diff --git a/0. Preamble.ipynb b/0. Preamble.ipynb
index 3547455..19b5a52 100644
--- a/0. Preamble.ipynb	
+++ b/0. Preamble.ipynb	
@@ -3,57 +3,32 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "<div>\n",
-    "    <h1 style=\"text-align: center;\">Deep Learning with Keras</h1>\n",
-    "    <img style=\"text-align: left\" src=\"imgs/keras-logo-small.jpg\" width=\"10%\" />\n",
-    "<div>\n",
-    "\n",
-    "<div>\n",
-    "    <h2 style=\"text-align: center;\">Tutorial @ EuroScipy 2016</h2>\n",
-    "    <img style=\"text-align: left\" src=\"imgs/euroscipy_2016_logo.png\" width=\"40%\" />\n",
-    "</div>    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Yam Peleg,  Valerio Maggio"
+    "# Deep Learning Tutorial with Keras and Tensorflow"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "# Goal of this Tutorial\n",
-    "\n",
-    "- **Introduce** main features of Keras\n",
-    "- **Learn** how simple and Pythonic is doing Deep Learning with Keras\n",
-    "- **Understand** how easy is to do basic and *advanced* DL models in Keras;\n",
-    "    - **Examples and Hand-on Excerises** along the way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Source\n",
+    "```shell\n",
     "\n",
-    "https://github.com/leriomaggio/deep-learning-keras-euroscipy2016/"
+    "git clone -b pydatait https://github.com/leriomaggio/deep-learning-keras-tensorflow.git\n",
+    "```"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---"
    ]
@@ -61,105 +36,124 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "# (Tentative) Schedule \n",
-    "\n",
-    "## Attention: Spoilers Warning!\n",
+    "# Tentative Outline\n",
     "\n",
+    "- **Warmup**\n",
     "\n",
-    "- **Setup** (`10 mins`)\n",
+    "- **Part I**: **Introduction**\n",
     "\n",
-    "- **Part I**: **Introduction** (`~65 mins`)\n",
-    "\n",
-    "    - Intro to ANN (`~20 mins`)\n",
+    "    - Intro to ANN\n",
     "        - naive pure-Python implementation\n",
     "        - fast forward, sgd, backprop\n",
     "        \n",
-    "    - Intro to Theano (`15 mins`)\n",
-    "        - Model + SGD with Theano\n",
-    "        \n",
-    "    - Introduction to Keras (`30 mins`)\n",
+    "    - Intro to Tensorflow \n",
+    "        - Model + SGD with Tensorflow\n",
+    "            \n",
+    "    - Introduction to Keras\n",
     "        - Overview and main features\n",
-    "            - Theano backend\n",
     "            - Tensorflow backend\n",
+    "            - Theano backend\n",
+    "            - Keras Backend\n",
+    "            - Overview of the main layers\n",
     "        - Multi-Layer Perceptron and Fully Connected\n",
     "            - Examples with `keras.models.Sequential` and `Dense`\n",
-    "            - HandsOn: MLP with keras\n",
+    "            - HandsOn: FC with keras\n",
+    "    \n",
+    "    - Extra Material:\n",
+    "        - Intro to Theano\n",
+    "        - Alternative ANN implementation for MNIST\n",
+    "    \n",
     "            \n",
-    "- **Coffe Break** (`30 mins`)\n",
+    "- **Break**\n",
     "\n",
-    "- **Part II**: **Supervised Learning and Convolutional Neural Nets** (`~45 mins`)\n",
+    "- **Part II**: **Supervised Learning and Convolutional Neural Nets**\n",
     "    \n",
-    "    - Intro: Focus on Image Classification (`5 mins`)\n",
+    "    - Intro: Focus on Image Classification\n",
     "\n",
-    "    - Intro to CNN (`25 mins`)\n",
+    "    - Intro to ConvNets\n",
     "        - meaning of convolutional filters\n",
     "            - examples from ImageNet    \n",
     "        - Meaning of dimensions of Conv filters (through an exmple of ConvNet) \n",
     "        - Visualising ConvNets\n",
     "        - HandsOn: ConvNet with keras \n",
     "\n",
-    "    - Advanced CNN (`10 mins`)\n",
+    "    - Advanced CNN\n",
     "        - Dropout\n",
     "        - MaxPooling\n",
     "        - Batch Normalisation\n",
     "        \n",
-    "    - Famous Models in Keras (likely moved somewhere else) (`10 mins`)\n",
-    "        (ref: https://github.com/fchollet/deep-learning-models)\n",
+    "    - Famous Models in Keras\n",
+    "        (ref: `keras.applications`)\n",
     "            - VGG16\n",
     "            - VGG19\n",
     "            - ResNet50\n",
     "            - Inception v3\n",
+    "        - Transfer Learning\n",
     "        - HandsOn: Fine tuning a network on new dataset \n",
     "        \n",
-    "- **Part III**: **Unsupervised Learning** (`10 mins`)\n",
+    "- **Part III**: **Unsupervised Learning**\n",
     "\n",
     "    - AutoEncoders (`5 mins`)\n",
-    "    - word2vec & doc2vec (gensim) & `keras.datasets` (`5 mins`)\n",
+    "    - word2vec & doc2vec (gensim) & `keras.datasets`\n",
     "        - `Embedding`\n",
     "        - word2vec and CNN\n",
     "    - Exercises\n",
     "\n",
-    "- **Part IV**: **Advanced Materials** (`20 mins`)\n",
+    "- **Part IV**: **Advanced Materials**\n",
     "    - RNN and LSTM (`10 mins`)\n",
     "        -  RNN, LSTM, GRU  \n",
-    "    - Example of RNN and LSTM with Text (`~10 mins`) -- *Tentative*\n",
+    "    - Example of RNN and LSTM with Text\n",
     "    - HandsOn: IMDB\n",
+    "    - **Multi-Input/Multi-Output Network Topologies**\n",
     "\n",
-    "- **Wrap up and Conclusions** (`5 mins`)"
+    "- **Wrap up and Conclusions**"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Requirements"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "This tutorial requires the following packages:\n",
     "\n",
-    "- Python version 3.4+ \n",
-    "    - likely Python 2.7 would be fine, but *who knows*? :P\n",
+    "- Python version 3.5.x\n",
+    "    - Python 3.4 should be fine as well\n",
+    "    - likely Python 2.7 would be also fine, but *who knows*? :P\n",
+    "    \n",
     "- `numpy` version 1.10 or later: http://www.numpy.org/\n",
     "- `scipy` version 0.16 or later: http://www.scipy.org/\n",
     "- `matplotlib` version 1.4 or later: http://matplotlib.org/\n",
     "- `pandas` version 0.16 or later: http://pandas.pydata.org\n",
     "- `scikit-learn` version 0.15 or later: http://scikit-learn.org\n",
-    "- `keras` version 1.0 or later: http://keras.io\n",
-    "- `theano` version 0.8 or later: http://deeplearning.net/software/theano/\n",
+    "- `keras` version 2.0 or later: http://keras.io\n",
+    "- `tensorflow` version 1.0 or later: https://www.tensorflow.org\n",
     "- `ipython`/`jupyter` version 4.0 or later, with notebook support\n",
     "\n",
     "(Optional but recommended):\n",
@@ -174,21 +168,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Python Version"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "I'm currently running this tutorial with **Python 3** on **Anaconda**"
    ]
@@ -197,7 +200,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -214,347 +219,94 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to set up your environment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "The quickest and simplest way to setup the environment is to use [conda](https://store.continuum.io) environment manager. \n",
+    "### Configure Keras with tensorflow\n",
+    "\n",
+    "1) Create the `keras.json` (if it does not exist):\n",
+    "\n",
+    "```shell\n",
+    "touch $HOME/.keras/keras.json\n",
+    "```\n",
+    "\n",
+    "2) Copy the following content into the file:\n",
     "\n",
-    "We provide in the materials a `deep-learning.yml` that is complete and **ready to use** to set up your virtual environment with conda."
+    "```\n",
+    "{\n",
+    "    \"epsilon\": 1e-07,\n",
+    "    \"backend\": \"tensorflow\",\n",
+    "    \"floatx\": \"float32\",\n",
+    "    \"image_data_format\": \"channels_last\"\n",
+    "}\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
-    "scrolled": false
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "name: deep-learning\r\n",
-      "channels:\r\n",
-      "- conda-forge\r\n",
-      "- defaults\r\n",
-      "dependencies:\r\n",
-      "- accelerate=2.3.0=np111py35_3\r\n",
-      "- accelerate_cudalib=2.0=0\r\n",
-      "- bokeh=0.12.1=py35_0\r\n",
-      "- cffi=1.6.0=py35_0\r\n",
-      "- backports.shutil_get_terminal_size=1.0.0=py35_0\r\n",
-      "- blas=1.1=openblas\r\n",
-      "- ca-certificates=2016.8.2=3\r\n",
-      "- cairo=1.12.18=8\r\n",
-      "- certifi=2016.8.2=py35_0\r\n",
-      "- cycler=0.10.0=py35_0\r\n",
-      "- cython=0.24.1=py35_0\r\n",
-      "- decorator=4.0.10=py35_0\r\n",
-      "- entrypoints=0.2.2=py35_0\r\n",
-      "- fontconfig=2.11.1=3\r\n",
-      "- freetype=2.6.3=1\r\n",
-      "- gettext=0.19.7=1\r\n",
-      "- glib=2.48.0=4\r\n",
-      "- h5py=2.6.0=np111py35_6\r\n",
-      "- harfbuzz=1.0.6=0\r\n",
-      "- hdf5=1.8.17=2\r\n",
-      "- icu=56.1=4\r\n",
-      "- ipykernel=4.3.1=py35_1\r\n",
-      "- ipython=5.1.0=py35_0\r\n",
-      "- ipywidgets=5.2.2=py35_0\r\n",
-      "- jinja2=2.8=py35_1\r\n",
-      "- jpeg=9b=0\r\n",
-      "- jsonschema=2.5.1=py35_0\r\n",
-      "- jupyter_client=4.3.0=py35_0\r\n",
-      "- jupyter_console=5.0.0=py35_0\r\n",
-      "- jupyter_core=4.1.1=py35_1\r\n",
-      "- libffi=3.2.1=2\r\n",
-      "- libiconv=1.14=3\r\n",
-      "- libpng=1.6.24=0\r\n",
-      "- libsodium=1.0.10=0\r\n",
-      "- libtiff=4.0.6=6\r\n",
-      "- libxml2=2.9.4=0\r\n",
-      "- markupsafe=0.23=py35_0\r\n",
-      "- matplotlib=1.5.2=np111py35_6\r\n",
-      "- mistune=0.7.3=py35_0\r\n",
-      "- nbconvert=4.2.0=py35_0\r\n",
-      "- nbformat=4.0.1=py35_0\r\n",
-      "- ncurses=5.9=8\r\n",
-      "- nose=1.3.7=py35_1\r\n",
-      "- notebook=4.2.2=py35_0\r\n",
-      "- numpy=1.11.1=py35_blas_openblas_201\r\n",
-      "- openblas=0.2.18=4\r\n",
-      "- openssl=1.0.2h=2\r\n",
-      "- pandas=0.18.1=np111py35_1\r\n",
-      "- pango=1.40.1=0\r\n",
-      "- path.py=8.2.1=py35_0\r\n",
-      "- pcre=8.38=1\r\n",
-      "- pexpect=4.2.0=py35_1\r\n",
-      "- pickleshare=0.7.3=py35_0\r\n",
-      "- pip=8.1.2=py35_0\r\n",
-      "- pixman=0.32.6=0\r\n",
-      "- prompt_toolkit=1.0.6=py35_0\r\n",
-      "- protobuf=3.0.0b3=py35_1\r\n",
-      "- ptyprocess=0.5.1=py35_0\r\n",
-      "- pygments=2.1.3=py35_1\r\n",
-      "- pyparsing=2.1.7=py35_0\r\n",
-      "- python=3.5.2=2\r\n",
-      "- python-dateutil=2.5.3=py35_0\r\n",
-      "- pytz=2016.6.1=py35_0\r\n",
-      "- pyyaml=3.11=py35_0\r\n",
-      "- pyzmq=15.4.0=py35_0\r\n",
-      "- qt=4.8.7=0\r\n",
-      "- qtconsole=4.2.1=py35_0\r\n",
-      "- readline=6.2=0\r\n",
-      "- requests=2.11.0=py35_0\r\n",
-      "- scikit-learn=0.17.1=np111py35_blas_openblas_201\r\n",
-      "- scipy=0.18.0=np111py35_blas_openblas_201\r\n",
-      "- setuptools=25.1.6=py35_0\r\n",
-      "- simplegeneric=0.8.1=py35_0\r\n",
-      "- sip=4.18=py35_0\r\n",
-      "- six=1.10.0=py35_0\r\n",
-      "- sqlite=3.13.0=1\r\n",
-      "- terminado=0.6=py35_0\r\n",
-      "- tk=8.5.19=0\r\n",
-      "- tornado=4.4.1=py35_1\r\n",
-      "- traitlets=4.2.2=py35_0\r\n",
-      "- wcwidth=0.1.7=py35_0\r\n",
-      "- wheel=0.29.0=py35_0\r\n",
-      "- widgetsnbextension=1.2.6=py35_3\r\n",
-      "- xz=5.2.2=0\r\n",
-      "- yaml=0.1.6=0\r\n",
-      "- zeromq=4.1.5=0\r\n",
-      "- zlib=1.2.8=3\r\n",
-      "- cudatoolkit=7.5=0\r\n",
-      "- ipython_genutils=0.1.0=py35_0\r\n",
-      "- jupyter=1.0.0=py35_3\r\n",
-      "- libgfortran=3.0.0=1\r\n",
-      "- llvmlite=0.11.0=py35_0\r\n",
-      "- mkl=11.3.3=0\r\n",
-      "- mkl-service=1.1.2=py35_2\r\n",
-      "- numba=0.26.0=np111py35_0\r\n",
-      "- pycparser=2.14=py35_1\r\n",
-      "- pyqt=4.11.4=py35_4\r\n",
-      "- snakeviz=0.4.1=py35_0\r\n",
-      "- pip:\r\n",
-      "  - backports.shutil-get-terminal-size==1.0.0\r\n",
-      "  - certifi==2016.8.2\r\n",
-      "  - cycler==0.10.0\r\n",
-      "  - cython==0.24.1\r\n",
-      "  - decorator==4.0.10\r\n",
-      "  - h5py==2.6.0\r\n",
-      "  - ipykernel==4.3.1\r\n",
-      "  - ipython==5.1.0\r\n",
-      "  - ipython-genutils==0.1.0\r\n",
-      "  - ipywidgets==5.2.2\r\n",
-      "  - jinja2==2.8\r\n",
-      "  - jsonschema==2.5.1\r\n",
-      "  - jupyter-client==4.3.0\r\n",
-      "  - jupyter-console==5.0.0\r\n",
-      "  - jupyter-core==4.1.1\r\n",
-      "  - keras==1.0.7\r\n",
-      "  - mako==1.0.4\r\n",
-      "  - markupsafe==0.23\r\n",
-      "  - matplotlib==1.5.2\r\n",
-      "  - mistune==0.7.3\r\n",
-      "  - nbconvert==4.2.0\r\n",
-      "  - nbformat==4.0.1\r\n",
-      "  - nose==1.3.7\r\n",
-      "  - notebook==4.2.2\r\n",
-      "  - numpy==1.11.1\r\n",
-      "  - pandas==0.18.1\r\n",
-      "  - path.py==8.2.1\r\n",
-      "  - pexpect==4.2.0\r\n",
-      "  - pickleshare==0.7.3\r\n",
-      "  - pip==8.1.2\r\n",
-      "  - prompt-toolkit==1.0.6\r\n",
-      "  - protobuf==3.0.0b2\r\n",
-      "  - ptyprocess==0.5.1\r\n",
-      "  - pygments==2.1.3\r\n",
-      "  - pygpu==0.2.1\r\n",
-      "  - pyparsing==2.1.7\r\n",
-      "  - python-dateutil==2.5.3\r\n",
-      "  - pytz==2016.6.1\r\n",
-      "  - pyyaml==3.11\r\n",
-      "  - pyzmq==15.4.0\r\n",
-      "  - qtconsole==4.2.1\r\n",
-      "  - requests==2.11.0\r\n",
-      "  - scikit-learn==0.17.1\r\n",
-      "  - scipy==0.18.0\r\n",
-      "  - setuptools==25.1.4\r\n",
-      "  - simplegeneric==0.8.1\r\n",
-      "  - six==1.10.0\r\n",
-      "  - tensorflow==0.10.0rc0\r\n",
-      "  - terminado==0.6\r\n",
-      "  - theano==0.8.2\r\n",
-      "  - tornado==4.4.1\r\n",
-      "  - traitlets==4.2.2\r\n",
-      "  - wcwidth==0.1.7\r\n",
-      "  - wheel==0.29.0\r\n",
-      "  - widgetsnbextension==1.2.6\r\n",
-      "prefix: /home/valerio/anaconda3/envs/deep-learning\r\n",
-      "\r\n"
+      "{\r\n",
+      "\t\"epsilon\": 1e-07,\r\n",
+      "\t\"backend\": \"tensorflow\",\r\n",
+      "\t\"floatx\": \"float32\",\r\n",
+      "\t\"image_data_format\": \"channels_last\"\r\n",
+      "}"
      ]
     }
    ],
    "source": [
-    "!cat deep-learning.yml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Recreate the Conda Environment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### A. Create the Environment\n",
-    "\n",
-    "```\n",
-    "conda env create -f deep-learning.yml  # this file is for Linux channels.\n",
-    "```\n",
-    "\n",
-    "If you're using a **Mac OSX**, we also provided in the repo the conda file \n",
-    "that is compatible with `osx-channels`:\n",
-    "\n",
-    "```\n",
-    "conda env create -f deep-learning-osx.yml  # this file is for OSX channels.\n",
-    "```\n",
-    "\n",
-    "#### B. Activate the new `deep-learning` Environment\n",
-    "\n",
-    "```\n",
-    "source activate deep-learning\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optionals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Enabling Conda-Forge"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is strongly suggested to enable [**conda forge**](https://conda-forge.github.io/) in your Anaconda installation.\n",
-    "\n",
-    "**Conda-Forge** is a github organisation containing repositories of conda recipies.\n",
-    "\n",
-    "To add `conda-forge` as an additional anaconda channel it is just required to type:\n",
-    "\n",
-    "```shell\n",
-    "conda config --add channels conda-forge\n",
-    "```"
+    "!cat ~/.keras/keras.json"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Configure Theano\n",
-    "\n",
-    "1) Create the `theanorc` file:\n",
-    "\n",
-    "```shell\n",
-    "touch $HOME/.theanorc\n",
-    "```\n",
-    "\n",
-    "2) Copy the following content into the file:\n",
-    "\n",
-    "```\n",
-    "[global]\n",
-    "floatX = float32\n",
-    "device = gpu  # switch to cpu if no GPU is available on your machine\n",
-    "\n",
-    "[nvcc]\n",
-    "fastmath = True\n",
-    "\n",
-    "[lib]\n",
-    "cnmem=.90\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**More on [theano documentation](http://theano.readthedocs.io/en/latest/library/config.html)**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Installing Tensorflow as backend "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```shell\n",
-    "# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5\n",
-    "# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see \"Install from sources\" below.\n",
-    "export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl\n",
-    "\n",
-    "pip install --ignore-installed --upgrade $TF_BINARY_URL\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**More on [tensorflow documentation](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html)**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Test if everything is up&running"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## 1. Check import"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -567,17 +319,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using Theano backend.\n",
-      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n"
+      "Using TensorFlow backend.\n"
      ]
     }
    ],
@@ -587,16 +340,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## 2. Check installeded Versions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -607,7 +365,7 @@
       "scipy: 0.18.0\n",
       "matplotlib: 1.5.2\n",
       "iPython: 5.1.0\n",
-      "scikit-learn: 0.17.1\n"
+      "scikit-learn: 0.18\n"
      ]
     }
    ],
@@ -632,16 +390,18 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "keras:  1.0.7\n",
-      "Theano:  0.8.2\n",
-      "Tensorflow:  0.10.0rc0\n"
+      "keras:  2.0.2\n",
+      "Theano:  0.9.0\n",
+      "Tensorflow:  1.0.1\n"
      ]
     }
    ],
@@ -649,17 +409,20 @@
     "import keras\n",
     "print('keras: ', keras.__version__)\n",
     "\n",
+    "# optional\n",
     "import theano\n",
     "print('Theano: ', theano.__version__)\n",
     "\n",
-    "# optional\n",
     "import tensorflow as tf\n",
     "print('Tensorflow: ', tf.__version__)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<br>\n",
     "<h1 style=\"text-align: center;\">If everything worked till down here, you're ready to start!</h1>"
@@ -667,65 +430,13 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Consulting Material"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You have two options to go through the material presented in this tutorial:\n",
-    "\n",
-    "* Read (and execute) the material as **iPython/Jupyter** notebooks\n",
-    "* (just) read the material as (HTML) slides"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the first case, all you need to do is just execute `ipython notebook` (or `jupyter notebook`) depending on the version of `iPython` you have installed on your machine\n",
-    "\n",
-    "(`jupyter` command works in case you have `iPython 4.0.x` installed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the second case, you may simply convert the provided notebooks in `HTML` slides and see them into your browser\n",
-    "thanks to `nbconvert`.\n",
-    "\n",
-    "Thus, move to the folder where notebooks are stored and execute the following command:\n",
-    "\n",
-    "    jupyter nbconvert --to slides ./*.ipynb --post serve\n",
-    "    \n",
-    "   \n",
-    "(Please substitute `jupyter` with `ipython` in the previous command if you have `iPython 3.x` installed on your machine)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## In case..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..you wanna do **both** (interactive and executable slides), I highly suggest to install the terrific `RISE` ipython notebook extension: [https://github.com/damianavila/RISE](https://github.com/damianavila/RISE)"
-   ]
   }
  ],
  "metadata": {
diff --git a/1.1 Introduction - Deep Learning and ANN.ipynb b/1.1 Introduction - Deep Learning and ANN.ipynb
index 9232a07..8639b59 100644
--- a/1.1 Introduction - Deep Learning and ANN.ipynb	
+++ b/1.1 Introduction - Deep Learning and ANN.ipynb	
@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "24ca50a0-b9ad-425d-a28f-6bd3ca3ac378"
     },
@@ -17,6 +19,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "3896ecb2-47fa-4dbc-a2ff-9c76e9dfe93e"
     },
@@ -31,6 +35,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "8c3060aa-fee9-438c-bc60-4685c0eb4750"
     },
@@ -45,6 +51,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "6287766f-972f-4b4d-bee7-5418f0af74de"
     },
@@ -59,6 +67,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "2f1c6299-954a-461a-b5c1-a86a1d12ad15"
     },
@@ -73,6 +83,26 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## This Section will cover:\n",
+    "\n",
+    "* Getting a conceptual understanding of multi-layer neural networks\n",
+    "* Training neural networks for image classification\n",
+    "* Implementing the powerful backpropagation algorithm\n",
+    "* Debugging neural network implementations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "5e13607b-3ec5-4a95-a2d8-f898f20748da"
     },
@@ -81,12 +111,14 @@
     }
    },
    "source": [
-    "# Artificial Neural Networks (ANN)"
+    "# Building Blocks: Artificial Neural Networks (ANN)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "4fa2e86a-be32-4e78-96d9-f511a07e3908"
     },
@@ -101,6 +133,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "df0121bc-10f1-4ace-840e-6fc89c6fdc7f"
     },
@@ -115,6 +149,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "c25d7194-10bd-4196-9d4c-592bf6e188f9"
     },
@@ -129,6 +165,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "15260f90-13d1-4fcc-afc6-379c507cb950"
     },
@@ -143,6 +181,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "92d4603e-7e39-4156-818c-785df6189fe8"
     },
@@ -158,6 +198,8 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "nbpresent": {
      "id": "356d5ec7-3392-4daa-9671-4cc7111c5c91"
     },
@@ -174,6 +216,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -184,7 +228,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src =\"imgs/MLP.png\" width=\"85%\">"
    ]
@@ -192,50 +239,141 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
-    "nbpresent": {
-     "id": "5678486b-caf4-440b-be62-2f1286982c71"
-    },
+    "deletable": true,
+    "editable": true,
     "slideshow": {
-     "slide_type": "subslide"
+     "slide_type": "slide"
     }
    },
    "source": [
-    "The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight."
+    "# Single Layer Neural Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src =\"imgs/single_layer.png\" width=\"85%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Weights Update Rule"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
    "source": [
-    "Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**."
+    "- We use a **gradient descent** optimization algorithm to learn the _Weights Coefficients_ of the model.\n",
+    "<br><br>\n",
+    "- In every **epoch** (pass over the training set), we update the weight vector $w$ using the following update rule:\n",
+    "\n",
+    "$$\n",
+    "w = w + \\Delta w, \\text{where } \\Delta w = - \\eta \\nabla J(w)\n",
+    "$$\n",
+    "\n",
+    "<br><br>\n",
+    "\n",
+    "In other words, we computed the gradient based on the whole training set and updated the weights of the model by taking a step into the **opposite direction** of the gradient $ \\nabla J(w)$. \n",
+    "\n",
+    "In order to fin the **optimal weights of the model**, we optimized an objective function (e.g. the Sum of Squared Errors (SSE)) cost function $J(w)$. \n",
+    "\n",
+    "Furthermore, we multiply the gradient by a factor, the learning rate $\\eta$ , which we choose carefully to balance the **speed of learning** against the risk of overshooting the global minimum of the cost function."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src =\"imgs/backprop.png\" width=\"80%\">"
+    "### Gradient Descent"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In **gradient descent optimization**, we update all the **weights simultaneously** after each epoch, and we define the _partial derivative_ for each weight $w_j$ in the weight vector $w$ as follows:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial}{\\partial w_j} J(w) = \\sum_{i} ( y^{(i)} - a^{(i)} )  x^{(i)}_j\n",
+    "$$\n",
+    "\n",
+    "**Note**: _The superscript $(i)$ refers to the ith sample. The subscript $j$ refers to the jth dimension/feature_\n",
+    "\n",
+    "\n",
+    "Here $y^{(i)}$ is the target class label of a particular sample $x^{(i)}$ , and $a^{(i)}$ is the **activation** of the neuron \n",
+    "\n",
+    "(which is a linear function in the special case of _Perceptron_)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define the **activation function** $\\phi(\\cdot)$ as follows:\n",
+    "\n",
+    "$$\n",
+    "\\phi(z) = z = a = \\sum_{j} w_j x_j = \\mathbf{w}^T \\mathbf{x}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Binary Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While we used the **activation** $\\phi(z)$ to compute the gradient update, we may use a **threshold function** _(Heaviside function)_ to squash the continuous-valued output into binary class labels for prediction:\n",
+    "\n",
+    "$$\n",
+    "\\hat{y} = \n",
+    "\\begin{cases}\n",
+    "    1 & \\text{if } \\phi(z) \\geq 0 \\\\\n",
+    "    0 & \\text{otherwise}\n",
+    "\\end{cases}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
-    "# Building Neural Nets from scratch \n"
+    "## Building Neural Nets from scratch \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -253,9 +391,219 @@
     "Libraries like `theano` have highly optimized code."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"1.1.1 Perceptron and Adaline.ipynb\" target=\"_blank_\"> Perceptron and Adaline </a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want a sneak peek of alternate (production ready) implementation of _Perceptron_ for instance try:\n",
+    "```python\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing the multi-layer neural network architecture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/multi-layers-1.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will see how to connect **multiple single neurons** to a **multi-layer feedforward neural network**; this special type of network is also called a **multi-layer perceptron** (MLP). \n",
+    "\n",
+    "The figure shows the concept of an **MLP** consisting of three layers: one _input_ layer, one _hidden_ layer, and one _output_ layer. \n",
+    "\n",
+    "The units in the hidden layer are fully connected to the input layer, and the output layer is fully connected to the hidden layer, respectively. \n",
+    "\n",
+    "If such a network has **more than one hidden layer**, we also call it a **deep artificial neural network**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we denote the `ith` activation unit in the `lth` layer as $a_i^{(l)}$ , and the activation units $a_0^{(1)}$ and \n",
+    "$a_0^{(2)}$ are the **bias units**, respectively, which we set equal to $1$. \n",
+    "<br><br>\n",
+    "The _activation_ of the units in the **input layer** is just its input plus the bias unit:\n",
+    "\n",
+    "$$\n",
+    "\\mathbf{a}^{(1)} = [a_0^{(1)}, a_1^{(1)}, \\ldots, a_m^{(1)}]^T = [1, x_1^{(i)}, \\ldots, x_m^{(i)}]^T\n",
+    "$$\n",
+    "<br><br>\n",
+    "**Note**: $x_j^{(i)}$ refers to the jth feature/dimension of the ith sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notes on Notation (usually) Adopted\n",
+    "\n",
+    "The terminology around the indices (subscripts and superscripts) may look a little bit confusing at first. \n",
+    "<br><br>\n",
+    "\n",
+    "You may wonder why we wrote $w_{j,k}^{(l)}$ and not $w_{k,j}^{(l)}$ to refer to \n",
+    "the **weight coefficient** that connects the *kth* unit in layer $l$ to the jth unit in layer $l+1$. \n",
+    "<br><br>\n",
+    "\n",
+    "What may seem a little bit quirky at first will make much more sense later when we **vectorize** the neural network representation. \n",
+    "<br><br>\n",
+    "\n",
+    "For example, we will summarize the weights that connect the input and hidden layer by a matrix \n",
+    "$$ W^{(1)} \\in \\mathbb{R}^{h×[m+1]}$$\n",
+    "\n",
+    "where $h$ is the number of hidden units and $m + 1$ is the number of hidden units plus bias unit. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/multi-layers-2.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Forward Propagation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Starting at the input layer, we forward propagate the patterns of the training data through the network to generate an output.\n",
+    "\n",
+    "* Based on the network's output, we calculate the error that we want to minimize using a cost function that we will describe later.\n",
+    "\n",
+    "* We backpropagate the error, find its derivative with respect to each weight in the network, and update the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sigmoid Activation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/logistic_function.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/fwd_step.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/fwd_step_net.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Backward Propagation"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "nbpresent": {
+     "id": "5678486b-caf4-440b-be62-2f1286982c71"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src =\"imgs/bkwd_step_net.png\" width=\"60%\" />\n",
+    "\n",
+    "_(Source: Python Machine Learning, S. Raschka)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src =\"imgs/backprop.png\" width=\"80%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -269,6 +617,8 @@
    "execution_count": 1,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -288,13 +638,15 @@
    "execution_count": 2,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
    },
    "outputs": [],
    "source": [
-    "# Display plots inline \n",
+    "# Display plots in notebook \n",
     "%matplotlib inline\n",
     "# Define plot's default figure size\n",
     "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)"
@@ -305,6 +657,8 @@
    "execution_count": 3,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -320,6 +674,8 @@
    "execution_count": 4,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -335,6 +691,8 @@
    "execution_count": 5,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -349,6 +707,8 @@
    "execution_count": 6,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -374,6 +734,8 @@
    "execution_count": 7,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -399,6 +761,8 @@
    "execution_count": 8,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -434,6 +798,8 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -447,6 +813,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -457,7 +825,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/mlp_details.png\" width=\"85%\" />"
    ]
@@ -465,6 +836,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -480,6 +853,8 @@
    "execution_count": 9,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -496,6 +871,8 @@
    "execution_count": 10,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -510,6 +887,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -522,7 +901,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -535,6 +916,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -549,7 +932,9 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -561,6 +946,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -576,6 +963,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -611,6 +1000,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -621,7 +1012,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "```python\n",
     "def activate(self, inputs):\n",
@@ -655,6 +1049,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -665,7 +1061,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "```python\n",
     "def backPropagate(self, targets, N, M):\n",
@@ -717,6 +1116,8 @@
    "execution_count": 13,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -847,6 +1248,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -859,7 +1262,9 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -883,6 +1288,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -895,7 +1302,9 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -915,6 +1324,8 @@
    "execution_count": 16,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -987,6 +1398,8 @@
    "execution_count": 17,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -1010,6 +1423,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -1023,6 +1438,8 @@
    "execution_count": 18,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -1054,7 +1471,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -1086,6 +1505,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -1102,7 +1523,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1115,6 +1538,8 @@
    "execution_count": 21,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -1162,6 +1587,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -1178,7 +1605,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1190,6 +1619,8 @@
    "execution_count": 23,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
@@ -1355,6 +1786,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -1362,7 +1795,7 @@
    "source": [
     "# Addendum\n",
     "\n",
-    "There is an additional notebook in the repo, i.e. [A simple implementation of ANN for MNIST](1.4 (Extra) A Simple Implementation of ANN for MNIST.ipynb) for a *naive* implementation of **SGD** and **MLP** applied on **MNIST** dataset.\n",
+    "There is an additional notebook in the repo, i.e. [A simple implementation of ANN for MNIST](1.1.2 MLP and MNIST.ipynb) for a *naive* implementation of **SGD** and **MLP** applied on **MNIST** dataset.\n",
     "\n",
     "This accompanies the online text http://neuralnetworksanddeeplearning.com/ . The book is highly recommended. "
    ]
@@ -1385,7 +1818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.3"
   },
   "nbpresent": {
    "slides": {
diff --git a/1.1.1 Perceptron and Adaline.ipynb b/1.1.1 Perceptron and Adaline.ipynb
new file mode 100644
index 0000000..0fc0a7f
--- /dev/null
+++ b/1.1.1 Perceptron and Adaline.ipynb	
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "##### (exceprt from Python Machine Learning Essentials, Supplementary Materials)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Sections\n",
+    "\n",
+    "- [Implementing a perceptron learning algorithm in Python](#Implementing-a-perceptron-learning-algorithm-in-Python)\n",
+    "    - [Training a perceptron model on the Iris dataset](#Training-a-perceptron-model-on-the-Iris-dataset)\n",
+    "- [Adaptive linear neurons and the convergence of learning](#Adaptive-linear-neurons-and-the-convergence-of-learning)\n",
+    "    - [Implementing an adaptive linear neuron in Python](#Implementing-an-adaptive-linear-neuron-in-Python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Display plots in notebook \n",
+    "%matplotlib inline\n",
+    "# Define plot's default figure size\n",
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Implementing a perceptron learning algorithm in Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "class Perceptron(object):\n",
+    "    \"\"\"Perceptron classifier.\n",
+    "\n",
+    "    Parameters\n",
+    "    ------------\n",
+    "    eta : float\n",
+    "        Learning rate (between 0.0 and 1.0)\n",
+    "    n_iter : int\n",
+    "        Passes over the training dataset.\n",
+    "\n",
+    "    Attributes\n",
+    "    -----------\n",
+    "    w_ : 1d-array\n",
+    "        Weights after fitting.\n",
+    "    errors_ : list\n",
+    "        Number of misclassifications in every epoch.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, eta=0.01, n_iter=10):\n",
+    "        self.eta = eta\n",
+    "        self.n_iter = n_iter\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        \"\"\"Fit training data.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        X : {array-like}, shape = [n_samples, n_features]\n",
+    "            Training vectors, where n_samples is the number of samples and\n",
+    "            n_features is the number of features.\n",
+    "        y : array-like, shape = [n_samples]\n",
+    "            Target values.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        self : object\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.w_ = np.zeros(1 + X.shape[1])\n",
+    "        self.errors_ = []\n",
+    "\n",
+    "        for _ in range(self.n_iter):\n",
+    "            errors = 0\n",
+    "            for xi, target in zip(X, y):\n",
+    "                update = self.eta * (target - self.predict(xi))\n",
+    "                self.w_[1:] += update * xi\n",
+    "                self.w_[0] += update\n",
+    "                errors += int(update != 0.0)\n",
+    "            self.errors_.append(errors)\n",
+    "        return self\n",
+    "\n",
+    "    def net_input(self, X):\n",
+    "        \"\"\"Calculate net input\"\"\"\n",
+    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        \"\"\"Return class label after unit step\"\"\"\n",
+    "        return np.where(self.net_input(X) >= 0.0, 1, -1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training a perceptron model on the Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Reading-in the Iris data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0    1    2    3               4\n",
+       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
+       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
+       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
+       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
+       "149  5.9  3.0  5.1  1.8  Iris-virginica"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
+    "        'machine-learning-databases/iris/iris.data', header=None)\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Plotting the Iris data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UuGfNiuYnTNi/Df/II2HlSmhujq7sm5uj+SOP3D/ph3qsn0ixlPBFYjKD/rm+6ZMmRfOT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\nE0r4IiJ1QglfRKRO/H9dfFAdFqad1gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10fc969e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# select setosa and versicolor\n",
+    "y = df.iloc[0:100, 4].values\n",
+    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
+    "\n",
+    "# extract sepal length and petal length\n",
+    "X = df.iloc[0:100, [0, 2]].values\n",
+    "\n",
+    "# plot data\n",
+    "plt.scatter(X[:50, 0], X[:50, 1],\n",
+    "            color='red', marker='o', label='setosa')\n",
+    "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
+    "            color='blue', marker='x', label='versicolor')\n",
+    "\n",
+    "plt.xlabel('petal length [cm]')\n",
+    "plt.ylabel('sepal length [cm]')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "# plt.savefig('./iris_1.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Training the perceptron model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dxLBnTnYIaUSanyAF6pcJOvZ6oHe1172AjQk6lkijm1iYz63TFlFW8UU383SL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\npAIlIiKR9P8BFlwC8omIyqkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e6c5588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
+    "\n",
+    "ppn.fit(X, y)\n",
+    "\n",
+    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Number of misclassifications')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./perceptron_1.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### A function for plotting decision regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "\n",
+    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
+    "\n",
+    "    # setup marker generator and color map\n",
+    "    markers = ('s', 'x', 'o', '^', 'v')\n",
+    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
+    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
+    "\n",
+    "    # plot the decision surface\n",
+    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
+    "                         np.arange(x2_min, x2_max, resolution))\n",
+    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
+    "    Z = Z.reshape(xx1.shape)\n",
+    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
+    "    plt.xlim(xx1.min(), xx1.max())\n",
+    "    plt.ylim(xx2.min(), xx2.max())\n",
+    "\n",
+    "    # plot class samples\n",
+    "    for idx, cl in enumerate(np.unique(y)):\n",
+    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
+    "                    alpha=0.8, c=cmap(idx),\n",
+    "                    marker=markers[idx], label=cl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2WteXBlKOS0QyoBxUK1B+QkRaUOYByswWA4sBDpo6NePWNCl1s4lIC+rIugHu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\nIiK59P8BVGnEoi1X/oUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110650358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_decision_regions(X, y, classifier=ppn)\n",
+    "plt.xlabel('sepal length [cm]')\n",
+    "plt.ylabel('petal length [cm]')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Adaptive linear neurons and the convergence of learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Implementing an adaptive linear neuron in Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "class AdalineGD(object):\n",
+    "    \"\"\"ADAptive LInear NEuron classifier.\n",
+    "\n",
+    "    Parameters\n",
+    "    ------------\n",
+    "    eta : float\n",
+    "        Learning rate (between 0.0 and 1.0)\n",
+    "    n_iter : int\n",
+    "        Passes over the training dataset.\n",
+    "\n",
+    "    Attributes\n",
+    "    -----------\n",
+    "    w_ : 1d-array\n",
+    "        Weights after fitting.\n",
+    "    errors_ : list\n",
+    "        Number of misclassifications in every epoch.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, eta=0.01, n_iter=50):\n",
+    "        self.eta = eta\n",
+    "        self.n_iter = n_iter\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        \"\"\" Fit training data.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        X : {array-like}, shape = [n_samples, n_features]\n",
+    "            Training vectors, where n_samples is the number of samples and\n",
+    "            n_features is the number of features.\n",
+    "        y : array-like, shape = [n_samples]\n",
+    "            Target values.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        self : object\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.w_ = np.zeros(1 + X.shape[1])\n",
+    "        self.cost_ = []\n",
+    "\n",
+    "        for i in range(self.n_iter):\n",
+    "            output = self.net_input(X)\n",
+    "            errors = (y - output)\n",
+    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
+    "            self.w_[0] += self.eta * errors.sum()\n",
+    "            cost = (errors**2).sum() / 2.0\n",
+    "            self.cost_.append(cost)\n",
+    "        return self\n",
+    "\n",
+    "    def net_input(self, X):\n",
+    "        \"\"\"Calculate net input\"\"\"\n",
+    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
+    "\n",
+    "    def activation(self, X):\n",
+    "        \"\"\"Compute linear activation\"\"\"\n",
+    "        return self.net_input(X)\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        \"\"\"Return class label after unit step\"\"\"\n",
+    "        return np.where(self.activation(X) >= 0.0, 1, -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zGysSD3odyZgCOWuBIyLLRWRZbreiCmnMufhlw17uGjefaudF8fGgdlS24iZQ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\nzv8AazTyZ2LDK04AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1108f9320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
+    "\n",
+    "ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)\n",
+    "ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')\n",
+    "ax[0].set_xlabel('Epochs')\n",
+    "ax[0].set_ylabel('log(Sum-squared-error)')\n",
+    "ax[0].set_title('Adaline - Learning rate 0.01')\n",
+    "\n",
+    "ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
+    "ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')\n",
+    "ax[1].set_xlabel('Epochs')\n",
+    "ax[1].set_ylabel('Sum-squared-error')\n",
+    "ax[1].set_title('Adaline - Learning rate 0.0001')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./adaline_1.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "#### Standardizing features and re-training adaline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# standardize features\n",
+    "X_std = np.copy(X)\n",
+    "X_std[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()\n",
+    "X_std[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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d1sgD7mIzs9Er5xzUs5snikFed64mnHqYMwfuuyEj6QA8/kRKOpMnt1/WF4Ka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\n+5C+9PeIiMeURpweV7y8NCI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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10fc16898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kBe7O4s37eHTJNuau2M7BI7UU5WXziQlFXH36IIb16w7A7GUluhBYpB2pQIm0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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110aa1588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ada = AdalineGD(n_iter=15, eta=0.01)\n",
+    "ada.fit(X_std, y)\n",
+    "\n",
+    "plot_decision_regions(X_std, y, classifier=ada)\n",
+    "plt.title('Adaline - Gradient Descent')\n",
+    "plt.xlabel('sepal length [standardized]')\n",
+    "plt.ylabel('petal length [standardized]')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./adaline_2.png', dpi=300)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Sum-squared-error')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./adaline_3.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Large scale machine learning and stochastic gradient descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy.random import seed\n",
+    "\n",
+    "class AdalineSGD(object):\n",
+    "    \"\"\"ADAptive LInear NEuron classifier.\n",
+    "\n",
+    "    Parameters\n",
+    "    ------------\n",
+    "    eta : float\n",
+    "        Learning rate (between 0.0 and 1.0)\n",
+    "    n_iter : int\n",
+    "        Passes over the training dataset.\n",
+    "\n",
+    "    Attributes\n",
+    "    -----------\n",
+    "    w_ : 1d-array\n",
+    "        Weights after fitting.\n",
+    "    errors_ : list\n",
+    "        Number of misclassifications in every epoch.\n",
+    "    shuffle : bool (default: True)\n",
+    "        Shuffles training data every epoch if True to prevent cycles.\n",
+    "    random_state : int (default: None)\n",
+    "        Set random state for shuffling and initializing the weights.\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):\n",
+    "        self.eta = eta\n",
+    "        self.n_iter = n_iter\n",
+    "        self.w_initialized = False\n",
+    "        self.shuffle = shuffle\n",
+    "        if random_state:\n",
+    "            seed(random_state)\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        \"\"\" Fit training data.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        X : {array-like}, shape = [n_samples, n_features]\n",
+    "            Training vectors, where n_samples is the number of samples and\n",
+    "            n_features is the number of features.\n",
+    "        y : array-like, shape = [n_samples]\n",
+    "            Target values.\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        self : object\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self._initialize_weights(X.shape[1])\n",
+    "        self.cost_ = []\n",
+    "        for i in range(self.n_iter):\n",
+    "            if self.shuffle:\n",
+    "                X, y = self._shuffle(X, y)\n",
+    "            cost = []\n",
+    "            for xi, target in zip(X, y):\n",
+    "                cost.append(self._update_weights(xi, target))\n",
+    "            avg_cost = sum(cost)/len(y)\n",
+    "            self.cost_.append(avg_cost)\n",
+    "        return self\n",
+    "\n",
+    "    def partial_fit(self, X, y):\n",
+    "        \"\"\"Fit training data without reinitializing the weights\"\"\"\n",
+    "        if not self.w_initialized:\n",
+    "            self._initialize_weights(X.shape[1])\n",
+    "        if y.ravel().shape[0] > 1:\n",
+    "            for xi, target in zip(X, y):\n",
+    "                self._update_weights(xi, target)\n",
+    "        else:\n",
+    "            self._update_weights(X, y)\n",
+    "        return self\n",
+    "\n",
+    "    def _shuffle(self, X, y):\n",
+    "        \"\"\"Shuffle training data\"\"\"\n",
+    "        r = np.random.permutation(len(y))\n",
+    "        return X[r], y[r]\n",
+    "    \n",
+    "    def _initialize_weights(self, m):\n",
+    "        \"\"\"Initialize weights to zeros\"\"\"\n",
+    "        self.w_ = np.zeros(1 + m)\n",
+    "        self.w_initialized = True\n",
+    "        \n",
+    "    def _update_weights(self, xi, target):\n",
+    "        \"\"\"Apply Adaline learning rule to update the weights\"\"\"\n",
+    "        output = self.net_input(xi)\n",
+    "        error = (target - output)\n",
+    "        self.w_[1:] += self.eta * xi.dot(error)\n",
+    "        self.w_[0] += self.eta * error\n",
+    "        cost = 0.5 * error**2\n",
+    "        return cost\n",
+    "    \n",
+    "    def net_input(self, X):\n",
+    "        \"\"\"Calculate net input\"\"\"\n",
+    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
+    "\n",
+    "    def activation(self, X):\n",
+    "        \"\"\"Compute linear activation\"\"\"\n",
+    "        return self.net_input(X)\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        \"\"\"Return class label after unit step\"\"\"\n",
+    "        return np.where(self.activation(X) >= 0.0, 1, -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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81Hzkgv1GP8A5X308Y2/ubGDV1KwF9WlJTzRZLuDDwAYlKEkjgW8DrwMWA/Ml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\ns1DS/hnxvETSvGIbt0vaX9L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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110dfc080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+xywELgIeAs4F7omIkFRPNlF1S2oE5gKrImKTpG2S3gQ8DHwA+GaK32GfI4mT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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1108fe4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)\n",
+    "ada.fit(X_std, y)\n",
+    "\n",
+    "plot_decision_regions(X_std, y, classifier=ada)\n",
+    "plt.title('Adaline - Stochastic Gradient Descent')\n",
+    "plt.xlabel('sepal length [standardized]')\n",
+    "plt.ylabel('petal length [standardized]')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig('./adaline_4.png', dpi=300)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Average Cost')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./adaline_5.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.AdalineSGD at 0x110dfc9e8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ada.partial_fit(X_std[0, :], y[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/1.1.2 MLP and MNIST.ipynb b/1.1.2 MLP and MNIST.ipynb
new file mode 100644
index 0000000..e21c231
--- /dev/null
+++ b/1.1.2 MLP and MNIST.ipynb	
@@ -0,0 +1,1503 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "##### (exceprt from Python Machine Learning Essentials, Supplementary Materials)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Sections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "- [Classifying handwritten digits](#Classifying-handwritten-digits)\n",
+    "    - [Obtaining the MNIST dataset](#Obtaining-the-MNIST-dataset)\n",
+    "    - [Implementing a multi-layer perceptron](#Implementing-a-multi-layer-perceptron)\n",
+    "- [Training an artificial neural network](#Training-an-artificial-neural-network)\n",
+    "- [Debugging neural networks with gradient checking](#Debugging-neural-networks-with-gradient-checking)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Classifying handwritten digits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Obtaining the MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST dataset is publicly available at http://yann.lecun.com/exdb/mnist/ and consists of the following four parts:\n",
+    "\n",
+    "- Training set images: train-images-idx3-ubyte.gz (9.9 MB, 47 MB unzipped, 60,000 samples)\n",
+    "- Training set labels: train-labels-idx1-ubyte.gz (29 KB, 60 KB unzipped, 60,000 labels)\n",
+    "- Test set images: t10k-images-idx3-ubyte.gz (1.6 MB, 7.8 MB, 10,000 samples)\n",
+    "- Test set labels: t10k-labels-idx1-ubyte.gz (5 KB, 10 KB unzipped, 10,000 labels)\n",
+    "\n",
+    "In this section, we will only be working with a subset of MNIST, thus, we only need to download the training set images and training set labels. After downloading the files, I recommend unzipping the files using the Unix/Linux gzip tool from the terminal for efficiency, e.g., using the command \n",
+    "\n",
+    "    gzip *ubyte.gz -d\n",
+    " \n",
+    "in your local MNIST download directory, or, using your favorite unzipping tool if you are working with a machine running on Microsoft Windows. The images are stored in byte form, and using the following function, we will read them into NumPy arrays that we will use to train our MLP.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import struct\n",
+    "import numpy as np\n",
+    " \n",
+    "def load_mnist(path, kind='train'):\n",
+    "    \"\"\"Load MNIST data from `path`\"\"\"\n",
+    "    labels_path = os.path.join(path, \n",
+    "                               '%s-labels-idx1-ubyte' \n",
+    "                                % kind)\n",
+    "    images_path = os.path.join(path, \n",
+    "                               '%s-images-idx3-ubyte' \n",
+    "                               % kind)\n",
+    "        \n",
+    "    with open(labels_path, 'rb') as lbpath:\n",
+    "        magic, n = struct.unpack('>II', \n",
+    "                                 lbpath.read(8))\n",
+    "        labels = np.fromfile(lbpath, \n",
+    "                             dtype=np.uint8)\n",
+    "\n",
+    "    with open(images_path, 'rb') as imgpath:\n",
+    "        magic, num, rows, cols = struct.unpack(\">IIII\", \n",
+    "                                               imgpath.read(16))\n",
+    "        images = np.fromfile(imgpath, \n",
+    "                             dtype=np.uint8).reshape(len(labels), 784)\n",
+    " \n",
+    "    return images, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 60000, columns: 784\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, y_train = load_mnist('mnist', kind='train')\n",
+    "print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 10000, columns: 784\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_test, y_test = load_mnist('mnist', kind='t10k')\n",
+    "print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Visualize the first digit of each class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ASgoJCgAQJRIUACBKJCgAQJRIUACAKJGgAABRIkEBAKJEggIARIkEBQCIEgkKABAlEhQA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\n/wHTK4KPiGYqugAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110041390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)\n",
+    "ax = ax.flatten()\n",
+    "for i in range(10):\n",
+    "    img = X_train[y_train == i][0].reshape(28, 28)\n",
+    "    ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
+    "\n",
+    "ax[0].set_xticks([])\n",
+    "ax[0].set_yticks([])\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/mnist_all.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Visualize 25 different versions of \"7\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KHeGiShIiIhJVwnoPasyYMfTs2RPwpuoG4+px79699r6WGVv95ptvqFq16lm/\ndl5iavJ9+eWXduza1OUKZ+8pN9mwYQN33323/d1U6Fi6dCngr3EmgTH3msGbch7unlM0yGpTx2+/\n/dbeZzLuvPNOWxg63D2nrIS1gfL5fEEv5/Lqq6/aL1JTafuyyy7LsHsrqZmGyTRCx48f5+mnnwb8\nM38ke2JjYwGvAK/5+aKLLmLJkiWAGqacMmv6wPuMS+DMrOYBAwbYahLmfTh06FA7O9o1GuITEREn\nOVFJIidMCf6xY8faoT2zf0wkVz5HG1OI1xSYbdy4MUOGDIlgRNHPTMlPubThnXfe0VBpDpnC0IcP\nH7ZVOtq3bx/JkKJKfHy8vbWyYsUK/v73vwP+ShHhmEiWU+pBiYiIk6Kuq2Gmj48ePRrw7pmYzbaa\nNm0KZDy9UlI7efKknWpqcvb6669HMqSotWbNGru43CzEbdiwIU8++SSQcX0+ydrgwYMBr5KE2dEg\nL1XVP1sLFy60W2WUKFGCTz75BPBXmXFZ1DVQplTSr7/+ao+ZyREa2sue1157zc4oM2V8rrrqqghG\nFH3MzeennnrKri8xBXj79u2b7lo/CYxZQzZv3jzA2/lVZY0CZ3a/7t69uz02ffr0qGiYDA3xiYiI\nk6JiR13jkUce4Z133gH8K59nzJhhp0NHqgcVbZUk9uzZA3g5NMNRpkBsKHfKDZTLK/VT2rBhAz16\n9AC8m8+GKdZZpUoVJ4aboyWf4N/AsWvXrrbnZIZHzbY8keZ6Pk3PKWXRaLPd0Lhx45zc4FGVJERE\nJKpExU0bs43G22+/bcf8zVh0lSpVdO8pm8xkkri4OAYMGAC40XOKFikX4pqe00UXXWR79yk3L5Ts\nufHGGwGv4sHNN98M+KtuS9Y2btxod3AwS0e6d++e7oaE0SAqvtnNflGmcQLsWh01Ttnz6aefcuDA\nAcDbi8jMMpPAZbTOSTP1csZ8rp955hm+/fZbwJttZnbXdmHbh2gxfPhw2zBVqlQJSD1JItpoiE9E\nRJzkfPdj+/btPPHEE/b3sWPHAme/KV9eY6bld+3alVOnTgHQtm1b7eQaoISEBLp06QKkXuekwq85\nt3XrVgC7LueVV16xj40fP97Z+nAuMttkzJw50xZ8/eWXX4DoHr53voFaunQpf/31l/3dzEBxYXZU\nNDFDogkJCUyZMgWAf/zjHxGMKLoMGTKE2bNnA6nXOalhypk1a9bY3bNNqTKAn376CYAGDRpEJK5o\nY0qVtWyEYHr+AAAWO0lEQVTZEvAW3y9evBiI7obJ0BCfiIg4yfke1JmuvPLKSIcQlfbu3Wt/btOm\nDZD6Jr+kz6zLMcNR4O06DKTa60myp1GjRnY2pORMUlIS3bp1A/wz9pYuXZpqW5Jopx6UiIg4yflK\nEsnJyfamNPjH/126+o+GShLNmjUDvF1yzZWrqwU3XVqpb3pOZjdcgMTExJD9faHgUj5zA1fyOXny\nZDuF/Pjx4wBOVokIREY5db6BigbR0EBFE1e+AEANlKSlfAafSh2JiEhUyXKShEtDabmB8hl84c5p\nbv8/zO3/vnBTPnMu0yE+ERGRSNEQn4iIOEkNlIiIOEkNlIiIOEkNlIiIOEkNlIiIOEkNlIiIOEkN\nlIiIOEkNlIiIOEkNlIiIOCnTUkc+n09lJgIUaLHYcMSSG2SnGGeoY8kNlM/gUj6DL72cZlmLT6WQ\nspadWlvKZ9ayW7tMOc2c8hlcymfwZZRTDfGJiIiT1ECJiIiT1ECJiIiT1ECJiIiT1ECJiIiToqqB\n2rVrFz179qRnz57ExMQQExNDx44dOXr0KEePHo10eCIiEkRR1UCJiEjekemW7z6fL9mFOfyzZs0C\noH379pQrVw6Aq6++GoC1a9dy8OBBADZs2ABA2bJlwxqfz+cLeKGuC/l0XaD5/N+5YclpbGwspUqV\nAiApKQmAmJgYnn32WQBq1qxpzzXxNGvWjO+//94ev+qqqwCoXLlyyONNycV8RjPlM/gyyqnzDdRv\nv/1Go0aNAChUqBDLli0DoG7dugDs27eP8847D4AFCxYAcN1114U1RlcaqO3btwNQvXp1AE6fPh2y\nvyuUXPwCiI+Ptw3MunXrzN+d7rkmntKlS3Po0CF73DRws2fPBqBp06bkz58/ZDEbLuYzmuWFfCYk\nJPD++++nOpYvXz7uueeekPx9GeVUQ3wiIuKkLEsdRYoZRpk1axbnnOOFuWrVKmrUqJHqvGLFilGo\nUCEAWrduDcDhw4c599xzwxitG8wVvflz+fLlXHbZZZEMKdcoXLgwTzzxBAAdO3YM6Dkpe08pf2/e\nvDngvU/D0YOKRkePHuXHH39MdSwxMZHu3btn+rxevXrZPytVqhSq8HKFY8eOATBjxgx7bPLkyQB8\n9913nDp1Ckg9UrB06VIAPvjgg7DE6OwQ3/r16wFo0KABEydOBODuu+9O99y+ffsCMHbsWMC7X1Ck\nSJEwROlxdYgvKSmJxo0bAzB//nwAypcvn+3XXb58OcuXLwfggQceAAjpF6vrQygpvzi3bt0KwJAh\nQ+wx0xCd2UAZJt7Dhw9TrFixEEXp53o+Dxw4AHhDSNOnTwfgpZdeYtu2bTl+zYoVK7Jnz56gxHcm\n1/N5pqVLl7Jv3z4ARo4cyaZNmwB/JyAuLi7br2meGywa4hMRkaji7BBfyi5k27ZtIxhJ9DhziK9x\n48asXbsWgAsuuACAJk2a2Ekkt99+O7Vq1UrzOqa3NG3aNAAmTpzI8ePHAWjXrh0Q/ploLrn88svT\n/NylSxd77KOPPgLgrrvuSvf5JocFCxYMVYhOMT3706dPM3r0aAA2b95sHzejJfnz57dX92erZ8+e\nQXmdaLN48WIWL14MwPjx4wE4ePBg0CZMvfLKK0F5nUCpByUiIk5ytgdl3HHHHZmO0ycnJ5OYmAh4\nvQMI7f0Rl5mxbvNn8eLFbW7MPSiAhQsXAl4Py9wovffeewF49913bQ/M3MuqWLGiHXMuU6ZMqP8Z\nUa9z586At04qPYMGDQKgQIECYYspEn766SfAPynk5MmT2Xq+6aVXq1YN8O6fpPTNN98A8Nhjj6V5\nbnojA7nN7t27ef311wH/aMfu3buzfX+oQ4cONG3aFID7778f8N6j5p6+ccMNN9h70OHibANVpUoV\nwLuBar5k8+XLl+a8uLg43nnnHQB69+4N5P4PfkbOHOJLqVWrVml+fvLJJzlx4kSq8ypUqGBnqZkZ\nk0uXLqV///4hiTk3eeuttwB/w3Tm/4MZ3jIXUrndqFGjAOxssIyYWbgFCxa0OSpVqhT/+Mc/AO9C\n60ybNm1i9erVqY7FxMQwYMAAAP75z3+eXfBRoEmTJuzduzfL87p27cozzzwDeGvzzlSkSBF7UR8b\nGwvAhx9+mOa8Ll26hH1YWkN8IiLiJGd7UA8++GBA55npkwB33nlnqMKJCmZIxFx5Hj582F69pjfs\nacpGpfTcc8+l+9rnn38+QJ5cXxaoI0eOZPhYoUKFaNCgQRijibyPP/4YgFtvvRXwr7s50zXXXAP4\nJ/IEYuPGjfb1jXLlytlRFFNdJjd6+umnAdLtPd199922t2SW2pQqVcquJU3PiRMn7C0AM/wcGxtL\nixYtABg8eDAAV155ZZD+BYFTD0pERJzkbA8qUObqC7z7J+LvSd533332KutspoVPmTIlKHHlZvHx\n8XZ6eXouueQSW8svrzGTRoLh999/B1JP+jFuuummqK0/mR1mosKePXto1qwZANdffz3gLcRP7159\nZg4ePGiXPhiFCxe2EzAi2fN3roFKucr+THFxcbz33nsA/PLLL4C3xsLcjN6/fz/gNVSFCxcOR7hO\nMiVekpOT7QSSjIbuAhGqFfm5gXmfjhw50r4n02MmDEjOJSUl0adPHwDmzZtnj9euXRvwcpzeJIDc\nxny+zTqnnDIXrynXmZoh/FmzZjkxJK0hPhERcZITPSgz1Xnbtm289tprgLce50zJyclppu7GxMRQ\nsmRJwH+VOmrUKHtTPy8ya0Ay2g4iUKZG186dO20hXknN7EX2wgsv2GMp94u66aabgNTVJyR7du/e\nDXgjJKYyCvh7EnfccQdAWOtvRjPzfWsmW6Qswr1kyRLAnUoxEW2gPvnkEwA762Tjxo32MbPpYI8e\nPVI9x1Te3bJlC+DN3DEbFZr9dvI600DNnTv3rBYs/vXXX4CX65wUmc3tYmNjbfXslBcDKddBne1F\ngvjLnpkZZsaXX34JQP369cMeUzTbuXMngN1bD/wzLLN7/yrUNMQnIiJOimgPqn379oC/asT69evp\n1KkT4F+3k3L9TlJSEn/88Qfg70EtXrxYPacMpKwekRNmBX/FihW55JJLghFSrjJ79mxbmDMjZ+5f\nJp5Dhw7ZCSampFZKiYmJ9ko/5ciKUaVKFfu9IYHbv38/9913H+Av0luvXj2GDx8OuLd+TD0oERFx\nkrMbFqZnzJgxPPTQQwDcdtttgLe1QUZFOcPFlQ0LQ+Xaa6+1P2fVYwiGaNkQrmbNmrZHn5KJp1ix\nYvz6668AEZ2042I+//vf/2ZaOeKZZ57h2WefTXUsX7589n5Uhw4dIna/xMV8ZiUhIQGA119/3d7L\nM1PKR4wYYTd9jZSMcurELL6smB03+/XrZ286Dx06FMi4YrRIqJg9izLaMdeYO3dunp5NmpmMGiez\n5m7ChAnpPt6wYUPAvZv5LktISLAXloMGDbINk1nQH+nGKTP6dhcRESc534NKSkqya0nA644C1KlT\nJ1Ih5Rlm/57Y2Nh0tzzIq1atWgVkXBzWrIMyV/sSmN27d9utSPbs2WOXNgwcOBCAPn365JldiIPB\nfH4ffvhhuxVM69at7XfoxRdfHLHYAuV8A7VmzRrWrFkDeCWMzIZaGtoLPVMpfu3atbZCumS+7xbo\nvZlTTZs2TVVWq0uXLgD83//9X6RCikpbt24F/A37zJkz7Yzejz76KKoWNOuTJCIiTnK+B/Xf//7X\n/vzWW29RokSJCEaTt5hJAMnJyVx33XURjsYdZuJDwYIF7ewoyT7TW+rWrZv93ZQvmj17tipE5EB8\nfLwtuzVz5kzA20X37bffBqKvHJR6UCIi4iTne1AVK1a0e56kLAsvoTd9+nTAu9eiShJ+5v3YoEED\nVqxYkeZx08tXHb7MjRkzBoCvvvrKHrv99tsB736UZN8ll1ySatQJYPXq1fztb3+LUERnx/kGqkWL\nFnzzzTeRDiNPS05OPuuySbnR3Llzad68OQCbN2+2x3/88UcAihYtGpG4okWxYsWA1BuNdu/ePULR\n5A5jx46lTZs2gL+C/t69e6O2LJSG+ERExElRVerIVbm11NHgwYMBb1uD9IayQiUaS8m4TPkMLuUz\n+DLKqRqoIMitDVSk6AsguJTP4FI+gy+jnGqIT0REnJTlJAnNRAou5TP4lNPgUj6DS/nMuUyH+ERE\nRCJFQ3wiIuIkNVAiIuIkNVAiIuIkNVAiIuIkNVAiIuIkNVAiIuIkNVAiIuIkNVAiIuIkNVAiIuKk\nTEsd+Xw+lZkIUKDFYsMRS26QnWKcoY4lN1A+g0v5DL70cpplLT6VQspadmptKZ9Zy27tMuU0c8pn\ncCmfwZdRTjXEJyIiTlIDJSIiTlIDJSIiTlIDJSIiTlIDJSIiTlIDJSIiTlIDJSIiTspyHZTkLsnJ\nyYwbNw6A3r172+PTpk0DoFOnThGJS0TkTOpBiYiIk9SDymPeffdd+vbtC0BMjP/6ZPHixYB6UNnx\nxx9/8J///AeAQ4cOceeddwLQpEkTAM477zx69eplzz/nHO/jdt1114U5Urdt27YNgBo1alCyZEnA\ny2egtm7dCsC///1vOnToAEChQoWCHGX0SUpKAuDUqVNMnz4dgL/++ss+vnDhQgAWLFhgj91zzz0A\n1KtXjwsvvBDwv19Tfl+Eiy+zMhw+ny852GU6Dh8+DMDXX3+d7uN79+4FoF+/foCX5PQSY5IfExND\nxYoVAbj88ssB6Ny5MzfffDMQnjeqz+cLuBZfpMqe/PnnnwA0b96cTZs2pXn8oosuAmDZsmUULVo0\nrLGdKdB8/u/coOV0zJgxALRv3x6Ae++9l8TExAzP37RpEzt27Aj49QsWLAjAvn37AChWrFhOQ82W\nSOUzUKaBuuCCC2zJmz59+tjPcJUqVQCoVKkSx44dA2D9+vWcOnUKgI4dOwJw/PhxypYtC8DKlSup\nXLlySOJ1PZ/x8fEAvPTSSwA8++yz6Z5n4sqqdNOkSZMA6Nq1a5AiTCujnGqIT0REnBT2HtS6desA\naNy4cUDnJycnp9vCZ9b6Jycnc+DAAQBKly6d01AD5nIPyvSc/vGPfwCwceNG6tWrB3hXRE8//TQA\nJ0+eBGDXrl22RxopkbhC3bJlCy1atABgz549Z/16mbniiisA+OKLL8LSi3L9ij9lDyo9ZtivVKlS\nnDhxAsj6/2jr1q1UrVo1iFH6uZzPXbt20bx5c8AbggaoWbMm5557rj1nxIgRAOTPn9/EaB/bvHkz\nkHoC1ZNPPglk3BMLhoxyGvZ7UIULFwa84Y2jR4+medwMg9SuXRuA06dPky9fvjTnnT59GoBjx46x\nffv2UIUb1Q4cOGC/dDdu3Ah4wyRDhgwBvKEsM9S6aNEiAOLi4sIfqAOqV69OnTp1gNRffubLsXv3\n7mf1+pMmTbLD28uWLQO8i4avvvoKgDJlypzV6+dmJm+HDx+mSJEigJe7b7/9Ns257dq1A4j4RVak\nTJgwwTZMXbp0AWD8+PEB3+ooV66c/dm8980960jQEJ+IiDgp7D0o0zOaPn06N910U4aPr169Gsj6\nBt6RI0e4/vrrAVi1alUwQ41aphfUokUL23MyV1CLFy+2OU7Pq6++yujRo0MfpGNiYmIYOXIk4E2O\nAJg6daodGjE36nPq+uuvp1WrVqmO/fzzz6xcuRKAG2644axeP5q9//77aY716dPH9gBSKlGiBODN\nkCxVqlSaxx9//HHAPxKT1zz22GPceuutgP+7NJDek5lw8txzz9ljDz74IJC6VxVu6kGJiIiTIrYO\n6tJLL033+C+//ALAZ599Bvin/GbkxIkTxMbGpjluelN5cc2JWW/TsmVLLr74YgA7GSKz3lNeZ96T\npvceTDVq1Aj6a+YWZuJUSvXr1+eyyy7L8DmffvppmmMFChSgZs2aQY0t2hQsWJAGDRpk6zmJiYkM\nHjwYgJkzZwJerynlRIlIiVgDVbJkSSZMmAD4F4eBf21ItWrVAnqdChUq2IWRZgYKwNq1a4G82UAV\nKFAAgDfeeCPCkUhmrr76alq2bBnpMCLOrHeaM2eOHY66+uqr0z3XrIN6+eWX0zw2YMAAe2NfAjdp\n0qQ0+Zw6daoTE000xCciIk6KWA8qX758dOvWDfAPrTzxxBO2B5XREGCgUvbKRCLFrNt5/vnn0zyW\nP3/+dJdQ5DW33XYb4FWRMaWKMloTdf/99wOwYsUKe8yMkphhKgmMKRE1aNAge8wM65m1VJGmHpSI\niDgposVizRTy+vXrAzB37tygvbZ5rbvvvjtor5kX9OjRI9Ih5CozZswA4MMPP4xwJO4y941MxYKM\nbN68mVmzZtnfTY1Os/Dc3HuVwDRs2BDwahiaReqvvPIK4J9oFWluRHEWDh8+zG+//Qb4yx8lJSXR\npk2bSIYlwokTJ+jTp0+a4+bL4L333gt3SFHJTIx49NFH7ZBpoUKF7B5mpki0BKZnz56AP68lSpRg\n3rx5gDsNk6EhPhERcZJbzWUO/PHHH6xZswbwDxlGYt+S3GLixIl5spJEqKS3Rq9z586AVxdRsjZ7\n9mwAPv/8c3usQoUK3HLLLZEKKWp9+umndnmPGXGaPHky1atXj2RYGdI3uYiIOCnqe1Aikjs98sgj\ngFeN2zATIQYMGBCRmKKVqc85Z84cO9LUv39/AKfv16uBklTSu6kvOXPm2icz9JzdUjR5UVJSEuPG\njQP8e5WBf5bZAw88EJG4olFCQoKdsbdlyxZb/NXk0mUa4hMRESflih7UmTtWJiUlRSiS6Ge2M5Cc\nM5tpnrnrq9kCom3btmGPKVokJiYC0KlTp1Q9J4C6devSsWPHSIQVlRISEgCvOsSWLVsAbyPH5cuX\nRzKsbFEPSkREnBT1PajHH388zaaGkyZNUk8gh8aMGZNu3TgJ3IYNGwDvfZiSrv6z9sILLwD+7XYA\n6tWrB8DSpUspXbp0ROKKRqaAQcoF4S+99BLnn39+pELKtqhtoL788ksAfvjhB3vMlIdXeaOcO3Lk\nSKRDiHqbNm1Kc6xUqVJ2Vpqk78iRI+neuDefdTVO2WPWO4F/+yJTmDdaaIhPREScFLU9qFatWgGk\nGt7TBn1n7+eff05V70yyb+DAgWmOtWjRwg5VSWrmZv6QIUNS9eBHjRoFQPny5SMSV7TasWMH4FWI\nMBYtWgRE32c6ahsoM1NPZY2C69///jdDhw4FYMSIERGORvKC3bt3A2kvMPv27QugPbOyyeyOe/To\nUcArTlyhQoVIhpRj+nYXEREnRW0PyvSczpzBJ9ljhgHMnjqrVq3iqaeeimBEudOwYcMiHUJUMTtr\nS/bExcWlmjgG3t54RYoUiVBEZ0c9KBERcVLU9qBSMiv0y5QpE+FIoo+5AT127NgIR5J7PPzwwwD0\n69fPbq1hpvlK5sxN/NWrV+veUw4cPHiQ1atXA141DsDZrTQC4TuzTFCqB32+5Mwej6SZM2cCMHXq\nVHsztUWLFhGJxefzkZycnOVYo8v5dEmg+fzfucppFpTP4FI+gy+jnGqIT0REnJRlDyqMsUS1QHtQ\n4YglN8jOFWqoY8kNlM/gUj6DL72cZtpAiYiIRIqG+ERExElqoERExElqoERExElqoERExElqoERE\nxEn/D4pZX87LFMIKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109d0c198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n",
+    "ax = ax.flatten()\n",
+    "for i in range(25):\n",
+    "    img = X_train[y_train == 7][i].reshape(28, 28)\n",
+    "    ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
+    "\n",
+    "ax[0].set_xticks([])\n",
+    "ax[0].set_yticks([])\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/mnist_7.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Uncomment the following lines to optionally save the data in CSV format. \n",
+    "However, note that those CSV files will take up a substantial amount of storage space:\n",
+    "\n",
+    "- train_img.csv 1.1 GB (gigabytes)\n",
+    "- train_labels.csv 1.4 MB (megabytes)\n",
+    "- test_img.csv 187.0 MB\n",
+    "- test_labels 144 KB (kilobytes)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "#np.savetxt('train_img.csv', X_train, fmt='%i', delimiter=',')\n",
+    "#np.savetxt('train_labels.csv', y_train, fmt='%i', delimiter=',')\n",
+    "X_train = np.genfromtxt('train_img.csv', dtype=int, delimiter=',')\n",
+    "y_train = np.genfromtxt('train_labels.csv', dtype=int, delimiter=',')\n",
+    "\n",
+    "#np.savetxt('test_img.csv', X_test, fmt='%i', delimiter=',')\n",
+    "#np.savetxt('test_labels.csv', y_test, fmt='%i', delimiter=',')\n",
+    "X_test = np.genfromtxt('test_img.csv', dtype=int, delimiter=',')\n",
+    "y_test = np.genfromtxt('test_labels.csv', dtype=int, delimiter=',')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Implementing a multi-layer perceptron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.special import expit\n",
+    "import sys\n",
+    "\n",
+    "\n",
+    "class NeuralNetMLP(object):\n",
+    "    \"\"\" Feedforward neural network / Multi-layer perceptron classifier.\n",
+    "\n",
+    "    Parameters\n",
+    "    ------------\n",
+    "    n_output : int\n",
+    "      Number of output units, should be equal to the\n",
+    "      number of unique class labels.\n",
+    "\n",
+    "    n_features : int\n",
+    "      Number of features (dimensions) in the target dataset.\n",
+    "      Should be equal to the number of columns in the X array.\n",
+    "\n",
+    "    n_hidden : int (default: 30)\n",
+    "      Number of hidden units.\n",
+    "\n",
+    "    l1 : float (default: 0.0)\n",
+    "      Lambda value for L1-regularization.\n",
+    "      No regularization if l1=0.0 (default)\n",
+    "\n",
+    "    l2 : float (default: 0.0)\n",
+    "      Lambda value for L2-regularization.\n",
+    "      No regularization if l2=0.0 (default)\n",
+    "\n",
+    "    epochs : int (default: 500)\n",
+    "      Number of passes over the training set.\n",
+    "\n",
+    "    eta : float (default: 0.001)\n",
+    "      Learning rate.\n",
+    "\n",
+    "    alpha : float (default: 0.0)\n",
+    "      Momentum constant. Factor multiplied with the\n",
+    "      gradient of the previous epoch t-1 to improve\n",
+    "      learning speed\n",
+    "      w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
+    "    \n",
+    "    decrease_const : float (default: 0.0)\n",
+    "      Decrease constant. Shrinks the learning rate\n",
+    "      after each epoch via eta / (1 + epoch*decrease_const)\n",
+    "\n",
+    "    shuffle : bool (default: False)\n",
+    "      Shuffles training data every epoch if True to prevent circles.\n",
+    "\n",
+    "    minibatches : int (default: 1)\n",
+    "      Divides training data into k minibatches for efficiency.\n",
+    "      Normal gradient descent learning if k=1 (default).\n",
+    "\n",
+    "    random_state : int (default: None)\n",
+    "      Set random state for shuffling and initializing the weights.\n",
+    "\n",
+    "    Attributes\n",
+    "    -----------\n",
+    "    cost_ : list\n",
+    "      Sum of squared errors after each epoch.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, n_output, n_features, n_hidden=30,\n",
+    "                 l1=0.0, l2=0.0, epochs=500, eta=0.001, \n",
+    "                 alpha=0.0, decrease_const=0.0, shuffle=True, \n",
+    "                 minibatches=1, random_state=None):\n",
+    "\n",
+    "        np.random.seed(random_state)\n",
+    "        self.n_output = n_output\n",
+    "        self.n_features = n_features\n",
+    "        self.n_hidden = n_hidden\n",
+    "        self.w1, self.w2 = self._initialize_weights()\n",
+    "        self.l1 = l1\n",
+    "        self.l2 = l2\n",
+    "        self.epochs = epochs\n",
+    "        self.eta = eta\n",
+    "        self.alpha = alpha\n",
+    "        self.decrease_const = decrease_const\n",
+    "        self.shuffle = shuffle\n",
+    "        self.minibatches = minibatches\n",
+    "\n",
+    "    def _encode_labels(self, y, k):\n",
+    "        \"\"\"Encode labels into one-hot representation\n",
+    "\n",
+    "        Parameters\n",
+    "        ------------\n",
+    "        y : array, shape = [n_samples]\n",
+    "            Target values.\n",
+    "\n",
+    "        Returns\n",
+    "        -----------\n",
+    "        onehot : array, shape = (n_labels, n_samples)\n",
+    "\n",
+    "        \"\"\"\n",
+    "        onehot = np.zeros((k, y.shape[0]))\n",
+    "        for idx, val in enumerate(y):\n",
+    "            onehot[val, idx] = 1.0\n",
+    "        return onehot\n",
+    "\n",
+    "    def _initialize_weights(self):\n",
+    "        \"\"\"Initialize weights with small random numbers.\"\"\"\n",
+    "        w1 = np.random.uniform(-1.0, 1.0, size=self.n_hidden*(self.n_features + 1))\n",
+    "        w1 = w1.reshape(self.n_hidden, self.n_features + 1)\n",
+    "        w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden + 1))\n",
+    "        w2 = w2.reshape(self.n_output, self.n_hidden + 1)\n",
+    "        return w1, w2\n",
+    "\n",
+    "    def _sigmoid(self, z):\n",
+    "        \"\"\"Compute logistic function (sigmoid)\n",
+    "\n",
+    "        Uses scipy.special.expit to avoid overflow\n",
+    "        error for very small input values z.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # return 1.0 / (1.0 + np.exp(-z))\n",
+    "        return expit(z)\n",
+    "\n",
+    "    def _sigmoid_gradient(self, z):\n",
+    "        \"\"\"Compute gradient of the logistic function\"\"\"\n",
+    "        sg = self._sigmoid(z)\n",
+    "        return sg * (1 - sg)\n",
+    "\n",
+    "    def _add_bias_unit(self, X, how='column'):\n",
+    "        \"\"\"Add bias unit (column or row of 1s) to array at index 0\"\"\"\n",
+    "        if how == 'column':\n",
+    "            X_new = np.ones((X.shape[0], X.shape[1]+1))\n",
+    "            X_new[:, 1:] = X\n",
+    "        elif how == 'row':\n",
+    "            X_new = np.ones((X.shape[0]+1, X.shape[1]))\n",
+    "            X_new[1:, :] = X\n",
+    "        else:\n",
+    "            raise AttributeError('`how` must be `column` or `row`')\n",
+    "        return X_new\n",
+    "\n",
+    "    def _feedforward(self, X, w1, w2):\n",
+    "        \"\"\"Compute feedforward step\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ----------\n",
+    "        a1 : array, shape = [n_samples, n_features+1]\n",
+    "          Input values with bias unit.\n",
+    "\n",
+    "        z2 : array, shape = [n_hidden, n_samples]\n",
+    "          Net input of hidden layer.\n",
+    "\n",
+    "        a2 : array, shape = [n_hidden+1, n_samples]\n",
+    "          Activation of hidden layer.\n",
+    "\n",
+    "        z3 : array, shape = [n_output_units, n_samples]\n",
+    "          Net input of output layer.\n",
+    "\n",
+    "        a3 : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of output layer.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        a1 = self._add_bias_unit(X, how='column')\n",
+    "        z2 = w1.dot(a1.T)\n",
+    "        a2 = self._sigmoid(z2)\n",
+    "        a2 = self._add_bias_unit(a2, how='row')\n",
+    "        z3 = w2.dot(a2)\n",
+    "        a3 = self._sigmoid(z3)\n",
+    "        return a1, z2, a2, z3, a3\n",
+    "\n",
+    "    def _L2_reg(self, lambda_, w1, w2):\n",
+    "        \"\"\"Compute L2-regularization cost\"\"\"\n",
+    "        return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) + np.sum(w2[:, 1:] ** 2))\n",
+    "\n",
+    "    def _L1_reg(self, lambda_, w1, w2):\n",
+    "        \"\"\"Compute L1-regularization cost\"\"\"\n",
+    "        return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() + np.abs(w2[:, 1:]).sum())\n",
+    "\n",
+    "    def _get_cost(self, y_enc, output, w1, w2):\n",
+    "        \"\"\"Compute cost function.\n",
+    "\n",
+    "        y_enc : array, shape = (n_labels, n_samples)\n",
+    "          one-hot encoded class labels.\n",
+    "\n",
+    "        output : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of the output layer (feedforward)\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "        cost : float\n",
+    "          Regularized cost.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        term1 = -y_enc * (np.log(output))\n",
+    "        term2 = (1 - y_enc) * np.log(1 - output)\n",
+    "        cost = np.sum(term1 - term2)\n",
+    "        L1_term = self._L1_reg(self.l1, w1, w2)\n",
+    "        L2_term = self._L2_reg(self.l2, w1, w2)\n",
+    "        cost = cost + L1_term + L2_term\n",
+    "        return cost\n",
+    "\n",
+    "    def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):\n",
+    "        \"\"\" Compute gradient step using backpropagation.\n",
+    "\n",
+    "        Parameters\n",
+    "        ------------\n",
+    "        a1 : array, shape = [n_samples, n_features+1]\n",
+    "          Input values with bias unit.\n",
+    "\n",
+    "        a2 : array, shape = [n_hidden+1, n_samples]\n",
+    "          Activation of hidden layer.\n",
+    "\n",
+    "        a3 : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of output layer.\n",
+    "\n",
+    "        z2 : array, shape = [n_hidden, n_samples]\n",
+    "          Net input of hidden layer.\n",
+    "\n",
+    "        y_enc : array, shape = (n_labels, n_samples)\n",
+    "          one-hot encoded class labels.\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "\n",
+    "        grad1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Gradient of the weight matrix w1.\n",
+    "\n",
+    "        grad2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "            Gradient of the weight matrix w2.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # backpropagation\n",
+    "        sigma3 = a3 - y_enc\n",
+    "        z2 = self._add_bias_unit(z2, how='row')\n",
+    "        sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)\n",
+    "        sigma2 = sigma2[1:, :]\n",
+    "        grad1 = sigma2.dot(a1)\n",
+    "        grad2 = sigma3.dot(a2.T)\n",
+    "\n",
+    "        # regularize\n",
+    "        grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))\n",
+    "        grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))\n",
+    "\n",
+    "        return grad1, grad2\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        \"\"\"Predict class labels\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        Returns:\n",
+    "        ----------\n",
+    "        y_pred : array, shape = [n_samples]\n",
+    "          Predicted class labels.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        if len(X.shape) != 2:\n",
+    "            raise AttributeError('X must be a [n_samples, n_features] array.\\n'\n",
+    "                                 'Use X[:,None] for 1-feature classification,'\n",
+    "                                 '\\nor X[[i]] for 1-sample classification')\n",
+    "\n",
+    "        a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)\n",
+    "        y_pred = np.argmax(z3, axis=0)\n",
+    "        return y_pred\n",
+    "\n",
+    "    def fit(self, X, y, print_progress=False):\n",
+    "        \"\"\" Learn weights from training data.\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        y : array, shape = [n_samples]\n",
+    "          Target class labels.\n",
+    "\n",
+    "        print_progress : bool (default: False)\n",
+    "          Prints progress as the number of epochs\n",
+    "          to stderr.\n",
+    "\n",
+    "        Returns:\n",
+    "        ----------\n",
+    "        self\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.cost_ = []\n",
+    "        X_data, y_data = X.copy(), y.copy()\n",
+    "        y_enc = self._encode_labels(y, self.n_output)\n",
+    "\n",
+    "        delta_w1_prev = np.zeros(self.w1.shape)\n",
+    "        delta_w2_prev = np.zeros(self.w2.shape)\n",
+    "\n",
+    "        for i in range(self.epochs):\n",
+    "            \n",
+    "            # adaptive learning rate\n",
+    "            self.eta /= (1 + self.decrease_const*i)\n",
+    "\n",
+    "            if print_progress:\n",
+    "                sys.stderr.write('\\rEpoch: %d/%d' % (i+1, self.epochs))\n",
+    "                sys.stderr.flush()\n",
+    "\n",
+    "            if self.shuffle:\n",
+    "                idx = np.random.permutation(y_data.shape[0])\n",
+    "                X_data, y_data = X_data[idx], y_data[idx]\n",
+    "\n",
+    "            mini = np.array_split(range(y_data.shape[0]), self.minibatches)\n",
+    "            for idx in mini:\n",
+    "\n",
+    "                # feedforward\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X[idx], self.w1, self.w2)\n",
+    "                cost = self._get_cost(y_enc=y_enc[:, idx],\n",
+    "                                      output=a3,\n",
+    "                                      w1=self.w1,\n",
+    "                                      w2=self.w2)\n",
+    "                self.cost_.append(cost)\n",
+    "\n",
+    "                # compute gradient via backpropagation\n",
+    "                grad1, grad2 = self._get_gradient(a1=a1, a2=a2,\n",
+    "                                                  a3=a3, z2=z2,\n",
+    "                                                  y_enc=y_enc[:, idx],\n",
+    "                                                  w1=self.w1,\n",
+    "                                                  w2=self.w2)\n",
+    "\n",
+    "                delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2\n",
+    "                self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))\n",
+    "                self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))\n",
+    "                delta_w1_prev, delta_w2_prev = delta_w1, delta_w2\n",
+    "\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Training an artificial neural network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "nn = NeuralNetMLP(n_output=10, \n",
+    "                  n_features=X_train.shape[1], \n",
+    "                  n_hidden=50, \n",
+    "                  l2=0.1, \n",
+    "                  l1=0.0, \n",
+    "                  epochs=1000, \n",
+    "                  eta=0.001,\n",
+    "                  alpha=0.001,\n",
+    "                  decrease_const=0.00001,\n",
+    "                  minibatches=50, \n",
+    "                  random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1000/1000"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.NeuralNetMLP at 0x109d527b8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nn.fit(X_train, y_train, print_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QnE4GZrl7qtoWcV3uq3+OWA+fmP33h5dfbnp7rlp79gzjAGbh/pyIlFZcl/uaTFJmNpjQ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\nRERSS0lKRERS6/8D1eIjaZLpzHsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109c374e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(range(len(nn.cost_)), nn.cost_)\n",
+    "plt.ylim([0, 2000])\n",
+    "plt.ylabel('Cost')\n",
+    "plt.xlabel('Epochs * 50')\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/cost.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "batches = np.array_split(range(len(nn.cost_)), 1000)\n",
+    "cost_ary = np.array(nn.cost_)\n",
+    "cost_avgs = [np.mean(cost_ary[i]) for i in batches]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Jc+ZES6xVq7RrXpMUUgUUUiI1xD1aT2vWwNy58Mor8MEHMa5rypQYz9WlS8xr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a101588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(len(cost_avgs)), cost_avgs, color='red')\n",
+    "plt.ylim([0, 2000])\n",
+    "plt.ylabel('Cost')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('./figures/cost2.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training accuracy: 97.74%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_train_pred = nn.predict(X_train)\n",
+    "acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
+    "print('Training accuracy: %.2f%%' % (acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training accuracy: 96.18%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = nn.predict(X_test)\n",
+    "acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
+    "print('Training accuracy: %.2f%%' % (acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TgZ5hwl+O1aI6DPzLPjeD8GOa/bC61JlY3chnwvh9B3gdq9WahXVjbOTnHjat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\nZFBBSaFQKBQhgwpKCoVCoQgZ/hfAHLPf42Z3yAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d7a6978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "miscl_img = X_test[y_test != y_test_pred][:25]\n",
+    "correct_lab = y_test[y_test != y_test_pred][:25]\n",
+    "miscl_lab= y_test_pred[y_test != y_test_pred][:25]\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n",
+    "ax = ax.flatten()\n",
+    "for i in range(25):\n",
+    "    img = miscl_img[i].reshape(28, 28)\n",
+    "    ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
+    "    ax[i].set_title('%d) t: %d p: %d' % (i+1, correct_lab[i], miscl_lab[i]))\n",
+    "\n",
+    "ax[0].set_xticks([])\n",
+    "ax[0].set_yticks([])\n",
+    "plt.tight_layout()\n",
+    "# plt.savefig('./figures/mnist_miscl.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Debugging neural networks with gradient checking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.special import expit\n",
+    "import sys\n",
+    "\n",
+    "\n",
+    "class MLPGradientCheck(object):\n",
+    "    \"\"\" Feedforward neural network / Multi-layer perceptron classifier.\n",
+    "\n",
+    "    Parameters\n",
+    "    ------------\n",
+    "    n_output : int\n",
+    "      Number of output units, should be equal to the\n",
+    "      number of unique class labels.\n",
+    "\n",
+    "    n_features : int\n",
+    "      Number of features (dimensions) in the target dataset.\n",
+    "      Should be equal to the number of columns in the X array.\n",
+    "\n",
+    "    n_hidden : int (default: 30)\n",
+    "      Number of hidden units.\n",
+    "\n",
+    "    l1 : float (default: 0.0)\n",
+    "      Lambda value for L1-regularization.\n",
+    "      No regularization if l1=0.0 (default)\n",
+    "\n",
+    "    l2 : float (default: 0.0)\n",
+    "      Lambda value for L2-regularization.\n",
+    "      No regularization if l2=0.0 (default)\n",
+    "\n",
+    "    epochs : int (default: 500)\n",
+    "      Number of passes over the training set.\n",
+    "\n",
+    "    eta : float (default: 0.001)\n",
+    "      Learning rate.\n",
+    "\n",
+    "    alpha : float (default: 0.0)\n",
+    "      Momentum constant. Factor multiplied with the\n",
+    "      gradient of the previous epoch t-1 to improve\n",
+    "      learning speed\n",
+    "      w(t) := w(t) - (grad(t) + alpha*grad(t-1))\n",
+    "    \n",
+    "    decrease_const : float (default: 0.0)\n",
+    "      Decrease constant. Shrinks the learning rate\n",
+    "      after each epoch via eta / (1 + epoch*decrease_const)\n",
+    "\n",
+    "    shuffle : bool (default: False)\n",
+    "      Shuffles training data every epoch if True to prevent circles.\n",
+    "\n",
+    "    minibatches : int (default: 1)\n",
+    "      Divides training data into k minibatches for efficiency.\n",
+    "      Normal gradient descent learning if k=1 (default).\n",
+    "\n",
+    "    random_state : int (default: None)\n",
+    "      Set random state for shuffling and initializing the weights.\n",
+    "\n",
+    "    Attributes\n",
+    "    -----------\n",
+    "    cost_ : list\n",
+    "      Sum of squared errors after each epoch.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, n_output, n_features, n_hidden=30,\n",
+    "                 l1=0.0, l2=0.0, epochs=500, eta=0.001, \n",
+    "                 alpha=0.0, decrease_const=0.0, shuffle=True, \n",
+    "                 minibatches=1, random_state=None):\n",
+    "\n",
+    "        np.random.seed(random_state)\n",
+    "        self.n_output = n_output\n",
+    "        self.n_features = n_features\n",
+    "        self.n_hidden = n_hidden\n",
+    "        self.w1, self.w2 = self._initialize_weights()\n",
+    "        self.l1 = l1\n",
+    "        self.l2 = l2\n",
+    "        self.epochs = epochs\n",
+    "        self.eta = eta\n",
+    "        self.alpha = alpha\n",
+    "        self.decrease_const = decrease_const\n",
+    "        self.shuffle = shuffle\n",
+    "        self.minibatches = minibatches\n",
+    "\n",
+    "    def _encode_labels(self, y, k):\n",
+    "        \"\"\"Encode labels into one-hot representation\n",
+    "\n",
+    "        Parameters\n",
+    "        ------------\n",
+    "        y : array, shape = [n_samples]\n",
+    "            Target values.\n",
+    "\n",
+    "        Returns\n",
+    "        -----------\n",
+    "        onehot : array, shape = (n_labels, n_samples)\n",
+    "\n",
+    "        \"\"\"\n",
+    "        onehot = np.zeros((k, y.shape[0]))\n",
+    "        for idx, val in enumerate(y):\n",
+    "            onehot[val, idx] = 1.0\n",
+    "        return onehot\n",
+    "\n",
+    "    def _initialize_weights(self):\n",
+    "        \"\"\"Initialize weights with small random numbers.\"\"\"\n",
+    "        w1 = np.random.uniform(-1.0, 1.0, size=self.n_hidden*(self.n_features + 1))\n",
+    "        w1 = w1.reshape(self.n_hidden, self.n_features + 1)\n",
+    "        w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden + 1))\n",
+    "        w2 = w2.reshape(self.n_output, self.n_hidden + 1)\n",
+    "        return w1, w2\n",
+    "\n",
+    "    def _sigmoid(self, z):\n",
+    "        \"\"\"Compute logistic function (sigmoid)\n",
+    "\n",
+    "        Uses scipy.special.expit to avoid overflow\n",
+    "        error for very small input values z.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # return 1.0 / (1.0 + np.exp(-z))\n",
+    "        return expit(z)\n",
+    "\n",
+    "    def _sigmoid_gradient(self, z):\n",
+    "        \"\"\"Compute gradient of the logistic function\"\"\"\n",
+    "        sg = self._sigmoid(z)\n",
+    "        return sg * (1 - sg)\n",
+    "\n",
+    "    def _add_bias_unit(self, X, how='column'):\n",
+    "        \"\"\"Add bias unit (column or row of 1s) to array at index 0\"\"\"\n",
+    "        if how == 'column':\n",
+    "            X_new = np.ones((X.shape[0], X.shape[1]+1))\n",
+    "            X_new[:, 1:] = X\n",
+    "        elif how == 'row':\n",
+    "            X_new = np.ones((X.shape[0]+1, X.shape[1]))\n",
+    "            X_new[1:, :] = X\n",
+    "        else:\n",
+    "            raise AttributeError('`how` must be `column` or `row`')\n",
+    "        return X_new\n",
+    "\n",
+    "    def _feedforward(self, X, w1, w2):\n",
+    "        \"\"\"Compute feedforward step\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ----------\n",
+    "        a1 : array, shape = [n_samples, n_features+1]\n",
+    "          Input values with bias unit.\n",
+    "\n",
+    "        z2 : array, shape = [n_hidden, n_samples]\n",
+    "          Net input of hidden layer.\n",
+    "\n",
+    "        a2 : array, shape = [n_hidden+1, n_samples]\n",
+    "          Activation of hidden layer.\n",
+    "\n",
+    "        z3 : array, shape = [n_output_units, n_samples]\n",
+    "          Net input of output layer.\n",
+    "\n",
+    "        a3 : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of output layer.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        a1 = self._add_bias_unit(X, how='column')\n",
+    "        z2 = w1.dot(a1.T)\n",
+    "        a2 = self._sigmoid(z2)\n",
+    "        a2 = self._add_bias_unit(a2, how='row')\n",
+    "        z3 = w2.dot(a2)\n",
+    "        a3 = self._sigmoid(z3)\n",
+    "        return a1, z2, a2, z3, a3\n",
+    "\n",
+    "    def _L2_reg(self, lambda_, w1, w2):\n",
+    "        \"\"\"Compute L2-regularization cost\"\"\"\n",
+    "        return (lambda_/2.0) * (np.sum(w1[:, 1:] ** 2) + np.sum(w2[:, 1:] ** 2))\n",
+    "\n",
+    "    def _L1_reg(self, lambda_, w1, w2):\n",
+    "        \"\"\"Compute L1-regularization cost\"\"\"\n",
+    "        return (lambda_/2.0) * (np.abs(w1[:, 1:]).sum() + np.abs(w2[:, 1:]).sum())\n",
+    "\n",
+    "    def _get_cost(self, y_enc, output, w1, w2):\n",
+    "        \"\"\"Compute cost function.\n",
+    "\n",
+    "        y_enc : array, shape = (n_labels, n_samples)\n",
+    "          one-hot encoded class labels.\n",
+    "\n",
+    "        output : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of the output layer (feedforward)\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "        cost : float\n",
+    "          Regularized cost.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        term1 = -y_enc * (np.log(output))\n",
+    "        term2 = (1 - y_enc) * np.log(1 - output)\n",
+    "        cost = np.sum(term1 - term2)\n",
+    "        L1_term = self._L1_reg(self.l1, w1, w2)\n",
+    "        L2_term = self._L2_reg(self.l2, w1, w2)\n",
+    "        cost = cost + L1_term + L2_term\n",
+    "        return cost\n",
+    "\n",
+    "    def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):\n",
+    "        \"\"\" Compute gradient step using backpropagation.\n",
+    "\n",
+    "        Parameters\n",
+    "        ------------\n",
+    "        a1 : array, shape = [n_samples, n_features+1]\n",
+    "          Input values with bias unit.\n",
+    "\n",
+    "        a2 : array, shape = [n_hidden+1, n_samples]\n",
+    "          Activation of hidden layer.\n",
+    "\n",
+    "        a3 : array, shape = [n_output_units, n_samples]\n",
+    "          Activation of output layer.\n",
+    "\n",
+    "        z2 : array, shape = [n_hidden, n_samples]\n",
+    "          Net input of hidden layer.\n",
+    "\n",
+    "        y_enc : array, shape = (n_labels, n_samples)\n",
+    "          one-hot encoded class labels.\n",
+    "\n",
+    "        w1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Weight matrix for input layer -> hidden layer.\n",
+    "\n",
+    "        w2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "          Weight matrix for hidden layer -> output layer.\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "\n",
+    "        grad1 : array, shape = [n_hidden_units, n_features]\n",
+    "          Gradient of the weight matrix w1.\n",
+    "\n",
+    "        grad2 : array, shape = [n_output_units, n_hidden_units]\n",
+    "            Gradient of the weight matrix w2.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # backpropagation\n",
+    "        sigma3 = a3 - y_enc\n",
+    "        z2 = self._add_bias_unit(z2, how='row')\n",
+    "        sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)\n",
+    "        sigma2 = sigma2[1:, :]\n",
+    "        grad1 = sigma2.dot(a1)\n",
+    "        grad2 = sigma3.dot(a2.T)\n",
+    "\n",
+    "        # regularize\n",
+    "        grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))\n",
+    "        grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))\n",
+    "\n",
+    "        return grad1, grad2\n",
+    "\n",
+    "    def _gradient_checking(self, X, y_enc, w1, w2, epsilon, grad1, grad2):\n",
+    "        \"\"\" Apply gradient checking (for debugging only)\n",
+    "\n",
+    "        Returns\n",
+    "        ---------\n",
+    "        relative_error : float\n",
+    "          Relative error between the numerically\n",
+    "          approximated gradients and the backpropagated gradients.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        num_grad1 = np.zeros(np.shape(w1))\n",
+    "        epsilon_ary1 = np.zeros(np.shape(w1))\n",
+    "        for i in range(w1.shape[0]):\n",
+    "            for j in range(w1.shape[1]):\n",
+    "                epsilon_ary1[i, j] = epsilon\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X, w1 - epsilon_ary1, w2)\n",
+    "                cost1 = self._get_cost(y_enc, a3, w1-epsilon_ary1, w2)\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X, w1 + epsilon_ary1, w2)\n",
+    "                cost2 = self._get_cost(y_enc, a3, w1 + epsilon_ary1, w2)\n",
+    "                num_grad1[i, j] = (cost2 - cost1) / (2 * epsilon)\n",
+    "                epsilon_ary1[i, j] = 0\n",
+    "\n",
+    "        num_grad2 = np.zeros(np.shape(w2))\n",
+    "        epsilon_ary2 = np.zeros(np.shape(w2))\n",
+    "        for i in range(w2.shape[0]):\n",
+    "            for j in range(w2.shape[1]):\n",
+    "                epsilon_ary2[i, j] = epsilon\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X, w1, w2 - epsilon_ary2)\n",
+    "                cost1 = self._get_cost(y_enc, a3, w1, w2 - epsilon_ary2)\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X, w1, w2 + epsilon_ary2)\n",
+    "                cost2 = self._get_cost(y_enc, a3, w1, w2 + epsilon_ary2)\n",
+    "                num_grad2[i, j] = (cost2 - cost1) / (2 * epsilon)\n",
+    "                epsilon_ary2[i, j] = 0\n",
+    "\n",
+    "        num_grad = np.hstack((num_grad1.flatten(), num_grad2.flatten()))\n",
+    "        grad = np.hstack((grad1.flatten(), grad2.flatten()))\n",
+    "        norm1 = np.linalg.norm(num_grad - grad)\n",
+    "        norm2 = np.linalg.norm(num_grad)\n",
+    "        norm3 = np.linalg.norm(grad)\n",
+    "        relative_error = norm1 / (norm2 + norm3)\n",
+    "        return relative_error\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        \"\"\"Predict class labels\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        Returns:\n",
+    "        ----------\n",
+    "        y_pred : array, shape = [n_samples]\n",
+    "          Predicted class labels.\n",
+    "\n",
+    "        \"\"\"\n",
+    "        if len(X.shape) != 2:\n",
+    "            raise AttributeError('X must be a [n_samples, n_features] array.\\n'\n",
+    "                                 'Use X[:,None] for 1-feature classification,'\n",
+    "                                 '\\nor X[[i]] for 1-sample classification')\n",
+    "\n",
+    "        a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)\n",
+    "        y_pred = np.argmax(z3, axis=0)\n",
+    "        return y_pred\n",
+    "\n",
+    "    def fit(self, X, y, print_progress=False):\n",
+    "        \"\"\" Learn weights from training data.\n",
+    "\n",
+    "        Parameters\n",
+    "        -----------\n",
+    "        X : array, shape = [n_samples, n_features]\n",
+    "          Input layer with original features.\n",
+    "\n",
+    "        y : array, shape = [n_samples]\n",
+    "          Target class labels.\n",
+    "\n",
+    "        print_progress : bool (default: False)\n",
+    "          Prints progress as the number of epochs\n",
+    "          to stderr.\n",
+    "\n",
+    "        Returns:\n",
+    "        ----------\n",
+    "        self\n",
+    "\n",
+    "        \"\"\"\n",
+    "        self.cost_ = []\n",
+    "        X_data, y_data = X.copy(), y.copy()\n",
+    "        y_enc = self._encode_labels(y, self.n_output)\n",
+    "\n",
+    "        delta_w1_prev = np.zeros(self.w1.shape)\n",
+    "        delta_w2_prev = np.zeros(self.w2.shape)\n",
+    "\n",
+    "        for i in range(self.epochs):\n",
+    "            \n",
+    "            # adaptive learning rate\n",
+    "            self.eta /= (1 + self.decrease_const*i)\n",
+    "\n",
+    "            if print_progress:\n",
+    "                sys.stderr.write('\\rEpoch: %d/%d' % (i+1, self.epochs))\n",
+    "                sys.stderr.flush()\n",
+    "\n",
+    "            if self.shuffle:\n",
+    "                idx = np.random.permutation(y_data.shape[0])\n",
+    "                X_data, y_data = X_data[idx], y_data[idx]\n",
+    "\n",
+    "            mini = np.array_split(range(y_data.shape[0]), self.minibatches)\n",
+    "            for idx in mini:\n",
+    "\n",
+    "                # feedforward\n",
+    "                a1, z2, a2, z3, a3 = self._feedforward(X[idx], self.w1, self.w2)\n",
+    "                cost = self._get_cost(y_enc=y_enc[:, idx],\n",
+    "                                      output=a3,\n",
+    "                                      w1=self.w1,\n",
+    "                                      w2=self.w2)\n",
+    "                self.cost_.append(cost)\n",
+    "\n",
+    "                # compute gradient via backpropagation\n",
+    "                grad1, grad2 = self._get_gradient(a1=a1, a2=a2,\n",
+    "                                                  a3=a3, z2=z2,\n",
+    "                                                  y_enc=y_enc[:, idx],\n",
+    "                                                  w1=self.w1,\n",
+    "                                                  w2=self.w2)\n",
+    "                \n",
+    "                ## start gradient checking\n",
+    "                grad_diff = self._gradient_checking(X=X[idx], y_enc=y_enc[:, idx],\n",
+    "                                                  w1=self.w1, w2=self.w2,\n",
+    "                                                  epsilon=1e-5,\n",
+    "                                                  grad1=grad1, grad2=grad2)\n",
+    "                \n",
+    "                if grad_diff <= 1e-7:\n",
+    "                    print('Ok: %s' % grad_diff)\n",
+    "                elif grad_diff <= 1e-4:\n",
+    "                    print('Warning: %s' % grad_diff)\n",
+    "                else:\n",
+    "                    print('PROBLEM: %s' % grad_diff)\n",
+    "\n",
+    "                # update weights; [alpha * delta_w_prev] for momentum learning\n",
+    "                delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2\n",
+    "                self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))\n",
+    "                self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))\n",
+    "                delta_w1_prev, delta_w2_prev = delta_w1, delta_w2\n",
+    "\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "nn_check = MLPGradientCheck(n_output=10, \n",
+    "                            n_features=X_train.shape[1], \n",
+    "                            n_hidden=10, \n",
+    "                            l2=0.0, \n",
+    "                            l1=0.0, \n",
+    "                            epochs=10, \n",
+    "                            eta=0.001,\n",
+    "                            alpha=0.0,\n",
+    "                            decrease_const=0.0,\n",
+    "                            minibatches=1, \n",
+    "                            random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ok: 2.56712936241e-10\n",
+      "Ok: 2.94603251069e-10\n",
+      "Ok: 2.37615620231e-10\n",
+      "Ok: 2.43469423226e-10\n",
+      "Ok: 3.37872073158e-10\n",
+      "Ok: 3.63466384861e-10\n",
+      "Ok: 2.22472120785e-10\n",
+      "Ok: 2.33163708438e-10\n",
+      "Ok: 3.44653686551e-10\n",
+      "Ok: 2.17161707211e-10\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.MLPGradientCheck at 0x10a13ab70>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nn_check.fit(X_train[:5], y_train[:5], print_progress=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/1.2 Introduction - Tensorflow.ipynb b/1.2 Introduction - Tensorflow.ipynb
new file mode 100644
index 0000000..f2d774c
--- /dev/null
+++ b/1.2 Introduction - Tensorflow.ipynb	
@@ -0,0 +1,1768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"imgs/tensorflow_head.png\" />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Tensorflow\n",
+    "\n",
+    ">**TensorFlow** (https://www.tensorflow.org/) is a software library, developed by Google Brain Team within Google's Machine Learning Intelligence research organization, for the purposes of conducting machine learning and deep neural network research. \n",
+    "\n",
+    ">TensorFlow combines the computational algebra of compilation optimization techniques, making easy the calculation of many mathematical expressions that would be difficult to calculate, instead.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Tensorflow Main Features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "* Defining, optimizing, and efficiently calculating mathematical expressions involving multi-dimensional arrays (tensors).\n",
+    "\n",
+    "* Programming support of **deep neural networks** and machine learning techniques.\n",
+    "\n",
+    "* Transparent use of GPU computing, automating management and optimization of the same memory and the data used. You can write the same code and run it either on CPUs or GPUs. More specifically, TensorFlow will figure out which parts of the computation should be moved to the GPU.\n",
+    "\n",
+    "* High scalability of computation across machines and huge data sets.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    ">TensorFlow is available with Python and C++ support, but the **Python API** is better supported and much easier to learn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Very Preliminary Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A simple calculation in Python\n",
+    "x = 1\n",
+    "y = x + 10\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(\"y/read:0\", shape=(), dtype=int32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The ~same simple calculation in Tensorflow\n",
+    "x = tf.constant(1, name='x')\n",
+    "y = tf.Variable(x+10, name='y')\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "**Meaning**: \"When the variable `y` is computed, take the value of the constant `x` and add `10` to it\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sessions and Models\n",
+    "\n",
+    "To actually calculate the value of the `y` variable and to evaluate expressions, we need to **initialise** the variables, and then create a **session** where the actual computation happens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = tf.global_variables_initializer()  # model is used by convention"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tf.Session() as session:\n",
+    "    session.run(model)\n",
+    "    print(session.run(y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Data Flow Graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* (**IDEA**) \n",
+    "_A Machine Learning application is the result of the repeated computation of complex mathematical expressions, thus \n",
+    "we could describe this computation by using a **Data Flow Graph**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* **Data Flow Graph**: a graph where:\n",
+    "    - each Node represents the _instance_ of a mathematical operation \n",
+    "        - `multiply`, `add`, `divide`\n",
+    "    - each Edge is a multi-dimensional data set (`tensors`) on which the operations are performed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Tensorflow Graph Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "* **Node**: In TensorFlow, each node represents the instantion of an operation. \n",
+    "    - Each operation has inputs (`>= 2`) and outputs `>= 0`.\n",
+    "    \n",
+    "* **Edges**: In TensorFlow, there are two types of edge:\n",
+    "    - Data Edges: \n",
+    "    They are carriers of data structures (`tensors`), where an output of one operation (from one node) becomes the input for another operation.\n",
+    "    - Dependency Edges: These edges indicate a _control dependency_ between two nodes (i.e. \"happens before\" relationship). \n",
+    "        + Let's suppose we have two nodes `A` and `B` and a dependency edge connecting `A` to `B`. This means that `B` will start its operation only when the operation in `A` ends. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Tensorflow Graph Model (cont.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "* **Operation**: This represents an abstract computation, such as adding or multiplying matrices. \n",
+    "    - An operation manages tensors, and It can just be polymorphic: the same operation can manipulate different tensor element types. \n",
+    "        + For example, the addition of two int32 tensors, the addition of two float tensors, and so on.\n",
+    "\n",
+    "* **Kernel**: This represents the concrete implementation of that operation. \n",
+    "    - A kernel defines the implementation of the operation on a particular device. \n",
+    "        + For example, an `add matrix` operation can have a CPU implementation and a GPU one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Tensorflow Graph Model Session"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "**Session**: When the client program has to establish communication with the TensorFlow runtime system, a session must be created. \n",
+    "    \n",
+    "As soon as the session is created for a client, an initial graph is created and is empty. It has two fundamental methods:\n",
+    "\n",
+    "* `session.extend`: To be used during a computation, requesting to add more operations (nodes) and edges (data). The execution graph is then extended accordingly.\n",
+    "\n",
+    "* `session.run`: The execution graphs are executed to get the outputs (sometimes, subgraphs are executed thousands/millions of times using run invocations)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Tensorboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "**TensorBoard** is a visualization tool, devoted to analyzing Data Flow Graph and also to better understand the machine learning models. \n",
+    "\n",
+    "It can view different types of statistics about the parameters and details of any part of a computer graph graphically. It often happens that a graph of computation can be very complex."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Tensorboard Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Run the **TensorBoard** Server:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "```shell\n",
+    "tensorboard --logdir=/tmp/tf_logs\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "[Open TensorBoard](http://localhost:6006)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "95\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = tf.constant(5, name=\"a\")\n",
+    "b = tf.constant(45, name=\"b\")\n",
+    "y = tf.Variable(a+b*2, name='y')\n",
+    "model = tf.global_variables_initializer()\n",
+    "\n",
+    "with tf.Session() as session:\n",
+    "    # Merge all the summaries collected in the default graph.\n",
+    "    merged = tf.summary.merge_all() \n",
+    "    \n",
+    "    # Then we create `SummaryWriter`. \n",
+    "    # It will write all the summaries (in this case the execution graph) \n",
+    "    # obtained from the code's execution into the specified path”\n",
+    "    writer = tf.summary.FileWriter(\"tmp/tf_logs_simple\", session.graph)\n",
+    "    session.run(model)\n",
+    "    print(session.run(y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Data Types (Tensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## One Dimensional Tensor (Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "tensor_1d = np.array([1, 2.5, 4.6, 5.75, 9.7])\n",
+    "tf_tensor=tf.convert_to_tensor(tensor_1d,dtype=tf.float64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.    2.5   4.6   5.75  9.7 ]\n",
+      "1.0\n",
+      "4.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tf.Session() as sess: \n",
+    "    print(sess.run(tf_tensor))\n",
+    "    print(sess.run(tf_tensor[0]))\n",
+    "    print(sess.run(tf_tensor[2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Two Dimensional Tensor (Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]\n",
+      " [12 13 14 15]]\n",
+      "[[  0.   1.   2.   3.]\n",
+      " [  4.   5.   6.   7.]\n",
+      " [  8.   9.  10.  11.]\n",
+      " [ 12.  13.  14.  15.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensor_2d = np.arange(16).reshape(4, 4)\n",
+    "print(tensor_2d)\n",
+    "tf_tensor = tf.placeholder(tf.float32, shape=(4, 4))\n",
+    "with tf.Session() as sess:\n",
+    "    print(sess.run(tf_tensor, feed_dict={tf_tensor: tensor_2d}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Basic Operations (Examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "matrix1 = np.array([(2,2,2),(2,2,2),(2,2,2)],dtype='float32') \n",
+    "matrix2 = np.array([(1,1,1),(1,1,1),(1,1,1)],dtype='float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "tf_mat1 = tf.constant(matrix1) \n",
+    "tf_mat2 = tf.constant(matrix2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "matrix_product = tf.matmul(tf_mat1, tf_mat2)\n",
+    "matrix_sum = tf.add(tf_mat1, tf_mat2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "matrix_det = tf.matrix_determinant(matrix2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with tf.Session() as sess: \n",
+    "    prod_res = sess.run(matrix_product) \n",
+    "    sum_res = sess.run(matrix_sum) \n",
+    "    det_res = sess.run(matrix_det)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matrix1*matrix2 : \n",
+      " [[ 6.  6.  6.]\n",
+      " [ 6.  6.  6.]\n",
+      " [ 6.  6.  6.]]\n",
+      "matrix1+matrix2 : \n",
+      " [[ 3.  3.  3.]\n",
+      " [ 3.  3.  3.]\n",
+      " [ 3.  3.  3.]]\n",
+      "det(matrix2) : \n",
+      " 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"matrix1*matrix2 : \\n\", prod_res)\n",
+    "print(\"matrix1+matrix2 : \\n\", sum_res)\n",
+    "print(\"det(matrix2) : \\n\", det_res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Handling Tensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.image as mp_image\n",
+    "filename = \"imgs/keras-logo-small.jpg\"\n",
+    "input_image = mp_image.imread(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input dim = 3\n",
+      "input shape = (300, 300, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#dimension\n",
+    "print('input dim = {}'.format(input_image.ndim))\n",
+    "#shape\n",
+    "print('input shape = {}'.format(input_image.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xD4QAKgRAYolSgA8wcSKzv3wOE194ADVVsijkNtwY1ZhwjGc6HmflV7/Mwx8+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\nuqg9fwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1149e2278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(input_image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "my_image = tf.placeholder(\"uint8\",[None,None,3])\n",
+    "slice = tf.slice(my_image,[10,0,0],[16,-1,-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(16, 300, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tf.Session() as session:\n",
+    "    result = session.run(slice,feed_dict={my_image: input_image})\n",
+    "    print(result.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAAyCAYAAACXroq0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116bd49e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(result)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Transpose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "x = tf.Variable(input_image,name='x')\n",
+    "model = tf.global_variables_initializer()\n",
+    "\n",
+    "with tf.Session() as session:\n",
+    "    x = tf.transpose(x, perm=[1,0,2])\n",
+    "    session.run(model)\n",
+    "    result=session.run(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5cgdjX/Eqpp79DcZM3YlSsgGNnkRANLLm5jtZcMz7qC9aTF03EHZtEk9VuCUG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\nfy8nLy7LAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116ba6898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(result)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Computing the Gradient\n",
+    "\n",
+    "- Gradients are free!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = tf.placeholder(tf.float32)\n",
+    "y = tf.log(x)   \n",
+    "var_grad = tf.gradients(y, x)\n",
+    "with tf.Session() as session:\n",
+    "    var_grad_val = session.run(var_grad, feed_dict={x:2})\n",
+    "    print(var_grad_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Warming up: Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## MNIST Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST dataset (available at http://yann.lecun.com/exdb/mnist/), is widely used for training and testing in the field of machine learning, and we will use it in the examples of this book. It contains black and white images of handwritten digits from 0 to 9.\n",
+    "\n",
+    "The data set is divided into two groups: 60,000 to train the model and an additional 10,000 to test it. The original images, in black and white, were normalized to fit into a box of size 28×28 pixels and centered by calculating the center of mass of the pixels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "![](imgs/mnist.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "```\n",
+    "%load mnist_data.py\n",
+    "```\n",
+    "\n",
+    "(Adapted from [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# %load mnist_data.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import mnist_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/mnist/train-images-idx3-ubyte.gz\n",
+      "Extracting ./data/mnist/train-labels-idx1-ubyte.gz\n",
+      "Extracting ./data/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Extracting ./data/mnist/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "mnist = mnist_data.read_data_sets(\"./data/mnist/\", one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Dataset Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55000\n",
+      "5000\n",
+      "10000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(mnist.train.images))\n",
+    "print(len(mnist.validation.images))\n",
+    "print(len(mnist.test.images))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SP9b433uaBbxffGQeznVxPpRwpkbtDHp3PzedxtQTth6wtcNhsCeNPyXcHMlv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\n4ScSW8j/AfkWSZR2cVBlAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1171245f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image = mnist.train.images[5, :]\n",
+    "image = np.reshape(image,[28,28])\n",
+    "plt.imshow(image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This algorithm has nothing to do with the canonical linear regression, but it is an algorithm that allows us to solve problems of classification(supervised learning). \n",
+    "\n",
+    "In fact, to estimate the dependent variable, now we make use of the so-called **logistic function** or **sigmoid**. \n",
+    "\n",
+    "It is precisely because of this feature we call this algorithm logistic regression.\n",
+    "\n",
+    "![](imgs/sigmoid.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Hands On - Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Parameters\n",
+    "learning_rate = 0.01\n",
+    "training_epochs = 25\n",
+    "batch_size = 100\n",
+    "display_step = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# tf Graph Input\n",
+    "x = tf.placeholder(\"float\", [None, 784]) # mnist data image of shape 28*28=784\n",
+    "y = tf.placeholder(\"float\", [None, 10]) # 0-9 digits recognition => 10 classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## The Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Set model weights\n",
+    "W = tf.Variable(tf.zeros([784, 10]))\n",
+    "b = tf.Variable(tf.zeros([10]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Construct model\n",
+    "activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Minimize error using cross entropy\n",
+    "cross_entropy = y*tf.log(activation)\n",
+    "cost = tf.reduce_mean(-tf.reduce_sum(cross_entropy,reduction_indices=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set the Optimizer\n",
+    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Initializing the variables\n",
+    "init = tf.global_variables_initializer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def training_phase():\n",
+    "    avg_set, epoch_set = [], []\n",
+    "    # Training cycle\n",
+    "    for epoch in range(training_epochs):\n",
+    "        avg_cost = 0.\n",
+    "        total_batch = int(mnist.train.num_examples/batch_size)\n",
+    "        # Loop over all batches\n",
+    "        for i in range(total_batch):\n",
+    "            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
+    "            # Fit training using batch data\n",
+    "            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n",
+    "            # Compute average loss\n",
+    "            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys}) / total_batch\n",
+    "        \n",
+    "        # Display logs per epoch step\n",
+    "        if epoch % display_step == 0:\n",
+    "            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n",
+    "        \n",
+    "        avg_set.append(avg_cost)\n",
+    "        epoch_set.append(epoch+1)\n",
+    "    return avg_set, epoch_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def testing_phase():\n",
+    "    # Test model\n",
+    "    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n",
+    "    # Calculate accuracy\n",
+    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
+    "    print(\"Model accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## TF Session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0001 cost= 1.174406662\n",
+      "Epoch: 0002 cost= 0.662001156\n",
+      "Epoch: 0003 cost= 0.550464896\n",
+      "Epoch: 0004 cost= 0.496610615\n",
+      "Epoch: 0005 cost= 0.463699823\n",
+      "Epoch: 0006 cost= 0.440842383\n",
+      "Epoch: 0007 cost= 0.423908034\n",
+      "Epoch: 0008 cost= 0.410584232\n",
+      "Epoch: 0009 cost= 0.399826936\n",
+      "Epoch: 0010 cost= 0.390904010\n",
+      "Epoch: 0011 cost= 0.383289029\n",
+      "Epoch: 0012 cost= 0.376714404\n",
+      "Epoch: 0013 cost= 0.370987393\n",
+      "Epoch: 0014 cost= 0.365903865\n",
+      "Epoch: 0015 cost= 0.361352632\n",
+      "Epoch: 0016 cost= 0.357227456\n",
+      "Epoch: 0017 cost= 0.353577220\n",
+      "Epoch: 0018 cost= 0.350052592\n",
+      "Epoch: 0019 cost= 0.347016569\n",
+      "Epoch: 0020 cost= 0.344181476\n",
+      "Epoch: 0021 cost= 0.341469029\n",
+      "Epoch: 0022 cost= 0.338967005\n",
+      "Epoch: 0023 cost= 0.336669330\n",
+      "Epoch: 0024 cost= 0.334486312\n",
+      "Epoch: 0025 cost= 0.332483579\n",
+      "Training phase finished\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CkXRBRXKf5nRZRTKoroH4+CKCrqS4v+E3ulAFg7QhTQ2Tqvu0tzGQWIeQHE/B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\nB7TXZhORdklGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112ba8c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model accuracy: 0.9137\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Launch the graph\n",
+    "with tf.Session() as sess:\n",
+    "    # Plug TensorBoard Visualisation\n",
+    "    merged = tf.summary.merge_all() \n",
+    "    writer = tf.summary.FileWriter(\"/tmp/logistic_logs\", session.graph)\n",
+    "    \n",
+    "    sess.run(init)\n",
+    "    avg_set, epoch_set = training_phase()\n",
+    "    print(\"Training phase finished\")\n",
+    "    \n",
+    "    #plotting\n",
+    "    plt.plot(epoch_set,avg_set, 'o', label='Logistic Regression Training phase')\n",
+    "    plt.ylabel('cost')\n",
+    "    plt.xlabel('epoch')\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    #Testing\n",
+    "    testing_phase()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "[Open TensorBoard](http://localhost:6006)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Why Tensorflow ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "On a typical system, there are multiple computing devices. \n",
+    "\n",
+    "In TensorFlow, the supported device types are **CPU** and **GPU**. \n",
+    "\n",
+    "They are represented as strings. For example:\n",
+    "\n",
+    "* `\"/cpu:0\"`: The CPU of your machine.\n",
+    "* `\"/gpu:0\"`: The GPU of your machine, if you have one.\n",
+    "* `\"/gpu:1\"`: The second GPU of your machine, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "If a TensorFlow operation has both **CPU** and **GPU** implementations, the GPU devices will be given priority when the operation is assigned to a device. \n",
+    "\n",
+    "For example, `matmul` has both CPU and GPU kernels. On a system with devices `cpu:0` and `gpu:0`, `gpu:0` will be selected to run `matmul`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Example 1. Logging Device Placement\n",
+    "\n",
+    "`tf.Session(config=tf.ConfigProto(log_device_placement=True))`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "```python\n",
+    "# Creates a graph.\n",
+    "a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')\n",
+    "b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')\n",
+    "c = tf.matmul(a, b)\n",
+    "# Creates a session with log_device_placement set to True.\n",
+    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
+    "# Runs the op.\n",
+    "print(sess.run(c))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "```\n",
+    "Device mapping:\n",
+    "/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 760, pci bus\n",
+    "id: 0000:05:00.0\n",
+    "b: /job:localhost/replica:0/task:0/gpu:0\n",
+    "a: /job:localhost/replica:0/task:0/gpu:0\n",
+    "MatMul: /job:localhost/replica:0/task:0/gpu:0\n",
+    "[[ 22.  28.]\n",
+    " [ 49.  64.]]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Using Multiple GPUs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "```python\n",
+    "# Creates a graph.\n",
+    "c = []\n",
+    "for d in ['/gpu:0', '/gpu:1']:\n",
+    "  with tf.device(d):\n",
+    "    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])\n",
+    "    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2])\n",
+    "    c.append(tf.matmul(a, b))\n",
+    "with tf.device('/cpu:0'):\n",
+    "  sum = tf.add_n(c)\n",
+    "# Creates a session with log_device_placement set to True.\n",
+    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
+    "# Runs the op.\n",
+    "print sess.run(sum)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "```\n",
+    "Device mapping:\n",
+    "/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 760, pci bus\n",
+    "id: 0000:02:00.0\n",
+    "/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 760, pci bus\n",
+    "id: 0000:03:00.0\n",
+    "Const_3: /job:localhost/replica:0/task:0/gpu:0\n",
+    "Const_2: /job:localhost/replica:0/task:0/gpu:0\n",
+    "MatMul_1: /job:localhost/replica:0/task:0/gpu:0\n",
+    "Const_1: /job:localhost/replica:0/task:0/gpu:1\n",
+    "Const: /job:localhost/replica:0/task:0/gpu:1\n",
+    "MatMul: /job:localhost/replica:0/task:0/gpu:1\n",
+    "AddN: /job:localhost/replica:0/task:0/cpu:0\n",
+    "[[  44.   56.]\n",
+    " [  98.  128.]]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## More on Tensorflow\n",
+    "\n",
+    "[Official Documentation](https://www.tensorflow.org/versions/r0.10/get_started/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/1.3 Introduction - Keras.ipynb b/1.3 Introduction - Keras.ipynb
index 8428f8e..2431089 100644
--- a/1.3 Introduction - Keras.ipynb	
+++ b/1.3 Introduction - Keras.ipynb	
@@ -1,5 +1,33 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -8,12 +36,14 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline"
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -22,21 +52,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using Theano backend.\n"
+      "Using TensorFlow backend.\n"
      ]
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import theano\n",
-    "import theano.tensor as T\n",
-    "import matplotlib.pyplot as plt\n",
-    "import keras \n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.preprocessing import LabelEncoder \n",
     "from keras.utils import np_utils\n",
-    "from sklearn.cross_validation import train_test_split\n",
     "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
     "from keras.models import Sequential\n",
     "from keras.layers import Dense, Activation"
@@ -44,7 +65,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "For this section we will use the Kaggle otto challenge.\n",
     "If you want to follow, Get the data from Kaggle: https://www.kaggle.com/c/otto-group-product-classification-challenge/data"
@@ -52,14 +76,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "#### About the data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\n",
     "For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \n",
@@ -70,9 +100,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -108,9 +140,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -137,9 +171,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -154,19 +190,19 @@
       "    0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   0.   1.   1.\n",
       "    0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
       "    0.  11.   1.  20.   0.   0.   0.   0.   0.]]\n",
-      "(9L, 'classes')\n",
-      "(93L, 'dims')\n"
+      "9 classes\n",
+      "93 dims\n"
      ]
     }
    ],
    "source": [
     "print(\"Loading data...\")\n",
-    "X, labels = load_data('train.csv', train=True)\n",
+    "X, labels = load_data('data/train.csv', train=True)\n",
     "X, scaler = preprocess_data(X)\n",
     "Y, encoder = preprocess_labels(labels)\n",
     "\n",
     "\n",
-    "X_test, ids = load_data('test.csv', train=False)\n",
+    "X_test, ids = load_data('data/test.csv', train=False)\n",
     "X_test, ids = X_test[:1000], ids[:1000]\n",
     "\n",
     "#Plotting the data\n",
@@ -183,21 +219,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Now lets create and train a logistic regression model."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Keras\n",
     "\n",
@@ -206,7 +251,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.\n",
     "ref: https://keras.io/"
@@ -214,7 +262,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Why this name, Keras?\n",
     "\n",
@@ -227,54 +278,59 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Hands On - Keras Logistic Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(93L, 'dims')\n",
+      "93 dims\n",
       "Building model...\n",
-      "(9L, 'classes')\n",
+      "9 classes\n",
       "Epoch 1/10\n",
-      "61878/61878 [==============================] - 1s - loss: 1.0574     \n",
+      "61878/61878 [==============================] - 2s - loss: 1.0935     \n",
       "Epoch 2/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.7730     \n",
+      "61878/61878 [==============================] - 2s - loss: 0.7749     \n",
       "Epoch 3/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.7297     \n",
+      "61878/61878 [==============================] - 3s - loss: 0.7303     \n",
       "Epoch 4/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.7080     \n",
+      "61878/61878 [==============================] - 2s - loss: 0.7084     \n",
       "Epoch 5/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6948     \n",
+      "61878/61878 [==============================] - 3s - loss: 0.6949     \n",
       "Epoch 6/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6854     \n",
+      "61878/61878 [==============================] - 3s - loss: 0.6856     \n",
       "Epoch 7/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6787     \n",
+      "61878/61878 [==============================] - 3s - loss: 0.6788     \n",
       "Epoch 8/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6734     \n",
+      "61878/61878 [==============================] - 2s - loss: 0.6737     \n",
       "Epoch 9/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6691     \n",
+      "61878/61878 [==============================] - 3s - loss: 0.6693     \n",
       "Epoch 10/10\n",
-      "61878/61878 [==============================] - 1s - loss: 0.6657     \n"
+      "61878/61878 [==============================] - 2s - loss: 0.6660     \n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x23d330f0>"
+       "<keras.callbacks.History at 0x1182474e0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +352,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Simplicity is pretty impressive right? :)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Now lets understand:\n",
     "<pre>The core data structure of Keras is a <b>model</b>, a way to organize layers. The main type of model is the <b>Sequential</b> model, a linear stack of layers.</pre>\n"
@@ -311,14 +373,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "What we did here is stacking a Fully Connected (<b>Dense</b>) layer of trainable weights from the input to the output and an <b>Activation</b> layer on top of the weights layer."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "##### Dense"
    ]
@@ -326,22 +394,99 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "```python\n",
     "from keras.layers.core import Dense\n",
     "\n",
-    "Dense(output_dim, init='glorot_uniform', activation='linear', \n",
-    "      weights=None, W_regularizer=None, b_regularizer=None,\n",
-    "      activity_regularizer=None, W_constraint=None, \n",
-    "      b_constraint=None, bias=True, input_dim=None)\n",
+    "Dense(units, activation=None, use_bias=True, \n",
+    "      kernel_initializer='glorot_uniform', bias_initializer='zeros', \n",
+    "      kernel_regularizer=None, bias_regularizer=None, \n",
+    "      activity_regularizer=None, kernel_constraint=None, bias_constraint=None)\n",
     "```"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "* `units`: int > 0.\n",
+    "\n",
+    "* `init`: name of initialization function for the weights of the layer (see initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.\n",
+    "\n",
+    "* `activation`: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. \"linear\" activation: a(x) = x).\n",
+    "\n",
+    "* `weights`: list of Numpy arrays to set as initial weights. The list should have 2 elements, of shape (input_dim, output_dim) and (output_dim,) for weights and biases respectively.\n",
+    "\n",
+    "* `kernel_regularizer`: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.\n",
+    "\n",
+    "* `bias_regularizer`: instance of WeightRegularizer, applied to the bias.\n",
+    "\n",
+    "* `activity_regularizer`: instance of ActivityRegularizer, applied to the network output.\n",
+    "\n",
+    "* `kernel_constraint`: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.\n",
+    "\n",
+    "* `bias_constraint`: instance of the constraints module, applied to the bias.\n",
+    "\n",
+    "* `use_bias`: whether to include a bias (i.e. make the layer affine rather than linear)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## (some) others `keras.core.layers`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "* `keras.layers.core.Flatten()`\n",
+    "* `keras.layers.core.Reshape(target_shape)`\n",
+    "* `keras.layers.core.Permute(dims)`\n",
+    "\n",
+    "```python\n",
+    "model = Sequential()\n",
+    "model.add(Permute((2, 1), input_shape=(10, 64)))\n",
+    "# now: model.output_shape == (None, 64, 10)\n",
+    "# note: `None` is the batch dimension\n",
+    "```\n",
+    "\n",
+    "* `keras.layers.core.Lambda(function, output_shape=None, arguments=None)`\n",
+    "* `keras.layers.core.ActivityRegularization(l1=0.0, l2=0.0)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src=\"imgs/dl_overview.png\" >\n",
+    "\n",
+    "Credits: Yam Peleg ([@Yampeleg](https://twitter.com/yampeleg))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "##### Activation"
    ]
@@ -349,7 +494,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "```python\n",
@@ -361,14 +508,32 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "**Supported Activations** : [https://keras.io/activations/]\n",
+    "\n",
+    "**Advanced Activations**: [https://keras.io/layers/advanced-activations/]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "##### Optimizer"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).\n",
     "Here we used <b>SGD</b> (stochastic gradient descent) as an optimization algorithm for our trainable weights.  "
@@ -376,7 +541,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src=\"imgs/optimizers.png\" width=\"90%\">\n",
+    "\n",
+    "Credits: Yam Peleg ([@Yampeleg](https://twitter.com/yampeleg))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\"Data Sciencing\" this example a little bit more\n",
     "====="
@@ -384,7 +564,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "What we did here is nice, however in the real world it is not useable because of overfitting.\n",
     "Lets try and solve it with cross validation."
@@ -392,14 +575,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "##### Overfitting"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. \n",
     "\n",
@@ -409,7 +598,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "\n",
@@ -418,7 +609,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<pre>To avoid overfitting, we will first split out data to training set and test set and test out model on the test set.\n",
     "Next: we will use two of keras's callbacks <b>EarlyStopping</b> and <b>ModelCheckpoint</b></pre>"
@@ -426,38 +620,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train on 19835 samples, validate on 3501 samples\n",
+      "Train on 44706 samples, validate on 7890 samples\n",
       "Epoch 1/20\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6391 - val_loss: 0.6680\n",
+      "44706/44706 [==============================] - 0s - loss: 0.6569 - val_loss: 0.6697\n",
       "Epoch 2/20\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6386 - val_loss: 0.6689\n",
+      "44706/44706 [==============================] - 0s - loss: 0.6563 - val_loss: 0.6702\n",
       "Epoch 3/20\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6384 - val_loss: 0.6695\n",
+      "44706/44706 [==============================] - 0s - loss: 0.6558 - val_loss: 0.6706\n",
       "Epoch 4/20\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6381 - val_loss: 0.6702\n",
+      "44706/44706 [==============================] - 0s - loss: 0.6552 - val_loss: 0.6708\n",
       "Epoch 5/20\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6378 - val_loss: 0.6709\n",
+      "44706/44706 [==============================] - 0s - loss: 0.6548 - val_loss: 0.6709\n",
       "Epoch 6/20\n",
-      "19328/19835 [============================>.] - ETA: 0s - loss: 0.6380Epoch 00005: early stopping\n",
-      "19835/19835 [==============================] - 0s - loss: 0.6375 - val_loss: 0.6716\n"
+      "44706/44706 [==============================] - 1s - loss: 0.6543 - val_loss: 0.6710\n",
+      "Epoch 00005: early stopping\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x1d7245f8>"
+       "<keras.callbacks.History at 0x117eb8828>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -468,21 +664,27 @@
     "fBestModel = 'best_model.h5' \n",
     "early_stop = EarlyStopping(monitor='val_loss', patience=4, verbose=1) \n",
     "best_model = ModelCheckpoint(fBestModel, verbose=0, save_best_only=True)\n",
-    "model.fit(X, Y, validation_data = (X_test, Y_test), nb_epoch=20, \n",
+    "model.fit(X, Y, validation_data = (X_test, Y_test), epochs=20, \n",
     "          batch_size=128, verbose=True, validation_split=0.15, \n",
     "          callbacks=[best_model, early_stop]) "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Multi-Layer Perceptron and Fully Connected"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "So, how hard can it be to build a Multi-Layer percepton with keras?\n",
     "It is baiscly the same, just add more layers!"
@@ -490,11 +692,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.8963     \n",
+      "Epoch 2/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.7089     \n",
+      "Epoch 3/10\n",
+      "44706/44706 [==============================] - 3s - loss: 0.6809     \n",
+      "Epoch 4/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6678     \n",
+      "Epoch 5/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6596     \n",
+      "Epoch 6/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6542     \n",
+      "Epoch 7/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6505     \n",
+      "Epoch 8/10\n",
+      "44706/44706 [==============================] - 3s - loss: 0.6472     \n",
+      "Epoch 9/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6451     \n",
+      "Epoch 10/10\n",
+      "44706/44706 [==============================] - 2s - loss: 0.6434     \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x119a00588>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model = Sequential()\n",
     "model.add(Dense(100, input_shape=(dims,)))\n",
@@ -506,21 +747,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Your Turn!"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Hands On - Keras Fully Connected\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Take couple of minutes and Try and optimize the number of layers and the number of parameters in the layers to get the best results. "
    ]
@@ -529,7 +779,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -548,56 +800,80 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Building a question answering system, an image classification model, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "#### Theoretical Motivations for depth"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     ">Much has been studied about the depth of neural nets. Is has been proven mathematically[1] and empirically that convolutional neural network benifit from depth! "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "[1] - On the Expressive Power of Deep Learning: A Tensor Analysis - Cohen, et al 2015"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "#### Theoretical Motivations for depth"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "One much quoted theorem about neural network states that:"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     ">Universal approximation theorem states[1] that a feed-forward network with a single hidden layer containing a finite number of neurons (i.e., a multilayer perceptron), can approximate continuous functions on compact subsets of $\\mathbb{R}^n$, under mild assumptions on the activation function. The theorem thus states that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters; however, it does not touch upon the algorithmic learnability of those parameters."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "[1] - Approximation Capabilities of Multilayer Feedforward Networks - Kurt Hornik 1991"
    ]
diff --git a/1.4 Keras Backend.ipynb b/1.4 Keras Backend.ipynb
new file mode 100755
index 0000000..fc60bf1
--- /dev/null
+++ b/1.4 Keras Backend.ipynb	
@@ -0,0 +1,1075 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Linear Regression\n",
+    "To get familiar with automatic differentiation, we start by learning a simple linear regression model using Stochastic Gradient Descent (SGD).\n",
+    "\n",
+    "Recall that given a dataset $\\{(x_i, y_i)\\}_{i=0}^N$, with $x_i, y_i \\in \\mathbb{R}$, the objective of linear regression is to find two scalars $w$ and $b$ such that $y = w\\cdot x + b$ fits the dataset. In this tutorial we will learn $w$ and $b$ using SGD and a Mean Square Error (MSE) loss:\n",
+    "\n",
+    "$$\\mathcal{l} = \\frac{1}{N} \\sum_{i=0}^N (w\\cdot x_i + b - y_i)^2$$\n",
+    "\n",
+    "Starting from random values, parameters $w$ and $b$ will be updated at each iteration via the following rule:\n",
+    "\n",
+    "$$w_t = w_{t-1} - \\eta \\frac{\\partial \\mathcal{l}}{\\partial w}$$\n",
+    "<br>\n",
+    "$$b_t = b_{t-1} - \\eta \\frac{\\partial \\mathcal{l}}{\\partial b}$$\n",
+    "\n",
+    "where $\\eta$ is the learning rate.\n",
+    "\n",
+    "**NOTE:** Recall that **linear regression** is indeed a **simple neuron** with a linear activation function!!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Placeholders and variables\n",
+    "To implement and run this simple model, we will use the [Keras backend module](http://keras.io/backend/), which provides an abstraction over Theano and Tensorflow, two popular tensor manipulation libraries that provide automatic differentiation.\n",
+    "\n",
+    "First of all, we define the necessary variables and placeholders for our computational graph. Variables maintain state across executions of the computational graph, while placeholders are ways to feed the graph with external data.\n",
+    "\n",
+    "For the linear regression example, we need three variables: `w`, `b`, and the learning rate for SGD, `lr`. Two placeholders `x` and `target` are created to store $x_i$ and $y_i$ values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import keras.backend as K\n",
+    "import numpy as np\n",
+    "\n",
+    "# Placeholders and variables\n",
+    "x = K.placeholder()\n",
+    "target = K.placeholder()\n",
+    "lr = K.variable(0.1)\n",
+    "w = K.variable(np.random.rand())\n",
+    "b = K.variable(np.random.rand())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Model definition\n",
+    "Now we can define the $y = w\\cdot x + b$ relation as well as the MSE loss in the computational graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Define model and loss\n",
+    "y = w * x + b\n",
+    "loss = K.mean(K.square(y-target))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Then, given the gradient of MSE wrt to `w` and `b`, we can define how we update the parameters via SGD:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grads = K.gradients(loss, [w,b])\n",
+    "updates = [(w, w-lr*grads[0]), (b, b-lr*grads[1])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The whole model can be encapsulated in a `function`, which takes as input `x` and `target`, returns the current loss value and updates its parameter according to `updates`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "train = K.function(inputs=[x, target], outputs=[loss], updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Training\n",
+    "Training is now just a matter of calling the `function` we have just defined. Each time `train` is called, indeed, `w` and `b` will be updated using the SGD rule.\n",
+    "\n",
+    "Having generated some random training data, we will feed the `train` function for several epochs and observe the values of `w`, `b`, and loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loss: 0.054, w, b: [0.65, 0.24]\n",
+      "Loss: 0.003, w, b: [0.77, 0.34]\n",
+      "Loss: 0.002, w, b: [0.82, 0.32]\n",
+      "Loss: 0.001, w, b: [0.85, 0.30]\n",
+      "Loss: 0.001, w, b: [0.87, 0.29]\n",
+      "Loss: 0.000, w, b: [0.89, 0.27]\n",
+      "Loss: 0.000, w, b: [0.91, 0.27]\n",
+      "Loss: 0.000, w, b: [0.92, 0.26]\n",
+      "Loss: 0.000, w, b: [0.93, 0.26]\n",
+      "Loss: 0.000, w, b: [0.94, 0.25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate data\n",
+    "np_x = np.random.rand(1000)\n",
+    "np_target = 0.96*np_x + 0.24\n",
+    "\n",
+    "# Training\n",
+    "loss_history = []\n",
+    "for epoch in range(200):\n",
+    "    current_loss = train([np_x, np_target])[0]\n",
+    "    loss_history.append(current_loss)\n",
+    "    if epoch % 20 == 0:\n",
+    "        print(\"Loss: %.03f, w, b: [%.02f, %.02f]\" % (current_loss, K.eval(w), K.eval(b)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can also plot the loss history:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x110164240>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot loss history\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib notebook\n",
+    "plt.plot(loss_history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Your Turn\n",
+    "\n",
+    "Please switch to the **Theano** backend and re-run the notebook.\n",
+    "\n",
+    "You _should_ see no difference in the execution!\n",
+    "\n",
+    "**Reminder**: please keep in mind that you *can* execute shell commands from a notebook (pre-pending a `!` sign).\n",
+    "Thus:\n",
+    "\n",
+    "```shell\n",
+    "    !cat ~/.keras/keras.json\n",
+    "```\n",
+    "should show you the content of your keras configuration file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/1.5.1 (Extra) Introduction - Theano.ipynb b/1.5.1 (Extra) Introduction - Theano.ipynb
new file mode 100644
index 0000000..7578386
--- /dev/null
+++ b/1.5.1 (Extra) Introduction - Theano.ipynb	
@@ -0,0 +1,1011 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Theano \n",
+    "===\n",
+    "A language in a language"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dealing with weights matrices and gradients can be tricky and sometimes not trivial.\n",
+    "Theano is a great framework for handling vectors, matrices and high dimensional tensor algebra. \n",
+    "Most of this tutorial will refer to Theano however TensorFlow is another great framework capable of providing an incredible abstraction for complex algebra.\n",
+    "More on TensorFlow in the next chapters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import theano\n",
+    "import theano.tensor as T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Symbolic variables\n",
+    "=========="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Theano has it's own variables and functions, defined the following"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = T.scalar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Variables can be used in expressions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = 3*(x**2) + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "y is an expression now "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Result is symbolic as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Shape.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(y)\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#####printing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "As we are about to see, normal printing isn't the best when it comes to theano"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elemwise{add,no_inplace}.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'((TensorConstant{3} * (<TensorType(float32, scalar)> ** TensorConstant{2})) + TensorConstant{1})'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theano.pprint(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elemwise{add,no_inplace} [@A] ''   \n",
+      " |Elemwise{mul,no_inplace} [@B] ''   \n",
+      " | |TensorConstant{3} [@C]\n",
+      " | |Elemwise{pow,no_inplace} [@D] ''   \n",
+      " |   |<TensorType(float32, scalar)> [@E]\n",
+      " |   |TensorConstant{2} [@F]\n",
+      " |TensorConstant{1} [@G]\n"
+     ]
+    }
+   ],
+   "source": [
+    "theano.printing.debugprint(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Evaluating expressions\n",
+    "============\n",
+    "\n",
+    "Supply a `dict` mapping variables to values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(13.0, dtype=float32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.eval({x: 2})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Or compile a function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function([x], y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(13.0, dtype=float32)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Other tensor types\n",
+    "=========="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = T.vector()\n",
+    "X = T.matrix()\n",
+    "X = T.tensor3()\n",
+    "X = T.tensor4()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Automatic differention\n",
+    "============\n",
+    "- Gradients are free!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = T.scalar()\n",
+    "y = T.log(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elemwise{true_div}.0\n",
+      "0.5\n",
+      "Elemwise{mul,no_inplace}.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "gradient = T.grad(y, x)\n",
+    "print gradient\n",
+    "print gradient.eval({x: 2})\n",
+    "print (2 * gradient)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Shared Variables\n",
+    "\n",
+    "- Symbolic + Storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "x = theano.shared(np.zeros((2, 3), dtype=theano.config.floatX))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<CudaNdarrayType(float32, matrix)>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "We can get and set the variable's value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3)\n",
+      "[[ 0.  0.  0.]\n",
+      " [ 0.  0.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = x.get_value()\n",
+    "print(values.shape)\n",
+    "print(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x.set_value(values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Shared variables can be used in expressions as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Elemwise{pow,no_inplace}.0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x + 2) ** 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Their value is used as input when evaluating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4.,  4.,  4.],\n",
+       "       [ 4.,  4.,  4.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((x + 2) ** 2).eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4.,  4.,  4.],\n",
+       "       [ 4.,  4.,  4.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theano.function([], (x + 2) ** 2)()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Updates\n",
+    "\n",
+    "- Store results of function evalution\n",
+    "- `dict` mapping shared variables to new values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "count = theano.shared(0)\n",
+    "new_count = count + 1\n",
+    "updates = {count: new_count}\n",
+    "\n",
+    "f = theano.function([], count, updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(0)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(1)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(2)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warming up! Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using Theano backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import theano\n",
+    "import theano.tensor as T\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import LabelEncoder \n",
+    "from keras.utils import np_utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this section we will use the Kaggle otto challenge.\n",
+    "If you want to follow, Get the data from Kaggle: https://www.kaggle.com/c/otto-group-product-classification-challenge/data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### About the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\n",
+    "For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \n",
+    "Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\n",
+    "\n",
+    "https://www.kaggle.com/c/otto-group-product-classification-challenge/data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def load_data(path, train=True):\n",
+    "    \"\"\"Load data from a CSV File\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    path: str\n",
+    "        The path to the CSV file\n",
+    "        \n",
+    "    train: bool (default True)\n",
+    "        Decide whether or not data are *training data*.\n",
+    "        If True, some random shuffling is applied.\n",
+    "        \n",
+    "    Return\n",
+    "    ------\n",
+    "    X: numpy.ndarray \n",
+    "        The data as a multi dimensional array of floats\n",
+    "    ids: numpy.ndarray\n",
+    "        A vector of ids for each sample\n",
+    "    \"\"\"\n",
+    "    df = pd.read_csv(path)\n",
+    "    X = df.values.copy()\n",
+    "    if train:\n",
+    "        np.random.shuffle(X)  # https://youtu.be/uyUXoap67N8\n",
+    "        X, labels = X[:, 1:-1].astype(np.float32), X[:, -1]\n",
+    "        return X, labels\n",
+    "    else:\n",
+    "        X, ids = X[:, 1:].astype(np.float32), X[:, 0].astype(str)\n",
+    "        return X, ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def preprocess_data(X, scaler=None):\n",
+    "    \"\"\"Preprocess input data by standardise features \n",
+    "    by removing the mean and scaling to unit variance\"\"\"\n",
+    "    if not scaler:\n",
+    "        scaler = StandardScaler()\n",
+    "        scaler.fit(X)\n",
+    "    X = scaler.transform(X)\n",
+    "    return X, scaler\n",
+    "\n",
+    "\n",
+    "def preprocess_labels(labels, encoder=None, categorical=True):\n",
+    "    \"\"\"Encode labels with values among 0 and `n-classes-1`\"\"\"\n",
+    "    if not encoder:\n",
+    "        encoder = LabelEncoder()\n",
+    "        encoder.fit(labels)\n",
+    "    y = encoder.transform(labels).astype(np.int32)\n",
+    "    if categorical:\n",
+    "        y = np_utils.to_categorical(y)\n",
+    "    return y, encoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data...\n",
+      "[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   3.\n",
+      "    2.   1.   0.   0.   0.   0.   0.   0.   0.   5.   3.   1.   1.   0.\n",
+      "    0.   0.   0.   0.   1.   0.   0.   1.   0.   1.   0.   1.   0.   0.\n",
+      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "    0.   0.   0.   0.   0.   0.   0.   3.   0.   0.   0.   0.   1.   1.\n",
+      "    0.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "    0.  11.   1.  20.   0.   0.   0.   0.   0.]]\n",
+      "(9L, 'classes')\n",
+      "(93L, 'dims')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Loading data...\")\n",
+    "X, labels = load_data('train.csv', train=True)\n",
+    "X, scaler = preprocess_data(X)\n",
+    "Y, encoder = preprocess_labels(labels)\n",
+    "\n",
+    "\n",
+    "X_test, ids = load_data('test.csv', train=False)\n",
+    "X_test, ids = X_test[:1000], ids[:1000]\n",
+    "\n",
+    "#Plotting the data\n",
+    "print(X_test[:1])\n",
+    "\n",
+    "X_test, _ = preprocess_data(X_test, scaler)\n",
+    "\n",
+    "nb_classes = Y.shape[1]\n",
+    "print(nb_classes, 'classes')\n",
+    "\n",
+    "dims = X.shape[1]\n",
+    "print(dims, 'dims')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets create and train a logistic regression model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Hands On - Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1\n",
+      "target values for Data:\n",
+      "[ 0.  0.  1. ...,  0.  0.  0.]\n",
+      "prediction on training set:\n",
+      "[0 0 0 ..., 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Based on example from DeepLearning.net\n",
+    "rng = np.random\n",
+    "N = 400\n",
+    "feats = 93\n",
+    "training_steps = 1\n",
+    "\n",
+    "# Declare Theano symbolic variables\n",
+    "x = T.matrix(\"x\")\n",
+    "y = T.vector(\"y\")\n",
+    "w = theano.shared(rng.randn(feats), name=\"w\")\n",
+    "b = theano.shared(0., name=\"b\")\n",
+    "\n",
+    "# Construct Theano expression graph\n",
+    "p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))   # Probability that target = 1\n",
+    "prediction = p_1 > 0.5                    # The prediction thresholded\n",
+    "xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function\n",
+    "cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize\n",
+    "gw, gb = T.grad(cost, [w, b])             # Compute the gradient of the cost\n",
+    "                                          # (we shall return to this in a\n",
+    "                                          # following section of this tutorial)\n",
+    "\n",
+    "# Compile\n",
+    "train = theano.function(\n",
+    "          inputs=[x,y],\n",
+    "          outputs=[prediction, xent],\n",
+    "          updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)),\n",
+    "          allow_input_downcast=True)\n",
+    "predict = theano.function(inputs=[x], outputs=prediction, allow_input_downcast=True)\n",
+    "\n",
+    "#Transform for class1\n",
+    "y_class1 = []\n",
+    "for i in Y:\n",
+    "    y_class1.append(i[0])\n",
+    "y_class1 = np.array(y_class1)\n",
+    "\n",
+    "# Train\n",
+    "for i in range(training_steps):\n",
+    "    print('Epoch %s' % (i+1,))\n",
+    "    pred, err = train(X, y_class1)\n",
+    "\n",
+    "print(\"target values for Data:\")\n",
+    "print(y_class1)\n",
+    "print(\"prediction on training set:\")\n",
+    "print(predict(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/2.1 Supervised Learning - ConvNets.ipynb b/2.1 Supervised Learning - ConvNets.ipynb
index 8eb93b1..5ad0869 100644
--- a/2.1 Supervised Learning - ConvNets.ipynb	
+++ b/2.1 Supervised Learning - ConvNets.ipynb	
@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -14,6 +16,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -27,6 +31,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -38,6 +44,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -51,6 +59,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -63,6 +73,8 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "-"
     }
@@ -76,6 +88,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -89,6 +103,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -102,6 +118,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -113,6 +131,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -123,7 +143,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/cnn1.png\" width=\"70%\" />\n",
     "\n",
@@ -133,6 +156,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -148,6 +173,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -158,7 +185,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "What we want the computer to do is to be able to differentiate between all the images it’s given and figure out the unique features that make a dog a dog or that make a cat a cat. "
    ]
@@ -166,6 +196,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -179,6 +211,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -189,7 +223,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "> A more detailed overview of what CNNs do would be that you take the image, pass it through a series of convolutional, nonlinear, pooling (downsampling), and fully connected layers, and get an output. As we said earlier, the output can be a single class or a probability of classes that best describes the image. \n",
     "\n",
@@ -199,6 +236,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -210,6 +249,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -220,7 +261,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "\n",
     "<img src =\"imgs/conv.png\" width=\"50%\">"
@@ -229,6 +273,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -240,7 +286,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "A Convolutional Filter much like a **kernel** in image recognition is a small matrix useful for blurring, sharpening, embossing, edge detection, and more. \n",
     "\n",
@@ -252,6 +301,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -263,6 +314,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -273,7 +326,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/cnn2.png\" width=\"80%\">"
    ]
@@ -281,6 +337,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -292,6 +350,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -303,6 +363,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -316,6 +378,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -327,6 +391,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -338,6 +404,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -348,7 +416,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/cnn4.png\" width=\"80%\" />"
    ]
@@ -356,6 +427,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -367,6 +440,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -378,6 +453,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -389,6 +466,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -400,6 +479,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -413,6 +494,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -424,6 +507,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -438,6 +523,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -449,6 +536,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -460,6 +549,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -477,6 +568,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -490,6 +583,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -511,6 +606,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -522,6 +619,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -533,6 +632,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -544,6 +645,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -554,7 +657,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/MaxPool.png\" width=\"80%\" />"
    ]
@@ -562,6 +668,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -573,6 +681,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -588,6 +698,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -601,6 +713,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -612,6 +726,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -625,6 +741,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -636,6 +754,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -647,6 +767,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -657,7 +779,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<img src=\"imgs/ConvNet LeNet.png\" width=\"90%\" />"
    ]
@@ -665,6 +790,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -676,6 +803,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -693,6 +822,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -701,20 +832,65 @@
     "#### Convolution1D\n",
     "\n",
     "```python\n",
-    "from keras.layers.convolutional import Convolution1D\n",
-    "Convolution1D(nb_filter, filter_length, init='uniform',\n",
-    "              activation='linear', weights=None,\n",
-    "              border_mode='valid', subsample_length=1,\n",
-    "              W_regularizer=None, b_regularizer=None,\n",
-    "              activity_regularizer=None, W_constraint=None,\n",
-    "              b_constraint=None, bias=True, input_dim=None,\n",
-    "              input_length=None)\n",
-    "```"
+    "from keras.layers.convolutional import Conv1D\n",
+    "\n",
+    "Conv1D(filters, kernel_size, strides=1, padding='valid', \n",
+    "       dilation_rate=1, activation=None, use_bias=True, \n",
+    "       kernel_initializer='glorot_uniform', bias_initializer='zeros', \n",
+    "       kernel_regularizer=None, bias_regularizer=None, \n",
+    "       activity_regularizer=None, kernel_constraint=None, \n",
+    "       bias_constraint=None)\n",
+    "```\n",
+    "\n",
+    "#### Arguments:\n",
+    "\n",
+    "<ul>\n",
+    "<li><strong>filters</strong>: Integer, the dimensionality of the output space\n",
+    "    (i.e. the number output of filters in the convolution).</li>\n",
+    "<li><strong>kernel_size</strong>: An integer or tuple/list of a single integer,\n",
+    "    specifying the length of the 1D convolution window.</li>\n",
+    "<li><strong>strides</strong>: An integer or tuple/list of a single integer,\n",
+    "    specifying the stride length of the convolution.\n",
+    "    Specifying any stride value != 1 is incompatible with specifying\n",
+    "    any <code>dilation_rate</code> value != 1.</li>\n",
+    "<li><strong>padding</strong>: One of <code>\"valid\"</code>, <code>\"causal\"</code> or <code>\"same\"</code> (case-insensitive).\n",
+    "    <code>\"causal\"</code> results in causal (dilated) convolutions, e.g. output[t]\n",
+    "    does not depend on input[t+1:]. Useful when modeling temporal data\n",
+    "    where the model should not violate the temporal order.\n",
+    "    See <a href=\"https://arxiv.org/abs/1609.03499\">WaveNet: A Generative Model for Raw Audio, section 2.1</a>.</li>\n",
+    "<li><strong>dilation_rate</strong>: an integer or tuple/list of a single integer, specifying\n",
+    "    the dilation rate to use for dilated convolution.\n",
+    "    Currently, specifying any <code>dilation_rate</code> value != 1 is\n",
+    "    incompatible with specifying any <code>strides</code> value != 1.</li>\n",
+    "<li><strong>activation</strong>: Activation function to use\n",
+    "    (see <a href=\"../../activations/\">activations</a>).\n",
+    "    If you don't specify anything, no activation is applied\n",
+    "    (ie. \"linear\" activation: <code>a(x) = x</code>).</li>\n",
+    "<li><strong>use_bias</strong>: Boolean, whether the layer uses a bias vector.</li>\n",
+    "<li><strong>kernel_initializer</strong>: Initializer for the <code>kernel</code> weights matrix\n",
+    "    (see <a href=\"../../initializers/\">initializers</a>).</li>\n",
+    "<li><strong>bias_initializer</strong>: Initializer for the bias vector\n",
+    "    (see <a href=\"../../initializers/\">initializers</a>).</li>\n",
+    "<li><strong>kernel_regularizer</strong>: Regularizer function applied to\n",
+    "    the <code>kernel</code> weights matrix\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>bias_regularizer</strong>: Regularizer function applied to the bias vector\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>activity_regularizer</strong>: Regularizer function applied to\n",
+    "    the output of the layer (its \"activation\").\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>kernel_constraint</strong>: Constraint function applied to the kernel matrix\n",
+    "    (see <a href=\"../../constraints/\">constraints</a>).</li>\n",
+    "<li><strong>bias_constraint</strong>: Constraint function applied to the bias vector\n",
+    "    (see <a href=\"../../constraints/\">constraints</a>).</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -726,6 +902,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -738,11 +916,11 @@
     "# apply a convolution 1d of length 3 to a sequence with 10 timesteps,\n",
     "# with 64 output filters\n",
     "model = Sequential()\n",
-    "model.add(Convolution1D(64, 3, border_mode='same', input_shape=(10, 32)))\n",
+    "model.add(Conv1D(64, 3, border_mode='same', input_shape=(10, 32)))\n",
     "# now model.output_shape == (None, 10, 64)\n",
     "\n",
     "# add a new conv1d on top\n",
-    "model.add(Convolution1D(32, 3, border_mode='same'))\n",
+    "model.add(Conv1D(32, 3, border_mode='same'))\n",
     "# now model.output_shape == (None, 10, 32)\n",
     "```"
    ]
@@ -750,6 +928,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -758,21 +938,77 @@
     "#### Convolution2D\n",
     "\n",
     "```python\n",
-    "from keras.layers.convolutional import Convolution2D\n",
-    "Convolution2D(nb_filter, nb_row, nb_col, \n",
-    "              init='glorot_uniform',\n",
-    "              activation='linear', weights=None,\n",
-    "              border_mode='valid', subsample=(1, 1),\n",
-    "              dim_ordering='default', W_regularizer=None,\n",
-    "              b_regularizer=None, activity_regularizer=None,\n",
-    "              W_constraint=None, b_constraint=None, \n",
-    "              bias=True)\n",
-    "```"
+    "from keras.layers.convolutional import Conv2D\n",
+    "\n",
+    "Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', \n",
+    "       data_format=None, dilation_rate=(1, 1), activation=None, \n",
+    "       use_bias=True, kernel_initializer='glorot_uniform', \n",
+    "       bias_initializer='zeros', kernel_regularizer=None, \n",
+    "       bias_regularizer=None, activity_regularizer=None, \n",
+    "       kernel_constraint=None, bias_constraint=None)\n",
+    "```\n",
+    "\n",
+    "#### Arguments:\n",
+    "\n",
+    "<ul>\n",
+    "<li><strong>filters</strong>: Integer, the dimensionality of the output space\n",
+    "    (i.e. the number output of filters in the convolution).</li>\n",
+    "<li><strong>kernel_size</strong>: An integer or tuple/list of 2 integers, specifying the\n",
+    "    width and height of the 2D convolution window.\n",
+    "    Can be a single integer to specify the same value for\n",
+    "    all spatial dimensions.</li>\n",
+    "<li><strong>strides</strong>: An integer or tuple/list of 2 integers,\n",
+    "    specifying the strides of the convolution along the width and height.\n",
+    "    Can be a single integer to specify the same value for\n",
+    "    all spatial dimensions.\n",
+    "    Specifying any stride value != 1 is incompatible with specifying\n",
+    "    any <code>dilation_rate</code> value != 1.</li>\n",
+    "<li><strong>padding</strong>: one of <code>\"valid\"</code> or <code>\"same\"</code> (case-insensitive).</li>\n",
+    "<li><strong>data_format</strong>: A string,\n",
+    "    one of <code>channels_last</code> (default) or <code>channels_first</code>.\n",
+    "    The ordering of the dimensions in the inputs.\n",
+    "    <code>channels_last</code> corresponds to inputs with shape\n",
+    "    <code>(batch, height, width, channels)</code> while <code>channels_first</code>\n",
+    "    corresponds to inputs with shape\n",
+    "    <code>(batch, channels, height, width)</code>.\n",
+    "    It defaults to the <code>image_data_format</code> value found in your\n",
+    "    Keras config file at <code>~/.keras/keras.json</code>.\n",
+    "    If you never set it, then it will be \"channels_last\".</li>\n",
+    "<li><strong>dilation_rate</strong>: an integer or tuple/list of 2 integers, specifying\n",
+    "    the dilation rate to use for dilated convolution.\n",
+    "    Can be a single integer to specify the same value for\n",
+    "    all spatial dimensions.\n",
+    "    Currently, specifying any <code>dilation_rate</code> value != 1 is\n",
+    "    incompatible with specifying any stride value != 1.</li>\n",
+    "<li><strong>activation</strong>: Activation function to use\n",
+    "    (see <a href=\"../../activations/\">activations</a>).\n",
+    "    If you don't specify anything, no activation is applied\n",
+    "    (ie. \"linear\" activation: <code>a(x) = x</code>).</li>\n",
+    "<li><strong>use_bias</strong>: Boolean, whether the layer uses a bias vector.</li>\n",
+    "<li><strong>kernel_initializer</strong>: Initializer for the <code>kernel</code> weights matrix\n",
+    "    (see <a href=\"../../initializers/\">initializers</a>).</li>\n",
+    "<li><strong>bias_initializer</strong>: Initializer for the bias vector\n",
+    "    (see <a href=\"../../initializers/\">initializers</a>).</li>\n",
+    "<li><strong>kernel_regularizer</strong>: Regularizer function applied to\n",
+    "    the <code>kernel</code> weights matrix\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>bias_regularizer</strong>: Regularizer function applied to the bias vector\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>activity_regularizer</strong>: Regularizer function applied to\n",
+    "    the output of the layer (its \"activation\").\n",
+    "    (see <a href=\"../../regularizers/\">regularizer</a>).</li>\n",
+    "<li><strong>kernel_constraint</strong>: Constraint function applied to the kernel matrix\n",
+    "    (see <a href=\"../../constraints/\">constraints</a>).</li>\n",
+    "<li><strong>bias_constraint</strong>: Constraint function applied to the bias vector\n",
+    "    (see <a href=\"../../constraints/\">constraints</a>).</li>\n",
+    "</ul>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -784,12 +1020,12 @@
     "\n",
     "# apply a 3x3 convolution with 64 output filters on a 256x256 image:\n",
     "model = Sequential()\n",
-    "model.add(Convolution2D(64, 3, 3, border_mode='same', \n",
+    "model.add(Conv2D(64, 3, 3, border_mode='same', \n",
     "          input_shape=(3, 256, 256)))\n",
     "# now model.output_shape == (None, 64, 256, 256)\n",
     "\n",
     "# add a 3x3 convolution on top, with 32 output filters:\n",
-    "model.add(Convolution2D(32, 3, 3, border_mode='same'))\n",
+    "model.add(Conv2D(32, 3, 3, border_mode='same'))\n",
     "# now model.output_shape == (None, 32, 256, 256)\n",
     "\n",
     "```"
@@ -798,6 +1034,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -809,6 +1047,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -820,6 +1060,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     }
@@ -831,6 +1073,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -840,16 +1084,18 @@
     "\n",
     "**Input Shape**:\n",
     "\n",
-    "**3D** tensor with shape: (`samples`, `steps`, `input_dim`).\n",
+    "**3D** tensor with shape: (`batch_size`, `steps`, `input_dim`).\n",
     "\n",
     "**Output Shape**:\n",
     "\n",
-    "**3D** tensor with shape: (`samples`, `new_steps`, `nb_filter`)."
+    "**3D** tensor with shape: (`batch_size`, `new_steps`, `filters`)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -861,21 +1107,29 @@
     "\n",
     "**4D** tensor with shape: \n",
     "\n",
-    "- (`samples`, `channels`, `rows`, `cols`) if `dim_ordering='th'`\n",
-    "- (`samples`, `rows`, `cols`, `channels`) if `dim_ordering='tf'`\n",
+    "- (`batch_size`, `channels`, `rows`, `cols`) if `image_data_format='channels_last'`\n",
+    "- (`batch_size`, `rows`, `cols`, `channels`) if `image_data_format='channels_first'`\n",
     "\n",
     "**Output Shape**:\n",
     "\n",
     "**4D** tensor with shape:\n",
     "\n",
-    "- (`samples`, `nb_filter`, `new_rows`, `new_cols`) \n",
-    "if `dim_ordering='th'`\n",
-    "- (`samples`, `new_rows`, `new_cols`, `nb_filter`) if `dim_ordering='tf'`"
+    "- (`batch_size`, `filters`, `new_rows`, `new_cols`) \n",
+    "if `image_data_format='channels_first'`\n",
+    "- (`batch_size`, `new_rows`, `new_cols`, `filters`) if `image_data_format='channels_last'`"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/2.2.1 ConvNet HandsOn Part I.ipynb b/2.2.1 ConvNet HandsOn Part I.ipynb
new file mode 100644
index 0000000..d6b2839
--- /dev/null
+++ b/2.2.1 ConvNet HandsOn Part I.ipynb	
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# ConvNet HandsOn with Keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Problem Definition\n",
+    "\n",
+    "*Recognize handwritten digits*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Data\n",
+    "\n",
+    "The MNIST database ([link](http://yann.lecun.com/exdb/mnist)) has a database of handwritten digits. \n",
+    "\n",
+    "The training set has $60,000$ samples. \n",
+    "The test set has $10,000$ samples.\n",
+    "\n",
+    "The digits are size-normalized and centered in a fixed-size image. \n",
+    "\n",
+    "The data page has description on how the data was collected. It also has reports the benchmark of various algorithms on the test dataset. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Load the data\n",
+    "\n",
+    "The data is available in the repo's `data` folder. Let's load that using the `keras` library. \n",
+    "\n",
+    "For now, let's load the data and see how it looks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "from keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the datasets\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Basic data analysis on the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the type of X_train?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the type of y_train?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find number of observations in training data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find number of observations in test data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Display first 2 records of X_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Display the first 10 records of y_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the number of observations for each digit in the y_train dataset \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the number of observations for each digit in the y_test dataset \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the dimension of X_train?. What does that mean?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Display Images\n",
+    "\n",
+    "Let's now display some of the images and see how they look\n",
+    "\n",
+    "We will be using `matplotlib` library for displaying the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot\n",
+    "import matplotlib as mpl\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Displaying the first training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "fig = pyplot.figure()\n",
+    "ax = fig.add_subplot(1,1,1)\n",
+    "imgplot = ax.imshow(X_train[0], cmap=mpl.cm.Greys)\n",
+    "imgplot.set_interpolation('nearest')\n",
+    "ax.xaxis.set_ticks_position('top')\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Let's now display the 11th record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/2.2.2 (Exercise) FC MNIST.ipynb b/2.2.2 (Exercise) FC MNIST.ipynb
new file mode 100755
index 0000000..1fa9eae
--- /dev/null
+++ b/2.2.2 (Exercise) FC MNIST.ipynb	
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Fully Connected Feed-Forward Network\n",
+    "\n",
+    "In this notebook we will play with Feed-Forward FC-NN (Fully Connected Neural Network) for a *classification task*: Image Classification on MNIST Dataset\n",
+    "\n",
+    "**RECALL**\n",
+    "\n",
+    "In the FC-NN, the output of each layer is computed using the activations from the previous one, as follows:\n",
+    "\n",
+    "$$h_{i} = \\sigma(W_i h_{i-1} + b_i)$$\n",
+    "\n",
+    "where ${h}_i$ is the activation vector from the $i$-th layer (or the input data for $i=0$), ${W}_i$ and ${b}_i$ are  the weight matrix and the bias vector for the $i$-th layer, respectively. \n",
+    "<br><rb>\n",
+    "$\\sigma(\\cdot)$ is the activation function. In our example, we will use the *ReLU* activation function for the hidden layers and *softmax* for the last layer.\n",
+    "\n",
+    "To regularize the model, we will also insert a Dropout layer between consecutive hidden layers. \n",
+    "\n",
+    "Dropout works by “dropping out” some unit activations in a given layer, that is setting them to zero with a given probability.\n",
+    "\n",
+    "Our loss function will be the **categorical crossentropy**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Model definition\n",
+    "Keras supports two different kind of models: the [Sequential](http://keras.io/models/#sequential) model and the [Graph](http://keras.io/models/#graph) model. The former is used to build linear stacks of layer (so each layer has one input and one output), and the latter supports any kind of connection graph.\n",
+    "\n",
+    "In our case we build a Sequential model with three [Dense](http://keras.io/layers/core/#dense) (aka fully connected) layers, with some [Dropout](http://keras.io/layers/core/#dropout). Notice that the output layer has the softmax activation function. \n",
+    "\n",
+    "The resulting model is actually a `function` of its own inputs implemented using the Keras backend. \n",
+    "\n",
+    "We apply the binary crossentropy loss and choose SGD as the optimizer. \n",
+    "\n",
+    "Please remind that Keras supports a variety of different [optimizers](http://keras.io/optimizers/) and [loss functions](http://keras.io/objectives/), which you may want to check out. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers.core import Dense, Dropout\n",
+    "from keras.optimizers import SGD\n",
+    "\n",
+    "nb_classes = 10\n",
+    "\n",
+    "# FC@512+relu -> DropOut(0.2) -> FC@512+relu -> DropOut(0.2) -> FC@nb_classes+softmax\n",
+    "# ... your Code Here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# %load solutions/sol_161.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data preparation (`keras.dataset`)\n",
+    "\n",
+    "We will train our model on the MNIST dataset, which consists of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. \n",
+    "\n",
+    "Since this dataset is **provided** with Keras, we just ask the `keras.dataset` model for training and test data.\n",
+    "\n",
+    "We will:\n",
+    "\n",
+    "* download the data\n",
+    "* reshape data to be in vectorial form (original data are images)\n",
+    "* normalize between 0 and 1.\n",
+    "\n",
+    "The `binary_crossentropy` loss expects a **one-hot-vector** as input, therefore we apply the `to_categorical` function from `keras.utilis` to convert integer labels to **one-hot-vectors**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "X_train = X_train.reshape(60000, 784)\n",
+    "X_test = X_test.reshape(10000, 784)\n",
+    "X_train = X_train.astype(\"float32\")\n",
+    "X_test = X_test.astype(\"float32\")\n",
+    "X_train /= 255\n",
+    "X_test /= 255\n",
+    "\n",
+    "# convert class vectors to binary class matrices\n",
+    "Y_train = np_utils.to_categorical(y_train, 10)\n",
+    "Y_test = np_utils.to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Training\n",
+    "Having defined and compiled the model, it can be trained using the `fit` function. We also specify a validation dataset to monitor validation loss and accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.3078 - acc: 0.9000 - val_loss: 0.2843 - val_acc: 0.9000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.2680 - acc: 0.9013 - val_loss: 0.2379 - val_acc: 0.9042\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.2230 - acc: 0.9094 - val_loss: 0.1888 - val_acc: 0.9218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.1812 - acc: 0.9274 - val_loss: 0.1490 - val_acc: 0.9414\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.1497 - acc: 0.9431 - val_loss: 0.1223 - val_acc: 0.9567\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 6/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.1289 - acc: 0.9532 - val_loss: 0.1049 - val_acc: 0.9655\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 7/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.1148 - acc: 0.9596 - val_loss: 0.0933 - val_acc: 0.9703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 8/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.1049 - acc: 0.9638 - val_loss: 0.0851 - val_acc: 0.9732\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 9/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0975 - acc: 0.9666 - val_loss: 0.0789 - val_acc: 0.9750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 10/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0921 - acc: 0.9685 - val_loss: 0.0743 - val_acc: 0.9765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 11/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0871 - acc: 0.9706 - val_loss: 0.0706 - val_acc: 0.9776\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 12/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0838 - acc: 0.9716 - val_loss: 0.0675 - val_acc: 0.9783\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 13/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0805 - acc: 0.9731 - val_loss: 0.0650 - val_acc: 0.9790\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 14/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0779 - acc: 0.9738 - val_loss: 0.0629 - val_acc: 0.9796\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 15/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0755 - acc: 0.9745 - val_loss: 0.0611 - val_acc: 0.9802\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 16/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0734 - acc: 0.9752 - val_loss: 0.0595 - val_acc: 0.9807\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 17/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0717 - acc: 0.9760 - val_loss: 0.0581 - val_acc: 0.9812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 18/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0701 - acc: 0.9764 - val_loss: 0.0569 - val_acc: 0.9815\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 19/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0688 - acc: 0.9769 - val_loss: 0.0558 - val_acc: 0.9818\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 20/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0675 - acc: 0.9774 - val_loss: 0.0547 - val_acc: 0.9822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 21/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0660 - acc: 0.9779 - val_loss: 0.0538 - val_acc: 0.9826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 22/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0652 - acc: 0.9781 - val_loss: 0.0530 - val_acc: 0.9828\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 23/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0639 - acc: 0.9784 - val_loss: 0.0521 - val_acc: 0.9832\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 24/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0630 - acc: 0.9788 - val_loss: 0.0513 - val_acc: 0.9835\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 25/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0620 - acc: 0.9791 - val_loss: 0.0506 - val_acc: 0.9835\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 26/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0613 - acc: 0.9796 - val_loss: 0.0500 - val_acc: 0.9838\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 27/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0603 - acc: 0.9799 - val_loss: 0.0493 - val_acc: 0.9840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 28/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0596 - acc: 0.9801 - val_loss: 0.0487 - val_acc: 0.9843\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 29/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0588 - acc: 0.9805 - val_loss: 0.0481 - val_acc: 0.9844\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 30/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0577 - acc: 0.9806 - val_loss: 0.0475 - val_acc: 0.9846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 31/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0573 - acc: 0.9811 - val_loss: 0.0470 - val_acc: 0.9848\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 32/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0566 - acc: 0.9813 - val_loss: 0.0465 - val_acc: 0.9851\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 33/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0557 - acc: 0.9816 - val_loss: 0.0460 - val_acc: 0.9851\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 34/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0553 - acc: 0.9815 - val_loss: 0.0455 - val_acc: 0.9854\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 35/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0548 - acc: 0.9819 - val_loss: 0.0451 - val_acc: 0.9855\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 36/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0542 - acc: 0.9821 - val_loss: 0.0447 - val_acc: 0.9856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 37/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0536 - acc: 0.9822 - val_loss: 0.0442 - val_acc: 0.9857\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 38/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0533 - acc: 0.9824 - val_loss: 0.0438 - val_acc: 0.9859\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 39/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0523 - acc: 0.9827 - val_loss: 0.0434 - val_acc: 0.9859\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 40/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0519 - acc: 0.9828 - val_loss: 0.0431 - val_acc: 0.9861\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 41/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0514 - acc: 0.9830 - val_loss: 0.0426 - val_acc: 0.9863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 42/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0511 - acc: 0.9830 - val_loss: 0.0423 - val_acc: 0.9863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 43/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0506 - acc: 0.9832 - val_loss: 0.0419 - val_acc: 0.9865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 44/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0499 - acc: 0.9836 - val_loss: 0.0415 - val_acc: 0.9866\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 45/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0496 - acc: 0.9835 - val_loss: 0.0412 - val_acc: 0.9867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 46/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0493 - acc: 0.9836 - val_loss: 0.0408 - val_acc: 0.9868\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 47/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0487 - acc: 0.9842 - val_loss: 0.0405 - val_acc: 0.9870\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 48/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0481 - acc: 0.9841 - val_loss: 0.0402 - val_acc: 0.9871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 49/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0480 - acc: 0.9843 - val_loss: 0.0398 - val_acc: 0.9872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 50/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0476 - acc: 0.9842 - val_loss: 0.0396 - val_acc: 0.9873\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 51/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0471 - acc: 0.9846 - val_loss: 0.0392 - val_acc: 0.9874\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 52/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0471 - acc: 0.9845 - val_loss: 0.0389 - val_acc: 0.9873\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 53/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0465 - acc: 0.9847 - val_loss: 0.0386 - val_acc: 0.9876\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 54/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0459 - acc: 0.9849 - val_loss: 0.0383 - val_acc: 0.9878\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 55/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0458 - acc: 0.9849 - val_loss: 0.0380 - val_acc: 0.9879\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 56/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0453 - acc: 0.9851 - val_loss: 0.0378 - val_acc: 0.9880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 57/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0447 - acc: 0.9854 - val_loss: 0.0375 - val_acc: 0.9880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 58/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0446 - acc: 0.9854 - val_loss: 0.0372 - val_acc: 0.9881\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 59/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0442 - acc: 0.9855 - val_loss: 0.0370 - val_acc: 0.9882\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 60/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0441 - acc: 0.9855 - val_loss: 0.0367 - val_acc: 0.9882\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 61/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0436 - acc: 0.9857 - val_loss: 0.0365 - val_acc: 0.9883\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 62/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0435 - acc: 0.9858 - val_loss: 0.0362 - val_acc: 0.9884\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 63/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0428 - acc: 0.9860 - val_loss: 0.0359 - val_acc: 0.9885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 64/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0425 - acc: 0.9862 - val_loss: 0.0357 - val_acc: 0.9886\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 65/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0422 - acc: 0.9862 - val_loss: 0.0355 - val_acc: 0.9886\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 66/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0422 - acc: 0.9862 - val_loss: 0.0352 - val_acc: 0.9888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 67/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0419 - acc: 0.9864 - val_loss: 0.0350 - val_acc: 0.9888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 68/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0414 - acc: 0.9864 - val_loss: 0.0348 - val_acc: 0.9889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 69/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0411 - acc: 0.9866 - val_loss: 0.0346 - val_acc: 0.9890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 70/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0408 - acc: 0.9866 - val_loss: 0.0343 - val_acc: 0.9891\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 71/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0405 - acc: 0.9868 - val_loss: 0.0341 - val_acc: 0.9891\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 72/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0401 - acc: 0.9868 - val_loss: 0.0339 - val_acc: 0.9892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 73/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0400 - acc: 0.9868 - val_loss: 0.0337 - val_acc: 0.9893\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 74/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0396 - acc: 0.9871 - val_loss: 0.0335 - val_acc: 0.9893\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 75/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0396 - acc: 0.9871 - val_loss: 0.0333 - val_acc: 0.9894\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 76/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0393 - acc: 0.9871 - val_loss: 0.0331 - val_acc: 0.9894\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 77/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0389 - acc: 0.9874 - val_loss: 0.0329 - val_acc: 0.9895\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 78/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0389 - acc: 0.9874 - val_loss: 0.0327 - val_acc: 0.9896\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 79/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0387 - acc: 0.9874 - val_loss: 0.0325 - val_acc: 0.9896\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 80/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0382 - acc: 0.9875 - val_loss: 0.0323 - val_acc: 0.9898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 81/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0382 - acc: 0.9875 - val_loss: 0.0321 - val_acc: 0.9898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 82/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0377 - acc: 0.9877 - val_loss: 0.0320 - val_acc: 0.9899\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 83/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0376 - acc: 0.9878 - val_loss: 0.0318 - val_acc: 0.9899\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 84/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0376 - acc: 0.9878 - val_loss: 0.0315 - val_acc: 0.9900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 85/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0372 - acc: 0.9879 - val_loss: 0.0314 - val_acc: 0.9900\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 86/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0370 - acc: 0.9879 - val_loss: 0.0312 - val_acc: 0.9901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 87/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0367 - acc: 0.9880 - val_loss: 0.0310 - val_acc: 0.9902\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 88/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0366 - acc: 0.9880 - val_loss: 0.0309 - val_acc: 0.9902\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 89/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0362 - acc: 0.9883 - val_loss: 0.0307 - val_acc: 0.9903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 90/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0359 - acc: 0.9884 - val_loss: 0.0305 - val_acc: 0.9903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 91/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0356 - acc: 0.9885 - val_loss: 0.0304 - val_acc: 0.9903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 92/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0355 - acc: 0.9886 - val_loss: 0.0302 - val_acc: 0.9904\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 93/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0354 - acc: 0.9885 - val_loss: 0.0300 - val_acc: 0.9904\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 94/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0352 - acc: 0.9885 - val_loss: 0.0299 - val_acc: 0.9905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 95/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0348 - acc: 0.9886 - val_loss: 0.0297 - val_acc: 0.9905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 96/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0347 - acc: 0.9886 - val_loss: 0.0296 - val_acc: 0.9905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 97/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0347 - acc: 0.9885 - val_loss: 0.0294 - val_acc: 0.9905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 98/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0345 - acc: 0.9887 - val_loss: 0.0293 - val_acc: 0.9905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 99/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0341 - acc: 0.9888 - val_loss: 0.0291 - val_acc: 0.9906\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 100/100\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0343 - acc: 0.9888 - val_loss: 0.0289 - val_acc: 0.9907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    }
+   ],
+   "source": [
+    "network_history = model.fit(X_train, Y_train, batch_size=128, nb_epoch=100, \n",
+    "                    verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Plotting Network Performance Trend\n",
+    "The return value of the `fit` function is a `keras.callbacks.History` object which contains the entire history of training/validation loss and accuracy, for each epoch. We can therefore plot the behaviour of loss and accuracy during the training phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f05b40e8c88>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0603b041d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/iEj1gO3FInJARO4eyvXSqrsRAlW0dkcALFgYYzLKUIJFtqpG+ha8z7mpDhIR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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f05ec01c5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.plot(network_history.history['loss'])\n",
+    "plt.plot(network_history.history['val_loss'])\n",
+    "plt.legend(['Training', 'Validation'])\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.plot(network_history.history['acc'])\n",
+    "plt.plot(network_history.history['val_acc'])\n",
+    "plt.legend(['Training', 'Validation'], loc='lower right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "After `100` epochs, we get a `98.8%` validation accuracy. \n",
+    "\n",
+    "If you continue training, at some point the validation loss will start to increase: that is when the model starts to **overfit**. \n",
+    "\n",
+    "It is always necessary to monitor training and validation loss during the training of any kind of Neural Network, either to detect overfitting or to evaluate the behaviour of the model **(any clue on how to do it??)**"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/2.2.3 ConvNet HandsOn Part II.ipynb b/2.2.3 ConvNet HandsOn Part II.ipynb
new file mode 100755
index 0000000..eb89d42
--- /dev/null
+++ b/2.2.3 ConvNet HandsOn Part II.ipynb	
@@ -0,0 +1,1151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Convolution Nets for MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Deep Learning models can take quite a bit of time to run, particularly if GPU isn't used. \n",
+    "\n",
+    "In the interest of time, you could sample a subset of observations (e.g. $1000$) that are a particular number of your choice (e.g. $6$) and $1000$ observations that aren't that particular number (i.e. $\\neq 6$). \n",
+    "\n",
+    "We will build a model using that and see how it performs on the test dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Import the required libraries\n",
+    "import numpy as np\n",
+    "np.random.seed(1338)\n",
+    "\n",
+    "from keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras.layers.core import Dense, Dropout, Activation, Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras.layers.convolutional import Conv2D\n",
+    "from keras.layers.pooling import MaxPooling2D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras.utils import np_utils\n",
+    "from keras.optimizers import SGD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Loading Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Load the training and testing data\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X_test_orig = X_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "img_rows, img_cols = 28, 28\n",
+    "\n",
+    "if K.image_data_format() == 'channel_first':\n",
+    "    shape_ord = (1, img_rows, img_cols)\n",
+    "else:  # channel_last\n",
+    "    shape_ord = (img_rows, img_cols, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X_train = X_train.reshape((X_train.shape[0],) + shape_ord)\n",
+    "X_test = X_test.reshape((X_test.shape[0],) + shape_ord)\n",
+    "\n",
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "\n",
+    "X_train /= 255\n",
+    "X_test /= 255"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(1338)  # for reproducibilty!!\n",
+    "\n",
+    "# Test data\n",
+    "X_test = X_test.copy()\n",
+    "Y = y_test.copy()\n",
+    "\n",
+    "# Converting the output to binary classification(Six=1,Not Six=0)\n",
+    "Y_test = Y == 6\n",
+    "Y_test = Y_test.astype(int)\n",
+    "\n",
+    "# Selecting the 5918 examples where the output is 6\n",
+    "X_six = X_train[y_train == 6].copy()\n",
+    "Y_six = y_train[y_train == 6].copy()\n",
+    "\n",
+    "# Selecting the examples where the output is not 6\n",
+    "X_not_six = X_train[y_train != 6].copy()\n",
+    "Y_not_six = y_train[y_train != 6].copy()\n",
+    "\n",
+    "# Selecting 6000 random examples from the data that \n",
+    "# only contains the data where the output is not 6\n",
+    "random_rows = np.random.randint(0,X_six.shape[0],6000)\n",
+    "X_not_six = X_not_six[random_rows]\n",
+    "Y_not_six = Y_not_six[random_rows]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Appending the data with output as 6 and data with output as <> 6\n",
+    "X_train = np.append(X_six,X_not_six)\n",
+    "\n",
+    "# Reshaping the appended data to appropraite form\n",
+    "X_train = X_train.reshape((X_six.shape[0] + X_not_six.shape[0],) + shape_ord)\n",
+    "\n",
+    "# Appending the labels and converting the labels to \n",
+    "# binary classification(Six=1,Not Six=0)\n",
+    "Y_labels = np.append(Y_six,Y_not_six)\n",
+    "Y_train = Y_labels == 6 \n",
+    "Y_train = Y_train.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(11918, 28, 28, 1) (11918,) (10000, 28, 28, 1) (10000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape, Y_labels.shape, X_test.shape, Y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Converting the classes to its binary categorical form\n",
+    "nb_classes = 2\n",
+    "Y_train = np_utils.to_categorical(Y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(Y_test, nb_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# A simple CNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Initializing the values for the convolution neural network\n",
+    "nb_epoch = 2\n",
+    "batch_size = 64\n",
+    "# number of convolutional filters to use\n",
+    "nb_filters = 32\n",
+    "# size of pooling area for max pooling\n",
+    "nb_pool = 2\n",
+    "# convolution kernel size\n",
+    "nb_conv = 3\n",
+    "\n",
+    "sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Step 1: Model Definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n",
+    "                        padding='valid',\n",
+    "                        input_shape=shape_ord))\n",
+    "model.add(Activation('relu'))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(nb_classes))\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Step 2: Compile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Step 3: Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 11918 samples, validate on 10000 samples\n",
+      "Epoch 1/2\n",
+      "11918/11918 [==============================] - 0s - loss: 0.2124 - acc: 0.9456 - val_loss: 0.0968 - val_acc: 0.9712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/2\n",
+      "11918/11918 [==============================] - 0s - loss: 0.1050 - acc: 0.9669 - val_loss: 0.0998 - val_acc: 0.9660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fcbee563f28>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, Y_train, batch_size=batch_size, \n",
+    "          epochs=nb_epoch, verbose=1,\n",
+    "          validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Step 4: Evaluate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.0997865243077\n",
+      "Test accuracy: 0.966\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluating the model on the test data    \n",
+    "score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Let's plot our model Predictions!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p9UOUJhWkUfZlcyeJfe+08VYAKnpA8qdaA/fRe25fAL5LkEQ+hkJaFFc++SAA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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcbe8f13898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "slice = 15\n",
+    "predicted = model.predict(X_test[:slice]).argmax(-1)\n",
+    "\n",
+    "plt.figure(figsize=(16,8))\n",
+    "for i in range(slice):\n",
+    "    plt.subplot(1, slice, i+1)\n",
+    "    plt.imshow(X_test_orig[i], interpolation='nearest')\n",
+    "    plt.text(0, 0, predicted[i], color='black', \n",
+    "             bbox=dict(facecolor='white', alpha=1))\n",
+    "    plt.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Adding more Dense Layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n",
+    "                        padding='valid',\n",
+    "                        input_shape=shape_ord))\n",
+    "model.add(Activation('relu'))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128))\n",
+    "model.add(Activation('relu'))\n",
+    "\n",
+    "model.add(Dense(nb_classes))\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 11918 samples, validate on 10000 samples\n",
+      "Epoch 1/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.2141 - acc: 0.9506 - val_loss: 0.1102 - val_acc: 0.9642\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.0927 - acc: 0.9698 - val_loss: 0.0896 - val_acc: 0.9678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fcbe8b13048>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, Y_train, batch_size=batch_size, \n",
+    "          epochs=nb_epoch,verbose=1,\n",
+    "          validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.0895933916539\n",
+      "Test accuracy: 0.9678\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluating the model on the test data    \n",
+    "score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Adding Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n",
+    "                        padding='valid',\n",
+    "                        input_shape=shape_ord))\n",
+    "model.add(Activation('relu'))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(nb_classes))\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 11918 samples, validate on 10000 samples\n",
+      "Epoch 1/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.2288 - acc: 0.9315 - val_loss: 0.1059 - val_acc: 0.9661\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.1046 - acc: 0.9661 - val_loss: 0.1002 - val_acc: 0.9639\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fcbe7af2d68>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, Y_train, batch_size=batch_size, \n",
+    "          epochs=nb_epoch,verbose=1,\n",
+    "          validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.100196693107\n",
+      "Test accuracy: 0.9639\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluating the model on the test data    \n",
+    "score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Adding more Convolution Layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n",
+    "                        padding='valid',\n",
+    "                        input_shape=shape_ord))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
+    "model.add(Dropout(0.25))\n",
+    "    \n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(nb_classes))\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 11918 samples, validate on 10000 samples\n",
+      "Epoch 1/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.3320 - acc: 0.8805 - val_loss: 0.1356 - val_acc: 0.9560\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.1326 - acc: 0.9539 - val_loss: 0.0824 - val_acc: 0.9713\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fcbe0bfa908>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(X_train, Y_train, batch_size=batch_size, \n",
+    "          epochs=nb_epoch,verbose=1,\n",
+    "          validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.0824499245822\n",
+      "Test accuracy: 0.9713\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluating the model on the test data    \n",
+    "score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score)\n",
+    "print('Test accuracy:', accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Exercise\n",
+    "\n",
+    "The above code has been written as a function. \n",
+    "\n",
+    "Change some of the **hyperparameters** and see what happens. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Function for constructing the convolution neural network\n",
+    "# Feel free to add parameters, if you want\n",
+    "\n",
+    "def build_model():\n",
+    "    \"\"\"\"\"\"\n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n",
+    "                        padding='valid',\n",
+    "                        input_shape=shape_ord))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "    \n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(nb_classes))\n",
+    "    model.add(Activation('softmax'))\n",
+    "    \n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='sgd',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "    model.fit(X_train, Y_train, batch_size=batch_size, \n",
+    "              epochs=nb_epoch,verbose=1,\n",
+    "              validation_data=(X_test, Y_test))\n",
+    "          \n",
+    "\n",
+    "    #Evaluating the model on the test data    \n",
+    "    score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "    print('Test score:', score)\n",
+    "    print('Test accuracy:', accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 11918 samples, validate on 10000 samples\n",
+      "Epoch 1/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.4027 - acc: 0.8598 - val_loss: 0.1124 - val_acc: 0.9708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/2\n",
+      "11918/11918 [==============================] - 1s - loss: 0.1422 - acc: 0.9526 - val_loss: 0.1887 - val_acc: 0.9288\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Test score: 0.188707275963\n",
+      "Test accuracy: 0.9288\n",
+      "1 loop, best of 1: 5.67 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Timing how long it takes to build the model and test it.\n",
+    "%timeit -n1 -r1 build_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Batch Normalisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## How to BatchNorm in Keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "\n",
+    "BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, \n",
+    "                   beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros',\n",
+    "                   moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None,\n",
+    "                   beta_constraint=None, gamma_constraint=None)\n",
+    "```\n",
+    "\n",
+    "#### Arguments\n",
+    "\n",
+    "<ul>\n",
+    "<li><strong>axis</strong>: Integer, the axis that should be normalized\n",
+    "    (typically the features axis).\n",
+    "    For instance, after a <code>Conv2D</code> layer with\n",
+    "    <code>data_format=\"channels_first\"</code>,\n",
+    "    set <code>axis=1</code> in <code>BatchNormalization</code>.</li>\n",
+    "<li><strong>momentum</strong>: Momentum for the moving average.</li>\n",
+    "<li><strong>epsilon</strong>: Small float added to variance to avoid dividing by zero.</li>\n",
+    "<li><strong>center</strong>: If True, add offset of <code>beta</code> to normalized tensor.\n",
+    "    If False, <code>beta</code> is ignored.</li>\n",
+    "<li><strong>scale</strong>: If True, multiply by <code>gamma</code>.\n",
+    "    If False, <code>gamma</code> is not used.\n",
+    "    When the next layer is linear (also e.g. <code>nn.relu</code>),\n",
+    "    this can be disabled since the scaling\n",
+    "    will be done by the next layer.</li>\n",
+    "<li><strong>beta_initializer</strong>: Initializer for the beta weight.</li>\n",
+    "<li><strong>gamma_initializer</strong>: Initializer for the gamma weight.</li>\n",
+    "<li><strong>moving_mean_initializer</strong>: Initializer for the moving mean.</li>\n",
+    "<li><strong>moving_variance_initializer</strong>: Initializer for the moving variance.</li>\n",
+    "<li><strong>beta_regularizer</strong>: Optional regularizer for the beta weight.</li>\n",
+    "<li><strong>gamma_regularizer</strong>: Optional regularizer for the gamma weight.</li>\n",
+    "<li><strong>beta_constraint</strong>: Optional constraint for the beta weight.</li>\n",
+    "<li><strong>gamma_constraint</strong>: Optional constraint for the gamma weight.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Try to add a new BatchNormalization layer to the Model \n",
+    "# (after the Dropout layer) - before or after the ReLU Activation"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/2.2.4 (Exercise) CNN CIFAR10.ipynb b/2.2.4 (Exercise) CNN CIFAR10.ipynb
new file mode 100755
index 0000000..6c94b50
--- /dev/null
+++ b/2.2.4 (Exercise) CNN CIFAR10.ipynb	
@@ -0,0 +1,1112 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Convolutional Neural Network\n",
+    "\n",
+    "In this second exercise-notebook we will play with Convolutional Neural Network (CNN). \n",
+    "\n",
+    "As you should have seen, a CNN is a feed-forward neural network tipically composed of Convolutional, MaxPooling and Dense layers. \n",
+    "\n",
+    "If the task implemented by the CNN is a classification task, the last Dense layer should use the **Softmax** activation, and the loss should be the **categorical crossentropy**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network Topology Model\n",
+    "\n",
+    "A simple CNN, with one input branch and one output branch can be defined using a [Sequential](http://keras.io/models/#sequential) model and stacking together all its layers. \n",
+    "\n",
+    "In this exercise we want to build a (_quite shallow_) network which contains two \n",
+    "[Convolution, Convolution, MaxPooling] stages, and two Dense layers.\n",
+    "\n",
+    "To test a different optimizer, we will use [AdaDelta](http://keras.io/optimizers/), which is a bit more complex than the simple Vanilla SGD with momentum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers.core import Dense, Dropout, Flatten, Activation\n",
+    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
+    "from keras.optimizers import Adadelta\n",
+    "\n",
+    "input_shape = (3, 32, 32)\n",
+    "nb_classes = 10\n",
+    "\n",
+    "## [conv@32x3x3+relu]x2 --> MaxPool@2x2 --> DropOut@0.25 -->\n",
+    "## [conv@64x3x3+relu]x2 --> MaxPool@2x2 --> DropOut@0.25 -->\n",
+    "## Flatten--> FC@512+relu --> DropOut@0.5 --> FC@nb_classes+SoftMax\n",
+    "## NOTE: each couple of Conv filters must have `border_mode=\"same\"` and `\"valid\"`, respectively\n",
+    "\n",
+    "## your code here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# %load solutions/sol_162.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Understanding layer shapes\n",
+    "\n",
+    "An important feature of Keras layers is that each of them has an `input_shape` attribute, which you can use to visualize the shape of the input tensor, and an `output_shape` attribute, for inspecting the shape of the output tensor.\n",
+    "\n",
+    "As we can see, the input shape of the first convolutional layer corresponds to the `input_shape` attribute (which must be specified by the user). \n",
+    "\n",
+    "In this case, it is a `32x32` image with three color channels. \n",
+    "\n",
+    "Since this convolutional layer has the `border_mode` set to `same`, its output width and height will remain the same, and the number of output channel will be equal to the number of filters learned by the layer, 16. \n",
+    "\n",
+    "The following convolutional layers, instead, have the default `border_mode`, and therefore reduce width and height by $(k-1)$, where $k$ is the size of the kernel. \n",
+    "\n",
+    "MaxPooling layers, instead, reduce width and height of the input tensor, but keep the same number of channels. Activation layers, of course, don't change the shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer 0 \t (None, 3, 32, 32) \t (None, 32, 32, 32)\n",
+      "Layer 1 \t (None, 32, 32, 32) \t (None, 32, 32, 32)\n",
+      "Layer 2 \t (None, 32, 32, 32) \t (None, 32, 30, 30)\n",
+      "Layer 3 \t (None, 32, 30, 30) \t (None, 32, 30, 30)\n",
+      "Layer 4 \t (None, 32, 30, 30) \t (None, 32, 15, 15)\n",
+      "Layer 5 \t (None, 32, 15, 15) \t (None, 32, 15, 15)\n",
+      "Layer 6 \t (None, 32, 15, 15) \t (None, 64, 15, 15)\n",
+      "Layer 7 \t (None, 64, 15, 15) \t (None, 64, 15, 15)\n",
+      "Layer 8 \t (None, 64, 15, 15) \t (None, 64, 13, 13)\n",
+      "Layer 9 \t (None, 64, 13, 13) \t (None, 64, 13, 13)\n",
+      "Layer 10 \t (None, 64, 13, 13) \t (None, 64, 6, 6)\n",
+      "Layer 11 \t (None, 64, 6, 6) \t (None, 64, 6, 6)\n",
+      "Layer 12 \t (None, 64, 6, 6) \t (None, 2304)\n",
+      "Layer 13 \t (None, 2304) \t (None, 512)\n",
+      "Layer 14 \t (None, 512) \t (None, 512)\n",
+      "Layer 15 \t (None, 512) \t (None, 512)\n",
+      "Layer 16 \t (None, 512) \t (None, 10)\n",
+      "Layer 17 \t (None, 10) \t (None, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, layer in enumerate(model.layers):\n",
+    "    print (\"Layer\", i, \"\\t\", layer.input_shape, \"\\t\", layer.output_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Understanding weights shape\n",
+    "In the same way, we can visualize the shape of the weights learned by each layer. In particular, Keras lets you inspect weights by using the `get_weights` method of a layer object. This will return a list with two elements, the first one being the weight tensor and the second one being the bias vector.\n",
+    "\n",
+    "Of course, MaxPooling layer don't have any weight tensor, since they don't have learnable parameters. Convolutional layers, instead, learn a $(n_o, n_i, k, k)$ weight tensor, where $k$ is the size of the kernel, $n_i$ is the number of channels of the input tensor, and $n_o$ is the number of filters to be learned. For each of the $n_o$ filters, a bias is also learned. Dense layers learn a $(n_i, n_o)$ weight tensor, where $n_o$ is the output size and $n_i$ is the input size of the layer. Each of the $n_o$ neurons also has a bias."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer 0 \t (32, 3, 3, 3) \t (32,)\n",
+      "Layer 2 \t (32, 32, 3, 3) \t (32,)\n",
+      "Layer 6 \t (64, 32, 3, 3) \t (64,)\n",
+      "Layer 8 \t (64, 64, 3, 3) \t (64,)\n",
+      "Layer 13 \t (2304, 512) \t (512,)\n",
+      "Layer 16 \t (512, 10) \t (10,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, layer in enumerate(model.layers):\n",
+    "    if len(layer.get_weights()) > 0:\n",
+    "        print(\"Layer\", i, \"\\t\", layer.get_weights()[0].shape, \"\\t\", layer.get_weights()[1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Training the network\n",
+    "\n",
+    "We will train our network on the **CIFAR10** [dataset](https://www.cs.toronto.edu/~kriz/cifar.html), which contains `50,000` 32x32 color training images, labeled over 10 categories, and 10,000 test images. \n",
+    "\n",
+    "As this dataset is also included in Keras datasets, we just ask the `keras.datasets` module for the dataset.\n",
+    "\n",
+    "Training and test images are normalized to lie in the $\\left[0,1\\right]$ interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.datasets import cifar10\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+    "X_train = X_train.astype(\"float32\")\n",
+    "X_test = X_test.astype(\"float32\")\n",
+    "X_train /= 255\n",
+    "X_test /= 255"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To reduce the risk of overfitting, we also apply some image transformation, like rotations, shifts and flips. All these can be easily implemented using the Keras [Image Data Generator](http://keras.io/preprocessing/image/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "generated_images = ImageDataGenerator(\n",
+    "    featurewise_center=True,  # set input mean to 0 over the dataset\n",
+    "    samplewise_center=False,  # set each sample mean to 0\n",
+    "    featurewise_std_normalization=True,  # divide inputs by std of the dataset\n",
+    "    samplewise_std_normalization=False,  # divide each input by its std\n",
+    "    zca_whitening=False,  # apply ZCA whitening\n",
+    "    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
+    "    width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)\n",
+    "    height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)\n",
+    "    horizontal_flip=True,  # randomly flip images\n",
+    "    vertical_flip=False)  # randomly flip images\n",
+    "\n",
+    "generated_images.fit(X_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now we can start training. \n",
+    "\n",
+    "At each iteration, a batch of 500 images is requested to the `ImageDataGenerator` object, and then fed to the network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000, 3, 32, 32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "gen = generated_images.flow(X_train, Y_train, batch_size=500, shuffle=True)\n",
+    "X_batch, Y_batch = next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(500, 3, 32, 32)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_batch.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0\n",
+      "Training...\n",
+      "50000/50000 [==============================] - 115s - train loss: 1.7522     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+      "405000/50000 [===================================================================================================================================================================================================================================================] - 941s - train loss: 1.4780\n",
+      "405500/50000 [===================================================================================================================================================================================================================================================] - 943s - train loss: 1.4777\n",
+      "406000/50000 [===================================================================================================================================================================================================================================================] - 944s - train loss: 1.4774\n",
+      "406500/50000 [===================================================================================================================================================================================================================================================] - 945s - train loss: 1.4771\n",
+      "407000/50000 [====================================================================================================================================================================================================================================================] - 946s - train loss: 1.4768\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-bc943df0200b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgenerated_images\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, class_weight, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    764\u001b[0m         return self.model.train_on_batch(x, y,\n\u001b[1;32m    765\u001b[0m                                          \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m                                          class_weight=class_weight)\n\u001b[0m\u001b[1;32m    767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m     def test_on_batch(self, x, y,\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1318\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1321\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1941\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1942\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 1943\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   1944\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    764\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 766\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    767\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    962\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 964\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    965\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1014\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1015\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1019\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1020\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1001\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1002\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "from keras.utils import generic_utils\n",
+    "\n",
+    "n_epochs = 2\n",
+    "for e in range(n_epochs):\n",
+    "    print('Epoch', e)\n",
+    "    print('Training...')\n",
+    "    progbar = generic_utils.Progbar(X_train.shape[0])\n",
+    "    \n",
+    "    for X_batch, Y_batch in generated_images.flow(X_train, Y_train, batch_size=500, shuffle=True):\n",
+    "        loss = model.train_on_batch(X_batch, Y_batch)\n",
+    "        progbar.add(X_batch.shape[0], values=[('train loss', loss[0])])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/2.3 Supervised Learning - Famous Models with Keras.ipynb b/2.3 Supervised Learning - Famous Models with Keras.ipynb
old mode 100644
new mode 100755
index 612f756..511f753
--- a/2.3 Supervised Learning - Famous Models with Keras.ipynb	
+++ b/2.3 Supervised Learning - Famous Models with Keras.ipynb	
@@ -3,17 +3,21 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
-    "# Practical Deep Learning"
+    "# Deep Learning Models"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -27,6 +31,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -38,6 +44,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -54,6 +62,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -69,6 +79,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -79,186 +91,34 @@
     "For instance, if you have set `image_dim_ordering=tf`, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, \"Width-Height-Depth\"."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Keras Configuration File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\r\n",
-      "    \"image_dim_ordering\": \"th\",\r\n",
-      "    \"floatx\": \"float32\",\r\n",
-      "    \"epsilon\": 1e-07,\r\n",
-      "    \"backend\": \"theano\"\r\n",
-      "}"
-     ]
-    }
-   ],
-   "source": [
-    "!cat ~/.keras/keras.json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\r\n",
-      "    \"image_dim_ordering\": \"th\",\r\n",
-      "    \"floatx\": \"float32\",\r\n",
-      "    \"epsilon\": 1e-07,\r\n",
-      "    \"backend\": \"tensorflow\"\r\n",
-      "}"
-     ]
-    }
-   ],
-   "source": [
-    "!sed -i 's/theano/tensorflow/g' ~/.keras/keras.json\n",
-    "!cat ~/.keras/keras.json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import theano"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\r\n",
-      "    \"image_dim_ordering\": \"th\",\r\n",
-      "    \"backend\": \"theano\",\r\n",
-      "    \"floatx\": \"float32\",\r\n",
-      "    \"epsilon\": 1e-07\r\n",
-      "}"
-     ]
-    }
-   ],
-   "source": [
-    "!sed -i 's/tensorflow/theano/g' ~/.keras/keras.json\n",
-    "!cat ~/.keras/keras.json"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using Theano backend.\n",
-      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import keras"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using Theano backend.\n",
-      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %load deep_learning_models/imagenet_utils.py\n",
     "import numpy as np\n",
@@ -308,9 +168,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": true,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -323,6 +185,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -331,55 +195,77 @@
     "# VGG16"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src=\"imgs/vgg16.png\" >"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
    "outputs": [],
    "source": [
-    "# %load deep_learning_models/vgg16.py\n",
-    "'''VGG16 model for Keras.\n",
+    "# -*- coding: utf-8 -*-\n",
+    "\"\"\"VGG16 model for Keras.\n",
     "\n",
-    "# Reference:\n",
+    "# Reference\n",
     "\n",
     "- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)\n",
     "\n",
-    "'''\n",
+    "\"\"\"\n",
     "from __future__ import print_function\n",
+    "from __future__ import absolute_import\n",
     "\n",
-    "import numpy as np\n",
     "import warnings\n",
     "\n",
     "from keras.models import Model\n",
-    "from keras.layers import Flatten, Dense, Input\n",
-    "from keras.layers import Convolution2D, MaxPooling2D\n",
-    "from keras.preprocessing import image\n",
-    "from keras.utils.layer_utils import convert_all_kernels_in_model\n",
+    "from keras.layers import Flatten\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Input\n",
+    "from keras.layers import Conv2D\n",
+    "from keras.layers import MaxPooling2D\n",
+    "from keras.layers import GlobalAveragePooling2D\n",
+    "from keras.layers import GlobalMaxPooling2D\n",
+    "from keras.engine.topology import get_source_inputs\n",
+    "from keras.utils import layer_utils\n",
     "from keras.utils.data_utils import get_file\n",
     "from keras import backend as K\n",
+    "from keras.applications.imagenet_utils import decode_predictions\n",
+    "from keras.applications.imagenet_utils import preprocess_input\n",
+    "from keras.applications.imagenet_utils import _obtain_input_shape\n",
     "\n",
-    "TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5'\n",
-    "TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n",
-    "TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'\n",
-    "TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
+    "\n",
+    "WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n",
+    "WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
     "\n",
     "\n",
     "def VGG16(include_top=True, weights='imagenet',\n",
-    "          input_tensor=None):\n",
-    "    '''Instantiate the VGG16 architecture,\n",
-    "    optionally loading weights pre-trained\n",
+    "          input_tensor=None, input_shape=None,\n",
+    "          pooling=None,\n",
+    "          classes=1000):\n",
+    "    \"\"\"Instantiates the VGG16 architecture.\n",
+    "\n",
+    "    Optionally loads weights pre-trained\n",
     "    on ImageNet. Note that when using TensorFlow,\n",
     "    for best performance you should set\n",
-    "    `image_dim_ordering=\"tf\"` in your Keras config\n",
+    "    `image_data_format=\"channels_last\"` in your Keras config\n",
     "    at ~/.keras/keras.json.\n",
     "\n",
     "    The model and the weights are compatible with both\n",
-    "    TensorFlow and Theano. The dimension ordering\n",
+    "    TensorFlow and Theano. The data format\n",
     "    convention used by the model is the one\n",
     "    specified in your Keras config file.\n",
     "\n",
@@ -390,59 +276,83 @@
     "            or \"imagenet\" (pre-training on ImageNet).\n",
     "        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n",
     "            to use as image input for the model.\n",
+    "        input_shape: optional shape tuple, only to be specified\n",
+    "            if `include_top` is False (otherwise the input shape\n",
+    "            has to be `(224, 224, 3)` (with `channels_last` data format)\n",
+    "            or `(3, 224, 244)` (with `channels_first` data format).\n",
+    "            It should have exactly 3 inputs channels,\n",
+    "            and width and height should be no smaller than 48.\n",
+    "            E.g. `(200, 200, 3)` would be one valid value.\n",
+    "        pooling: Optional pooling mode for feature extraction\n",
+    "            when `include_top` is `False`.\n",
+    "            - `None` means that the output of the model will be\n",
+    "                the 4D tensor output of the\n",
+    "                last convolutional layer.\n",
+    "            - `avg` means that global average pooling\n",
+    "                will be applied to the output of the\n",
+    "                last convolutional layer, and thus\n",
+    "                the output of the model will be a 2D tensor.\n",
+    "            - `max` means that global max pooling will\n",
+    "                be applied.\n",
+    "        classes: optional number of classes to classify images\n",
+    "            into, only to be specified if `include_top` is True, and\n",
+    "            if no `weights` argument is specified.\n",
     "\n",
     "    # Returns\n",
     "        A Keras model instance.\n",
-    "    '''\n",
+    "\n",
+    "    # Raises\n",
+    "        ValueError: in case of invalid argument for `weights`,\n",
+    "            or invalid input shape.\n",
+    "    \"\"\"\n",
     "    if weights not in {'imagenet', None}:\n",
     "        raise ValueError('The `weights` argument should be either '\n",
     "                         '`None` (random initialization) or `imagenet` '\n",
     "                         '(pre-training on ImageNet).')\n",
+    "\n",
+    "    if weights == 'imagenet' and include_top and classes != 1000:\n",
+    "        raise ValueError('If using `weights` as imagenet with `include_top`'\n",
+    "                         ' as true, `classes` should be 1000')\n",
     "    # Determine proper input shape\n",
-    "    if K.image_dim_ordering() == 'th':\n",
-    "        if include_top:\n",
-    "            input_shape = (3, 224, 224)\n",
-    "        else:\n",
-    "            input_shape = (3, None, None)\n",
-    "    else:\n",
-    "        if include_top:\n",
-    "            input_shape = (224, 224, 3)\n",
-    "        else:\n",
-    "            input_shape = (None, None, 3)\n",
+    "    input_shape = _obtain_input_shape(input_shape,\n",
+    "                                      default_size=224,\n",
+    "                                      min_size=48,\n",
+    "                                      data_format=K.image_data_format(),\n",
+    "                                      include_top=include_top)\n",
     "\n",
     "    if input_tensor is None:\n",
     "        img_input = Input(shape=input_shape)\n",
     "    else:\n",
     "        if not K.is_keras_tensor(input_tensor):\n",
-    "            img_input = Input(tensor=input_tensor)\n",
+    "            img_input = Input(tensor=input_tensor, shape=input_shape)\n",
     "        else:\n",
     "            img_input = input_tensor\n",
     "    # Block 1\n",
-    "    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)\n",
-    "    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
+    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
     "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
     "\n",
     "    # Block 2\n",
-    "    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)\n",
-    "    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)\n",
+    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
+    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
     "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
     "\n",
     "    # Block 3\n",
-    "    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)\n",
-    "    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)\n",
-    "    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)\n",
+    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
+    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
+    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
     "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
     "\n",
     "    # Block 4\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
     "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
     "\n",
     "    # Block 5\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)\n",
-    "    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
+    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
     "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
     "\n",
     "    if include_top:\n",
@@ -450,74 +360,83 @@
     "        x = Flatten(name='flatten')(x)\n",
     "        x = Dense(4096, activation='relu', name='fc1')(x)\n",
     "        x = Dense(4096, activation='relu', name='fc2')(x)\n",
-    "        x = Dense(1000, activation='softmax', name='predictions')(x)\n",
-    "\n",
-    "    # Create model\n",
-    "    model = Model(img_input, x)\n",
+    "        x = Dense(classes, activation='softmax', name='predictions')(x)\n",
+    "    else:\n",
+    "        if pooling == 'avg':\n",
+    "            x = GlobalAveragePooling2D()(x)\n",
+    "        elif pooling == 'max':\n",
+    "            x = GlobalMaxPooling2D()(x)\n",
+    "\n",
+    "    # Ensure that the model takes into account\n",
+    "    # any potential predecessors of `input_tensor`.\n",
+    "    if input_tensor is not None:\n",
+    "        inputs = get_source_inputs(input_tensor)\n",
+    "    else:\n",
+    "        inputs = img_input\n",
+    "    # Create model.\n",
+    "    model = Model(inputs, x, name='vgg16')\n",
     "\n",
     "    # load weights\n",
     "    if weights == 'imagenet':\n",
-    "        print('K.image_dim_ordering:', K.image_dim_ordering())\n",
-    "        if K.image_dim_ordering() == 'th':\n",
+    "        if include_top:\n",
+    "            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',\n",
+    "                                    WEIGHTS_PATH,\n",
+    "                                    cache_subdir='models')\n",
+    "        else:\n",
+    "            weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',\n",
+    "                                    WEIGHTS_PATH_NO_TOP,\n",
+    "                                    cache_subdir='models')\n",
+    "        model.load_weights(weights_path)\n",
+    "        if K.backend() == 'theano':\n",
+    "            layer_utils.convert_all_kernels_in_model(model)\n",
+    "\n",
+    "        if K.image_data_format() == 'channels_first':\n",
     "            if include_top:\n",
-    "                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels.h5',\n",
-    "                                        TH_WEIGHTS_PATH,\n",
-    "                                        cache_subdir='models')\n",
-    "            else:\n",
-    "                weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5',\n",
-    "                                        TH_WEIGHTS_PATH_NO_TOP,\n",
-    "                                        cache_subdir='models')\n",
-    "            model.load_weights(weights_path)\n",
+    "                maxpool = model.get_layer(name='block5_pool')\n",
+    "                shape = maxpool.output_shape[1:]\n",
+    "                dense = model.get_layer(name='fc1')\n",
+    "                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')\n",
+    "\n",
     "            if K.backend() == 'tensorflow':\n",
     "                warnings.warn('You are using the TensorFlow backend, yet you '\n",
     "                              'are using the Theano '\n",
-    "                              'image dimension ordering convention '\n",
-    "                              '(`image_dim_ordering=\"th\"`). '\n",
+    "                              'image data format convention '\n",
+    "                              '(`image_data_format=\"channels_first\"`). '\n",
     "                              'For best performance, set '\n",
-    "                              '`image_dim_ordering=\"tf\"` in '\n",
+    "                              '`image_data_format=\"channels_last\"` in '\n",
     "                              'your Keras config '\n",
     "                              'at ~/.keras/keras.json.')\n",
-    "                convert_all_kernels_in_model(model)\n",
-    "        else:\n",
-    "            if include_top:\n",
-    "                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',\n",
-    "                                        TF_WEIGHTS_PATH,\n",
-    "                                        cache_subdir='models')\n",
-    "            else:\n",
-    "                weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',\n",
-    "                                        TF_WEIGHTS_PATH_NO_TOP,\n",
-    "                                        cache_subdir='models')\n",
-    "            model.load_weights(weights_path)\n",
-    "            if K.backend() == 'theano':\n",
-    "                convert_all_kernels_in_model(model)\n",
-    "    return model"
+    "    return model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "vgg16 = VGG16(include_top=True, weights='imagenet')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "K.image_dim_ordering: th\n",
-      "Input image shape: (1, 3, 224, 224)\n",
-      "Predicted: [['n07745940', 'strawberry']]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
-    "model = VGG16(include_top=True, weights='imagenet')\n",
-    "\n",
     "img_path = os.path.join(IMAGENET_FOLDER, 'strawberry_1157.jpeg')\n",
     "img = image.load_img(img_path, target_size=(224, 224))\n",
     "x = image.img_to_array(img)\n",
@@ -525,132 +444,100 @@
     "x = preprocess_input(x)\n",
     "print('Input image shape:', x.shape)\n",
     "\n",
-    "preds = model.predict(x)\n",
+    "preds = vgg16.predict(x)\n",
     "print('Predicted:', decode_predictions(preds))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "# Fine Tuning of a Pre-Trained Model"
+    "<img src=\"imgs/imagenet/strawberry_1157.jpeg\" >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "!ls ./imgs/imagenet/"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "```python\n",
-    "def VGG16_FT(weights_path = None, \n",
-    "             img_width = 224, img_height = 224, \n",
-    "             f_type = None, n_labels = None ):\n",
-    "    \n",
-    "    \"\"\"Fine Tuning of a VGG16 based Net\"\"\"\n",
-    "\n",
-    "    # VGG16 Up to the layer before the last!\n",
-    "    model = Sequential()\n",
-    "    model.add(ZeroPadding2D((1, 1), \n",
-    "                            input_shape=(3, \n",
-    "                            img_width, img_height)))\n",
-    "\n",
-    "    model.add(Convolution2D(64, 3, 3, activation='relu', \n",
-    "                            name='conv1_1'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(64, 3, 3, activation='relu', \n",
-    "                            name='conv1_2'))\n",
-    "    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
-    "\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(128, 3, 3, activation='relu', \n",
-    "                            name='conv2_1'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(128, 3, 3, activation='relu', \n",
-    "                            name='conv2_2'))\n",
-    "    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
-    "\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(256, 3, 3, activation='relu', \n",
-    "                            name='conv3_1'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(256, 3, 3, activation='relu', \n",
-    "                            name='conv3_2'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(256, 3, 3, activation='relu', \n",
-    "                            name='conv3_3'))\n",
-    "    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
-    "\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv4_1'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv4_2'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv4_3'))\n",
-    "    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
-    "\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv5_1'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv5_2'))\n",
-    "    model.add(ZeroPadding2D((1, 1)))\n",
-    "    model.add(Convolution2D(512, 3, 3, activation='relu', \n",
-    "                            name='conv5_3'))\n",
-    "    model.add(MaxPooling2D((2, 2), strides=(2, 2)))\n",
-    "    model.add(Flatten())\n",
-    "\n",
-    "    # Plugging new Layers\n",
-    "    model.add(Dense(768, activation='sigmoid'))\n",
-    "    model.add(Dropout(0.0))\n",
-    "    model.add(Dense(768, activation='sigmoid'))\n",
-    "    model.add(Dropout(0.0))\n",
-    "    \n",
-    "    last_layer = Dense(n_labels, activation='sigmoid')\n",
-    "    loss = 'categorical_crossentropy'\n",
-    "    optimizer = optimizers.Adam(lr=1e-4, epsilon=1e-08)\n",
-    "    batch_size = 128\n",
-    "    \n",
-    "    assert os.path.exists(weights_path), 'Model weights not found (see \"weights_path\" variable in script).'\n",
-    "    #model.load_weights(weights_path)\n",
-    "    f = h5py.File(weights_path)\n",
-    "    for k in range(len(f.attrs['layer_names'])):\n",
-    "       g = f[f.attrs['layer_names'][k]]\n",
-    "       weights = [g[g.attrs['weight_names'][p]] \n",
-    "                   for p in range(len(g.attrs['weight_names']))]\n",
-    "       if k >= len(model.layers):\n",
-    "           break\n",
-    "       else:\n",
-    "           model.layers[k].set_weights(weights)\n",
-    "    f.close()\n",
-    "    print('Model loaded.')\n",
-    "\n",
-    "    model.add(last_layer)\n",
-    "\n",
-    "    # set the first 25 layers (up to the last conv block)\n",
-    "    # to non-trainable (weights will not be updated)\n",
-    "    for layer in model.layers[:25]:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "    # compile the model with a SGD/momentum optimizer\n",
-    "    # and a very slow learning rate.\n",
-    "    model.compile(loss=loss,\n",
-    "                  optimizer=optimizer,\n",
-    "                  metrics=['accuracy'])\n",
-    "    return model, batch_size\n",
-    "\n",
-    "```"
+    "<img src=\"imgs/imagenet/apricot_696.jpeg\" >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "img_path = os.path.join(IMAGENET_FOLDER, 'apricot_696.jpeg')\n",
+    "img = image.load_img(img_path, target_size=(224, 224))\n",
+    "x = image.img_to_array(img)\n",
+    "x = np.expand_dims(x, axis=0)\n",
+    "x = preprocess_input(x)\n",
+    "print('Input image shape:', x.shape)\n",
+    "\n",
+    "preds = vgg16.predict(x)\n",
+    "print('Predicted:', decode_predictions(preds))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src=\"imgs/imagenet/apricot_565.jpeg\" >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "img_path = os.path.join(IMAGENET_FOLDER, 'apricot_565.jpeg')\n",
+    "img = image.load_img(img_path, target_size=(224, 224))\n",
+    "x = image.img_to_array(img)\n",
+    "x = np.expand_dims(x, axis=0)\n",
+    "x = preprocess_input(x)\n",
+    "print('Input image shape:', x.shape)\n",
+    "\n",
+    "preds = vgg16.predict(x)\n",
+    "print('Predicted:', decode_predictions(preds))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     }
@@ -658,18 +545,97 @@
    "source": [
     "# Hands On:\n",
     "\n",
-    "### Try to do the same with other models "
+    "### Try to do the same with VGG19 Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<img src=\"imgs/vgg19.png\" >"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "%load deep_learning_models/vgg19.py"
+    "# %load deep_learning_models/vgg19.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# `keras.applications`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.applications import vgg16, vgg19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "vgg16?  # Jupyter way to see documentation\n",
+    "\n",
+    "# please focus on parameters (e.g. include top)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Residual Networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"imgs/resnet_bb.png\" >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ResNet 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"imgs/resnet34.png\" >"
    ]
   },
   {
@@ -680,12 +646,20 @@
    },
    "outputs": [],
    "source": [
-    "%load deep_learning_models/resnet50.py"
+    "## from keras.applications import ..."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/2.4 Supervised Learning - Transfer Learning.ipynb b/2.4 Supervised Learning - Transfer Learning.ipynb
new file mode 100755
index 0000000..df0518e
--- /dev/null
+++ b/2.4 Supervised Learning - Transfer Learning.ipynb	
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transfer Learning and Fine Tuning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Train a simple convnet on the MNIST dataset the first 5 digits [0..4].\n",
+    "* Freeze convolutional layers and fine-tune dense layers for the classification of digits [5..9].\n",
+    "\n",
+    "Run on GPU: \n",
+    "`THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import datetime\n",
+    "\n",
+    "np.random.seed(1337)  # for reproducibility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using Theano backend.\n",
+      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 5110)\n",
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
+      "  warnings.warn(warn)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras import backend as K\n",
+    "from numpy import nan\n",
+    "\n",
+    "now = datetime.datetime.now"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "now = datetime.datetime.now\n",
+    "\n",
+    "batch_size = 128\n",
+    "nb_classes = 5\n",
+    "nb_epoch = 5\n",
+    "\n",
+    "# input image dimensions\n",
+    "img_rows, img_cols = 28, 28\n",
+    "# number of convolutional filters to use\n",
+    "nb_filters = 32\n",
+    "# size of pooling area for max pooling\n",
+    "pool_size = 2\n",
+    "# convolution kernel size\n",
+    "kernel_size = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "if K.image_dim_ordering() == 'th':\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    input_shape = (img_rows, img_cols, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def train_model(model, train, test, nb_classes):\n",
+    "    \n",
+    "    X_train = train[0].reshape((train[0].shape[0],) + input_shape)\n",
+    "    X_test = test[0].reshape((test[0].shape[0],) + input_shape)\n",
+    "    X_train = X_train.astype('float32')\n",
+    "    X_test = X_test.astype('float32')\n",
+    "    X_train /= 255\n",
+    "    X_test /= 255\n",
+    "    \n",
+    "    print('X_train shape:', X_train.shape)\n",
+    "    print(X_train.shape[0], 'train samples')\n",
+    "    print(X_test.shape[0], 'test samples')\n",
+    "\n",
+    "    # convert class vectors to binary class matrices\n",
+    "    Y_train = np_utils.to_categorical(train[1], nb_classes)\n",
+    "    Y_test = np_utils.to_categorical(test[1], nb_classes)\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                  optimizer='adadelta',\n",
+    "                  metrics=['accuracy'])\n",
+    "\n",
+    "    t = now()\n",
+    "    model.fit(X_train, Y_train,\n",
+    "              batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "              verbose=1,\n",
+    "              validation_data=(X_test, Y_test))\n",
+    "    print('Training time: %s' % (now() - t))\n",
+    "    score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "    print('Test score:', score[0])\n",
+    "    print('Test accuracy:', score[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# the data, shuffled and split between train and test sets\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "# create two datasets one with digits below 5 and one with 5 and above\n",
+    "X_train_lt5 = X_train[y_train < 5]\n",
+    "y_train_lt5 = y_train[y_train < 5]\n",
+    "X_test_lt5 = X_test[y_test < 5]\n",
+    "y_test_lt5 = y_test[y_test < 5]\n",
+    "\n",
+    "X_train_gte5 = X_train[y_train >= 5]\n",
+    "y_train_gte5 = y_train[y_train >= 5] - 5  # make classes start at 0 for\n",
+    "X_test_gte5 = X_test[y_test >= 5]         # np_utils.to_categorical\n",
+    "y_test_gte5 = y_test[y_test >= 5] - 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# define two groups of layers: feature (convolutions) and classification (dense)\n",
+    "feature_layers = [\n",
+    "    Convolution2D(nb_filters, kernel_size, kernel_size,\n",
+    "                  border_mode='valid',\n",
+    "                  input_shape=input_shape),\n",
+    "    Activation('relu'),\n",
+    "    Convolution2D(nb_filters, kernel_size, kernel_size),\n",
+    "    Activation('relu'),\n",
+    "    MaxPooling2D(pool_size=(pool_size, pool_size)),\n",
+    "    Dropout(0.25),\n",
+    "    Flatten(),\n",
+    "]\n",
+    "classification_layers = [\n",
+    "    Dense(128),\n",
+    "    Activation('relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(nb_classes),\n",
+    "    Activation('softmax')\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (30596, 1, 28, 28)\n",
+      "30596 train samples\n",
+      "5139 test samples\n",
+      "Train on 30596 samples, validate on 5139 samples\n",
+      "Epoch 1/5\n",
+      "30596/30596 [==============================] - 3s - loss: 0.2071 - acc: 0.9362 - val_loss: 0.0476 - val_acc: 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+      "Epoch 2/5\n",
+      "30596/30596 [==============================] - 3s - loss: 0.0787 - acc: 0.9774 - val_loss: 0.0370 - val_acc: 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+      "Epoch 3/5\n",
+      "30596/30596 [==============================] - 3s - loss: 0.0528 - acc: 0.9846 - val_loss: 0.0195 - val_acc: 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+      "Epoch 4/5\n",
+      "30596/30596 [==============================] - 3s - loss: 0.0409 - acc: 0.9880 - val_loss: 0.0152 - val_acc: 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+      "Epoch 5/5\n",
+      "30596/30596 [==============================] - 3s - loss: 0.0336 - acc: 0.9901 - val_loss: 0.0135 - val_acc: 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+      "Training time: 0:00:40.094398\n",
+      "Test score: 0.0135238260214\n",
+      "Test accuracy: 0.995913601868\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create complete model\n",
+    "model = Sequential(feature_layers + classification_layers)\n",
+    "\n",
+    "# train model for 5-digit classification [0..4]\n",
+    "train_model(model,\n",
+    "            (X_train_lt5, y_train_lt5),\n",
+    "            (X_test_lt5, y_test_lt5), nb_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (29404, 1, 28, 28)\n",
+      "29404 train samples\n",
+      "4861 test samples\n",
+      "Train on 29404 samples, validate on 4861 samples\n",
+      "Epoch 1/5\n",
+      "29404/29404 [==============================] - 1s - loss: 0.3810 - acc: 0.8846 - val_loss: 0.0897 - val_acc: 0.9728\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/5\n",
+      "29404/29404 [==============================] - 1s - loss: 0.1245 - acc: 0.9607 - val_loss: 0.0596 - val_acc: 0.9825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/5\n",
+      "29404/29404 [==============================] - 1s - loss: 0.0927 - acc: 0.9714 - val_loss: 0.0467 - val_acc: 0.9860\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/5\n",
+      "29404/29404 [==============================] - 1s - loss: 0.0798 - acc: 0.9755 - val_loss: 0.0408 - val_acc: 0.9868\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/5\n",
+      "29404/29404 [==============================] - 1s - loss: 0.0704 - acc: 0.9783 - val_loss: 0.0353 - val_acc: 0.9887\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Training time: 0:00:07.964140\n",
+      "Test score: 0.0352752654647\n",
+      "Test accuracy: 0.988685455557\n"
+     ]
+    }
+   ],
+   "source": [
+    "# freeze feature layers and rebuild model\n",
+    "for l in feature_layers:\n",
+    "    l.trainable = False\n",
+    "\n",
+    "# transfer: train dense layers for new classification task [5..9]\n",
+    "train_model(model,\n",
+    "            (X_train_gte5, y_train_gte5),\n",
+    "            (X_test_gte5, y_test_gte5), nb_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your Turn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try to Fine Tune a VGG16 Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "...\n",
+    "...\n",
+    "# Plugging new Layers\n",
+    "    model.add(Dense(768, activation='sigmoid'))\n",
+    "    model.add(Dropout(0.0))\n",
+    "    model.add(Dense(768, activation='sigmoid'))\n",
+    "    model.add(Dropout(0.0))\n",
+    "    model.add(Dense(n_labels, activation='softmax'))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2.5 - Supervised Learning - HyperParameter Tuning.ipynb b/2.5 - Supervised Learning - HyperParameter Tuning.ipynb
new file mode 100755
index 0000000..e6e3407
--- /dev/null
+++ b/2.5 - Supervised Learning - HyperParameter Tuning.ipynb	
@@ -0,0 +1,1216 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HyperParameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `keras.wrappers.scikit_learn`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem: \n",
+    "Builds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(1337)  # for reproducibility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "nb_classes = 10\n",
+    "\n",
+    "# input image dimensions\n",
+    "img_rows, img_cols = 28, 28"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# load training data and do basic data normalization\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "X_train /= 255\n",
+    "X_test /= 255\n",
+    "\n",
+    "# convert class vectors to binary class matrices\n",
+    "y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "y_test = np_utils.to_categorical(y_test, nb_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_model(dense_layer_sizes, nb_filters, nb_conv, nb_pool):\n",
+    "    '''Creates model comprised of 2 convolutional layers followed by dense layers\n",
+    "\n",
+    "    dense_layer_sizes: List of layer sizes. This list has one number for each layer\n",
+    "    nb_filters: Number of convolutional filters in each convolutional layer\n",
+    "    nb_conv: Convolutional kernel size\n",
+    "    nb_pool: Size of pooling area for max pooling\n",
+    "    '''\n",
+    "\n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
+    "                            border_mode='valid',\n",
+    "                            input_shape=input_shape))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "\n",
+    "    model.add(Flatten())\n",
+    "    for layer_size in dense_layer_sizes:\n",
+    "        model.add(Dense(layer_size))\n",
+    "    model.add(Activation('relu'))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(nb_classes))\n",
+    "    model.add(Activation('softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                  optimizer='adadelta',\n",
+    "                  metrics=['accuracy'])\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dense_size_candidates = [[32], [64], [32, 32], [64, 64]]\n",
+    "my_classifier = KerasClassifier(make_model, batch_size=32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GridSearch HyperParameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 1.0825 - acc: 0.6222     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.7591 - acc: 0.7419     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6405 - acc: 0.7849     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8896/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38560/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.9208 - acc: 0.6857     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5617 - acc: 0.8196     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4684 - acc: 0.8531     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8896/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38272/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.9945 - acc: 0.6619     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6438 - acc: 0.7877     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5229 - acc: 0.8311     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8832/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38240/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.8949 - acc: 0.6963     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5775 - acc: 0.8084     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4681 - acc: 0.8483     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3943 - acc: 0.8732     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3621 - acc: 0.8825     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3374 - acc: 0.8935     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8896/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38400/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.9276 - acc: 0.6925     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6084 - acc: 0.8049     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5138 - acc: 0.8364     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4329 - acc: 0.8625     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3879 - acc: 0.8770     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3573 - acc: 0.8873     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8960/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38624/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.8132 - acc: 0.7273     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4917 - acc: 0.8406     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4218 - acc: 0.8670     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3825 - acc: 0.8806     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3533 - acc: 0.8903     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3355 - acc: 0.8943     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8960/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38560/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6596 - acc: 0.7909     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3696 - acc: 0.8883     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2956 - acc: 0.9102     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8736/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37696/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6467 - acc: 0.7952     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3801 - acc: 0.8850     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3116 - acc: 0.9075     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8736/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37856/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6584 - acc: 0.7864     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3693 - acc: 0.8865     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2913 - acc: 0.9110     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8832/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37888/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6325 - acc: 0.7968     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3469 - acc: 0.8953     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2730 - acc: 0.9176     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2437 - acc: 0.9255     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2202 - acc: 0.9352     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2030 - acc: 0.9400     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8864/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38112/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5953 - acc: 0.8116     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3288 - acc: 0.9016     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2771 - acc: 0.9174     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2388 - acc: 0.9294     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2107 - acc: 0.9389     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1952 - acc: 0.9425     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8800/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37952/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6209 - acc: 0.8027     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3469 - acc: 0.8961     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2878 - acc: 0.9154     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2475 - acc: 0.9269     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2185 - acc: 0.9362     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1997 - acc: 0.9417     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8832/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38048/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.7584 - acc: 0.7527     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4407 - acc: 0.8619     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3767 - acc: 0.8825     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8800/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38176/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.7053 - acc: 0.7702     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3982 - acc: 0.8756     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3448 - acc: 0.8926     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8800/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38016/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.9118 - acc: 0.6914     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5370 - acc: 0.8269     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4312 - acc: 0.8612     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8864/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38240/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.7750 - acc: 0.7430      \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4436 - acc: 0.8617     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3732 - acc: 0.8818     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3338 - acc: 0.8943     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3024 - acc: 0.9051     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2807 - acc: 0.9118     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8832/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38432/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.8562 - acc: 0.7113     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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4873 - acc: 0.8475     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4067 - acc: 0.8739     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3594 - acc: 0.8860     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3229 - acc: 0.9014     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2996 - acc: 0.9093     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8864/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38208/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.8505 - acc: 0.7146     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.4756 - acc: 0.8477     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3917 - acc: 0.8792     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3467 - acc: 0.8901     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3203 - acc: 0.8995     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2908 - acc: 0.9098     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8832/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38112/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5228 - acc: 0.8346     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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2651 - acc: 0.9220     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2170 - acc: 0.9361     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20000/20000 [==============================] - 0s     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      " 8736/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37408/40000 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5966 - acc: 0.8105     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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3128 - acc: 0.9085     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2497 - acc: 0.9272     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8672/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39808/40000 [============================>.] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.6228 - acc: 0.8036      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+      "Epoch 2/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2961 - acc: 0.9141     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+      "Epoch 3/3\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2309 - acc: 0.9318     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8672/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37312/40000 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5508 - acc: 0.8260     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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2587 - acc: 0.9217     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2092 - acc: 0.9389     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1818 - acc: 0.9482     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1635 - acc: 0.9516     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1502 - acc: 0.9560     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8672/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39776/40000 [============================>.] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5957 - acc: 0.8138     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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.3026 - acc: 0.9110     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2335 - acc: 0.9324     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2045 - acc: 0.9409     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1824 - acc: 0.9463     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1693 - acc: 0.9500     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8608/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39648/40000 [============================>.] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.5756 - acc: 0.8177     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+      "Epoch 2/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2785 - acc: 0.9165     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+      "Epoch 3/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2308 - acc: 0.9325     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+      "Epoch 4/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.2000 - acc: 0.9419     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+      "Epoch 5/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1770 - acc: 0.9486     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+      "Epoch 6/6\n",
+      "40000/40000 [==============================] - 2s - loss: 0.1646 - acc: 0.9532     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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8576/40000 [=====>........................] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/sklearn/metrics/scorer.py:127: DeprecationWarning: Scoring method log_loss was renamed to neg_log_loss in version 0.18 and will be removed in 0.20.\n",
+      "  sample_weight=sample_weight)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39456/40000 [============================>.] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 1/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.5110 - acc: 0.8414     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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.2501 - acc: 0.9262     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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.1995 - acc: 0.9430     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+      "Epoch 4/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.1721 - acc: 0.9509     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+      "Epoch 5/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.1547 - acc: 0.9557     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+      "Epoch 6/6\n",
+      "60000/60000 [==============================] - 3s - loss: 0.1414 - acc: 0.9598     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+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=None, error_score='raise',\n",
+       "       estimator=<keras.wrappers.scikit_learn.KerasClassifier object at 0x7f49fac5c8d0>,\n",
+       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       param_grid={'nb_conv': [3], 'nb_filters': [8], 'nb_pool': [2], 'dense_layer_sizes': [[32], [64], [32, 32], [64, 64]], 'nb_epoch': [3, 6]},\n",
+       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
+       "       scoring='log_loss', verbose=0)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "validator = GridSearchCV(my_classifier,\n",
+    "                         param_grid={'dense_layer_sizes': dense_size_candidates,\n",
+    "                                     # nb_epoch is avail for tuning even when not\n",
+    "                                     # an argument to model building function\n",
+    "                                     'nb_epoch': [3, 6],\n",
+    "                                     'nb_filters': [8],\n",
+    "                                     'nb_conv': [3],\n",
+    "                                     'nb_pool': [2]},\n",
+    "                         scoring='log_loss',\n",
+    "                         n_jobs=1)\n",
+    "validator.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The parameters of the best model are: \n",
+      "{'nb_pool': 2, 'nb_conv': 3, 'nb_filters': 8, 'dense_layer_sizes': [64, 64], 'nb_epoch': 6}\n",
+      " 9024/10000 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bloss :  0.058321340532\n",
+      "acc :  0.9828\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('The parameters of the best model are: ')\n",
+    "print(validator.best_params_)\n",
+    "\n",
+    "# validator.best_estimator_ returns sklearn-wrapped version of best model.\n",
+    "# validator.best_estimator_.model returns the (unwrapped) keras model\n",
+    "best_model = validator.best_estimator_.model\n",
+    "metric_names = best_model.metrics_names\n",
+    "metric_values = best_model.evaluate(X_test, y_test)\n",
+    "for metric, value in zip(metric_names, metric_values):\n",
+    "    print(metric, ': ', value)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/3.1 Unsupervised Learning - AutoEncoders and Embeddings.ipynb b/3.1 Unsupervised Learning - AutoEncoders and Embeddings.ipynb
old mode 100644
new mode 100755
diff --git a/3.2 RNN and LSTM.ipynb b/3.2 RNN and LSTM.ipynb
old mode 100644
new mode 100755
diff --git a/3.3 (Extra) LSTM for Sentence Generation.ipynb b/3.3 (Extra) LSTM for Sentence Generation.ipynb
old mode 100644
new mode 100755
diff --git a/4. Conclusions.ipynb b/4. Conclusions.ipynb
index c37aa52..7d090ae 100644
--- a/4. Conclusions.ipynb	
+++ b/4. Conclusions.ipynb	
@@ -86,6 +86,21 @@
     "* Hyperas: https://github.com/maxpumperla/hyperas\n",
     "    - A web dashboard for Keras Models"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "#### Super-Cool\n",
+    "\n",
+    "* Keras.js: https://github.com/transcranial/keras-js\n",
+    "    - Your Keras Model **inside your Browser**\n",
+    "    - Showcase: https://transcranial.github.io/keras-js/"
+   ]
   }
  ],
  "metadata": {
diff --git a/4.1 Extra - Custom Layer.ipynb b/4.1 Extra - Custom Layer.ipynb
new file mode 100755
index 0000000..825c05b
--- /dev/null
+++ b/4.1 Extra - Custom Layer.ipynb	
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Custom Keras Layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Idea:\n",
+    "\n",
+    "We build a custom activation layer called **Antirectifier**,\n",
+    "which modifies the shape of the tensor that passes through it.\n",
+    "\n",
+    "We need to specify two methods: `get_output_shape_for` and `call`.\n",
+    "\n",
+    "Note that the same result can also be achieved via a `Lambda` layer (`keras.layer.core.Lambda`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "\n",
+    "keras.layers.core.Lambda(function, output_shape=None, arguments=None)\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because our custom layer is written with primitives from the Keras backend (`K`), our code can run both on TensorFlow and Theano."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using Theano backend.\n",
+      "Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 5110)\n",
+      "/home/valerio/anaconda3/envs/deep-learning/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
+      "  warnings.warn(warn)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Layer, Activation\n",
+    "from keras.datasets import mnist\n",
+    "from keras import backend as K\n",
+    "from keras.utils import np_utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AntiRectifier Layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Antirectifier(Layer):\n",
+    "    '''This is the combination of a sample-wise\n",
+    "    L2 normalization with the concatenation of the\n",
+    "    positive part of the input with the negative part\n",
+    "    of the input. The result is a tensor of samples that are\n",
+    "    twice as large as the input samples.\n",
+    "\n",
+    "    It can be used in place of a ReLU.\n",
+    "\n",
+    "    # Input shape\n",
+    "        2D tensor of shape (samples, n)\n",
+    "\n",
+    "    # Output shape\n",
+    "        2D tensor of shape (samples, 2*n)\n",
+    "\n",
+    "    # Theoretical justification\n",
+    "        When applying ReLU, assuming that the distribution\n",
+    "        of the previous output is approximately centered around 0.,\n",
+    "        you are discarding half of your input. This is inefficient.\n",
+    "\n",
+    "        Antirectifier allows to return all-positive outputs like ReLU,\n",
+    "        without discarding any data.\n",
+    "\n",
+    "        Tests on MNIST show that Antirectifier allows to train networks\n",
+    "        with twice less parameters yet with comparable\n",
+    "        classification accuracy as an equivalent ReLU-based network.\n",
+    "    '''\n",
+    "\n",
+    "    def get_output_shape_for(self, input_shape):\n",
+    "        shape = list(input_shape)\n",
+    "        assert len(shape) == 2  # only valid for 2D tensors\n",
+    "        shape[-1] *= 2\n",
+    "        return tuple(shape)\n",
+    "\n",
+    "    def call(self, x, mask=None):\n",
+    "        x -= K.mean(x, axis=1, keepdims=True)\n",
+    "        x = K.l2_normalize(x, axis=1)\n",
+    "        pos = K.relu(x)\n",
+    "        neg = K.relu(-x)\n",
+    "        return K.concatenate([pos, neg], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parametrs and Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# global parameters\n",
+    "batch_size = 128\n",
+    "nb_classes = 10\n",
+    "nb_epoch = 40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60000 train samples\n",
+      "10000 test samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the data, shuffled and split between train and test sets\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "X_train = X_train.reshape(60000, 784)\n",
+    "X_test = X_test.reshape(10000, 784)\n",
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "X_train /= 255\n",
+    "X_test /= 255\n",
+    "print(X_train.shape[0], 'train samples')\n",
+    "print(X_test.shape[0], 'test samples')\n",
+    "\n",
+    "# convert class vectors to binary class matrices\n",
+    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model with Custom Layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.6011 - acc: 0.9140 - val_loss: 0.1505 - val_acc: 0.9613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 2/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.1260 - acc: 0.9656 - val_loss: 0.0982 - val_acc: 0.9703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 3/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0831 - acc: 0.9763 - val_loss: 0.0782 - val_acc: 0.9747\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 4/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0636 - acc: 0.9813 - val_loss: 0.0827 - val_acc: 0.9741\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 5/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0511 - acc: 0.9841 - val_loss: 0.0724 - val_acc: 0.9758\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 6/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0429 - acc: 0.9866 - val_loss: 0.0667 - val_acc: 0.9788\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 7/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0366 - acc: 0.9883 - val_loss: 0.0715 - val_acc: 0.9792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 8/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0315 - acc: 0.9904 - val_loss: 0.0809 - val_acc: 0.9771\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 9/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0282 - acc: 0.9913 - val_loss: 0.0706 - val_acc: 0.9803\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 10/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0236 - acc: 0.9925 - val_loss: 0.0687 - val_acc: 0.9803\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 11/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0215 - acc: 0.9931 - val_loss: 0.0670 - val_acc: 0.9795\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 12/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0195 - acc: 0.9938 - val_loss: 0.0704 - val_acc: 0.9811\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 13/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0181 - acc: 0.9941 - val_loss: 0.0667 - val_acc: 0.9820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 14/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0149 - acc: 0.9955 - val_loss: 0.0687 - val_acc: 0.9823\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 15/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0146 - acc: 0.9959 - val_loss: 0.0723 - val_acc: 0.9799\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 16/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0137 - acc: 0.9958 - val_loss: 0.0795 - val_acc: 0.9799\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 17/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0130 - acc: 0.9957 - val_loss: 0.0697 - val_acc: 0.9826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 18/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0113 - acc: 0.9965 - val_loss: 0.0688 - val_acc: 0.9823\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 19/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0107 - acc: 0.9963 - val_loss: 0.0737 - val_acc: 0.9819\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 20/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0103 - acc: 0.9967 - val_loss: 0.0746 - val_acc: 0.9812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 21/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0094 - acc: 0.9968 - val_loss: 0.0727 - val_acc: 0.9811\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 22/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0084 - acc: 0.9972 - val_loss: 0.0805 - val_acc: 0.9820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 23/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0088 - acc: 0.9971 - val_loss: 0.0809 - val_acc: 0.9809\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 24/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0075 - acc: 0.9974 - val_loss: 0.0773 - val_acc: 0.9817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 25/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0078 - acc: 0.9975 - val_loss: 0.0758 - val_acc: 0.9817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 26/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0074 - acc: 0.9976 - val_loss: 0.0751 - val_acc: 0.9816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 27/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0076 - acc: 0.9975 - val_loss: 0.0785 - val_acc: 0.9809\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 28/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0067 - acc: 0.9978 - val_loss: 0.0782 - val_acc: 0.9816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 29/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0070 - acc: 0.9976 - val_loss: 0.0834 - val_acc: 0.9808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 30/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0055 - acc: 0.9983 - val_loss: 0.0775 - val_acc: 0.9817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 31/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0930 - val_acc: 0.9814\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 32/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0056 - acc: 0.9981 - val_loss: 0.0886 - val_acc: 0.9812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 33/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0778 - val_acc: 0.9812\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 34/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0047 - acc: 0.9984 - val_loss: 0.0839 - val_acc: 0.9824\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 35/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0049 - acc: 0.9984 - val_loss: 0.0900 - val_acc: 0.9809\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 36/40\n",
+      "60000/60000 [==============================] - 1s - loss: 0.0046 - acc: 0.9984 - val_loss: 0.0851 - val_acc: 0.9816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 37/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0053 - acc: 0.9985 - val_loss: 0.0932 - val_acc: 0.9801\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 38/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0049 - acc: 0.9983 - val_loss: 0.0917 - val_acc: 0.9804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 39/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0044 - acc: 0.9984 - val_loss: 0.0931 - val_acc: 0.9816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
+      "Epoch 40/40\n",
+      "60000/60000 [==============================] - 0s - loss: 0.0047 - acc: 0.9986 - val_loss: 0.0874 - val_acc: 0.9820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f1630520da0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# build the model\n",
+    "model = Sequential()\n",
+    "model.add(Dense(256, input_shape=(784,)))\n",
+    "model.add(Antirectifier())\n",
+    "model.add(Dropout(0.1))\n",
+    "model.add(Dense(256))\n",
+    "model.add(Antirectifier())\n",
+    "model.add(Dropout(0.1))\n",
+    "model.add(Dense(10))\n",
+    "model.add(Activation('softmax'))\n",
+    "\n",
+    "# compile the model\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='rmsprop',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# train the model\n",
+    "model.fit(X_train, Y_train,\n",
+    "          batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Excercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare with an equivalent network that is **2x bigger** (in terms of Dense layers) + **ReLU**)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/4.2 Extra - Multi-Modal Networks.ipynb b/4.2 Extra - Multi-Modal Networks.ipynb
new file mode 100755
index 0000000..3dbc9e6
--- /dev/null
+++ b/4.2 Extra - Multi-Modal Networks.ipynb	
@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi-Input Networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras Merge Layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multiple Sequential instances can be merged into a single output via a Merge layer. The output is a layer that can be added as first layer in a new Sequential model. For instance, here's a model with two separate input branches getting merged:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example (from documentation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://s3.amazonaws.com/keras.io/img/two_branches_sequential_model.png\" >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from keras.layers import Merge, Dense\n",
+    "from keras.models import Sequential\n",
+    "\n",
+    "left_branch = Sequential()\n",
+    "left_branch.add(Dense(32, input_dim=784))\n",
+    "\n",
+    "right_branch = Sequential()\n",
+    "right_branch.add(Dense(32, input_dim=784))\n",
+    "\n",
+    "merged = Merge([left_branch, right_branch], mode='concat')\n",
+    "\n",
+    "final_model = Sequential()\n",
+    "final_model.add(merged)\n",
+    "final_model.add(Dense(10, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Such a two-branch model can then be trained via e.g.:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "final_model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
+    "final_model.fit([input_data_1, input_data_2], targets)  # we pass one data array per model input\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Merge layer supports a number of pre-defined modes:\n",
+    "\n",
+    "* `sum` (default): element-wise sum\n",
+    "* `concat`: tensor concatenation. You can specify the concatenation axis via the argument concat_axis.\n",
+    "* `mul`: element-wise multiplication\n",
+    "* `ave`: tensor average\n",
+    "* `dot`: dot product. You can specify which axes to reduce along via the argument dot_axes.\n",
+    "* `cos`: cosine proximity between vectors in 2D tensors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also pass a function as the mode argument, allowing for arbitrary transformations:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "merged = Merge([left_branch, right_branch], mode=lambda x: x[0] - x[1])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras Functional API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### All models (layers) are callables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from keras.layers import Input, Dense\n",
+    "from keras.models import Model\n",
+    "\n",
+    "# this returns a tensor\n",
+    "inputs = Input(shape=(784,))\n",
+    "\n",
+    "# a layer instance is callable on a tensor, and returns a tensor\n",
+    "x = Dense(64, activation='relu')(inputs)\n",
+    "x = Dense(64, activation='relu')(x)\n",
+    "predictions = Dense(10, activation='softmax')(x)\n",
+    "\n",
+    "# this creates a model that includes\n",
+    "# the Input layer and three Dense layers\n",
+    "model = Model(input=inputs, output=predictions)\n",
+    "model.compile(optimizer='rmsprop',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "model.fit(data, labels)  # starts training\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ex2 - CNN MNIST.ipynb b/Ex2 - CNN MNIST.ipynb
new file mode 100755
index 0000000..51ca9b0
--- /dev/null
+++ b/Ex2 - CNN MNIST.ipynb	
@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Convolutional Neural Network\n",
+    "In this third tutorial we will see how to define, train and visualize a Convolutional Neural Network (CNN) for image classification using the [Keras](http://keras.io/) library. \n",
+    "\n",
+    "As you should have seen, a CNN is a feed-forward neural network tipically composed of Convolutional, MaxPooling and Dense layers. If the task implemented by the CNN is a classification task, the last Dense layer shoud use the Softmax activation, and the loss should be the categorical crossentropy.\n",
+    "\n",
+    "## Define the network\n",
+    "A simple CNN, with one input branch and one output branch can be defined using a [Sequential](http://keras.io/models/#sequential) model and stacking together all its layer. In this example the network will contain two [Convolution, Convolution, MaxPooling] stages, and two Dense layers.\n",
+    "\n",
+    "To test a different optimizer, we will use [AdaDelta](http://keras.io/optimizers/), which is a bit more complex than the simple Vanilla SGD with momentum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers.core import Dense, Dropout, Flatten, Activation\n",
+    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
+    "from keras.optimizers import Adadelta\n",
+    "\n",
+    "input_shape = (3, 32, 32)\n",
+    "nb_classes = 10\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Convolution2D(32, 3, 3, border_mode='same',\n",
+    "                        input_shape=input_shape))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Convolution2D(32, 3, 3))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Convolution2D(64, 3, 3, border_mode='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Convolution2D(64, 3, 3))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(512))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(nb_classes))\n",
+    "model.add(Activation('softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer=Adadelta())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Understanding layer shapes\n",
+    "An important feature of Keras layers is that each of them has an `input_shape` attribute, which you can use to visualize the shape of the input tensor, and an `output_shape` attribute, for inspecting the shape of the output tensor.\n",
+    "\n",
+    "As we can see, the input shape of the first convolutional layer corresponds to the `input_shape` attribute (which must be specified by the user). In this case, it is a 32x32 image with three color channels. Since this convolutional layer has the `border_mode` set to `same`, its output width and height will remain the same, and the number of output channel will be equal to the number of filters learned by the layer, 16. The following convolutional layers, instead, have the default `border_mode`, and therefore reduce width and height by $(k-1)$, where $k$ is the size of the kernel. \n",
+    "\n",
+    "MaxPooling layers, instead, reduce width and height of the input tensor, but keep the same number of channels. Activation layers, of course, don't change the shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer 0 \t (None, 3, 32, 32) \t (None, 32, 32, 32)\n",
+      "Layer 1 \t (None, 32, 32, 32) \t (None, 32, 32, 32)\n",
+      "Layer 2 \t (None, 32, 32, 32) \t (None, 32, 30, 30)\n",
+      "Layer 3 \t (None, 32, 30, 30) \t (None, 32, 30, 30)\n",
+      "Layer 4 \t (None, 32, 30, 30) \t (None, 32, 15, 15)\n",
+      "Layer 5 \t (None, 32, 15, 15) \t (None, 32, 15, 15)\n",
+      "Layer 6 \t (None, 32, 15, 15) \t (None, 64, 15, 15)\n",
+      "Layer 7 \t (None, 64, 15, 15) \t (None, 64, 15, 15)\n",
+      "Layer 8 \t (None, 64, 15, 15) \t (None, 64, 13, 13)\n",
+      "Layer 9 \t (None, 64, 13, 13) \t (None, 64, 13, 13)\n",
+      "Layer 10 \t (None, 64, 13, 13) \t (None, 64, 6, 6)\n",
+      "Layer 11 \t (None, 64, 6, 6) \t (None, 64, 6, 6)\n",
+      "Layer 12 \t (None, 64, 6, 6) \t (None, 2304)\n",
+      "Layer 13 \t (None, 2304) \t (None, 512)\n",
+      "Layer 14 \t (None, 512) \t (None, 512)\n",
+      "Layer 15 \t (None, 512) \t (None, 512)\n",
+      "Layer 16 \t (None, 512) \t (None, 10)\n",
+      "Layer 17 \t (None, 10) \t (None, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "for i, layer in enumerate(model.layers):\n",
+    "    print (\"Layer\", i, \"\\t\", layer.input_shape, \"\\t\", layer.output_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Understanding weights shape\n",
+    "In the same way, we can visualize the shape of the weights learned by each layer. In particular, Keras lets you inspect weights by using the `get_weights` method of a layer object. This will return a list with two elements, the first one being the weight tensor and the second one being the bias vector.\n",
+    "\n",
+    "Of course, MaxPooling layer don't have any weight tensor, since they don't have learnable parameters. Convolutional layers, instead, learn a $(n_o, n_i, k, k)$ weight tensor, where $k$ is the size of the kernel, $n_i$ is the number of channels of the input tensor, and $n_o$ is the number of filters to be learned. For each of the $n_o$ filters, a bias is also learned. Dense layers learn a $(n_i, n_o)$ weight tensor, where $n_o$ is the output size and $n_i$ is the input size of the layer. Each of the $n_o$ neurons also has a bias."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer 0 \t (32, 3, 3, 3) \t (32,)\n",
+      "Layer 2 \t (32, 32, 3, 3) \t (32,)\n",
+      "Layer 6 \t (64, 32, 3, 3) \t (64,)\n",
+      "Layer 8 \t (64, 64, 3, 3) \t (64,)\n",
+      "Layer 13 \t (2304, 512) \t (512,)\n",
+      "Layer 16 \t (512, 10) \t (10,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, layer in enumerate(model.layers):\n",
+    "    if len(layer.get_weights()) > 0:\n",
+    "        print(\"Layer\", i, \"\\t\", layer.get_weights()[0].shape, \"\\t\", layer.get_weights()[1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Training the network\n",
+    "We will train our network on the Cifar10 dataset, which contains 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images. As this dataset is also included in Keras datasets, we just ask the `keras.datasets` module for the dataset.\n",
+    "\n",
+    "Training and test images are normalized to lie in the $\\left[0,1\\right]$ interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.datasets import cifar10\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+    "X_train = X_train.astype(\"float32\")\n",
+    "X_test = X_test.astype(\"float32\")\n",
+    "X_train /= 255\n",
+    "X_test /= 255"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To reduce the risk of overfitting, we also apply some image transformation, like rotations, shifts and flips. All these can be easily implemented using the Keras [Image Data Generator](http://keras.io/preprocessing/image/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "datagen = ImageDataGenerator(\n",
+    "    featurewise_center=True,  # set input mean to 0 over the dataset\n",
+    "    samplewise_center=False,  # set each sample mean to 0\n",
+    "    featurewise_std_normalization=True,  # divide inputs by std of the dataset\n",
+    "    samplewise_std_normalization=False,  # divide each input by its std\n",
+    "    zca_whitening=False,  # apply ZCA whitening\n",
+    "    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
+    "    width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)\n",
+    "    height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)\n",
+    "    horizontal_flip=True,  # randomly flip images\n",
+    "    vertical_flip=False)  # randomly flip images\n",
+    "\n",
+    "datagen.fit(X_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now we can start training. At each iteration, a batch of 500 images is requested to the `ImageDataGenerator` object, and then fed to the network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.utils import generic_utils\n",
+    "for e in range(200):\n",
+    "    print('Epoch', e)\n",
+    "    print('Training...')\n",
+    "    progbar = generic_utils.Progbar(X_train.shape[0])\n",
+    "    for X_batch, Y_batch in datagen.flow(X_train, Y_train, batch_size=500, shuffle=True):\n",
+    "        loss = model.train_on_batch(X_batch, Y_batch)\n",
+    "        progbar.add(X_batch.shape[0], values=[('train loss', loss[0])])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..4a10435
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2017 MPBA
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 7956457..547d09c 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,8 @@
-
-<div>
-    <h1 style="text-align: center;">Deep Learning with Keras</h1>
-    <img style="text-align: left" src="imgs/keras-logo-small.jpg" width="10%" />
 <div>
-
+    <h1 style="text-align: center;">Deep Learning with Keras and TFlow</h1>
+    <img style="text-align: left" src="https://blog.keras.io/img/keras-tensorflow-logo.jpg" width="15%" />
 <div>
-    <h2 style="text-align: center;">Tutorial @ EuroScipy 2016</h2>
-    <img style="text-align: left" src="imgs/euroscipy_2016_logo.png" width="40%" />
-</div>    
-
+<br>
 ##### Yam Peleg,  Valerio Maggio
 
 # Goal of this Tutorial
@@ -18,78 +12,9 @@
 - **Understand** how easy is to do basic and *advanced* DL models in Keras;
     - **Examples and Hand-on Excerises** along the way.
 
-## Source
-
-https://github.com/leriomaggio/deep-learning-keras-euroscipy2016/
 
 ---
-
-# (Tentative) Schedule 
-
-## Attention: Spoilers Warning!
-
-
-- **Setup** (`10 mins`)
-
-- **Part I**: **Introduction** (`~65 mins`)
-
-    - Intro to ANN (`~20 mins`)
-        - naive pure-Python implementation
-        - fast forward, sgd, backprop
-        
-    - Intro to Theano (`15 mins`)
-        - Model + SGD with Theano
-        
-    - Introduction to Keras (`30 mins`)
-        - Overview and main features
-            - Theano backend
-            - Tensorflow backend
-        - Multi-Layer Perceptron and Fully Connected
-            - Examples with `keras.models.Sequential` and `Dense`
-            - HandsOn: MLP with keras
-            
-- **Coffe Break** (`30 mins`)
-
-- **Part II**: **Supervised Learning and Convolutional Neural Nets** (`~45 mins`)
-    
-    - Intro: Focus on Image Classification (`5 mins`)
-
-    - Intro to CNN (`25 mins`)
-        - meaning of convolutional filters
-            - examples from ImageNet    
-        - Meaning of dimensions of Conv filters (through an exmple of ConvNet) 
-        - Visualising ConvNets
-        - HandsOn: ConvNet with keras 
-
-    - Advanced CNN (`10 mins`)
-        - Dropout
-        - MaxPooling
-        - Batch Normalisation
-        
-    - Famous Models in Keras (likely moved somewhere else) (`10 mins`)
-        (ref: https://github.com/fchollet/deep-learning-models)
-            - VGG16
-            - VGG19
-            - ResNet50
-            - Inception v3
-        - HandsOn: Fine tuning a network on new dataset 
-        
-- **Part III**: **Unsupervised Learning** (`10 mins`)
-
-    - AutoEncoders (`5 mins`)
-    - word2vec & doc2vec (gensim) & `keras.datasets` (`5 mins`)
-        - `Embedding`
-        - word2vec and CNN
-    - Exercises
-
-- **Part IV**: **Advanced Materials** (`20 mins`)
-    - RNN and LSTM (`10 mins`)
-        -  RNN, LSTM, GRU  
-    - Example of RNN and LSTM with Text (`~10 mins`) -- *Tentative*
-    - HandsOn: IMDB
-
-- **Wrap up and Conclusions** (`5 mins`)
-
+### !!!PROGRAM WILL BE PUBLISHED HERE!!!
 ---
 
 # Requirements
@@ -132,9 +57,27 @@ I'm currently running this tutorial with **Python 3** on **Anaconda**
 
 # How to set up your environment
 
+
 The quickest and simplest way to setup the environment is to use [conda](https://store.continuum.io) environment manager. 
+Download Anaconda 4.2.0 (based on python 3.5) here:
++ Mac OSX 64Bit (https://repo.continuum.io/archive/Anaconda3-4.2.0-MacOSX-x86_64.sh)
++ Linux 64Bit (https://repo.continuum.io/archive/Anaconda2-4.2.0-Linux-x86_64.sh)
 
-We provide in the materials a `deep-learning.yml` that is complete and **ready to use** to set up your virtual environment with conda.
+If you need anaconda for different archs please refers to: https://repo.continuum.io/archive/index.html
+<br>
+To install anaconda: 
+```bash
+bash Anaconda3-4.2.0-MacOSX-x86_64.sh [mac users]
+bash Anaconda2-4.2.0-Linux-x86_64.sh [linux users]
+```
+
+<br>
+#### For Windows Users
+**We never tested the env on windows machines**. If you want you can install Anaconda (https://repo.continuum.io/archive/Anaconda2-4.2.0-Windows-x86_64.exe) for windows and create your env manually (https://conda.io/docs/using/envs.html#create-an-environment).
+In ```envs/packages_list.txt``` you can find all the software you need to install within your virtual environment.
+
+<br>
+We provide in the materials a `envs/deep-learning-linux.yml` for linux users `envs/deep-learning-osx.yml` for mac users that is complete and **ready to use** to set up your virtual environment with conda.
 
 
 ```python
@@ -300,23 +243,25 @@ We provide in the materials a `deep-learning.yml` that is complete and **ready t
       - wcwidth==0.1.7
       - wheel==0.29.0
       - widgetsnbextension==1.2.6
-    prefix: /home/valerio/anaconda3/envs/deep-learning
-    
+
 
 
 # Recreate the Conda Environment
 
+#### Preface
+For first clone this repo ```git clone https://gitlab.fbk.eu/MPBA/deep-learning-keras-tensorflow.git``` and then enter into deep-learning-keras-tensorflow directory.
+
 #### A. Create the Environment
 
 ```
-conda env create -f deep-learning.yml  # this file is for Linux channels.
+conda env create -f envs/deep-learning-linux.yml  # this file is for Linux channels.
 ```
 
 If you're using a **Mac OSX**, we also provided in the repo the conda file 
 that is compatible with `osx-channels`:
 
 ```
-conda env create -f deep-learning-osx.yml  # this file is for OSX channels.
+conda env create -f envs/deep-learning-osx.yml  # this file is for OSX channels.
 ```
 
 #### B. Activate the new `deep-learning` Environment
@@ -365,14 +310,30 @@ cnmem=.90
 
 ### 3. Installing Tensorflow as backend 
 
+For Linux Users<br>
+`!!!SELECT THE whl FILE ACCORDING TO YOUR HARDWARE!!!`
 ```shell
 # Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
 # Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
 export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl
 
+# Ubuntu/Linux 64-bit, CPU only, Python 3.5
+export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp34-cp34m-linux_x86_64.whl
+
 pip install --ignore-installed --upgrade $TF_BINARY_URL
 ```
 
+For Mac OSX Users<br>
+`!!!SELECT THE whl FILE ACCORDING TO YOUR HARDWARE!!!`
+```shell
+# Mac OS X, CPU only, Python 3.4 or 3.5:
+export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py3-none-any.whl
+
+# Mac OS X, GPU enabled, Python 3.4 or 3.5:
+export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.10.0-py3-none-any.whl
+
+pip install --ignore-installed --upgrade $TF_BINARY_URL
+```
 **More on [tensorflow documentation](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html)**
 
 ---
diff --git a/best_model.h5 b/best_model.h5
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diff --git a/data/mnist/t10k-images-idx3-ubyte.gz b/data/mnist/t10k-images-idx3-ubyte.gz
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index 0000000..5ace8ea
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diff --git a/data/mnist/t10k-labels-idx1-ubyte.gz b/data/mnist/t10k-labels-idx1-ubyte.gz
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index 0000000..a7e1415
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diff --git a/data/mnist/train-images-idx3-ubyte.gz b/data/mnist/train-images-idx3-ubyte.gz
new file mode 100644
index 0000000..b50e4b6
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diff --git a/data/mnist/train-labels-idx1-ubyte.gz b/data/mnist/train-labels-idx1-ubyte.gz
new file mode 100644
index 0000000..707a576
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diff --git a/data/test.csv b/data/test.csv
new file mode 100644
index 0000000..2c31158
--- /dev/null
+++ b/data/test.csv
@@ -0,0 +1,144369 @@
+id,feat_1,feat_2,feat_3,feat_4,feat_5,feat_6,feat_7,feat_8,feat_9,feat_10,feat_11,feat_12,feat_13,feat_14,feat_15,feat_16,feat_17,feat_18,feat_19,feat_20,feat_21,feat_22,feat_23,feat_24,feat_25,feat_26,feat_27,feat_28,feat_29,feat_30,feat_31,feat_32,feat_33,feat_34,feat_35,feat_36,feat_37,feat_38,feat_39,feat_40,feat_41,feat_42,feat_43,feat_44,feat_45,feat_46,feat_47,feat_48,feat_49,feat_50,feat_51,feat_52,feat_53,feat_54,feat_55,feat_56,feat_57,feat_58,feat_59,feat_60,feat_61,feat_62,feat_63,feat_64,feat_65,feat_66,feat_67,feat_68,feat_69,feat_70,feat_71,feat_72,feat_73,feat_74,feat_75,feat_76,feat_77,feat_78,feat_79,feat_80,feat_81,feat_82,feat_83,feat_84,feat_85,feat_86,feat_87,feat_88,feat_89,feat_90,feat_91,feat_92,feat_93
+1,0,0,0,0,0,0,0,0,0,3,0,0,0,3,2,1,0,0,0,0,0,0,0,5,3,1,1,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,20,0,0,0,0,0
+2,2,2,14,16,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,4,0,4,0,0,0,0,2,0,0,0,8,0,0,0,0,0,0,0,0,2,0,4,0,4,0,0,0,0,0,24,0,0,0,0,0,0,0,0,0,6,8,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,2,0,0,0,0,0,0,4,0,0,2,0
+3,0,1,12,1,0,0,0,0,0,0,7,1,0,0,0,7,0,2,0,0,0,4,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,4,0,2,3,0,0,0,0,10,1,0,2,0,0,1,6,1,1,0,0,1,1,1,2,0,0,2,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,2,0,0,0,0,1
+4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,21,3,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,2,0,0,0,0,6,0,8,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0
+5,1,0,0,1,0,0,1,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,4,0,5,16,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0
+6,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,7,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0
+7,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,4,0,1,0,4,0,2,0,0,1,0,0,1,0,0,1,0,0,0,2,0,2,1,1,0,1,0,0,2,0,0,0,0,0,2,0,3,0,1,0,1,1,0,1,0,0,2,0,0,2,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,18,1,0,0
+8,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0
+9,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0
+10,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+11,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,5,0,0,0,0,0
+12,0,1,2,0,0,0,0,3,1,0,0,0,1,0,0,2,0,1,0,1,0,5,0,0,0,0,0,0,1,0,0,0,1,0,0,7,0,2,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,12,0,3,0,7,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,1,0,0,1,0
+13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0
+14,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+15,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0
+16,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,3,0,1,11,10,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,1,0,0,12,5,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,2,1,1,7,1,0,1,0,0,3,0,0,6,0,8,0,0,0,0,4,0,0,1,3,1,0,1,1,0,0
+17,0,0,0,4,0,0,0,0,0,1,1,0,0,1,1,5,0,5,0,0,0,3,0,6,0,8,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,2,0,5,0,0,0,1,0,0,0,0,0,7,1,0,2,0,1,10,0,0,0,1,0,3,6,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1
+18,0,0,9,2,0,0,0,0,0,0,5,0,0,1,0,2,0,0,1,0,3,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,6,0,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,0,0
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+22,1,2,1,0,0,1,3,0,0,1,1,0,0,0,0,0,5,1,1,7,0,1,0,10,0,0,0,1,0,0,0,2,0,1,0,0,0,3,2,0,1,0,0,0,0,1,0,7,2,2,0,0,0,5,0,0,0,0,0,0,0,2,0,1,0,0,4,0,12,6,0,1,1,2,1,15,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0
+23,0,0,0,0,0,0,0,1,0,0,0,0,1,7,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0
+24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+25,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,11,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0
+26,0,1,0,0,0,0,8,1,0,1,0,0,11,0,0,0,1,0,1,0,0,0,0,8,1,1,0,0,1,0,0,2,0,0,0,3,1,2,0,1,0,0,0,0,2,0,0,6,0,1,4,0,0,1,3,0,2,0,0,0,0,0,0,3,0,0,5,0,2,0,2,0,2,0,0,12,0,71,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0
+27,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+28,1,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+29,4,0,0,0,0,0,0,0,0,0,0,0,0,18,2,3,0,1,0,1,0,1,0,1,5,0,0,0,0,0,0,0,3,0,0,0,0,1,0,19,1,4,0,0,0,0,0,15,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,8,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0
+30,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,2,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,3,9,0,0,0,0,1,1,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,5,0,0,2,0,0,0,0,1,0,0
+31,0,0,0,0,0,0,0,0,0,0,1,1,0,14,0,0,0,0,0,0,0,0,0,0,9,0,1,0,0,0,0,0,1,1,0,1,0,0,0,3,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0
+32,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+33,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,4,0,0,0,0,0
+34,0,2,2,0,0,0,4,2,0,0,1,0,2,0,1,2,3,3,0,24,0,2,0,15,0,0,0,0,2,1,0,5,9,1,0,4,1,11,0,1,9,1,1,3,0,0,0,1,2,2,0,0,4,7,4,0,1,3,0,1,0,3,0,0,1,1,33,0,0,11,0,1,0,2,2,4,0,1,0,0,0,0,2,0,2,0,5,0,4,0,2,0,0
+35,0,0,0,0,0,0,0,0,1,0,9,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+19902,0,0,0,0,0,0,0,21,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,3,1,0,0,1,2,0,0,1,2,4,0,12,1,0,0,4,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,1,0,0,0,0,0,11,0,0,0,2,10,0,0,0,1,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0
+19903,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
+19904,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0
+19905,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+19906,0,14,0,1,0,0,4,0,0,0,2,1,1,0,0,2,0,4,0,4,0,2,0,3,0,0,0,1,1,0,0,8,1,1,2,2,2,2,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,4,1,1,2,6,1,2,0,2,0,0,0,3,1,0,23,1,0,0,2,2,0,6,0,2,0,0,0,0,28,0,0,0,0,0,2,4,0,0,1
+19907,0,0,0,0,1,0,1,1,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,9,0,0,0,0,0,2,0,1,0,0,0,2,1,2,2,0,0,0,0,0,1,0,4,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,12,1,0,0,0,2,5,1,1,4,0,11,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0
+19908,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,1,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,1,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0
+19909,0,0,0,0,0,2,0,0,1,0,0,0,0,5,2,7,0,0,0,0,0,0,0,7,19,0,0,0,0,0,0,0,2,0,0,1,0,0,0,8,0,0,1,0,0,2,0,3,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,1,1,5,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,6,1,3,0,0,0,0,0
+19910,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,1,0,1,1,0,0,1,0,6,0,1,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,2,0,1,0,0,0,1,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0
+19911,2,0,3,0,0,0,0,5,6,0,2,0,1,0,3,2,1,3,0,0,0,1,0,3,1,1,0,0,0,0,0,8,3,1,0,1,0,0,0,2,1,1,1,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,24,2,3,21,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1
+19912,5,0,0,0,0,0,3,2,0,1,0,0,0,0,0,0,2,1,0,5,0,0,0,8,0,0,0,0,2,0,0,3,0,0,0,2,1,0,0,2,1,0,0,4,0,0,1,1,0,1,0,0,1,2,3,0,0,0,1,0,0,1,0,0,1,1,4,0,3,2,1,1,0,0,0,12,1,5,1,0,0,0,2,0,1,2,2,1,0,0,0,0,0
+19913,0,0,1,1,0,0,0,0,0,0,1,0,0,4,0,3,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,1,0,0,0,0,1,0,7,1,1,3,0,0,1,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,1,1,0,0,2,0,2,0,0,0,2,0,0,0,0,0,1,1,1,11,0,0,0,0
+19914,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0
+19915,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0
+19916,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,8,0,0,0,0,0,0,0,0,1,15,0,2,0,0,1,1,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,1,1,0,0,1,1,0,0,0,1,0,0,5,0,0,1,1,0,1,0,0,0,0
+19917,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,3,0,0,0,2,1,1,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,3,0,0,0,1,1,0,0,0,0,9,1,4,0,0,0,0,0,6,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,2,0
+19918,6,0,0,0,0,0,0,1,1,0,1,0,0,0,0,3,0,3,0,0,0,0,0,6,8,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,1,2,1,1,0,0,1,1,0,0,0,0,2,1,0,0,0,0,21,0,0,1,0,0,0,2,22,1,0,6,0,2,6,0,2,8,2,0,4,0,0,0,0,0,1,10,1,1,1,0,0,0,0
+19919,0,0,0,0,0,0,0,2,0,0,0,0,0,2,1,1,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,3,1,0,4,1,1,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0
+19920,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+19921,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
+19922,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,3,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+19923,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,6,0,0,0,0,0,0,0,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+19924,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,7,1,0,0,0,0,0,0,2,0,1,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
+19925,0,0,0,0,0,0,0,1,1,0,0,0,0,3,3,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+19926,0,0,14,14,0,0,0,0,0,1,5,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,3,0,8,0,0,1,1,0,0,0,17,0,4,0,0,0,0,5,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,3,0,0,1,0
+19927,0,0,0,1,0,0,0,1,0,0,0,0,0,10,0,3,0,0,0,1,0,2,0,1,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,1,0,1,0,1,0,0,0,0,1,2,1,0,0,0,0,0,0,3,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0
+19928,4,0,1,0,0,0,0,0,0,0,0,1,0,15,1,6,0,0,0,0,0,2,0,1,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9,0,1,0,0,0,0,0,10,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,1,1,4,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0
+19929,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0
+19930,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,7,0,2,0,0,0,0,0,0,0,0,0,0,3,3,0,2,0,0,1,1,2,23,0,0,0,0,0,0,0,2,0,0,17,2,0,0,3,2,0,1,0,0,0,1,0,0,4,0,0,0,0,2,0,15,0,1,0
+19931,0,0,3,0,0,0,0,0,0,1,0,1,0,0,12,7,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,7,0,3,0,0,1,1,0,0,0,0,0,0,0,0,0,4,0,1,0,2,1,0,0,3,0,1,0,2,0,0,0,1,0,12,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0
+19932,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,12,0,0,0,0,1,3,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+19933,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,1,0,3,0,0,1,0,7,0,2,0,0,0,0,4,0,0,0,0,0,0,1,13,0,0
+19934,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,2,0,0,0,2,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,0,0,3,2,0,0,0,1,0,4,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0
+19935,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0
+19936,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,3,1,0,0,1,0,7,0,2,0,0,0,0,12,0,3,1,0,0,0,0,1,0,0
+19937,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,1,0,0,0,1,2,0,0,1,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1
+19938,0,0,1,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,17,0,0,0,0,10,0,0,0,0,0,0,0,4,0,0,0,0,0,6,2,0,1,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,5,0,1,0,2,0,0,0,1,4,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0
+19939,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0
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diff --git a/data/test.csv.zip b/data/test.csv.zip
new file mode 100644
index 0000000..5045420
Binary files /dev/null and b/data/test.csv.zip differ
diff --git a/data/train.csv b/data/train.csv
new file mode 100644
index 0000000..157ef1f
--- /dev/null
+++ b/data/train.csv
@@ -0,0 +1,61879 @@
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+12,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,1,0,2,0,1,0,0,0,0,0,1,0,0,1,0,0,6,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,2,0,0,0,0,0,0,0,1,1,0,0,2,0,2,0,0,0,0,0,1,0,Class_1
+13,1,0,1,0,0,0,0,1,0,3,0,0,0,2,0,2,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,4,0,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_1
+14,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,4,1,0,1,0,3,0,0,1,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,2,5,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,0,8,0,7,1,0,0,1,0,0,0,0,0,11,1,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_1
+15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_1
+16,0,0,0,2,0,0,0,1,0,1,2,0,3,0,1,0,4,2,0,1,0,2,0,3,0,1,1,0,0,0,0,0,0,1,0,1,0,0,1,1,0,1,0,2,0,0,1,1,1,0,0,0,0,1,3,0,0,0,0,1,1,1,0,0,0,3,9,1,0,2,1,0,0,2,1,2,0,0,0,1,2,2,0,0,1,0,1,0,2,0,0,0,0,Class_1
+17,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_1
+18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+19,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,3,1,0,0,0,1,0,0,3,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,13,0,3,2,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,3,1,0,0,1,0,1,Class_1
+20,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,7,0,0,6,0,0,0,24,1,1,0,0,0,0,0,9,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,8,0,1,0,2,0,0,0,1,10,0,5,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,Class_1
+21,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,3,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_1
+22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,Class_1
+23,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+24,0,0,0,0,1,0,0,1,1,3,0,0,0,0,0,3,0,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,3,0,2,0,0,1,0,1,1,6,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+25,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_1
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+27,2,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,1,5,1,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,7,1,0,8,0,2,0,2,9,1,0,0,8,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_1
+28,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,Class_1
+29,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,2,1,1,0,0,0,2,0,1,0,0,0,0,1,0,1,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,Class_1
+30,2,0,0,0,0,0,4,1,0,0,1,1,0,0,0,0,5,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,11,0,1,2,1,0,0,1,0,3,0,6,1,0,0,0,4,0,0,0,3,2,0,0,0,0,1,Class_1
+31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_1
+32,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,14,0,0,0,0,0,0,3,0,0,0,1,1,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,16,0,1,0,0,1,1,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+33,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_1
+34,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_1
+35,2,0,0,0,0,2,4,1,0,0,0,0,2,0,0,0,4,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_1
+36,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,6,0,0,0,0,0,0,0,4,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,16,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+37,0,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_1
+38,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,4,0,0,0,0,0,0,0,1,0,0,0,6,0,0,3,0,1,0,0,0,2,0,1,1,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,Class_1
+39,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,Class_1
+40,2,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,1,0,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,1,2,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,0,0,3,0,0,0,3,0,2,6,0,1,3,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_1
+41,2,0,2,2,0,0,3,0,0,2,0,1,3,0,1,1,0,0,0,2,0,3,0,10,1,0,0,0,1,0,0,2,1,0,1,0,0,6,2,0,2,1,0,0,0,0,0,1,0,1,0,0,1,3,1,0,2,0,0,0,1,0,0,2,1,4,4,0,0,0,0,0,1,0,2,5,1,7,0,0,0,0,6,0,1,0,2,0,1,0,0,0,0,Class_1
+42,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+43,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,14,1,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_1
+44,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,1,8,0,4,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_1
+45,1,0,1,0,0,0,2,0,0,2,0,4,1,0,2,2,0,1,0,2,0,3,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,5,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,1,1,0,7,0,1,0,4,1,0,0,3,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_1
+46,0,2,0,1,0,0,0,0,0,0,1,0,3,2,0,3,2,7,0,0,0,2,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,9,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_1
+47,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,Class_1
+48,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,2,0,1,0,2,0,0,2,0,2,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+49,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
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+1140,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1141,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,4,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
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+1148,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,Class_1
+1149,0,0,0,0,1,0,0,1,2,3,0,0,1,0,0,0,1,1,0,0,0,1,0,5,2,0,0,0,0,0,0,3,1,0,0,0,0,1,0,1,0,1,0,5,0,0,0,4,0,0,0,0,1,1,1,0,2,0,0,1,0,2,1,1,0,2,12,0,0,9,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,12,1,0,0,0,1,0,0,Class_1
+1150,3,1,0,1,0,0,0,38,1,0,1,7,2,2,1,2,5,0,0,1,0,5,0,6,4,0,0,0,14,0,0,1,0,0,0,18,1,1,1,0,0,2,0,1,0,0,0,2,1,0,0,0,0,3,0,0,0,0,0,0,0,15,0,2,1,8,6,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,4,0,1,2,1,0,0,0,5,0,0,Class_1
+1151,0,0,0,0,0,0,2,3,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,3,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_1
+1152,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,2,1,0,0,0,25,0,0,0,0,0,1,0,4,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,2,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,2,0,1,8,0,1,0,0,0,0,2,1,17,0,5,0,0,0,0,9,0,0,0,1,0,0,0,0,0,0,Class_1
+1153,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,3,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+1154,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,2,0,3,0,5,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,2,2,13,0,0,1,1,0,1,0,0,3,0,3,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,Class_1
+1155,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,8,1,1,0,1,0,2,0,1,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,1,5,0,0,0,6,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,2,0,0,0,0,Class_1
+1156,1,1,0,0,0,0,0,2,1,0,0,0,0,0,0,2,2,2,0,0,0,0,1,6,0,0,0,0,0,1,0,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,12,0,0,0,1,0,0,0,4,2,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_1
+1157,0,0,0,0,0,0,0,0,0,0,0,1,6,0,1,0,5,0,0,1,0,2,0,7,2,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,1,0,1,0,0,9,0,1,1,1,1,0,0,0,2,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1158,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,4,0,7,0,0,0,0,1,0,0,3,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,5,0,0,0,0,0,3,1,0,0,0,0,2,0,1,0,0,0,0,10,0,1,3,1,3,0,0,0,5,0,1,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,Class_1
+1159,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,1,0,0,3,0,0,1,5,0,0,0,0,0,0,0,3,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,8,0,0,3,0,1,1,0,1,10,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1160,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_1
+1161,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,10,0,0,2,0,0,0,11,0,0,0,0,0,0,0,2,1,1,0,3,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,1,1,1,14,0,2,0,2,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_1
+1162,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_1
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+1164,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,1,8,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+1165,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,Class_1
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+1168,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,3,0,1,0,15,0,0,0,0,0,1,0,3,0,0,0,4,1,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,1,0,2,0,7,0,16,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,Class_1
+1169,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,Class_1
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+1415,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,1,0,4,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,1,0,1,0,1,0,0,0,1,0,2,0,0,1,0,0,0,0,3,2,0,0,0,5,4,0,0,0,0,0,9,1,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_1
+1416,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,5,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_1
+1417,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1418,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,1,1,0,1,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1419,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,5,0,0,1,0,0,0,6,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,4,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+1420,0,0,0,0,0,0,0,3,0,1,0,1,1,6,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1421,0,0,0,0,0,0,1,0,0,0,1,0,6,0,0,1,0,0,0,0,0,0,0,8,2,0,0,0,0,0,0,3,0,1,0,0,0,1,1,2,0,1,0,0,0,0,0,3,0,0,1,0,16,1,0,1,0,0,3,3,0,2,0,0,0,1,5,1,1,1,4,0,1,0,2,3,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_1
+1422,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1423,2,1,0,0,7,0,0,0,0,0,0,0,2,0,0,0,6,0,0,2,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,2,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,0,0,5,0,1,0,1,4,2,7,0,2,4,2,0,0,1,0,2,0,4,3,0,0,0,0,1,0,3,0,0,0,0,2,0,0,Class_1
+1424,0,0,0,0,1,0,0,3,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,10,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,1,1,2,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_1
+1425,1,1,0,1,0,0,3,0,0,2,0,1,1,4,0,7,2,3,0,10,0,6,1,2,1,0,0,0,12,0,0,0,1,0,0,3,1,2,0,2,0,0,0,3,0,0,5,3,1,3,0,0,1,8,27,0,0,0,1,1,0,0,0,3,3,4,4,3,1,8,3,1,0,0,0,3,3,1,0,0,2,0,6,0,0,0,0,0,1,0,0,0,1,Class_1
+1426,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_1
+1427,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,0,0,0,0,0,0,7,3,0,0,0,0,1,0,2,0,0,0,2,0,1,1,0,1,0,0,0,0,0,0,1,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,15,0,0,2,5,0,0,0,14,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,Class_1
+1428,2,0,1,0,4,1,4,1,0,0,2,0,1,1,0,2,2,2,0,1,0,1,2,7,1,0,0,0,1,61,0,16,0,1,2,0,0,0,0,0,0,0,2,5,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,3,4,0,0,5,0,1,3,2,0,1,0,0,1,0,1,0,0,20,0,1,2,1,3,0,0,0,2,Class_1
+1429,1,0,0,0,0,0,4,2,0,1,0,0,0,3,1,0,0,0,0,1,0,0,0,15,0,0,0,0,0,0,0,6,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,3,0,0,48,0,3,0,6,0,0,1,3,11,0,3,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_1
+1430,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1431,0,0,0,0,0,0,1,3,0,0,0,0,0,1,3,1,0,0,0,0,1,0,0,2,1,0,1,0,0,0,0,1,2,0,1,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,1,0,0,1,2,0,0,0,0,1,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,Class_1
+1432,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1433,0,0,1,0,0,0,0,5,1,0,0,0,0,0,0,0,2,1,0,0,0,2,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,1,0,0,1,0,0,3,2,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,6,2,4,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,1,1,0,0,0,0,Class_1
+1434,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,1,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,3,0,0,0,0,0,1,0,2,1,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,Class_1
+1435,0,0,1,0,0,0,3,1,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,3,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,2,0,1,0,2,0,5,2,0,0,0,0,0,0,4,0,0,0,0,0,1,0,3,0,0,0,0,4,0,0,1,1,3,1,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_1
+1436,0,0,0,0,0,0,5,0,0,3,2,0,3,0,0,1,0,1,0,2,0,0,5,14,0,0,0,0,0,0,0,3,0,0,1,2,2,1,0,1,0,0,0,4,0,0,0,0,2,2,2,1,2,3,7,0,1,0,0,1,0,0,0,6,0,0,6,0,2,4,0,0,2,0,0,2,0,20,1,2,1,0,2,0,1,1,0,0,0,2,0,2,0,Class_1
+1437,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,6,1,0,0,0,0,1,0,11,0,0,0,4,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,19,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1438,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,Class_1
+1439,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,2,0,0,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_1
+1440,5,0,0,0,0,0,0,0,0,8,0,0,2,0,1,1,0,0,0,0,0,1,0,2,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,19,1,0,0,0,0,3,2,0,4,1,0,0,2,2,0,1,4,0,0,0,3,1,0,0,0,7,1,0,0,0,0,0,5,1,0,1,0,0,0,0,Class_1
+1441,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,2,2,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_1
+1442,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,3,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_1
+1443,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,4,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_1
+1444,0,0,1,0,0,0,0,16,0,0,1,0,0,1,0,4,5,3,0,4,0,0,0,14,0,0,0,0,0,0,0,1,0,0,0,2,0,4,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,1,3,0,0,0,0,1,0,2,0,0,0,11,16,0,0,2,1,2,1,0,0,0,0,6,0,0,3,0,0,0,2,0,1,0,2,0,0,0,0,Class_1
+1445,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,3,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,3,4,0,0,0,1,0,0,0,0,0,0,1,0,1,2,0,0,0,0,2,1,0,0,0,0,0,0,Class_1
+1446,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,1,0,0,0,2,1,0,0,1,0,0,0,2,0,0,2,1,0,1,0,0,1,5,0,1,0,0,1,2,0,0,1,0,0,0,2,0,1,0,0,2,0,0,0,1,0,0,7,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,Class_1
+1447,1,1,0,0,7,0,1,3,1,1,0,0,0,1,0,0,23,1,0,1,0,0,0,30,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,1,0,4,0,0,1,0,2,0,2,1,0,41,5,2,1,6,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,2,0,0,2,0,0,0,Class_1
+1448,1,0,0,0,0,3,0,0,0,1,1,0,0,0,0,3,3,1,0,0,0,1,0,6,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,7,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,10,0,1,1,1,0,1,12,3,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_1
+1449,0,0,0,0,0,0,0,0,1,2,0,0,0,2,0,0,1,0,0,3,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,1,0,0,2,0,0,0,1,6,0,0,0,0,0,0,0,0,0,3,0,1,0,1,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
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+1500,0,0,0,1,0,0,0,8,2,0,0,0,0,1,1,0,10,0,0,0,0,0,0,7,4,0,0,0,0,0,0,0,1,0,0,3,3,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,4,0,1,7,0,1,0,3,18,4,0,0,5,1,0,0,5,0,0,0,0,0,0,0,0,0,0,5,4,0,2,0,0,0,0,Class_1
+1501,2,0,0,0,0,0,0,0,0,3,0,0,0,1,0,7,0,0,0,0,0,2,0,2,4,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,1,0,0,3,0,0,0,2,2,1,0,0,1,5,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,4,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_1
+1502,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,1,17,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_1
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+1504,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
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+1506,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,15,1,0,0,0,0,0,0,3,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,9,0,4,0,2,0,1,0,2,16,0,3,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_1
+1507,0,0,2,0,0,0,0,3,0,2,0,0,0,0,0,1,5,0,0,0,0,0,0,4,1,1,0,1,1,0,0,0,1,0,0,2,2,2,0,0,1,0,0,0,0,0,0,10,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,3,9,0,0,1,1,3,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,Class_1
+1508,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,2,8,0,0,0,1,0,5,0,0,0,0,0,0,0,0,1,0,0,15,0,4,0,1,1,0,0,0,0,0,2,1,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,0,4,1,0,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,Class_1
+1509,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+1510,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,14,0,6,1,0,0,0,9,0,1,0,0,0,0,0,0,0,0,Class_1
+1511,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_1
+1512,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,Class_1
+1513,0,0,1,0,1,0,10,0,0,1,0,0,0,1,0,0,0,1,0,1,0,2,0,2,1,1,0,0,1,0,0,0,2,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,2,7,0,0,0,1,1,0,0,0,0,2,1,0,0,0,3,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1514,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,1,2,0,0,0,0,0,1,6,1,0,0,0,0,0,0,1,0,5,0,5,2,1,0,0,1,2,0,1,0,0,1,0,1,1,0,1,0,0,0,1,1,1,0,2,0,0,1,4,0,3,10,1,0,3,1,2,1,0,1,4,1,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,Class_1
+1515,0,1,0,0,0,0,4,2,0,0,0,0,1,2,0,0,5,0,0,4,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,1,0,0,1,10,0,0,1,1,1,0,0,0,0,2,2,0,0,3,0,0,0,3,0,5,1,1,9,0,1,6,0,1,1,0,0,10,0,0,0,0,0,0,1,0,1,0,1,0,0,1,4,0,0,Class_1
+1516,0,0,0,0,0,0,0,10,0,0,0,0,2,0,1,1,2,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,5,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,2,0,0,0,0,0,0,0,4,0,0,Class_1
+1517,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,4,1,0,3,0,2,2,3,0,0,0,0,0,0,1,1,0,2,1,3,0,2,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,7,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,Class_1
+1518,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,3,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,5,0,1,0,2,0,1,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_1
+1519,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,9,0,3,0,1,1,7,0,7,0,0,1,0,1,0,0,0,0,2,0,5,1,0,2,2,2,0,1,1,0,3,7,0,0,0,0,2,0,1,0,0,2,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,2,0,0,1,0,Class_1
+1520,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,9,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_1
+1521,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,13,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,Class_1
+1522,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,4,0,1,0,3,1,1,0,0,1,0,0,0,0,0,2,0,1,2,0,0,0,1,0,2,0,1,3,0,0,0,0,0,0,2,4,0,0,0,0,0,0,1,0,1,3,5,0,2,0,6,1,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,Class_1
+1523,2,0,1,0,8,3,2,0,0,0,5,0,0,0,8,1,0,2,2,1,0,4,0,30,2,3,0,1,0,81,1,31,0,0,2,2,2,2,1,1,0,1,2,5,0,0,0,4,0,0,0,2,0,1,1,1,1,0,0,0,0,2,1,0,3,2,14,1,0,3,0,2,2,8,2,4,0,0,0,0,2,1,0,51,0,3,0,0,3,0,0,0,1,Class_1
+1524,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1525,3,0,2,0,1,0,1,1,0,4,3,0,1,0,0,0,4,1,0,1,0,2,0,10,3,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,1,0,0,2,0,0,2,4,1,1,0,0,0,3,2,0,0,0,0,1,0,2,2,0,1,1,19,2,0,5,3,0,2,0,0,4,0,0,1,0,4,1,0,1,0,0,1,0,2,0,1,0,0,Class_1
+1526,0,1,0,1,0,0,1,2,0,2,0,0,2,0,0,2,0,0,0,3,0,1,0,3,0,0,1,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,0,3,0,1,1,1,1,0,4,0,0,1,0,0,0,0,1,1,0,4,0,2,0,0,4,0,3,0,3,0,0,0,0,0,0,0,2,0,4,0,1,0,0,0,0,0,1,0,0,1,0,Class_1
+1527,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,1,0,0,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,Class_1
+1528,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1529,0,1,0,5,0,0,0,3,0,2,1,0,1,1,0,1,8,3,0,1,0,1,0,9,0,2,0,0,0,0,0,3,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,6,1,0,15,2,0,0,2,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,Class_1
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+1531,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,7,1,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,5,0,0,6,0,0,3,0,8,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,Class_1
+1532,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,3,0,0,0,0,0,0,0,Class_1
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+1538,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,1,0,0,0,0,0,6,1,0,0,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,2,0,5,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1539,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,2,0,0,3,0,0,0,2,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,2,1,1,1,0,0,0,0,2,2,0,2,0,0,0,0,3,0,0,0,1,1,0,1,2,0,2,Class_1
+1540,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,2,0,0,0,0,0,Class_1
+1541,1,0,0,0,0,0,0,5,0,0,0,0,7,0,0,0,1,0,0,2,0,0,0,7,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,1,2,2,0,0,0,0,1,0,3,0,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,Class_1
+1542,0,0,0,0,1,0,1,4,0,0,0,1,0,0,0,0,4,0,0,1,0,1,0,8,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,1,2,3,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,Class_1
+1543,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,16,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1544,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,0,0,0,0,0,0,4,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,4,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
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+1547,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,2,5,3,0,0,1,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_1
+1548,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,5,1,0,0,0,3,1,0,0,2,0,0,2,2,1,1,0,1,0,0,0,0,0,0,1,0,0,0,1,6,0,3,0,0,0,0,0,0,0,0,0,1,0,9,0,0,1,1,1,0,0,11,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_1
+1549,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,6,0,0,0,0,0,1,0,6,1,0,0,2,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,1,0,4,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_1
+1550,0,0,0,0,0,0,3,1,0,0,1,0,2,0,0,1,0,3,0,2,0,0,1,12,0,0,0,0,0,0,0,2,0,0,0,2,2,0,0,1,0,0,0,3,0,0,0,12,0,0,1,1,2,2,5,0,5,0,0,1,2,1,0,7,0,0,7,0,2,2,0,0,5,0,0,9,0,20,0,1,0,2,1,0,0,0,0,0,0,1,0,2,0,Class_1
+1551,3,1,1,0,3,0,16,2,1,2,3,0,0,1,0,2,22,7,0,7,0,1,0,24,2,0,0,0,1,0,0,7,2,3,0,4,0,9,0,1,2,0,0,0,0,1,1,7,3,4,1,0,0,3,0,0,0,0,0,5,0,2,1,3,0,0,36,6,6,10,18,1,0,0,0,6,0,9,4,0,0,0,5,1,1,1,4,0,5,0,0,0,0,Class_1
+1552,0,1,0,0,0,0,3,1,1,0,0,0,0,1,0,0,0,1,0,4,0,0,0,14,1,0,0,0,0,0,0,4,0,0,0,0,0,5,2,0,0,0,0,2,0,0,0,2,1,2,0,0,0,3,2,0,1,0,0,0,0,0,1,1,1,1,1,0,0,3,0,0,0,0,0,9,0,5,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,Class_1
+1553,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_1
+1554,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,1,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,4,1,0,3,0,0,0,3,0,0,0,0,15,1,1,0,0,0,0,0,0,3,0,0,0,3,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,1,0,0,0,2,0,Class_1
+1555,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,2,0,2,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,Class_1
+1556,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,1,1,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,34,0,0,0,3,0,0,0,2,0,0,0,1,0,0,0,0,0,0,6,4,0,0,0,0,2,0,Class_1
+1557,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0,15,1,0,0,0,0,0,13,0,0,0,0,0,0,0,5,0,1,0,4,0,0,1,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,12,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_1
+1558,2,0,0,0,0,0,1,3,0,0,0,0,0,5,1,0,1,0,0,1,0,0,0,19,0,0,0,0,0,0,0,7,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,42,0,0,0,6,0,1,1,1,8,0,2,5,0,0,0,4,0,0,2,0,0,0,0,0,0,0,Class_1
+1559,0,0,0,0,0,0,0,9,1,0,0,0,7,0,0,0,3,1,0,3,0,3,0,8,3,0,1,0,1,0,0,2,1,2,1,3,0,0,0,0,2,0,0,1,0,0,0,4,0,1,0,0,4,0,2,0,0,0,1,2,0,1,0,0,0,0,3,0,1,1,5,3,0,1,2,0,0,0,1,0,3,0,0,0,0,1,0,1,0,0,1,0,1,Class_1
+1560,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,Class_1
+1561,1,0,0,0,0,0,1,1,4,0,0,0,0,0,1,4,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,2,0,0,0,0,0,0,1,5,0,2,0,0,0,0,0,0,0,1,0,0,0,0,Class_1
+1562,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,1,0,0,0,0,1,0,Class_1
+1563,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_1
+1564,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,3,2,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,7,1,0,0,0,4,3,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1565,0,0,3,1,0,0,0,2,0,0,4,0,9,2,0,3,11,4,0,0,0,1,0,4,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,6,2,0,2,0,0,0,0,2,0,0,0,5,39,0,0,1,1,0,5,0,0,12,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,Class_1
+1566,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,4,2,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1567,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1568,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,1,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,2,1,0,0,0,0,0,0,0,8,0,3,1,0,10,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,Class_1
+1569,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_1
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+1613,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,1,11,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_1
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+1616,0,0,0,0,0,0,3,0,1,3,1,0,0,3,0,0,1,0,0,1,0,0,0,6,0,0,0,0,2,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,5,0,2,0,0,0,0,0,0,6,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_1
+1617,0,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_1
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+1619,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,Class_1
+1620,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,6,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,Class_1
+1621,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,1,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,2,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_1
+1622,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,2,0,0,2,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,1,0,7,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,Class_1
+1623,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,Class_1
+1624,0,0,0,0,0,0,0,14,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_1
+1625,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,2,8,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0,3,0,2,0,0,5,0,1,0,0,0,0,10,0,3,0,0,0,0,20,0,2,0,0,1,0,8,1,0,0,Class_1
+1626,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,2,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,5,1,0,0,0,0,0,0,Class_1
+1627,0,0,0,0,1,0,0,5,1,0,0,0,0,0,0,0,14,2,0,3,0,1,0,24,0,0,2,0,1,0,0,2,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,7,3,1,0,0,0,0,0,0,3,0,4,1,0,1,0,0,0,0,0,1,1,0,0,0,0,1,Class_1
+1628,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1629,0,0,0,0,0,0,0,2,1,2,0,0,2,0,0,2,5,3,0,1,0,2,0,1,0,1,0,0,1,0,0,0,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,1,1,1,1,9,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,2,0,0,0,0,1,0,0,Class_1
+1630,1,0,0,1,1,0,1,2,1,2,0,0,0,1,0,2,5,0,0,0,0,5,0,6,0,0,2,0,1,1,0,2,1,0,1,1,1,0,0,1,2,0,0,0,0,0,0,3,0,0,1,1,1,3,1,0,0,0,0,0,0,6,0,0,2,2,9,0,0,2,1,0,0,0,3,14,0,12,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,Class_1
+1631,0,0,0,0,3,2,2,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,1,1,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,2,0,0,0,0,0,10,0,0,0,0,0,0,5,5,0,2,0,0,0,0,3,2,1,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,Class_1
+1632,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,4,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,4,2,0,0,0,0,0,0,2,0,3,0,5,8,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_1
+1633,5,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,3,1,0,0,0,0,0,0,4,0,0,0,3,1,1,0,0,1,1,0,1,0,0,0,8,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1634,0,0,0,0,1,0,0,0,3,4,0,0,1,0,0,0,1,1,0,0,0,1,0,5,2,0,0,0,0,0,0,3,2,1,0,0,0,1,0,1,1,1,0,5,0,0,0,6,0,0,0,0,1,1,1,0,2,0,0,1,0,1,1,0,0,2,10,0,0,9,0,0,4,0,0,0,0,2,0,0,0,0,0,0,1,10,1,0,0,0,1,0,0,Class_1
+1635,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1636,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1637,0,0,1,2,3,0,4,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,0,3,0,2,0,1,0,0,0,0,0,0,2,1,0,2,0,0,0,0,0,13,0,0,1,0,0,1,0,0,0,2,4,0,1,0,0,0,0,0,0,0,10,0,1,0,1,5,0,0,0,0,1,1,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,Class_1
+1638,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,4,0,0,2,2,0,0,0,0,0,0,0,0,0,1,5,0,Class_1
+1639,3,0,0,0,7,4,0,0,0,0,6,0,0,0,3,25,0,1,0,1,0,2,2,29,0,0,0,1,0,80,0,149,0,3,1,1,1,1,2,1,1,0,5,4,0,0,0,5,0,0,1,0,0,2,1,0,1,2,0,5,0,1,0,1,6,1,29,0,0,1,0,1,15,8,30,12,0,0,0,2,0,0,0,55,1,3,1,0,1,0,0,0,3,Class_1
+1640,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,3,0,2,0,0,0,4,0,4,2,0,0,0,1,0,0,2,1,0,0,0,0,1,0,1,1,1,0,1,0,0,0,4,0,1,0,0,34,2,8,0,0,0,0,0,0,1,0,0,0,0,3,0,0,3,0,0,0,0,2,0,1,0,6,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_1
+1641,1,1,0,0,0,0,5,1,1,0,1,0,0,0,1,0,5,1,1,6,0,2,0,3,0,0,0,0,0,0,0,6,1,1,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,9,0,1,3,7,1,0,0,1,2,0,7,2,0,1,0,5,0,1,3,3,0,0,0,1,0,1,Class_1
+1642,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,1,1,1,0,3,3,0,0,0,2,0,0,3,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,2,0,0,0,0,27,8,2,2,0,0,0,1,0,9,1,0,2,2,1,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,4,0,0,0,0,Class_1
+1643,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,Class_1
+1644,0,0,0,0,0,0,0,4,0,0,0,0,1,0,5,1,0,0,0,0,7,0,0,2,0,0,0,0,0,0,0,0,0,0,0,19,1,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,27,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_1
+1645,1,1,0,0,0,0,2,0,2,0,0,0,3,1,2,4,1,0,0,2,0,1,0,5,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,1,2,0,0,0,0,0,1,0,3,0,0,0,0,0,3,0,0,0,5,1,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,1,2,1,0,0,0,1,Class_1
+1646,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1647,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,5,4,0,0,1,2,0,0,6,0,0,0,1,0,1,3,0,0,0,0,0,2,1,0,1,0,0,0,0,1,1,1,1,0,0,3,1,0,1,0,0,0,0,8,0,1,1,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_1
+1648,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,2,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,2,0,0,0,0,0,4,0,0,5,1,0,0,0,0,0,0,0,0,0,1,1,0,Class_1
+1649,2,0,0,0,0,0,7,0,0,1,1,0,4,1,0,1,2,6,0,2,0,1,0,5,1,0,0,0,1,0,0,3,1,1,0,2,0,0,0,0,0,0,0,1,0,0,0,3,3,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,1,9,1,4,2,6,0,0,0,0,7,0,3,4,1,0,0,3,0,0,0,2,0,1,2,0,0,0,Class_1
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+1679,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,10,1,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
+1680,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,2,0,0,0,5,0,3,0,1,0,0,1,2,0,0,1,1,0,1,1,0,1,1,0,0,0,0,0,1,1,0,0,1,2,0,0,1,0,0,0,1,0,1,1,1,0,4,6,2,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,Class_1
+1681,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,5,0,2,0,0,0,0,0,0,1,0,2,3,0,1,0,2,1,1,0,1,0,0,0,1,1,0,0,0,0,5,1,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_1
+1682,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,3,0,0,4,0,0,0,3,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,1,0,0,0,0,Class_1
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+1685,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,5,0,0,1,1,0,1,2,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,4,1,0,3,0,0,0,1,0,0,7,0,3,0,0,1,0,3,0,0,0,0,0,0,0,0,0,2,Class_1
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+1713,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_1
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+1726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_1
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+1970,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+1971,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,1,0,0,0,0,0,Class_2
+1972,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,3,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,9,0,0,0,1,0,Class_2
+1973,0,0,0,1,0,0,0,0,0,0,0,0,0,0,19,6,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,1,0,14,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,0,0,0,0,Class_2
+1974,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,3,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,3,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,6,0,0,0,1,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,Class_2
+1975,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+1976,1,0,0,0,1,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+1977,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+1978,0,0,0,0,0,0,0,1,0,0,0,0,0,4,4,6,0,0,0,0,2,0,0,0,6,0,2,0,0,0,0,0,4,0,1,1,0,0,0,8,0,0,0,0,0,2,0,0,0,0,0,3,2,4,1,0,0,0,0,0,0,2,0,1,0,2,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+1979,0,0,1,0,0,0,0,4,0,0,0,0,0,6,0,4,0,2,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,22,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,1,1,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+1980,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+1981,0,1,0,0,0,0,0,1,0,1,1,0,1,10,0,4,0,1,0,0,0,1,0,5,2,0,0,1,0,0,0,3,2,1,0,0,0,4,8,5,1,1,1,0,1,0,0,3,0,3,0,0,1,0,0,0,0,0,0,0,0,1,3,3,0,1,1,0,0,4,0,1,0,0,0,2,0,1,0,0,0,0,0,0,1,27,2,3,0,0,0,0,0,Class_2
+1982,0,0,0,0,0,0,0,0,19,0,3,0,0,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+1983,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+1984,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_2
+1985,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+1986,2,0,0,0,0,0,0,0,3,0,0,0,1,14,1,2,0,2,0,0,0,0,0,7,16,0,0,0,0,0,0,0,4,2,0,0,0,0,0,13,0,2,1,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,1,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,6,0,0,0,1,0,Class_2
+1987,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,1,2,1,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+1988,0,0,0,0,0,0,0,0,4,1,1,1,0,3,3,1,0,2,1,0,3,0,0,0,0,1,0,0,0,0,1,0,3,2,2,0,2,1,0,2,2,1,0,0,0,1,0,0,2,0,2,1,1,1,0,7,1,0,0,0,0,1,0,5,0,1,0,0,0,1,1,0,1,6,0,0,0,0,0,0,1,1,0,0,0,1,0,3,1,0,0,1,0,Class_2
+1989,0,0,0,0,0,0,0,0,0,0,0,0,1,1,27,3,1,0,0,0,0,2,0,1,5,0,3,0,0,0,0,1,5,1,0,0,0,2,0,2,0,0,0,0,0,0,1,0,1,0,0,0,1,9,1,0,0,0,0,0,0,2,1,1,0,0,1,0,0,3,0,15,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,2,3,0,0,0,0,Class_2
+1990,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+1991,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,Class_2
+1992,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,0,0,0,0,Class_2
+1993,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+1994,6,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,1,0,1,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,Class_2
+1995,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+1996,0,0,0,0,0,0,0,0,0,3,1,0,0,2,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,1,0,2,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,2,2,0,1,0,0,1,0,0,7,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,4,0,0,0,Class_2
+1997,1,0,0,0,0,0,0,0,20,0,0,0,1,0,3,3,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,1,2,14,0,1,3,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,Class_2
+1998,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+1999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2000,0,0,0,0,0,0,0,0,0,0,0,0,0,1,24,4,0,1,0,1,0,1,0,2,7,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,3,0,0,5,0,11,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,1,0,Class_2
+2001,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,3,4,0,1,0,0,0,0,1,4,0,0,0,0,1,0,4,0,0,1,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+2002,1,0,0,0,0,0,0,1,7,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,19,0,1,12,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_2
+2003,1,0,0,1,0,0,0,0,0,0,0,1,0,2,21,4,0,0,0,0,1,1,0,5,1,0,0,0,0,0,0,0,10,1,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,4,1,0,3,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2004,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,1,0,0,0,0,0,2,3,1,0,0,0,0,0,0,3,0,0,0,0,1,0,10,1,1,1,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
+2005,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_2
+2006,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+2007,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,6,0,0,0,0,0,0,0,0,1,5,0,0,0,0,1,1,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,Class_2
+2008,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+2009,1,0,0,0,0,0,0,2,0,0,0,0,0,6,0,0,0,2,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+2010,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+2011,0,0,0,2,0,0,0,0,1,0,0,0,1,1,15,8,0,1,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,2,0,0,1,1,0,0,0,0,1,16,0,0,0,0,2,0,0,3,0,0,1,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_2
+2012,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,13,5,0,0,0,0,0,0,0,Class_2
+2013,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+2014,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+2015,1,0,3,0,1,0,1,0,0,0,0,0,0,2,19,10,0,4,0,1,0,0,0,2,1,0,0,0,0,0,0,0,6,0,0,0,0,2,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,1,2,1,0,2,0,0,4,0,9,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,0,2,0,0,1,1,Class_2
+2016,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2017,0,0,0,0,0,0,0,5,5,1,0,0,1,1,3,0,0,3,0,0,0,0,0,3,1,0,0,0,0,0,0,10,5,0,0,0,0,1,0,2,0,0,3,0,0,1,0,10,0,0,0,0,1,2,0,0,0,0,0,4,0,5,1,22,0,0,26,0,0,2,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_2
+2018,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,3,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,5,0,1,0,1,0,0,0,0,1,0,0,0,2,7,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,5,0,0,0,0,0,Class_2
+2019,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,6,0,0,0,0,0,Class_2
+2020,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,1,13,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,1,0,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,5,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_2
+2021,0,0,0,0,0,0,0,0,0,0,0,0,0,8,2,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+2022,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,1,Class_2
+2023,0,0,0,0,0,0,0,1,0,0,0,0,0,10,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_2
+2024,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+2025,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+2026,4,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,3,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,Class_2
+2027,1,0,1,0,0,0,0,0,0,0,0,0,0,4,2,2,0,1,0,0,1,0,0,3,1,0,0,0,0,0,0,2,3,1,0,0,0,0,0,5,0,0,0,1,0,0,0,2,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,Class_2
+2028,0,0,0,0,0,0,0,0,0,0,1,0,0,21,0,0,0,0,0,0,0,0,0,1,19,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+2029,0,0,0,0,0,0,0,0,0,1,0,1,0,1,2,2,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,6,0,0,0,0,0,0,1,0,0,0,0,0,17,9,0,1,0,0,0,1,0,Class_2
+2030,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_2
+2031,2,0,0,0,0,0,0,0,0,0,2,0,0,4,0,1,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,0,0,4,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+2032,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,6,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,3,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+2033,0,1,1,1,0,0,0,0,0,0,0,1,0,0,22,11,1,0,0,0,2,2,0,2,2,0,0,0,0,0,0,0,5,0,0,1,0,0,0,4,0,0,0,0,0,0,0,6,1,0,0,0,2,2,0,0,0,0,0,2,0,2,0,0,1,1,3,0,0,3,1,14,0,1,1,0,0,0,0,1,0,0,0,0,3,5,0,1,0,0,0,0,0,Class_2
+2034,0,0,0,0,0,0,0,0,10,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2035,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,2,0,0,1,1,0,0,3,0,1,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,3,0,0,0,0,0,Class_2
+2036,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2037,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,Class_2
+2038,0,0,0,0,0,0,0,0,14,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+2039,0,0,0,0,0,0,0,0,2,0,0,1,0,16,0,3,0,0,0,0,0,1,0,3,16,0,0,0,0,0,0,1,1,0,0,0,0,0,0,18,0,0,1,0,0,2,0,6,0,0,0,0,3,2,0,0,0,0,0,1,0,1,0,0,0,1,2,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+2040,0,0,0,0,0,0,0,0,2,0,0,0,0,3,1,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,5,0,7,0,0,4,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_2
+2041,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,11,0,0,1,0,0,0,3,0,1,0,0,1,0,0,3,1,1,0,0,0,1,1,0,0,0,0,0,0,4,0,7,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_2
+2042,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+2043,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+2044,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,5,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+2045,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2046,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+2047,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+2048,0,0,0,0,0,0,0,0,8,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+2049,0,0,0,0,0,0,0,2,0,0,0,0,1,0,4,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+3741,0,0,0,0,0,0,0,1,2,0,1,0,0,21,0,3,0,0,0,1,0,0,0,0,15,0,0,0,0,0,0,1,4,1,0,1,0,0,0,12,0,0,1,1,0,2,0,5,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,2,1,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,Class_2
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+3743,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+3744,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+3745,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
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+3749,3,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+3751,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+3752,0,0,0,2,0,0,0,0,3,0,1,0,1,1,24,5,1,0,0,0,2,2,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,10,0,0,0,0,0,0,1,2,0,0,0,1,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+3753,0,0,0,1,0,0,0,0,0,1,0,1,0,11,0,6,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,5,0,0,0,0,0,16,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+3754,0,0,0,0,0,0,0,0,2,0,1,0,0,11,2,1,0,0,0,0,3,0,0,4,10,0,0,0,0,0,0,0,4,0,0,1,0,0,0,18,0,1,1,0,0,0,0,6,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,Class_2
+3755,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_2
+3756,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+3757,0,0,1,0,0,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,4,2,0,0,0,0,0,6,0,1,0,0,0,0,0,39,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,28,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,Class_2
+3758,0,0,1,0,0,0,1,0,0,0,0,0,2,1,0,4,1,1,0,0,0,3,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,6,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,1,0,0,0,1,0,Class_2
+3759,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,Class_2
+3760,0,0,0,0,0,0,0,0,0,0,0,0,0,1,23,7,0,1,0,0,0,2,0,2,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,5,0,0,1,3,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,1,1,2,0,0,3,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_2
+3761,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,4,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,10,1,1,0,0,1,1,0,0,0,1,0,0,0,0,10,0,0,1,0,0,0,0,0,Class_2
+3762,0,0,1,2,0,0,0,0,0,0,0,0,0,2,19,8,0,1,0,0,0,3,0,2,2,0,0,0,2,0,0,0,2,1,1,0,0,1,0,3,0,0,0,3,3,0,0,0,0,0,0,1,1,11,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,2,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_2
+3763,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+3764,0,0,0,0,1,0,0,0,0,0,0,0,0,18,0,0,0,0,0,1,0,0,0,0,10,0,1,0,0,0,0,1,4,0,0,1,0,1,0,3,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,Class_2
+3765,7,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_2
+3766,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,1,0,0,4,0,0,1,0,1,0,0,2,0,0,1,0,0,0,14,2,0,0,0,0,0,0,2,0,0,0,0,2,4,0,0,0,0,0,1,0,3,0,0,2,1,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+3767,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,1,1,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+3768,0,0,0,0,0,0,0,0,1,0,0,0,0,5,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+3769,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_2
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+4027,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+4028,0,0,0,0,0,0,0,0,0,0,2,0,2,16,1,3,0,3,0,0,1,3,1,3,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,1,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,Class_2
+4029,0,0,0,0,0,0,0,0,0,0,0,0,0,1,20,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,2,1,0,0,0,0,1,0,13,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,Class_2
+4030,1,0,0,0,0,0,0,0,0,0,1,0,1,3,1,0,0,1,0,0,0,4,0,1,7,0,1,0,0,0,0,0,1,0,1,3,0,0,0,6,0,1,2,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+4031,0,0,0,0,0,0,0,0,2,0,0,0,0,3,1,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,9,0,0,0,0,0,Class_2
+4032,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+4033,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+4034,1,0,0,0,0,1,0,8,6,0,0,1,1,1,0,5,0,2,0,0,0,1,0,4,1,1,0,0,0,0,0,4,4,0,0,0,0,1,0,1,1,0,3,1,0,0,0,3,0,0,0,0,0,5,0,0,0,0,0,1,0,4,0,13,0,2,14,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,Class_2
+4035,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+4036,1,0,0,0,0,0,0,0,0,0,0,0,0,0,19,10,0,1,0,0,0,4,0,3,0,0,2,0,1,0,0,1,1,0,0,0,0,0,0,4,0,0,0,1,0,0,0,6,0,0,0,0,0,2,0,1,0,0,0,0,0,5,0,1,1,3,4,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,2,0,1,2,0,Class_2
+4037,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+4038,0,0,2,0,0,0,0,1,0,0,0,0,5,1,0,1,0,3,0,0,0,2,0,0,3,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,8,0,1,0,5,1,0,0,0,1,4,0,0,0,0,1,1,0,1,0,0,2,1,5,0,0,2,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,3,1,0,0,0,1,0,0,Class_2
+4039,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+4040,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,2,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_2
+4041,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+4042,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+4043,0,0,1,0,0,0,0,0,0,0,1,0,0,6,6,2,0,0,0,0,1,0,0,1,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,14,0,0,1,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,7,0,5,0,0,0,0,0,Class_2
+4044,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,4,0,0,0,0,0,Class_2
+4045,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,1,0,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,5,2,0,1,0,0,0,0,1,Class_2
+4046,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,Class_2
+4047,1,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+4048,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
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+4240,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+4877,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+4880,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+5012,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+5013,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,2,0,0,0,2,0,2,0,0,3,0,0,0,0,0,0,0,1,0,4,0,0,1,0,3,0,0,5,2,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,5,0,0,0,0,1,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,9,1,11,0,0,0,0,0,Class_2
+5014,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,5,0,0,0,0,0,0,0,1,1,4,0,0,1,0,1,1,0,0,5,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,Class_2
+5015,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_2
+5016,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+5018,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,5,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5019,0,0,0,1,0,1,0,1,0,0,0,0,1,10,0,6,0,1,0,0,3,3,0,0,3,0,2,0,0,0,0,0,3,1,0,0,0,0,0,6,1,0,0,1,0,0,0,0,0,0,0,2,2,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+5020,1,0,0,0,0,0,0,0,0,0,0,0,0,5,1,11,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,10,0,0,2,4,0,0,0,1,1,0,0,0,0,7,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,1,1,0,0,2,0,Class_2
+5021,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,5,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,8,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,6,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+5022,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,7,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5023,1,0,0,0,0,0,0,0,0,0,0,1,0,10,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+5024,2,0,0,2,2,0,0,0,0,19,4,0,0,9,0,5,0,1,0,0,2,3,0,3,6,0,0,0,1,1,0,2,4,1,0,1,1,5,2,8,0,0,4,2,1,0,0,4,0,3,0,1,3,1,0,0,0,1,0,1,0,1,1,2,3,4,2,1,1,18,0,3,0,9,0,0,2,0,0,0,1,1,0,0,0,28,1,6,2,0,0,0,0,Class_2
+5025,0,0,1,0,1,0,0,0,14,0,1,0,3,0,3,2,0,1,0,0,0,2,0,6,2,0,0,0,0,0,0,11,0,1,0,0,3,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,3,0,0,0,2,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5026,0,0,0,2,0,0,0,0,0,0,0,0,0,1,21,9,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,1,1,0,0,0,2,2,0,0,1,2,0,0,0,0,0,0,0,4,0,2,1,3,3,0,0,2,0,18,2,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,Class_2
+5027,0,0,0,0,0,0,0,1,2,0,0,0,0,18,2,0,0,0,0,0,0,2,0,0,16,0,0,0,0,0,0,0,1,1,0,0,0,0,0,16,0,1,1,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,3,0,Class_2
+5028,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+5029,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,1,0,1,0,0,0,7,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+5030,0,0,0,1,0,0,0,0,0,0,0,1,0,4,23,14,0,1,0,0,0,3,1,4,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,2,1,0,2,0,0,1,0,3,0,0,0,0,2,9,0,0,0,0,0,0,0,4,2,1,0,2,2,0,0,2,0,19,0,0,0,0,0,0,0,2,0,0,0,0,3,2,0,3,1,0,0,0,0,Class_2
+5031,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5032,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,Class_2
+5033,0,0,0,0,0,0,0,1,0,0,2,0,0,1,20,3,0,1,0,0,0,1,0,4,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,5,0,0,0,0,0,0,0,0,1,3,0,1,0,3,0,0,0,0,4,0,0,5,0,1,2,0,5,0,1,2,3,16,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,Class_2
+5034,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,3,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,2,1,0,1,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,Class_2
+5035,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5036,0,0,0,0,0,0,0,0,2,0,0,0,0,17,3,2,0,1,0,0,0,1,0,5,16,0,2,0,0,0,0,0,4,1,0,0,0,0,0,7,0,0,1,0,0,0,0,7,0,0,0,0,1,2,0,0,0,0,1,1,0,1,0,0,0,0,4,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,Class_2
+5037,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,1,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,Class_2
+5038,0,0,0,0,0,0,0,3,0,0,0,0,0,7,1,9,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,2,2,0,0,0,1,0,15,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,Class_2
+5039,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5040,0,0,3,0,0,0,0,0,0,0,0,0,0,0,4,8,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,1,0,9,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,Class_2
+5041,0,0,0,0,0,0,0,0,0,0,0,2,0,1,20,7,0,0,1,0,1,1,0,5,3,1,2,0,0,0,0,0,6,0,1,0,0,0,0,3,3,0,0,0,0,0,0,6,1,0,0,0,2,4,0,0,0,0,0,1,0,3,0,0,0,0,6,0,0,2,0,22,1,0,0,0,1,0,1,1,0,0,0,0,2,2,0,1,0,0,0,0,0,Class_2
+5042,0,0,0,0,0,0,0,0,0,0,1,0,2,9,2,4,0,3,0,0,1,2,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,6,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+5043,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5044,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,3,0,1,0,0,1,1,0,0,4,0,0,1,0,0,0,0,2,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+5045,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+5046,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5047,5,0,1,0,0,0,0,0,0,0,1,0,0,19,2,2,0,0,0,0,0,0,0,4,10,0,0,0,0,0,1,0,1,0,1,0,1,4,0,21,0,0,0,1,0,1,0,10,0,5,0,0,1,1,0,0,0,0,0,0,0,2,0,0,1,1,6,0,0,4,0,3,0,1,1,0,0,0,0,0,0,0,0,0,1,4,3,8,0,1,0,3,0,Class_2
+5048,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+5049,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,8,0,0,4,0,0,0,0,0,2,11,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+5075,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_2
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+5079,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,5,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,3,5,0,0,0,0,1,0,0,5,0,0,0,0,1,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+5080,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+5081,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,3,0,0,0,Class_2
+5082,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+5083,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+5086,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+5425,0,1,0,0,0,0,0,4,7,0,0,0,1,0,4,3,0,3,0,0,0,0,0,6,1,0,0,0,0,0,0,5,2,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,4,0,22,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,Class_2
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+5428,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,1,1,1,0,2,12,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5429,0,0,0,0,0,0,0,1,0,0,0,0,0,7,4,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,1,0,0,0,0,0,9,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+5430,0,0,1,0,0,0,0,2,0,0,0,1,0,0,5,1,0,0,0,2,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,5,1,0,2,1,1,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,11,0,1,0,0,0,3,0,0,0,0,0,0,0,0,18,0,0,0,1,0,0,0,0,Class_2
+5431,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5432,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+5433,0,0,0,1,0,0,0,1,0,0,0,0,0,7,0,2,0,1,0,0,0,3,0,0,3,1,0,0,0,0,0,0,1,0,1,2,0,1,1,9,0,0,5,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,4,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_2
+5434,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,4,0,0,0,0,0,1,1,2,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5435,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+5436,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+5437,0,0,0,0,0,0,0,0,0,0,0,1,0,7,7,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,1,0,0,0,5,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,7,0,0,0,1,0,Class_2
+5438,0,0,0,2,0,0,1,1,0,0,0,0,0,2,16,1,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,2,0,4,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_2
+5439,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,Class_2
+5440,0,0,0,0,0,0,0,1,1,0,1,2,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+5441,0,0,0,0,0,0,0,0,0,0,0,1,0,0,16,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+5443,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,8,0,0,0,0,0,Class_2
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+5445,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,6,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,20,0,0,0,2,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,1,1,0,1,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
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+5448,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+5469,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+5471,0,0,1,0,0,0,0,0,2,0,0,0,0,4,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
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+5475,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,3,1,5,0,0,0,1,0,Class_2
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+5693,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
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+5695,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+5708,0,0,0,2,0,0,0,0,0,0,0,0,1,2,16,10,0,0,0,0,0,2,0,1,1,1,0,0,0,0,0,0,2,1,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,1,1,1,0,0,1,0,7,0,3,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,1,0,Class_2
+5709,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,10,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,Class_2
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+5711,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,1,0,0,1,1,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+5712,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+5713,0,0,0,0,0,0,4,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,8,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5714,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+5715,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+5716,0,0,1,0,0,0,0,2,0,0,0,0,0,5,0,7,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,18,0,2,0,0,0,0,0,0,0,0,0,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_2
+5717,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,8,0,0,6,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,Class_2
+5718,0,0,0,0,0,0,0,0,0,0,0,0,2,8,6,3,0,3,0,0,1,2,0,2,8,0,0,0,0,0,0,0,2,0,2,0,0,0,0,9,0,1,0,0,0,0,0,6,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,3,0,0,3,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,16,1,8,0,0,0,1,0,Class_2
+5719,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5720,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5721,1,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,4,0,0,0,0,0,0,9,0,0,1,1,1,0,3,0,0,0,0,0,1,0,2,4,0,2,0,0,0,1,0,1,0,0,0,0,0,0,1,13,0,0,10,0,0,0,2,0,1,3,0,0,0,0,0,0,0,0,0,0,1,11,1,8,0,1,0,0,1,Class_2
+5722,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,4,0,2,0,1,0,2,0,0,0,1,0,0,0,0,1,0,2,1,0,0,0,3,0,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,3,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,Class_2
+5723,0,0,1,0,0,0,0,0,0,1,2,0,0,2,24,12,0,3,0,0,0,0,0,2,5,0,0,0,0,0,0,0,5,0,0,1,0,0,0,3,0,0,0,2,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,2,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+5724,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5725,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+5727,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_2
+5728,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,9,0,0,6,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+5729,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
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+5738,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,6,2,0,0,1,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,6,0,10,0,0,0,0,0,Class_2
+5739,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+5740,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,3,0,0,0,0,0,Class_2
+5741,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,Class_2
+5742,0,0,0,0,0,0,0,0,0,0,0,1,0,2,17,2,0,0,0,0,1,1,0,4,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,5,0,2,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,7,0,2,1,0,2,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,2,3,0,0,0,0,Class_2
+5743,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5744,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,1,1,0,0,5,0,0,0,0,0,0,0,2,1,0,0,0,1,0,17,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,2,0,3,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+5745,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,2,0,1,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5746,0,0,1,0,0,0,0,0,0,0,0,0,0,0,20,5,0,0,0,0,0,0,0,1,3,1,0,1,0,0,0,0,3,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,4,1,0,0,0,1,0,0,3,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,Class_2
+5747,0,0,0,0,0,0,0,1,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,1,3,0,0,1,0,0,0,4,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+5748,0,0,0,0,0,0,0,0,19,0,0,0,0,0,4,1,0,0,0,0,0,0,0,13,1,0,4,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+5749,3,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+5750,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+5751,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+5752,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,11,3,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+5754,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,1,1,0,0,1,0,0,3,1,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_2
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+5756,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,3,0,0,0,0,0,2,0,4,8,0,0,0,0,0,0,0,0,0,1,0,0,1,0,26,0,0,1,1,0,1,0,3,0,0,0,0,2,2,1,0,0,0,0,0,0,1,2,0,1,0,2,0,0,5,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_2
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+5760,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+5761,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,1,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,7,0,2,0,0,0,0,0,Class_2
+5762,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,Class_2
+5763,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,3,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,1,0,17,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,Class_2
+5764,0,0,0,0,0,0,0,0,0,0,0,0,3,0,21,4,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,1,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,2,1,0,0,0,0,Class_2
+5765,1,0,0,0,0,0,0,0,17,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+5766,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,1,0,27,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+5767,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+5768,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
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+5782,0,0,0,0,0,0,0,0,0,0,2,0,0,3,2,0,0,0,0,0,0,0,0,16,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,36,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_2
+5783,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,3,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,1,1,0,9,0,0,2,3,0,0,0,1,1,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,2,0,2,2,0,0,0,0,Class_2
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+5787,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
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+5794,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,4,0,2,0,1,0,0,0,5,3,0,0,0,1,0,0,0,1,0,0,0,0,1,1,27,1,3,2,0,0,0,0,10,0,0,0,0,0,0,0,2,0,1,0,2,0,4,0,0,0,1,5,1,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,1,0,0,11,0,0,1,0,Class_2
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+6412,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+6416,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+6419,0,0,0,1,1,0,0,0,0,0,0,0,1,4,4,1,0,0,0,0,0,0,0,2,2,0,0,0,1,1,0,0,0,0,0,0,0,2,0,2,1,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,1,0,0,0,3,0,5,0,0,0,0,0,0,0,0,0,0,0,0,12,3,2,2,2,0,0,0,0,Class_2
+6420,0,0,0,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,10,0,0,4,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+6421,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,Class_2
+6422,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+6423,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,17,0,0,3,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+6424,0,0,0,0,0,0,0,0,0,0,0,0,0,10,2,0,0,1,0,0,0,0,0,2,13,0,0,0,0,0,0,0,2,0,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,5,0,0,0,0,0,Class_2
+6425,0,0,0,0,0,0,0,2,0,0,0,0,0,10,5,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,Class_2
+6426,0,0,0,0,0,0,0,0,27,0,0,1,0,3,3,0,0,1,0,1,0,0,0,11,1,0,0,0,0,0,0,7,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,30,0,1,11,1,0,0,0,0,2,0,1,1,0,0,0,0,0,0,1,0,0,0,1,1,0,3,0,0,0,Class_2
+6427,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_2
+6428,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+6429,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,3,0,1,0,0,2,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,1,14,1,1,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,2,0,Class_2
+6430,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+6431,0,0,0,0,0,0,0,3,0,0,0,0,1,0,3,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,2,9,0,0,0,0,1,0,0,0,3,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+6432,0,0,0,0,0,0,0,0,2,0,0,1,0,19,1,1,0,0,0,0,1,0,0,2,10,0,0,0,0,0,0,0,4,0,0,1,0,0,0,15,0,0,1,0,0,3,0,8,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,1,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,Class_2
+6433,0,0,0,0,0,0,0,1,6,1,1,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,9,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,3,0,0,0,0,0,0,Class_2
+6434,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+6435,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+6436,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_2
+6437,0,0,0,1,0,0,0,1,0,0,0,0,0,2,1,12,0,0,0,1,0,3,0,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,10,0,0,3,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,1,0,0,0,0,Class_2
+6438,0,0,0,0,0,0,0,0,0,0,0,0,0,1,19,5,0,2,0,0,1,1,0,2,1,0,3,0,0,0,0,0,3,0,0,0,0,1,0,8,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,5,0,0,1,1,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+6439,0,0,0,0,0,0,0,0,0,2,0,0,0,3,24,6,0,0,0,0,2,2,0,2,3,0,0,0,0,0,0,0,5,1,0,0,1,1,0,1,1,1,0,0,0,0,0,7,0,0,0,0,1,2,0,1,0,0,0,0,0,3,0,1,2,0,10,1,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,1,0,1,3,0,Class_2
+6440,1,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,1,0,0,0,0,Class_2
+6441,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+6442,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,1,0,Class_2
+6443,0,1,1,0,0,0,0,0,0,0,0,0,0,2,21,2,1,1,0,1,0,2,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,4,0,0,0,2,2,0,0,5,1,5,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,2,1,0,0,0,0,Class_2
+6444,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+6445,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+6446,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+6447,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+6448,1,1,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+6449,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_2
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+7028,0,0,0,0,0,0,0,0,0,1,0,0,0,1,23,4,0,1,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,2,0,0,1,0,4,0,1,0,0,0,0,0,0,0,2,0,0,1,3,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,3,0,16,0,0,0,0,0,0,0,1,0,0,1,0,5,0,1,0,2,0,0,1,0,Class_2
+7029,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,Class_2
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+7032,1,0,0,0,0,0,0,0,20,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,13,0,2,9,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+7033,2,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7034,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7035,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7036,1,0,1,4,0,0,0,0,0,1,3,0,0,0,1,12,0,3,0,3,0,3,0,2,1,1,0,0,0,0,0,3,1,1,2,2,0,4,2,2,0,1,0,1,0,0,3,9,2,0,0,0,0,3,1,0,0,0,1,1,0,8,6,6,0,25,7,0,1,0,0,6,1,1,1,1,0,0,0,4,0,0,1,0,3,3,6,7,8,0,0,1,2,Class_2
+7037,1,0,1,0,0,0,0,0,0,0,0,1,0,2,25,8,0,0,0,1,0,2,0,1,0,3,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,7,1,0,0,0,0,1,0,3,0,2,0,0,0,0,1,3,0,8,0,3,0,0,0,0,0,1,0,0,0,0,4,0,0,0,2,0,0,3,0,Class_2
+7038,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+7039,0,0,0,0,0,0,0,0,0,0,0,0,0,4,11,4,0,0,0,1,0,3,0,1,0,0,0,0,0,3,0,1,4,0,0,0,0,1,0,2,2,1,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,3,0,0,0,0,1,0,1,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,Class_2
+7040,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,10,0,0,0,1,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,3,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,2,1,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,Class_2
+7041,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,12,0,0,4,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+7042,1,0,0,0,0,0,0,0,0,0,1,0,0,5,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+7043,0,0,1,0,0,0,0,3,14,0,0,0,2,1,6,1,0,2,0,1,0,2,0,8,2,0,0,0,1,0,0,4,4,1,0,1,2,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,0,0,1,4,0,20,0,6,8,0,0,4,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,1,0,1,0,0,0,Class_2
+7044,0,0,0,0,0,0,0,0,0,0,0,0,2,8,1,2,0,3,0,0,1,3,0,1,3,0,0,0,1,0,0,0,2,1,0,0,0,0,0,9,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+7045,0,0,0,0,0,0,0,0,0,2,2,0,0,3,0,1,0,0,0,0,0,0,1,1,3,3,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,10,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7046,0,0,0,0,0,0,0,0,21,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,20,0,1,12,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_2
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+7048,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,7,0,0,0,0,0,Class_2
+7049,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+7060,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+7061,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+7068,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,5,0,1,0,0,2,1,0,2,5,0,0,0,0,0,0,0,1,0,0,0,1,1,0,12,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+7069,1,0,0,0,0,0,0,0,17,0,4,0,0,0,4,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,2,0,0,0,0,0,0,0,0,1,0,5,0,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7070,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7071,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7072,0,0,0,0,0,0,0,4,5,0,0,0,1,1,4,5,0,2,0,0,2,2,0,4,2,0,0,0,0,0,0,4,8,2,0,0,0,1,0,4,0,0,1,0,0,1,0,1,0,0,0,0,1,2,1,1,1,0,1,0,0,7,0,13,0,1,10,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,1,0,Class_2
+7073,0,0,0,0,0,0,0,1,2,0,1,1,0,21,0,3,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,2,2,0,0,0,0,0,19,0,0,1,1,0,0,0,5,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+7074,0,0,0,0,0,0,0,0,0,0,0,0,1,1,20,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,4,0,0,1,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+7075,0,0,0,1,0,0,0,0,1,0,0,0,0,2,6,1,0,1,0,1,1,1,0,0,2,0,0,0,0,0,0,0,3,0,1,1,0,0,0,2,0,0,10,1,0,1,0,0,0,0,0,2,0,3,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,2,0,3,0,1,0,0,0,0,0,7,0,0,0,0,8,4,0,1,1,0,0,0,0,Class_2
+7076,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+7077,0,0,0,2,0,0,0,0,0,0,0,0,0,0,14,5,0,2,0,0,0,3,0,2,0,1,0,0,0,0,0,1,3,1,0,0,0,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,0,0,0,0,Class_2
+7078,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,5,6,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,1,9,2,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,32,0,0,0,0,0,1,0,1,1,0,0,0,0,0,9,1,1,5,0,0,0,0,0,Class_2
+7079,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+7080,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,4,15,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+7081,0,0,1,0,0,0,0,0,3,0,0,0,0,6,1,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,10,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,1,0,Class_2
+7082,0,0,0,0,0,0,0,1,0,0,0,0,0,9,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7083,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,Class_2
+7084,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+7085,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,Class_2
+7086,2,0,0,0,0,0,0,0,1,0,1,0,0,6,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7087,0,0,0,0,0,0,0,0,0,0,0,0,0,2,17,5,1,0,0,1,0,1,0,1,5,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,5,1,11,1,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,Class_2
+7088,1,0,0,0,0,0,0,0,0,0,0,0,0,7,1,2,0,1,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,2,1,0,0,0,4,0,0,2,7,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+7145,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,1,0,0,0,0,0,Class_2
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+7149,0,0,0,0,0,0,0,0,18,0,1,0,0,0,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,8,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+7151,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,14,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_2
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+7158,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,6,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,10,0,6,0,0,0,0,0,Class_2
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+7428,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,1,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,Class_2
+7429,1,0,1,0,0,0,0,0,0,0,0,0,0,9,2,1,0,0,0,1,0,1,0,0,2,0,0,0,1,0,0,1,12,0,0,0,0,0,0,2,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,4,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,4,1,1,0,0,0,3,0,Class_2
+7430,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+7431,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+7432,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7433,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+7434,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7435,0,0,0,0,0,0,0,0,0,0,0,1,0,0,18,3,0,0,0,1,0,2,0,5,2,0,0,0,0,0,0,0,1,0,0,0,0,4,0,2,0,1,0,2,0,0,0,2,0,0,0,0,1,1,1,0,0,0,0,0,0,8,0,1,0,2,4,0,0,4,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+7436,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+7438,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7439,0,0,4,2,0,0,0,0,0,0,0,0,1,2,18,3,0,1,0,0,0,5,0,4,1,0,0,3,2,0,0,0,3,0,2,0,0,1,1,2,0,0,0,1,0,0,0,0,0,2,0,1,0,4,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,3,3,8,0,1,0,0,0,0,0,0,0,0,0,0,6,1,0,0,2,0,0,0,0,Class_2
+7440,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,3,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+7441,0,0,0,0,0,0,0,0,0,0,0,0,0,8,3,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,0,Class_2
+7442,0,0,1,0,0,0,0,0,0,0,0,0,0,1,5,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_2
+7443,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7444,0,0,0,0,0,0,0,1,0,0,0,0,1,5,0,4,0,0,0,0,0,2,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,16,0,0,0,1,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+7445,1,0,0,0,1,0,0,0,0,0,0,0,1,5,0,2,0,1,0,0,0,2,0,0,3,0,1,0,0,0,0,0,2,0,2,1,0,0,0,7,0,0,2,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+7446,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,2,0,6,0,0,3,2,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,2,3,0,0,2,0,0,0,5,0,1,0,0,0,0,0,2,4,0,0,0,0,1,0,3,0,0,6,0,0,0,0,Class_2
+7447,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,Class_2
+7448,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,1,1,6,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,Class_2
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+7631,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+7632,0,1,0,0,0,0,0,0,1,0,0,0,0,9,0,2,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,17,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_2
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+7634,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7635,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,4,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,1,1,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
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+7637,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,Class_2
+7638,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_2
+7639,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,9,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,1,2,1,0,0,0,0,10,0,1,0,1,0,0,0,0,0,1,0,0,1,3,0,0,1,0,0,0,0,2,0,0,0,0,2,0,0,5,0,7,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,1,0,Class_2
+7640,2,0,0,0,0,0,0,0,0,0,1,0,0,7,3,2,1,0,0,0,0,0,0,0,15,0,0,0,0,0,0,2,1,2,0,1,0,0,0,5,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,3,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,2,0,0,0,1,0,Class_2
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+7643,1,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+7693,0,0,0,0,0,0,0,0,0,0,2,0,0,2,20,5,1,1,0,1,0,1,0,2,4,0,0,0,0,0,0,0,6,1,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,1,2,0,1,4,0,10,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,2,1,0,0,2,0,Class_2
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+7740,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+7741,0,0,0,0,0,0,0,1,2,0,0,0,0,1,3,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,5,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7742,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,0,6,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7743,0,0,0,0,0,0,1,0,0,0,0,1,0,3,15,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,10,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,2,0,0,0,0,0,2,0,10,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_2
+7744,0,0,0,0,0,0,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,2,1,0,0,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+7745,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7746,0,0,0,0,0,0,0,0,1,0,1,2,0,2,19,4,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,6,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,5,0,1,1,1,1,0,0,2,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_2
+7747,5,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,1,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,8,0,0,0,0,0,0,0,0,0,0,0,0,6,4,0,0,0,0,0,0,0,Class_2
+7748,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,3,0,2,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7749,0,0,0,0,0,0,0,0,0,0,0,0,0,15,1,0,0,0,0,1,1,0,0,1,7,0,0,0,0,0,0,0,7,0,0,0,0,0,0,13,1,2,0,0,0,0,0,9,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,2,0,0,4,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,10,0,0,0,0,0,Class_2
+7750,0,0,0,0,0,0,0,1,2,0,0,0,0,8,1,0,0,1,0,0,0,1,0,4,13,0,0,0,0,0,0,0,0,2,1,2,0,0,1,13,0,1,1,0,0,0,0,8,0,0,0,0,2,2,0,0,0,0,0,1,0,2,0,0,1,0,2,0,0,1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,1,0,Class_2
+7751,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,1,4,0,1,0,0,0,0,0,Class_2
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+7759,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7760,0,0,0,0,0,0,0,1,0,0,0,0,1,4,1,4,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,6,1,1,3,1,0,0,0,1,0,0,0,0,0,5,0,0,0,1,0,0,0,1,0,2,0,3,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,3,0,1,2,0,Class_2
+7761,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+7762,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,Class_2
+7763,0,0,0,0,0,0,1,0,18,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,5,0,0,1,0,0,1,0,0,0,1,0,3,0,0,0,2,0,3,0,0,0,3,0,0,0,1,0,0,0,0,0,18,0,1,8,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,4,1,2,0,0,0,0,0,Class_2
+7764,0,0,0,0,0,0,0,0,1,1,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,2,2,0,0,0,Class_2
+7765,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+7766,0,0,0,0,0,0,0,2,0,1,0,0,0,10,0,3,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,3,2,0,8,0,1,12,3,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,3,1,9,0,0,0,0,0,Class_2
+7767,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7768,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+7769,0,0,0,0,0,0,0,1,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_2
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+7795,0,0,0,0,0,0,0,1,0,0,0,0,0,7,1,1,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,1,0,2,0,0,0,17,0,0,1,0,0,0,0,5,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,2,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,Class_2
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+7830,0,0,0,0,0,0,0,0,1,0,1,0,0,6,1,1,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,1,2,0,0,0,0,0,0,0,48,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,38,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+7831,0,0,0,0,0,0,1,3,1,0,0,0,0,0,1,1,1,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,6,0,1,0,0,2,5,0,0,1,0,0,0,0,2,1,1,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,2,0,0,0,0,0,0,0,Class_2
+7832,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,6,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,7,0,0,4,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+7833,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,2,2,3,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,Class_2
+7834,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+7835,0,0,0,0,0,0,0,0,0,0,0,1,0,10,1,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,3,0,0,1,0,0,0,10,0,2,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,3,1,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,Class_2
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+7838,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+7839,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+7840,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,4,2,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,1,1,0,0,0,1,0,0,0,0,10,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,Class_2
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+7842,0,0,0,0,0,0,0,0,3,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+7843,1,0,0,1,0,0,0,0,0,2,0,1,3,9,0,1,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,3,1,1,0,1,0,0,3,0,0,0,1,0,0,0,6,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,1,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,Class_2
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+7845,3,0,0,0,0,0,0,0,0,0,2,0,0,4,0,9,0,0,0,0,1,0,0,4,8,0,0,0,0,0,0,0,0,1,0,1,0,0,0,14,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,1,Class_2
+7846,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,5,0,0,1,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,Class_2
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+7849,0,0,1,1,0,0,0,0,1,0,2,0,1,0,16,9,0,1,0,1,2,4,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,1,1,0,1,0,0,0,6,0,0,0,0,0,5,2,0,0,0,0,0,0,3,0,1,2,1,5,0,0,1,1,9,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+7919,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,0,Class_2
+7920,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,2,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,6,0,1,1,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,1,0,Class_2
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+7922,0,0,0,0,1,0,0,1,0,0,0,1,0,1,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+7923,0,0,1,0,0,0,0,0,0,0,0,1,0,1,28,3,0,1,0,1,0,2,0,1,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,1,0,10,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,Class_2
+7924,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,2,1,0,0,0,0,5,0,1,0,0,0,0,0,46,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,3,0,Class_2
+7925,0,0,0,0,0,0,0,14,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,1,4,0,1,0,0,0,3,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+7926,0,0,0,0,0,0,0,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,0,2,8,0,2,0,0,0,0,0,1,0,0,0,0,0,0,23,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,3,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+7927,0,0,0,0,0,0,0,1,0,0,0,0,2,10,2,2,0,3,0,0,1,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,18,0,0,0,0,0,0,0,5,1,1,0,0,0,2,0,0,0,0,0,1,0,0,0,1,1,1,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,Class_2
+7928,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,3,0,2,0,0,1,0,0,5,1,8,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,Class_2
+7929,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+8238,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,7,0,0,0,1,0,1,0,2,2,0,0,0,0,0,0,0,2,1,1,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+8239,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
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+8428,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+8429,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,0,1,5,22,0,1,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,2,1,0,0,1,0,0,0,Class_2
+8430,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,1,1,0,0,5,0,1,2,3,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_2
+8431,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,14,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,2,0,7,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,1,0,Class_2
+8432,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,4,5,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+8433,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+8434,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+8435,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,2,0,1,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,Class_2
+8436,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,3,0,0,1,0,0,1,0,1,8,0,0,1,0,0,0,0,2,0,1,0,0,1,0,5,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,Class_2
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+8438,0,0,0,0,0,0,0,0,0,0,0,0,0,6,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+8439,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8440,0,0,0,0,0,0,0,0,0,0,1,0,0,19,0,1,0,0,0,0,0,1,0,0,14,0,0,0,0,0,0,0,3,0,0,2,0,0,0,3,1,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,1,2,0,0,0,0,0,Class_2
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+8443,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+8446,0,0,0,0,0,0,0,0,0,1,1,0,0,7,0,4,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,1,0,0,0,0,0,2,3,0,1,0,0,1,0,0,1,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1,3,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,0,0,2,0,Class_2
+8447,0,0,0,0,0,0,0,0,0,0,0,1,0,0,16,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+8448,0,0,0,1,0,0,0,5,5,0,0,1,2,3,2,2,0,4,0,1,0,1,0,8,1,0,0,0,0,0,0,4,5,0,0,1,0,1,0,1,1,1,3,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,5,0,9,1,2,6,2,0,3,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,1,0,0,5,0,Class_2
+8449,0,0,0,0,0,0,0,0,3,0,0,1,0,11,2,3,0,0,0,0,0,0,0,2,18,0,0,0,0,0,0,0,4,1,1,1,0,1,0,17,0,1,1,0,0,1,0,5,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,0,6,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
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+8541,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,1,2,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,Class_2
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+8549,0,5,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,0,0,0,0,2,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,2,3,0,1,0,0,3,1,0,1,2,0,0,0,0,0,1,0,2,4,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,9,0,0,2,1,0,1,3,1,0,0,Class_2
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+8551,0,0,0,0,0,0,0,0,9,0,0,0,0,1,3,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,22,0,0,4,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+8553,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,2,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,4,0,10,0,0,0,0,0,Class_2
+8554,1,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+8555,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+8556,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+8557,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
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+8559,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+8560,0,0,0,1,0,0,0,0,7,0,1,0,0,2,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,5,1,0,3,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,16,0,1,10,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+8561,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+8620,1,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,2,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+8621,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8622,0,0,0,0,0,0,0,4,4,4,0,0,1,1,3,3,1,3,0,0,0,2,0,4,1,0,0,0,0,1,0,3,2,0,0,2,0,0,0,2,3,1,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,6,0,8,0,1,7,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,7,2,0,0,1,0,Class_2
+8623,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,5,0,2,0,0,0,0,1,0,0,0,0,0,0,0,6,0,1,2,2,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,Class_2
+8624,0,0,0,1,0,0,0,1,3,0,0,0,1,0,25,7,1,0,0,0,1,4,0,1,2,1,0,0,2,0,0,0,6,0,3,0,0,0,0,7,1,0,0,0,0,1,0,3,0,0,0,0,0,6,1,0,0,0,0,0,0,10,3,0,2,1,1,0,0,4,0,12,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,1,3,0,0,1,0,Class_2
+8625,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8626,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8627,0,0,0,0,1,0,0,0,0,2,0,0,0,5,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+8628,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+8629,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,5,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,4,5,1,0,1,1,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,2,0,Class_2
+8630,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+8631,3,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,2,6,0,0,0,0,0,0,2,1,0,0,0,0,1,0,6,0,1,0,0,0,0,0,9,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,2,0,0,2,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8632,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,0,0,0,5,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,25,0,0,0,0,0,Class_2
+8633,0,0,0,0,0,0,0,0,0,0,0,0,1,0,20,5,0,0,2,1,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,1,0,0,0,0,5,1,0,0,3,3,0,0,5,1,7,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_2
+8634,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,2,9,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+8635,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8636,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,10,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+8637,0,0,0,0,0,0,0,0,0,0,1,0,0,21,0,0,0,0,0,0,0,1,0,2,9,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_2
+8638,0,0,0,0,0,0,0,0,2,0,0,0,0,5,2,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+8639,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8640,0,0,1,1,0,0,0,0,0,0,0,1,0,2,23,4,0,1,0,0,0,0,0,2,2,1,0,0,0,0,0,1,3,1,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,1,0,2,0,0,6,0,12,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,Class_2
+8641,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,2,2,0,0,0,0,0,1,2,2,1,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,8,1,2,6,0,0,1,1,0,0,0,0,0,1,0,0,3,0,0,0,0,1,2,0,2,1,0,1,0,0,Class_2
+8642,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+8643,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,1,1,6,0,0,0,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_2
+8644,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+8645,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8646,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,1,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8647,0,0,0,0,0,0,0,0,25,0,0,1,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,9,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,3,0,3,0,0,1,0,1,0,0,0,0,0,0,0,1,12,0,1,8,1,0,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+8648,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,1,1,4,0,0,0,0,2,2,0,0,3,0,1,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,Class_2
+8649,5,0,0,0,0,0,0,0,0,0,0,0,0,0,6,7,0,1,0,0,0,2,0,0,10,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,1,2,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,Class_2
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+8741,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
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+8821,1,0,0,0,0,0,0,0,0,0,0,0,0,8,2,4,0,0,0,0,1,1,0,0,2,0,0,0,1,0,0,0,8,0,0,0,0,0,0,9,1,1,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,7,0,0,0,0,0,Class_2
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+8823,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,4,0,0,0,1,2,1,0,0,6,0,0,0,1,0,0,0,3,0,0,0,0,0,0,14,0,0,0,2,0,0,0,6,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+8825,1,0,0,0,0,0,0,0,1,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
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+8827,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,10,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8828,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+8829,1,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+8830,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,Class_2
+8831,0,0,0,1,0,0,0,0,0,0,0,0,0,0,24,12,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,3,1,1,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,1,0,3,0,0,0,0,1,0,0,3,0,21,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,1,2,0,0,0,0,Class_2
+8832,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8833,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,9,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,19,0,0,0,1,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+8834,0,0,0,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+8835,0,1,0,2,0,0,0,0,1,0,0,0,1,1,25,8,0,1,0,0,0,1,0,1,1,1,0,0,1,0,0,0,6,0,0,1,1,2,0,7,1,2,0,1,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,7,1,0,1,1,2,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,Class_2
+8836,5,0,1,0,0,0,0,0,0,0,0,0,0,18,2,3,0,0,0,0,0,4,0,0,5,0,0,0,0,0,0,0,2,1,1,1,0,1,1,13,0,0,0,0,0,0,0,2,0,0,0,0,2,3,2,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8837,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+8838,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8839,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8840,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,Class_2
+8841,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,1,0,1,0,1,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+8842,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,Class_2
+8843,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+8844,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+8845,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,3,0,0,0,0,0,Class_2
+8846,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+8847,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,3,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+8848,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+8849,6,0,0,0,0,0,0,2,0,0,1,0,0,2,0,3,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,4,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,Class_2
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+8919,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+8922,0,0,0,0,1,0,0,0,0,2,0,0,0,5,1,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,1,0,0,0,7,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,4,0,0,0,0,0,Class_2
+8923,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,3,1,4,1,7,0,0,2,1,0,1,0,1,0,1,0,0,0,7,0,2,0,0,0,0,0,5,0,0,2,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,1,0,Class_2
+8924,0,0,0,0,0,0,0,3,0,0,0,0,0,2,3,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,1,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,1,0,Class_2
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+8926,0,0,0,0,0,0,0,0,0,0,0,0,0,6,13,3,0,1,0,0,3,0,0,2,2,0,0,0,0,0,0,0,6,0,1,0,0,1,0,3,0,0,0,5,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,5,0,1,0,0,1,0,0,7,1,11,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
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+8928,0,0,0,0,0,0,0,1,0,1,1,0,0,3,0,1,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,6,0,0,0,1,1,0,10,0,0,0,2,0,0,0,1,0,0,0,0,2,9,0,0,0,0,0,0,1,2,0,0,0,2,0,1,0,4,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+8929,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
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+8940,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,6,0,0,0,0,0,Class_2
+8941,0,0,1,0,0,0,0,1,0,0,0,0,0,4,0,9,0,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,7,0,1,2,3,0,0,0,0,0,0,0,0,0,6,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,6,0,0,0,0,0,Class_2
+8942,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,6,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,Class_2
+8943,0,0,0,0,0,0,0,0,0,0,2,0,0,4,3,2,0,0,0,0,0,2,0,2,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,13,1,0,0,1,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,2,0,Class_2
+8944,0,0,0,0,0,0,0,0,0,0,1,2,0,9,0,5,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,1,1,2,1,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,Class_2
+8945,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+8946,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+8947,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,Class_2
+8948,1,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8949,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,5,0,0,0,0,0,0,0,1,2,4,0,0,0,0,1,1,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+8950,0,0,0,0,0,0,0,2,0,3,0,1,0,2,0,5,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,2,0,1,0,2,1,0,8,0,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,1,2,0,0,3,0,1,1,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+8951,0,0,0,0,0,0,0,0,0,0,0,1,2,2,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,6,0,0,0,0,0,Class_2
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+8957,0,0,2,0,0,0,0,0,0,0,0,0,0,0,10,5,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,5,0,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,2,1,0,1,0,0,0,0,1,1,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
+8958,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+8959,0,0,0,0,11,1,0,1,0,0,0,0,4,0,3,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,1,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_2
+8960,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+8961,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8962,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+8963,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,6,0,0,0,0,0,Class_2
+8964,0,0,0,0,0,0,0,1,0,0,1,0,0,7,0,4,0,0,0,0,0,0,0,0,8,0,0,0,3,0,0,0,6,1,0,0,0,0,0,3,0,0,1,0,0,0,0,3,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8965,0,0,1,2,0,0,0,0,0,0,0,0,0,1,22,8,0,1,0,1,0,3,0,2,1,0,2,1,0,0,0,0,1,0,3,0,0,0,0,2,0,2,0,1,0,0,0,6,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,2,4,0,0,2,0,20,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,3,2,0,0,1,0,Class_2
+8966,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,1,0,5,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+8967,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+8968,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,6,0,1,0,2,0,0,0,8,6,0,0,0,0,0,0,12,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,9,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,16,1,1,9,0,1,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,15,2,2,0,0,0,0,0,Class_2
+8969,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,1,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+9032,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
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+9038,0,0,0,1,0,0,0,0,0,1,0,0,0,1,24,5,0,1,1,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,1,0,2,1,0,0,2,0,7,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,3,0,0,0,0,1,Class_2
+9039,0,0,0,1,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9040,0,0,0,0,0,0,0,1,1,0,0,0,0,6,1,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,4,1,0,0,0,1,0,18,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,2,2,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+9041,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+9042,0,0,0,0,0,0,0,0,17,0,0,0,0,0,2,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,5,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+9043,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+9045,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,4,1,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9046,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
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+9059,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,11,0,0,1,0,0,0,0,0,0,0,Class_2
+9060,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+9061,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,2,0,0,0,1,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,21,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
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+9063,0,0,1,0,1,0,0,0,0,1,0,1,1,1,0,3,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,3,0,5,0,0,11,2,0,0,0,0,2,3,0,0,0,10,1,0,0,0,0,0,0,0,1,0,3,1,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,1,0,0,0,Class_2
+9064,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,2,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9065,3,0,0,0,0,0,0,0,1,0,1,1,0,2,0,1,0,0,0,0,0,1,0,2,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,1,Class_2
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+9067,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,6,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9068,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,7,0,0,0,1,2,2,0,1,2,0,0,1,0,0,0,0,9,0,0,0,0,2,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,2,0,0,3,0,6,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,3,0,0,0,0,0,Class_2
+9069,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9070,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,1,0,1,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,1,0,Class_2
+9071,1,0,0,0,0,0,0,0,0,0,1,1,2,10,0,0,0,0,0,0,0,0,0,4,9,0,0,0,0,1,0,0,5,0,0,1,0,0,0,5,0,1,0,0,0,0,0,10,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,2,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,Class_2
+9072,0,0,0,0,0,0,0,1,1,0,0,0,1,5,16,5,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,14,0,1,0,0,0,1,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,4,0,0,3,2,14,0,0,2,0,0,0,0,0,0,1,0,0,5,0,0,3,2,0,0,0,0,Class_2
+9073,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9074,3,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,1,0,0,11,0,0,0,0,0,0,2,2,1,0,1,1,0,0,11,0,1,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,1,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,Class_2
+9075,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9076,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,1,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+9077,0,0,0,0,0,0,0,0,0,0,1,0,0,11,2,0,0,0,0,1,0,0,0,3,8,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,1,1,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,17,0,0,0,0,0,Class_2
+9078,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,1,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9079,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,15,0,0,8,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9080,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9081,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9082,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9083,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,1,0,1,0,Class_2
+9084,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9085,0,0,0,0,0,0,0,0,12,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9086,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,1,0,11,1,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+9087,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9088,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,6,0,1,0,0,0,1,0,2,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,4,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,4,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,Class_2
+9089,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
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+9150,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9151,2,0,0,0,1,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+9154,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9155,1,0,0,1,0,0,0,5,5,0,0,0,1,0,2,3,0,2,0,0,0,0,0,8,2,0,0,0,0,0,0,3,3,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,1,2,0,13,0,0,6,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,2,2,0,0,1,0,Class_2
+9156,0,0,1,1,0,0,0,0,0,0,0,1,0,5,0,9,0,0,0,0,0,2,0,0,3,0,0,1,0,0,2,0,1,1,1,0,0,0,0,4,0,0,5,0,0,0,0,1,0,0,0,0,0,11,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,Class_2
+9157,0,0,0,0,0,0,0,0,13,1,0,0,0,0,2,1,0,0,0,0,0,1,0,9,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,17,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+9158,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+9159,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+9161,1,0,0,0,0,0,0,0,0,0,0,0,1,4,21,7,0,0,0,0,0,1,0,3,4,0,0,1,0,0,0,0,3,1,0,0,0,0,0,6,0,0,0,0,0,0,0,6,1,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,3,0,0,1,0,11,1,0,0,0,1,0,0,0,0,0,0,0,3,0,2,2,0,0,0,1,0,Class_2
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+9165,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,Class_2
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+9167,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,1,0,1,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,9,0,0,0,0,0,Class_2
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+9236,3,0,1,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,2,9,0,1,0,0,0,0,1,3,0,0,0,0,0,0,14,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_2
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+9238,0,0,0,0,0,0,0,0,18,0,0,0,0,0,4,2,0,0,0,0,0,0,0,9,1,0,0,1,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,2,0,1,0,0,0,1,0,0,3,0,5,1,0,4,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+9239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+9412,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9413,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
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+9415,0,0,0,0,0,0,0,0,0,0,0,0,0,1,23,9,0,2,0,0,0,2,0,3,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,2,0,2,0,0,1,0,17,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,1,1,0,0,0,0,Class_2
+9416,7,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,10,0,0,0,0,0,1,0,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9417,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9418,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+9419,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,1,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,2,13,0,0,0,0,1,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,1,1,0,0,1,1,0,0,Class_2
+9420,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,1,0,0,0,0,0,1,5,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+9421,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,1,9,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,5,0,0,0,0,1,2,0,0,0,0,0,1,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+9422,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+9423,0,0,0,1,0,0,0,1,0,0,0,0,0,9,1,0,0,0,0,0,1,2,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9424,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9425,1,0,0,0,0,0,0,0,0,0,0,0,0,0,13,5,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,1,6,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+9426,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9427,0,0,2,0,0,0,0,0,0,0,0,0,0,2,19,6,0,0,0,0,2,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,14,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,2,2,0,0,0,0,Class_2
+9428,0,0,0,0,0,0,1,0,0,0,0,0,0,1,16,6,1,1,0,0,4,2,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,1,0,0,0,0,0,3,0,5,0,0,0,0,0,0,0,7,0,1,0,0,1,0,0,1,0,11,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,1,0,Class_2
+9429,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,1,0,0,1,1,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_2
+9430,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,2,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,20,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_2
+9431,0,0,0,1,0,0,0,0,0,0,0,1,0,1,17,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,6,0,0,0,0,0,1,4,1,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,1,0,0,0,2,0,1,0,1,3,0,0,2,0,27,0,0,2,0,0,0,0,0,0,0,0,0,3,1,0,1,0,0,0,1,0,Class_2
+9432,0,0,0,0,1,0,0,0,2,0,1,0,0,9,1,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,1,0,9,3,0,4,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,4,0,2,0,0,0,1,0,0,1,0,0,1,0,0,2,1,0,4,1,0,0,0,0,Class_2
+9433,0,0,0,0,0,0,0,0,10,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9434,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9435,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,10,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,Class_2
+9436,0,0,1,0,0,0,0,0,0,0,0,0,0,0,21,10,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,1,0,0,1,1,0,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,12,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9437,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9438,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,5,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,2,1,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,Class_2
+9439,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9440,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9441,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,13,0,0,4,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_2
+9442,0,0,0,0,0,0,0,1,0,0,0,0,0,3,20,13,0,0,0,0,0,2,0,1,3,0,2,0,0,0,0,0,4,0,1,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,6,0,1,0,0,0,0,0,4,1,0,0,0,1,0,0,0,0,12,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,2,3,0,0,1,0,Class_2
+9443,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9444,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,4,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,8,2,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,19,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,4,0,0,0,0,0,Class_2
+9445,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,Class_2
+9446,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+9447,0,0,0,0,0,0,0,0,2,0,0,1,0,2,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,1,1,1,0,1,0,3,1,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,7,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,Class_2
+9448,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,8,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,5,1,0,0,0,0,Class_2
+9449,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,6,0,0,0,1,0,1,0,3,4,0,0,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,1,0,0,1,5,0,0,0,1,0,0,0,0,1,3,0,1,2,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,22,1,8,2,0,0,0,0,Class_2
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+9452,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9453,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,2,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
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+9461,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9462,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9463,2,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,0,Class_2
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+9465,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+9468,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+9469,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,2,1,0,0,0,0,Class_2
+9470,0,0,0,0,0,0,0,0,0,1,0,0,0,0,20,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,3,0,1,0,0,0,0,5,0,0,0,3,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,21,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_2
+9471,0,0,0,0,0,0,0,0,0,0,1,0,0,7,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+9472,1,0,0,0,0,0,0,1,0,0,0,1,0,10,1,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,5,1,0,1,0,0,0,4,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,1,0,Class_2
+9473,0,0,2,0,0,0,0,1,0,0,0,0,0,1,14,6,0,2,0,1,0,1,0,3,1,0,4,0,0,0,0,0,2,0,4,0,0,1,0,10,2,1,0,0,0,0,0,0,1,2,0,0,0,8,1,0,0,0,0,0,0,5,1,0,1,0,3,0,0,5,0,16,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,1,0,0,0,0,Class_2
+9474,1,0,0,0,0,0,0,0,0,0,0,0,0,4,3,4,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,7,0,1,1,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9475,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9476,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,5,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+9477,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9478,0,0,0,0,0,0,0,0,2,0,1,0,0,4,8,0,1,0,0,1,0,0,0,1,4,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,15,0,0,8,0,0,2,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,6,2,4,0,0,0,0,0,Class_2
+9479,0,0,1,0,0,0,0,0,0,1,0,0,3,2,19,2,0,0,0,1,1,3,0,3,1,0,0,0,0,0,0,0,3,1,1,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,3,0,1,0,1,0,0,0,1,0,1,0,0,5,0,0,1,2,17,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,0,0,0,1,Class_2
+9480,1,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+9481,0,0,0,0,0,0,1,0,19,0,0,0,0,0,0,0,0,0,0,1,0,1,0,5,2,0,0,0,1,0,0,9,0,0,0,1,0,3,0,1,1,0,0,0,0,1,0,7,2,1,0,0,0,9,0,0,0,0,0,0,0,2,0,16,0,0,5,0,0,5,1,0,1,1,0,0,0,0,0,0,0,0,1,0,3,2,1,2,4,0,0,0,0,Class_2
+9482,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+9483,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,6,6,0,2,0,0,0,0,0,Class_2
+9484,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,Class_2
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+9486,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+9491,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,1,0,0,0,Class_2
+9492,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_2
+9493,0,0,0,0,0,0,0,0,0,0,1,0,1,24,1,0,0,0,0,2,0,1,0,1,8,0,0,0,0,0,0,1,2,1,1,1,0,0,0,3,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_2
+9494,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,1,0,0,0,0,1,0,0,3,7,0,0,0,0,0,0,0,2,0,0,2,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9495,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,1,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,0,21,0,1,1,0,0,Class_2
+9496,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9497,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,1,0,0,0,0,0,3,4,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,4,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+9498,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,6,0,0,0,0,0,Class_2
+9499,0,0,0,0,0,0,0,0,0,0,0,0,0,1,21,11,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,2,1,1,0,0,3,0,1,0,0,0,3,0,1,0,0,1,0,0,0,0,7,0,0,0,0,1,0,0,3,0,1,1,0,2,0,0,1,1,11,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,Class_2
+9500,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9501,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,10,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,2,0,22,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_2
+9502,0,0,0,0,0,0,0,0,0,0,1,0,0,0,20,2,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,2,0,10,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,2,0,0,0,0,Class_2
+9503,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9504,5,0,0,0,0,0,0,2,0,0,2,0,2,0,0,2,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,2,1,0,0,2,2,0,0,4,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,5,1,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,0,2,1,4,0,0,0,0,1,Class_2
+9505,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,1,2,0,0,0,0,2,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,6,3,0,2,0,0,0,0,0,Class_2
+9506,0,0,0,0,0,0,0,1,0,0,1,1,0,8,0,0,0,0,0,0,0,0,0,3,12,0,0,0,0,0,0,0,1,0,0,2,1,0,0,1,0,1,0,1,0,0,0,7,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,1,0,3,0,0,4,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9507,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,2,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9508,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9509,0,0,0,0,0,0,0,3,6,3,0,0,2,0,3,4,1,2,0,1,1,1,0,4,2,1,0,0,1,0,0,5,4,0,1,0,0,0,0,2,0,0,3,1,0,0,0,5,0,0,0,0,1,2,0,0,0,0,1,1,0,3,0,15,3,3,11,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_2
+9510,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,2,1,3,0,1,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,9,0,0,2,3,0,1,0,2,1,0,0,0,1,3,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+9511,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9512,0,0,0,0,0,0,0,0,19,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,8,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,7,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9513,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+9514,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9515,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+9516,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,9,0,8,0,0,0,0,0,Class_2
+9517,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,1,0,0,0,2,0,9,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+9518,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,2,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,3,0,2,1,1,10,0,0,0,0,1,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+9519,2,0,0,0,0,1,0,0,0,0,0,0,0,0,14,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,6,6,0,1,0,0,0,0,0,Class_2
+9520,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,Class_2
+9521,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,2,12,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9522,1,0,0,0,0,0,1,0,3,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,1,6,0,0,0,0,1,1,6,0,1,0,0,0,0,0,0,0,0,0,2,0,0,5,1,5,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,17,1,0,0,3,1,0,0,0,Class_2
+9523,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,4,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,Class_2
+9524,0,0,0,1,0,0,0,0,0,0,0,0,0,0,14,7,0,0,0,0,0,2,0,2,4,0,0,0,0,0,0,0,2,1,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,4,1,7,1,0,1,0,0,0,1,0,0,0,0,0,2,1,0,1,1,0,0,2,0,Class_2
+9525,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,5,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,13,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,4,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_2
+9526,0,0,0,0,0,0,0,0,0,0,0,1,1,0,14,0,1,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+9527,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,4,0,1,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,1,0,0,0,0,11,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9528,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,6,0,0,0,0,1,2,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+9529,0,0,0,0,0,0,0,0,13,0,0,0,0,2,3,0,0,0,0,0,0,0,0,2,1,0,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,29,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9530,0,0,0,0,0,0,0,1,0,0,0,0,0,13,0,1,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,12,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_2
+9531,0,0,0,1,0,0,0,0,0,0,0,0,1,0,13,4,0,2,0,1,0,3,0,4,2,0,0,0,1,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,6,0,0,0,0,1,8,0,0,0,0,0,0,0,1,0,1,0,1,5,1,0,1,0,12,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9532,0,0,0,0,0,0,0,1,0,0,0,0,0,2,3,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,Class_2
+9533,2,0,0,0,0,0,0,5,3,0,0,0,1,2,2,2,0,3,0,4,0,1,0,6,1,0,0,0,0,0,0,8,2,1,0,0,0,2,0,0,1,1,1,2,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,21,0,3,12,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,2,0,0,0,1,0,Class_2
+9534,0,0,0,0,0,0,0,1,12,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,2,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,28,0,4,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,1,4,1,1,0,0,0,Class_2
+9535,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9536,0,0,0,1,0,0,1,1,5,0,0,0,0,1,0,0,1,0,0,1,0,1,0,5,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,18,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,1,3,0,0,0,0,0,Class_2
+9537,0,0,0,0,0,0,0,0,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,7,1,0,0,1,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,18,0,0,9,0,1,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+9538,0,0,0,0,0,0,0,1,1,0,0,1,0,8,1,9,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,2,1,0,0,1,0,12,0,2,0,3,0,0,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+9539,0,0,0,0,0,0,0,0,21,0,0,0,0,1,3,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9540,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+9541,0,0,0,0,0,0,1,1,0,3,1,0,0,3,0,2,0,0,0,1,0,1,0,0,6,0,0,0,0,0,0,1,1,2,1,0,6,0,0,1,0,1,16,2,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,3,2,0,1,0,1,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,3,1,9,0,1,0,0,0,Class_2
+9542,0,0,0,0,0,0,0,0,12,0,0,0,0,0,5,0,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,2,4,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9543,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+9544,0,0,0,0,0,0,1,0,18,0,0,0,0,0,0,0,2,0,0,1,0,0,0,4,2,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,18,0,0,10,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_2
+9545,0,0,0,0,0,0,0,0,12,0,3,0,0,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9546,0,0,2,0,0,0,0,0,0,0,0,1,0,1,26,3,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,3,0,0,1,0,16,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_2
+9547,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,1,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,4,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9548,0,0,0,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9549,0,0,1,1,0,0,0,0,0,0,0,0,1,0,22,3,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,5,0,1,0,0,1,1,3,0,1,0,0,0,0,0,6,0,0,0,0,0,1,1,0,0,1,0,1,0,2,0,0,0,0,3,0,0,2,0,21,0,0,1,0,0,0,0,0,0,0,0,0,2,2,0,3,1,0,1,0,0,Class_2
+9550,0,0,0,0,0,0,0,0,0,0,0,0,0,13,3,0,0,0,0,0,0,1,0,1,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,3,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,3,0,9,0,0,0,0,0,Class_2
+9551,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+9552,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9553,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9554,0,0,0,0,0,0,0,1,18,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,6,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_2
+9555,0,0,0,0,0,0,0,1,7,0,2,0,0,2,0,0,0,1,0,0,0,0,0,4,2,0,0,0,0,0,0,1,2,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,16,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,1,0,Class_2
+9556,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_2
+9557,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9558,0,0,0,0,0,0,0,0,21,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,6,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9559,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,4,0,3,0,0,0,2,3,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9560,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,8,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+9561,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0,3,0,3,0,1,0,0,0,0,0,0,0,3,0,3,0,0,4,0,6,1,0,8,1,0,0,0,0,0,2,0,2,0,6,0,0,0,0,1,0,1,4,0,2,1,0,0,0,0,8,1,6,0,5,0,0,0,0,0,2,0,1,0,0,12,4,1,2,3,0,0,0,0,Class_2
+9562,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,8,0,0,0,0,0,Class_2
+9563,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,9,0,0,0,0,0,Class_2
+9564,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9565,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+9566,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9567,1,0,0,0,0,0,0,0,0,7,1,0,0,2,0,0,0,0,0,1,0,2,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,2,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,7,0,0,0,0,0,Class_2
+9568,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,3,0,0,0,0,0,Class_2
+9569,0,0,0,0,0,0,0,0,0,1,0,0,0,7,1,3,0,0,0,0,0,1,0,3,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,7,0,1,0,0,1,0,1,0,0,0,0,0,0,3,0,2,0,0,2,0,0,3,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,14,0,7,0,0,0,0,0,Class_2
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+9581,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
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+9588,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,10,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+9591,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9592,2,0,0,1,0,0,0,1,0,0,0,0,1,0,22,7,0,1,0,1,0,1,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,4,0,1,0,1,1,0,0,4,0,13,2,0,0,0,0,0,0,1,0,0,0,0,2,1,2,0,0,0,0,2,0,Class_2
+9593,0,0,0,0,0,0,0,0,0,2,1,0,0,8,1,1,0,0,1,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,4,1,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,2,0,0,0,0,0,Class_2
+9594,0,0,0,0,0,0,0,1,0,0,0,0,0,2,3,2,0,1,0,0,0,0,0,0,5,0,0,0,0,1,0,0,8,0,0,0,0,0,1,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,Class_2
+9595,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+9596,0,0,0,0,0,0,0,2,1,0,0,0,1,1,17,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,2,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9597,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+9598,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9599,3,0,0,0,0,0,0,0,0,0,1,2,6,11,0,1,0,0,0,2,0,0,0,3,9,0,0,0,0,0,0,0,2,0,1,1,0,0,0,7,0,1,0,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,0,0,2,1,0,0,0,2,0,0,2,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,2,3,0,0,0,0,Class_2
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+9601,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,4,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9602,0,0,0,1,0,0,0,0,0,0,0,1,0,6,1,5,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,5,0,0,0,0,0,0,17,0,0,0,0,0,0,0,2,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
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+9612,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,6,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9613,1,0,0,0,0,0,0,2,0,0,1,1,0,14,1,1,0,0,0,0,4,0,0,2,15,0,0,0,0,0,0,0,4,1,0,0,0,0,1,24,0,0,1,0,0,0,0,3,0,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_2
+9614,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9615,0,0,0,0,0,0,0,0,0,0,0,0,0,9,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+9616,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,13,0,0,0,0,0,Class_2
+9617,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9618,0,0,0,0,0,0,0,0,11,0,0,1,0,0,1,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,4,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9619,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9620,0,0,0,0,0,0,0,1,1,0,0,1,0,6,1,1,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,6,1,0,1,0,0,0,3,0,1,0,0,0,0,0,2,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,Class_2
+9621,0,0,0,0,0,0,0,0,12,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9622,1,0,0,0,0,0,0,0,0,0,1,0,0,10,0,1,0,2,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,1,0,Class_2
+9623,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,Class_2
+9624,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,13,0,0,0,0,0,Class_2
+9625,0,0,0,0,0,0,0,0,0,0,2,1,0,8,5,5,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,2,0,0,2,0,20,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+9626,0,0,0,0,0,0,0,0,2,0,0,0,0,12,0,1,0,0,1,0,2,1,0,0,15,0,0,0,0,1,0,0,1,1,0,0,0,0,0,17,0,0,1,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+9627,0,0,0,0,0,0,0,0,0,0,0,0,2,5,1,1,0,3,0,0,2,3,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,10,0,0,0,0,0,0,0,6,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,2,4,1,2,0,0,0,0,0,Class_2
+9628,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,3,0,0,2,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9629,1,0,0,0,0,0,1,0,1,0,0,0,0,7,3,0,0,0,0,0,0,0,0,1,2,0,0,0,2,0,0,0,1,0,0,1,0,0,0,5,0,0,1,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,1,0,Class_2
+9630,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+9631,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,0,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9632,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_2
+9633,0,0,1,0,0,0,0,0,0,0,0,1,1,1,18,4,0,0,0,0,0,1,0,3,2,0,0,1,0,0,0,0,2,1,0,0,0,0,0,6,0,0,1,1,0,0,0,6,0,0,0,0,0,4,1,0,1,0,1,0,0,0,0,1,1,0,3,0,0,4,0,21,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,1,0,0,Class_2
+9634,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_2
+9635,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9636,0,0,0,0,0,0,0,0,16,0,0,0,0,1,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,4,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+9637,0,0,0,0,0,0,0,0,21,2,1,0,0,0,2,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,6,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,21,0,0,7,0,0,0,0,1,2,0,0,1,0,0,0,1,0,0,0,0,2,1,1,1,0,0,0,0,0,Class_2
+9638,1,0,0,0,2,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,16,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,10,1,2,0,0,0,0,0,Class_2
+9639,0,0,1,0,0,0,0,0,0,0,0,0,0,3,15,4,0,0,0,0,2,0,0,1,3,0,0,0,0,0,0,1,5,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,3,1,0,0,0,1,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,1,1,0,0,0,0,Class_2
+9640,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+9641,1,0,0,0,0,0,0,0,17,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,0,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9642,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9643,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_2
+9644,0,1,0,0,0,0,0,4,8,0,1,1,1,2,2,8,0,2,0,1,3,0,0,3,1,0,0,0,0,0,0,4,4,1,0,0,0,4,0,1,1,0,3,1,0,0,0,3,1,0,0,0,1,2,0,0,0,0,0,0,0,4,2,23,0,1,11,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_2
+9645,0,0,0,0,0,0,0,1,0,0,1,1,0,10,0,0,0,0,0,0,0,0,0,1,20,0,0,0,0,0,0,0,4,0,0,1,0,0,0,3,0,1,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+9646,0,0,0,0,0,0,0,0,11,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+9647,0,0,1,0,0,0,0,0,0,0,0,0,0,15,0,4,0,1,0,0,0,0,0,2,11,0,0,0,0,0,0,0,4,2,0,0,0,0,0,4,0,2,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9648,0,0,0,0,0,0,0,0,0,0,0,0,0,7,5,10,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,1,0,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+9649,2,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,2,2,0,0,0,0,Class_2
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+9651,0,0,0,1,0,0,1,0,0,0,2,0,1,2,15,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,5,1,1,0,1,1,0,0,0,0,1,0,0,0,2,0,0,0,0,2,0,0,1,0,1,0,0,2,0,1,4,1,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,1,0,Class_2
+9652,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+9662,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,3,0,0,0,0,0,1,0,6,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,1,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,1,0,0,0,0,0,1,0,0,0,1,16,0,29,0,0,0,0,1,Class_2
+9663,0,0,0,1,0,0,0,0,0,0,0,2,0,2,25,4,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,2,0,1,1,0,0,0,3,1,0,1,0,0,1,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,4,0,1,1,0,2,0,0,2,0,14,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
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+9665,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9666,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9667,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,8,0,1,0,2,0,1,0,0,2,0,0,0,0,0,1,2,3,0,0,0,0,1,0,12,0,0,0,1,0,1,0,0,1,0,0,2,2,1,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_2
+9668,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+9669,0,0,0,0,0,0,0,0,2,0,0,0,0,14,1,3,0,0,0,0,2,2,0,0,9,0,0,0,0,0,0,0,1,1,0,1,0,0,0,20,0,0,1,0,0,0,0,8,0,0,0,0,3,1,0,0,0,1,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+9670,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9671,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9672,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,2,0,0,0,1,0,0,0,Class_2
+9673,1,1,0,0,0,0,0,0,1,0,0,1,3,12,0,0,0,0,0,0,0,0,0,4,10,0,0,0,0,1,0,0,1,0,0,1,0,0,0,7,1,1,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+9674,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+9677,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9678,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,5,0,0,0,0,0,2,0,0,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,20,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_2
+9679,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,2,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_2
+9680,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+9682,0,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9683,0,0,0,0,0,0,0,0,0,0,0,0,0,3,20,4,0,0,0,0,0,1,0,3,2,0,3,0,1,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,23,1,0,0,0,1,0,0,0,0,0,0,0,5,3,0,0,0,0,0,0,0,Class_2
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+9686,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9687,0,0,0,0,0,0,0,0,0,0,2,0,0,5,2,1,0,0,0,1,0,1,0,0,9,0,0,0,1,0,0,0,1,0,0,0,0,0,0,14,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+9693,0,0,0,0,0,0,0,1,0,0,0,2,0,16,1,2,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,2,0,3,0,0,0,12,0,0,1,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,Class_2
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+9695,1,0,0,1,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+9696,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_2
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+9698,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9699,1,0,0,0,0,0,0,0,0,2,0,0,0,0,7,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,4,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,1,3,0,3,0,0,1,1,0,0,2,0,0,0,0,0,5,7,0,0,0,0,0,0,0,Class_2
+9700,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+9708,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9709,0,0,0,1,0,0,0,0,0,0,0,2,0,0,22,9,0,1,0,0,2,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,7,0,0,1,1,0,1,0,0,1,0,0,0,3,2,0,2,0,0,0,0,0,1,0,2,1,0,2,0,0,5,0,12,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
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+9716,0,0,0,0,0,0,0,0,0,0,0,1,0,0,15,7,0,0,0,1,3,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,4,0,0,0,2,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,2,0,4,0,0,2,1,1,0,0,1,0,19,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,Class_2
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+9718,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,4,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,11,0,1,1,2,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,1,0,Class_2
+9719,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+9720,1,0,0,0,0,0,0,0,0,0,1,0,0,10,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,5,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,9,0,0,0,1,0,Class_2
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+9723,0,0,0,0,0,0,0,0,15,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1,0,0,0,0,0,Class_2
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+9726,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_2
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+9741,0,0,3,1,0,0,0,0,0,0,0,0,0,1,20,2,0,2,0,0,0,1,0,2,1,0,0,2,0,0,0,0,1,1,0,2,0,2,0,3,0,1,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,5,1,0,0,2,2,0,1,2,0,6,0,0,0,0,0,0,0,2,0,0,0,0,2,1,1,1,0,2,0,0,0,Class_2
+9742,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+9743,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,10,0,0,0,0,0,0,0,1,0,0,1,1,0,0,7,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_2
+9744,0,0,1,0,0,0,0,0,1,0,0,0,0,16,2,2,1,0,0,0,0,0,0,5,17,0,1,0,2,0,0,0,2,0,0,0,0,0,1,10,0,0,1,0,0,1,0,10,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,8,0,0,0,0,0,Class_2
+9745,1,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,1,5,1,1,0,1,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,Class_2
+9746,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9747,0,0,0,0,0,0,0,1,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,1,0,12,0,1,0,1,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,1,1,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,Class_2
+9748,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+9749,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+9750,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,10,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,15,1,0,0,1,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,6,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+9751,1,0,0,0,0,0,0,0,14,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9752,2,0,0,0,0,0,0,1,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
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+9754,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,2,0,0,0,0,0,1,0,3,10,0,0,0,0,0,0,0,2,0,0,0,0,0,0,9,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+9755,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,7,0,0,0,0,1,2,0,0,2,1,0,0,0,0,0,0,1,3,0,0,0,0,0,9,0,0,0,1,0,1,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,3,0,Class_2
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+9759,1,0,0,1,0,0,0,0,18,3,1,0,0,0,0,1,0,0,0,2,0,0,0,3,1,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,2,0,0,0,1,2,0,0,0,1,23,0,2,8,0,0,0,0,1,2,0,0,2,0,0,0,1,0,0,0,0,1,3,0,0,1,1,0,0,0,Class_2
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+9761,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9762,0,0,0,0,2,0,1,2,0,8,0,0,0,3,1,0,0,0,0,0,0,0,1,1,5,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,14,2,0,0,0,2,1,1,0,0,0,2,2,0,0,0,0,0,0,1,0,1,1,2,0,0,0,10,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,7,3,2,0,0,0,0,0,Class_2
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+9820,0,0,0,1,0,0,0,1,0,1,0,0,0,1,2,1,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,2,1,0,1,0,3,1,0,1,0,1,11,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,1,2,0,0,1,0,Class_2
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+9822,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+9823,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_2
+9824,0,0,0,0,0,0,0,1,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9825,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,5,0,0,0,0,0,Class_2
+9826,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9827,4,0,0,0,0,0,0,2,0,0,2,0,1,2,1,0,0,0,0,0,0,1,0,4,9,0,0,0,1,0,0,0,0,0,0,1,1,0,0,5,0,0,1,0,0,0,0,4,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+9828,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+9829,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,9,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9830,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,2,0,1,0,0,0,1,0,0,7,0,0,0,0,0,0,0,1,0,1,2,0,0,0,9,1,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_2
+9831,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9832,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,1,1,0,1,0,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,7,0,0,1,0,0,0,0,Class_2
+9833,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,2,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9834,0,0,0,0,0,0,0,1,2,0,0,0,0,2,14,2,0,0,0,1,0,1,0,1,4,0,0,0,2,0,0,1,3,0,1,0,0,2,0,3,0,0,0,2,1,0,0,0,1,0,0,0,4,7,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,2,1,18,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,Class_2
+9835,0,0,0,3,0,0,0,0,0,0,1,0,0,2,20,7,0,0,0,0,0,1,0,0,3,4,0,0,0,0,0,0,2,0,0,0,1,0,0,7,0,0,0,2,0,0,0,0,0,0,0,3,1,5,0,0,0,0,0,0,1,2,0,0,1,1,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,1,0,0,0,1,Class_2
+9836,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+9837,0,0,0,0,0,0,1,0,0,0,0,1,0,0,27,6,0,0,0,0,0,2,0,1,1,0,0,1,0,0,0,0,4,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,4,2,0,2,2,1,0,0,0,0,14,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,Class_2
+9838,0,1,1,4,0,0,0,0,3,0,1,0,1,1,1,6,0,2,0,2,0,4,0,3,0,1,0,0,0,0,0,4,2,0,1,0,1,3,0,2,0,1,0,0,0,0,3,2,1,0,0,1,1,4,1,0,0,0,0,1,0,8,6,5,0,23,6,0,1,3,1,4,1,2,1,1,0,1,0,4,0,0,1,0,2,0,7,2,8,0,0,1,2,Class_2
+9839,0,0,0,1,1,0,0,1,1,0,0,1,1,1,23,6,0,1,0,3,1,2,0,3,6,0,0,1,0,0,0,1,2,0,0,0,0,2,0,5,1,2,0,1,0,1,0,7,2,0,0,0,0,9,0,0,0,0,0,0,0,2,7,0,0,1,6,1,0,2,1,20,2,0,0,0,1,0,1,0,0,0,0,0,6,0,0,3,3,0,0,0,0,Class_2
+9840,0,0,1,0,0,0,0,0,0,1,0,0,1,3,11,5,1,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,4,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,Class_2
+9841,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+9842,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,14,0,0,0,0,0,0,0,1,Class_2
+9843,0,0,0,0,0,0,0,1,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+9844,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,Class_2
+9845,0,0,1,0,0,0,0,1,3,0,1,1,0,15,2,0,0,0,0,0,0,0,0,0,17,0,2,0,0,0,0,0,2,0,0,1,0,0,0,13,0,0,1,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,4,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+9846,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,1,0,2,9,0,1,0,0,0,0,0,3,0,0,2,0,0,0,1,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+9847,0,0,0,0,0,0,0,0,0,0,0,0,1,3,17,8,0,0,0,0,2,0,0,1,1,0,0,0,0,1,0,0,2,0,2,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,3,1,0,2,0,0,6,1,15,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,Class_2
+9848,0,0,1,0,0,0,0,0,0,0,0,0,0,0,19,6,0,0,0,0,3,2,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,6,1,0,0,0,0,3,0,0,0,0,1,0,0,3,0,0,0,0,4,0,0,3,0,19,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,Class_2
+9849,0,0,0,0,0,0,0,1,0,0,1,1,0,4,1,6,0,1,0,0,0,2,0,0,7,0,0,0,0,0,0,0,1,1,0,0,0,1,0,9,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
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+9861,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,4,0,3,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+9862,0,0,1,0,0,0,0,0,0,0,0,0,0,6,3,2,0,0,0,1,0,0,0,2,12,0,0,0,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,1,3,1,0,0,0,0,Class_2
+9863,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_2
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+9865,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+9868,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,1,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,10,0,0,0,1,1,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+9869,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,6,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9870,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9871,0,0,0,2,0,0,0,0,3,0,0,0,0,2,2,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,8,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+9872,0,0,0,1,0,0,0,0,0,1,0,0,0,4,18,7,0,0,0,0,0,1,0,1,2,0,0,1,0,0,1,0,2,3,0,0,0,0,0,3,1,0,0,1,0,0,0,0,1,0,0,0,2,5,0,0,0,0,0,0,0,6,0,4,1,0,1,0,0,2,0,11,1,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_2
+9873,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+9874,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,3,0,2,0,0,0,2,0,0,2,1,0,0,0,0,0,0,1,1,0,0,0,0,0,20,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+9875,0,0,1,1,0,0,0,0,0,0,1,0,0,0,17,10,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,3,0,1,0,0,0,0,0,7,0,0,0,0,0,8,0,0,0,0,0,1,0,1,0,0,0,1,4,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,1,2,0,0,0,0,Class_2
+9876,0,0,0,2,1,0,0,0,0,1,1,0,0,7,0,2,0,0,1,0,0,0,0,2,4,0,0,0,0,0,0,0,2,0,3,2,0,0,0,4,0,0,6,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,2,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_2
+9877,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+9878,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,3,0,0,3,0,0,0,1,0,0,0,1,0,0,1,0,0,0,9,1,1,3,1,0,0,0,0,1,0,0,0,1,4,0,0,0,2,0,0,0,2,1,3,0,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,Class_2
+9879,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,Class_2
+9880,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,7,0,0,0,0,0,Class_2
+9881,0,1,0,0,0,0,0,0,0,1,0,0,0,1,19,9,0,0,0,0,0,2,0,2,1,0,0,1,0,0,0,1,4,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,1,0,2,0,1,0,0,5,0,18,0,0,1,0,0,0,0,0,0,0,0,0,4,2,1,0,0,0,0,0,0,Class_2
+9882,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,4,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+9883,0,0,0,0,0,0,0,0,2,0,0,0,0,6,2,0,0,0,0,1,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,14,0,10,1,0,0,0,0,Class_2
+9884,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,5,1,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+9885,22,0,5,0,0,0,0,0,0,1,0,0,1,3,2,1,1,10,0,1,0,1,0,1,4,0,1,0,1,0,0,0,1,0,2,1,1,11,0,7,0,0,5,1,0,0,0,5,1,0,0,0,0,4,2,7,0,0,0,0,0,1,1,0,2,1,1,0,0,8,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,3,7,17,0,0,0,1,Class_2
+9886,0,0,0,0,0,0,1,2,0,1,1,0,0,6,0,1,0,2,1,0,0,1,0,2,10,0,0,0,0,0,1,3,3,0,1,0,3,0,0,10,1,1,14,3,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,3,1,0,0,0,0,16,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,6,1,0,0,1,0,Class_2
+9887,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+9888,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,1,0,1,0,0,2,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,10,2,0,0,0,1,0,0,0,Class_2
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+9926,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,7,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,0,5,0,0,1,0,0,0,14,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+10122,0,0,0,0,0,0,0,0,0,0,2,0,0,10,0,0,0,0,0,0,0,0,0,1,23,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+10125,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,Class_2
+10126,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,3,0,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_2
+10127,0,0,0,1,0,0,0,0,0,3,0,0,0,0,15,2,0,0,1,2,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,4,2,0,0,0,0,0,3,0,0,0,3,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,4,0,5,0,2,0,0,0,0,4,0,0,0,0,0,12,8,0,1,0,1,0,0,0,Class_2
+10128,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+10129,1,0,1,0,0,0,0,0,0,0,0,0,0,13,2,0,0,2,0,0,0,0,0,1,3,0,0,1,1,0,0,0,5,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,1,2,1,0,0,1,0,Class_2
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+10146,0,0,0,0,0,1,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10147,0,0,0,0,0,0,0,0,2,12,0,1,1,5,1,2,1,0,1,1,0,1,0,5,10,1,0,0,0,0,0,12,2,0,0,2,0,1,1,1,0,2,0,0,1,0,1,5,0,5,0,0,2,3,1,1,2,0,0,0,0,5,0,12,0,2,14,2,0,2,1,1,1,1,2,4,0,1,0,0,1,0,0,0,0,15,3,5,0,0,0,0,0,Class_2
+10148,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,17,0,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10149,0,0,0,0,0,0,0,0,0,0,0,0,3,11,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10150,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,Class_2
+10151,0,0,0,0,0,0,0,0,0,1,0,0,1,0,24,2,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,3,0,1,0,2,1,0,0,3,2,11,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10152,0,0,0,0,0,1,0,2,0,0,1,0,1,0,1,2,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,15,0,0,0,0,1,3,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,Class_2
+10153,0,0,1,0,0,0,0,0,0,1,0,1,1,2,16,3,0,1,0,0,0,1,0,1,1,0,0,0,1,0,0,0,4,1,0,1,0,0,0,13,0,1,0,1,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,Class_2
+10154,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10155,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10156,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,1,0,0,0,0,0,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_2
+10157,0,0,0,0,0,1,0,2,0,0,0,0,0,4,3,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,8,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,1,1,0,0,0,0,Class_2
+10158,0,0,0,0,0,0,0,1,0,0,0,0,0,14,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10159,1,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+10160,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+10161,0,0,0,0,0,0,0,0,0,0,2,0,0,9,4,4,0,1,0,1,0,0,0,2,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,15,0,1,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10162,4,0,0,0,0,0,0,0,0,0,0,1,0,12,0,3,0,0,0,0,1,2,1,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,1,2,0,0,0,0,0,22,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,1,0,7,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,3,0,Class_2
+10163,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10164,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10165,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10166,0,0,0,0,0,0,0,0,0,0,2,0,0,7,4,6,0,0,0,1,0,2,0,1,5,0,0,0,0,0,0,0,1,1,0,0,0,1,0,9,0,0,2,0,0,1,0,4,0,0,0,0,0,4,0,0,0,0,0,1,0,1,1,1,0,2,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,Class_2
+10167,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,9,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,4,2,1,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,Class_2
+10168,0,0,0,1,0,0,0,1,0,0,1,0,0,2,19,3,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,4,1,0,0,0,0,0,4,0,0,0,1,0,0,0,5,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,2,0,0,6,1,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,0,0,1,0,Class_2
+10169,1,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
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+10206,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10207,0,0,0,0,0,0,2,1,0,9,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,4,4,0,3,0,1,14,4,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,3,2,1,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,7,1,1,0,0,0,Class_2
+10208,2,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+10209,7,0,0,0,0,0,0,2,0,0,0,0,1,4,0,7,0,0,0,1,0,0,0,4,6,0,0,0,0,0,0,0,3,0,0,1,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,1,1,4,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,Class_2
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+10212,0,0,0,1,0,0,0,0,1,0,1,0,2,1,17,9,0,1,0,0,1,6,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,6,0,1,0,0,0,0,0,1,1,1,0,0,0,11,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+10213,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10214,0,0,0,1,0,0,0,0,0,0,0,0,0,0,20,10,0,1,1,0,1,0,0,2,2,1,0,0,0,1,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,2,0,1,0,2,3,0,0,2,0,9,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
+10215,0,0,0,0,0,0,0,0,0,0,0,0,0,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+10216,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10220,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10224,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,5,0,1,0,0,0,1,0,0,2,0,0,1,0,0,0,7,0,0,0,0,1,0,1,1,0,0,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,1,0,2,1,0,0,1,0,Class_2
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+10227,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10228,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,0,0,0,0,0,6,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10229,0,0,0,4,0,0,0,0,1,0,1,0,0,2,1,8,0,2,0,1,0,2,0,4,2,2,0,0,0,0,0,3,2,2,1,0,0,6,0,3,0,0,0,1,0,2,3,1,1,0,0,0,0,7,0,0,0,1,0,1,0,9,6,3,0,27,6,0,2,6,0,2,0,1,1,2,0,0,0,4,0,0,1,0,1,2,6,3,8,2,0,1,2,Class_2
+10230,0,0,0,0,0,0,0,2,0,0,0,0,0,7,0,2,0,0,0,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,1,0,3,0,0,9,0,1,11,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,2,1,0,0,0,0,Class_2
+10231,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10232,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+10233,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+10234,0,0,0,0,0,0,0,0,15,0,0,0,0,1,7,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,11,0,0,6,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+10235,0,0,0,0,0,0,0,0,1,0,0,0,0,5,4,8,0,0,0,0,2,0,0,0,2,0,2,0,0,0,0,0,0,3,2,1,1,0,0,7,0,1,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,1,0,1,0,2,0,0,1,3,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,1,0,Class_2
+10236,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,2,0,0,0,5,0,1,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,Class_2
+10237,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10238,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10239,1,0,0,0,0,0,0,1,0,0,0,0,0,14,2,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10240,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10241,0,0,0,0,0,0,0,0,14,0,1,0,0,0,5,0,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10242,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10243,0,0,0,0,0,0,0,1,1,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_2
+10244,0,0,1,1,0,0,0,0,0,0,1,0,0,3,19,6,1,0,0,0,0,4,0,2,2,0,0,0,0,0,0,0,5,1,1,0,0,0,0,1,0,0,0,1,0,0,0,6,0,0,0,0,2,7,1,0,0,0,1,0,0,2,0,1,0,1,4,0,0,1,0,13,0,0,3,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,0,Class_2
+10245,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,4,0,0,0,0,0,1,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,Class_2
+10246,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,1,0,0,0,8,0,1,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+10247,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,5,0,0,0,0,0,Class_2
+10248,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,2,1,0,0,2,0,0,19,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10249,1,0,0,0,0,0,0,0,12,0,0,0,0,2,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,18,0,2,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+10311,0,0,0,0,0,0,0,1,0,0,1,0,0,12,0,1,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,2,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+10313,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10314,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+10318,0,0,0,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,13,0,0,3,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,1,0,Class_2
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+10320,0,0,0,0,0,0,0,0,4,0,0,0,0,6,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,8,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+10325,0,0,0,0,0,0,0,1,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10326,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,1,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,1,2,0,1,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
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+10328,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10412,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+10413,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10414,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10415,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,8,0,1,0,0,0,Class_2
+10416,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_2
+10417,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10418,0,1,0,0,0,0,0,0,11,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,2,0,2,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+10419,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10420,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,Class_2
+10421,1,0,1,0,0,0,0,0,0,0,0,0,1,7,0,0,0,1,0,0,0,1,0,1,9,0,0,0,0,0,0,0,2,0,3,0,0,0,0,7,0,1,2,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10422,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,3,0,0,2,0,0,0,5,0,1,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,Class_2
+10423,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,Class_2
+10424,2,0,1,0,0,0,0,0,0,0,0,0,0,25,1,5,0,0,0,1,0,2,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,26,0,1,0,0,0,1,0,16,0,0,0,0,1,0,1,0,0,0,0,1,0,4,0,1,0,1,6,0,0,1,1,2,0,0,1,0,1,0,0,0,0,0,0,0,1,5,1,1,0,0,0,0,0,Class_2
+10425,0,0,0,0,0,0,0,0,1,0,1,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1,0,0,0,0,0,Class_2
+10426,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,1,0,0,0,0,Class_2
+10427,0,0,0,0,0,0,0,0,0,0,0,0,1,22,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,6,0,0,1,0,0,0,7,0,1,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_2
+10428,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10429,0,0,0,0,0,0,0,2,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,Class_2
+10430,0,0,0,0,0,0,0,0,23,0,0,0,0,0,1,0,0,0,2,0,0,0,0,8,1,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,18,0,0,9,0,0,1,1,1,2,0,3,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,Class_2
+10431,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+10432,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,2,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,3,0,0,0,2,1,0,28,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+10433,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,Class_2
+10434,0,0,2,4,0,1,0,0,3,0,1,0,0,2,1,8,2,3,0,1,0,2,0,8,2,1,0,1,0,0,0,3,0,0,1,0,1,5,0,6,0,1,0,1,0,1,0,5,1,1,0,0,0,6,0,0,0,0,1,1,0,9,6,15,0,14,5,1,1,5,0,2,0,1,1,1,0,1,0,5,0,0,0,0,1,2,4,4,8,0,0,0,2,Class_2
+10435,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,8,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,13,0,0,0,0,0,Class_2
+10436,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10437,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10438,0,0,0,1,0,0,0,6,15,0,0,3,2,8,2,4,0,4,0,1,0,3,0,15,1,0,0,3,0,0,1,6,12,1,1,0,0,0,0,1,3,2,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,2,23,0,3,16,0,1,0,0,7,0,0,0,0,0,0,2,0,0,0,1,0,0,1,1,3,2,0,0,0,0,Class_2
+10439,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10440,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10441,1,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10442,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+10443,3,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,2,9,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,Class_2
+10444,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,1,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10445,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,10,1,0,0,0,0,0,0,0,Class_2
+10446,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,4,0,0,0,0,0,Class_2
+10447,0,0,0,0,0,0,0,1,18,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,7,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,13,0,0,9,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10448,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10449,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+10450,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,Class_2
+10451,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10452,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,1,0,0,0,0,1,1,2,0,0,0,0,0,0,0,5,3,0,0,1,1,0,26,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,Class_2
+10453,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,2,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+10454,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+10459,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10460,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_2
+10461,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+10462,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,9,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+10463,1,0,0,0,0,0,0,0,6,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,3,0,0,0,0,0,Class_2
+10464,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10465,0,0,0,0,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10466,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,Class_2
+10467,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,Class_2
+10468,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,Class_2
+10469,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10470,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+10471,0,0,0,0,0,0,0,2,0,0,1,0,0,18,0,0,1,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,0,0,0,0,6,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+10472,0,0,0,0,0,0,0,0,0,0,1,0,1,8,1,4,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,15,0,0,0,0,0,1,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_2
+10473,1,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10474,2,0,0,0,0,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,1,1,0,2,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,6,0,1,2,0,0,0,0,Class_2
+10475,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+10476,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10477,1,0,0,0,0,0,0,0,3,0,1,0,0,17,1,1,0,0,0,1,1,0,0,0,8,0,1,1,0,0,0,0,3,0,0,0,0,0,0,9,0,0,1,0,0,0,0,5,0,0,0,0,2,2,0,0,0,0,0,1,0,1,0,0,1,0,4,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,1,0,Class_2
+10478,0,0,0,0,0,0,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+10479,0,0,0,0,0,0,0,0,0,0,0,0,0,3,26,0,0,2,0,1,0,1,0,6,5,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,3,0,0,3,0,10,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,Class_2
+10480,0,0,0,0,0,0,0,0,0,0,1,1,1,3,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+10481,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,8,0,1,0,0,0,3,0,0,2,0,0,0,0,0,0,4,3,0,0,1,0,0,0,14,1,0,1,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10482,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+10483,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,Class_2
+10484,0,0,0,1,0,0,0,0,1,0,0,0,1,6,1,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,6,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10485,4,0,0,0,0,0,0,1,0,0,2,1,0,3,0,4,0,0,0,0,0,1,0,4,11,1,0,0,0,0,0,0,2,0,0,0,1,0,0,9,0,0,2,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,1,0,0,0,1,0,0,0,0,9,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,Class_2
+10486,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10487,0,0,0,0,0,0,0,0,4,0,0,0,0,5,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,Class_2
+10488,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+10489,2,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,2,7,0,0,0,0,0,1,0,3,1,0,0,0,0,2,5,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+10491,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10492,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10493,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10494,1,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,0,0,0,4,0,0,1,0,1,0,0,1,0,1,0,6,5,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,2,Class_2
+10495,1,0,0,0,0,0,0,1,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,2,0,0,0,0,2,0,0,2,0,1,13,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,2,1,2,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,4,1,0,0,0,0,Class_2
+10496,0,0,0,0,0,0,0,0,1,0,0,0,0,6,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,Class_2
+10497,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10498,2,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,Class_2
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+10500,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10501,4,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10502,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+10503,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,3,0,1,0,0,0,2,0,2,1,0,0,0,0,0,0,1,2,0,0,1,0,1,0,1,0,2,0,3,0,0,0,0,1,1,0,0,1,4,1,1,0,0,0,0,0,0,0,2,0,1,4,0,0,4,0,14,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,Class_2
+10504,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10505,0,0,0,0,0,0,0,0,20,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10506,1,0,0,0,0,0,0,1,1,0,2,0,0,9,0,2,0,0,0,0,0,0,0,2,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,2,0,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10507,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10508,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10509,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,3,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,2,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10510,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,8,0,2,0,0,0,3,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,17,1,0,0,1,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_2
+10511,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+10512,0,0,0,0,0,0,0,0,0,0,0,0,0,14,2,2,1,0,0,0,0,1,0,2,12,0,0,0,0,0,0,0,2,0,0,0,0,1,0,11,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,Class_2
+10513,1,0,0,0,0,0,0,4,5,0,0,1,1,0,3,2,0,2,0,1,0,1,0,2,1,0,0,0,0,0,0,4,2,0,2,0,0,1,0,3,0,0,3,0,0,0,0,5,0,0,0,0,0,4,0,0,1,0,0,0,0,3,0,21,0,1,12,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,4,0,0,1,0,Class_2
+10514,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,3,2,0,0,1,0,0,0,1,3,0,0,2,0,0,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,16,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_2
+10515,0,0,0,0,1,0,0,1,11,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,4,3,2,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,1,0,3,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10516,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,2,3,0,2,0,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+10517,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+10518,0,0,1,0,0,0,0,0,0,0,0,0,0,2,18,8,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,2,2,1,0,0,0,0,1,4,0,1,0,1,0,0,0,6,1,0,0,0,1,4,0,0,0,0,0,0,0,4,0,1,0,1,3,0,0,3,1,11,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,2,0,0,1,0,Class_2
+10519,0,0,0,0,0,1,0,0,0,0,0,0,2,12,1,3,0,3,0,0,1,2,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,0,0,7,0,0,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10520,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+10521,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10522,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,0,0,1,3,6,0,0,0,0,1,0,0,0,3,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,Class_2
+10523,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,3,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10524,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,6,0,1,0,0,0,1,0,1,1,2,0,0,0,0,0,0,1,1,0,0,1,0,0,20,1,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_2
+10525,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,15,1,0,0,0,0,Class_2
+10526,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10527,0,0,0,0,0,0,0,0,18,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,6,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10528,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10529,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,9,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,10,0,1,8,0,0,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,Class_2
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+10551,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10552,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+10554,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,1,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,Class_2
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+10558,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,13,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,Class_2
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+10567,0,0,1,0,0,0,0,0,1,0,0,0,1,1,16,4,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,0,2,1,2,0,0,1,0,0,3,0,7,1,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,1,0,Class_2
+10568,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
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+10583,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+10587,1,0,0,0,1,0,0,0,0,0,0,0,0,7,0,5,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,3,0,0,1,2,0,0,0,0,0,0,0,0,1,6,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,1,0,Class_2
+10588,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,2,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10589,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+10590,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+10591,0,0,0,0,0,2,0,0,0,0,0,1,0,1,1,4,0,0,0,2,0,3,0,0,12,0,2,0,0,0,0,0,0,1,0,0,0,0,2,7,0,0,1,0,0,0,0,4,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,4,0,0,1,0,0,1,0,Class_2
+10592,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,2,0,5,0,0,4,6,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,1,2,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_2
+10593,3,2,1,1,1,0,0,0,0,3,0,0,2,0,3,11,0,0,0,1,0,3,0,1,3,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,5,4,0,0,1,0,0,0,1,1,2,1,0,0,0,0,4,1,6,0,0,0,0,0,0,1,0,0,0,0,0,5,9,0,0,0,0,0,0,0,Class_2
+10594,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10595,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10596,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,3,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,3,0,0,0,1,0,Class_2
+10597,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,4,0,0,0,0,0,0,0,2,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,0,0,1,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,1,0,1,1,0,0,1,2,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10598,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,2,0,0,0,0,2,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,17,0,0,6,0,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,Class_2
+10599,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,0,2,0,0,1,4,0,0,1,0,0,0,0,2,0,1,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,8,1,3,0,0,0,0,0,Class_2
+10600,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10601,0,0,0,0,0,0,0,0,0,0,1,0,0,14,2,0,0,0,0,0,0,0,0,1,12,0,0,0,0,0,0,1,5,3,0,2,0,0,0,3,0,2,0,0,0,0,0,11,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,2,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,8,0,0,0,0,0,Class_2
+10602,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,Class_2
+10603,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,1,0,1,0,3,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,3,0,2,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,Class_2
+10604,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10605,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10606,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10607,0,0,0,0,0,0,0,1,0,0,0,0,0,1,5,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,7,0,0,0,0,0,0,0,0,0,0,0,0,9,9,1,1,0,0,0,1,0,Class_2
+10608,0,0,0,0,0,0,0,1,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,3,0,0,0,0,11,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+10609,0,0,0,0,0,0,0,1,11,0,0,3,0,0,3,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+10672,1,0,0,0,0,0,0,0,1,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,1,0,Class_2
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+10674,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
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+10693,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+10695,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10696,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_2
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+10698,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+10699,0,0,0,0,0,0,0,0,0,0,0,0,0,4,14,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,2,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,Class_2
+10700,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+10701,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10702,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+10703,0,0,0,1,0,0,0,0,2,0,0,0,1,3,2,4,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,3,1,0,2,0,0,0,0,0,0,3,0,1,0,0,1,3,1,5,3,0,1,0,0,Class_2
+10704,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,2,0,0,5,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+10705,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10707,0,0,0,0,0,0,0,0,0,0,1,1,0,18,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,3,0,0,3,0,0,1,5,0,2,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,Class_2
+10708,0,0,0,0,0,0,0,0,0,0,0,0,1,1,21,3,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,1,3,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,3,1,0,2,0,0,0,0,3,1,11,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,6,0,0,1,0,Class_2
+10709,0,3,0,0,0,0,0,0,7,0,0,0,0,0,3,0,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10710,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,4,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,8,0,0,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10711,0,0,0,0,0,1,0,0,0,0,0,1,0,0,16,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,4,0,1,0,0,0,0,0,Class_2
+10712,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+10713,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+10714,0,0,0,0,0,0,0,0,0,0,0,0,0,10,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+10715,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,11,1,0,1,0,0,0,0,3,2,0,0,0,0,0,16,1,0,1,0,0,0,0,2,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,Class_2
+10716,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+10717,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10718,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10719,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10720,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,1,0,0,0,0,7,0,0,2,3,0,0,0,2,1,0,0,2,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10721,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+10722,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,8,0,0,0,0,0,2,0,1,4,0,0,0,0,0,0,0,0,2,0,0,0,2,0,27,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,2,0,Class_2
+10723,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,Class_2
+10724,0,0,0,0,0,0,0,1,0,0,2,1,0,15,2,1,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,1,0,0,1,0,0,0,5,0,1,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,1,3,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+10725,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,3,0,0,0,Class_2
+10726,0,0,0,0,0,0,0,0,0,0,1,0,0,2,2,4,0,0,0,0,1,0,0,0,11,0,0,0,0,0,0,0,2,0,0,0,0,0,1,10,0,0,1,1,0,0,0,3,0,0,0,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,Class_2
+10727,0,0,0,0,0,0,0,0,2,0,0,0,0,2,1,13,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,5,1,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,Class_2
+10728,0,0,0,0,0,0,0,0,0,0,0,1,0,10,0,5,1,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,2,0,0,0,10,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,Class_2
+10729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
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+10738,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10739,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,4,0,1,0,0,1,2,0,0,1,1,0,0,0,0,0,0,2,3,1,0,0,0,0,19,1,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,5,0,Class_2
+10740,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10741,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,6,0,0,0,0,0,Class_2
+10742,0,0,1,0,0,0,0,0,0,0,1,0,0,19,3,0,0,0,0,0,0,0,0,5,10,0,0,0,0,0,0,0,4,1,0,1,0,0,0,4,0,0,1,0,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,6,1,1,9,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,10,0,0,0,0,0,Class_2
+10743,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,3,0,0,0,0,0,Class_2
+10744,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,0,0,0,0,Class_2
+10745,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+10746,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10747,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,7,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,3,0,0,0,1,0,15,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+10748,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10749,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27,6,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,1,0,2,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,2,0,1,0,0,2,0,0,0,0,5,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,4,0,0,4,0,Class_2
+10750,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+10751,0,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10752,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+10753,0,0,0,0,0,0,0,0,13,0,0,0,1,0,3,0,0,0,0,0,0,0,0,10,2,0,0,0,0,0,0,10,3,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,10,0,0,3,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10754,0,0,0,0,0,0,0,0,0,2,0,0,0,1,3,0,0,0,0,0,0,0,1,2,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,4,0,2,0,2,0,0,0,0,0,0,0,0,0,0,3,10,0,7,0,0,0,0,0,Class_2
+10755,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10756,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,Class_2
+10757,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10758,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+10759,0,0,2,0,0,0,0,0,0,0,0,0,0,1,23,10,1,1,0,0,0,2,0,1,1,0,0,0,0,0,0,2,4,0,0,1,0,0,0,2,0,0,1,2,0,2,0,0,0,0,0,0,1,9,0,1,0,0,0,0,0,4,3,0,0,1,1,0,0,1,1,12,1,0,0,0,0,2,1,1,0,0,0,0,4,0,0,0,3,0,0,2,0,Class_2
+10760,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,1,4,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,Class_2
+10761,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,2,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,Class_2
+10762,0,0,0,0,0,0,0,0,1,0,0,1,1,7,0,1,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10763,2,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10764,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10765,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10766,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_2
+10767,1,0,0,1,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,3,1,0,1,0,0,0,8,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10768,0,0,0,0,0,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,9,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,Class_2
+10769,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10782,0,0,0,0,0,3,0,0,9,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10783,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10794,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+10805,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,Class_2
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+10812,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+10814,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,6,0,0,0,0,0,Class_2
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+10816,0,0,0,0,0,0,1,1,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,Class_2
+10817,0,0,0,0,0,0,0,0,0,0,0,0,3,9,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,9,0,0,0,0,0,Class_2
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+10819,4,0,0,0,0,0,0,0,1,0,0,0,1,8,2,0,0,0,1,0,0,0,0,3,7,0,0,0,0,0,0,0,1,1,0,0,1,0,1,10,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+10820,0,0,0,0,0,0,0,0,0,0,2,0,0,7,1,2,0,0,0,0,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,14,0,1,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+10821,0,0,1,0,0,0,0,0,0,0,0,1,1,5,1,7,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,4,0,0,1,0,0,0,12,0,0,0,4,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+10822,0,0,0,0,0,0,0,1,0,0,0,0,0,13,0,8,0,2,0,0,0,2,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10823,1,0,0,0,0,0,0,0,0,2,1,0,0,4,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,Class_2
+10824,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,5,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,1,0,1,0,9,0,1,3,1,0,0,0,0,1,0,0,2,2,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,1,0,Class_2
+10825,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,13,0,0,8,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10826,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,1,0,0,0,1,0,1,6,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,Class_2
+10827,0,0,0,0,0,0,0,0,0,0,1,0,0,23,0,0,0,0,0,0,0,0,0,1,9,1,0,0,0,0,0,0,4,0,0,1,1,0,0,3,0,1,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+10828,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,Class_2
+10829,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_2
+10830,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10831,4,0,0,0,0,0,0,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,5,0,0,1,0,0,0,0,7,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10832,0,0,0,0,0,0,1,0,0,0,0,0,0,1,13,3,0,2,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,Class_2
+10833,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,1,0,0,0,0,9,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,8,0,0,0,3,0,Class_2
+10834,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+10835,0,0,0,0,1,0,0,0,10,0,1,0,1,0,3,2,0,1,0,0,0,1,0,8,1,0,0,0,1,0,0,10,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,2,3,6,0,0,3,0,0,0,1,1,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10836,1,1,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,16,0,3,7,0,0,0,1,1,2,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+10837,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,4,10,0,1,0,0,0,0,0,1,0,0,0,0,1,0,8,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_2
+10838,4,0,0,0,0,0,0,0,0,0,0,0,0,20,2,5,0,0,0,1,0,2,0,0,7,0,0,0,0,0,0,0,4,0,0,0,0,0,0,29,1,1,0,0,0,0,0,17,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,1,0,0,6,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+10839,0,0,0,1,0,0,0,0,0,1,0,1,0,2,20,12,0,0,0,0,0,6,0,3,2,0,0,0,0,0,0,1,7,0,0,1,0,0,0,7,1,0,0,1,0,0,0,6,0,0,0,0,1,7,1,0,0,0,0,0,0,7,0,0,1,0,3,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,2,0,0,1,1,Class_2
+10840,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,6,0,0,0,0,0,Class_2
+10841,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_2
+10842,0,0,1,0,0,0,0,0,0,0,0,0,0,0,20,7,0,0,0,0,2,5,0,5,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,7,1,0,0,0,1,0,0,0,0,0,0,1,3,0,0,1,0,10,0,2,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+10843,0,0,0,1,0,0,0,2,1,0,0,1,0,3,5,3,0,1,0,0,0,1,0,1,7,0,2,1,0,0,0,0,9,1,0,1,0,0,0,8,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+10844,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_2
+10845,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,1,1,5,0,0,1,0,0,0,0,5,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,2,0,0,1,0,0,0,17,0,0,4,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,5,0,0,0,0,1,Class_2
+10846,0,0,1,1,0,0,0,0,1,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,2,0,0,0,2,0,9,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,1,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,Class_2
+10847,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,1,0,3,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,33,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,19,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,Class_2
+10848,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,9,0,1,0,0,2,1,0,0,0,1,0,0,0,0,0,0,5,2,0,0,1,1,0,3,1,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,5,1,4,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,Class_2
+10849,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,1,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,22,0,0,0,0,0,Class_2
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+10911,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10912,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,6,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+10917,1,0,0,1,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,8,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+10918,1,0,0,0,0,0,0,0,0,0,0,0,0,11,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+10919,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,3,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,4,0,0,0,0,0,Class_2
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+10922,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+10923,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
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+10925,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,17,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+10926,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,3,0,0,0,1,1,1,0,3,0,0,0,0,0,0,0,1,3,1,0,0,1,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,9,0,0,0,0,4,0,1,4,0,12,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,1,0,Class_2
+10927,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,8,0,0,0,0,0,Class_2
+10928,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,Class_2
+10929,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+10943,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,Class_2
+10944,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,1,0,0,0,0,0,8,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+10945,0,0,0,0,0,0,0,0,0,0,0,1,0,6,2,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,2,0,1,0,13,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,1,0,1,0,3,0,0,0,1,1,0,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10946,7,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10947,7,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,2,1,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,3,0,3,0,1,0,0,0,Class_2
+10948,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,0,1,0,0,1,0,Class_2
+10949,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,5,0,1,0,0,0,3,0,4,2,0,0,0,0,0,0,1,0,1,0,1,1,1,0,3,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,3,0,0,2,3,0,0,2,1,5,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,Class_2
+10950,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
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+10962,1,0,0,0,0,0,0,0,0,0,0,0,0,5,3,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,1,0,1,0,1,0,0,1,0,2,0,0,6,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,10,4,0,0,2,0,0,0,0,Class_2
+10963,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_2
+10964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10965,1,0,0,0,0,0,0,0,22,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+10966,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+10967,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,3,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+10968,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+10969,2,0,0,0,0,0,0,0,16,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+10994,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,7,1,0,1,3,0,1,0,0,0,0,0,0,1,0,11,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+11000,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11001,0,0,0,0,0,0,0,0,0,6,0,0,0,2,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,5,0,0,0,0,2,1,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,Class_2
+11002,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+11004,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+11005,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,Class_2
+11006,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11007,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11008,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,0,0,0,0,Class_2
+11009,0,0,0,0,0,0,0,0,0,1,0,0,0,0,20,4,0,1,0,1,2,1,0,0,1,0,0,1,0,0,0,0,3,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,2,0,3,2,0,0,0,0,4,0,10,2,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,2,0,0,0,0,Class_2
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+11028,0,0,0,0,0,0,0,0,11,0,0,0,0,2,3,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,13,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+11029,0,0,0,0,0,0,0,0,23,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,6,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,Class_2
+11030,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+11031,0,0,0,0,0,0,0,0,1,0,1,0,0,9,2,9,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,5,3,1,0,0,0,0,8,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
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+11033,0,0,0,0,0,0,0,1,0,0,0,1,0,11,1,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,13,1,1,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,Class_2
+11034,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11035,2,0,0,0,0,0,0,0,0,4,0,0,1,1,18,4,0,0,0,0,0,5,0,3,1,0,1,0,0,0,0,0,2,0,1,0,0,1,1,1,0,0,0,3,0,1,0,4,2,0,0,0,0,4,1,3,0,0,0,0,0,4,0,1,0,0,4,0,0,3,0,14,2,1,1,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,0,0,0,Class_2
+11036,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,4,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+11037,0,0,0,0,0,0,0,0,9,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,3,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11038,0,0,0,0,0,0,0,0,0,4,0,0,0,5,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,1,10,0,0,0,0,0,Class_2
+11039,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,1,0,0,0,0,1,0,0,2,5,0,1,0,0,0,0,0,3,2,0,0,1,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+11040,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+11041,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,2,10,0,0,0,0,0,0,0,6,0,0,0,0,0,0,11,0,0,1,0,0,0,0,7,0,0,0,0,4,2,0,0,0,0,0,0,0,1,0,4,0,0,7,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,Class_2
+11042,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11043,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11044,0,0,0,0,0,0,0,0,1,0,0,0,0,13,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11045,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+11046,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+11047,0,0,0,0,0,0,0,0,2,0,0,0,0,2,17,7,0,1,0,0,3,0,0,2,0,0,0,0,0,0,0,0,2,0,1,1,1,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,2,0,6,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_2
+11048,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+11049,0,0,4,1,0,0,0,0,0,0,0,0,0,8,14,2,0,0,0,0,0,2,0,2,2,1,1,0,1,0,0,0,3,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,2,0,3,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,1,0,0,0,1,0,Class_2
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+11422,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_2
+11423,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,2,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,Class_2
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+11426,0,0,0,0,0,0,0,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,42,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,Class_2
+11427,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,Class_2
+11428,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+11429,3,0,0,0,0,0,0,2,1,0,0,0,0,0,0,4,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,1,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,Class_2
+11430,1,0,0,1,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,3,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,Class_2
+11431,0,0,0,0,0,0,0,0,0,0,0,1,1,0,8,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+11432,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,2,0,2,0,1,1,1,0,0,0,7,1,0,0,0,1,1,3,0,0,0,1,0,0,0,0,3,1,3,2,0,0,2,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,11,1,10,2,0,0,0,0,Class_2
+11433,0,0,0,0,0,0,0,2,0,0,0,0,0,5,1,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+11434,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+11435,2,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+11436,0,0,1,1,0,0,0,0,0,0,0,1,0,1,14,2,0,0,0,0,0,6,0,3,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,2,1,0,4,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_2
+11437,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+11438,0,0,0,1,0,0,0,9,3,0,1,1,1,1,4,4,2,5,0,0,0,1,0,8,3,0,0,0,0,0,0,16,15,2,0,0,0,1,0,6,0,2,3,0,0,0,0,6,1,1,0,0,1,2,1,1,0,0,0,0,0,5,0,0,0,1,26,0,0,3,1,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,3,0,0,1,1,0,Class_2
+11439,0,0,0,0,0,0,0,1,2,0,0,0,0,3,2,2,0,0,0,1,0,0,0,4,4,0,0,0,0,0,0,1,13,0,0,1,0,0,0,9,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11440,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,6,0,2,0,0,0,0,3,2,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,2,0,17,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,3,0,0,1,0,Class_2
+11441,2,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,Class_2
+11442,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,1,1,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,22,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,3,0,0,0,2,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_2
+11443,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_2
+11444,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,Class_2
+11445,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11446,0,0,0,1,0,0,0,0,0,0,0,0,0,1,20,7,0,2,0,1,0,1,0,1,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,7,0,0,1,0,1,0,0,1,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,Class_2
+11447,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+11448,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11449,0,0,0,0,0,0,0,0,0,0,0,0,0,1,15,5,0,1,0,0,1,2,0,2,2,0,0,0,0,0,0,2,0,1,1,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,2,0,0,1,1,3,0,0,4,0,22,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
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+11632,0,0,0,0,0,0,0,0,1,0,0,0,1,1,21,3,0,1,0,1,0,1,0,2,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,4,1,0,0,1,0,0,0,8,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,0,0,3,1,0,2,0,14,0,0,2,1,0,0,0,0,0,1,0,0,2,1,2,3,0,0,0,0,0,Class_2
+11633,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
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+11635,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,2,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+11640,7,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+11700,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+11708,0,0,0,1,0,0,0,0,0,0,0,0,0,0,26,10,0,0,0,1,0,1,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,1,1,6,1,0,0,3,0,0,0,6,1,4,0,0,0,9,0,0,0,0,0,0,0,3,0,1,0,0,3,0,0,1,1,20,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,3,0,0,0,0,0,Class_2
+11709,7,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,8,14,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,Class_2
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+11711,0,0,1,0,0,0,0,0,0,0,1,0,0,7,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,2,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,2,0,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
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+11713,1,0,1,0,1,0,0,0,0,0,0,0,0,1,14,9,0,0,0,0,0,2,0,2,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,3,0,0,2,7,0,1,0,0,0,0,0,3,0,0,1,0,1,0,0,4,0,7,0,0,0,0,0,0,0,1,0,0,0,0,5,2,0,0,0,1,0,1,0,Class_2
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+11716,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,3,0,0,0,0,1,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11717,0,0,0,0,0,0,0,1,0,0,1,0,0,1,19,3,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,Class_2
+11718,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,6,0,0,0,1,1,2,3,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,16,0,0,8,0,0,1,1,0,2,2,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+11719,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+11721,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,3,0,0,0,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,6,0,1,0,0,0,0,0,Class_2
+11722,0,0,0,0,0,0,1,0,18,1,0,0,0,0,0,2,0,1,0,2,0,1,0,4,2,0,0,0,0,0,0,7,2,0,0,0,0,2,0,0,1,0,0,1,0,0,0,6,0,4,0,0,0,1,1,0,0,0,0,0,0,0,1,27,2,1,7,0,0,3,3,2,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,0,2,0,0,0,Class_2
+11723,1,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,2,0,0,0,0,1,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,2,0,2,0,Class_2
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+11725,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+11726,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,6,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11727,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11728,1,0,0,0,0,0,0,0,20,0,0,2,0,0,3,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,2,0,0,1,0,0,0,3,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7,1,8,0,0,6,0,0,2,0,1,0,0,0,0,2,0,0,0,6,0,0,0,0,1,0,2,1,0,0,0,0,Class_2
+11729,2,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+11782,0,0,0,0,0,0,0,0,1,0,0,0,2,19,0,0,1,0,0,0,0,1,0,1,6,1,0,0,0,0,0,0,1,0,1,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+11783,5,0,0,0,0,0,0,0,0,0,0,0,0,25,3,3,0,0,0,1,0,2,0,7,13,0,0,0,0,0,0,0,7,0,0,4,0,0,0,16,0,4,0,0,0,1,0,18,0,0,0,0,1,1,0,0,0,0,0,4,0,3,0,3,2,1,7,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,1,3,0,3,0,0,0,1,0,Class_2
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+11785,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+11789,2,0,0,0,0,0,0,0,0,0,1,1,0,11,0,1,0,0,0,0,0,0,0,1,12,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,13,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,Class_2
+11790,0,0,0,0,0,0,0,0,10,4,0,1,0,0,0,0,1,0,0,1,0,0,0,3,1,0,0,0,0,0,0,5,2,0,0,0,1,0,0,0,1,0,0,1,0,0,1,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,8,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,2,0,0,0,0,Class_2
+11791,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,Class_2
+11792,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,6,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11793,0,0,0,0,0,0,0,0,0,0,1,0,0,9,1,3,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,2,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
+11794,0,0,0,0,0,0,0,0,17,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11795,0,0,0,0,0,0,0,1,13,0,0,0,0,0,0,3,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,8,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,20,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+11796,0,0,2,3,0,0,0,0,0,0,0,1,0,1,0,2,0,2,0,0,0,0,0,2,3,1,2,0,0,0,0,0,0,1,1,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,1,1,0,0,0,1,0,2,0,0,2,0,0,0,0,Class_2
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+11798,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+11799,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,Class_2
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+11801,1,0,0,0,0,3,0,0,0,0,0,0,0,5,1,4,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,1,2,0,0,0,0,0,12,1,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,6,1,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+11802,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11803,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+11804,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+11805,0,0,0,0,0,0,0,0,0,0,0,1,0,0,22,0,0,0,0,1,1,2,0,4,3,0,1,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,1,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,6,1,0,0,0,12,1,0,1,0,0,0,0,0,0,0,0,0,4,0,2,0,1,0,0,2,0,Class_2
+11806,0,0,0,0,0,0,0,0,7,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+11807,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,2,0,0,0,0,1,1,0,0,15,0,0,0,0,0,0,2,0,0,0,0,0,0,0,18,0,1,1,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,Class_2
+11808,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_2
+11809,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,1,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,1,0,Class_2
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+11925,0,0,0,1,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+11926,0,0,0,0,0,0,0,0,1,0,0,0,0,1,16,10,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,1,1,0,4,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,2,0,4,0,1,1,4,1,1,1,3,0,11,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_2
+11927,0,0,0,1,0,0,0,2,0,0,0,1,0,4,0,7,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,2,3,2,0,0,0,0,12,0,1,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,1,0,Class_2
+11928,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+12045,0,0,0,2,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+12046,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,6,2,1,0,1,0,0,1,0,4,0,0,0,0,0,2,0,1,0,0,2,0,3,2,4,2,2,0,0,0,0,1,0,0,0,2,2,3,0,0,0,0,0,0,4,0,0,0,2,0,0,1,2,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,8,3,1,5,0,0,0,0,Class_2
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+12811,0,0,0,1,0,0,0,0,0,0,0,0,0,1,19,6,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,3,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,10,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_2
+12812,0,1,1,2,0,0,1,0,12,1,0,0,0,0,0,0,0,0,0,3,0,2,0,3,3,0,0,0,0,0,0,3,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,5,1,1,0,0,0,1,0,0,0,0,0,1,0,0,1,6,0,0,4,0,1,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,6,0,1,0,0,0,0,Class_2
+12813,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12814,0,0,0,0,0,0,0,0,0,0,1,1,0,16,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,1,0,1,0,2,0,1,0,0,0,0,0,9,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+12815,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+12816,0,0,0,1,0,0,0,1,9,0,0,0,0,0,0,0,1,1,0,1,0,0,0,3,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,8,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12817,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,Class_2
+12818,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+12819,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12820,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+12821,0,0,0,0,1,0,0,0,0,0,0,0,0,0,22,5,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,3,0,5,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,1,0,0,0,0,Class_2
+12822,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,7,0,0,0,0,0,1,0,0,1,3,0,1,0,0,0,0,2,0,1,1,0,0,1,15,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12823,0,0,0,0,0,0,0,0,0,0,0,1,0,0,16,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+12824,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+12825,0,1,2,0,0,0,0,0,0,0,0,0,0,2,6,5,0,0,0,0,4,1,0,1,2,0,2,0,0,0,0,0,0,0,1,2,0,1,0,1,0,0,13,0,0,4,0,0,0,1,0,0,0,3,0,0,1,0,0,1,1,1,0,1,1,0,0,0,0,4,1,1,0,1,0,0,1,0,0,5,0,1,0,0,11,4,0,0,0,0,0,0,0,Class_2
+12826,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+12827,0,0,0,0,0,0,0,2,7,0,0,0,0,3,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,4,0,0,2,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,13,1,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+12828,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,3,2,0,0,0,0,0,4,0,1,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,3,0,Class_2
+12829,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,Class_2
+12830,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
+12831,0,0,0,0,0,0,0,0,18,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,1,0,0,0,0,0,0,0,0,1,0,6,0,0,4,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12832,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+12833,0,0,0,1,0,0,0,0,0,1,0,0,0,0,6,1,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,3,1,0,3,2,0,0,0,1,13,2,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,4,0,1,0,0,0,0,0,0,0,0,0,0,27,3,0,0,1,0,0,0,0,Class_2
+12834,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+12835,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+12836,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_2
+12837,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,Class_2
+12838,0,0,0,2,0,0,0,0,0,0,0,0,0,3,19,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,7,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,0,4,0,0,4,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+12839,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,2,0,1,0,1,0,2,0,4,0,0,0,1,0,0,0,0,4,1,0,0,0,1,0,1,2,0,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,2,0,0,1,1,0,1,0,7,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,Class_2
+12840,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,Class_2
+12841,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+12842,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,7,0,0,0,0,2,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,1,1,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,3,6,0,1,0,0,0,Class_2
+12843,0,0,1,0,0,0,1,0,0,0,0,0,0,6,0,2,0,0,0,0,0,1,0,2,4,0,0,0,0,0,0,2,3,1,0,3,0,0,0,20,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+12844,1,0,0,0,0,0,0,0,0,0,0,1,0,5,9,3,0,0,0,1,0,1,1,1,3,0,0,0,4,0,0,1,1,0,0,0,0,1,0,4,1,1,1,0,0,1,0,0,0,0,0,1,0,7,1,0,0,0,0,0,0,3,0,3,1,0,1,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_2
+12845,0,0,0,0,0,0,0,0,0,0,0,0,0,18,1,0,0,0,0,1,0,1,0,1,12,0,0,1,0,0,0,0,5,0,0,0,0,0,0,15,1,2,0,0,0,0,0,16,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,4,0,0,6,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,Class_2
+12846,6,0,0,0,0,0,0,1,0,0,2,0,0,2,0,4,0,0,0,0,0,0,0,2,12,0,0,0,0,0,2,0,0,1,0,1,1,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,4,1,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+12847,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,2,0,0,0,0,0,0,0,2,3,0,0,0,1,0,0,0,3,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,Class_2
+12848,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+12849,0,0,0,0,0,0,0,0,13,0,0,0,1,1,0,0,0,0,0,0,0,2,0,5,1,0,3,0,0,0,0,5,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,Class_2
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+12907,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,2,0,1,0,0,0,0,18,0,0,0,0,0,0,0,2,2,0,0,0,0,0,3,0,2,1,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+12908,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+12909,0,0,0,0,0,0,0,0,2,0,0,0,0,10,4,2,0,0,0,0,0,0,0,8,19,0,0,0,0,0,0,0,1,1,0,0,0,0,0,13,0,0,1,0,0,1,0,7,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,2,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+12910,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+12911,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,7,0,6,0,0,0,0,0,0,1,1,0,0,0,0,4,0,1,0,0,0,0,0,0,Class_2
+12912,0,0,0,1,0,0,0,0,2,0,0,0,0,21,1,2,0,0,0,1,0,0,0,0,13,0,0,0,0,0,0,0,3,0,0,0,0,0,0,20,0,0,1,0,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,2,4,0,1,0,0,0,0,0,Class_2
+12913,0,0,0,0,0,0,0,1,0,0,0,0,0,0,16,3,0,1,0,0,0,7,0,2,2,0,0,0,0,0,0,0,3,1,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,1,0,3,1,0,0,3,5,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,Class_2
+12914,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,4,0,1,1,0,0,0,0,0,13,0,0,0,0,0,0,0,6,1,0,0,0,0,0,12,0,0,1,0,0,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,2,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,Class_2
+12915,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+12916,1,0,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_2
+12917,0,0,0,0,0,0,0,0,0,0,1,0,0,0,24,6,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,4,0,1,0,0,2,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_2
+12918,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12919,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+12920,0,0,0,0,0,0,0,1,0,0,0,1,0,5,20,3,0,1,0,0,0,2,0,2,0,0,0,0,0,1,0,0,2,1,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,22,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_2
+12921,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+12922,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+12923,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,1,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,1,0,0,0,0,0,17,4,0,1,0,0,0,0,0,Class_2
+12924,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+12925,0,0,1,0,0,0,0,0,0,0,0,2,1,1,22,6,0,2,0,0,0,1,0,3,1,0,0,0,0,0,0,0,10,0,0,1,0,0,0,2,0,0,0,1,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,0,0,0,4,1,1,3,0,14,0,0,2,0,1,0,0,0,0,0,0,0,4,2,1,2,2,0,0,0,0,Class_2
+12926,0,0,1,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,9,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,8,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+12927,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+12928,0,0,0,0,0,0,0,1,0,0,0,1,0,10,3,1,0,0,0,1,0,1,1,2,8,0,0,0,0,0,0,0,1,1,0,1,0,0,0,2,1,1,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,8,0,14,0,0,1,0,0,Class_2
+12929,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
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+13024,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,7,0,3,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,Class_2
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+13027,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13028,2,0,0,0,0,0,0,1,0,0,2,1,0,7,0,4,0,0,0,0,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,1,1,0,0,8,0,0,1,1,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,1,0,1,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_2
+13029,0,0,0,0,1,0,0,0,0,0,0,0,0,15,0,2,1,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_2
+13030,0,0,0,0,0,0,0,3,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,7,2,2,0,2,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,2,0,0,0,2,0,Class_2
+13031,0,0,0,0,0,0,1,0,0,0,0,0,0,0,15,6,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,5,0,2,0,3,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,5,0,1,0,3,0,0,0,1,0,19,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+13032,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,13,0,0,10,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,Class_2
+13033,0,0,0,0,0,0,0,1,0,3,1,0,0,4,0,4,0,0,0,1,0,1,0,4,3,0,0,0,0,0,1,1,0,1,1,0,4,3,2,3,0,2,16,1,0,0,0,2,0,3,0,0,0,0,0,0,0,0,1,0,0,3,0,3,0,0,2,0,0,8,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,2,0,10,0,0,0,0,0,Class_2
+13034,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13035,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,Class_2
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+13037,1,0,0,1,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_2
+13038,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13039,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+13040,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_2
+13041,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_2
+13042,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13043,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13044,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,1,14,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13045,0,0,0,0,0,0,0,0,0,1,0,1,0,7,1,2,1,0,0,4,0,1,0,4,5,0,0,0,0,0,0,1,0,0,1,0,1,4,0,5,0,1,8,3,0,0,0,1,0,2,0,0,0,2,2,1,0,1,0,0,0,0,0,2,1,0,0,0,1,16,0,0,0,0,0,4,0,0,2,0,1,0,0,0,1,16,0,15,1,0,0,0,0,Class_2
+13046,2,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13047,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,9,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_2
+13048,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,8,0,0,0,0,1,3,0,0,6,0,0,0,0,0,0,0,1,2,0,0,0,0,0,11,0,1,0,1,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,2,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,Class_2
+13049,0,0,0,0,0,1,0,0,0,0,0,0,2,13,1,2,0,3,0,0,1,2,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,11,0,1,0,0,0,0,0,5,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
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+13079,0,0,1,0,0,0,0,0,1,0,0,1,0,1,2,6,0,0,0,0,1,3,0,0,1,0,0,0,1,0,0,0,6,0,0,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,Class_2
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+13081,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_2
+13082,1,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,1,0,2,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
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+13084,0,0,0,0,0,0,0,0,0,0,0,0,2,17,1,2,0,3,0,0,1,2,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,14,0,0,0,0,0,0,0,6,1,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,Class_2
+13085,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13086,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13087,2,0,0,1,0,0,0,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,1,0,1,1,0,0,1,0,0,0,1,0,0,0,2,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,4,1,1,0,0,0,0,0,Class_2
+13088,0,0,0,1,0,0,1,0,19,0,0,0,0,0,0,0,0,0,0,2,0,0,0,6,2,0,0,0,0,0,0,10,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,3,0,1,0,0,1,0,1,0,0,1,1,0,0,0,0,12,0,0,8,0,0,1,3,1,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_2
+13089,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,17,2,0,1,0,0,0,0,0,Class_2
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+13425,0,0,0,0,0,0,0,0,17,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13426,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,10,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,3,0,0,0,Class_2
+13427,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,2,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13428,0,0,0,0,0,0,0,0,0,0,0,3,1,4,1,3,0,0,0,1,0,3,0,0,3,0,0,0,0,0,0,0,4,2,0,0,0,0,0,20,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,Class_2
+13429,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_2
+13430,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+13431,0,0,0,0,0,0,0,0,0,0,0,1,0,2,18,4,0,0,0,0,2,1,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,10,0,1,0,0,2,0,0,4,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13432,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13433,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+13434,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13435,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+13436,0,0,0,0,0,0,0,1,0,0,0,0,0,6,1,2,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13437,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,Class_2
+13438,1,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_2
+13439,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+13440,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13441,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,Class_2
+13442,0,0,0,0,0,0,0,0,0,0,0,0,0,3,22,13,0,2,0,1,2,4,0,2,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,2,0,0,1,0,0,0,0,6,0,0,0,0,0,3,0,0,0,0,1,1,0,1,0,0,1,0,5,0,0,3,0,16,0,0,0,0,0,0,2,0,0,0,0,0,2,4,0,2,0,0,0,1,0,Class_2
+13443,0,0,0,0,0,0,0,0,0,0,1,0,1,4,1,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13444,1,0,0,0,0,0,0,0,0,0,1,0,0,4,1,1,0,1,0,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13445,0,0,1,0,0,0,0,1,3,0,0,0,2,0,23,5,0,1,0,1,4,3,0,3,1,1,0,0,0,0,0,0,2,0,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,1,0,2,0,0,1,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+13446,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13447,2,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,6,0,0,0,0,0,1,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,13,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13448,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,1,Class_2
+13449,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+13465,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
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+13467,0,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+13468,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,0,1,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13469,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+13470,0,0,0,0,0,0,1,0,0,0,1,0,0,1,9,2,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,5,0,2,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,3,0,0,8,0,0,2,1,12,0,0,0,2,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,Class_2
+13471,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+13472,2,0,0,2,0,0,0,0,0,0,0,1,1,12,0,2,0,0,0,0,0,0,0,5,11,0,0,0,0,1,0,1,3,0,1,1,0,0,0,7,0,1,0,0,0,0,0,10,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,3,0,0,3,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,Class_2
+13473,0,1,1,0,1,0,0,0,0,0,0,1,0,0,18,5,0,0,0,1,0,2,0,1,2,2,0,0,0,0,0,0,3,0,1,0,0,1,0,4,0,0,0,2,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,10,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,Class_2
+13474,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+13475,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+13476,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+13477,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,Class_2
+13478,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13479,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+13480,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+13481,0,0,1,0,0,0,0,0,16,0,1,0,0,1,0,1,0,0,2,3,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,6,0,1,0,0,1,2,0,0,0,0,0,1,0,0,0,18,1,0,7,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,1,7,1,0,0,0,0,Class_2
+13482,0,0,0,0,0,0,0,0,0,0,0,0,2,12,2,2,0,3,0,0,1,3,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,10,1,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,7,1,0,0,0,0,Class_2
+13483,0,0,1,1,0,0,1,0,0,0,0,0,0,1,11,3,0,0,0,1,0,2,0,0,1,0,3,0,0,0,0,0,4,0,0,0,1,1,0,5,0,1,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,4,0,11,0,0,1,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,Class_2
+13484,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+13485,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+13486,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,4,0,0,0,2,0,1,0,1,1,0,0,0,1,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,1,1,1,1,0,0,1,0,19,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,Class_2
+13487,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13488,0,0,0,0,0,0,0,0,0,0,0,0,2,14,1,2,0,3,0,0,1,2,0,5,9,0,0,0,0,0,0,0,2,0,0,0,0,0,0,11,0,0,0,0,0,0,0,8,1,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_2
+13489,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+13501,0,0,0,0,0,0,0,0,12,0,3,0,0,0,3,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+13505,2,0,0,0,0,0,0,0,16,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,4,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+13507,0,0,0,0,0,0,0,1,0,0,0,0,0,5,2,6,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,1,0,0,0,0,7,1,1,1,1,0,0,0,0,0,0,0,2,2,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,2,0,0,1,0,Class_2
+13508,0,0,0,0,0,0,0,1,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,0,0,0,39,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13509,2,0,0,0,0,0,0,0,0,0,0,0,0,13,3,8,0,1,0,1,0,2,0,1,7,0,0,0,1,0,0,0,4,0,0,1,0,0,0,8,0,2,0,2,0,0,0,16,0,0,0,0,0,2,0,0,1,0,3,0,0,0,0,1,0,0,5,1,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,6,0,15,0,0,0,0,0,Class_2
+13510,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13511,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,4,1,0,0,0,0,Class_2
+13512,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+13513,0,0,0,1,0,0,0,0,0,1,0,1,0,8,0,2,0,2,0,0,0,2,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,1,0,22,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,Class_2
+13514,0,0,0,0,0,0,0,0,20,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,18,0,0,7,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,Class_2
+13515,0,1,0,1,0,0,0,0,0,0,0,0,0,2,15,8,3,0,0,1,0,0,0,3,2,0,0,1,0,0,0,0,2,1,1,1,0,1,0,4,0,0,0,0,0,0,0,6,0,0,0,0,0,3,0,1,0,0,3,2,0,5,0,2,0,1,4,0,0,6,0,30,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,1,0,Class_2
+13516,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0,11,1,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13517,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13518,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,1,1,0,4,0,0,0,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,1,0,2,0,Class_2
+13519,1,0,0,0,0,0,0,0,0,0,0,0,0,1,23,6,0,1,0,0,3,1,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,1,3,0,1,0,0,1,1,16,1,0,0,0,0,0,0,0,0,0,1,0,2,0,1,1,3,0,0,2,0,Class_2
+13520,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13521,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13522,0,0,0,0,0,0,0,0,0,0,0,1,0,1,14,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,2,0,0,0,2,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,5,1,7,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,1,0,0,0,0,0,Class_2
+13523,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,4,1,0,0,0,0,0,2,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,15,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,2,0,0,0,0,0,Class_2
+13524,0,0,0,0,0,0,0,0,2,0,0,0,0,2,21,3,1,1,0,0,2,1,0,2,0,0,0,0,0,0,0,2,6,1,1,0,0,1,0,2,0,0,1,1,2,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,18,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,Class_2
+13525,0,0,0,0,0,0,0,0,0,0,0,1,0,4,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,17,0,0,0,0,0,Class_2
+13526,0,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,11,0,0,4,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13527,0,0,0,0,0,0,0,1,1,0,0,0,0,12,0,2,0,0,0,0,2,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,18,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,1,0,Class_2
+13528,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+13529,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,6,0,0,0,0,0,0,0,0,2,6,0,0,0,0,1,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,Class_2
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+13540,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,9,0,1,1,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,Class_2
+13541,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,8,1,8,0,0,0,0,0,Class_2
+13542,1,0,0,2,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,1,0,0,0,0,0,0,4,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,1,Class_2
+13543,0,0,0,0,0,0,0,0,21,0,1,0,0,3,2,2,0,0,0,0,0,0,0,9,1,0,0,0,1,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,7,0,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13544,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13545,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13546,0,0,0,0,0,0,0,0,16,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,11,0,0,4,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13547,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13548,0,0,1,0,0,0,1,0,1,0,1,0,1,3,1,5,0,1,0,0,0,2,0,0,4,0,1,0,0,0,0,0,2,0,0,0,0,2,1,14,0,3,1,0,0,0,0,5,0,0,0,2,1,8,0,0,0,1,0,1,0,4,1,1,0,0,1,0,0,0,0,2,0,4,0,1,0,0,0,7,0,0,0,0,0,1,0,1,6,0,0,0,0,Class_2
+13549,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,5,0,0,0,0,0,0,0,2,0,0,0,0,1,0,6,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,Class_2
+13550,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+13551,0,0,0,0,0,0,0,0,17,0,0,0,0,0,2,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,5,0,9,0,0,8,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13552,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13553,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13554,0,0,0,0,0,0,0,2,0,0,0,0,1,11,1,2,0,0,0,1,0,1,0,0,8,0,0,0,0,0,0,0,3,1,0,0,0,0,0,7,1,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,2,0,Class_2
+13555,2,0,0,0,0,0,0,0,1,0,2,0,0,3,0,2,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,2,0,0,4,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+13556,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+13557,0,0,0,0,0,0,0,3,4,0,0,0,1,0,2,6,0,3,0,0,2,0,0,2,1,0,0,0,1,0,0,4,4,0,0,0,0,0,0,3,0,0,3,0,0,0,0,4,1,0,0,0,1,4,0,0,0,0,0,0,0,6,0,14,1,1,11,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,2,4,0,0,0,0,Class_2
+13558,0,0,0,0,0,0,0,0,16,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,9,0,0,4,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+13559,7,0,1,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,1,0,0,1,4,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13560,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13561,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13562,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13563,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13564,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,Class_2
+13565,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,Class_2
+13566,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13567,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,Class_2
+13568,1,0,0,0,0,0,0,4,0,0,0,0,0,10,1,4,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,1,2,0,0,0,0,11,0,0,2,7,0,0,0,1,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,Class_2
+13569,0,0,0,0,0,0,0,0,14,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,6,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+13631,1,0,0,0,0,0,0,2,0,0,0,0,0,4,0,3,0,0,0,0,0,0,1,2,4,0,0,0,0,0,0,0,0,0,0,2,4,1,0,6,0,2,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_2
+13632,0,0,0,0,0,0,0,0,0,0,2,0,2,16,1,2,0,3,0,0,1,2,1,3,9,0,0,0,0,0,1,0,1,0,0,0,0,1,0,14,0,1,0,0,0,0,0,2,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,6,0,0,0,2,0,Class_2
+13633,2,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,0,Class_2
+13634,0,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13635,0,0,0,0,0,0,0,0,0,0,0,0,2,12,1,2,0,3,0,0,1,2,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+13637,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+13638,0,0,1,0,0,0,0,0,0,0,1,0,0,2,4,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,2,0,11,0,1,5,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,1,Class_2
+13639,0,0,1,1,0,0,0,5,4,0,0,0,1,4,5,3,0,3,0,1,0,2,0,9,1,1,0,0,0,0,0,13,4,2,0,0,0,5,0,1,0,0,3,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,17,0,7,26,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_2
+13640,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,10,0,0,0,0,0,Class_2
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+13645,0,2,0,1,0,0,0,0,1,0,0,0,1,3,21,7,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,4,0,1,0,2,0,0,0,1,0,0,0,0,0,4,1,0,0,0,1,1,0,3,0,0,0,2,2,0,0,2,0,8,0,1,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,Class_2
+13646,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,1,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13647,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,Class_2
+13648,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,10,0,11,0,0,0,0,0,Class_2
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+13700,0,0,0,1,0,0,0,0,0,1,0,0,0,2,5,1,0,0,0,1,0,2,0,2,1,1,0,0,1,0,0,0,2,0,1,1,0,0,0,4,1,0,6,1,2,0,0,0,0,2,0,0,1,3,0,0,0,0,0,1,0,2,0,1,1,0,0,0,0,6,0,6,0,0,0,0,0,0,0,1,0,0,0,0,18,5,1,0,2,0,0,0,0,Class_2
+13701,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_2
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+13705,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,12,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13706,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_2
+13707,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+13708,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,1,1,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,2,0,1,0,0,0,0,0,0,0,0,0,0,15,1,0,2,0,0,0,0,0,Class_2
+13709,0,0,0,0,0,0,0,0,0,0,0,0,0,7,6,2,0,0,0,0,2,0,0,0,7,0,0,0,0,0,0,0,1,1,0,0,0,0,0,8,0,1,1,0,0,0,0,4,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,7,0,6,0,0,0,0,0,Class_2
+13710,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+13711,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+13712,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,6,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,6,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13713,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13714,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,0,0,0,0,0,0,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13715,0,0,0,0,0,0,0,0,0,4,0,0,2,4,19,1,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,1,0,1,1,1,0,0,0,0,0,6,0,1,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,3,0,13,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,Class_2
+13716,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,0,0,0,0,0,0,0,15,1,0,0,0,1,0,0,0,Class_2
+13717,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
+13718,0,0,0,0,0,0,0,0,1,3,0,0,0,6,0,3,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,7,1,1,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,Class_2
+13719,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,1,0,0,2,4,0,0,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13720,0,0,0,0,0,0,0,0,1,3,0,0,1,0,2,1,0,0,1,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,1,0,0,8,0,2,0,1,0,0,0,0,1,0,0,0,0,0,26,9,0,0,0,0,0,0,0,Class_2
+13721,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13722,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,2,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,1,1,1,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,Class_2
+13723,0,0,0,0,0,0,0,0,0,0,1,0,0,9,6,4,0,0,0,1,0,1,0,2,12,0,0,0,0,0,0,2,2,0,0,1,0,0,0,10,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,0,2,0,1,0,0,0,0,9,0,1,6,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,10,2,16,0,0,0,0,0,Class_2
+13724,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,10,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13725,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,2,0,0,0,0,1,1,0,0,17,0,0,0,0,0,0,0,1,0,1,0,0,0,0,27,0,0,1,0,0,0,0,9,0,0,0,0,1,2,0,0,0,0,2,0,0,1,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_2
+13726,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,Class_2
+13727,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13728,0,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+13729,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,3,1,0,3,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,15,0,0,11,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
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+13739,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+13740,0,0,0,0,0,0,0,0,0,1,0,1,1,5,22,11,0,1,0,0,1,1,0,2,1,0,0,0,0,0,0,0,5,0,2,0,0,0,0,6,2,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,5,0,10,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+13741,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,0,0,0,0,1,0,1,1,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_2
+13742,0,0,0,0,0,0,0,1,0,0,1,1,0,11,0,0,0,0,0,0,0,0,0,2,10,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,10,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13743,0,0,1,0,0,0,0,0,3,0,0,0,0,3,3,0,0,0,0,0,2,0,0,3,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,1,0,0,0,0,0,0,6,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,22,1,0,11,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,1,0,0,0,Class_2
+13744,0,0,0,3,0,0,0,0,0,0,0,0,0,0,20,4,0,0,1,1,0,3,0,2,3,1,0,0,0,0,0,0,3,0,0,0,2,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,2,1,8,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_2
+13745,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+13746,0,0,0,0,0,0,0,0,15,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,6,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+13747,0,0,0,0,0,0,0,0,0,0,0,0,1,2,5,1,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,12,2,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+13748,2,0,0,4,0,1,0,0,2,0,1,0,0,4,1,9,1,5,0,3,0,16,0,2,1,1,0,0,1,0,0,6,0,0,3,4,0,3,0,1,0,0,0,0,0,0,4,3,1,2,0,0,0,2,2,0,1,0,0,1,0,8,6,11,0,19,7,1,1,2,0,3,0,1,0,2,0,1,0,4,0,0,0,0,2,0,15,9,8,1,0,1,2,Class_2
+13749,1,0,0,0,0,0,0,1,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+13750,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13751,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,1,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,Class_2
+13752,0,0,0,0,0,0,0,0,1,0,0,0,0,1,21,8,0,0,0,0,0,2,2,2,3,0,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,6,0,0,0,0,0,1,0,5,0,2,0,1,1,0,0,3,0,10,0,0,1,0,0,0,1,0,1,0,0,0,4,0,1,0,0,0,0,1,0,Class_2
+13753,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13754,0,0,0,2,1,0,1,0,0,0,0,0,0,7,0,7,0,1,0,1,0,2,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,12,0,1,2,0,0,0,0,0,1,0,0,0,0,3,0,2,1,0,0,0,0,1,0,3,0,2,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,1,0,Class_2
+13755,0,0,1,0,0,0,0,0,0,0,0,0,0,5,1,6,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,1,0,1,2,1,24,0,0,0,0,0,0,1,1,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_2
+13756,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+13757,0,0,0,0,0,0,0,0,0,0,1,0,0,19,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,2,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+13758,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13759,0,0,0,0,0,0,0,0,2,0,0,0,0,11,1,3,0,1,0,0,0,1,0,0,6,0,0,0,0,0,0,0,2,0,0,1,0,0,3,9,0,1,3,1,0,0,0,5,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,5,0,9,0,0,0,0,0,Class_2
+13760,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+13761,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13762,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,9,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,14,1,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,1,Class_2
+13763,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+13764,0,1,1,1,0,0,0,0,2,0,0,0,0,0,21,6,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,8,0,0,0,0,0,0,0,3,0,1,1,0,0,2,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+13765,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,1,0,2,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,Class_2
+13766,0,0,0,0,0,0,0,2,0,0,1,1,0,5,0,6,0,0,0,0,0,1,0,0,7,0,0,0,0,0,1,0,4,1,0,2,0,2,0,10,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13767,0,0,0,0,0,0,1,0,0,0,0,0,0,2,6,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,5,1,0,1,0,0,1,1,4,0,0,1,1,2,0,0,1,0,1,0,0,0,2,0,1,2,0,0,0,0,3,0,1,1,0,1,0,1,3,1,5,0,3,1,1,1,0,0,0,0,0,0,0,6,10,0,0,1,0,0,0,1,Class_2
+13768,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13769,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_2
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+13788,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_2
+13789,0,0,0,0,0,0,0,3,2,0,0,1,1,12,1,3,0,1,0,0,2,1,0,0,13,0,2,0,0,0,0,0,3,0,0,0,0,0,0,20,0,1,1,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,7,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,0,0,Class_2
+13790,0,0,0,0,0,0,0,2,0,0,0,0,0,10,0,10,0,1,0,0,0,1,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,10,0,0,0,2,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+13791,0,0,0,0,0,0,0,0,16,0,0,0,0,0,2,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,Class_2
+13792,0,0,0,0,0,0,0,0,10,0,1,0,0,0,5,0,0,0,0,0,0,0,0,2,1,0,2,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13793,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,Class_2
+13794,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,8,0,2,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+13797,1,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,10,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+13799,0,0,0,0,0,0,0,0,0,0,1,0,2,15,1,1,0,3,0,0,1,2,0,4,11,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,11,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,Class_2
+13800,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13801,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,5,0,0,0,0,0,Class_2
+13802,2,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13803,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13804,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,2,0,0,0,0,0,Class_2
+13805,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13806,0,0,1,0,0,0,0,0,1,0,0,0,0,2,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,8,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+13807,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13808,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13809,0,0,0,0,0,0,0,0,0,0,0,0,2,14,1,3,0,3,0,0,1,2,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,6,0,1,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
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+13811,0,0,1,1,0,0,0,0,1,0,0,3,0,0,16,11,0,1,0,0,2,2,0,1,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,1,3,0,2,0,0,0,0,0,4,1,0,1,0,5,0,0,2,0,19,0,3,2,0,0,0,0,1,0,0,0,0,3,1,1,0,1,0,0,1,0,Class_2
+13812,2,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,1,0,Class_2
+13813,0,0,1,0,0,0,0,0,0,1,0,0,0,3,0,3,0,1,0,0,2,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,1,0,0,10,0,0,0,3,0,1,1,0,0,0,0,0,1,4,0,0,0,0,1,0,0,2,0,0,1,0,0,0,1,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
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+13816,0,0,1,0,0,0,0,1,0,0,0,0,0,7,1,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,8,0,0,0,5,0,Class_2
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+13819,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,7,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,0,2,1,2,0,14,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+13820,0,0,0,0,0,0,0,0,0,0,0,1,0,5,2,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,1,0,1,1,4,0,0,0,0,0,0,0,7,1,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,3,1,Class_2
+13821,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,2,0,0,0,4,0,1,0,0,0,0,0,1,0,1,0,2,0,0,23,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,Class_2
+13822,0,0,1,1,0,0,0,0,0,0,0,0,0,2,2,2,0,0,0,0,0,0,0,2,7,0,0,0,1,0,0,0,1,2,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,1,0,0,1,5,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,3,0,2,0,1,0,0,0,0,1,0,0,0,0,0,1,14,0,3,2,0,0,0,0,Class_2
+13823,0,0,0,0,0,0,0,0,0,0,0,1,3,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13824,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13825,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+13826,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,Class_2
+13827,0,0,3,0,0,0,0,1,0,0,0,0,1,3,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,2,0,0,0,0,0,12,0,2,0,1,0,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,0,4,0,0,0,1,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+13828,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13829,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,12,0,0,0,1,0,1,0,3,0,1,0,1,0,0,0,1,3,0,0,0,0,0,0,7,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,3,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_2
+13830,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13831,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,Class_2
+13832,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13833,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_2
+13834,0,0,0,0,0,0,0,0,0,0,1,0,0,15,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,Class_2
+13835,2,0,0,1,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+13836,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13837,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_2
+13838,1,0,0,0,0,0,0,0,0,1,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,2,0,0,0,0,0,1,6,0,0,0,0,0,0,0,Class_2
+13839,1,0,0,0,0,0,0,0,0,0,0,1,0,17,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,20,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+13840,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,1,0,1,0,0,6,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,Class_2
+13841,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+13842,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13843,0,0,0,0,0,0,0,1,0,1,1,0,0,9,0,0,0,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,0,2,0,0,2,1,0,7,0,1,13,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,1,1,1,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,Class_2
+13844,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,23,0,0,0,0,0,1,0,1,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+13845,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_2
+13846,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+13847,0,0,0,0,0,0,0,1,14,0,3,0,0,0,3,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,7,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13848,0,0,0,0,0,0,0,0,12,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,1,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_2
+13849,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
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+13885,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13886,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
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+13888,0,0,0,0,0,0,1,0,1,7,0,0,0,0,0,0,1,1,1,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,5,6,2,0,0,0,3,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,1,0,1,1,6,0,0,0,1,0,2,0,1,0,0,0,0,0,0,3,1,0,0,0,1,0,0,3,Class_2
+13889,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
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+13905,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,Class_2
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+13907,0,0,0,0,0,0,0,1,1,0,0,1,0,1,4,1,0,0,0,0,1,1,0,3,4,0,0,0,0,8,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,1,1,2,5,0,0,0,0,0,0,4,9,3,0,4,14,1,0,2,4,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,2,3,5,0,0,0,0,Class_2
+13908,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13909,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,4,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,1,16,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,0,1,6,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13910,5,0,0,0,0,0,0,0,0,0,0,0,0,8,1,8,0,0,0,0,0,2,0,0,13,0,0,0,0,0,0,0,1,0,0,0,0,0,0,14,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_2
+13911,0,0,0,0,0,0,0,2,1,0,1,1,0,1,2,4,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,1,2,1,0,0,1,0,0,1,1,0,5,2,1,2,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,Class_2
+13912,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,9,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,1,1,0,0,0,1,0,13,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_2
+13913,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13914,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_2
+13915,2,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13916,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+13917,0,0,0,0,0,0,0,0,0,0,0,0,1,1,20,3,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,8,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,1,0,1,3,0,0,0,0,1,0,0,2,0,10,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,4,1,0,0,0,1,Class_2
+13918,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13919,0,0,0,0,0,0,0,0,0,0,0,0,0,13,7,3,0,0,0,1,0,2,0,1,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,2,1,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,2,0,Class_2
+13920,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,14,1,0,0,0,0,4,0,2,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,6,0,0,0,0,0,6,0,0,0,0,0,1,0,1,0,0,0,0,8,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+13921,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,Class_2
+13922,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+13923,0,1,0,2,0,0,1,1,0,6,1,0,0,1,1,2,1,1,0,1,0,3,2,1,1,0,0,0,0,0,0,0,0,0,2,0,6,2,2,1,0,1,1,1,1,0,0,0,0,10,0,0,0,2,0,0,1,0,1,1,0,0,0,2,1,0,3,0,0,4,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,4,2,1,1,1,1,0,0,Class_2
+13924,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,1,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+13925,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+13926,0,0,1,3,0,0,0,0,0,0,0,1,0,5,0,2,0,0,0,0,0,1,0,0,6,2,0,2,1,0,0,0,3,1,0,0,0,0,0,11,0,0,0,4,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_2
+13927,0,0,0,0,0,0,0,1,0,0,0,0,0,0,23,4,0,0,0,1,0,0,0,2,5,0,0,0,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,1,1,0,0,1,0,9,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,0,Class_2
+13928,0,0,0,0,0,0,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,9,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,Class_2
+13929,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
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+13940,0,0,0,2,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13941,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+13942,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,23,0,2,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,Class_2
+13943,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_2
+13944,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+13945,0,0,0,0,0,0,0,0,0,0,1,0,1,1,19,7,0,0,1,1,0,3,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,3,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,2,1,2,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,1,0,Class_2
+13946,0,0,0,0,0,0,0,0,0,0,0,0,0,1,23,2,0,2,0,0,0,1,0,3,7,0,0,2,0,0,0,0,1,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,3,2,0,0,2,0,0,2,0,10,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
+13947,0,1,0,0,0,0,0,6,16,0,1,2,2,9,2,9,0,4,0,1,0,2,0,13,1,0,0,0,0,0,1,6,16,0,1,0,0,1,0,1,3,0,4,2,0,0,0,0,0,0,0,0,0,3,2,1,0,0,0,0,0,5,1,22,1,2,16,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,3,0,0,0,0,0,Class_2
+13948,0,0,0,0,0,0,0,0,1,0,1,0,0,3,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,20,0,0,0,0,0,Class_2
+13949,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,4,6,0,3,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,2,2,1,1,0,0,0,3,Class_2
+13950,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_2
+13951,0,0,0,0,0,0,0,0,1,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+13952,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13953,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_2
+13954,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+13955,2,0,1,0,0,1,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,Class_2
+13956,1,0,1,0,0,0,0,0,0,0,0,0,0,5,0,2,0,2,0,0,0,1,0,1,7,0,2,0,0,0,0,0,1,0,0,0,1,0,0,8,0,0,2,3,0,0,0,2,1,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_2
+13957,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,8,9,0,2,0,0,0,0,0,Class_2
+13958,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,Class_2
+13959,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+13960,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+13961,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+13962,0,0,0,0,7,2,0,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,Class_2
+13963,2,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+13964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+13965,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_2
+13966,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+13967,7,0,0,0,0,0,0,0,0,0,3,0,0,4,0,2,0,0,0,0,0,0,0,4,9,0,0,0,0,0,0,0,0,1,0,1,0,0,0,7,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+13968,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+13969,0,0,1,0,0,0,1,0,0,2,0,0,0,2,10,4,1,0,0,0,0,2,2,0,2,0,0,0,2,0,0,5,1,0,0,0,0,1,2,2,0,1,3,3,2,2,0,4,0,5,0,0,1,3,0,1,1,0,2,0,0,0,2,2,1,0,1,1,0,4,6,10,1,7,2,0,1,0,0,0,0,0,0,0,13,29,0,0,1,0,0,0,2,Class_2
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+14430,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+14431,1,0,0,0,0,0,0,2,1,0,0,0,0,1,4,1,0,0,0,0,2,1,0,1,5,0,0,0,0,4,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,1,1,2,5,0,0,0,0,0,0,4,9,3,0,4,10,1,0,2,4,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,2,3,5,0,0,0,0,Class_2
+14432,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+14433,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,1,1,0,0,1,Class_2
+14434,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14435,0,0,0,0,0,0,0,0,0,0,0,0,2,9,1,1,0,3,0,0,1,3,0,0,5,0,0,0,0,0,0,0,1,2,0,0,0,0,0,4,0,1,0,0,0,0,0,6,1,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,4,0,2,0,0,0,0,0,Class_2
+14436,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,14,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+14438,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,Class_2
+14439,0,0,0,0,1,0,0,0,0,0,0,1,0,2,25,10,0,0,0,1,2,1,0,2,2,0,0,0,0,0,0,0,3,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,9,0,2,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,4,0,2,0,Class_2
+14440,0,0,0,0,1,0,0,3,0,0,0,0,0,5,1,3,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_2
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+14445,0,0,0,0,0,0,0,0,9,0,0,0,0,0,3,0,0,0,3,0,0,0,0,9,1,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_2
+14446,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+14447,0,0,0,0,0,0,0,1,0,0,0,0,0,5,5,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,7,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,Class_2
+14448,0,0,1,0,0,0,0,1,0,0,0,1,0,5,30,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,1,0,2,2,0,3,0,8,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,Class_2
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+14469,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_2
+14470,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+14471,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+14474,0,0,0,0,0,0,0,0,0,0,0,0,2,11,1,1,0,3,0,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+14475,0,0,1,2,0,0,0,0,0,0,0,0,0,0,18,7,0,1,0,0,2,3,0,1,4,0,0,1,0,0,0,1,2,1,0,0,0,0,0,1,0,1,0,1,0,0,0,1,1,0,0,0,0,7,0,1,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,9,0,2,0,0,0,0,0,1,0,0,0,0,5,0,0,1,3,0,0,3,0,Class_2
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+14519,0,0,0,0,1,0,1,3,0,3,0,0,0,1,1,1,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,1,4,1,0,1,0,3,13,2,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,0,0,11,0,2,0,0,0,0,0,1,0,0,0,0,0,0,30,3,0,1,0,1,0,0,0,Class_2
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+14526,1,0,0,1,0,0,0,0,0,1,0,1,2,2,0,2,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
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+14631,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+14632,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,24,0,0,0,0,0,Class_2
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+14634,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_2
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+14638,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+14639,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14640,0,0,0,0,0,0,0,0,0,0,0,0,0,8,2,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,4,0,0,1,0,0,1,1,1,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,12,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,6,0,2,0,0,0,1,0,Class_2
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+14711,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,3,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,Class_2
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+14722,0,0,0,1,0,0,1,0,0,2,0,0,0,2,17,5,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,8,0,0,0,0,0,0,0,0,0,0,0,0,5,1,1,2,0,0,0,0,0,Class_2
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+14747,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+14748,0,0,0,1,0,0,0,0,0,0,0,0,2,0,23,6,0,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,3,0,0,0,0,1,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,2,0,Class_2
+14749,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,0,0,1,2,4,0,0,0,0,1,1,0,0,3,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,Class_2
+14750,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+14751,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,1,0,0,0,1,0,1,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,1,3,2,0,0,0,2,1,0,0,0,1,4,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+14752,0,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_2
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+14760,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+14766,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14767,0,1,0,0,0,0,0,0,0,0,0,0,1,2,21,9,0,1,0,0,1,1,0,2,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,3,1,14,0,0,0,0,0,0,0,1,1,0,1,0,5,1,0,1,0,0,0,1,0,Class_2
+14768,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,3,1,0,0,0,0,1,16,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_2
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+14940,0,0,0,1,0,0,0,0,0,0,0,0,0,8,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+14941,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14942,0,0,0,0,0,0,0,0,13,0,0,0,0,1,2,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+14943,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,5,0,11,0,0,0,0,0,Class_2
+14944,0,0,0,0,0,0,0,3,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,Class_2
+14945,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14946,0,0,0,2,0,0,0,0,0,1,0,0,0,0,25,3,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,2,1,0,1,2,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+14947,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,1,0,6,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14948,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_2
+14949,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14950,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14951,0,0,0,0,0,0,1,1,0,0,0,0,0,0,11,11,0,0,0,0,0,2,0,0,5,0,1,0,2,0,0,0,3,0,0,0,0,0,0,4,0,3,0,3,0,0,0,1,0,0,0,0,0,4,0,0,1,0,0,0,0,5,0,0,1,0,0,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,1,Class_2
+14952,22,0,5,0,0,0,0,0,1,1,0,0,1,5,1,2,1,10,0,1,1,1,0,2,7,0,1,0,1,0,0,0,2,0,2,1,1,12,0,6,1,0,5,1,0,0,0,8,1,0,0,0,0,4,2,8,0,0,0,0,0,1,1,0,2,0,0,0,0,9,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,2,5,17,0,0,0,1,Class_2
+14953,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,9,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,6,0,0,1,2,0,0,0,6,0,0,0,0,0,4,0,0,0,0,0,1,0,2,0,0,0,2,4,0,0,0,0,18,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,2,1,0,0,0,0,Class_2
+14954,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+14955,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14956,0,0,0,0,0,0,0,0,0,0,0,0,2,5,1,1,0,3,0,0,1,2,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,1,0,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_2
+14957,0,0,0,0,0,0,0,1,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,Class_2
+14958,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,6,1,2,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,Class_2
+14959,3,0,0,0,0,0,0,0,0,0,0,0,0,3,1,7,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,1,0,0,0,0,2,0,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,6,0,5,0,0,0,0,0,Class_2
+14960,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14961,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+14962,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+14963,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,4,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,1,0,6,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,4,1,5,0,0,1,0,0,0,0,0,0,0,0,0,10,3,0,0,1,0,1,0,0,Class_2
+14964,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_2
+14965,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_2
+14966,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14967,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+14968,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+14969,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,10,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,2,0,1,0,0,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,1,0,Class_2
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+15040,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,5,6,0,0,0,0,0,0,0,2,0,0,0,0,0,1,20,1,0,2,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+15045,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,8,0,1,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+15069,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,2,10,1,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
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+15074,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,Class_2
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+15077,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,Class_2
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+15079,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,2,0,0,1,0,0,Class_2
+15080,0,0,0,0,0,0,0,0,1,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_2
+15081,0,0,0,0,0,0,0,0,0,0,0,0,2,11,1,3,0,3,0,0,1,2,0,4,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,12,0,0,0,0,0,0,0,4,1,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_2
+15082,0,0,0,0,0,0,0,0,0,1,0,0,0,0,23,7,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,4,0,0,2,1,1,0,0,1,0,18,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,1,1,0,0,0,0,Class_2
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+15085,0,0,1,3,2,0,0,0,0,24,1,2,0,3,1,1,3,1,0,3,0,3,1,4,2,0,0,0,0,0,0,1,2,1,1,2,4,9,2,3,1,0,1,5,0,0,0,2,3,15,0,0,0,5,5,0,1,1,2,0,0,5,0,0,3,1,0,2,0,20,2,1,2,0,2,1,0,1,0,0,1,1,1,0,8,4,5,0,4,2,1,0,0,Class_2
+15086,0,0,0,0,0,0,0,0,1,0,0,0,0,6,1,1,0,0,0,0,0,0,0,0,13,0,2,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,Class_2
+15087,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_2
+15088,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,6,0,0,0,0,0,2,6,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15089,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+15431,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15432,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15433,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,1,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15434,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+15435,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,1,0,0,0,0,0,Class_2
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+15437,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15438,0,0,0,0,0,0,0,0,1,0,1,0,0,1,20,6,0,0,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,1,1,3,0,0,0,9,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+15439,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15440,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,1,1,5,0,0,1,0,1,1,0,0,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,Class_2
+15441,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,Class_2
+15442,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,5,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+15443,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
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+15445,0,0,0,2,0,0,0,0,4,0,0,0,0,2,6,4,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,1,0,0,1,0,7,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,3,0,0,0,0,0,0,1,0,0,0,0,0,12,3,0,1,3,0,0,0,0,Class_2
+15446,0,0,0,1,0,0,0,3,3,0,0,0,1,0,2,3,0,2,0,0,0,0,0,5,1,0,0,0,0,0,0,4,3,2,1,0,0,0,0,1,0,0,3,0,0,0,0,1,1,1,0,0,0,3,0,0,0,0,0,0,0,2,1,12,2,3,7,1,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+15447,0,0,0,0,0,0,0,0,1,0,0,0,0,11,1,0,0,1,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_2
+15448,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,9,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
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+15472,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+15473,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,Class_2
+15474,0,0,0,0,0,0,0,0,4,0,0,0,0,2,4,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,4,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,31,1,0,12,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_2
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+15478,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
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+15484,0,0,0,0,0,0,0,0,1,0,0,0,1,0,22,2,0,0,0,1,1,2,0,2,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,Class_2
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+15631,0,0,0,0,0,0,0,0,0,0,1,1,0,26,0,0,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,1,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+15632,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,2,0,0,0,0,0,0,0,2,4,0,1,0,0,0,0,0,1,3,0,0,0,0,0,14,0,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,Class_2
+15633,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,5,0,0,0,0,0,0,0,2,1,8,0,0,0,0,1,2,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,1,0,Class_2
+15634,0,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15635,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,4,12,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+15636,0,0,0,0,0,0,0,0,18,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,8,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15637,0,0,0,0,0,0,1,0,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15638,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,Class_2
+15639,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,Class_2
+15640,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_2
+15641,0,0,0,0,0,0,0,0,13,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,3,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15642,0,0,0,0,0,0,0,1,0,0,0,1,0,13,0,1,0,0,0,0,1,0,0,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15643,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+15645,0,0,1,0,0,0,0,0,17,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,1,0,0,2,1,0,0,0,0,0,0,0,1,5,1,0,0,0,0,1,2,0,0,0,0,1,0,3,0,19,0,2,7,0,1,1,1,0,2,0,1,0,0,0,1,1,0,0,0,0,1,2,1,0,0,0,0,0,0,Class_2
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+15648,0,0,0,0,0,0,0,0,14,0,3,0,0,0,3,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+15668,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_2
+15669,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+15672,0,0,0,0,0,0,0,0,2,0,0,0,0,2,1,3,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_2
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+15674,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
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+15680,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,3,0,3,0,3,0,0,0,0,0,0,0,0,2,0,2,21,0,0,4,0,1,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,Class_2
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+15686,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_2
+15687,0,0,0,0,0,0,0,1,0,0,0,0,0,15,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,9,0,0,0,0,0,Class_2
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+15741,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,1,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15742,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,Class_2
+15743,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,3,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,1,2,0,0,2,0,0,1,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,3,0,4,0,1,0,0,0,Class_2
+15744,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,4,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,0,Class_2
+15745,1,0,0,0,0,0,0,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+15746,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,1,0,1,0,0,0,2,0,1,0,0,0,0,0,8,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15747,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,0,0,0,0,7,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15748,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15749,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,Class_2
+15750,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,1,0,0,0,1,1,1,1,0,1,1,0,0,0,0,0,0,2,0,0,0,0,1,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,1,2,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,1,0,0,0,0,Class_2
+15751,0,0,0,0,0,1,0,0,0,0,0,0,2,12,1,2,1,3,0,0,1,3,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+15752,3,0,0,1,0,0,0,0,0,0,2,0,2,2,0,3,0,0,0,0,0,1,0,2,9,0,0,1,1,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,Class_2
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+15754,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15755,1,0,0,0,0,0,0,0,0,0,0,0,0,7,1,1,0,1,0,0,1,0,0,1,4,0,0,0,0,0,0,0,2,0,1,0,0,0,0,6,1,0,2,3,0,0,0,3,1,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
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+15759,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15760,3,0,0,0,0,0,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,Class_2
+15761,0,0,0,0,0,0,0,0,0,0,0,1,0,1,18,6,0,0,0,0,2,1,0,1,3,0,0,0,0,0,0,2,2,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,4,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,2,0,0,1,0,Class_2
+15762,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15763,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+15764,0,0,3,1,0,0,0,0,0,0,0,0,1,2,0,8,0,0,0,0,0,3,0,0,5,0,0,0,0,0,0,0,0,1,1,0,0,1,0,11,0,0,0,1,0,0,0,1,1,0,0,0,0,6,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+15765,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15766,0,0,0,0,0,0,0,2,0,0,1,0,0,18,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,Class_2
+15767,1,0,0,0,0,0,0,0,0,0,1,0,0,5,1,1,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15768,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15769,0,0,0,0,0,0,0,0,0,2,0,0,1,2,24,8,0,0,0,0,1,0,0,1,6,1,0,0,0,0,0,0,1,1,1,0,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,1,2,5,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,3,1,14,1,0,1,0,0,0,0,0,0,0,1,0,4,0,0,3,1,0,0,0,0,Class_2
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+15782,0,0,0,0,0,0,0,0,1,0,0,0,1,4,2,5,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,7,1,0,0,0,0,0,2,0,0,0,0,0,1,0,5,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,9,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,Class_2
+15783,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+15785,0,0,0,0,0,0,0,1,0,0,1,0,0,13,0,0,0,4,1,0,0,0,0,1,5,0,2,0,0,0,0,0,3,1,1,1,1,0,0,24,1,1,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,1,0,Class_2
+15786,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+15787,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,14,0,0,0,1,1,0,1,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,1,0,0,2,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,1,0,0,0,0,Class_2
+15788,2,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+15789,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,14,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+15790,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_2
+15791,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,1,1,0,0,0,0,0,7,1,0,2,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,0,2,0,2,0,0,0,2,0,0,0,0,0,1,1,0,11,0,0,1,0,Class_2
+15792,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15793,1,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,5,0,0,3,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,3,0,0,0,0,0,Class_2
+15794,0,0,0,0,0,0,0,0,14,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,Class_2
+15795,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,1,0,0,0,0,1,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_2
+15796,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,1,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,21,0,0,9,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15797,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+15798,0,0,0,2,0,0,0,0,0,0,0,0,0,0,20,3,0,0,0,0,0,0,0,4,2,0,0,0,1,2,0,0,2,1,1,0,0,0,0,1,0,1,0,2,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,3,1,0,1,0,2,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,Class_2
+15799,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15800,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+15801,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,4,0,2,0,0,0,3,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,1,0,1,0,0,2,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,6,0,0,1,0,2,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,4,0,0,0,0,0,1,0,Class_2
+15802,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,15,0,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,0,Class_2
+15803,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,2,1,12,0,0,1,0,1,1,0,0,3,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_2
+15804,0,0,0,0,0,0,0,0,0,0,1,1,0,17,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,Class_2
+15805,0,0,0,0,0,0,0,0,0,0,0,0,0,8,3,5,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,1,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,11,0,10,0,0,0,0,0,Class_2
+15806,0,0,0,0,0,0,0,0,0,0,0,0,0,2,14,4,0,0,0,0,0,1,0,4,2,0,0,1,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,7,0,1,0,0,1,1,0,4,0,9,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_2
+15807,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,7,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15808,0,0,0,0,0,0,0,0,0,0,0,1,0,1,14,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15809,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,9,0,0,0,0,0,2,0,0,8,0,0,0,0,0,0,0,3,0,0,2,0,0,0,15,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_2
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+15831,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+15832,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
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+15834,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_2
+15835,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15836,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+15838,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15839,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+15840,0,0,2,1,0,0,0,0,0,0,0,0,0,0,23,9,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,6,1,1,0,0,0,10,0,0,0,0,0,0,0,4,1,0,1,1,6,0,0,1,0,6,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,3,0,0,0,0,Class_2
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+15846,1,1,0,0,0,0,0,0,0,0,1,0,0,9,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+15868,0,0,0,0,0,0,0,0,12,0,0,0,0,1,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15869,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,7,4,0,0,0,0,0,0,0,Class_2
+15870,0,0,2,0,0,0,0,0,0,1,0,1,0,0,24,8,0,0,0,0,0,2,0,2,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,2,0,6,1,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,1,6,0,0,1,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,Class_2
+15871,0,0,0,0,0,0,0,0,0,1,0,0,0,0,20,10,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,4,1,1,0,0,2,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,3,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_2
+15872,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,3,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,Class_2
+15873,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,3,0,0,0,0,11,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15874,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,Class_2
+15875,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,1,0,1,2,1,0,0,0,0,0,0,0,5,0,0,0,0,1,0,10,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15876,0,0,0,1,0,0,0,0,1,0,0,1,2,1,20,11,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,4,0,10,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,Class_2
+15877,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15878,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_2
+15879,0,0,0,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+15880,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+15881,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+15882,0,0,0,0,0,0,0,1,1,0,0,0,0,4,0,3,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15883,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15884,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,1,0,2,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15885,0,0,0,0,0,0,0,2,2,0,0,0,0,5,0,2,0,2,0,0,0,1,0,0,8,2,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,3,0,0,0,1,0,Class_2
+15886,0,0,1,0,0,0,1,0,0,0,0,0,0,2,20,11,0,1,0,0,0,3,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,3,0,0,0,0,1,0,0,3,2,7,0,0,1,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,Class_2
+15887,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15888,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,6,0,0,0,0,0,0,0,2,1,2,0,0,0,0,1,1,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,7,0,0,2,1,0,0,0,1,0,0,Class_2
+15889,0,0,0,0,0,0,0,0,0,0,0,1,3,10,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+15892,0,0,1,2,0,0,0,0,0,0,0,0,0,1,21,7,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,2,0,1,0,0,1,0,4,0,0,1,1,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,Class_2
+15893,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15894,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15895,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,1,0,0,0,0,1,0,0,10,0,0,0,0,0,0,0,1,0,0,2,0,0,0,4,0,1,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15896,0,0,1,0,0,0,0,0,0,0,0,0,1,1,5,6,1,1,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,5,0,0,0,0,0,0,0,0,1,20,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,4,0,Class_2
+15897,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
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+15899,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15900,3,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,2,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+15901,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,4,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,2,0,0,0,1,0,17,0,0,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15902,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+15903,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15904,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_2
+15905,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15906,0,0,0,0,0,0,0,0,4,0,0,0,0,3,1,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,10,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,3,Class_2
+15907,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_2
+15908,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_2
+15909,0,0,0,0,0,0,0,1,24,0,0,0,0,2,2,3,0,0,0,1,0,0,1,2,3,0,0,0,0,0,0,7,7,1,1,0,0,0,1,2,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,6,0,13,0,2,4,0,0,1,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15910,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,9,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,3,0,1,2,0,16,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,Class_2
+15911,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15912,0,0,0,0,0,0,0,0,0,1,0,0,1,1,15,9,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,3,0,0,0,0,0,0,0,0,2,11,1,0,0,0,0,0,0,4,0,0,1,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,2,0,Class_2
+15913,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,1,0,7,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15914,0,0,0,0,0,0,0,1,0,0,0,0,0,9,0,3,0,4,0,0,0,2,1,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,21,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15915,0,0,0,0,0,0,0,0,27,0,0,0,0,0,4,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,18,0,1,7,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,Class_2
+15916,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,Class_2
+15917,0,0,0,0,0,0,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,7,0,0,0,0,0,Class_2
+15918,0,0,0,0,0,0,0,0,0,0,0,1,0,6,1,12,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,2,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+15919,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15920,3,0,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,6,0,7,0,0,0,0,0,Class_2
+15921,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
+15922,0,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,15,0,0,4,0,0,0,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15923,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,6,0,0,0,0,0,0,3,0,0,1,2,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,Class_2
+15924,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,6,1,3,0,0,0,0,0,0,0,0,0,0,2,24,0,0,4,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+15925,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,1,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_2
+15926,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15927,0,0,0,0,0,0,0,2,0,0,0,0,0,1,4,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_2
+15928,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_2
+15929,0,0,0,0,0,0,0,1,0,0,0,0,0,4,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,12,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,3,0,Class_2
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+15931,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,10,0,0,0,1,0,1,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,6,1,0,0,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,3,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,Class_2
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+15936,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+15937,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_2
+15938,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,Class_2
+15939,0,0,1,1,0,0,1,0,0,0,0,0,2,1,17,14,1,0,0,1,0,5,0,1,2,0,0,2,1,0,0,2,2,0,0,0,1,2,0,3,0,0,0,4,0,0,0,0,1,0,0,0,1,20,3,0,0,0,0,0,0,2,0,0,1,2,1,0,0,9,1,11,0,0,0,0,0,0,0,1,0,1,1,0,4,0,0,1,0,0,0,1,0,Class_2
+15940,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_2
+15941,0,0,0,0,0,0,0,0,19,0,1,0,0,0,0,1,0,2,0,1,0,0,0,1,2,0,0,0,0,0,0,6,1,0,4,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,5,0,7,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+15942,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,7,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15943,0,0,0,0,0,0,0,0,0,0,1,0,0,2,4,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,11,0,1,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,1,0,0,0,0,0,Class_2
+15944,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,3,0,0,0,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
+15945,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+15946,0,0,0,0,0,0,0,0,0,0,0,0,0,10,2,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,1,3,0,0,2,0,0,0,1,0,1,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_2
+15947,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15948,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+15949,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15950,2,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15951,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_2
+15952,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,5,1,0,0,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,Class_2
+15953,0,0,0,0,0,0,0,0,15,0,0,0,0,0,4,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+15954,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,0,1,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,2,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,5,0,0,0,0,0,0,2,0,0,0,0,0,16,9,0,0,0,0,0,0,0,Class_2
+15955,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_2
+15956,1,0,0,0,0,0,0,1,0,4,0,1,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,1,11,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,4,0,0,0,1,0,0,0,Class_2
+15957,0,0,0,0,0,0,0,0,2,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,Class_2
+15958,0,0,0,0,0,0,0,0,0,0,1,0,0,1,23,7,0,1,0,0,1,1,0,3,1,0,0,0,0,0,0,2,2,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,7,0,0,1,0,3,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,Class_2
+15959,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15960,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+15961,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_2
+15962,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,6,0,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,1,0,0,0,Class_2
+15963,0,0,0,0,0,0,0,0,0,0,0,0,0,2,17,4,0,2,0,1,1,0,0,2,2,0,0,0,0,0,1,0,3,0,1,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,1,5,0,0,0,0,0,1,0,3,0,0,0,1,4,0,0,4,0,15,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,2,1,0,0,1,0,Class_2
+15964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,6,0,5,0,0,0,0,0,Class_2
+15965,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_2
+15966,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,1,0,0,1,0,0,3,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,4,0,0,0,0,7,0,0,1,0,0,0,2,0,0,0,0,0,1,8,0,1,0,0,0,0,0,Class_2
+15967,0,0,0,0,0,0,0,0,2,0,0,0,0,10,1,2,0,0,0,0,2,1,0,0,6,1,0,0,0,0,0,0,2,0,0,0,0,0,0,20,0,0,1,0,0,1,0,4,0,0,0,0,0,4,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,1,1,1,1,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,1,0,Class_2
+15968,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,4,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,7,1,5,0,0,0,0,0,Class_2
+15969,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,1,0,2,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,0,Class_2
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+15988,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,Class_2
+15989,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
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+16001,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,2,0,0,1,1,0,0,3,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,Class_2
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+16003,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+16004,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,Class_2
+16005,1,0,0,0,0,0,1,0,0,0,1,0,0,9,2,1,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_2
+16006,0,0,0,0,0,3,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,3,0,5,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
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+16008,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_2
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+16630,0,0,0,0,0,0,0,0,1,0,0,1,0,2,18,2,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,2,0,15,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_2
+16631,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,1,0,0,0,0,0,Class_2
+16632,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,5,0,2,0,1,0,0,0,0,0,0,0,3,2,0,4,1,1,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,15,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,1,0,3,0,0,0,0,0,Class_2
+16633,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,4,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,7,0,0,0,0,0,Class_2
+16634,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
+16635,0,0,0,0,0,0,0,1,0,0,1,0,0,9,0,5,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,4,1,0,2,0,0,0,16,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_2
+16636,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_2
+16637,0,0,0,0,0,0,0,0,0,0,0,1,1,2,10,2,0,0,0,0,2,1,0,3,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,4,0,0,5,0,0,5,2,13,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,Class_2
+16638,3,0,0,1,0,0,0,0,5,0,0,0,1,3,0,2,1,0,1,9,0,3,0,4,7,0,0,0,0,0,0,14,1,1,0,3,0,1,5,1,0,0,0,1,0,0,0,9,0,17,0,0,1,5,0,1,1,0,0,0,0,1,0,29,0,3,11,0,1,10,0,1,5,4,1,0,0,1,0,0,0,0,1,0,2,13,5,3,0,0,0,0,0,Class_2
+16639,0,0,1,0,1,0,0,1,0,0,0,0,0,7,0,5,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,6,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,4,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_2
+16640,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_2
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+17830,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,Class_2
+17831,0,0,0,0,0,0,0,0,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,3,0,6,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_2
+17832,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+17833,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,1,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,1,12,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,Class_2
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+17838,0,3,0,0,0,0,0,0,0,0,1,0,0,1,24,3,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,1,1,0,0,2,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,4,2,0,0,0,0,0,0,3,0,0,2,0,3,0,0,2,0,20,0,0,1,0,0,0,1,0,0,0,0,0,4,0,1,3,0,0,0,0,0,Class_2
+17839,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,5,0,0,0,0,0,4,0,0,4,1,0,0,0,0,0,0,1,2,0,0,0,0,0,16,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,Class_2
+17840,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
+17841,1,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_2
+17842,0,0,0,0,0,0,0,1,1,0,0,1,0,1,4,1,0,0,0,0,1,1,0,2,4,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,1,1,2,5,0,0,0,0,0,0,4,9,3,0,4,9,1,0,2,4,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,2,3,5,0,0,0,0,Class_2
+17843,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,Class_2
+17844,0,0,1,0,0,0,0,0,2,0,0,0,0,2,1,5,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,Class_2
+17845,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,4,0,0,0,1,0,Class_2
+17846,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_2
+17847,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_2
+17848,1,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,1,0,1,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_2
+17849,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_2
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+17911,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,2,5,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_2
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+17926,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_2
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+17929,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,Class_2
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+18067,0,0,0,0,0,1,0,1,0,2,1,1,0,5,0,0,0,1,0,1,0,0,0,1,6,0,0,0,0,0,0,0,2,3,0,0,4,1,0,2,0,1,14,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,15,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,3,0,9,0,0,0,0,0,Class_3
+18068,2,0,1,0,0,0,0,0,0,0,0,0,0,3,0,8,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,17,0,1,0,1,0,0,1,0,1,1,0,0,0,10,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,Class_3
+18069,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+18070,0,0,0,0,0,0,0,2,0,3,1,0,0,4,0,0,0,0,1,4,0,0,0,1,4,0,0,0,0,1,0,0,1,0,0,0,3,0,0,9,1,2,19,0,0,0,0,0,0,0,0,0,0,2,0,12,0,0,0,0,0,0,0,1,1,0,0,2,0,9,0,1,0,0,1,0,0,0,0,0,0,0,0,0,4,4,0,14,0,0,0,0,0,Class_3
+18071,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+18072,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,1,4,0,0,0,0,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,Class_3
+18073,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+18074,0,0,1,1,0,0,0,1,0,0,0,2,0,0,12,5,0,1,0,0,0,0,2,1,1,0,0,0,1,0,0,2,2,0,0,0,0,3,0,6,0,0,2,1,0,1,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,2,0,1,0,0,2,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,0,Class_3
+18075,0,0,0,0,0,0,0,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,5,0,0,0,0,0,Class_3
+18076,0,0,0,0,0,0,0,0,2,0,0,0,0,0,20,8,0,0,0,0,4,4,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,Class_3
+18077,0,0,0,1,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,1,1,8,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,6,0,0,0,0,1,1,0,0,0,1,0,0,0,2,0,1,0,0,2,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+18078,0,0,0,0,0,0,0,0,22,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,0,10,0,0,2,1,0,2,0,1,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_3
+18079,0,0,0,0,0,0,0,0,0,1,0,1,0,0,16,2,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
+18080,0,0,0,2,0,0,0,0,0,0,0,1,1,1,10,7,0,0,0,1,2,1,0,1,3,0,0,0,0,0,0,0,2,1,0,1,0,0,2,19,1,0,4,1,0,0,0,0,1,0,0,1,1,1,0,2,0,1,0,0,0,2,0,1,1,0,0,2,0,1,0,6,0,0,0,0,0,0,0,3,0,0,0,0,5,1,0,0,8,0,0,0,0,Class_3
+18081,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+18082,0,0,0,0,0,0,0,1,14,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,15,0,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,3,0,0,0,0,0,Class_3
+18083,0,0,0,0,0,0,0,3,0,0,1,0,1,0,1,3,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,1,9,0,0,0,1,1,0,0,0,2,0,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,6,0,0,1,1,0,0,0,1,0,0,Class_3
+18084,0,0,1,0,0,0,1,1,0,0,0,1,0,4,0,6,0,4,0,0,0,1,0,1,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,11,2,1,1,4,0,0,0,1,0,0,0,0,6,4,1,0,0,0,1,0,0,3,0,1,0,1,1,1,0,11,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,Class_3
+18085,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,10,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,11,0,0,1,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,Class_3
+18086,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+18087,0,0,0,0,0,0,0,0,0,1,0,0,0,1,4,5,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,5,1,0,0,0,0,0,2,0,0,0,0,1,2,0,0,1,0,5,0,0,0,1,0,1,1,0,0,0,0,2,0,2,0,0,1,0,0,3,0,10,1,0,1,0,1,0,0,0,0,0,0,0,17,10,0,1,0,0,1,0,0,Class_3
+18088,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,3,0,0,4,1,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,4,0,0,1,0,0,0,0,3,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,6,1,1,0,0,0,Class_3
+18089,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,5,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
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+18827,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,Class_3
+18828,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,2,1,1,0,13,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+18829,0,0,0,0,0,0,0,0,21,0,2,0,0,0,0,2,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,22,0,1,7,0,0,0,1,1,2,0,3,1,0,0,0,1,0,0,0,0,2,2,2,0,0,0,0,0,0,Class_3
+18830,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,3,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,2,0,0,5,0,0,1,0,0,0,0,0,0,0,Class_3
+18831,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,Class_3
+18832,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,1,1,3,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_3
+18833,1,1,0,0,0,0,0,0,0,0,0,0,3,18,0,0,0,0,0,0,0,0,0,2,9,0,0,0,0,0,0,0,2,0,0,1,0,0,0,7,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,6,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_3
+18834,1,0,1,0,0,0,0,0,0,0,0,0,0,8,1,1,0,1,0,0,0,2,0,0,6,0,0,0,0,0,0,0,2,0,1,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+18835,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,4,0,3,0,0,3,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_3
+18836,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,2,2,0,0,0,0,0,0,5,1,0,0,0,0,0,0,7,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,7,0,0,0,3,0,1,6,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,Class_3
+18837,0,0,0,0,0,0,0,0,21,0,1,0,0,3,1,2,0,0,0,0,0,0,0,12,1,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,7,0,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+18838,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,Class_3
+18839,0,0,0,0,0,0,0,1,0,0,0,0,0,16,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+18840,0,0,0,0,0,0,0,0,7,0,0,0,0,4,2,0,0,0,0,1,0,0,0,4,4,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,1,0,1,0,0,0,0,3,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,30,0,0,11,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+18841,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_3
+18842,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+18843,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,2,0,0,0,0,1,0,9,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_3
+18844,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+18845,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+18846,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_3
+18847,0,0,1,0,0,0,0,0,0,0,1,1,0,1,1,5,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,1,5,0,0,6,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,Class_3
+18848,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,16,0,0,6,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,Class_3
+18849,1,0,0,0,0,0,0,0,0,7,1,0,0,2,0,0,0,0,0,2,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,10,2,0,0,0,0,0,0,0,0,3,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,8,1,2,0,0,0,0,0,Class_3
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+19240,0,0,0,0,0,0,0,0,0,0,1,0,0,3,4,3,0,1,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,1,0,0,3,1,19,1,2,1,0,0,0,0,8,0,1,0,0,0,5,0,0,0,1,0,0,0,8,0,0,0,0,2,1,0,2,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,20,0,0,0,0,Class_3
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+19245,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,Class_3
+19246,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,6,0,1,0,0,0,Class_3
+19247,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
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+19249,0,0,0,0,2,0,1,3,0,3,1,0,0,3,4,2,0,1,0,1,0,2,0,4,1,0,0,0,0,0,1,5,0,0,3,0,4,5,0,0,0,1,12,6,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,1,1,0,2,0,0,5,0,2,0,0,0,0,0,0,0,1,0,0,0,0,20,2,0,0,2,0,0,0,0,Class_3
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+19447,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,11,0,22,0,0,0,0,0,Class_3
+19448,0,0,1,1,0,0,0,0,0,0,0,0,2,4,15,9,0,0,0,0,3,1,2,1,1,0,4,0,0,0,0,0,4,0,1,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,1,0,1,0,0,1,0,8,0,0,1,0,0,0,0,3,0,0,0,0,9,0,0,0,0,0,0,0,0,Class_3
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+19621,3,0,0,1,0,0,0,0,0,0,1,0,0,5,2,7,0,1,0,0,0,0,0,4,6,0,0,0,0,0,0,0,1,0,0,0,0,1,2,1,0,0,0,3,1,0,0,5,0,4,0,0,1,2,1,0,0,0,0,1,0,1,0,2,1,0,0,0,0,10,0,1,1,0,0,0,0,0,3,0,0,1,0,0,1,65,0,2,2,0,0,0,0,Class_3
+19622,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,1,0,1,8,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,1,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,1,0,0,0,Class_3
+19623,0,0,1,1,0,0,0,0,18,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,2,0,0,2,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,11,1,0,4,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,0,0,0,Class_3
+19624,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,3,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,2,5,0,0,0,0,1,0,0,0,1,0,0,0,0,6,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,1,1,0,0,0,0,0,0,Class_3
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+19626,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,Class_3
+19627,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,1,0,0,1,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,1,0,9,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,3,0,0,0,0,0,Class_3
+19628,0,2,0,0,0,0,0,0,17,1,0,0,0,0,3,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,3,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19629,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_3
+19630,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,Class_3
+19631,0,0,0,0,0,0,0,0,12,0,0,0,0,2,0,0,0,0,0,0,3,0,0,2,1,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_3
+19632,0,0,0,0,0,0,1,3,0,2,0,0,0,1,2,0,0,2,0,1,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,1,4,3,0,0,0,1,12,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,2,0,0,0,4,0,0,0,0,0,0,0,0,32,0,1,0,0,0,0,0,0,Class_3
+19633,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,10,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+19634,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,2,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,Class_3
+19635,1,0,0,0,0,1,0,0,1,0,0,0,1,3,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,1,0,0,0,2,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,1,0,0,1,0,0,0,0,0,13,5,0,0,0,0,0,0,1,Class_3
+19636,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+19637,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,7,0,0,0,0,0,Class_3
+19638,0,0,0,1,0,0,0,0,11,0,3,0,0,0,4,1,0,0,0,1,0,0,0,5,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,4,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_3
+19639,1,0,0,0,0,2,0,0,0,0,0,0,0,10,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,8,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19640,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_3
+19641,0,0,0,0,0,0,0,0,0,0,0,0,2,13,1,1,0,3,0,0,1,2,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_3
+19642,0,0,0,0,0,0,0,0,0,0,3,0,0,7,0,0,0,0,0,0,0,0,0,2,12,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_3
+19643,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_3
+19644,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,9,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,Class_3
+19645,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19646,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,5,0,0,0,0,1,1,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,Class_3
+19647,1,0,0,0,0,0,0,0,0,0,0,0,0,2,13,8,0,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,1,0,1,1,2,3,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,6,0,0,0,1,1,0,2,0,0,0,0,0,7,8,1,3,0,0,0,0,0,Class_3
+19648,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,1,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,1,0,12,0,0,0,3,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,1,0,Class_3
+19649,0,0,1,0,0,0,0,2,0,0,1,0,0,0,1,6,2,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,11,2,2,0,0,2,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,6,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,Class_3
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+19668,0,0,1,1,0,0,0,0,0,0,0,0,0,1,14,16,0,1,0,1,0,5,0,4,0,0,0,0,0,0,0,0,3,1,0,0,0,1,0,1,0,1,1,3,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,4,0,2,0,2,2,0,0,4,0,14,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,1,0,0,0,0,Class_3
+19669,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,Class_3
+19670,0,0,1,0,0,0,0,3,0,0,0,0,1,9,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,1,0,0,0,1,3,0,0,0,0,0,2,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19671,6,0,0,0,0,0,0,0,0,0,1,0,0,18,2,4,0,0,0,0,0,1,0,2,7,0,2,0,0,0,0,0,2,0,0,0,1,0,0,41,0,0,1,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,5,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,6,0,0,0,0,0,Class_3
+19672,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,Class_3
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+19674,0,0,0,0,0,0,0,1,0,0,0,0,0,10,1,1,0,0,0,0,1,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19675,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+19676,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,11,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,2,16,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+19677,1,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+19678,3,0,0,0,0,0,0,0,10,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19679,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,Class_3
+19680,0,0,0,3,0,0,0,0,1,0,0,2,0,3,1,0,2,0,1,4,0,3,3,0,3,0,0,0,0,0,1,1,2,1,0,0,1,9,0,0,1,2,14,0,0,0,0,1,2,5,0,0,0,0,0,1,0,0,1,1,0,1,2,1,0,1,1,1,0,19,0,2,1,1,4,0,0,1,0,0,0,0,0,0,32,4,0,1,2,1,0,0,0,Class_3
+19681,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,1,0,0,0,0,0,7,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_3
+19682,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,6,0,0,0,0,1,1,1,0,2,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,6,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,Class_3
+19683,0,0,0,0,0,0,0,2,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,9,0,0,0,0,0,0,0,0,0,0,0,0,7,5,0,1,0,0,0,0,0,Class_3
+19684,0,0,0,1,0,1,0,0,0,1,0,0,1,3,22,7,0,0,1,0,0,1,0,2,2,0,0,0,0,0,0,0,1,0,1,1,1,2,1,4,0,0,2,3,0,0,0,0,1,3,0,0,0,3,0,0,0,0,0,1,0,6,0,0,0,0,1,0,0,3,0,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+19685,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,13,1,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,Class_3
+19686,10,0,0,0,0,0,0,0,0,0,0,0,1,13,0,3,0,0,1,0,0,0,0,0,4,0,0,0,2,0,0,0,4,5,0,0,0,0,1,4,0,0,0,0,1,0,1,0,0,0,0,0,0,1,2,1,0,0,0,1,0,2,0,0,0,0,2,0,1,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,Class_3
+19687,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+19688,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,10,0,0,0,0,1,Class_3
+19689,0,0,0,0,0,0,0,0,9,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,2,1,10,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
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+19700,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,3,0,0,9,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19701,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+19705,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,10,0,0,0,0,0,Class_3
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+19707,0,0,1,0,0,0,0,1,0,0,0,0,0,5,2,4,0,1,0,0,0,1,0,3,1,0,0,0,0,0,1,0,2,0,1,0,0,1,0,3,2,0,4,1,0,2,0,0,2,1,0,0,0,5,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,Class_3
+19708,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,3,2,0,0,0,2,0,1,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,Class_3
+19709,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19710,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,1,6,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,8,0,0,0,0,0,Class_3
+19711,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,Class_3
+19712,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,1,0,1,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_3
+19713,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,3,0,0,0,2,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,Class_3
+19714,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+19715,2,0,2,0,0,0,0,0,0,0,0,1,0,9,0,2,0,0,0,0,0,2,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,16,2,0,0,0,0,1,0,8,0,0,0,0,1,0,1,0,0,3,0,0,0,5,0,0,0,0,2,0,0,4,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,9,0,0,0,0,Class_3
+19716,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,1,0,1,0,Class_3
+19717,0,0,0,0,0,0,0,1,0,0,1,0,0,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19718,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,Class_3
+19719,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,Class_3
+19720,0,0,1,0,0,0,0,0,0,0,1,0,1,2,4,4,0,0,0,0,0,0,0,0,2,2,0,0,1,0,0,0,3,0,1,0,0,0,0,7,0,1,1,1,0,0,0,1,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,4,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,1,1,0,0,0,1,Class_3
+19721,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,5,0,0,0,0,0,Class_3
+19722,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+19723,0,0,0,0,0,0,0,0,14,0,0,0,0,0,8,0,0,0,0,0,0,0,0,8,2,0,1,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19724,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,Class_3
+19725,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19726,0,0,0,0,0,0,0,0,0,0,0,0,0,9,2,1,0,2,0,0,0,0,0,2,9,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,2,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,2,0,0,6,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_3
+19727,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19728,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,8,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19729,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,7,1,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_3
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+19765,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19766,2,0,0,0,0,0,0,0,0,2,0,0,0,3,3,2,0,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,1,0,0,0,0,0,0,10,1,0,0,0,0,0,0,7,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,10,0,0,0,0,0,Class_3
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+19828,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
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+19830,0,0,0,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19831,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,Class_3
+19832,6,0,0,0,0,0,0,0,1,0,0,0,0,14,1,7,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,17,0,1,0,1,0,0,0,1,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,3,1,7,0,0,0,0,0,Class_3
+19833,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+19834,2,0,0,0,0,0,0,0,0,0,0,0,1,13,1,2,0,0,0,0,0,0,0,2,4,0,0,0,1,0,0,0,3,0,0,0,0,0,0,23,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_3
+19835,1,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_3
+19836,0,0,0,0,0,0,0,1,0,0,0,0,0,2,23,10,0,1,0,1,0,1,0,0,4,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,1,2,0,0,0,0,0,0,0,0,1,2,1,0,0,2,0,1,0,0,0,1,3,0,0,0,0,1,0,9,0,0,0,0,0,0,4,0,0,0,1,0,3,5,0,0,1,0,0,0,0,Class_3
+19837,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19838,0,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,0,0,0,1,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,2,0,0,0,0,0,6,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19839,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19840,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19841,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,2,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+19842,1,0,0,0,0,0,0,0,0,0,0,0,0,9,2,4,0,3,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,7,0,0,0,0,0,Class_3
+19843,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,1,9,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,Class_3
+19844,0,0,0,0,0,0,0,0,0,0,0,1,3,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,1,0,Class_3
+19845,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+19846,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,3,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_3
+19847,0,0,0,0,0,0,0,0,0,0,0,1,0,4,3,4,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,4,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19848,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,4,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,4,0,0,0,0,0,Class_3
+19849,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,1,0,1,0,2,0,0,3,1,0,0,0,0,0,0,1,0,1,0,0,2,0,15,0,1,0,2,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_3
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+19868,0,0,0,0,0,0,0,0,20,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,17,0,0,11,0,0,1,2,0,1,0,0,0,0,1,0,0,0,0,0,0,2,3,0,1,0,0,0,0,0,Class_3
+19869,1,0,1,2,3,0,0,0,2,4,0,0,2,3,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,1,1,0,4,0,0,20,6,0,0,0,0,1,0,0,0,0,5,1,0,0,0,0,0,0,0,0,1,2,3,0,0,1,8,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,Class_3
+19870,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_3
+19871,0,0,0,1,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+19872,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,3,1,1,1,1,0,2,0,0,1,2,0,0,0,0,0,0,1,1,0,0,1,1,1,1,2,0,1,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,5,0,2,0,5,0,0,0,0,1,0,0,0,0,0,6,3,0,0,1,0,0,0,0,Class_3
+19873,3,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,1,9,0,0,0,1,0,0,0,1,0,0,1,0,0,0,6,1,0,0,0,0,0,0,5,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,1,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+19874,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,0,6,0,0,0,0,0,Class_3
+19875,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,1,2,4,0,0,1,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,Class_3
+19876,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,0,15,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,Class_3
+19877,0,0,0,0,0,0,0,0,0,0,0,0,0,20,1,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,Class_3
+19878,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+19879,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,6,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19880,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,Class_3
+19881,0,0,0,0,0,0,0,1,3,1,0,0,0,1,0,3,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,1,2,1,0,5,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,0,Class_3
+19882,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,1,2,0,0,0,0,0,0,5,1,0,0,0,0,0,0,11,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,5,5,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,3,0,Class_3
+19883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19884,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19885,1,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,12,0,1,10,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,Class_3
+19886,0,0,1,0,0,0,0,0,0,1,0,0,0,2,1,2,0,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,2,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,9,0,3,0,0,0,0,0,Class_3
+19887,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,4,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,8,0,0,0,0,0,Class_3
+19888,0,0,0,0,0,0,3,1,2,13,0,0,1,2,1,7,0,2,0,1,0,1,0,2,7,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,1,0,3,0,0,0,7,1,2,0,1,1,0,2,0,0,0,2,1,0,1,1,1,2,3,2,0,0,0,0,1,0,1,0,1,1,0,19,2,1,0,0,0,0,12,0,3,1,0,0,0,0,Class_3
+19889,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19890,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,4,0,7,0,0,0,0,0,Class_3
+19891,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,11,0,0,0,0,0,0,0,Class_3
+19892,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19893,0,0,0,0,0,0,0,0,0,0,0,0,3,4,17,7,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,3,1,1,0,2,2,0,8,1,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,3,0,18,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,Class_3
+19894,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,Class_3
+19895,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,4,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+19896,0,0,0,0,0,0,0,0,0,1,0,0,1,3,7,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,6,0,10,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,Class_3
+19897,0,0,0,0,0,0,1,0,0,3,1,0,0,3,0,0,0,0,0,0,0,2,0,3,7,0,0,0,0,0,0,3,0,0,0,1,0,1,1,1,0,1,0,0,1,0,0,2,0,1,0,0,4,1,0,0,2,0,3,0,0,0,0,2,0,0,3,1,0,1,1,2,1,0,0,0,0,0,0,0,0,0,0,0,2,31,0,8,0,0,0,0,0,Class_3
+19898,1,1,0,0,1,0,0,0,0,0,0,0,1,3,4,2,0,4,1,1,0,1,0,0,3,0,0,2,0,0,2,0,1,0,3,0,0,2,0,5,0,0,7,0,0,0,0,1,1,4,0,0,3,4,1,1,0,0,1,1,0,3,3,0,0,1,0,0,0,0,0,4,0,3,0,0,0,0,2,1,0,1,0,0,8,5,0,0,3,0,0,0,1,Class_3
+19899,0,0,0,0,0,0,0,0,20,0,0,0,0,2,0,0,0,0,0,5,0,1,0,10,2,0,0,0,0,0,0,8,4,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,19,1,1,10,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_3
+19900,4,0,0,0,0,0,0,0,0,2,0,0,0,4,2,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,14,1,1,0,0,0,0,0,7,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,11,1,0,0,0,0,Class_3
+19901,0,0,0,0,0,0,0,2,0,1,3,3,0,4,0,0,0,0,0,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,12,0,0,0,0,0,0,2,0,0,0,1,0,13,0,0,0,1,0,0,0,2,0,0,0,0,0,11,0,2,0,1,0,0,0,0,0,0,0,0,0,0,6,2,0,6,0,0,0,0,0,Class_3
+19902,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+19903,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,3,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+19904,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,Class_3
+19905,1,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,8,0,0,0,1,0,2,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,Class_3
+19906,2,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,3,0,0,0,8,0,0,0,0,0,0,0,8,0,0,0,0,2,2,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19907,0,0,0,1,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,1,0,0,2,3,15,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,1,0,0,0,0,1,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_3
+19908,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19909,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,Class_3
+19910,0,0,0,0,0,0,0,0,26,0,0,0,0,0,0,0,1,0,0,0,0,0,2,5,2,0,0,0,0,0,1,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,20,0,0,8,0,0,0,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,2,0,1,1,0,0,0,0,Class_3
+19911,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,2,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,3,1,1,0,0,6,0,0,0,0,1,0,1,0,0,0,3,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,5,1,1,0,0,0,0,0,0,1,1,1,0,0,0,8,6,0,0,0,0,0,0,0,Class_3
+19912,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,1,9,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,9,1,0,0,1,1,0,0,0,Class_3
+19913,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,1,0,1,0,15,0,0,0,2,1,0,0,0,0,0,0,2,0,12,0,0,0,0,0,0,0,8,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,Class_3
+19914,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19915,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,11,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,1,1,0,3,3,0,24,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,Class_3
+19916,0,0,0,0,0,0,0,0,0,1,4,0,0,2,0,0,0,0,1,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,16,0,0,0,0,1,0,2,0,0,0,0,0,13,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,7,0,0,0,0,0,Class_3
+19917,0,0,0,0,0,0,0,0,15,0,0,0,0,0,3,1,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,16,1,1,4,0,0,0,0,2,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19918,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,7,0,1,0,0,0,Class_3
+19919,0,0,0,0,0,0,0,0,14,0,1,0,0,0,2,1,0,0,0,0,0,0,0,6,2,0,0,0,1,0,0,8,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,6,0,0,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,Class_3
+19920,1,0,0,0,0,0,0,0,13,0,0,0,0,1,3,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,16,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_3
+19921,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,9,0,0,0,0,0,Class_3
+19922,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+19923,0,0,0,0,0,0,0,0,20,0,0,0,1,1,0,3,3,0,1,0,0,3,0,3,2,0,0,0,0,0,0,7,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,5,0,0,1,0,0,0,0,0,5,19,1,1,11,0,1,1,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,Class_3
+19924,0,0,0,1,0,0,0,2,0,1,0,0,0,4,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,8,0,0,2,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,8,0,0,0,0,0,Class_3
+19925,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,1,0,0,2,6,0,0,0,0,0,0,0,2,0,4,0,0,0,1,1,1,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,7,0,0,1,1,0,0,0,0,0,0,Class_3
+19926,1,0,0,0,0,0,0,0,0,0,0,1,4,12,1,0,0,0,0,0,0,0,0,3,10,0,0,0,0,0,0,1,3,0,0,1,0,0,0,9,0,2,0,0,0,0,0,6,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,1,1,0,3,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19927,0,0,0,0,0,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,6,0,0,0,0,1,Class_3
+19928,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_3
+19929,3,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,1,0,0,0,1,0,1,7,0,0,0,1,0,0,0,1,0,0,1,0,0,0,13,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0,0,0,0,0,Class_3
+19930,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,Class_3
+19931,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,1,0,9,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19932,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,9,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19933,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,10,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,5,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
+19934,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,13,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_3
+19935,0,0,1,0,0,0,0,0,3,1,0,0,0,0,23,6,1,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,3,0,2,2,1,0,0,0,0,0,0,0,0,1,7,0,2,0,0,1,0,0,8,1,1,0,1,1,0,0,6,0,5,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,1,2,0,0,0,0,Class_3
+19936,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,5,0,1,0,0,0,1,0,1,2,0,0,1,0,0,0,7,0,0,0,0,0,0,1,1,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_3
+19937,2,0,0,0,0,0,0,1,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,10,0,0,0,0,0,Class_3
+19938,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,4,0,4,0,0,0,0,1,6,1,0,0,1,0,0,0,0,4,0,1,0,16,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,1,0,1,0,Class_3
+19939,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19940,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19941,0,0,0,0,0,0,0,0,3,0,0,0,0,0,15,13,0,0,0,0,1,4,0,4,0,0,0,0,0,0,0,0,5,0,1,0,0,1,0,3,1,0,2,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,3,2,0,1,1,4,0,0,1,0,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,Class_3
+19942,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_3
+19943,1,0,0,0,0,0,0,1,0,0,0,1,3,18,0,0,1,0,0,0,1,0,0,0,14,0,0,0,0,0,0,1,0,0,0,2,0,1,0,3,1,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,9,0,9,0,0,0,0,0,Class_3
+19944,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,5,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,1,0,7,0,0,0,0,1,0,0,0,4,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,Class_3
+19945,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,1,1,0,0,0,0,0,0,0,0,2,7,2,1,0,0,0,0,0,Class_3
+19946,1,0,0,1,0,0,1,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,1,1,0,0,2,4,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,4,0,0,0,2,1,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,13,0,0,2,0,0,3,0,1,0,0,Class_3
+19947,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,Class_3
+19948,0,0,0,0,0,0,0,0,3,0,0,0,0,1,21,3,0,0,0,0,2,3,0,1,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,11,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,Class_3
+19949,0,0,0,1,0,0,0,1,17,8,0,0,0,0,0,1,1,0,0,2,0,4,4,3,3,0,0,1,0,0,0,8,0,0,0,0,0,1,0,1,1,0,0,4,0,0,0,5,1,0,0,0,1,3,1,0,0,0,0,0,0,1,0,14,0,2,6,0,0,0,4,0,0,1,0,0,0,0,0,0,3,0,0,0,3,3,1,0,0,1,0,0,0,Class_3
+19950,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,7,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19951,0,0,0,0,0,0,0,0,17,0,0,0,0,0,1,0,0,0,0,0,0,0,2,4,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,8,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+19952,0,0,0,0,0,0,0,0,9,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+19953,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,Class_3
+19954,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,1,Class_3
+19955,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_3
+19956,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19957,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,6,0,1,0,1,1,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,Class_3
+19958,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19959,0,0,0,0,0,0,0,0,19,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,18,0,0,10,0,0,1,2,0,1,0,0,0,0,1,0,0,0,0,0,0,2,3,0,1,0,0,0,0,0,Class_3
+19960,0,0,0,0,0,0,0,0,0,2,0,0,0,3,1,0,0,0,0,2,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,2,0,1,1,0,1,0,1,1,0,0,6,0,2,0,0,2,0,0,0,2,0,1,0,0,0,2,20,0,16,0,0,0,0,0,Class_3
+19961,0,0,0,0,0,0,0,0,15,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19962,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,27,0,1,7,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,5,1,0,0,0,0,Class_3
+19963,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,8,0,0,0,0,0,Class_3
+19964,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19965,1,0,0,0,0,0,0,0,10,0,6,1,0,1,3,0,0,0,0,3,0,1,0,15,1,0,0,0,0,0,0,11,1,1,0,0,1,1,2,2,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,6,0,0,6,0,1,1,4,1,0,1,2,0,0,2,21,1,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+19966,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19967,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+19968,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,6,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,8,1,4,0,0,0,0,0,0,0,0,0,0,0,0,16,1,0,1,1,0,0,0,0,Class_3
+19969,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,4,0,0,0,0,0,2,3,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+19970,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,1,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+19971,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,13,0,0,0,0,0,Class_3
+19972,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_3
+19973,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_3
+19974,0,0,0,0,0,0,0,1,11,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,5,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,14,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_3
+19975,0,0,0,0,0,0,0,0,14,0,0,0,0,0,3,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,3,0,5,0,0,4,0,0,0,1,1,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19976,0,0,0,0,0,0,0,0,18,0,1,0,0,0,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,8,0,0,4,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19977,1,0,0,0,0,0,0,0,3,2,0,0,0,1,0,2,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,8,1,0,2,1,0,0,1,0,0,0,1,0,1,0,2,5,0,3,0,0,2,2,1,1,0,0,0,0,0,1,0,20,0,0,9,0,0,3,2,0,1,2,1,0,0,1,0,0,0,0,0,0,3,3,1,6,0,1,0,0,0,Class_3
+19978,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,2,0,0,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_3
+19979,0,0,0,1,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,10,0,0,2,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_3
+19980,1,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,1,0,1,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,10,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,3,0,0,0,0,0,Class_3
+19981,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19982,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,4,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19983,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+19984,0,0,0,0,0,0,0,0,10,0,1,0,0,0,3,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+19985,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,9,0,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+19986,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,8,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19987,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19988,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,10,0,0,0,0,0,Class_3
+19989,0,2,0,1,0,0,0,0,24,0,1,0,0,3,2,0,0,4,2,4,0,2,3,8,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,2,1,0,0,0,0,1,0,0,1,34,1,3,22,0,0,1,1,0,2,1,0,2,0,0,0,0,0,0,0,0,3,1,4,2,0,0,0,0,0,Class_3
+19990,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,1,0,1,2,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,16,0,0,0,0,0,Class_3
+19991,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,15,0,0,0,0,0,Class_3
+19992,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,Class_3
+19993,1,0,0,0,0,0,0,0,0,1,0,1,0,0,20,7,0,1,0,1,0,0,0,0,5,0,3,0,0,0,0,1,2,0,0,0,0,1,0,1,0,0,0,5,0,0,0,0,0,1,0,0,1,7,0,0,0,0,0,0,0,0,1,1,2,0,0,0,1,3,0,13,0,0,0,0,0,0,4,0,1,0,0,0,6,8,1,0,3,0,0,0,0,Class_3
+19994,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+19995,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,1,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
+19996,0,0,0,0,2,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_3
+19997,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,Class_3
+19998,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_3
+19999,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+20000,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,10,2,0,0,0,0,0,0,0,Class_3
+20001,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,8,5,0,0,0,0,0,0,0,Class_3
+20002,0,0,0,1,0,0,0,0,0,0,0,0,0,8,1,2,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,Class_3
+20003,0,0,0,0,1,0,0,2,0,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,2,1,1,3,0,0,2,1,1,9,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,2,1,0,0,0,Class_3
+20004,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,Class_3
+20005,0,0,0,0,0,0,0,0,0,0,0,0,1,11,1,1,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,14,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+20006,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,15,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+20007,0,1,1,0,0,0,0,0,0,0,2,0,0,8,1,3,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,3,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,5,0,2,0,0,0,Class_3
+20008,1,0,0,0,0,0,0,0,0,0,0,0,0,1,7,2,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,1,0,0,0,0,0,2,1,4,3,0,1,3,0,0,0,1,0,0,0,0,1,2,1,0,0,0,0,0,0,Class_3
+20009,2,0,0,0,1,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,1,0,9,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
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+20011,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,0,14,5,0,0,2,0,2,0,0,0,0,2,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_3
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+20829,1,0,0,0,0,0,0,0,21,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,1,0,0,2,0,0,0,0,3,0,0,0,0,0,14,0,0,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+20831,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,1,1,1,0,0,0,0,11,1,1,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_3
+20832,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,8,0,0,8,0,2,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+20833,22,0,5,0,0,0,0,0,0,2,0,0,1,2,1,1,1,10,0,1,0,1,0,3,2,0,1,0,1,0,0,0,1,0,2,1,1,12,0,5,0,0,5,1,0,0,0,10,1,0,0,1,0,4,2,7,0,0,0,0,0,1,1,1,2,1,0,0,0,11,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,6,3,8,17,0,0,0,1,Class_3
+20834,2,0,0,0,0,0,0,0,0,0,0,0,0,9,2,4,1,1,0,0,0,0,0,2,7,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,0,0,1,0,0,0,5,0,0,0,0,1,0,2,0,0,0,0,0,0,2,0,2,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,6,0,0,0,0,0,Class_3
+20835,0,0,0,0,0,0,1,1,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,11,0,0,0,0,0,Class_3
+20836,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,Class_3
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+20838,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,Class_3
+20839,22,0,5,0,0,0,0,0,0,1,0,0,1,5,1,1,1,10,0,1,0,1,0,0,5,0,1,0,1,0,0,1,2,0,2,1,0,11,0,5,0,0,5,1,0,0,0,4,1,0,0,0,0,3,2,7,0,0,0,0,0,1,1,0,3,1,1,0,0,8,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,2,2,4,17,0,0,0,1,Class_3
+20840,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+20841,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+20842,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,0,1,0,0,1,0,0,0,4,2,0,0,0,0,0,0,0,2,0,1,1,0,2,0,7,1,0,4,1,0,0,0,2,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,1,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,1,7,1,0,0,0,0,Class_3
+20843,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,5,0,0,0,0,0,Class_3
+20844,0,0,0,0,0,0,0,0,18,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21,0,0,7,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_3
+20845,0,0,0,0,0,0,0,0,5,0,1,1,0,0,1,0,0,0,0,0,0,1,0,3,2,0,0,0,0,0,0,4,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,8,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_3
+20846,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_3
+20847,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+20848,0,0,0,0,0,0,0,0,17,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,4,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+20849,2,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,1,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+20943,1,0,0,0,0,0,0,1,14,0,0,0,0,1,4,0,1,0,0,0,0,0,0,1,4,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,32,0,3,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_3
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+20945,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
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+20948,0,0,0,0,0,0,0,0,0,0,1,0,0,16,1,5,0,1,0,0,0,1,0,2,1,0,1,0,0,0,0,0,1,0,1,0,0,1,0,22,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,3,0,4,0,0,0,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,7,0,0,0,0,0,Class_3
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+20966,0,0,0,0,0,0,0,1,2,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_3
+20967,0,0,0,0,0,0,0,2,0,1,0,0,1,1,3,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,4,0,7,0,0,0,0,0,0,0,0,0,0,0,0,13,11,0,1,0,0,0,0,0,Class_3
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+21045,1,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,Class_3
+21046,0,0,0,0,0,0,0,2,0,0,0,0,0,15,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
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+21082,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,5,0,1,2,0,0,0,1,0,1,0,0,0,0,1,0,1,1,0,3,0,0,8,0,2,0,2,11,0,0,0,0,0,1,3,0,2,0,3,0,0,0,0,0,0,0,3,0,0,1,0,0,0,3,9,0,5,0,3,0,0,0,0,0,0,0,0,0,0,18,1,0,0,1,2,0,1,0,Class_3
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+21084,0,0,0,1,0,0,1,1,0,2,0,0,0,1,3,0,0,2,0,1,0,0,1,2,2,0,0,0,0,0,0,0,0,0,2,0,3,3,0,2,0,1,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,1,0,1,0,0,0,0,0,0,0,12,1,0,0,0,0,0,0,0,Class_3
+21085,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,1,0,2,0,0,1,0,0,0,0,1,1,0,2,0,0,1,4,0,0,6,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21086,0,0,0,0,0,0,0,0,0,3,0,0,0,5,1,0,0,0,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,9,0,0,0,0,1,Class_3
+21087,0,0,0,0,0,0,0,1,0,3,4,1,0,2,22,6,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,4,1,0,0,1,0,0,3,0,0,1,0,0,0,0,2,1,0,0,0,0,1,0,1,0,2,0,0,0,1,0,1,0,2,2,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,0,Class_3
+21088,0,0,0,0,0,0,0,0,1,1,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,3,1,3,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,0,1,0,0,0,0,Class_3
+21089,0,0,2,2,0,0,0,0,0,0,0,0,3,0,15,6,0,1,0,0,0,3,0,2,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,8,0,0,0,1,0,0,0,2,1,1,0,0,0,10,0,1,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
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+21321,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,11,0,0,0,0,0,Class_3
+21322,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,0,1,0,0,0,6,1,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,3,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,2,0,1,0,31,0,2,12,0,0,1,0,0,1,0,0,2,0,0,1,0,0,0,1,0,1,3,1,6,0,0,0,0,0,Class_3
+21323,0,0,0,0,0,0,0,0,15,0,2,0,0,0,2,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,11,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+21324,1,0,0,0,0,0,0,0,0,0,0,0,0,4,2,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,1,1,3,0,0,0,0,15,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,12,0,3,1,0,0,0,0,Class_3
+21325,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,3,0,0,0,0,3,1,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21326,0,0,0,0,0,0,0,1,0,0,0,1,0,7,0,4,0,1,0,0,2,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,2,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,3,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21327,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21328,0,0,0,0,0,1,0,1,0,0,0,0,0,5,3,1,0,0,1,0,0,0,0,3,1,0,0,0,0,0,2,1,3,3,0,0,4,0,0,7,0,1,13,1,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,7,0,1,0,0,0,0,0,0,0,1,0,0,0,0,15,0,1,0,1,0,0,0,0,Class_3
+21329,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,5,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,8,0,1,0,1,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,5,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,0,0,0,Class_3
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+21399,0,0,0,0,0,0,0,1,0,0,2,0,0,10,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,1,0,2,0,1,0,9,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,1,0,1,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,Class_3
+21400,2,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,0,0,0,0,0,11,0,0,0,0,0,0,0,11,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_3
+21401,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,1,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21402,0,0,0,0,0,0,0,0,0,0,3,0,1,14,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,6,0,0,1,1,0,0,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,4,1,3,0,1,0,0,0,3,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,9,0,1,0,0,0,Class_3
+21403,0,0,0,0,0,0,0,0,12,0,3,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,1,4,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21404,2,0,0,0,0,0,0,0,1,0,0,1,1,9,1,4,0,1,0,0,2,1,0,4,3,0,0,0,1,0,0,0,1,0,1,0,0,0,0,14,0,0,1,0,0,1,0,7,1,0,0,0,1,1,2,0,0,0,0,0,0,1,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,6,0,0,0,0,Class_3
+21405,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,1,0,0,0,2,5,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,0,0,3,4,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,2,2,2,0,0,1,0,1,0,0,0,0,0,0,0,14,0,11,0,0,0,0,0,Class_3
+21406,0,0,1,1,0,0,0,0,0,0,1,0,0,2,0,3,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,7,1,0,2,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,2,0,1,0,0,0,2,0,0,0,0,0,1,1,0,11,0,0,0,0,Class_3
+21407,0,0,0,0,0,0,0,0,15,0,1,0,0,0,3,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21408,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21409,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
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+21411,0,0,0,1,0,0,0,0,21,0,0,0,0,1,0,0,0,0,0,0,0,2,0,5,1,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,6,1,4,0,0,0,4,1,0,1,0,0,0,0,0,0,18,0,0,7,1,0,0,2,0,4,0,1,0,0,1,0,0,0,0,0,0,1,6,2,6,0,0,0,0,0,Class_3
+21412,0,0,0,0,0,0,0,1,1,0,0,0,0,5,0,2,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,5,0,0,0,1,0,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,Class_3
+21413,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,Class_3
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+21415,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,4,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+21416,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,Class_3
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+21419,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+21420,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,Class_3
+21421,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21422,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21423,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,6,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,5,0,0,0,1,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,Class_3
+21424,0,0,0,0,0,0,0,0,0,0,0,0,2,11,1,1,0,3,0,0,1,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,0,0,0,0,0,0,7,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
+21425,0,0,0,0,0,0,0,0,0,0,0,1,0,15,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,Class_3
+21426,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,1,0,0,0,7,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_3
+21427,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+21428,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+21429,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_3
+21430,0,0,0,0,0,0,1,1,1,0,1,0,0,3,0,2,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,3,1,0,0,1,0,4,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,8,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,8,0,2,0,0,0,0,0,Class_3
+21431,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+21432,0,0,0,0,0,0,0,0,0,0,0,0,1,0,19,5,0,0,0,0,0,1,0,3,1,0,0,0,1,0,0,0,3,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,7,0,1,1,1,4,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21433,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21434,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,Class_3
+21435,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+21436,3,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_3
+21437,0,0,0,0,1,0,1,1,0,4,0,0,0,5,0,0,0,0,0,1,0,1,1,0,4,0,1,0,0,0,0,0,2,1,1,0,3,0,0,2,0,1,21,2,0,1,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,Class_3
+21438,0,0,0,0,0,0,0,0,0,1,0,1,3,11,0,2,0,1,0,0,0,0,0,0,8,0,0,0,1,0,0,0,2,0,0,0,0,0,0,8,0,0,2,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_3
+21439,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21440,1,0,0,0,0,0,0,3,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,1,6,0,0,0,0,2,0,0,3,0,0,0,0,1,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_3
+21441,0,0,1,1,1,0,0,0,0,0,0,0,1,1,28,2,0,0,0,1,1,0,0,0,4,0,2,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,1,0,0,2,1,1,0,1,0,0,0,4,0,7,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,Class_3
+21442,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,3,0,0,0,0,8,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21443,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,3,1,0,0,0,1,0,0,0,4,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,10,0,0,0,0,0,Class_3
+21444,0,0,0,0,0,0,0,0,0,0,0,1,0,0,21,6,0,0,0,0,0,3,0,2,1,1,0,0,0,0,0,0,1,2,1,0,0,1,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,4,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_3
+21445,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
+21446,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21447,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,8,0,0,0,0,0,0,0,1,1,7,0,1,0,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,1,0,0,0,0,0,0,Class_3
+21448,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+21449,0,0,0,0,0,0,0,0,23,0,0,0,0,0,2,0,0,0,0,0,0,0,1,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,10,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,12,0,1,5,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
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+21451,0,0,0,0,0,0,0,0,0,0,0,1,1,0,11,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
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+21468,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,1,4,1,0,2,0,1,2,0,0,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,10,0,0,2,0,0,1,0,0,0,0,Class_3
+21469,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,1,0,0,0,1,0,0,9,0,0,1,0,0,0,0,0,1,8,0,1,0,8,2,0,0,0,5,0,0,0,0,6,0,1,0,1,0,0,0,0,0,0,0,0,0,3,5,0,0,1,5,0,0,2,0,3,1,0,0,0,0,0,0,0,0,0,5,0,0,2,1,0,1,0,0,0,0,Class_3
+21470,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+21471,0,0,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,8,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21472,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,2,2,0,1,0,0,1,0,1,0,0,1,1,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,3,0,1,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,5,0,1,0,0,0,Class_3
+21473,0,0,0,0,0,0,0,0,0,0,0,0,1,11,1,1,0,0,0,0,0,0,0,3,6,0,0,0,0,0,0,2,1,0,0,0,0,0,0,26,0,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,5,0,0,0,0,0,Class_3
+21474,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,8,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,3,0,3,1,0,0,1,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,Class_3
+21475,0,0,0,0,0,0,0,0,0,0,0,0,0,21,0,0,1,1,0,0,0,1,0,0,4,0,0,0,0,1,0,0,7,0,1,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,0,Class_3
+21476,1,0,0,0,0,0,0,0,21,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,7,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+21477,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+21478,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+21479,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,13,0,0,0,1,1,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,4,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,2,0,0,3,0,1,1,0,4,0,12,0,0,0,0,0,0,2,0,0,0,0,0,4,5,4,0,1,0,0,0,0,Class_3
+21480,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+21481,0,0,0,1,0,0,1,1,0,2,0,0,0,0,4,2,0,0,0,1,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,3,10,1,1,0,0,0,0,0,Class_3
+21482,0,0,0,0,0,0,0,0,0,1,4,0,0,5,0,2,0,0,1,0,0,1,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,12,0,0,1,0,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,1,0,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,1,0,1,0,Class_3
+21483,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_3
+21484,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+21485,0,0,0,0,0,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,4,0,0,0,3,0,Class_3
+21486,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,7,0,0,0,0,1,0,0,0,0,0,0,0,12,0,0,0,0,0,2,0,2,0,0,1,0,1,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,4,2,0,0,0,0,Class_3
+21487,0,0,0,0,0,0,0,0,0,0,0,0,0,9,2,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,7,0,0,0,0,0,Class_3
+21488,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,6,0,1,0,0,0,1,0,0,5,0,1,1,0,0,0,0,1,1,0,0,1,0,0,12,1,0,0,1,0,1,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_3
+21489,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,4,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,2,0,1,0,0,9,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,Class_3
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+21522,4,0,0,0,0,0,0,1,0,0,0,0,0,14,1,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,2,0,0,0,1,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+21523,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_3
+21524,0,0,1,1,0,0,0,0,1,0,0,1,0,0,5,1,0,1,1,0,2,1,0,2,1,0,0,0,0,0,0,2,2,0,1,0,0,3,1,3,0,0,9,3,0,0,0,0,0,0,0,1,0,5,0,0,0,2,0,0,0,4,3,2,1,0,0,0,0,8,0,1,0,0,0,0,0,0,0,5,0,1,0,0,14,3,2,1,3,0,0,0,0,Class_3
+21525,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,Class_3
+21526,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_3
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+21528,0,0,0,0,0,0,0,0,10,0,0,0,0,1,2,2,0,0,0,0,0,1,0,1,2,0,0,0,1,0,0,5,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,18,0,0,6,0,0,0,0,0,2,3,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
+21529,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+21646,3,0,0,0,0,0,0,0,0,0,0,1,0,10,0,0,1,0,0,3,0,0,0,0,5,0,0,0,1,0,0,0,2,0,0,0,1,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,0,0,2,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
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+21649,1,0,0,0,0,0,0,0,1,0,0,0,0,9,0,4,0,3,0,0,0,1,0,2,3,1,0,0,1,0,0,0,5,0,1,1,1,1,0,20,2,1,0,0,0,1,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
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+21830,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,2,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+21831,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
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+21833,0,0,1,0,2,0,0,0,0,3,1,0,0,4,0,7,0,0,0,0,0,1,0,0,4,0,0,0,0,1,2,0,0,0,3,0,0,1,0,1,0,0,6,3,0,0,0,0,1,0,0,0,0,4,0,6,0,0,0,0,0,3,0,0,1,1,0,0,0,11,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,5,1,3,3,0,0,0,1,Class_3
+21834,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_3
+21835,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,2,1,0,4,0,1,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,0,3,1,0,0,0,0,Class_3
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+21837,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+21838,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+21839,1,0,0,0,0,0,0,0,24,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,3,0,0,0,0,0,0,6,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,21,0,0,8,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,1,Class_3
+21840,0,0,0,0,0,0,0,0,14,0,1,0,0,0,3,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_3
+21841,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,6,0,0,0,0,1,0,0,0,3,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,Class_3
+21842,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,Class_3
+21843,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,15,0,14,0,0,0,0,0,Class_3
+21844,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_3
+21845,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,9,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,6,0,1,0,0,0,1,0,4,0,4,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,1,4,0,0,0,0,Class_3
+21846,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+21847,1,0,0,0,0,0,0,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,2,7,0,0,0,0,0,2,19,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,Class_3
+21848,0,0,0,0,0,0,0,0,0,1,0,0,0,0,20,6,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,3,1,0,0,0,0,1,0,0,2,1,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,16,0,3,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,Class_3
+21849,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,2,4,0,0,0,0,1,1,0,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,Class_3
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+21886,1,0,0,0,0,0,0,1,24,0,0,0,0,1,0,1,1,0,0,0,0,1,0,5,1,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,27,0,3,6,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,1,1,4,0,1,0,0,0,Class_3
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+21920,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_3
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+21924,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
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+21926,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,3,0,0,0,0,0,3,0,3,0,0,0,0,1,0,0,1,2,1,1,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,1,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,1,1,0,0,0,0,Class_3
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+22012,0,0,1,1,1,0,0,1,0,3,0,0,0,0,0,2,0,0,1,0,0,2,0,0,4,0,0,0,0,0,0,0,0,1,2,1,3,0,0,6,0,1,20,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,3,2,1,1,0,0,0,0,0,Class_3
+22013,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,6,1,9,0,0,0,0,0,Class_3
+22014,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,4,0,0,0,0,0,Class_3
+22015,0,0,0,0,0,0,0,0,0,1,1,0,0,1,20,5,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,4,1,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,17,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,Class_3
+22016,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,17,0,1,0,0,0,0,0,Class_3
+22017,0,0,2,0,1,0,0,1,2,1,1,0,0,7,1,2,0,1,0,0,0,2,0,1,2,0,0,0,0,0,0,0,1,0,2,0,0,1,1,6,0,0,7,7,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_3
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+22019,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_3
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+22030,0,0,0,1,0,0,0,0,12,0,0,0,0,1,4,2,0,0,0,0,2,1,0,1,4,0,0,0,0,0,0,8,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,15,0,0,4,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,1,1,0,1,0,0,0,0,0,Class_3
+22031,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+22032,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,3,0,0,0,2,2,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,1,0,0,0,0,13,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,12,0,0,0,0,0,1,0,Class_3
+22033,0,0,0,0,0,0,0,0,1,0,0,0,1,0,24,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+22034,0,0,0,1,0,0,0,2,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,1,0,3,0,0,0,0,1,16,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,Class_3
+22035,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,7,0,0,0,0,0,Class_3
+22036,0,0,0,0,0,0,0,0,0,0,0,0,0,1,29,8,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,13,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,Class_3
+22037,1,0,2,0,2,3,0,0,0,2,1,0,1,2,2,3,1,0,0,2,0,3,0,0,3,0,1,0,1,0,1,0,1,0,5,1,0,8,1,3,0,0,5,5,0,1,0,5,1,8,0,0,1,8,1,0,0,0,1,1,1,2,2,2,1,2,0,0,1,12,1,4,1,4,2,0,0,0,0,1,0,0,0,0,31,5,2,1,2,2,0,0,0,Class_3
+22038,0,0,0,0,0,0,0,3,1,0,0,0,0,7,1,0,1,2,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,14,1,0,0,0,0,Class_3
+22039,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_3
+22040,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+22041,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+22042,0,0,1,0,0,0,1,1,0,1,0,0,0,6,0,5,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,0,0,1,2,0,0,17,2,0,0,0,0,0,0,0,1,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,Class_3
+22043,0,0,0,0,0,0,0,0,16,0,0,0,0,0,2,1,0,1,0,0,0,1,0,5,2,0,0,0,0,0,0,6,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,1,6,1,1,2,1,2,1,0,0,1,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,Class_3
+22044,1,0,0,0,0,0,0,0,0,0,0,0,0,3,3,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,7,0,1,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+22045,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,20,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+22046,0,0,0,0,0,0,0,0,0,0,0,1,2,3,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,Class_3
+22047,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+22048,1,0,0,0,0,0,0,1,0,0,0,0,0,11,1,0,0,0,0,0,1,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+22049,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+22431,0,0,0,0,0,0,0,0,1,0,0,0,0,0,21,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,Class_3
+22432,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,1,0,0,0,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+22433,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,5,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,1,0,12,1,0,4,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,Class_3
+22434,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_3
+22435,0,0,0,0,0,0,0,0,0,0,1,0,0,11,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+22436,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_3
+22437,0,0,1,1,0,0,1,0,0,3,0,0,0,2,4,2,0,1,3,1,0,3,0,2,1,0,0,0,1,0,1,0,0,0,1,0,0,4,0,0,0,0,4,5,0,0,0,0,4,4,0,0,2,10,1,1,0,0,1,0,0,0,2,0,2,0,0,0,0,20,0,8,0,0,0,2,1,0,1,0,0,0,0,0,45,0,1,4,1,0,0,1,0,Class_3
+22438,0,0,0,0,0,0,0,0,20,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,17,0,0,7,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+22439,0,0,0,0,0,0,0,0,27,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,11,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_3
+22440,0,0,0,0,1,0,0,0,0,0,0,0,0,1,18,10,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,3,0,0,1,2,0,20,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,Class_3
+22441,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_3
+22442,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,1,0,1,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,7,0,0,0,1,0,Class_3
+22443,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_3
+22444,0,0,1,0,0,0,0,0,0,0,0,1,0,3,1,1,0,1,1,0,0,1,0,0,4,0,2,0,0,0,0,0,1,0,0,2,1,1,0,22,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_3
+22445,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_3
+22446,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+22447,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_3
+22448,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+22449,1,0,1,0,0,0,0,1,0,2,0,0,0,1,0,4,0,0,0,2,0,0,0,2,7,0,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,2,2,0,0,0,0,1,2,0,0,0,3,0,0,0,3,0,0,0,1,0,5,0,0,0,0,2,6,0,1,0,0,2,2,0,1,0,0,0,0,0,0,1,21,0,20,1,1,0,0,0,Class_3
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+22646,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,6,0,0,0,0,1,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_3
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+23225,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,Class_3
+23226,0,0,0,0,0,0,0,0,0,0,0,0,0,6,4,1,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,8,0,0,0,0,0,Class_3
+23227,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,1,11,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,5,1,2,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,0,0,0,0,0,0,Class_3
+23228,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,Class_3
+23229,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,8,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+23230,0,0,0,0,0,0,0,0,0,0,1,0,0,2,4,2,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,1,1,16,1,3,2,0,0,0,0,13,0,1,0,0,1,5,0,1,0,1,0,0,0,4,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,2,20,0,0,0,0,Class_3
+23231,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_3
+23232,0,0,0,0,0,0,0,0,0,2,0,0,0,7,0,3,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,1,1,0,3,0,0,2,2,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,3,1,3,0,0,0,0,0,Class_3
+23233,1,0,0,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,18,0,0,4,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,Class_3
+23234,0,0,0,0,0,0,0,1,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,4,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,2,0,0,0,1,1,Class_3
+23235,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,9,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,Class_3
+23236,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,5,0,0,1,2,1,1,0,0,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,3,0,1,1,0,0,1,0,0,0,0,Class_3
+23237,0,0,0,0,0,0,0,0,2,0,0,0,1,4,1,2,0,0,0,0,0,0,0,4,7,0,0,0,0,0,0,0,1,0,0,1,0,0,0,28,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_3
+23238,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,Class_3
+23239,1,0,0,0,0,0,0,1,0,0,0,1,0,14,1,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,1,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,2,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,8,0,0,0,0,0,Class_3
+23240,0,0,1,2,0,0,0,0,2,0,0,0,0,0,2,7,0,2,1,0,0,1,0,0,5,0,0,2,1,0,1,2,2,0,0,0,0,3,0,2,1,0,6,0,0,1,0,0,0,1,0,0,1,11,1,0,0,0,1,0,0,1,2,0,2,0,0,0,0,11,0,7,0,2,0,0,0,0,0,2,0,1,0,0,13,3,0,3,5,0,0,0,0,Class_3
+23241,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,3,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,8,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_3
+23242,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_3
+23243,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,1,0,3,1,0,3,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_3
+23244,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,2,0,0,0,0,0,0,0,0,13,0,0,1,0,1,0,0,0,Class_3
+23245,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1,14,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,2,0,5,0,0,0,0,0,Class_3
+23246,1,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,Class_3
+23247,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,8,0,0,0,0,0,0,0,Class_3
+23248,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,Class_3
+23249,0,0,1,0,1,0,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,2,0,1,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
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+23431,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
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+23439,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+23441,0,0,0,0,0,0,0,1,0,0,0,0,0,15,0,1,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,4,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,12,0,0,0,0,0,Class_3
+23442,0,0,0,1,1,0,0,0,0,0,0,0,0,0,21,9,0,3,1,1,0,1,0,2,1,0,0,1,1,0,0,0,4,0,0,0,0,1,2,1,0,2,2,0,0,0,0,0,1,0,0,0,0,3,1,4,0,0,0,0,0,2,0,0,0,0,1,0,1,5,0,20,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,Class_3
+23443,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+23444,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,0,18,0,0,0,0,0,Class_3
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+23446,0,0,0,0,0,0,1,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_3
+23447,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_3
+23448,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,6,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+23449,0,0,0,0,0,0,0,1,14,0,1,0,0,0,3,0,0,0,0,0,0,0,0,8,2,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
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+23841,1,0,0,0,0,0,0,0,0,0,0,0,0,8,6,2,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,4,0,1,0,0,0,0,7,0,1,1,0,0,0,0,4,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,1,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,10,0,0,0,0,0,Class_3
+23842,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,2,0,1,0,0,1,1,0,0,6,2,0,1,0,0,0,2,1,0,0,0,0,1,0,9,0,0,3,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_3
+23843,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
+23844,0,0,0,0,0,0,1,1,0,0,0,1,0,1,1,1,0,2,0,2,0,0,0,0,3,0,0,0,0,0,0,0,1,1,2,0,6,3,0,1,0,2,23,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,1,0,20,0,0,0,1,1,0,0,0,Class_3
+23845,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_3
+23846,0,0,0,0,0,0,0,1,1,0,0,0,0,4,0,10,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,10,1,0,2,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+23847,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,2,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,2,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,Class_3
+23848,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+23849,0,0,0,0,0,0,0,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,14,0,0,0,0,0,Class_3
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+24045,2,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+24046,0,0,0,0,0,0,0,0,13,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
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+24446,0,0,1,0,0,0,0,0,1,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
+24447,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+24448,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
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+24640,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,8,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,Class_3
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+24846,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,44,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,1,0,0,0,0,Class_3
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+25628,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,1,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,17,2,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,9,0,10,0,0,0,0,0,Class_3
+25629,0,0,0,0,0,0,0,0,19,0,0,0,0,2,3,2,0,0,0,0,1,0,0,2,3,0,0,0,0,0,0,9,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,2,1,1,0,0,0,0,0,0,4,1,7,0,0,7,0,0,1,0,4,0,1,1,0,1,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_3
+25630,1,0,0,0,0,0,0,0,0,0,0,0,0,1,6,3,0,2,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,4,0,7,0,0,11,0,0,2,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,3,1,0,0,0,0,0,0,4,1,6,0,2,1,0,0,0,0,1,0,0,0,0,4,3,0,0,1,0,0,0,0,Class_3
+25631,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_3
+25632,0,0,0,0,0,0,0,1,0,0,0,1,0,1,22,4,0,1,0,0,0,2,0,0,2,2,0,1,0,0,0,0,2,0,0,0,0,2,0,3,1,2,2,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,8,2,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,Class_3
+25633,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,16,0,0,0,0,0,Class_3
+25634,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+25635,0,0,0,0,0,0,0,0,0,0,1,1,0,0,26,1,0,1,0,0,0,1,0,0,3,1,0,1,1,0,1,0,6,1,1,0,1,1,0,10,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,10,0,1,3,0,0,0,0,1,0,5,3,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,2,0,0,0,0,Class_3
+25636,4,1,5,2,1,0,1,0,0,3,0,1,7,4,0,4,0,3,0,0,0,2,0,1,12,1,1,1,0,0,0,1,2,0,1,0,0,1,1,7,1,4,0,8,1,0,0,5,3,1,0,0,3,12,1,2,0,0,0,0,0,10,3,0,0,4,1,1,0,3,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,1,14,0,0,0,1,Class_3
+25637,0,0,0,0,0,0,0,0,0,0,2,1,0,18,4,4,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,4,1,0,2,0,0,0,17,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,1,1,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_3
+25638,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,13,1,14,0,2,0,0,0,Class_3
+25639,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_3
+25640,0,0,0,0,0,0,0,0,11,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,12,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+25641,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_3
+25642,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_3
+25643,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,2,0,0,0,0,0,2,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,0,3,0,0,1,0,0,0,0,Class_3
+25644,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_3
+25645,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_3
+25646,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,2,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_3
+25647,0,0,0,0,0,0,0,0,23,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,10,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+25648,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,7,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,9,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,Class_3
+25649,0,0,0,0,0,0,0,1,0,1,0,0,0,1,29,5,0,1,1,0,0,2,0,0,5,1,0,0,0,0,0,1,2,1,2,0,0,3,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,6,1,0,1,1,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,Class_3
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+25959,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
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+25962,2,0,2,0,0,0,0,0,0,0,0,1,0,9,0,2,0,0,0,0,0,2,0,0,5,0,0,0,1,0,0,0,1,0,0,0,0,0,0,15,2,0,0,0,0,1,0,8,0,0,0,0,1,0,1,0,0,3,0,0,0,5,0,0,0,0,2,0,0,4,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,3,9,0,0,0,0,Class_3
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+25964,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_3
+25965,0,0,0,2,0,0,0,0,0,0,0,1,5,2,9,8,0,2,0,1,4,2,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,1,1,6,3,0,1,1,0,0,0,15,0,0,0,0,0,5,0,0,0,1,0,0,0,5,0,0,2,0,1,0,0,2,0,3,0,0,0,0,1,0,0,5,0,0,0,0,1,11,0,6,4,0,0,0,0,Class_3
+25966,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_3
+25967,0,0,1,0,0,0,0,1,1,1,0,0,0,6,0,5,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_3
+25968,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,4,6,0,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,Class_3
+25969,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_3
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+26440,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,1,0,1,4,1,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,1,7,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,1,0,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,3,0,10,0,0,0,0,0,Class_4
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+26631,9,0,0,0,0,0,0,1,0,0,0,0,1,4,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,2,0,1,0,7,2,2,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,2,0,3,0,0,0,0,1,0,0,3,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_4
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+26828,0,0,0,0,0,0,0,0,0,0,0,0,0,6,26,2,0,0,0,1,0,0,0,0,8,1,0,0,0,0,0,0,5,1,0,1,0,2,0,4,0,1,1,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,1,0,6,1,0,1,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,Class_4
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+26830,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,Class_4
+26831,0,0,0,2,0,0,1,0,0,0,0,0,0,4,19,6,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,2,0,1,1,3,0,0,0,0,0,0,0,0,1,13,0,0,0,0,0,2,2,2,1,0,0,2,0,0,0,3,0,9,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,2,0,0,0,1,0,Class_4
+26832,1,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,7,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,Class_4
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+26834,0,0,0,0,0,0,0,0,0,4,1,1,1,0,21,4,0,1,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,4,1,6,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,2,0,0,0,0,Class_4
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+26838,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,4,1,2,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,3,2,0,1,1,2,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,2,0,0,0,Class_4
+26839,0,0,0,0,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,14,0,0,0,0,0,Class_4
+26840,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,2,0,0,0,1,0,0,1,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_4
+26841,2,0,1,1,2,0,1,0,0,3,0,0,2,2,0,2,0,0,0,0,0,0,0,1,7,2,0,0,0,0,0,1,0,1,0,0,0,1,0,4,0,1,18,5,0,0,0,0,1,1,0,0,0,5,4,1,0,0,0,1,0,1,0,1,4,3,1,0,0,15,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,3,4,3,0,0,0,0,0,Class_4
+26842,0,0,0,0,0,0,0,0,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,9,0,0,0,0,0,Class_4
+26843,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_4
+26844,0,0,0,1,0,0,0,0,0,0,0,0,0,3,12,6,2,2,0,0,0,3,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,2,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,6,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,3,0,11,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,2,0,0,0,3,0,Class_4
+26845,0,0,0,0,0,0,0,1,0,0,0,0,0,0,18,15,0,2,0,2,0,5,0,1,4,4,0,0,1,0,0,0,6,0,1,1,1,2,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,3,0,13,0,0,0,0,0,0,0,0,0,0,0,0,2,4,1,3,1,0,0,1,0,Class_4
+26846,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,8,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,11,0,0,0,0,0,Class_4
+26847,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_4
+26848,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_4
+26849,0,0,2,0,0,0,0,0,0,0,0,0,0,0,15,8,0,0,0,2,0,4,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,3,2,3,0,2,0,1,0,1,0,0,1,8,1,0,0,0,0,1,0,4,1,0,2,1,0,0,0,3,0,11,0,0,0,0,0,0,0,0,0,0,0,0,3,2,1,1,1,0,0,0,0,Class_4
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+29812,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29813,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29814,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_5
+29815,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29816,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_5
+29817,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_5
+29818,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29819,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,24,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_5
+29820,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_5
+29821,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29822,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,3,0,0,2,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_5
+29823,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_5
+29824,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29825,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_5
+29826,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_5
+29827,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29828,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29829,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,18,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_5
+29830,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29831,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29832,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,19,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29833,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29834,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_5
+29835,0,0,0,0,0,0,0,0,12,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29836,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29837,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,44,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_5
+29838,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,17,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29839,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_5
+29840,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,37,0,0,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29841,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_5
+29842,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,4,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,Class_5
+29843,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,2,10,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
+29844,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_5
+29845,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_5
+29846,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,1,3,0,0,1,1,0,0,0,0,0,4,4,13,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_5
+29847,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_5
+29848,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,7,0,0,1,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,Class_5
+29849,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,3,11,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_5
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+31509,1,0,0,1,0,0,0,1,0,1,2,0,1,0,0,1,2,2,2,4,0,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,7,0,2,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,21,0,1,0,0,0,10,Class_6
+31510,1,0,2,11,0,0,0,0,1,0,6,0,0,0,0,4,0,1,2,0,0,1,0,0,0,0,1,2,0,0,5,2,0,0,2,0,0,0,0,1,1,0,0,2,0,5,0,0,2,0,0,0,0,5,0,1,0,0,0,1,5,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31511,0,0,4,3,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,1,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31512,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,1,0,0,1,0,1,17,0,0,0,2,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,4,1,0,0,0,0,0,0,Class_6
+31513,0,0,16,3,0,0,0,0,0,0,10,1,0,0,0,2,0,0,0,0,0,1,2,2,0,2,14,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,18,0,0,3,0,0,0,0,14,0,4,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31514,2,0,11,13,0,0,0,0,0,0,9,0,3,0,0,0,0,3,0,0,0,3,0,0,0,4,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,10,0,0,0,1,0,2,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31515,0,3,6,1,0,0,0,0,0,0,9,1,2,0,0,0,1,3,1,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,6,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,2,0,2,0,0,0,0,Class_6
+31516,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,16,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+31517,0,1,1,10,0,0,1,1,0,0,9,0,0,2,0,1,0,0,1,0,1,2,0,2,0,0,1,0,0,0,0,1,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,3,0,2,2,1,0,1,0,0,0,2,1,1,1,0,1,0,0,1,0,2,0,0,0,Class_6
+31518,0,0,11,0,0,0,0,0,0,0,10,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,5,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,1,4,0,8,0,1,1,0,0,1,0,8,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,1,Class_6
+31519,0,1,8,4,0,0,0,0,0,0,8,1,1,0,0,2,0,0,0,0,1,2,0,0,0,1,7,1,0,0,0,2,0,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,15,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,2,10,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31520,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31521,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,3,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,11,0,2,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,Class_6
+31522,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+31523,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,2,1,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31524,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31525,0,1,4,13,0,0,0,0,0,1,6,0,1,1,0,5,0,1,0,0,1,2,0,0,1,1,0,2,0,0,0,1,2,0,9,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,1,1,1,0,1,0,0,6,0,8,0,0,0,0,0,0,4,0,0,0,0,Class_6
+31526,1,0,2,4,0,0,0,0,1,0,18,0,1,0,0,0,0,1,0,0,0,1,0,14,0,0,6,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,2,0,8,1,0,0,0,0,0,0,3,0,0,0,15,0,0,2,0,0,1,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,6,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+31527,0,0,10,3,0,0,0,2,0,0,5,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,Class_6
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+31529,0,0,7,8,0,0,0,0,1,1,17,0,0,0,0,4,0,0,0,0,5,0,0,14,1,3,12,2,0,0,0,2,1,0,0,1,1,2,2,1,0,0,0,1,0,5,0,1,1,0,0,3,0,7,1,1,0,16,0,1,3,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
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+31537,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,1,0,0,1,4,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,7,0,2,0,2,0,1,1,0,0,0,0,3,0,1,0,5,1,1,1,0,0,0,1,5,1,0,0,0,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,1,1,Class_6
+31538,0,0,0,0,0,0,0,2,0,0,0,0,4,1,0,4,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+31539,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,1,0,3,0,2,0,1,0,0,0,0,1,0,0,2,0,1,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,4,0,0,5,0,0,6,0,1,0,1,0,5,3,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,1,0,0,0,0,0,0,0,0,2,0,Class_6
+31540,0,0,8,2,0,0,0,4,0,0,0,0,1,0,0,3,1,0,0,1,0,4,0,0,0,2,1,1,0,0,1,1,1,0,0,0,0,1,0,1,0,1,1,1,0,7,1,1,0,0,0,0,0,19,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31541,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,4,0,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,Class_6
+31542,0,0,13,13,0,0,0,0,1,1,11,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,14,1,0,0,0,0,0,5,4,0,0,5,0,0,0,0,0,0,0,6,0,0,1,0,1,0,1,11,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,6,0,1,0,0,0,0,0,1,4,0,0,0,0,Class_6
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+31544,1,0,1,2,0,0,0,0,0,0,10,0,0,0,1,4,0,0,0,0,0,2,0,9,0,1,0,0,0,0,0,1,0,0,8,0,0,0,0,10,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,0,1,0,0,0,0,Class_6
+31545,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,1,1,0,2,0,0,0,0,3,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+31546,1,1,1,1,0,0,0,1,0,1,0,2,3,1,0,3,5,1,0,0,0,1,0,6,1,1,1,1,0,1,0,4,0,1,2,0,4,2,2,0,0,3,0,3,0,1,0,4,1,3,0,0,0,3,1,0,0,0,3,3,0,1,1,1,1,5,9,0,0,4,1,2,1,0,4,2,0,0,0,2,0,0,0,0,4,0,2,0,0,0,0,0,0,Class_6
+31547,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,6,0,0,17,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,1,1,0,0,0,Class_6
+31548,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+31549,1,0,3,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,3,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+31550,0,1,18,14,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,2,2,1,2,14,1,5,1,0,0,0,0,1,0,1,1,0,0,1,0,6,0,4,0,7,0,0,0,0,0,3,0,22,0,0,0,1,0,0,3,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,3,1,0,4,0,Class_6
+31551,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,2,0,2,1,0,0,0,0,0,0,2,2,2,0,0,1,3,0,0,0,3,0,0,0,0,0,1,0,2,0,0,2,2,2,0,1,0,1,1,0,2,0,0,0,0,6,0,0,2,3,1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,6,0,0,0,0,0,0,Class_6
+31552,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,4,0,3,0,0,0,2,0,1,0,10,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,11,0,0,1,0,0,17,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+31553,0,0,0,2,0,0,0,0,0,0,2,0,1,0,1,2,1,1,0,0,1,0,0,6,0,7,0,0,0,0,0,1,0,1,0,1,0,2,0,2,0,3,0,1,0,0,0,1,0,0,0,0,0,3,0,0,3,0,0,12,0,1,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,1,8,0,0,2,0,Class_6
+31554,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31555,0,0,11,3,0,0,0,0,1,0,12,0,0,0,0,4,0,3,0,0,1,5,0,0,0,1,8,5,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,3,1,7,0,0,1,0,0,1,0,20,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,3,0,0,0,0,0,0,2,0,1,4,0,Class_6
+31556,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,1,0,1,0,5,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,1,0,0,Class_6
+31557,0,1,1,2,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,6,0,9,0,1,0,0,1,0,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,5,0,0,4,0,0,13,0,0,0,0,0,1,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,Class_6
+31558,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,10,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+31559,0,1,0,1,0,0,3,3,0,0,2,0,2,1,2,4,8,0,8,8,0,1,6,24,0,2,0,0,1,3,5,3,0,1,2,4,0,5,2,2,0,2,0,3,0,0,1,4,0,1,2,0,1,4,2,0,9,0,0,20,0,1,0,0,0,1,8,1,0,0,0,0,2,9,0,3,0,4,0,0,2,0,40,0,0,0,2,0,5,0,0,1,2,Class_6
+31560,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,19,0,4,0,0,2,0,1,0,1,1,1,0,0,0,0,0,1,2,0,1,0,0,2,10,0,0,0,0,0,0,2,0,6,0,0,18,0,0,1,0,0,0,18,4,0,0,2,0,0,2,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+31561,0,0,2,11,0,1,0,0,0,0,8,0,0,0,1,1,0,0,2,0,8,1,1,0,0,8,12,5,0,0,0,6,0,0,3,0,0,0,1,0,0,3,0,0,0,13,0,1,1,0,0,2,0,9,0,1,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,2,1,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31562,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Class_6
+31563,0,0,9,4,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,1,1,0,0,0,2,6,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,5,0,0,1,0,0,0,1,10,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+31564,0,0,1,0,0,0,0,0,1,0,10,0,1,0,0,5,0,0,0,0,2,1,0,0,0,1,3,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+31565,0,1,11,2,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,2,1,2,0,4,22,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,10,0,1,0,1,0,0,0,14,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+31566,0,0,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+31567,0,0,0,0,0,0,0,1,0,0,2,1,1,0,0,5,0,0,0,0,0,2,0,3,0,4,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,1,Class_6
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+31569,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,2,0,3,0,2,0,2,0,0,0,0,0,0,0,0,6,0,1,0,0,1,0,2,0,0,0,1,0,1,0,0,0,0,0,1,0,0,3,0,0,8,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+31587,0,0,8,0,0,0,0,0,0,0,9,0,0,1,0,4,0,0,0,0,2,1,0,2,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+31588,0,1,5,2,0,0,0,0,1,0,11,1,1,2,4,3,0,0,0,2,0,4,0,0,0,0,2,3,0,1,1,2,0,0,3,0,0,1,0,0,3,1,0,2,0,4,0,0,3,0,0,0,2,12,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,2,0,2,0,1,0,0,0,0,0,8,1,3,0,0,0,0,1,0,6,0,0,1,0,Class_6
+31589,0,0,2,1,0,0,0,0,0,0,7,0,0,0,1,3,0,1,0,0,2,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+31590,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,3,0,5,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,2,0,0,0,0,3,1,0,0,0,0,0,1,2,0,2,0,0,8,0,0,1,0,0,1,4,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+31591,0,0,16,8,0,0,0,0,0,3,7,1,1,1,0,6,0,0,1,0,1,7,0,0,1,1,6,3,0,0,0,0,1,0,2,0,0,1,1,1,0,0,0,3,0,14,1,0,1,0,0,0,0,23,0,0,1,0,0,0,5,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,7,1,0,0,0,0,0,1,1,0,0,0,Class_6
+31592,0,0,0,1,0,0,0,1,0,0,3,1,4,0,0,1,1,6,0,1,0,0,0,4,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,5,0,13,0,0,3,1,0,0,0,0,0,1,3,0,4,0,1,2,0,3,0,2,0,3,1,0,0,9,2,0,0,1,0,1,0,0,2,1,0,0,1,0,0,0,1,0,1,0,0,0,0,Class_6
+31593,0,0,22,10,0,0,0,0,0,1,9,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,5,4,1,0,0,1,0,1,3,1,0,0,0,1,0,3,1,1,0,5,0,0,3,0,0,1,1,5,0,1,0,0,0,0,3,1,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31594,0,0,3,6,0,0,0,0,0,0,2,0,1,2,0,1,0,2,4,1,1,3,0,16,0,5,0,0,0,0,11,2,0,0,2,0,0,2,1,2,0,2,0,0,0,0,0,3,0,0,0,0,0,4,1,1,5,0,0,12,1,1,0,0,0,1,9,1,0,1,0,1,1,2,0,2,0,0,7,5,0,0,0,0,0,0,0,0,1,6,0,0,2,Class_6
+31595,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,2,11,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+31596,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31597,0,0,3,0,0,0,0,0,0,0,8,1,0,0,0,0,0,2,0,0,2,0,0,0,0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,15,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31598,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,3,1,0,1,1,2,0,14,2,0,0,0,0,0,0,6,1,2,0,1,1,2,1,0,0,1,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,1,0,0,3,0,0,0,10,4,0,1,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,16,2,0,0,0,1,0,0,Class_6
+31599,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,0,1,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,0,7,0,0,0,0,0,1,3,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,2,1,0,0,0,1,0,0,2,0,0,0,0,0,0,Class_6
+31600,1,0,8,16,0,0,0,0,2,0,8,0,1,0,0,4,0,3,2,1,2,0,1,0,0,1,0,1,0,0,2,3,0,0,10,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,9,1,0,1,0,0,1,3,1,1,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,7,0,0,0,0,1,0,1,0,0,0,0,Class_6
+31601,0,0,0,3,0,0,0,0,2,0,0,0,1,0,0,6,0,3,0,0,0,0,1,2,0,11,0,0,1,0,0,0,4,5,6,0,0,7,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,6,0,0,8,0,2,0,0,1,2,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_6
+31602,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,2,0,2,1,0,0,0,0,0,3,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,7,0,Class_6
+31603,0,1,4,3,0,0,0,0,1,0,12,1,2,1,2,4,0,0,0,0,1,1,0,0,0,0,19,1,0,0,1,0,0,0,2,0,0,0,0,0,0,1,1,2,0,2,0,1,0,0,0,0,0,19,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,9,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+31604,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,3,0,1,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,2,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,Class_6
+31605,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+31606,0,0,3,0,0,0,1,0,4,0,1,0,0,3,1,7,1,2,0,1,0,9,0,1,1,0,0,2,1,0,1,0,5,2,1,2,4,2,1,7,2,12,0,1,0,0,1,7,0,0,0,0,1,4,0,3,0,0,1,12,0,8,1,1,0,3,10,0,0,1,1,2,0,0,0,0,0,0,1,1,0,0,1,0,0,0,27,3,3,1,0,6,5,Class_6
+31607,1,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,4,1,1,0,0,1,0,0,3,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,2,0,4,1,0,13,0,1,0,0,0,1,5,0,0,0,2,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+31629,0,1,0,0,0,0,0,1,1,0,0,0,3,1,2,0,0,4,0,2,0,3,0,5,0,0,0,0,1,0,1,3,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,2,0,3,1,0,10,0,1,0,2,1,0,5,3,0,1,0,3,0,0,0,1,4,1,0,1,2,0,0,0,0,0,0,1,2,0,0,0,0,Class_6
+31630,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,3,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,5,1,0,0,1,0,12,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+31631,0,0,0,0,0,0,0,1,0,0,1,1,2,0,0,1,0,1,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,1,1,5,0,1,0,0,0,2,0,0,0,0,1,3,0,1,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,4,0,Class_6
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+31636,1,0,1,1,0,0,0,0,1,0,5,0,0,0,3,1,0,2,0,0,1,0,1,1,0,1,3,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,4,0,1,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,32,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+31637,0,0,0,1,0,0,0,1,0,0,1,0,1,0,2,0,2,2,1,5,0,4,1,1,0,1,0,1,0,0,0,0,1,0,2,4,0,1,1,0,1,0,0,2,0,0,0,0,3,1,5,0,0,2,0,0,0,0,1,12,0,1,0,0,1,0,0,0,1,3,0,5,1,0,0,0,1,1,0,1,0,1,0,0,0,0,12,1,2,3,1,0,0,Class_6
+31638,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,2,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+31639,0,0,3,4,0,0,0,1,0,0,7,0,1,0,0,1,0,3,0,1,3,0,0,0,0,0,7,2,0,0,1,2,0,0,0,1,1,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,4,0,1,0,0,0,1,1,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31640,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,3,0,1,0,0,0,1,0,10,0,7,0,0,0,0,0,1,0,0,0,1,0,5,1,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,2,0,0,2,2,14,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Class_6
+31641,1,0,0,3,0,0,0,1,0,0,13,0,0,0,0,5,1,2,0,1,1,2,0,0,0,2,7,0,0,0,1,1,1,0,1,0,1,1,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,4,0,0,8,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,22,0,4,0,0,0,0,1,0,1,0,0,1,0,Class_6
+31642,0,0,1,0,0,0,0,2,0,3,1,1,0,2,0,5,2,1,0,0,0,2,0,3,1,4,0,2,0,0,3,0,2,0,1,1,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,2,2,5,0,4,0,0,4,0,4,1,2,1,2,6,1,0,7,1,0,0,7,0,0,0,0,8,1,1,0,0,0,1,2,3,0,1,0,0,0,0,Class_6
+31643,0,0,4,1,0,0,0,0,0,0,6,0,0,1,2,2,0,0,0,0,2,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,6,0,0,1,0,0,0,0,5,0,2,0,0,0,1,5,1,0,0,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31644,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,1,Class_6
+31645,0,0,2,2,0,0,0,4,0,0,2,0,2,2,1,0,1,5,0,0,0,2,1,3,1,1,0,0,0,0,0,6,0,1,2,0,0,3,1,0,1,0,0,1,0,0,1,1,3,1,0,0,0,10,0,0,9,0,1,0,0,1,0,0,0,6,10,2,1,2,0,0,0,0,0,3,0,0,0,2,1,0,1,0,0,1,0,1,0,0,0,0,0,Class_6
+31646,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,2,0,0,0,3,0,2,0,5,0,1,0,0,0,0,1,0,6,0,3,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,11,1,0,2,0,0,4,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,0,0,Class_6
+31647,0,0,9,5,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,0,3,0,0,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+31675,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,3,1,2,0,1,0,0,0,1,0,0,2,1,0,1,0,0,0,5,0,1,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,4,Class_6
+31676,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,3,1,0,12,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+31677,0,0,4,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31678,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31679,1,0,1,1,0,0,0,0,0,1,1,0,3,1,2,3,1,1,0,0,0,1,0,11,1,1,0,5,3,1,0,4,0,0,1,7,0,2,0,0,0,4,0,1,0,0,1,1,1,1,0,0,0,1,4,0,2,0,0,12,0,0,0,5,0,2,17,1,0,3,1,0,0,0,3,0,0,2,0,0,1,2,1,1,0,0,1,0,0,0,0,1,0,Class_6
+31680,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+31681,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31682,0,1,0,0,0,0,0,1,1,1,0,0,2,0,2,1,1,5,0,2,0,1,3,3,1,0,2,2,1,0,0,0,1,1,1,0,0,1,1,0,1,1,0,0,0,0,0,2,1,3,1,3,0,0,0,0,4,1,0,11,0,1,0,0,0,0,3,0,2,3,1,3,2,0,0,0,4,1,0,1,3,0,0,0,2,0,0,0,2,1,0,0,0,Class_6
+31683,0,0,0,1,0,0,0,0,16,0,0,0,0,0,1,3,1,0,0,0,0,11,0,1,2,1,0,0,0,0,0,2,0,0,3,1,0,0,0,1,0,0,0,1,0,0,0,2,0,2,0,0,0,13,2,0,0,0,0,1,0,0,0,24,0,1,5,0,0,2,1,1,2,1,0,0,0,0,0,0,0,0,0,0,4,1,1,2,0,0,0,1,0,Class_6
+31684,0,0,12,3,0,0,0,0,0,1,14,0,1,1,0,3,0,2,0,0,1,2,0,7,0,0,9,4,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,2,0,10,0,2,0,0,0,1,0,15,0,0,0,6,0,1,1,0,0,2,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,1,1,0,0,0,Class_6
+31685,0,1,9,6,0,0,0,0,0,0,6,0,0,0,0,9,0,0,1,0,2,2,0,0,0,11,4,3,0,0,0,2,0,0,1,1,0,0,0,1,0,1,0,2,1,5,0,1,0,0,1,0,0,9,0,0,0,1,0,1,3,0,0,0,3,0,0,0,0,0,0,1,3,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,Class_6
+31686,0,3,2,1,1,0,2,0,0,0,5,0,0,0,0,6,0,2,0,0,1,1,0,1,0,0,2,1,0,4,3,1,0,0,3,0,0,2,0,2,0,0,0,1,0,0,2,2,7,0,0,0,1,13,1,0,0,0,0,0,1,1,1,0,2,1,2,0,0,2,0,0,1,2,2,1,0,0,1,1,0,5,0,2,0,1,0,0,1,0,0,0,0,Class_6
+31687,4,0,0,0,0,0,0,1,0,2,0,0,1,1,1,2,0,0,0,0,0,4,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,2,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,5,1,0,0,0,0,5,Class_6
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+31715,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,3,0,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,0,0,2,0,0,1,1,0,0,14,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,2,Class_6
+31716,1,0,0,0,0,0,1,0,0,1,8,0,0,3,0,6,0,2,3,1,0,0,2,0,0,0,4,0,1,0,0,3,0,0,3,0,0,2,1,1,0,1,3,6,0,2,0,0,0,1,0,1,1,3,0,1,0,0,0,1,0,2,0,1,1,1,1,0,0,3,0,0,0,0,0,0,0,0,0,4,0,2,1,0,0,0,0,0,2,0,0,0,0,Class_6
+31717,0,0,0,0,0,0,0,0,1,1,2,0,1,0,0,2,3,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,4,1,0,1,0,0,0,0,0,0,3,0,4,6,0,0,3,0,0,1,2,0,0,0,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,Class_6
+31718,0,0,7,2,0,0,0,0,0,0,17,0,1,0,0,7,0,0,0,0,0,2,0,6,0,1,0,0,1,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,6,0,1,1,0,0,0,0,8,0,0,0,6,0,2,2,1,0,1,1,2,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+31719,0,0,16,5,0,0,0,0,0,1,8,1,0,1,0,1,0,2,0,0,0,3,0,2,0,1,9,3,0,0,0,0,2,1,1,0,0,0,0,0,0,0,1,3,1,9,0,0,1,0,0,0,0,7,1,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,3,0,0,0,0,Class_6
+31720,0,0,3,9,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,0,1,0,0,0,5,1,1,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+31721,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31722,0,0,9,2,0,0,0,0,0,0,5,0,0,2,0,9,0,3,0,0,2,5,1,2,1,4,3,4,1,0,0,0,0,1,1,1,0,0,0,1,0,2,0,1,0,8,0,2,1,0,0,0,0,21,1,0,0,0,0,0,1,1,0,0,2,1,0,0,0,2,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+31723,0,0,4,5,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,3,3,0,0,0,1,0,0,2,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31724,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,13,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31725,0,0,15,3,0,0,0,0,0,0,8,1,0,1,0,7,0,0,0,0,5,3,0,0,0,1,7,2,1,0,2,1,0,0,3,0,0,0,0,0,0,1,0,2,0,7,0,1,1,0,0,2,0,24,1,0,0,0,0,1,3,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31726,0,0,6,9,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,1,1,1,3,0,0,0,0,3,0,0,0,0,0,1,1,4,0,0,0,0,0,9,0,2,0,1,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,7,0,0,0,0,0,3,0,2,1,0,0,0,0,0,0,0,0,1,0,Class_6
+31727,0,0,3,0,0,0,0,1,0,0,19,0,0,0,0,1,0,0,0,0,2,0,0,12,0,1,8,0,0,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,2,0,0,1,7,0,0,2,0,0,1,0,1,2,0,0,0,0,0,0,4,0,0,0,0,0,11,0,2,0,0,0,0,0,1,0,0,1,1,0,Class_6
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+31748,0,0,3,3,0,0,1,0,0,0,20,0,1,0,0,1,1,0,0,1,1,1,0,7,0,0,9,2,0,0,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,1,0,1,0,13,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31749,0,0,25,7,0,0,0,0,0,0,8,1,0,3,0,3,0,0,1,0,1,3,0,2,0,2,14,1,0,0,0,0,0,2,3,0,0,1,0,0,0,1,1,2,0,13,0,0,1,0,0,0,0,14,0,1,1,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,1,0,3,1,0,2,0,Class_6
+31750,0,0,9,2,0,0,0,0,0,0,6,0,0,0,0,2,0,1,1,0,5,1,0,1,0,1,8,2,0,0,1,0,0,0,0,0,2,2,0,3,0,0,0,1,0,10,0,0,2,0,0,3,0,6,0,0,0,0,0,0,3,0,0,0,0,2,1,1,0,1,0,0,0,2,0,0,0,0,2,9,0,2,0,0,0,0,1,0,1,1,0,2,1,Class_6
+31751,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,3,0,1,0,2,0,0,0,0,0,0,1,1,0,0,1,0,0,8,0,4,1,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,1,1,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,Class_6
+31752,0,0,4,0,0,0,0,0,0,3,0,0,3,1,0,4,0,0,1,0,0,2,0,5,1,2,0,0,1,4,2,2,0,2,0,0,0,0,0,1,2,6,0,5,0,0,0,4,2,1,0,0,29,10,0,1,0,0,0,2,0,4,0,1,0,0,1,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,1,1,4,0,0,1,0,0,1,0,Class_6
+31753,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,2,0,3,0,1,0,0,0,0,1,0,1,0,0,1,0,0,3,7,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,19,0,1,0,0,0,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+31754,0,0,15,3,0,0,0,0,0,0,8,1,1,2,0,2,0,0,0,0,1,1,0,0,0,0,11,3,0,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,15,0,0,0,0,0,0,0,12,0,0,0,0,0,1,0,2,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,1,0,1,1,0,0,0,Class_6
+31755,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,13,0,2,1,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,Class_6
+31756,0,1,1,0,0,0,0,0,0,0,9,0,0,1,0,0,1,0,0,1,0,1,1,2,0,1,4,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,3,0,6,0,0,3,1,1,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,1,1,1,1,0,0,0,4,3,0,6,0,0,0,0,0,0,3,1,0,0,0,Class_6
+31757,0,0,2,1,0,0,0,0,0,0,9,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,4,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,1,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31758,1,0,1,2,3,0,1,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,4,4,4,0,0,1,0,0,0,0,2,0,0,0,0,2,4,1,0,1,0,5,0,0,1,1,2,9,0,0,0,5,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,9,0,7,0,0,1,0,0,0,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,Class_6
+31759,1,0,1,0,0,0,1,0,0,0,12,0,0,0,0,2,0,3,0,0,0,0,0,2,0,1,5,0,0,0,4,2,0,2,0,0,5,1,0,1,0,0,1,2,0,0,0,0,0,0,0,4,0,4,0,0,2,10,0,1,2,0,1,3,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,21,0,1,1,0,0,0,0,0,0,1,0,0,0,Class_6
+31760,0,0,2,0,0,0,0,0,0,0,0,0,3,1,0,2,0,1,1,0,0,1,0,2,0,5,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,2,0,1,5,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,Class_6
+31761,0,0,26,4,0,0,0,0,0,0,4,0,0,0,0,1,1,0,1,0,1,2,0,0,0,1,5,4,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,12,0,1,2,0,1,0,0,12,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,1,1,2,1,0,0,0,Class_6
+31762,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,2,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,13,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+31763,2,1,1,0,0,0,0,0,0,0,13,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,8,0,0,0,0,0,8,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,16,0,0,0,0,0,0,0,0,2,1,1,0,0,Class_6
+31764,0,0,0,1,0,0,0,1,0,0,0,0,0,2,1,4,0,0,0,0,0,2,0,1,1,1,0,1,0,0,0,0,0,1,0,0,0,1,0,10,1,4,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,0,0,16,0,0,0,1,0,1,2,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,Class_6
+31765,0,0,1,1,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,0,0,1,8,1,0,0,0,0,0,0,3,1,0,0,2,2,1,1,1,0,1,0,0,0,0,2,4,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,11,0,1,18,1,0,3,0,0,2,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+31766,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,2,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,2,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+31767,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,3,0,0,1,1,0,1,0,0,0,9,1,2,1,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,1,4,1,0,8,0,0,7,0,0,0,1,0,2,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,Class_6
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+31796,0,0,0,9,0,0,0,0,0,0,7,0,0,2,3,1,0,1,0,0,1,2,0,1,0,2,14,1,0,0,1,0,1,1,7,0,0,1,0,1,0,1,1,3,1,1,0,1,0,0,0,1,0,5,0,1,1,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31797,1,0,1,0,0,0,0,5,1,0,0,0,12,4,0,5,2,2,0,3,0,3,0,4,3,0,0,0,0,0,0,0,1,1,0,9,0,2,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,1,2,0,10,1,2,0,1,9,1,0,1,0,0,1,0,1,0,0,0,7,0,0,0,0,0,0,1,2,0,4,0,4,2,0,Class_6
+31798,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,14,0,4,0,0,0,0,0,0,0,1,4,0,0,0,0,0,2,7,0,0,0,0,0,3,1,0,0,0,0,7,0,0,2,0,4,12,1,1,0,0,0,0,3,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+31799,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,1,0,6,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,9,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31800,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,1,1,0,0,0,3,0,5,1,2,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,4,0,0,0,0,0,0,1,0,0,0,0,1,1,0,4,0,0,4,0,1,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31801,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,2,0,2,0,3,0,1,0,5,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,3,0,1,0,1,0,3,0,0,3,0,0,10,0,0,0,0,1,0,8,0,1,1,0,0,1,1,0,1,0,0,0,2,0,0,5,0,0,0,0,0,0,0,1,0,0,Class_6
+31802,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,2,2,6,1,2,2,0,0,0,0,8,0,0,0,1,0,1,0,0,1,2,0,0,0,0,0,5,0,0,0,0,0,3,0,1,0,0,2,2,0,0,0,2,0,1,4,0,0,6,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,3,9,0,0,0,0,0,17,Class_6
+31803,1,1,0,1,0,0,1,2,1,0,0,0,3,0,1,1,1,5,0,4,0,2,0,1,2,1,2,0,3,0,0,0,0,1,0,1,0,1,0,0,1,0,1,0,0,0,0,4,2,0,5,0,0,0,3,0,1,3,0,10,0,0,0,1,0,0,0,1,1,1,0,3,0,0,0,0,4,2,0,0,0,0,10,0,0,0,0,2,1,2,2,0,1,Class_6
+31804,0,1,10,8,0,0,0,0,0,0,19,0,0,1,0,1,0,0,0,0,0,1,0,10,0,3,7,1,0,0,2,0,1,0,3,0,4,2,0,0,1,0,0,0,0,3,0,1,2,0,0,3,0,7,0,0,0,4,0,0,3,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,2,2,0,5,1,0,1,0,0,0,0,0,1,0,0,2,0,Class_6
+31805,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,1,0,Class_6
+31806,0,0,5,2,0,0,0,0,0,1,12,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,4,0,0,0,0,0,0,0,2,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,13,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+31807,0,0,3,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,1,1,1,0,3,1,0,0,0,0,0,1,1,0,1,0,3,0,0,2,0,0,1,0,6,1,0,0,0,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,9,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
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+31823,1,0,3,1,0,0,0,0,0,0,12,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,28,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,7,1,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,1,0,0,0,0,1,0,1,1,0,1,0,0,0,Class_6
+31824,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,0,3,0,0,0,0,3,0,0,0,1,0,0,4,0,0,3,14,0,0,0,0,0,2,0,0,0,0,2,4,1,0,1,0,0,14,0,0,0,0,0,3,2,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31825,0,0,1,2,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,7,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31826,0,0,3,2,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,2,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31827,0,0,16,12,0,0,0,1,0,1,10,0,2,0,0,1,0,0,0,0,1,0,0,0,1,3,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,7,0,0,0,0,0,0,0,12,1,0,0,0,0,1,4,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+31828,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,7,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,6,0,0,5,0,0,12,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31829,0,0,7,1,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,2,0,0,0,0,11,0,1,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31830,1,2,1,1,0,0,0,0,4,0,14,0,0,0,0,8,0,0,0,0,1,3,0,8,0,3,5,0,0,0,2,2,0,2,2,2,0,4,0,0,0,1,0,0,0,0,0,3,2,0,0,1,0,5,0,5,0,0,0,1,1,0,0,0,0,1,4,0,0,0,0,0,0,3,3,0,1,0,0,15,0,3,4,0,0,0,0,0,0,0,0,0,0,Class_6
+31831,0,0,25,32,0,0,0,0,2,2,8,0,0,0,5,5,0,0,0,0,3,5,0,0,0,2,0,3,0,1,6,0,1,0,5,0,0,1,0,0,1,0,0,1,0,20,0,0,5,0,0,1,0,19,1,1,0,0,0,0,4,5,0,0,2,3,0,0,0,2,0,0,3,0,0,1,0,0,0,16,0,10,0,0,0,0,0,0,0,1,0,1,0,Class_6
+31832,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,2,0,1,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31833,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,1,3,0,0,0,3,0,1,0,4,0,0,0,0,0,0,0,0,4,4,1,2,0,1,0,9,0,1,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,13,0,3,0,0,1,1,16,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,2,0,0,0,2,1,Class_6
+31834,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,18,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31835,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,4,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,10,4,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,Class_6
+31836,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+31837,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,1,0,0,0,9,2,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,2,0,1,0,1,0,2,0,Class_6
+31838,0,0,1,1,0,0,0,1,2,0,0,0,0,0,0,0,1,1,0,4,0,0,0,11,0,1,0,0,0,0,0,0,0,1,1,1,0,2,5,0,1,1,1,1,0,0,0,11,0,7,0,0,0,2,0,0,0,0,0,1,0,0,3,2,0,1,4,0,0,5,0,2,0,2,0,0,1,1,0,0,0,0,0,0,4,0,3,3,1,0,0,0,0,Class_6
+31839,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,2,1,2,0,1,0,8,0,1,0,10,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,7,2,0,9,0,0,5,0,0,0,1,0,2,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,4,0,Class_6
+31840,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31841,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,1,0,2,0,11,0,2,0,0,0,0,1,3,1,0,0,0,0,1,0,3,0,0,0,5,0,0,0,0,0,0,0,7,0,0,11,0,0,12,0,1,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,3,0,Class_6
+31842,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,2,1,1,0,0,0,0,2,1,1,0,0,1,0,0,0,2,0,4,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,5,5,4,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+31843,0,0,0,4,0,0,0,0,0,0,9,0,0,0,0,1,1,3,0,2,0,2,0,2,0,0,0,0,0,0,4,0,0,0,2,2,0,2,0,0,0,0,0,0,0,0,0,2,1,0,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,6,0,0,2,0,0,0,0,4,2,0,0,0,6,1,0,0,0,0,0,0,0,8,1,0,0,0,Class_6
+31844,0,0,11,5,0,0,0,0,0,0,12,0,0,0,0,1,0,1,0,0,0,1,0,7,0,1,4,1,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,9,1,0,0,8,0,1,0,2,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,0,7,1,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31845,1,1,5,1,0,0,0,0,0,0,18,1,0,0,0,2,0,1,0,0,1,2,0,5,0,0,4,2,0,0,0,2,1,0,2,1,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,3,0,2,0,1,0,9,0,2,1,1,0,1,0,1,1,0,0,0,0,0,1,4,0,1,0,0,0,11,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+31846,2,0,0,0,0,0,0,1,0,1,1,0,0,1,0,0,0,1,0,2,0,1,0,5,1,0,0,0,0,0,1,5,0,0,2,0,2,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,9,0,0,1,1,0,0,11,0,0,1,0,2,0,0,0,1,0,0,3,0,0,0,6,0,0,0,6,1,0,0,0,0,0,Class_6
+31847,0,2,8,3,0,0,0,0,0,0,10,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,26,3,0,1,0,2,0,0,1,0,0,0,1,0,1,0,0,1,1,0,0,0,1,0,0,2,0,5,2,2,0,1,0,1,3,3,0,0,0,2,1,2,0,0,0,0,1,7,0,0,2,0,1,5,0,0,0,0,0,0,2,0,2,1,0,0,0,Class_6
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+31849,0,0,9,8,0,0,0,0,0,0,10,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,5,3,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,5,0,1,0,0,0,0,7,0,1,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+31868,0,2,10,2,1,0,0,0,0,1,6,1,0,1,0,3,0,1,0,0,0,3,0,1,0,7,1,2,1,0,0,0,1,3,3,0,0,0,0,0,0,1,0,1,0,3,0,2,2,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31869,0,0,2,6,0,0,0,0,0,1,6,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,3,1,1,0,1,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+31870,0,1,12,7,0,0,0,0,0,1,14,0,0,0,0,1,0,0,0,0,0,3,0,4,0,1,10,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,1,0,6,0,0,1,0,0,8,0,11,0,2,0,10,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31871,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,2,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,3,0,0,0,0,0,0,2,4,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31872,0,0,0,1,0,0,0,3,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,1,0,0,2,0,0,2,0,0,4,0,2,0,0,0,2,0,0,0,0,2,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,3,0,Class_6
+31873,0,0,1,4,2,0,0,0,2,0,12,0,0,2,1,1,0,0,0,0,2,0,2,0,0,1,3,2,0,1,0,1,0,0,2,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,13,0,1,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,1,22,0,3,0,0,0,7,0,3,0,0,1,0,0,0,2,0,1,1,0,Class_6
+31874,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+31875,0,0,11,3,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,16,0,0,1,0,0,0,0,7,0,0,0,0,0,1,7,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31876,0,0,0,1,0,0,0,1,0,1,1,0,2,0,0,1,0,0,2,0,0,2,0,8,0,9,0,1,0,0,0,1,2,2,0,0,0,1,0,5,0,1,0,0,0,0,0,6,0,0,0,1,1,1,0,0,4,0,1,10,0,1,1,1,1,1,6,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,Class_6
+31877,0,0,11,9,0,0,0,0,0,1,5,0,0,0,1,3,0,1,0,0,0,5,0,0,0,2,2,4,0,0,0,2,0,0,1,0,0,1,0,1,0,7,0,0,0,1,0,0,1,0,0,2,0,5,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+31878,0,2,5,4,0,0,0,0,1,0,10,0,0,0,0,2,0,4,0,1,1,2,0,0,0,4,8,3,0,0,0,0,0,0,3,0,0,0,0,1,0,3,1,0,0,13,0,0,6,0,0,0,0,16,0,0,0,0,0,0,3,1,0,1,0,2,1,0,0,1,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,0,0,1,Class_6
+31879,0,2,6,1,0,0,0,1,0,0,5,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,6,1,0,1,0,14,1,0,0,0,0,0,3,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31880,0,0,8,0,0,0,0,1,0,0,17,0,0,0,1,3,0,0,0,1,0,2,0,7,0,0,6,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,7,0,0,2,0,0,0,0,14,0,0,1,9,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+31881,4,0,1,0,0,0,0,0,0,0,2,0,12,1,0,2,0,1,0,0,0,0,0,5,5,0,0,0,2,1,0,0,1,0,0,1,0,1,0,0,1,3,0,0,0,0,0,3,0,0,0,0,5,3,0,0,0,2,23,2,0,2,2,1,0,1,9,1,1,12,1,0,0,0,0,0,0,1,3,0,0,0,0,0,2,3,1,1,0,0,0,0,0,Class_6
+31882,0,0,4,2,0,0,0,1,0,0,5,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,4,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+31883,0,0,0,1,0,0,0,0,2,0,0,0,0,2,0,1,1,5,0,1,0,2,2,1,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,1,0,1,0,2,0,0,0,0,0,4,0,0,5,0,0,16,0,1,0,0,0,1,7,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+31884,1,0,2,0,0,1,0,0,1,0,0,0,0,0,0,1,0,8,0,0,0,2,0,5,0,2,1,0,1,0,4,0,0,1,1,1,0,1,0,0,4,9,0,2,0,0,0,1,2,0,1,0,0,2,0,0,0,0,0,13,1,2,0,0,0,2,7,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,1,0,0,Class_6
+31885,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31886,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,8,0,1,0,0,0,1,0,1,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+31887,0,0,2,3,0,0,1,0,0,1,12,0,0,1,0,1,0,1,1,0,0,1,0,0,0,1,10,2,0,0,3,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,10,0,10,0,0,0,0,0,0,5,2,1,1,0,0,1,0,1,1,0,1,0,0,0,1,0,0,0,18,0,3,0,0,0,0,1,1,2,0,0,0,0,Class_6
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+31923,1,0,1,1,3,0,0,0,0,2,0,0,1,0,0,4,0,2,1,2,0,5,0,2,0,4,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,4,0,3,0,1,6,2,0,0,6,0,2,6,21,0,3,0,0,17,0,0,6,0,1,3,0,0,3,0,0,0,1,0,3,2,0,0,2,1,5,1,0,3,0,1,0,0,2,0,0,2,0,Class_6
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+31970,5,0,1,0,0,0,0,0,1,1,3,0,1,1,0,3,1,1,0,4,0,1,0,2,3,9,1,0,0,0,0,0,0,1,6,2,0,1,0,0,4,34,0,1,0,0,0,2,1,0,0,1,0,1,2,0,6,0,0,0,0,1,0,1,0,0,9,0,0,1,0,0,0,0,1,3,0,1,0,0,0,0,5,0,0,2,0,1,1,0,2,2,0,Class_6
+31971,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,18,0,0,0,1,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+31972,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,2,0,0,4,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,2,Class_6
+31973,0,0,6,6,0,0,0,1,0,0,6,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,5,4,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,5,2,3,0,1,1,0,0,1,0,14,2,1,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31974,0,0,2,2,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+31975,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+31977,0,0,10,1,0,0,0,1,0,0,7,0,0,1,0,1,0,0,0,0,1,0,0,2,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,10,0,1,1,0,0,0,4,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_6
+31978,2,0,1,0,1,0,0,0,0,0,4,0,0,0,0,6,1,2,0,0,0,5,0,6,3,1,0,0,0,0,0,1,2,0,1,0,0,0,0,1,1,7,0,12,0,0,2,0,2,1,0,0,1,0,1,0,5,0,3,0,0,1,2,0,3,2,4,2,0,2,2,0,0,1,0,0,0,0,3,1,0,0,1,0,6,0,2,0,1,0,1,3,0,Class_6
+31979,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31980,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,Class_6
+31981,0,0,11,7,0,0,1,0,0,0,8,0,0,0,0,8,0,2,0,0,1,4,0,1,0,6,6,2,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,7,0,1,0,0,0,0,1,14,0,0,0,0,0,0,7,0,0,0,0,1,4,0,0,0,0,0,1,1,1,2,0,0,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,Class_6
+31982,0,0,6,6,0,0,0,0,3,0,8,0,0,0,0,5,0,3,0,0,1,3,0,1,0,3,18,3,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,3,0,0,17,0,0,0,2,0,0,5,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+31983,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,13,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,2,0,12,0,0,0,0,0,0,4,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+31984,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,13,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,1,Class_6
+31985,1,0,0,0,1,0,1,0,1,0,0,0,2,0,0,3,2,1,0,1,0,3,0,7,1,5,0,0,0,0,0,8,0,1,1,0,1,1,0,0,0,7,0,10,0,0,0,6,1,0,1,1,0,6,2,1,6,0,1,20,0,1,0,2,1,5,14,0,0,3,2,1,2,1,0,9,0,7,5,0,0,0,1,0,2,7,1,2,0,0,1,0,0,Class_6
+31986,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31987,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31988,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,7,0,1,1,3,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,3,0,1,1,0,5,1,1,19,1,2,0,0,1,1,10,0,0,0,1,0,0,1,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31989,0,1,10,1,2,0,0,0,0,1,7,0,0,2,0,1,0,0,0,0,2,2,0,2,0,2,13,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,1,0,15,0,0,0,1,0,1,3,2,0,0,0,0,2,0,0,2,0,1,1,0,0,2,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,Class_6
+31990,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31991,2,0,1,0,0,0,0,1,0,0,0,2,1,1,0,4,0,2,0,0,0,6,0,2,2,3,0,0,28,0,0,1,0,3,0,1,0,0,0,0,3,2,0,1,0,0,0,1,0,0,0,0,1,3,5,0,1,0,0,1,0,4,0,0,0,3,6,1,0,1,1,0,1,0,2,1,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,3,0,Class_6
+31992,0,0,1,1,0,0,0,0,0,0,0,0,1,3,0,5,0,1,0,0,0,2,0,3,0,15,1,0,0,0,0,0,0,0,5,0,0,0,1,0,0,3,0,0,0,3,0,0,0,0,0,0,0,12,0,0,3,0,0,12,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+31993,1,0,1,1,0,0,0,9,0,0,0,0,11,0,1,4,3,0,0,2,0,2,0,1,2,0,0,0,0,0,0,0,1,2,0,7,0,2,0,2,0,2,0,0,0,0,0,3,0,0,0,0,0,1,2,0,0,0,1,2,0,9,1,0,0,2,9,1,0,0,0,1,1,0,1,0,0,0,7,0,0,0,0,0,0,0,3,0,3,0,4,1,0,Class_6
+31994,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,1,0,0,0,0,0,3,0,0,3,0,0,8,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+31995,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,2,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+31996,0,0,5,4,0,0,0,1,0,0,6,0,0,1,2,0,0,1,0,0,4,1,0,1,0,2,22,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+31997,0,0,11,2,0,0,0,0,0,0,7,0,1,0,0,1,0,0,0,0,0,1,0,0,0,3,1,4,0,0,0,2,0,1,0,0,0,1,0,0,0,0,1,0,0,6,0,1,0,0,0,0,0,7,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+31998,1,0,0,0,2,0,0,1,0,3,3,0,1,0,1,2,0,0,0,2,0,1,0,2,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,4,0,0,13,0,1,1,0,0,1,0,7,0,0,0,1,3,0,0,0,1,1,3,1,1,0,1,3,0,0,1,0,2,0,0,2,1,0,0,0,0,0,2,1,0,0,0,1,0,0,Class_6
+31999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,4,0,1,0,1,1,2,0,2,0,0,0,0,0,1,0,0,4,1,0,1,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,5,0,0,16,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32000,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,6,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,12,0,3,0,0,0,1,5,2,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,Class_6
+32001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,1,0,Class_6
+32002,0,0,0,0,0,0,0,4,0,0,2,1,0,0,0,0,1,2,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,7,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,2,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,Class_6
+32003,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,6,0,10,0,3,2,2,1,1,0,0,1,0,10,2,0,0,0,0,0,2,0,3,0,0,0,0,3,0,0,0,0,9,2,0,1,0,0,8,0,1,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+32004,3,0,0,0,0,0,0,1,0,0,0,0,4,0,1,3,0,3,0,0,0,4,0,1,1,4,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,15,0,1,1,0,0,2,0,0,0,0,0,1,1,0,0,0,4,1,0,0,1,1,1,2,1,0,2,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+32005,0,0,7,1,0,0,0,0,0,0,5,0,0,1,0,2,0,1,0,0,1,1,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,1,1,0,1,0,4,0,0,2,0,0,1,0,11,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,2,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32006,0,1,14,5,0,0,0,0,0,0,10,0,0,0,0,6,0,0,0,0,1,1,0,1,0,2,4,4,0,0,1,0,1,0,4,1,0,0,0,2,1,0,2,6,0,8,0,4,3,0,0,2,0,11,1,0,0,0,2,0,1,0,0,0,1,2,0,0,3,0,0,0,0,0,0,2,0,0,0,7,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+32007,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,3,0,1,0,0,2,0,0,0,0,1,0,0,0,2,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+32009,0,1,13,1,0,0,1,0,0,0,13,0,0,0,0,4,0,0,0,0,0,0,0,9,0,0,10,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,6,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
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+32018,2,0,0,0,0,0,0,4,0,0,0,0,2,0,0,1,1,2,0,0,0,0,0,3,2,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,2,7,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,2,6,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,3,2,0,1,0,0,0,0,Class_6
+32019,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32020,0,0,0,0,0,0,1,0,0,0,8,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,0,2,0,0,0,2,0,3,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,38,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+32022,0,0,5,14,0,0,0,1,0,0,7,1,3,0,0,1,0,0,0,0,0,3,0,2,1,2,7,2,0,0,2,0,0,0,1,0,0,1,0,3,0,1,3,6,0,6,0,0,0,0,0,2,0,18,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,2,0,0,1,0,1,1,1,0,0,0,1,Class_6
+32023,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,9,0,0,0,2,1,1,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,3,1,0,2,0,0,9,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,1,Class_6
+32024,1,0,0,0,0,0,1,2,0,1,0,0,2,1,3,0,5,4,0,1,0,1,2,4,0,0,0,0,0,0,0,1,1,0,0,2,4,1,0,0,1,0,0,1,0,0,0,6,0,0,0,0,0,3,1,0,0,0,3,4,1,0,0,0,0,5,14,0,0,2,2,0,0,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,1,0,Class_6
+32025,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32026,0,0,2,4,0,0,0,0,0,1,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,7,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,7,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32027,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,4,1,5,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,10,0,0,0,2,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32028,0,0,12,8,0,0,0,0,0,0,7,0,1,1,0,5,0,6,0,0,0,3,0,2,0,3,15,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,6,0,0,2,0,0,2,0,10,1,0,0,1,0,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,3,1,0,0,0,5,0,3,0,0,1,0,0,0,1,0,0,0,0,Class_6
+32029,9,0,0,0,0,0,0,0,0,0,2,0,3,1,1,1,0,0,0,0,0,1,0,2,4,0,0,0,1,0,1,0,4,0,0,2,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,3,3,2,2,0,0,8,1,0,1,1,0,0,0,10,1,0,0,0,0,0,1,1,0,0,0,10,0,2,0,0,0,0,2,0,0,2,0,1,13,0,Class_6
+32030,0,0,0,1,0,0,0,1,0,0,6,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+32031,0,0,5,4,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,4,4,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32032,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+32033,0,0,2,2,0,0,0,0,0,0,12,1,0,0,0,8,0,0,0,0,3,4,0,0,0,1,11,2,0,0,0,0,0,1,2,0,0,2,0,1,0,0,1,0,0,10,0,0,1,0,0,2,1,10,0,1,0,0,0,0,5,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,6,0,1,0,0,0,0,0,0,2,0,0,2,0,Class_6
+32034,0,2,0,0,0,0,0,2,0,0,1,0,0,2,1,0,9,0,0,1,0,0,0,2,0,0,0,0,1,0,0,4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,2,0,5,0,0,25,0,1,0,3,0,0,18,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32035,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,5,1,2,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,9,0,0,2,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32036,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,1,0,0,0,0,1,1,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32037,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,1,0,2,0,0,0,1,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,3,0,0,0,2,0,1,0,0,0,1,1,0,1,0,0,0,0,1,0,0,16,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,1,Class_6
+32038,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32039,0,1,6,1,0,0,0,0,0,0,7,0,0,2,0,1,1,0,0,0,0,2,0,0,0,1,3,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,1,0,14,0,1,1,0,0,0,0,19,0,1,0,0,0,1,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,Class_6
+32040,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,6,0,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,12,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32041,2,0,0,0,0,0,0,0,0,2,1,0,0,0,1,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,5,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,1,0,1,2,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,10,1,0,0,1,0,0,4,1,0,0,1,0,0,0,Class_6
+32042,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,5,0,0,0,0,0,0,0,3,0,1,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,16,0,0,0,0,0,0,8,0,1,0,0,1,0,0,0,2,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,Class_6
+32043,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+32044,0,0,14,3,0,0,0,1,0,0,7,0,1,0,0,1,0,0,2,0,0,2,0,0,0,0,1,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,13,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,3,0,0,0,0,0,0,0,0,Class_6
+32045,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,3,0,0,0,0,0,3,0,5,0,10,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,12,0,1,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,2,0,Class_6
+32046,1,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,1,4,2,1,0,0,1,0,0,8,3,0,0,2,0,0,0,0,0,1,0,0,0,2,1,5,1,3,0,0,0,2,0,0,0,0,1,5,0,3,0,22,0,6,8,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,4,5,0,1,0,0,2,Class_6
+32047,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,7,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+32049,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,1,1,1,0,0,0,0,0,3,1,3,0,0,0,0,1,1,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,8,0,1,0,0,0,2,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+32063,0,0,12,5,0,0,0,0,0,0,7,0,0,0,0,9,0,3,1,0,5,1,0,0,0,5,4,6,0,0,0,2,1,0,2,0,0,0,0,0,0,1,0,1,0,6,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,4,0,1,0,1,0,0,0,0,0,0,0,2,0,0,2,0,0,7,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+32064,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+32065,0,0,5,2,0,0,0,0,1,0,18,0,0,0,0,2,0,1,0,0,2,1,0,9,0,0,8,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,3,0,0,0,0,12,0,0,0,9,0,1,2,1,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+32066,0,0,6,20,0,0,0,0,1,2,13,2,0,0,0,0,0,0,1,0,1,0,0,0,0,1,18,4,0,0,5,0,0,0,1,0,0,0,2,1,0,0,0,1,1,6,0,3,0,0,0,0,0,5,0,1,0,1,0,0,3,0,1,0,0,0,3,0,0,1,0,0,0,0,0,4,0,0,0,6,0,1,0,0,0,0,0,1,2,0,0,2,0,Class_6
+32067,0,0,1,6,0,0,1,0,0,2,1,0,2,1,1,0,0,2,0,0,1,2,1,13,0,1,0,1,1,0,0,2,0,0,0,0,2,2,0,0,0,0,0,1,0,3,0,8,1,0,0,2,1,6,0,0,5,0,0,1,2,1,0,1,1,0,13,0,0,0,0,0,1,0,1,3,0,0,1,7,0,4,3,0,0,0,0,0,1,0,0,0,0,Class_6
+32068,1,0,8,6,0,0,0,1,0,0,18,0,0,0,0,2,0,1,0,0,1,1,0,6,0,2,9,2,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,0,0,10,0,1,0,10,0,0,4,0,0,1,0,0,1,0,0,2,0,1,0,3,0,0,0,1,0,10,0,1,2,0,0,0,1,0,2,0,0,0,0,Class_6
+32069,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,2,0,0,16,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,Class_6
+32070,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,3,0,3,2,0,0,0,0,0,0,0,5,0,0,0,0,0,5,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,10,0,1,0,0,0,0,3,1,0,3,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_6
+32071,0,0,1,2,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,2,0,1,0,0,0,3,0,0,1,0,2,2,10,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,12,0,0,2,0,0,9,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32072,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,4,2,0,0,0,0,0,2,0,1,0,2,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,10,0,1,0,0,0,0,1,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,10,0,0,0,1,0,1,1,0,0,0,Class_6
+32073,0,0,0,2,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,2,0,1,0,5,0,0,0,0,5,0,0,0,1,0,0,1,0,0,0,0,0,1,0,2,0,0,0,2,0,0,1,0,0,1,0,Class_6
+32074,1,0,3,8,0,0,0,0,0,1,13,1,0,0,0,5,0,1,0,0,1,1,0,6,0,1,5,0,0,0,1,2,0,2,10,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,3,0,0,0,11,0,0,2,1,0,0,0,1,1,0,1,3,0,0,0,0,0,0,0,0,0,8,0,5,0,0,0,0,1,0,1,0,0,0,0,Class_6
+32075,0,0,10,7,0,0,0,0,0,0,8,0,0,4,0,8,0,2,0,0,0,9,0,0,0,12,0,9,0,0,0,2,0,0,5,0,0,0,0,0,0,2,0,3,0,13,0,0,0,0,0,1,0,18,0,0,0,0,0,1,0,1,0,0,0,3,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,Class_6
+32076,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,2,0,0,1,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32077,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32078,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,7,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,9,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,5,0,0,0,1,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32079,0,0,8,9,0,0,0,0,1,1,7,0,0,1,0,2,0,2,0,0,0,1,0,2,0,4,1,3,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,2,0,2,0,0,1,1,0,0,0,18,0,1,0,2,0,2,2,0,0,2,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+32080,0,2,6,5,0,0,1,0,0,0,6,1,0,0,2,3,0,3,0,1,0,2,0,2,0,1,17,1,0,0,0,1,3,0,4,1,0,1,0,0,0,1,0,3,0,2,0,0,2,0,1,0,1,3,0,0,3,1,1,0,2,4,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,5,1,0,2,1,Class_6
+32081,2,1,0,2,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,0,13,0,1,1,0,0,0,0,1,1,0,0,8,1,0,1,2,1,2,0,2,0,1,0,4,0,0,0,0,0,0,0,0,7,0,0,12,0,1,0,2,0,0,10,0,0,1,1,1,0,2,0,4,0,1,0,0,0,0,15,0,0,0,1,0,1,0,0,1,3,Class_6
+32082,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,9,0,0,1,2,0,1,4,0,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32083,0,0,5,10,0,0,0,0,0,0,9,0,0,0,0,2,0,1,0,1,0,3,0,1,0,2,2,0,0,0,0,1,0,0,5,0,0,0,0,0,0,1,1,1,0,5,0,1,4,0,0,0,0,9,0,0,1,0,0,0,0,1,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32084,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,0,3,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,3,0,1,1,0,0,1,0,3,0,1,0,1,1,0,0,0,0,0,4,1,2,0,0,0,0,0,1,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32085,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,3,0,1,0,0,0,2,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,5,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32086,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,7,0,0,0,1,0,2,1,0,0,0,2,3,0,0,1,0,1,0,0,3,0,1,6,11,0,0,0,0,0,0,0,0,0,2,1,0,1,1,2,0,0,23,0,3,0,1,0,1,4,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,2,0,0,2,0,Class_6
+32087,0,0,1,4,0,0,0,1,0,0,0,0,0,0,0,3,0,4,0,0,1,2,0,9,0,4,1,3,0,0,1,0,1,1,12,2,0,0,0,0,1,0,0,0,0,2,0,2,2,0,0,0,0,8,0,0,6,0,0,13,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+32108,0,0,0,0,0,0,1,1,0,3,1,0,0,0,0,1,3,0,0,2,0,2,0,2,1,0,0,0,2,0,0,0,0,1,0,0,1,1,0,0,0,4,0,0,0,0,1,2,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,Class_6
+32109,0,0,7,7,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,2,0,24,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,2,0,2,4,1,0,0,0,0,0,2,0,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,Class_6
+32110,0,0,8,5,0,0,0,0,0,0,19,0,1,1,0,2,0,0,0,0,0,2,0,8,0,0,5,0,1,0,1,1,0,0,0,1,0,2,0,0,0,0,0,1,0,11,0,0,1,0,0,1,0,12,0,0,0,14,0,1,2,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32111,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,5,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32112,0,0,20,4,1,0,0,0,0,0,9,0,0,1,0,6,0,2,0,1,0,2,0,2,0,3,11,2,0,0,0,0,1,2,3,0,0,0,0,1,0,0,0,3,0,24,0,0,2,0,0,0,1,14,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+32113,1,0,0,2,0,0,0,1,0,2,0,2,3,1,0,3,6,0,0,3,0,2,0,7,2,0,1,1,0,1,0,4,0,2,0,0,7,5,2,0,0,5,2,3,0,1,0,4,1,4,0,0,0,1,2,0,0,0,3,2,0,1,0,1,0,7,9,1,0,5,1,3,0,0,4,4,0,0,0,1,2,0,0,0,4,0,0,0,1,0,0,0,0,Class_6
+32114,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,1,0,1,0,2,0,6,7,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,Class_6
+32115,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,5,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32116,0,0,0,3,0,0,3,0,0,0,1,1,1,0,4,2,0,1,2,2,0,3,2,17,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,8,2,0,0,0,0,1,1,1,1,0,0,6,0,0,0,0,0,1,10,0,1,0,1,0,0,2,1,4,0,2,0,1,0,0,16,0,0,0,1,0,0,3,0,0,0,Class_6
+32117,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,2,0,3,0,1,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,6,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,8,0,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32118,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,3,0,4,0,0,0,0,0,0,1,0,0,2,0,1,0,1,5,7,0,2,0,0,0,1,0,0,0,0,0,3,0,0,1,0,4,9,1,0,0,1,0,2,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,1,Class_6
+32119,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32120,0,2,3,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,7,1,1,0,0,3,0,0,1,1,0,0,1,0,1,0,0,0,0,0,2,0,2,1,2,3,0,0,9,0,0,1,0,0,12,0,2,0,0,0,0,2,1,0,1,0,1,0,2,3,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,1,Class_6
+32121,0,0,9,2,0,0,0,0,1,0,8,2,0,0,0,0,0,1,0,0,0,4,0,3,0,0,4,2,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,3,0,0,3,0,0,0,0,10,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32122,0,0,8,1,1,0,0,0,0,1,10,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,18,0,0,0,0,0,0,0,5,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32123,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,3,0,0,0,13,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32124,0,4,2,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,3,2,0,1,0,1,0,0,0,0,0,0,0,2,16,0,0,0,0,4,0,1,0,0,0,1,0,1,0,0,1,3,0,0,0,2,0,0,1,0,1,0,1,0,1,2,1,2,0,0,1,0,0,0,1,0,0,0,0,Class_6
+32125,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,4,0,0,1,0,0,1,0,Class_6
+32126,0,1,5,0,0,0,0,0,0,0,11,0,0,0,0,1,0,1,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,7,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,2,0,1,0,1,0,2,0,0,0,7,0,2,0,0,0,1,0,0,0,0,0,0,0,Class_6
+32127,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,12,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+32145,0,0,3,13,0,0,0,0,0,0,1,0,1,0,0,12,0,1,0,1,3,7,0,1,0,3,2,4,0,0,1,0,0,3,11,0,0,1,0,0,0,5,0,3,0,2,0,0,5,0,0,0,0,30,2,0,1,0,0,8,2,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_6
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+32147,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32148,0,0,2,0,0,0,0,0,0,0,13,0,0,0,0,1,0,2,0,0,0,0,0,3,0,5,4,0,0,0,1,3,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+32149,5,0,2,2,0,0,0,0,0,0,0,0,1,0,0,1,1,2,0,2,0,0,0,0,4,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,19,0,0,0,3,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32150,0,0,0,0,0,0,2,2,0,0,0,0,2,0,0,2,0,2,0,1,0,0,1,3,0,2,0,0,0,0,0,2,0,0,2,0,4,3,38,0,0,1,0,3,13,0,0,2,4,0,0,0,0,9,1,0,0,0,0,2,0,0,0,0,0,2,3,0,0,6,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,Class_6
+32151,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32152,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,Class_6
+32153,0,0,11,2,0,0,0,0,0,0,6,0,1,0,0,2,0,0,0,1,1,1,0,0,0,1,13,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,12,0,0,0,0,0,1,0,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32154,0,2,2,0,0,0,0,0,0,0,9,0,1,0,0,2,0,0,0,1,0,2,0,0,0,1,0,1,0,0,0,3,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,4,1,0,0,0,3,1,0,0,1,0,1,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32155,1,0,8,1,0,0,1,1,0,0,0,0,1,1,0,3,0,0,0,0,0,2,0,3,0,6,0,1,0,0,1,0,0,0,6,0,0,0,3,0,0,0,0,3,0,0,0,0,4,0,0,3,0,14,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,1,0,0,0,0,0,0,2,1,0,0,0,Class_6
+32156,0,0,0,1,1,0,0,2,0,0,0,1,1,0,0,4,0,4,0,0,0,2,0,9,1,3,0,0,0,0,1,0,0,0,1,0,0,0,1,0,2,9,1,1,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,16,0,1,1,1,0,1,0,2,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,Class_6
+32157,0,2,2,0,0,0,0,3,0,0,1,0,1,1,0,3,3,3,1,2,1,2,0,3,3,1,0,0,0,0,0,0,1,1,2,3,0,0,0,0,2,9,0,0,0,0,0,1,0,0,0,0,0,6,0,1,2,1,0,19,0,4,0,1,1,6,12,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,Class_6
+32158,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,5,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,10,0,2,0,0,0,0,0,Class_6
+32159,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,1,1,0,0,0,0,0,5,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,4,0,Class_6
+32160,0,0,10,9,0,0,1,0,0,0,11,1,1,0,0,1,0,0,0,0,4,3,0,0,0,1,11,5,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,1,0,12,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,5,0,1,1,0,0,0,1,0,0,0,0,0,0,Class_6
+32161,0,0,6,7,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,7,0,0,1,0,0,0,0,7,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32162,0,2,5,4,0,0,0,0,0,0,9,0,0,0,0,4,0,2,0,0,2,4,1,0,0,3,17,1,0,0,0,0,0,0,3,0,0,1,0,2,0,0,0,1,0,7,0,0,1,0,0,1,0,5,2,1,0,0,0,2,5,2,2,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,14,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+32163,0,0,4,3,0,0,0,0,0,1,13,1,0,1,1,7,0,0,0,0,8,1,0,5,0,0,6,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,1,0,1,0,2,0,18,1,1,0,6,0,1,1,2,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32164,0,1,3,2,1,0,0,0,0,0,11,0,0,1,0,2,0,1,0,0,0,1,0,1,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,1,1,1,0,8,0,2,0,0,0,0,2,0,0,0,0,1,0,1,0,1,0,1,0,1,0,0,0,0,1,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32165,0,2,34,22,0,0,0,0,0,2,10,1,1,0,0,6,0,2,0,0,1,4,0,4,0,2,3,3,3,0,1,0,0,0,4,1,0,2,0,0,0,0,0,2,0,10,0,0,8,0,0,1,0,15,1,1,0,1,0,0,13,1,0,2,0,0,0,0,0,1,0,1,0,1,0,1,0,0,2,4,0,7,0,0,0,0,1,0,4,0,0,0,0,Class_6
+32166,0,0,1,1,0,0,0,0,0,1,6,0,1,0,0,2,0,1,0,0,0,2,0,1,0,8,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,2,0,0,1,0,0,0,0,5,0,2,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32167,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,2,0,1,0,2,0,2,1,0,1,0,0,0,0,1,1,0,0,1,1,2,0,0,1,3,0,1,0,0,0,2,0,0,0,0,0,3,1,0,0,0,4,1,1,0,0,5,0,0,6,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,8,11,6,0,0,0,0,3,Class_6
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+32188,3,0,0,0,0,0,1,2,0,0,0,0,0,1,1,0,0,0,0,3,0,6,0,4,5,0,0,0,0,0,0,4,0,0,0,2,1,3,1,0,0,2,0,3,0,0,0,6,1,1,0,0,0,5,1,0,1,0,3,1,0,4,0,2,0,0,15,0,0,10,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,2,12,0,1,0,0,0,2,Class_6
+32189,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,12,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+32190,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,18,0,0,5,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,Class_6
+32191,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,1,0,0,0,2,0,0,1,0,0,0,13,0,0,2,0,0,0,3,0,0,0,0,2,3,0,0,0,0,0,3,0,0,0,1,2,4,2,0,0,0,0,2,0,1,0,0,0,0,7,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+32192,0,0,0,0,3,1,0,6,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,10,0,2,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32193,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
+32194,0,1,5,4,0,1,0,0,2,0,11,0,0,1,0,0,0,0,0,0,3,1,0,1,0,1,24,2,0,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,11,0,0,0,0,1,2,0,3,1,1,0,3,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,3,0,0,1,0,0,0,3,2,0,0,0,Class_6
+32195,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,1,0,1,0,0,0,0,0,0,0,1,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32196,0,0,1,1,0,0,0,0,1,0,1,0,0,2,0,3,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,2,1,0,0,0,0,2,0,0,4,0,0,7,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,0,1,0,0,0,Class_6
+32197,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,1,Class_6
+32198,0,0,14,1,0,0,0,0,1,0,6,0,0,0,0,3,0,0,2,0,0,4,0,2,0,2,10,2,0,0,1,0,2,0,1,1,0,0,0,0,0,1,0,0,0,12,0,0,1,0,0,0,0,12,1,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_6
+32199,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32200,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,17,0,0,4,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,Class_6
+32201,0,2,2,1,0,0,1,0,0,0,9,0,1,0,0,10,0,3,0,0,1,3,0,0,0,0,8,1,0,0,0,1,1,0,1,0,1,1,0,0,0,0,1,2,0,1,0,0,2,0,0,4,0,10,1,1,0,0,0,0,7,1,0,0,1,1,3,0,1,0,0,1,0,5,0,0,0,0,1,16,0,2,0,0,0,1,0,0,0,0,0,0,0,Class_6
+32202,0,1,8,1,0,0,0,6,0,0,9,0,2,0,1,4,1,0,0,1,0,8,0,1,0,2,1,1,0,0,0,1,1,0,2,1,0,1,0,2,0,1,1,1,0,8,1,3,0,0,0,0,0,23,0,5,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32203,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,3,0,4,0,0,1,0,0,0,1,0,0,1,0,0,2,7,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,19,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+32204,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,16,0,1,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32205,0,1,0,0,0,0,0,1,0,0,7,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,2,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,3,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,1,0,1,0,Class_6
+32206,0,1,12,25,1,0,0,0,8,0,11,1,0,0,0,4,0,1,0,0,3,1,0,6,0,2,39,4,0,1,1,1,1,2,2,0,0,0,0,1,0,1,0,4,0,6,0,0,4,0,0,0,0,10,0,0,1,2,0,1,5,0,0,0,1,0,7,0,1,1,1,1,0,0,0,6,1,1,0,3,0,9,8,0,0,0,0,0,2,0,0,0,1,Class_6
+32207,0,1,1,5,0,0,0,1,0,0,8,0,1,0,0,9,0,2,0,0,2,7,1,0,2,2,3,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,5,1,3,0,0,0,0,6,0,3,0,0,0,0,2,2,0,0,0,1,1,1,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,1,0,Class_6
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+32234,0,5,0,3,0,0,0,0,0,0,4,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,6,2,0,1,0,2,0,0,1,0,0,0,0,1,2,0,0,0,0,1,0,0,0,1,0,1,1,0,0,2,0,4,1,0,0,3,0,0,0,3,1,0,0,0,1,1,0,1,0,0,1,0,0,0,0,1,1,0,0,Class_6
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+32236,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,4,1,0,0,0,0,2,8,5,1,0,0,0,0,0,6,0,0,4,0,0,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,13,0,0,1,0,0,1,0,0,7,0,0,0,1,0,0,1,0,1,33,0,0,0,0,0,0,0,Class_6
+32237,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,2,1,1,1,0,0,0,1,1,0,0,0,0,0,0,2,0,0,1,0,0,1,12,0,1,0,2,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,Class_6
+32238,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,1,0,2,3,5,0,0,0,0,0,0,1,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,4,0,0,12,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+32239,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,2,0,0,1,1,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,14,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32240,0,0,6,0,0,0,0,1,0,0,8,1,1,1,0,1,0,2,0,0,1,1,0,0,0,2,6,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32241,0,0,10,5,0,0,0,0,0,0,17,0,0,1,1,0,0,0,0,0,0,1,0,7,0,0,4,1,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,5,0,0,2,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32242,0,0,5,11,0,0,0,0,0,0,1,0,0,0,2,2,0,1,0,0,0,2,0,1,0,1,0,1,0,0,1,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,0,9,1,0,0,0,0,8,0,1,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,1,2,0,3,1,0,0,0,0,0,1,0,0,0,0,Class_6
+32243,0,0,6,5,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,5,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32244,0,0,1,0,1,0,1,0,0,0,6,0,1,0,0,7,0,1,1,0,0,7,0,0,0,0,3,1,0,1,0,0,0,0,2,0,1,2,0,0,0,0,0,7,0,0,1,2,8,1,0,0,0,3,2,0,0,1,0,1,1,2,1,1,0,3,1,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,2,1,0,0,0,4,0,0,0,1,Class_6
+32245,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,5,0,0,0,0,3,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,3,0,4,0,0,0,12,0,0,0,1,0,0,0,1,0,0,0,0,0,4,3,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,1,0,Class_6
+32246,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32247,0,0,0,1,0,0,0,0,2,0,0,0,0,0,3,0,0,3,1,0,0,4,0,3,0,3,0,0,0,0,0,0,0,1,9,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,6,0,0,3,0,0,8,0,1,0,0,0,2,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+32472,6,0,1,1,0,0,0,3,0,0,0,1,20,0,0,2,0,2,0,0,0,0,0,9,3,0,0,0,2,0,0,0,0,0,0,2,0,3,0,1,1,1,0,5,0,0,0,3,0,1,0,0,8,6,0,0,0,0,44,2,0,1,2,2,1,1,32,4,3,10,0,1,0,0,0,0,2,0,15,0,1,0,0,0,4,6,0,1,1,1,0,0,0,Class_6
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+32478,3,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,6,0,3,0,0,0,1,0,0,0,0,0,8,0,0,0,0,3,4,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
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+32482,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,2,0,3,0,0,0,2,0,13,0,10,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,2,0,0,0,0,1,0,4,0,0,0,0,2,0,0,5,0,0,9,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+32483,1,0,0,0,1,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,6,0,1,0,0,0,1,0,0,0,0,1,2,0,0,0,0,4,4,0,0,6,1,0,0,1,0,0,2,0,3,1,0,0,0,0,0,14,0,0,0,0,0,0,3,0,0,1,0,0,0,0,Class_6
+32484,0,1,6,6,0,0,0,0,0,0,9,0,1,2,0,2,0,2,0,0,0,3,0,2,0,1,17,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,2,0,1,0,0,0,0,0,10,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
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+32486,0,0,2,0,0,0,0,0,0,0,6,0,1,1,0,0,0,2,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+32487,0,1,9,3,0,0,0,0,0,0,7,0,2,0,0,2,0,2,0,0,0,0,0,1,0,1,1,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,9,0,4,0,0,0,3,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+32609,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,17,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32610,2,1,1,3,0,0,0,0,0,0,0,1,2,0,0,2,0,3,0,1,0,1,0,0,1,5,0,0,1,0,0,0,0,0,3,3,0,3,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,7,2,1,3,0,0,6,0,1,0,0,1,2,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,Class_6
+32611,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,1,2,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,0,2,0,0,3,0,0,8,0,1,0,0,0,3,2,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,4,0,1,0,0,1,0,Class_6
+32612,0,0,8,8,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,3,0,2,6,0,0,1,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,4,0,0,0,0,Class_6
+32613,0,0,4,2,0,0,0,0,0,0,8,0,0,0,4,8,0,0,0,0,1,4,0,2,0,1,3,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,8,0,0,0,0,0,1,0,25,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32614,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+32615,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,3,0,2,0,1,0,10,2,0,0,0,0,0,0,3,0,1,1,2,0,0,1,0,2,3,0,1,0,0,0,1,1,0,0,0,0,2,1,0,1,0,0,24,0,0,1,0,1,0,11,0,2,0,1,0,0,0,1,2,0,4,0,0,0,0,1,0,0,0,3,1,0,1,0,1,0,Class_6
+32616,0,0,10,5,0,0,0,1,0,1,11,0,1,1,0,5,0,0,0,0,3,3,0,0,0,2,6,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,8,0,1,1,0,0,0,0,20,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+32617,0,1,19,8,0,0,0,0,0,0,13,1,1,1,0,2,0,0,0,0,1,3,0,2,0,5,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,1,1,12,0,0,0,0,0,2,6,0,0,3,0,0,2,0,0,0,0,0,0,0,0,2,0,1,0,4,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+32618,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32619,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,4,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,Class_6
+32620,0,0,21,8,0,0,0,0,0,0,8,2,1,1,0,4,0,0,0,0,1,4,0,4,0,7,20,3,0,0,0,0,0,0,4,1,0,0,0,1,1,4,0,6,0,8,0,2,2,0,0,1,0,36,1,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,2,0,0,0,2,0,0,2,0,Class_6
+32621,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,Class_6
+32622,0,0,3,1,0,0,0,0,0,0,5,0,1,0,0,0,2,2,1,1,0,0,0,2,0,2,1,0,0,0,0,1,1,0,3,0,0,3,0,0,0,0,0,3,0,1,0,1,15,5,0,0,0,2,0,1,0,2,0,1,0,4,2,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,1,3,3,1,0,0,1,0,1,3,0,1,0,0,Class_6
+32623,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+32624,0,1,5,3,0,1,0,0,0,1,11,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,11,1,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,2,0,7,0,0,1,0,0,0,0,9,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32625,0,0,0,0,0,0,0,2,0,0,2,0,2,1,2,0,18,0,0,1,0,0,0,14,0,0,0,0,1,0,0,2,0,1,0,2,0,0,0,0,0,3,0,0,0,0,0,7,0,0,1,0,0,0,0,0,12,1,0,15,0,1,2,1,0,1,18,1,1,1,1,0,1,2,2,1,0,1,6,1,1,0,2,0,0,1,0,0,2,0,0,0,0,Class_6
+32626,1,0,10,2,0,0,0,0,0,1,8,1,0,0,0,4,0,1,0,0,0,1,0,2,1,3,6,1,0,0,0,0,0,0,3,0,0,1,0,0,0,2,0,5,0,7,1,0,3,1,0,0,0,17,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32627,2,1,0,3,0,0,0,0,0,1,2,1,2,0,0,1,2,2,0,0,0,6,0,5,1,2,0,1,0,1,0,1,1,0,11,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,7,0,0,2,0,0,1,0,3,0,0,0,1,2,1,0,0,2,0,1,1,2,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+32628,0,0,14,4,0,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,1,2,0,1,1,5,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,13,0,0,0,0,0,0,0,21,1,0,0,0,0,0,3,1,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32629,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,2,0,1,2,0,1,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,2,2,0,0,0,0,1,1,Class_6
+32630,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,2,0,0,0,0,0,0,1,0,0,3,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32631,0,0,13,6,0,0,0,0,0,0,9,0,0,0,0,7,0,1,0,0,2,1,0,0,0,0,12,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,2,0,13,0,1,1,1,0,0,0,19,2,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,Class_6
+32632,0,0,5,3,0,0,0,0,0,0,0,2,1,0,0,3,0,1,0,0,0,0,0,7,1,4,0,2,0,0,0,0,1,0,0,1,0,0,0,0,2,8,0,1,0,1,0,0,2,0,0,0,0,5,0,0,8,0,0,12,1,2,0,1,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,2,0,Class_6
+32633,0,0,29,9,0,0,0,0,0,0,8,1,0,2,0,4,0,0,0,0,1,0,0,2,0,3,8,2,0,0,0,3,1,0,2,1,0,0,0,0,0,1,0,3,0,13,0,1,0,0,0,0,1,29,1,1,0,1,0,2,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+32634,0,0,5,1,0,0,0,0,0,0,17,1,0,1,0,2,1,0,0,0,1,1,0,7,0,2,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,2,0,2,0,1,0,1,0,0,0,2,0,5,0,1,0,5,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,1,0,0,0,1,0,0,0,0,1,0,0,Class_6
+32635,0,0,0,0,0,0,0,3,1,0,0,3,0,0,0,4,1,8,0,2,0,2,3,2,3,8,0,0,1,0,0,0,3,0,7,1,0,3,0,0,0,16,0,1,0,0,1,0,3,0,0,0,0,2,1,0,30,0,0,12,0,8,0,1,0,0,1,2,0,2,0,0,0,3,2,0,0,0,4,1,2,0,0,1,0,0,0,0,5,11,2,6,0,Class_6
+32636,0,0,9,3,0,0,0,0,0,0,10,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,9,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,5,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+32637,0,2,2,4,0,0,0,0,0,0,8,1,0,2,0,5,0,3,0,0,1,3,0,0,1,1,4,2,0,0,1,1,0,0,1,0,0,3,1,2,0,0,0,0,0,4,0,1,1,0,0,0,0,9,0,2,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,2,0,2,0,0,0,0,0,19,0,4,0,0,0,0,0,1,1,0,0,0,0,Class_6
+32638,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,8,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+32639,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,1,1,0,1,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+32640,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,1,0,0,1,1,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,2,0,0,4,0,0,2,0,1,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32641,2,1,1,1,0,0,5,2,0,0,0,0,1,0,0,2,0,0,0,4,0,2,1,4,2,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,7,0,2,1,0,0,2,2,0,0,0,0,3,3,0,1,0,0,2,0,0,0,0,1,1,1,1,0,3,1,0,0,1,0,3,0,2,0,0,0,0,11,0,0,0,1,0,0,0,1,0,0,Class_6
+32642,0,0,9,7,0,0,0,0,0,1,9,0,0,1,0,6,0,1,0,0,0,1,0,0,0,3,4,3,0,0,1,1,0,0,1,0,0,0,0,1,0,1,1,5,0,15,0,0,1,0,0,0,0,20,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,2,0,0,1,1,0,1,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,1,1,Class_6
+32643,1,1,3,6,0,0,1,0,1,0,0,0,2,1,0,3,0,10,0,0,1,4,0,6,1,8,0,1,0,0,0,1,1,1,5,0,1,1,3,0,0,18,0,1,0,1,1,0,4,0,0,8,0,4,1,0,4,0,0,4,2,2,0,0,1,5,0,1,0,2,0,0,0,9,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,1,1,Class_6
+32644,0,0,13,15,0,0,0,0,0,0,9,1,0,0,2,3,0,3,0,3,2,2,0,0,0,1,2,1,0,0,1,1,0,0,4,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,0,0,10,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32645,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+32646,0,0,7,2,0,0,0,0,0,0,8,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,3,3,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,0,1,8,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32647,0,2,3,4,0,0,2,1,0,1,5,0,0,1,2,1,0,5,2,2,0,1,4,8,2,1,0,2,0,1,2,0,0,1,4,0,1,1,0,3,0,0,1,0,1,2,0,5,1,1,4,3,0,2,1,0,5,0,0,12,1,1,0,3,0,0,4,0,5,0,0,0,3,2,0,1,0,0,0,5,2,3,3,0,0,1,0,2,3,0,1,0,0,Class_6
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+32705,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,2,0,1,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,4,0,0,13,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32706,0,1,4,4,0,0,1,0,0,1,8,1,0,0,0,6,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,5,0,0,2,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,14,0,2,0,0,0,0,1,1,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+32707,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32708,0,0,10,11,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,2,0,0,0,1,4,4,0,0,1,0,1,1,0,0,0,0,0,0,0,2,1,1,0,7,0,0,1,0,0,0,0,16,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32709,1,1,3,0,0,0,0,0,2,0,12,0,0,1,0,2,0,1,0,0,1,1,0,2,0,2,1,0,0,1,0,1,0,2,1,0,2,0,0,1,0,2,1,0,0,3,0,5,0,0,0,4,0,11,0,1,0,0,0,0,1,1,0,0,0,1,5,0,0,0,0,0,0,23,0,0,0,0,0,29,0,0,0,0,1,0,0,0,1,0,0,2,0,Class_6
+32710,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,3,3,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,15,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,Class_6
+32711,0,0,2,6,0,0,0,0,3,0,9,0,0,1,3,1,0,0,0,0,2,0,0,1,1,0,3,0,0,0,0,0,0,0,1,0,1,1,1,2,1,0,0,2,0,2,0,0,1,0,0,4,0,8,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,13,0,0,0,0,1,0,0,1,4,0,0,0,0,Class_6
+32712,1,0,6,0,0,0,0,0,0,0,14,0,1,1,0,3,0,1,0,0,0,0,0,0,0,1,12,1,0,0,0,1,0,2,6,0,0,1,0,2,0,0,0,0,0,3,0,0,1,0,0,0,0,8,0,1,1,1,0,4,6,0,0,0,1,2,2,0,0,0,0,1,0,1,2,1,0,0,0,17,0,3,1,0,1,0,0,0,3,1,0,0,0,Class_6
+32713,0,0,6,5,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,2,0,0,1,0,0,0,0,4,0,0,2,0,0,0,0,8,0,2,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32714,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,1,0,1,1,1,10,2,1,0,0,3,1,0,0,1,0,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32715,0,0,3,8,0,0,0,0,0,0,0,0,2,0,1,2,0,1,0,0,0,1,0,5,0,1,0,0,1,0,0,1,0,3,1,1,0,0,0,0,0,1,0,0,0,2,0,2,1,0,0,0,0,1,0,3,1,1,0,16,0,0,1,1,0,0,5,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+32716,0,4,2,2,0,0,4,1,0,0,3,0,2,0,2,3,8,2,1,11,0,2,5,27,0,1,0,0,1,3,5,4,0,2,2,5,0,5,1,2,0,2,1,3,1,1,1,9,1,0,1,2,0,3,1,0,18,0,0,28,0,2,0,0,0,3,8,0,0,0,1,0,2,3,0,8,0,3,1,2,0,0,30,0,0,2,1,0,1,0,0,2,2,Class_6
+32717,0,0,0,3,0,0,0,0,0,0,11,0,0,0,0,5,0,10,0,0,0,4,0,2,0,12,1,0,0,0,1,0,1,4,5,0,0,2,0,1,0,1,0,1,0,0,0,0,2,0,0,0,0,10,0,2,0,0,0,1,0,1,2,2,0,3,1,1,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,4,0,0,2,0,Class_6
+32718,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,12,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+32719,0,0,19,5,0,0,0,1,0,0,8,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,4,2,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,4,0,4,0,0,2,0,0,0,0,12,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32720,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,4,0,0,1,0,0,0,3,0,0,2,2,0,0,0,3,0,0,1,0,0,0,0,0,2,0,0,0,1,0,1,0,0,3,0,0,18,0,4,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,2,0,0,4,0,0,0,Class_6
+32721,0,0,14,7,0,0,0,0,0,2,10,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,10,3,0,0,0,2,0,0,2,0,2,1,0,1,0,0,1,2,0,3,0,0,2,0,0,0,0,9,0,0,0,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32722,0,1,10,9,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,4,0,1,0,0,0,0,1,2,0,0,0,0,1,0,0,1,2,0,16,0,0,1,0,1,1,0,16,0,1,0,0,0,1,6,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,18,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+32723,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,2,1,0,0,0,0,1,0,3,1,2,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,6,0,0,0,0,1,1,1,0,0,0,0,2,0,0,1,0,0,11,0,1,0,0,0,4,11,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32724,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,1,1,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,4,0,0,0,0,0,Class_6
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+32726,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+32727,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
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+32738,2,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,2,0,0,0,0,0,3,0,0,0,0,0,4,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,Class_6
+32739,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,2,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,13,0,1,0,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32740,4,1,1,1,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,2,0,1,0,4,4,1,0,0,0,0,0,2,0,0,0,2,0,1,0,0,0,3,0,3,0,0,1,4,1,0,0,0,1,5,1,0,0,0,5,0,0,0,0,0,0,2,10,0,0,0,1,1,0,4,0,1,0,0,0,0,0,0,0,0,0,8,12,2,0,0,0,0,2,Class_6
+32741,0,0,0,0,0,0,0,1,1,0,2,0,2,3,0,3,0,0,1,0,0,1,0,10,0,5,0,1,0,0,0,2,4,0,0,0,0,1,0,5,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,6,0,0,16,0,1,0,2,1,2,5,0,0,2,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+32742,0,0,0,0,0,0,0,0,1,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32743,0,2,0,0,0,0,0,0,0,0,0,0,2,1,1,1,1,5,0,0,0,2,0,3,0,2,0,0,1,0,0,1,1,0,2,1,0,3,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,3,1,1,17,0,1,1,0,1,4,1,1,0,2,1,3,0,1,0,0,4,2,1,0,0,1,0,0,0,0,4,1,5,1,0,2,0,Class_6
+32744,0,0,10,3,0,0,0,0,0,0,7,0,1,0,0,1,0,2,0,0,2,1,0,0,0,0,9,2,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,2,0,10,0,0,3,0,0,0,1,2,0,0,0,0,0,2,13,0,1,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,12,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32745,0,0,1,0,0,0,0,1,0,0,8,0,2,0,0,1,0,1,0,0,0,2,0,0,0,2,0,4,0,0,0,1,0,1,0,0,0,1,0,0,0,2,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32746,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32747,0,1,0,2,0,0,1,0,0,0,2,0,2,0,3,1,1,5,0,0,0,0,2,3,0,0,0,0,2,0,0,2,1,0,1,1,0,3,2,0,2,0,0,0,0,0,0,2,0,0,3,1,0,0,1,0,1,1,0,10,0,1,0,1,0,0,5,0,1,1,0,4,1,0,0,0,4,2,0,0,1,0,0,0,1,1,7,1,3,0,0,0,0,Class_6
+32748,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,1,3,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,8,0,2,0,0,0,1,1,1,0,0,0,1,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32749,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32750,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,1,0,1,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,6,0,2,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,2,0,0,2,0,Class_6
+32751,1,0,9,5,0,0,0,0,1,0,11,0,0,0,0,2,0,0,0,0,0,2,1,1,0,3,10,1,0,0,1,1,0,2,1,0,0,0,1,1,0,1,1,0,0,6,0,2,2,0,0,4,0,13,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,0,1,0,5,0,1,0,0,0,9,0,2,0,0,0,0,0,0,0,0,0,2,0,Class_6
+32752,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32753,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,2,0,0,0,0,0,0,0,1,1,1,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,6,0,6,0,0,0,1,3,0,0,0,0,1,2,0,3,0,0,2,0,0,0,1,0,0,0,1,1,3,0,1,0,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,1,0,0,2,0,Class_6
+32754,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,2,1,1,0,0,2,0,3,1,0,0,0,0,0,0,3,0,0,3,0,0,2,2,1,0,3,1,4,0,0,0,2,0,0,0,0,0,3,0,0,4,0,0,0,0,1,0,0,0,0,2,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,Class_6
+32755,0,0,3,1,0,0,0,0,0,1,5,0,0,0,0,1,0,3,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,5,0,0,0,1,9,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+32756,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32757,3,0,1,5,1,0,0,0,0,0,0,1,1,0,0,3,1,1,0,0,0,1,0,0,0,18,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,17,0,2,0,1,0,4,0,0,0,0,0,5,0,0,1,3,1,1,1,1,0,0,0,5,2,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+32758,0,1,11,2,0,0,1,0,0,2,8,0,1,0,0,3,0,1,0,0,4,0,0,1,0,2,13,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,3,0,18,0,0,1,0,0,0,0,10,0,0,0,0,0,1,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32759,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,7,0,0,0,2,0,2,1,0,0,0,1,0,0,0,1,0,2,1,0,6,0,0,6,20,1,0,0,1,0,1,0,0,0,0,1,2,0,0,2,0,0,14,0,3,2,2,0,3,2,0,0,2,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_6
+32760,0,1,1,1,0,0,0,0,0,0,6,0,0,1,0,3,0,1,0,0,0,1,0,0,0,0,4,1,0,0,0,1,1,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,3,2,0,0,0,0,0,1,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+32761,0,0,0,0,0,0,1,0,0,1,2,0,1,1,0,1,0,3,0,2,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,17,0,3,0,0,4,1,1,0,0,0,0,1,1,0,0,0,1,3,0,1,0,0,1,0,1,0,0,3,1,0,0,1,0,1,0,2,5,1,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_6
+32762,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,4,0,0,2,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,Class_6
+32763,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,1,1,0,14,0,25,1,3,1,0,0,2,0,0,3,0,0,0,0,0,0,2,2,0,0,1,0,0,0,0,0,1,1,6,0,0,0,2,0,1,0,1,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32764,0,1,6,5,0,0,0,0,0,1,10,0,0,0,0,1,0,2,0,1,0,0,0,1,0,2,16,1,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,2,0,7,0,0,0,0,0,1,6,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32765,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32766,3,0,0,0,0,0,0,14,1,0,0,0,14,2,0,2,2,0,0,2,0,1,0,4,3,0,0,0,0,0,0,0,2,3,1,16,0,0,0,4,0,1,0,1,0,0,0,4,0,1,0,0,1,3,2,0,0,0,1,2,0,9,1,0,0,3,11,1,0,0,3,0,1,0,1,0,0,0,7,0,0,0,0,0,0,1,6,1,4,0,4,1,0,Class_6
+32767,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,2,0,3,0,4,1,0,0,0,0,0,0,4,0,0,1,2,1,2,0,0,0,8,0,1,0,0,0,4,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,13,0,1,7,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,12,1,0,0,0,1,3,Class_6
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+32782,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,5,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+32783,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,4,0,1,0,4,0,1,0,0,0,0,1,4,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,0,0,0,0,1,2,0,2,0,0,7,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,2,0,Class_6
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+32785,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,2,0,Class_6
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+32787,1,0,1,2,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,4,1,1,0,0,0,0,0,2,1,0,1,0,1,5,24,3,0,0,0,1,31,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,2,0,0,2,1,1,0,2,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,Class_6
+32788,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,0,4,0,5,0,2,0,0,2,0,0,0,1,1,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,0,0,6,0,0,3,0,0,13,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32789,0,0,15,2,0,0,0,0,0,0,9,0,0,1,0,3,1,0,1,0,0,1,0,1,1,1,9,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,14,0,0,1,0,0,1,0,21,0,0,0,0,0,0,2,2,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,7,0,0,0,0,0,0,1,1,1,0,0,0,0,Class_6
+32790,1,2,0,1,0,0,0,0,0,0,2,0,0,1,0,0,3,5,0,0,0,2,0,7,0,8,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,18,0,0,0,1,0,1,2,0,0,0,0,11,0,0,11,0,0,4,0,2,0,3,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,1,2,Class_6
+32791,1,0,0,0,0,0,0,0,0,0,2,0,1,3,0,3,0,1,0,1,0,5,0,10,6,0,0,0,1,0,0,0,1,1,0,1,0,2,1,0,1,4,3,13,0,0,16,0,2,1,0,0,2,6,8,0,0,0,1,1,0,4,0,0,5,0,2,1,0,6,1,2,0,1,2,1,0,0,2,1,0,0,0,0,2,1,6,0,2,2,0,2,0,Class_6
+32792,0,1,9,6,0,0,0,0,0,0,16,0,1,0,0,3,0,0,0,0,0,1,0,7,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,4,0,0,0,0,0,1,0,5,0,1,0,9,0,0,1,1,0,1,0,0,1,1,0,0,0,0,0,2,1,1,0,0,0,6,0,1,0,0,0,0,1,0,2,1,0,0,0,Class_6
+32793,0,1,0,2,0,0,0,0,0,0,13,0,1,0,1,1,0,4,0,0,1,2,1,3,0,1,4,1,0,0,0,0,0,0,7,0,0,1,0,0,1,1,0,2,0,1,8,2,4,0,0,0,0,1,0,0,0,2,0,0,0,2,1,1,1,5,1,2,0,0,0,0,0,3,1,0,0,0,0,0,3,1,0,5,0,0,0,0,1,0,0,2,0,Class_6
+32794,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,1,1,0,2,1,9,2,2,0,0,0,2,0,0,2,1,0,0,0,0,1,12,0,1,0,5,0,0,1,0,0,0,0,6,0,0,0,0,0,7,0,1,0,1,0,3,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32795,0,2,0,1,0,0,0,0,2,0,4,0,3,1,0,0,0,4,1,0,1,3,0,0,0,1,4,0,1,0,1,0,0,0,2,0,2,0,0,0,0,1,0,4,0,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+32796,0,0,8,3,0,0,0,0,0,4,7,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,2,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32797,0,2,6,1,0,0,0,0,0,1,6,0,1,0,0,7,0,2,0,0,0,4,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,1,8,0,0,0,0,12,0,0,0,0,0,0,0,1,1,0,12,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32798,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,0,1,0,1,3,0,0,2,0,0,0,1,1,0,1,1,0,0,3,0,0,3,0,2,0,0,0,1,2,1,0,0,0,0,2,0,1,0,0,4,0,1,0,2,0,0,6,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,1,0,1,0,0,0,0,Class_6
+32799,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+32800,0,1,10,4,0,0,0,1,1,0,8,0,0,0,0,2,0,1,0,0,0,4,0,0,0,1,4,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,4,0,12,0,1,1,0,0,0,0,12,1,2,0,1,0,2,3,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_6
+32801,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,5,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+32802,0,1,9,1,0,0,0,0,0,1,7,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,2,0,0,2,0,0,0,0,14,1,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,1,0,Class_6
+32803,0,0,12,3,0,0,0,0,0,0,9,0,0,1,0,1,0,0,0,0,6,2,0,0,0,0,10,2,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,5,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+32804,0,0,5,5,0,0,0,0,0,0,8,0,0,1,0,3,0,1,0,0,0,3,0,0,0,1,2,4,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,Class_6
+32805,0,2,6,10,0,0,0,0,0,0,7,0,1,0,0,10,1,1,0,0,0,3,0,0,1,1,10,1,1,0,0,0,0,0,1,1,0,0,1,0,0,1,0,6,0,1,0,2,3,0,0,0,0,29,1,1,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,0,1,1,0,0,0,0,0,7,0,0,1,0,0,1,0,0,2,0,0,0,0,Class_6
+32806,0,0,0,0,0,0,1,3,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,10,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+32807,2,1,1,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,6,1,0,0,0,0,0,0,0,2,0,0,0,2,6,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,2,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+32843,1,0,1,1,1,0,0,0,0,9,0,0,0,3,0,1,1,0,0,2,0,1,0,0,4,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,9,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,2,1,3,1,0,1,0,8,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
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+32846,0,0,13,1,0,0,0,1,0,0,7,0,0,0,0,2,0,0,0,0,0,1,0,0,0,2,5,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,13,0,0,0,0,0,0,5,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+32907,5,0,1,1,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,1,1,2,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,12,0,2,0,0,0,1,0,0,0,0,0,6,2,0,0,0,3,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,Class_6
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+32917,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+32920,0,0,9,10,0,0,0,0,0,1,11,0,1,0,0,2,0,0,0,0,0,0,1,0,0,4,5,2,0,0,2,0,0,0,6,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,8,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,4,0,0,0,0,Class_6
+32921,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,4,0,0,0,0,0,1,0,7,0,2,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
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+32924,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,Class_6
+32925,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,0,0,1,2,0,0,0,1,4,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,0,0,5,1,0,3,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+32926,0,0,0,0,0,0,0,0,0,0,13,4,0,0,0,0,0,1,6,0,0,1,2,13,0,1,2,0,0,1,1,7,0,0,0,1,0,2,4,0,0,0,0,1,2,0,0,4,2,0,0,22,0,0,0,3,0,3,0,0,1,0,1,0,1,0,7,0,1,1,0,0,0,107,0,3,1,0,1,6,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+32927,1,0,11,7,0,0,2,0,1,0,8,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,27,1,0,0,0,0,2,1,5,0,1,0,0,3,0,0,0,1,0,4,0,1,0,0,0,4,0,6,0,0,2,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,1,5,0,3,0,0,0,0,2,1,2,0,0,0,0,Class_6
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+32945,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_6
+32946,0,0,5,5,0,0,0,0,0,0,14,2,0,0,0,1,0,2,0,0,0,1,0,0,0,2,10,1,0,0,0,0,2,2,1,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,2,0,5,0,0,1,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32947,3,0,0,0,0,0,0,1,0,0,0,0,5,2,0,3,0,0,0,0,0,1,0,0,8,1,0,0,0,0,0,1,2,0,0,0,0,0,0,1,5,4,0,1,0,0,0,1,0,0,0,0,8,3,0,0,0,1,1,0,0,6,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,3,0,0,0,0,0,7,1,Class_6
+32948,0,0,6,11,0,2,1,0,1,0,9,0,0,0,0,6,0,0,0,0,0,0,0,1,0,4,10,1,0,0,0,1,0,0,2,0,0,0,2,0,0,0,0,2,0,3,0,0,0,0,0,0,0,13,3,0,0,1,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32949,1,0,14,6,0,0,1,0,0,0,7,1,0,0,0,6,0,0,0,0,0,1,0,0,0,2,4,4,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,3,0,8,1,3,1,0,0,0,1,19,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+32950,0,0,3,2,0,0,0,0,0,0,13,0,0,0,0,2,0,1,0,0,1,3,0,0,0,3,11,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,1,0,1,4,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+32951,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,7,0,1,0,0,0,2,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,10,0,1,0,0,0,0,0,1,0,1,1,2,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+32952,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,12,1,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,5,0,0,0,3,0,Class_6
+32953,0,2,2,0,1,0,0,2,0,0,7,0,0,0,0,3,1,3,0,1,0,0,0,2,1,0,0,5,0,0,1,0,0,0,10,1,0,1,0,0,0,1,0,4,0,0,0,3,14,0,1,0,0,5,0,1,0,1,1,0,0,1,0,1,0,2,1,0,0,0,0,0,0,0,1,0,1,0,1,2,2,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32954,0,0,15,2,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,12,1,0,0,1,0,1,0,1,0,1,0,0,3,0,0,0,3,0,22,0,0,1,1,0,0,1,10,0,0,0,0,0,2,2,1,0,1,0,0,1,1,0,0,1,1,0,1,0,0,2,0,0,2,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+32955,0,1,3,5,0,0,0,0,0,0,9,0,0,0,0,6,0,1,1,1,4,1,0,0,0,3,1,2,0,2,0,2,0,0,2,2,0,1,0,0,1,0,1,1,0,1,0,1,3,0,0,0,0,15,0,1,0,0,0,1,4,4,0,0,2,1,0,0,0,0,0,0,0,0,1,1,0,0,0,5,0,2,0,0,0,0,1,0,3,0,1,0,0,Class_6
+32956,0,0,0,1,0,0,0,1,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,0,15,0,1,0,0,0,0,4,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+32957,0,2,12,2,0,0,0,0,0,0,7,0,0,0,0,4,0,1,0,0,0,5,0,1,1,0,5,2,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,3,0,1,2,0,0,0,0,27,0,2,0,1,0,1,5,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,5,0,1,1,0,0,1,1,1,2,0,0,0,1,Class_6
+32958,0,3,4,5,0,0,0,0,0,0,5,1,0,0,0,1,0,1,0,0,0,3,0,2,0,12,2,0,0,0,0,0,0,0,9,0,0,0,0,0,0,6,0,1,0,1,0,2,4,0,0,0,0,5,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32959,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32960,3,0,2,1,0,0,0,0,0,0,0,1,0,0,0,8,1,2,0,0,0,3,0,2,5,5,2,1,4,0,0,0,2,1,2,0,0,1,0,6,1,14,0,4,0,0,0,1,1,0,0,0,0,9,1,0,0,0,0,10,0,2,0,1,1,3,4,2,4,0,2,1,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,1,0,0,1,0,Class_6
+32961,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,5,0,7,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,11,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,12,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32962,0,0,1,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,9,0,0,0,2,0,1,3,3,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32963,0,0,2,0,0,0,0,1,0,0,9,1,0,0,0,2,0,4,0,0,0,1,0,1,0,1,3,1,0,0,3,1,0,0,2,2,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,0,1,2,0,0,3,0,Class_6
+32964,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,1,1,1,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,3,0,1,1,2,3,0,1,0,1,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,5,0,0,0,1,0,2,2,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+32965,0,0,4,5,0,0,0,0,1,0,0,0,0,0,1,5,2,24,0,0,0,1,2,1,0,3,2,2,0,0,0,0,0,2,7,0,0,3,4,2,0,4,4,2,0,1,0,0,4,6,0,0,0,14,1,0,3,1,0,9,0,0,0,0,0,0,1,1,3,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,Class_6
+32966,0,0,5,6,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,6,0,5,0,2,0,0,0,3,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,1,0,2,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+32967,0,1,3,6,0,0,0,0,0,0,10,0,1,0,1,2,0,0,0,1,0,0,0,0,0,0,10,1,0,0,0,0,0,1,2,0,0,2,0,0,0,1,0,1,0,6,0,0,0,0,0,2,0,6,0,4,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+33002,0,0,0,0,0,0,1,1,0,1,3,0,3,3,0,2,2,1,0,3,0,1,0,2,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,2,2,9,0,0,20,5,2,2,0,0,2,1,4,0,0,0,1,2,0,1,0,2,2,0,3,1,0,6,0,1,3,1,0,1,0,0,2,1,0,0,0,0,3,2,2,0,0,0,0,1,0,Class_6
+33003,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33004,1,0,0,0,0,0,0,0,0,3,2,0,1,1,0,3,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,8,0,0,2,0,0,1,2,6,0,0,0,1,0,0,1,0,0,1,3,1,1,0,0,2,0,0,2,0,1,0,0,2,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+33005,0,1,2,6,0,0,1,0,0,0,10,0,0,0,0,5,0,1,0,0,0,1,0,4,0,2,4,1,0,1,0,3,0,2,3,0,0,1,4,4,1,0,2,0,0,0,0,0,0,0,0,1,0,6,1,1,0,0,0,2,7,2,0,1,0,0,0,0,3,1,0,1,0,9,0,0,1,0,0,17,2,3,0,0,0,0,0,0,4,0,0,0,0,Class_6
+33006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,2,1,0,0,0,0,0,1,2,0,0,2,0,0,6,3,3,0,1,1,0,1,1,0,0,1,1,0,0,0,2,0,0,0,0,0,14,0,3,0,1,2,0,0,0,0,4,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,1,8,1,0,1,0,Class_6
+33007,0,2,22,8,0,0,0,0,0,1,11,0,0,1,0,7,0,2,0,0,0,1,0,0,0,0,8,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,10,0,0,1,1,0,1,0,10,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,4,0,0,0,0,0,0,2,0,0,0,1,Class_6
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+33046,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,Class_6
+33047,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+33069,1,0,11,3,0,0,0,0,0,0,7,2,0,0,0,3,0,1,1,0,0,1,0,0,0,0,4,1,0,0,1,4,0,0,1,0,0,0,0,0,0,0,0,4,0,7,0,0,3,0,0,0,0,13,0,0,0,0,0,1,1,1,0,0,1,0,1,0,1,0,0,2,0,2,0,0,0,0,2,10,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+33077,0,0,3,10,0,0,0,0,0,2,8,2,0,0,0,6,0,0,0,0,0,1,0,0,0,2,4,2,0,0,4,0,1,0,1,0,0,1,0,0,1,0,0,3,1,1,0,0,1,2,0,0,0,5,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,10,0,1,0,0,0,0,1,0,1,0,2,2,0,Class_6
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+33079,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+33080,0,0,14,4,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,1,0,7,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33081,9,0,0,0,0,0,0,0,0,0,1,0,3,0,1,2,0,0,0,0,0,1,0,2,5,0,0,0,1,0,0,0,3,1,1,2,0,1,0,5,4,0,0,1,0,0,0,0,0,0,0,1,2,2,2,3,0,0,6,1,0,3,0,2,0,0,10,1,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,4,0,Class_6
+33082,0,0,0,5,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,4,0,2,0,1,0,2,0,0,2,0,0,0,8,0,0,0,0,1,1,3,0,1,0,0,0,1,1,0,0,1,0,16,0,0,6,0,0,12,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33083,0,3,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,3,2,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,8,0,0,0,0,1,1,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,3,1,0,Class_6
+33084,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,3,0,1,0,1,0,2,0,3,3,1,0,0,0,0,0,3,1,0,0,5,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,4,0,2,0,1,0,6,6,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,Class_6
+33085,0,0,13,7,0,0,1,0,0,1,7,0,0,2,0,0,1,0,0,0,1,3,0,0,0,2,3,6,2,0,0,2,0,2,0,0,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,4,2,0,0,0,0,1,1,4,1,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,4,0,0,0,0,0,0,1,1,0,0,0,Class_6
+33086,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,12,0,1,0,0,0,0,5,2,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+33087,0,0,0,0,1,0,0,2,1,0,0,1,0,0,0,6,2,10,0,2,0,2,1,3,2,3,0,0,2,0,0,0,1,1,7,1,0,2,0,0,0,14,0,2,0,0,3,0,3,0,0,1,0,3,1,0,28,0,0,4,0,6,0,1,0,3,1,3,0,2,0,0,0,5,1,0,0,0,4,2,1,0,0,1,0,0,1,0,2,10,2,6,0,Class_6
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+33124,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,2,0,2,0,0,0,0,1,0,0,6,0,0,2,1,0,3,5,1,0,2,0,2,2,0,0,6,5,1,0,0,0,6,0,0,0,0,0,12,0,1,0,1,1,1,10,0,0,1,0,2,0,1,1,1,0,0,3,0,3,0,0,1,0,0,0,0,3,0,0,0,0,Class_6
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+33127,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+33206,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,4,0,3,1,2,0,2,0,0,0,0,0,0,8,1,0,1,0,0,0,3,0,2,0,0,0,0,1,0,0,0,0,5,0,1,4,1,0,12,0,1,0,0,0,0,8,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+33207,0,0,2,1,0,0,0,0,0,0,18,0,0,0,0,2,0,0,0,0,1,2,0,4,0,2,8,1,0,0,0,0,0,0,5,2,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,4,0,0,0,7,0,2,3,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,8,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+33230,0,0,2,19,0,0,0,0,0,0,9,0,1,0,0,2,0,2,2,0,0,1,0,1,1,20,3,0,0,0,0,0,0,0,4,0,0,1,1,1,0,4,1,1,0,5,1,0,2,0,0,6,0,2,0,1,1,0,0,0,0,2,0,0,0,1,2,0,0,1,0,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,1,0,2,0,0,1,0,Class_6
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+33236,0,0,1,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,13,0,0,0,0,0,1,0,1,3,0,0,0,0,Class_6
+33237,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,6,0,4,0,1,0,0,1,1,0,0,0,0,0,1,1,0,1,0,0,12,0,1,0,1,0,3,2,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33238,1,0,2,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,2,1,6,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,1,0,2,0,0,0,0,1,2,1,1,2,1,0,4,1,0,2,0,0,18,0,0,0,0,0,1,4,0,0,2,0,0,0,5,0,2,0,0,0,1,0,0,10,0,0,0,2,0,2,0,0,0,1,Class_6
+33239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,4,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,16,0,0,0,0,0,1,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+33240,0,1,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,2,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,9,0,0,4,0,0,12,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33241,0,0,0,4,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,4,0,7,0,2,0,0,0,1,0,0,1,1,0,1,0,0,0,3,0,0,0,3,0,1,0,0,0,1,0,2,0,1,1,0,0,8,1,0,0,0,0,0,8,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+33242,0,0,16,18,0,0,0,0,0,0,10,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,10,7,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,17,0,0,0,0,0,0,1,16,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33243,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,2,1,0,0,1,0,1,0,0,1,0,0,8,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+33244,0,0,8,5,0,0,0,0,0,0,10,0,0,0,0,2,0,1,0,0,2,0,0,0,0,1,12,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,6,0,0,1,0,0,1,0,5,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+33245,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,7,1,2,0,0,0,0,0,2,0,0,0,1,0,1,0,0,1,4,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,1,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33246,2,0,1,2,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,1,0,2,0,2,0,0,2,1,0,0,0,0,0,0,2,1,0,3,0,0,0,4,2,1,0,0,0,2,12,2,0,0,0,4,0,9,0,0,0,3,4,1,0,0,0,2,2,0,0,2,0,1,0,3,0,0,0,0,0,2,0,1,0,0,0,1,0,0,3,1,0,0,0,Class_6
+33247,0,3,3,0,0,0,0,0,0,1,10,0,0,0,0,5,0,0,0,0,4,2,0,7,0,2,4,2,0,0,0,2,0,0,0,0,1,3,0,0,0,0,0,2,0,1,0,0,4,0,0,2,0,9,1,0,0,2,0,2,3,4,0,2,1,1,1,1,0,0,0,1,3,0,0,0,0,0,1,10,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+33426,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,3,1,0,0,1,0,3,0,4,0,1,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,2,0,1,0,1,3,0,1,0,0,0,0,7,1,0,0,0,0,16,0,2,0,1,0,2,8,5,0,0,0,1,0,0,0,0,0,3,0,0,0,0,2,0,0,0,1,0,1,0,0,1,0,Class_6
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+33428,1,0,20,23,0,0,0,1,0,0,8,1,0,0,0,3,0,0,0,0,2,3,0,0,0,2,10,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,2,0,1,0,0,6,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,3,2,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33429,1,0,6,2,0,0,0,2,0,0,7,0,0,0,0,1,0,0,0,0,2,0,0,0,1,2,1,1,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,3,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33430,0,0,14,9,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,1,2,0,0,0,0,5,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,8,0,0,1,0,0,0,0,23,2,0,0,4,0,0,4,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33431,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,2,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,12,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+33432,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,3,1,5,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,6,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+33433,0,0,2,8,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,1,0,1,0,5,0,3,0,4,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,1,0,1,0,0,2,1,0,2,2,8,0,0,1,0,0,12,0,0,0,1,0,1,4,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,Class_6
+33434,0,0,2,5,0,0,0,1,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,14,0,7,0,2,0,3,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,1,0,11,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33435,0,0,7,4,0,0,0,0,1,0,8,0,0,0,0,3,0,2,0,0,0,0,0,2,0,2,13,4,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,4,0,1,0,0,0,0,3,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33436,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33437,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,1,0,1,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,3,0,1,14,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,Class_6
+33438,0,0,12,5,0,0,0,0,0,0,6,1,0,0,0,1,0,0,1,0,1,2,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,13,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33439,0,1,10,16,0,0,0,0,0,1,7,0,0,1,0,6,0,0,0,0,0,0,0,2,0,3,9,1,1,0,0,0,2,2,3,1,0,0,0,0,0,0,1,0,0,13,1,0,0,0,0,0,2,8,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,3,0,1,0,0,2,0,Class_6
+33440,0,0,23,5,0,0,0,0,0,1,10,0,1,1,0,1,0,0,0,0,0,3,0,0,0,2,3,5,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,3,2,11,0,0,5,0,0,0,0,15,0,0,0,0,0,0,3,4,1,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33441,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,4,3,4,0,1,0,0,0,0,0,3,0,0,0,0,1,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,Class_6
+33442,0,0,11,2,0,0,0,0,0,0,9,1,1,1,0,0,0,0,0,1,1,2,0,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,1,0,16,0,0,0,1,0,0,0,12,0,0,1,0,0,0,2,0,0,0,1,0,3,0,0,0,0,0,3,0,2,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33443,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,2,0,0,0,2,0,0,4,5,0,0,2,0,0,0,0,2,0,1,2,2,0,0,2,14,0,1,0,0,0,1,0,0,0,0,0,4,2,0,2,0,2,2,0,0,0,1,0,3,5,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,Class_6
+33444,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,2,0,7,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,17,0,2,0,0,0,1,1,0,0,2,0,3,0,0,3,0,0,5,0,1,0,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+33445,0,0,15,1,0,0,0,1,0,1,7,0,1,1,0,0,1,1,0,0,1,0,0,2,0,2,7,2,0,0,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,14,0,0,0,0,0,0,1,17,0,1,0,1,0,0,1,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+33446,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,1,4,6,0,1,0,0,0,0,14,0,0,3,4,0,0,0,0,1,0,1,0,1,0,0,0,2,0,0,0,1,0,0,0,0,2,0,0,16,0,0,0,1,0,0,5,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,Class_6
+33447,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,3,1,1,0,0,0,0,3,1,0,0,0,0,0,0,9,0,0,0,1,0,0,3,0,0,1,0,0,2,0,0,1,0,1,0,0,1,1,0,1,0,0,0,2,0,0,0,2,0,1,6,0,0,1,1,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,7,0,0,1,0,0,1,Class_6
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+33507,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,2,3,0,0,0,0,0,0,2,1,1,0,0,0,0,0,1,0,12,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,6,0,1,0,0,1,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+33508,0,1,9,3,0,0,0,0,0,0,8,0,0,0,1,4,0,0,0,0,9,1,0,0,0,2,36,2,0,0,1,0,1,0,1,1,0,1,0,2,0,0,1,0,0,5,0,0,1,7,0,2,0,7,1,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,Class_6
+33509,0,0,1,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,18,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33510,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,4,0,1,0,1,1,2,0,0,1,1,3,0,0,0,1,0,0,0,5,0,0,2,0,0,0,0,0,4,0,0,0,2,11,0,0,0,0,5,1,0,0,0,1,0,0,0,1,0,3,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,1,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+33511,0,0,8,2,0,0,0,0,0,0,10,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,10,0,0,0,1,2,1,1,0,2,0,1,0,0,0,1,1,1,0,15,0,0,1,0,0,0,0,15,0,1,0,0,0,1,11,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33512,0,0,3,0,0,0,0,1,0,0,0,1,0,0,0,3,0,2,0,0,0,8,0,1,1,5,0,2,26,0,0,0,0,1,1,0,0,0,0,0,3,4,0,1,0,0,0,1,0,0,0,0,1,2,8,0,3,0,0,1,0,5,0,0,0,2,3,0,0,1,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,Class_6
+33513,0,0,1,0,0,0,0,0,0,1,0,1,1,0,1,4,0,1,0,0,0,4,0,0,0,3,0,0,2,0,0,0,1,1,0,0,0,0,8,1,0,4,0,1,6,0,0,2,1,0,0,0,0,5,2,0,0,0,0,5,0,1,0,2,0,2,5,0,1,3,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,3,0,1,0,0,0,0,Class_6
+33514,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1,1,0,0,1,0,12,2,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,4,0,0,0,0,1,3,0,1,0,0,1,3,0,0,0,0,0,0,0,9,0,1,1,0,10,14,7,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,Class_6
+33515,0,0,1,1,0,0,0,0,0,0,9,0,0,0,0,6,1,3,0,0,0,0,2,0,0,5,7,4,0,0,1,2,0,1,5,0,0,2,3,0,0,5,1,4,0,1,0,0,7,0,0,0,0,6,0,0,0,0,0,4,3,2,1,0,1,0,2,0,0,1,0,2,0,1,0,0,0,0,0,4,0,1,0,0,2,0,0,0,1,0,0,0,0,Class_6
+33516,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,3,0,0,0,0,0,1,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33517,0,0,5,5,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,3,0,3,1,7,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,2,0,2,0,2,1,0,0,0,0,7,0,0,2,0,1,16,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+33518,0,0,2,1,0,0,0,0,1,0,1,1,0,3,0,3,3,10,0,3,0,2,0,9,1,2,0,1,1,0,0,0,0,0,4,0,0,0,0,0,0,2,0,2,0,0,1,1,8,2,0,0,0,1,3,0,1,0,0,11,0,1,0,1,0,1,2,0,1,4,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,1,Class_6
+33519,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33520,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,1,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_6
+33521,0,1,10,5,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,4,0,7,0,3,10,0,0,0,0,0,0,0,2,0,0,0,5,0,0,1,0,0,0,1,0,6,1,0,2,0,0,10,0,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,5,0,0,0,0,0,0,0,0,0,1,Class_6
+33522,0,2,1,3,0,0,0,0,0,0,6,0,0,1,0,3,0,1,0,1,0,1,0,1,0,4,0,0,0,0,0,1,0,0,3,0,0,0,1,0,1,3,0,0,0,0,0,2,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33523,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,4,0,0,1,0,0,2,0,0,0,0,1,2,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,3,2,5,0,0,0,0,9,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33524,0,0,9,7,0,0,0,0,0,0,6,0,1,0,0,3,1,0,0,0,0,0,0,0,0,1,3,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,13,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33525,0,1,0,4,0,0,0,1,0,0,2,0,1,0,0,0,2,4,0,2,0,4,0,4,0,4,0,0,1,0,1,0,0,0,1,0,0,0,2,1,1,23,0,1,0,0,0,1,1,0,0,0,0,20,1,0,8,1,0,3,0,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,2,Class_6
+33526,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33527,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,5,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+33548,0,1,4,3,0,0,0,0,1,0,16,0,0,0,0,1,0,3,0,0,1,0,0,5,0,1,2,3,0,0,0,1,0,0,4,1,0,0,0,1,1,0,0,2,0,2,0,0,2,0,0,1,0,4,0,0,0,4,0,1,1,0,1,0,0,1,0,1,1,0,0,0,1,2,0,0,0,2,0,6,1,1,0,0,0,0,0,0,1,2,1,0,0,Class_6
+33549,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,1,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33550,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+33552,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,5,0,7,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,12,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33553,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33554,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,7,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+33555,0,0,2,3,0,0,0,0,0,0,8,0,0,0,0,1,2,1,0,0,0,1,0,1,0,2,1,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
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+33557,0,0,5,5,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,0,0,0,0,0,0,2,0,Class_6
+33558,0,1,1,0,0,0,0,0,0,1,1,0,0,1,0,3,0,7,1,3,0,2,0,5,0,5,0,0,2,0,0,1,2,0,4,1,0,0,0,0,1,41,0,3,0,0,0,1,0,0,0,0,0,5,0,0,7,0,0,3,0,2,0,2,0,1,4,0,1,0,0,1,0,0,0,1,0,0,0,2,0,0,7,0,0,0,1,0,1,0,0,1,2,Class_6
+33559,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,2,4,0,0,18,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,Class_6
+33560,0,1,2,4,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,18,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33561,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,2,5,0,0,0,2,0,5,0,4,2,2,0,0,0,0,2,1,1,0,0,0,0,0,0,3,0,1,1,0,0,1,1,0,0,0,0,2,0,0,6,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_6
+33562,0,0,5,5,0,0,0,1,0,0,13,0,0,1,0,2,0,2,1,0,0,1,1,0,0,5,26,1,0,0,0,0,0,0,2,1,1,1,1,0,0,1,0,1,0,4,0,0,0,0,2,1,0,8,0,0,2,0,0,0,6,0,2,0,0,1,3,0,0,1,0,0,4,1,0,6,0,0,0,12,1,1,2,0,0,0,0,0,2,0,0,3,1,Class_6
+33563,0,0,7,5,0,0,0,0,0,0,6,1,4,0,0,4,0,1,1,0,0,4,0,10,1,14,10,2,1,0,12,1,0,0,2,0,0,1,0,0,0,1,0,2,0,10,0,7,2,1,0,2,0,23,0,1,4,0,0,18,3,2,0,2,0,1,4,1,0,0,0,1,1,0,0,0,0,0,0,10,0,1,3,0,3,1,1,0,3,0,0,5,0,Class_6
+33564,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,13,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+33565,0,1,15,4,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,0,0,10,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33566,1,0,1,0,0,0,1,8,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,3,1,1,0,0,1,0,0,3,0,0,1,0,0,1,4,0,0,5,0,2,1,0,0,3,1,0,0,0,0,2,0,0,0,0,0,7,0,3,0,1,0,0,12,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,1,0,3,0,1,0,2,1,0,Class_6
+33567,0,0,13,0,0,0,1,0,0,0,9,1,0,0,0,4,0,0,0,1,3,4,0,4,0,0,2,1,0,0,0,3,0,0,1,2,0,0,0,0,0,4,0,3,0,11,0,0,2,0,0,0,0,22,1,0,1,6,0,1,0,2,0,0,0,2,1,1,0,1,0,1,0,2,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+33605,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,2,0,2,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,17,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,4,0,0,0,1,0,1,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33606,0,0,10,1,0,0,0,0,0,0,13,0,2,1,0,2,0,0,0,0,1,1,0,1,0,1,4,0,1,1,0,1,0,0,1,1,0,1,0,0,1,2,1,0,0,1,0,0,3,0,0,0,0,8,0,1,1,2,0,0,11,2,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,0,19,0,5,0,0,0,1,0,0,3,0,0,0,0,Class_6
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+33687,0,0,9,10,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,1,4,0,2,0,0,8,7,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,3,0,12,4,0,2,0,0,0,0,13,0,0,1,0,0,0,7,0,0,0,0,1,0,2,0,1,0,2,0,2,0,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+33707,0,0,23,25,0,0,0,0,1,1,11,1,0,5,0,5,0,0,0,0,2,7,0,1,0,10,4,7,1,0,1,0,0,2,1,0,0,1,8,1,0,0,0,0,0,11,0,0,1,0,0,8,1,40,5,3,0,3,0,0,0,2,0,1,1,1,0,0,0,1,0,0,0,0,2,0,0,0,1,8,1,3,0,0,0,0,0,0,0,2,0,0,0,Class_6
+33708,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,6,0,1,0,0,0,1,0,4,3,2,0,0,0,0,0,2,3,0,0,0,0,0,0,1,3,6,0,0,0,0,0,3,0,0,0,0,21,2,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,8,0,Class_6
+33709,0,0,1,2,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,Class_6
+33710,0,0,16,1,0,0,0,0,0,0,8,1,1,1,1,5,0,2,0,0,7,2,0,0,0,2,6,3,0,0,0,2,0,1,2,3,0,0,0,0,0,2,0,1,0,2,0,0,2,0,0,1,1,24,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+33711,0,0,0,0,0,0,1,0,0,4,1,0,1,0,0,0,1,1,0,0,0,1,0,2,1,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,1,1,0,0,0,0,4,3,0,0,0,0,0,0,1,0,0,1,2,1,0,0,2,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,Class_6
+33712,0,0,31,6,0,0,0,0,0,1,9,0,1,1,0,4,0,4,0,0,1,2,0,0,0,8,9,3,0,0,0,2,0,0,0,1,0,1,0,1,1,1,1,1,0,19,1,0,0,0,1,0,1,28,0,1,2,0,0,2,3,1,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,12,0,2,0,1,0,1,0,0,1,0,0,1,0,Class_6
+33713,0,0,13,2,0,0,0,0,0,1,13,1,1,2,0,8,0,0,0,0,7,0,0,0,2,0,10,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,0,13,0,1,0,0,0,0,0,17,1,0,0,0,0,1,5,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,3,0,0,0,0,1,0,0,0,1,0,0,Class_6
+33714,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33715,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,1,0,4,1,0,5,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,4,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33716,0,0,0,0,0,0,1,1,0,0,0,2,0,1,0,1,0,2,0,0,0,0,0,2,1,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,2,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33717,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33718,0,0,12,5,0,0,0,0,0,1,6,0,1,0,0,5,0,0,0,0,0,0,0,1,0,17,0,4,0,0,0,2,0,1,3,0,0,0,0,1,0,1,0,1,0,15,0,0,0,0,0,0,0,14,0,0,0,0,0,1,1,1,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+33719,0,1,1,1,0,0,2,0,1,0,0,0,1,0,0,3,0,4,0,2,0,0,1,7,2,1,0,0,0,0,0,0,0,0,5,1,0,2,0,0,0,1,0,2,0,1,0,2,5,3,0,0,0,6,1,0,2,1,0,12,1,1,0,0,0,2,6,0,2,2,1,0,0,1,0,0,0,1,0,6,0,0,5,0,1,0,0,1,3,0,0,0,1,Class_6
+33720,0,1,2,9,0,0,0,0,2,0,11,0,0,0,1,1,0,1,0,0,1,0,0,2,0,1,5,2,0,0,1,0,0,0,8,0,0,1,0,2,0,1,0,1,0,2,0,0,2,0,0,2,0,5,0,0,1,2,0,0,2,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,1,0,0,0,1,1,Class_6
+33721,0,1,0,0,0,0,1,0,1,0,0,0,2,1,2,0,0,6,0,3,0,2,0,2,0,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,1,0,16,0,1,0,1,1,0,1,2,0,1,0,2,0,0,0,1,4,1,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+33722,2,0,0,1,0,0,1,1,0,0,1,0,4,1,0,3,0,0,0,1,0,3,0,0,3,1,0,0,16,0,0,0,0,0,0,1,0,0,0,0,2,3,0,2,0,0,0,1,0,0,0,0,0,2,1,0,0,0,1,2,0,0,1,1,0,1,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+33723,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,6,0,2,0,0,0,2,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,5,0,0,0,0,0,2,0,0,0,0,0,4,1,0,2,0,0,10,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33724,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,10,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,4,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+33725,0,2,1,1,0,0,1,0,0,0,1,0,1,0,0,2,0,1,0,1,0,2,0,4,0,4,0,0,0,0,1,0,0,0,12,1,0,1,0,0,1,3,0,5,0,0,0,1,3,0,0,0,0,9,0,0,3,0,0,9,0,0,0,0,1,1,1,1,0,2,0,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,Class_6
+33726,2,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,8,0,6,0,0,0,0,1,0,0,0,4,3,0,1,0,0,1,3,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,1,13,0,0,0,0,0,0,13,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33727,0,1,0,0,0,0,1,0,0,0,6,0,0,1,0,6,0,1,0,0,0,1,0,1,1,0,2,0,0,0,0,0,1,0,4,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,11,0,0,0,0,1,0,0,1,1,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,Class_6
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+33740,0,3,3,0,0,0,0,0,0,0,11,2,0,2,0,3,1,2,0,3,1,2,2,14,0,0,7,1,0,0,3,3,0,0,1,2,0,0,0,1,0,2,0,0,1,1,1,0,0,0,0,10,0,3,0,1,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,1,0,40,0,0,0,0,0,16,0,0,0,1,0,0,0,1,3,0,0,0,0,Class_6
+33741,0,9,1,5,0,0,1,0,0,0,18,0,0,0,0,2,1,9,2,2,1,1,0,1,0,3,7,1,1,0,0,1,2,0,11,1,0,1,0,0,1,0,0,0,0,0,0,2,17,0,0,0,0,6,1,0,0,7,0,1,0,1,0,6,0,3,0,0,1,1,0,2,0,0,3,2,0,0,2,7,0,2,0,0,0,0,2,0,0,9,0,0,2,Class_6
+33742,0,0,2,1,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,5,1,0,0,0,1,0,0,2,1,0,1,0,1,0,0,1,2,0,4,0,0,1,0,0,5,0,5,1,1,0,0,0,0,3,2,0,1,0,1,0,0,0,0,0,0,0,13,0,0,1,0,0,14,0,0,0,0,0,0,1,0,3,0,0,0,0,Class_6
+33743,0,0,17,3,0,0,0,0,1,0,9,0,1,1,1,3,0,0,0,0,4,2,0,0,0,0,8,2,0,0,0,2,0,0,1,1,1,0,0,0,0,0,0,2,0,17,0,0,1,0,0,2,0,11,0,0,1,0,0,2,2,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,Class_6
+33744,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,2,1,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33745,1,6,0,0,0,0,0,2,0,0,0,0,3,1,1,1,0,13,1,0,0,3,2,8,0,2,1,0,0,0,0,6,1,5,3,2,1,0,0,0,2,4,0,3,0,0,0,1,1,0,0,0,0,6,1,0,3,3,0,0,0,4,0,0,0,3,14,5,0,0,0,2,0,0,8,0,0,0,0,2,0,0,0,0,0,0,2,0,1,1,1,5,3,Class_6
+33746,0,8,0,2,0,0,0,1,0,1,8,4,0,0,0,12,0,18,0,4,0,7,0,6,0,4,0,1,1,0,1,0,1,2,17,0,1,1,0,0,1,13,0,16,0,0,0,2,81,0,0,0,0,17,3,0,8,0,0,4,0,2,2,1,0,7,0,0,0,2,0,3,0,0,2,0,0,0,1,17,2,2,0,0,0,0,0,0,0,0,0,0,2,Class_6
+33747,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,18,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33748,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,14,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33749,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,4,0,0,1,0,0,1,1,0,1,0,0,0,10,0,3,0,0,0,1,0,0,0,1,2,0,0,0,1,0,0,8,0,0,0,0,0,2,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,4,0,Class_6
+33750,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,5,4,0,2,0,2,5,0,1,0,0,1,0,0,0,3,2,0,0,1,0,1,1,2,0,1,1,1,0,0,0,6,0,0,0,0,1,2,0,0,2,3,1,2,0,3,2,16,0,6,7,0,5,3,0,3,0,0,0,0,0,1,0,0,0,0,0,0,4,4,8,5,0,4,0,0,0,Class_6
+33751,0,0,7,20,0,0,0,0,0,0,9,0,0,0,1,0,1,1,0,0,1,0,0,0,0,2,8,3,0,1,0,1,0,0,6,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,6,0,0,0,1,0,0,1,1,1,0,0,2,0,0,1,0,0,1,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,1,1,0,2,0,Class_6
+33752,0,0,1,0,0,0,0,0,0,0,1,0,1,2,1,6,0,0,0,0,0,3,0,5,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,8,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,Class_6
+33753,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,3,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,13,0,1,0,0,0,1,2,0,0,1,0,0,0,0,2,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,4,0,Class_6
+33754,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,1,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,14,0,0,1,1,0,8,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,Class_6
+33755,1,0,1,2,0,0,0,1,0,0,0,0,1,2,0,0,1,2,0,1,0,2,0,4,0,1,1,0,0,0,0,0,0,0,2,1,0,2,0,2,0,2,0,3,0,0,0,1,0,0,0,0,0,8,0,0,1,1,0,16,0,0,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,2,0,Class_6
+33756,5,0,1,0,0,0,0,0,0,0,2,0,12,0,0,1,0,1,0,0,0,0,0,3,4,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,4,3,0,0,0,0,22,2,0,2,0,1,1,1,5,1,0,5,0,0,0,0,1,1,0,0,9,0,0,0,0,0,0,5,0,1,0,0,0,0,0,Class_6
+33757,0,3,0,3,0,0,0,0,0,0,7,0,0,0,0,2,0,3,2,3,0,2,2,0,0,1,2,0,2,0,1,0,0,1,5,2,0,0,0,0,2,1,2,1,0,1,0,3,9,0,0,0,0,4,1,1,2,7,0,1,1,4,2,0,0,3,1,0,0,1,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_6
+33758,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,9,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33759,0,2,3,1,0,0,1,1,0,0,6,2,0,0,0,3,0,1,0,2,0,6,1,0,0,2,2,1,0,0,0,0,2,0,4,0,1,1,0,0,1,1,1,6,0,1,0,2,5,0,0,0,1,10,1,0,2,0,0,0,1,1,0,0,0,1,1,0,0,1,0,1,0,0,5,0,0,0,2,1,0,1,0,0,0,1,1,0,3,0,0,1,0,Class_6
+33760,1,0,0,0,1,0,0,1,0,0,0,0,4,0,1,1,3,4,0,1,0,0,0,2,1,2,0,0,1,0,0,3,0,0,0,2,2,2,0,0,0,2,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,4,8,0,5,0,0,0,3,10,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,Class_6
+33761,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33762,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,7,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,5,0,0,2,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+33763,0,0,0,4,0,0,0,0,0,0,8,0,0,0,0,8,0,0,0,0,0,2,0,1,0,3,0,0,0,0,1,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,13,0,2,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,12,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33764,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,2,1,0,0,1,0,0,0,5,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_6
+33765,1,0,14,8,0,0,0,0,1,0,12,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,11,2,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,9,0,1,0,0,0,2,0,4,0,0,0,1,0,0,8,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+33766,0,3,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,1,2,2,2,0,1,0,0,3,0,1,0,0,7,0,3,1,1,0,0,1,0,1,0,0,0,2,4,0,0,0,0,6,2,18,2,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,4,0,0,1,0,0,0,0,Class_6
+33767,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,5,0,0,0,5,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
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+33789,0,0,18,3,0,0,0,0,0,1,13,1,0,0,0,5,0,0,0,0,0,2,0,0,0,3,9,2,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,3,0,4,0,0,1,0,0,0,0,4,0,0,0,0,0,3,6,2,0,0,0,0,0,0,0,2,0,2,1,1,0,0,0,2,0,0,1,2,0,0,1,0,0,0,2,0,0,0,0,Class_6
+33790,0,0,8,3,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,1,0,2,0,2,7,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,7,0,0,0,0,0,0,0,9,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,1,0,3,0,Class_6
+33791,0,0,12,3,0,0,0,0,0,0,9,0,1,0,0,3,0,2,0,0,0,2,0,2,0,4,7,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,16,0,0,4,0,0,1,0,14,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,3,0,0,0,0,0,0,4,0,0,1,0,Class_6
+33792,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,2,1,6,0,0,0,0,0,4,0,4,0,0,0,0,1,0,0,3,3,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,6,1,0,12,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,1,2,Class_6
+33793,0,3,0,1,0,0,0,0,0,0,12,0,1,0,0,3,0,2,0,0,1,0,0,2,0,0,7,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,1,2,0,0,1,0,0,2,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33794,0,1,0,0,0,0,0,0,0,0,11,0,0,0,0,6,0,0,0,0,1,1,0,3,0,0,0,1,1,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,4,0,1,0,3,0,0,0,2,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,Class_6
+33795,0,0,0,0,0,0,0,0,1,0,3,0,1,1,0,0,1,0,0,2,0,3,0,3,1,1,0,0,1,0,0,0,1,0,1,1,0,1,0,0,0,0,1,3,0,0,5,2,0,1,0,0,1,0,6,0,1,0,1,0,0,0,0,0,1,1,1,2,0,1,1,0,1,1,3,1,0,0,2,1,0,0,1,0,0,1,2,0,0,0,0,0,0,Class_6
+33796,1,0,1,0,0,0,0,0,0,1,5,0,1,0,0,5,0,3,0,2,0,1,0,10,1,1,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,5,1,1,0,0,19,10,0,1,0,0,2,2,6,0,2,0,1,2,0,0,0,0,0,2,7,3,0,0,6,2,1,1,0,2,0,0,2,1,0,0,2,0,0,1,1,0,0,0,0,1,1,Class_6
+33797,0,0,11,5,0,0,0,0,1,0,7,0,0,0,0,4,1,1,0,0,1,2,1,0,0,5,2,5,0,0,1,3,0,1,0,0,0,1,0,0,0,0,0,2,1,6,0,0,1,0,0,0,0,6,0,0,0,0,0,1,4,1,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,1,1,1,0,0,Class_6
+33798,0,2,0,1,0,0,0,0,1,0,6,0,1,0,1,1,0,2,0,0,0,1,0,1,0,2,5,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,2,2,0,3,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+33799,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,4,0,0,1,0,0,4,0,1,0,5,0,0,0,0,0,0,0,1,2,0,0,1,0,0,1,24,0,2,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,9,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,Class_6
+33800,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,4,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33801,0,0,11,4,0,0,0,0,0,0,6,0,0,0,0,4,0,1,0,0,2,1,0,0,1,0,8,3,0,0,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,1,0,12,0,0,0,3,0,2,3,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,2,4,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+33802,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,1,0,1,0,8,0,5,0,0,0,0,0,1,0,0,7,1,0,0,0,0,1,8,2,0,0,0,0,2,4,0,0,0,0,5,0,0,3,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+33803,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33804,0,0,4,3,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,0,4,0,0,0,4,1,4,0,0,3,1,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,6,0,7,0,0,0,0,7,2,0,3,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,7,0,0,0,0,0,0,0,0,0,3,0,Class_6
+33805,0,2,3,2,0,0,0,2,0,0,2,1,0,0,0,3,0,3,0,0,0,1,0,1,1,5,0,0,0,0,1,0,1,0,3,0,1,0,0,0,0,2,0,1,0,0,0,1,0,2,0,1,0,2,0,0,0,0,0,9,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
+33806,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,16,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,12,0,0,0,0,0,1,2,0,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,Class_6
+33807,1,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+33828,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,3,1,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,4,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,2,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,Class_6
+33829,0,0,8,2,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,3,3,0,1,0,1,24,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,25,1,0,0,1,0,1,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,2,0,0,0,0,Class_6
+33830,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33831,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,1,0,1,0,0,0,5,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,1,1,0,7,0,0,16,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33832,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,1,0,1,0,0,1,1,0,1,0,2,13,0,0,0,0,1,0,1,2,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33833,0,2,2,1,0,0,1,0,0,0,0,0,3,0,0,2,1,2,0,0,0,4,0,16,0,12,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,4,3,2,0,0,0,0,1,8,0,0,3,0,0,12,0,7,0,2,0,6,7,2,1,0,0,0,1,0,3,0,0,1,0,0,0,0,5,0,0,0,2,0,0,0,0,1,2,Class_6
+33834,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33835,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,5,0,0,1,0,0,0,0,1,5,1,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,28,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,Class_6
+33836,0,0,7,4,0,0,0,0,0,1,10,0,1,1,0,4,0,6,0,0,0,1,2,0,0,0,0,3,1,0,0,1,0,0,4,0,0,2,0,1,0,0,0,2,0,0,0,5,2,0,0,0,0,6,1,0,0,1,0,2,0,0,0,0,0,2,5,0,1,1,0,0,0,0,0,2,0,0,0,19,0,3,2,0,0,0,2,0,3,0,0,0,1,Class_6
+33837,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+33838,0,0,4,7,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,2,0,2,0,2,2,0,0,0,0,6,0,2,0,1,0,0,1,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,17,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+33839,0,0,18,3,0,0,0,0,0,1,6,0,0,2,0,6,1,1,1,0,3,0,0,0,0,2,4,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,2,0,21,0,1,1,0,0,0,1,13,1,0,0,0,0,0,2,4,1,3,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33840,0,0,1,3,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,3,0,1,0,8,0,8,0,0,0,0,4,0,0,0,14,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,2,0,0,4,0,0,16,0,3,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,3,0,0,Class_6
+33841,0,0,0,0,1,0,1,0,1,0,1,0,1,0,0,4,0,3,0,0,0,3,0,3,1,11,0,2,0,0,0,0,0,0,1,0,2,1,0,2,0,6,0,0,0,0,0,0,2,0,0,1,0,9,1,0,2,0,0,17,1,2,0,1,0,2,2,1,0,1,0,1,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+33842,0,0,0,0,0,0,0,0,1,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33843,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33844,0,0,12,13,0,0,0,0,1,0,9,0,0,0,0,2,0,0,0,0,0,1,0,0,0,7,0,4,0,0,0,2,2,0,3,0,0,0,0,0,1,1,0,0,0,11,0,1,0,0,0,0,0,14,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33845,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33846,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33847,0,0,1,1,0,0,0,0,0,0,10,0,0,1,0,1,0,1,0,0,2,0,0,1,0,0,11,0,0,1,1,1,0,0,4,1,0,2,0,0,0,0,0,1,0,5,0,0,0,0,0,1,1,1,0,0,0,0,0,0,8,1,0,0,0,1,1,0,0,3,0,0,1,0,0,0,0,0,2,11,0,2,0,0,0,0,0,0,3,0,0,2,0,Class_6
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+33869,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+33871,0,0,5,4,0,0,0,0,0,0,12,0,0,0,1,1,0,1,0,0,0,0,0,4,0,1,4,0,0,0,1,2,0,0,2,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,4,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33872,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,2,0,0,1,0,0,0,3,0,1,0,0,1,0,1,2,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,2,0,1,3,0,0,2,1,0,0,0,0,1,0,5,0,0,0,2,7,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,0,0,Class_6
+33873,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,4,0,1,0,2,0,3,0,0,3,1,0,0,0,0,0,7,0,0,0,3,1,1,0,0,0,2,0,1,0,1,0,2,0,0,0,0,0,5,1,0,0,0,5,2,0,1,0,0,0,3,22,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,2,9,0,0,0,0,2,2,Class_6
+33874,0,0,0,2,0,0,0,1,0,0,1,0,2,0,0,3,0,0,0,0,0,5,0,6,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,3,0,0,0,0,0,8,0,0,0,0,0,7,0,0,3,0,0,9,0,1,1,1,0,2,3,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,4,0,Class_6
+33875,4,0,1,0,0,0,0,0,2,1,2,1,0,1,2,1,1,0,0,3,0,3,0,5,2,1,0,0,0,0,0,8,3,0,0,5,2,2,0,0,1,3,0,2,0,0,0,6,0,0,0,0,0,5,2,4,0,0,6,5,0,0,0,4,0,3,24,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,16,16,0,1,1,0,0,8,Class_6
+33876,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,4,1,0,0,0,0,1,15,1,0,0,0,0,0,0,5,0,5,0,0,3,0,1,0,0,1,0,1,0,0,0,4,1,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,6,6,0,0,3,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,10,1,1,0,0,0,0,0,Class_6
+33877,0,1,0,1,0,0,1,0,0,0,7,1,0,0,0,0,0,2,0,1,0,2,0,1,0,2,0,0,0,0,1,2,0,0,0,0,0,1,1,0,0,1,0,2,0,0,0,0,1,0,0,0,0,7,1,0,0,6,0,1,0,4,0,0,1,2,2,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,4,0,Class_6
+33878,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,7,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,6,0,0,0,1,0,0,5,1,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+33879,0,1,17,6,0,0,0,1,1,1,13,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,12,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,9,0,0,2,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,0,0,0,0,0,1,1,0,1,0,Class_6
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+33881,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,1,0,6,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+33882,1,0,15,9,0,0,0,0,0,0,9,0,0,0,0,4,0,0,1,0,1,3,0,0,0,1,2,2,0,0,0,0,0,1,1,1,0,0,0,1,0,0,1,0,0,5,0,0,2,0,0,1,0,10,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,4,0,0,0,0,Class_6
+33883,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,11,0,0,0,1,0,0,4,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,2,1,0,0,0,0,1,Class_6
+33884,0,0,1,2,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33885,0,0,0,0,0,0,0,1,0,0,12,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+33886,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,2,0,0,1,0,3,5,12,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,1,1,0,0,0,0,2,0,1,2,0,0,1,1,0,5,0,0,4,0,0,0,2,0,1,8,1,0,0,0,0,1,1,0,0,0,4,0,1,1,1,1,0,1,1,7,0,0,0,1,0,0,Class_6
+33887,0,1,8,9,0,0,0,0,0,0,12,0,0,0,0,5,0,0,0,0,0,2,0,6,0,1,17,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,16,0,0,1,0,0,1,0,5,0,0,0,5,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+33912,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,2,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,4,0,1,0,4,0,3,0,0,0,0,0,7,0,0,0,0,0,0,3,0,0,2,0,0,0,2,0,0,0,0,7,0,0,0,0,0,0,1,4,1,0,0,0,0,1,Class_6
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+33915,0,0,9,6,0,0,0,0,0,0,8,0,1,1,0,3,0,0,0,0,0,5,0,2,0,2,5,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,4,0,16,0,0,3,0,0,0,1,10,2,0,0,0,0,3,1,0,0,0,0,1,1,0,0,0,0,0,0,3,0,1,0,0,0,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33916,0,0,19,5,0,0,0,0,0,0,6,1,1,0,0,2,0,1,0,0,6,1,0,0,0,2,10,1,0,0,0,2,0,0,1,1,0,1,0,1,0,0,0,3,0,23,0,2,0,0,0,1,0,12,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33917,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,2,0,0,0,0,0,2,17,0,0,2,0,0,8,0,0,1,0,2,0,0,0,0,1,0,1,0,0,12,0,1,0,0,0,0,5,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,Class_6
+33918,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,4,0,3,0,4,0,1,0,0,0,0,0,0,3,0,0,1,0,1,0,25,0,2,0,0,0,0,0,0,0,0,0,3,3,0,8,0,0,4,0,0,0,1,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,3,0,Class_6
+33919,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,6,0,0,0,1,0,0,1,Class_6
+33920,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33921,0,0,0,0,0,0,0,1,0,0,0,0,1,3,0,0,1,2,0,1,0,7,0,3,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33922,0,0,2,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33923,0,2,7,8,0,0,0,0,0,0,6,0,0,0,0,2,0,3,0,0,0,2,0,1,0,7,5,0,0,0,0,0,0,0,2,1,0,2,0,1,0,0,0,3,1,4,0,2,4,0,0,0,0,12,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,4,0,0,0,Class_6
+33924,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33925,0,0,0,0,1,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33926,0,1,8,5,0,1,0,1,0,0,16,0,1,2,0,2,0,0,0,0,1,1,0,8,2,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,11,0,2,0,0,0,1,0,14,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
+33927,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,2,1,3,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
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+33938,0,0,12,3,0,0,0,0,0,0,10,0,0,1,0,2,0,1,1,2,1,0,0,0,0,1,3,2,0,0,0,0,1,1,2,0,0,0,0,2,0,1,0,1,0,14,0,0,1,0,0,0,1,8,0,2,0,0,0,0,0,0,0,0,1,2,2,0,1,2,0,3,0,0,1,0,0,0,0,3,1,2,0,0,0,0,0,0,1,2,0,0,0,Class_6
+33939,0,0,2,6,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,3,2,1,0,1,0,2,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,1,0,2,0,0,0,2,2,2,0,0,0,3,0,1,1,0,0,13,0,0,1,0,0,0,4,1,0,0,0,0,1,1,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,1,0,0,0,Class_6
+33940,0,0,0,0,0,0,0,1,2,0,1,0,0,1,1,1,0,0,0,0,0,0,0,7,3,0,0,0,0,0,0,0,1,1,0,0,0,0,5,1,1,3,2,0,0,0,0,4,0,1,0,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,9,0,0,0,3,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33941,0,0,3,4,0,0,0,1,0,0,8,0,0,0,0,1,0,1,0,0,3,1,0,2,0,3,2,2,0,0,0,0,2,0,1,0,0,0,0,1,0,1,0,1,0,10,0,0,2,0,0,0,0,9,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33942,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,6,6,1,0,2,1,12,0,1,0,0,0,1,1,3,0,0,2,3,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,2,1,0,5,0,0,9,0,0,0,2,1,0,16,0,7,1,0,0,0,1,1,7,0,0,0,0,0,0,12,0,0,0,5,1,2,0,0,0,2,Class_6
+33943,0,0,8,3,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,1,6,0,0,0,0,0,1,0,1,1,1,0,1,0,0,0,4,0,1,0,1,0,2,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,6,5,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_6
+33944,0,2,4,0,0,0,0,0,0,0,9,0,0,0,0,1,0,2,0,0,0,1,0,14,0,3,1,0,0,0,0,3,0,1,5,0,0,1,0,0,0,0,1,1,0,3,0,1,1,0,0,0,0,8,2,0,0,0,0,1,1,2,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33945,0,0,9,5,0,0,0,0,1,0,11,0,0,0,0,3,0,1,0,0,2,3,0,0,0,4,31,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33946,0,0,7,7,0,0,0,0,0,0,18,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,21,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,5,0,0,1,3,0,1,0,8,0,0,1,8,0,1,1,1,0,1,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,2,0,2,0,0,1,0,0,1,0,0,0,0,0,Class_6
+33947,0,0,7,12,0,0,0,0,2,0,7,2,0,2,1,6,1,0,0,0,1,0,0,0,1,1,5,2,0,0,0,1,0,1,5,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,2,0,3,0,1,0,1,0,0,2,3,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,2,0,Class_6
+33948,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,1,0,13,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,0,3,0,0,0,0,0,2,0,0,1,0,0,12,0,2,0,1,0,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+33949,0,3,1,3,0,0,0,0,0,1,0,0,0,1,3,3,0,4,1,0,0,0,0,4,0,4,0,1,1,0,0,0,0,0,19,0,0,1,0,0,1,1,0,0,0,0,0,0,3,0,0,2,0,5,0,0,4,0,0,8,3,1,0,1,0,0,5,0,0,1,0,0,0,2,1,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,1,0,Class_6
+33950,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33951,0,1,15,12,0,0,0,0,1,0,10,0,2,0,0,7,0,1,0,0,1,2,0,0,0,1,9,4,0,0,1,0,1,2,2,0,0,1,0,0,0,0,0,3,0,18,0,0,1,0,0,0,0,18,0,0,0,0,0,1,3,2,0,0,0,2,0,0,0,1,0,4,0,0,0,1,0,0,0,6,0,7,0,0,0,1,0,0,0,0,0,0,0,Class_6
+33952,0,1,1,0,0,0,0,0,0,0,9,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+33953,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0,0,1,0,0,3,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,1,0,9,0,4,10,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,2,1,0,0,0,0,1,Class_6
+33954,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,2,1,2,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,4,0,0,1,0,0,0,0,1,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33955,0,0,1,1,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,14,0,0,1,0,3,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33956,0,0,6,10,0,0,0,0,1,0,7,0,1,2,0,4,0,3,0,1,1,2,0,0,0,2,0,4,0,0,0,2,1,0,2,0,0,2,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,9,1,0,0,0,0,2,0,1,0,0,0,0,1,0,0,2,0,3,0,0,0,1,1,0,0,5,0,6,1,0,0,1,0,0,2,0,0,1,0,Class_6
+33957,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33958,0,0,1,9,0,0,0,0,0,0,12,1,0,0,0,2,0,0,0,0,0,1,0,6,0,1,3,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,1,0,5,0,0,0,7,0,0,2,0,0,1,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+33959,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33960,0,0,1,1,0,2,0,0,0,0,1,0,0,0,0,9,0,4,0,0,0,3,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,4,0,1,8,7,0,2,0,0,0,0,3,1,1,0,1,13,1,0,0,1,1,18,0,8,0,0,0,2,4,1,1,4,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_6
+33961,0,0,15,1,0,0,0,0,0,2,9,0,0,1,0,3,0,0,0,2,1,2,1,1,0,0,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,1,2,0,0,1,0,13,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+33962,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,1,0,1,2,0,1,1,0,1,5,0,1,0,0,3,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,8,1,0,9,0,1,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,3,0,0,0,0,0,Class_6
+33963,0,0,10,16,0,0,0,0,2,0,10,0,0,0,0,1,0,0,0,0,0,1,0,1,0,7,0,4,0,0,0,2,1,0,3,1,0,0,0,0,0,1,0,0,0,12,0,2,0,0,0,0,0,14,0,0,2,2,0,1,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+33964,0,0,4,2,0,0,0,0,1,0,8,0,0,1,0,7,0,1,0,0,2,1,0,1,0,2,9,0,0,0,0,1,2,0,3,1,0,0,0,0,0,2,3,3,0,11,0,0,2,0,0,2,0,14,0,0,0,0,0,1,2,1,0,0,0,1,0,0,0,1,0,1,1,4,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33965,2,0,1,4,0,0,1,0,0,0,0,0,0,0,0,2,1,3,0,1,0,2,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,18,4,4,0,0,0,0,0,0,0,0,0,7,0,0,2,0,2,4,0,0,0,0,1,1,7,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+33966,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+33967,0,0,25,7,0,0,0,0,0,0,9,1,0,3,0,3,0,0,1,0,1,3,0,2,0,2,14,1,0,0,0,0,0,2,3,0,0,1,0,0,0,1,1,2,0,13,0,0,1,0,0,0,0,14,0,1,1,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,1,0,3,1,0,2,0,Class_6
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+33999,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34002,0,0,1,0,1,0,0,2,1,4,1,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,16,0,0,0,0,0,1,1,0,0,0,0,3,6,0,0,0,0,6,0,2,2,0,1,0,1,0,0,3,4,0,0,0,2,0,0,0,7,0,2,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+34003,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34004,0,2,17,6,0,0,0,0,1,0,9,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,12,0,0,0,1,0,0,1,4,0,0,0,0,0,0,2,0,3,0,13,0,0,2,0,0,1,0,10,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,0,1,Class_6
+34005,0,0,10,1,0,0,0,1,0,0,8,0,0,1,0,5,0,3,0,0,0,2,0,1,2,2,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,17,0,0,4,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,Class_6
+34006,0,0,17,7,0,0,0,0,0,1,8,1,2,0,0,4,0,0,0,0,0,8,0,0,1,13,1,6,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,1,0,11,0,0,1,0,0,1,0,17,0,0,2,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
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+34018,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,Class_6
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+34022,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34023,0,0,0,2,0,0,0,0,3,0,1,0,0,0,0,2,0,4,0,1,0,5,0,2,1,24,0,1,1,0,3,0,2,6,2,1,0,0,0,1,0,13,0,0,0,0,2,2,1,0,0,0,0,7,1,0,2,0,0,4,0,1,0,3,0,4,5,1,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,Class_6
+34024,0,2,0,0,0,0,3,0,0,0,0,0,1,2,0,0,0,2,2,4,0,1,3,8,0,0,0,0,1,0,1,0,0,0,2,0,5,3,1,4,0,1,3,0,0,0,0,2,1,3,0,0,0,0,0,0,2,2,0,13,0,0,0,0,1,2,4,0,1,4,0,0,1,2,0,8,0,0,0,0,0,0,12,0,1,1,2,0,1,2,0,0,2,Class_6
+34025,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,3,0,1,0,0,0,0,0,4,1,0,3,0,0,0,0,1,0,0,0,0,0,13,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34026,5,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,2,0,4,2,8,3,2,1,0,0,0,0,9,0,0,0,2,0,1,0,1,1,1,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,5,5,0,0,0,0,0,0,12,0,0,7,1,2,0,0,0,0,0,0,1,1,0,0,0,0,3,3,8,0,0,0,0,0,20,Class_6
+34027,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+34028,0,0,9,3,0,0,0,2,1,0,8,0,0,0,1,0,0,0,0,1,4,1,0,2,0,1,34,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,8,0,0,1,0,0,0,0,5,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34029,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,1,2,0,0,0,0,1,12,0,6,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,8,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+34030,1,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,4,0,0,0,3,0,0,1,0,2,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,28,0,5,4,1,2,1,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_6
+34031,0,0,0,1,0,1,1,0,0,3,1,0,1,1,0,2,2,2,0,1,0,9,1,14,0,0,2,0,5,0,3,8,0,2,1,2,0,2,1,0,0,5,2,9,10,0,0,9,2,0,2,0,0,8,0,0,3,1,0,19,0,0,1,1,1,1,11,0,0,2,0,0,1,0,0,10,0,2,3,1,0,0,8,0,4,0,5,0,0,0,0,1,1,Class_6
+34032,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,25,1,0,1,0,0,0,16,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,7,0,0,0,0,0,2,3,0,0,0,0,5,0,2,0,3,0,1,8,5,0,0,1,0,2,0,2,1,0,1,4,0,8,0,0,0,2,0,0,2,0,0,0,0,0,Class_6
+34033,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+34034,0,2,0,0,0,0,0,0,0,0,0,0,6,1,0,4,2,0,0,6,0,8,1,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,0,0,0,0,2,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,36,Class_6
+34035,0,0,8,2,0,0,0,0,0,0,11,0,0,1,0,4,0,2,0,0,4,0,0,2,0,4,27,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,5,0,1,2,0,1,7,0,11,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34036,2,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,8,0,5,0,0,0,0,0,0,0,0,2,3,0,1,0,0,1,6,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,1,13,0,0,0,0,0,0,13,1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34037,1,0,3,2,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,3,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34038,0,0,6,7,0,0,0,1,0,0,7,0,0,0,1,2,1,0,0,0,2,2,0,0,0,1,30,1,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,1,0,6,0,0,2,0,0,0,0,10,0,0,1,0,0,0,1,1,2,0,0,3,0,1,0,0,0,0,0,3,0,1,0,0,0,3,0,2,1,0,1,0,0,0,3,2,0,0,0,Class_6
+34039,0,0,6,9,0,0,0,0,0,1,6,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,35,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,0,0,0,0,2,2,Class_6
+34040,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,0,0,2,0,1,1,2,0,0,2,0,0,2,0,0,0,0,0,1,2,0,1,9,0,3,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,39,0,1,0,1,0,2,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,1,0,3,0,Class_6
+34041,0,1,4,4,0,0,0,0,2,0,10,0,0,0,0,1,0,0,1,1,0,0,0,1,0,1,12,1,1,0,0,2,0,0,5,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,6,0,0,1,3,0,1,2,0,0,2,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,2,0,5,0,0,0,0,0,0,4,0,0,1,0,Class_6
+34042,0,0,4,1,0,0,0,0,0,0,5,1,0,0,0,1,0,1,1,0,1,0,0,0,0,1,11,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,7,0,0,1,0,0,0,0,1,0,0,0,0,0,0,11,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,1,0,Class_6
+34043,0,1,1,3,0,0,1,0,0,0,0,0,1,0,0,2,1,2,0,1,0,4,0,5,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,7,0,0,2,0,1,17,0,1,0,0,0,0,1,0,1,2,0,0,0,0,0,1,0,0,0,1,0,0,3,0,1,0,2,1,0,0,3,0,0,Class_6
+34044,3,1,1,0,0,0,0,1,0,1,1,0,0,0,0,1,0,1,0,0,0,3,0,0,4,4,0,0,0,0,0,0,0,5,1,0,1,1,0,0,1,34,0,7,0,1,0,2,0,0,0,0,1,9,1,0,2,0,0,1,0,2,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,4,0,0,0,0,0,Class_6
+34045,0,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,5,0,11,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,13,0,2,0,0,0,1,1,0,0,0,1,4,0,0,5,0,0,9,0,0,0,2,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,2,0,0,3,0,0,0,0,Class_6
+34046,0,1,15,3,0,0,0,0,0,0,10,0,1,0,0,0,0,2,1,1,1,1,0,0,0,0,9,5,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,13,3,0,0,0,0,1,5,0,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34047,0,0,2,0,0,0,0,0,0,0,6,0,1,0,2,1,1,0,0,2,0,0,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+34069,0,0,1,1,0,0,1,0,0,1,1,1,4,0,0,5,0,1,0,2,0,2,0,17,2,11,0,1,27,0,0,1,0,1,1,2,0,0,2,0,3,7,0,2,0,0,0,2,0,0,0,0,0,8,7,0,1,0,0,3,1,1,0,0,0,2,22,0,0,0,2,1,0,0,4,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,Class_6
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+34073,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,7,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,Class_6
+34074,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+34075,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34076,1,1,0,0,0,0,0,0,0,0,7,0,0,1,5,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,4,1,0,0,1,0,0,1,0,2,0,0,4,0,2,0,0,0,0,0,0,11,0,1,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,34,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34077,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34078,0,0,15,4,0,0,0,1,0,0,8,1,3,0,0,6,0,6,0,0,0,1,0,4,0,0,5,7,0,0,0,0,0,0,4,0,0,1,1,0,0,0,2,0,0,5,0,0,1,0,0,0,0,17,0,0,0,0,0,0,2,0,1,1,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,2,0,Class_6
+34079,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34080,1,0,0,2,0,0,0,1,0,0,6,0,2,0,0,4,0,1,2,0,0,1,0,4,5,6,0,0,0,0,0,1,5,3,1,0,0,0,0,4,0,2,0,0,0,0,0,7,0,0,0,0,0,3,0,0,3,0,0,16,1,1,1,1,0,0,8,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,0,Class_6
+34081,1,0,14,6,0,0,0,1,0,0,7,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,2,3,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,4,0,8,0,0,0,0,0,1,0,10,1,0,0,0,1,0,2,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34082,0,0,12,2,0,0,0,0,0,0,11,0,0,0,0,5,0,3,0,0,2,1,0,1,0,0,9,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,2,0,16,0,0,0,0,0,0,0,8,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34083,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,Class_6
+34084,0,1,1,0,0,0,0,0,0,0,1,0,0,1,1,2,2,0,0,2,0,4,0,3,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,1,1,1,0,0,0,0,2,1,0,1,0,0,0,2,9,0,0,4,0,2,0,0,0,2,0,1,0,0,0,0,0,0,1,0,1,2,0,0,0,0,1,Class_6
+34085,0,0,20,2,0,0,0,1,0,0,7,1,0,0,0,3,2,1,0,0,0,2,0,0,0,5,3,3,0,0,0,1,0,1,2,0,0,0,0,2,0,0,0,1,0,9,0,0,1,0,0,0,0,19,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,1,0,1,0,1,Class_6
+34086,0,0,0,0,0,1,0,0,0,0,8,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,0,4,0,2,0,0,0,14,1,1,0,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,60,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+34087,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,1,0,1,0,1,1,8,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,4,0,1,0,0,0,3,0,2,0,1,1,1,0,0,1,0,0,10,0,2,0,1,0,1,3,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,1,0,2,4,1,0,Class_6
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+34123,1,0,7,4,1,0,0,0,1,1,7,0,0,0,0,3,0,0,1,1,0,4,0,1,0,0,5,1,0,1,0,0,0,0,3,1,0,0,0,0,0,1,0,11,0,1,1,1,2,0,0,0,0,15,0,2,0,0,0,2,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+34124,0,0,3,4,0,0,0,0,1,0,7,0,0,0,0,2,0,0,0,0,1,0,0,2,0,1,8,2,0,0,1,0,0,0,1,0,0,1,0,0,0,0,2,0,0,5,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+34126,1,3,0,1,0,0,0,1,0,0,7,0,0,0,1,2,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,3,0,0,1,0,3,0,0,3,6,0,0,0,0,4,0,0,1,0,5,3,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,1,Class_6
+34127,0,2,7,1,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,3,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,2,Class_6
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+34145,1,0,7,6,0,0,0,1,2,0,9,0,0,0,0,1,0,1,0,0,0,1,0,0,0,2,5,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,16,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34146,0,0,9,3,0,0,0,1,0,0,9,0,1,0,0,0,0,2,0,0,2,0,0,2,0,5,12,2,0,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,11,0,0,0,0,0,1,0,6,0,0,0,0,0,3,3,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,0,0,1,0,Class_6
+34147,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,6,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,Class_6
+34148,1,0,0,0,0,0,0,2,2,3,0,0,2,2,0,2,2,1,0,1,0,1,0,1,1,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,4,0,8,0,0,5,0,0,0,0,0,1,6,6,0,0,2,0,2,0,0,2,1,0,0,2,1,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+34149,0,0,4,6,0,0,0,0,0,0,9,0,2,0,1,1,0,1,0,1,3,2,0,0,0,3,7,1,0,0,1,0,0,0,2,1,0,4,0,0,0,2,0,2,1,4,0,0,2,0,0,0,0,9,0,0,0,0,0,0,4,2,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,7,0,4,0,0,0,0,1,1,1,1,0,1,0,Class_6
+34150,2,1,10,2,1,0,0,1,0,1,0,0,3,3,0,6,26,7,0,1,0,7,2,4,1,9,0,0,1,0,0,3,0,0,2,9,0,0,0,0,0,25,6,7,0,1,0,4,4,2,0,0,1,29,3,1,20,1,0,18,0,1,0,2,1,3,17,0,0,4,0,1,0,2,0,0,0,0,8,0,1,0,0,0,2,2,7,1,2,2,0,3,3,Class_6
+34151,0,0,1,0,1,0,0,0,0,0,9,3,2,1,0,10,0,3,1,1,1,2,0,0,0,0,3,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,2,1,0,6,1,1,0,0,1,0,14,2,0,1,0,0,0,0,0,0,0,0,3,3,1,0,1,0,0,0,2,0,0,0,0,2,12,0,4,0,1,0,0,1,0,2,0,0,0,0,Class_6
+34152,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34153,0,1,2,1,0,0,0,0,0,0,13,0,0,0,0,4,0,1,1,2,0,1,0,2,0,0,1,2,0,0,1,0,0,1,2,0,1,2,0,1,0,0,1,0,0,0,0,0,1,0,0,8,0,12,0,0,0,1,0,0,1,0,0,1,1,2,1,1,0,1,0,0,0,9,0,1,0,0,0,19,0,5,0,0,0,1,0,0,2,0,0,0,0,Class_6
+34154,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,4,0,0,0,0,0,1,2,0,0,0,0,1,1,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34155,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,1,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+34156,3,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,Class_6
+34157,0,0,17,8,0,0,0,0,0,0,10,0,0,0,0,4,0,2,0,1,2,2,0,0,0,3,3,4,0,0,0,1,0,0,2,1,0,0,0,0,0,0,2,3,0,9,2,0,2,0,0,2,0,15,1,0,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,3,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34158,0,0,7,0,0,0,0,0,0,1,9,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,Class_6
+34159,0,0,2,2,0,0,0,0,1,0,0,1,0,0,0,4,0,1,0,0,0,3,0,4,0,9,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,6,0,0,2,0,0,8,1,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34160,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,4,2,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+34161,0,1,8,3,0,0,0,0,0,0,1,0,2,1,0,2,1,1,0,0,0,6,0,11,0,7,0,4,0,0,0,2,0,1,1,1,0,1,0,0,0,1,0,4,0,1,0,5,3,0,0,0,1,4,0,1,0,0,1,10,0,0,0,0,0,0,9,0,0,4,0,0,0,0,1,0,0,0,0,8,0,1,0,0,0,1,0,0,1,0,0,1,0,Class_6
+34162,5,0,1,1,0,0,0,1,0,1,0,1,19,0,0,4,1,4,0,0,0,1,3,4,3,0,0,0,2,0,0,0,0,0,0,2,0,3,0,3,2,0,0,2,1,1,0,5,0,3,0,0,7,5,1,0,0,0,32,4,0,2,3,3,0,2,33,1,3,20,0,3,0,0,0,1,2,1,3,0,0,0,0,0,0,7,4,1,0,0,0,0,0,Class_6
+34163,2,1,0,1,0,0,0,0,0,0,1,0,1,0,2,1,1,11,0,1,0,5,0,6,2,0,0,0,0,0,0,6,0,2,1,4,1,1,0,1,0,2,0,2,0,0,0,1,0,0,0,0,0,12,0,0,4,0,0,1,0,2,0,1,0,14,12,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Class_6
+34164,0,0,9,0,0,0,0,0,0,0,6,1,0,0,0,1,0,1,0,0,2,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,9,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,1,0,0,0,Class_6
+34165,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,2,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,8,0,1,0,0,5,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,6,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34166,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,12,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34167,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+34230,5,0,0,1,0,0,1,0,1,1,1,0,2,2,0,3,0,0,0,2,0,2,0,6,2,1,0,0,0,0,0,6,2,0,1,4,1,0,0,1,0,5,0,5,0,0,0,4,0,2,0,0,0,0,2,1,1,0,5,0,0,0,0,4,0,1,16,0,0,3,0,0,0,3,1,1,0,1,1,0,0,0,3,0,0,7,14,1,1,2,0,0,26,Class_6
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+34232,1,0,1,0,0,0,0,2,0,0,0,0,3,0,0,4,0,4,1,0,0,3,0,2,2,3,2,2,0,0,0,0,0,1,2,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,2,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+34234,0,1,23,11,0,0,0,0,1,0,8,0,2,0,0,4,0,0,0,0,0,3,0,2,0,0,32,1,0,0,1,0,1,0,0,0,0,1,0,0,0,1,1,1,0,25,0,1,1,0,0,1,0,18,0,1,0,0,0,1,5,0,0,0,0,0,1,0,0,0,0,1,0,0,1,4,0,0,0,1,0,1,1,0,0,0,2,0,2,0,0,0,0,Class_6
+34235,0,0,1,8,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+34236,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+34237,0,0,1,0,0,0,0,0,0,0,3,0,1,2,1,4,1,0,0,3,0,5,0,3,2,1,0,1,0,0,1,1,1,1,3,0,1,1,0,1,1,8,0,3,0,0,10,2,1,2,0,0,1,5,7,0,0,0,1,1,0,2,1,2,0,1,2,1,0,5,0,1,0,3,2,2,0,0,2,1,0,0,0,0,3,0,4,0,0,0,0,5,0,Class_6
+34238,0,1,21,5,0,0,0,0,0,0,11,0,0,0,0,1,0,1,0,2,2,0,0,0,0,0,0,0,0,0,0,2,0,0,3,2,0,0,7,0,0,0,0,1,0,16,0,0,0,0,0,1,0,23,0,0,0,0,0,0,1,2,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34239,0,0,4,1,0,0,0,0,0,0,17,0,0,0,0,3,0,0,0,0,1,2,1,9,0,1,9,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,12,0,0,0,0,1,1,0,8,0,0,0,10,1,0,2,0,0,0,0,1,1,0,0,1,0,0,0,2,0,1,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+34240,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,3,0,0,0,0,3,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34241,4,0,2,0,0,0,0,0,1,0,0,0,0,4,0,2,0,0,0,1,0,4,0,0,10,0,0,1,2,0,0,3,1,0,1,0,0,0,0,2,1,10,0,13,0,0,0,4,0,0,0,1,0,6,5,0,0,0,1,2,0,3,1,2,1,2,6,0,0,8,0,0,1,1,1,2,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,Class_6
+34242,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,4,0,2,0,2,0,4,3,0,0,0,0,0,2,1,1,1,0,3,0,0,0,0,1,4,0,0,0,1,0,2,1,0,6,0,2,0,0,0,0,0,0,5,0,2,1,2,1,1,8,0,0,2,2,2,0,0,0,0,0,2,2,0,0,0,0,0,0,0,9,0,1,0,0,0,0,Class_6
+34243,0,0,2,4,0,0,0,0,0,0,12,0,0,0,0,5,0,0,0,0,0,1,0,10,0,1,1,4,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,1,0,1,2,0,1,0,1,4,1,0,0,7,0,1,1,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+34244,0,2,1,9,0,0,0,0,0,0,1,2,0,0,0,1,0,2,0,0,0,0,0,5,0,9,0,0,1,0,0,0,0,0,15,0,0,0,1,0,0,10,0,0,0,2,0,0,3,0,0,3,0,3,0,0,1,1,0,11,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34245,0,0,2,1,0,0,0,1,7,1,0,0,0,1,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,17,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+34246,1,1,0,1,0,0,1,0,0,1,2,0,0,0,1,0,1,1,2,1,0,1,3,14,1,0,0,0,0,0,1,2,0,1,1,0,0,1,2,0,0,0,1,0,0,0,0,4,0,0,0,0,1,2,2,0,3,1,0,3,0,1,0,0,0,0,8,1,7,0,0,0,0,1,0,4,0,0,1,1,0,0,17,0,0,3,0,0,0,3,0,0,0,Class_6
+34247,0,0,8,6,0,0,0,0,1,0,8,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,6,2,0,0,2,0,0,1,1,1,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,2,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34403,1,0,12,7,0,0,1,0,0,2,23,0,1,1,0,0,0,0,0,0,0,0,0,7,0,0,5,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,7,0,1,0,10,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,3,0,1,0,1,0,11,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+34406,0,0,2,1,0,0,0,0,0,0,5,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,1,0,0,2,0,1,0,0,0,0,0,0,3,1,1,1,0,0,1,0,0,0,0,0,1,2,0,1,0,0,2,17,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34426,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,0,1,1,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_6
+34427,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34428,0,1,4,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,14,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34429,0,0,1,3,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,0,3,0,1,0,3,7,2,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,10,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,1,0,0,0,2,0,Class_6
+34430,0,1,3,4,1,0,0,0,2,0,15,0,0,1,0,4,0,1,1,0,1,3,0,1,0,3,29,6,0,0,0,0,0,0,3,0,0,1,0,3,0,1,1,1,0,8,0,0,2,0,0,2,0,13,0,2,0,0,1,0,11,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,7,0,6,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34431,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,4,2,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34432,0,2,19,15,0,0,0,0,0,2,13,0,1,0,0,6,0,0,0,1,1,1,0,2,0,5,8,1,0,0,0,0,1,0,0,0,0,3,0,1,0,1,1,2,0,4,0,1,4,1,0,2,0,11,0,2,0,0,0,0,4,0,1,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,11,0,2,0,0,1,1,0,0,2,0,1,0,0,Class_6
+34433,0,1,0,1,0,0,0,0,0,0,16,1,0,0,0,0,0,0,0,0,1,0,0,10,0,0,21,0,0,0,0,2,0,1,5,0,1,7,0,1,0,0,1,0,0,1,0,3,0,0,0,3,0,2,0,0,2,4,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,9,0,0,1,0,0,0,0,0,1,1,0,0,0,Class_6
+34434,0,0,2,4,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,3,0,1,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34435,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,2,Class_6
+34436,0,0,0,1,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+34437,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34438,0,0,1,1,0,0,0,0,0,0,11,0,1,0,2,4,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,0,12,0,0,3,0,0,0,1,2,1,0,0,1,0,8,0,0,0,1,3,0,2,3,0,0,0,0,0,0,1,0,7,2,0,1,2,0,0,1,4,0,2,0,0,0,2,0,1,0,0,0,0,0,0,3,1,0,0,0,Class_6
+34439,0,0,5,3,0,0,0,1,0,0,10,0,0,0,0,5,0,0,0,1,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,12,0,0,1,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,1,0,0,0,0,0,0,0,0,Class_6
+34440,0,1,1,1,0,0,0,3,0,0,0,2,1,0,0,3,0,3,0,0,0,1,0,3,1,0,1,0,4,0,0,0,2,0,3,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,0,3,0,0,3,0,0,10,1,1,0,0,0,0,0,0,0,4,1,1,0,1,2,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,1,1,0,Class_6
+34441,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34442,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,4,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,4,2,0,8,0,0,0,1,0,3,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,Class_6
+34443,0,0,10,3,0,0,0,0,2,1,4,0,0,0,0,1,0,2,0,0,1,1,0,0,0,0,5,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,10,1,0,1,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+34444,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34445,0,0,14,16,0,0,0,0,0,0,11,1,0,1,0,2,1,1,0,0,2,1,0,1,1,3,10,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,2,6,0,0,0,0,0,2,0,8,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,9,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34446,0,0,2,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34447,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,3,0,0,0,0,0,2,0,8,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,1,2,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,Class_6
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+34483,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+34484,2,0,0,0,0,0,0,21,0,0,0,0,0,5,1,2,1,0,0,0,0,0,0,10,1,4,0,0,0,0,0,0,1,5,0,0,2,2,61,1,0,2,0,0,53,4,0,1,0,3,0,0,0,0,0,0,1,0,2,2,0,0,0,4,0,1,5,0,1,1,0,0,0,1,1,6,0,0,2,0,0,0,0,0,2,0,1,0,0,0,4,0,0,Class_6
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+34486,0,1,15,1,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,1,0,4,0,3,0,0,0,0,0,1,0,1,4,0,0,1,0,0,0,0,0,2,0,11,0,0,0,0,0,0,0,10,0,1,0,0,0,0,11,2,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,24,0,5,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34487,0,0,1,2,0,0,0,0,0,0,9,0,0,0,2,1,0,2,0,1,0,2,0,2,0,0,0,1,0,0,1,0,1,1,6,0,0,0,1,0,1,1,2,0,0,1,0,1,0,0,0,3,0,6,0,0,0,0,0,0,3,0,0,0,0,1,2,0,0,0,0,0,0,5,2,0,0,0,0,13,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+34489,0,0,7,1,0,0,0,0,1,0,14,0,1,1,0,4,0,0,0,0,2,1,0,8,0,1,12,2,0,0,0,2,1,0,4,0,2,0,1,1,0,1,0,3,0,3,0,0,0,1,0,7,1,5,0,0,0,1,0,1,2,1,0,0,0,2,1,1,0,2,0,0,0,6,0,0,0,0,0,7,0,0,1,0,0,0,0,0,2,0,0,1,0,Class_6
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+34491,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,Class_6
+34492,0,1,5,1,0,0,0,0,0,0,5,0,1,0,0,1,0,2,0,0,0,0,0,0,0,1,6,1,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,4,0,0,0,0,4,0,0,0,0,0,1,3,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,4,1,0,0,0,0,0,0,0,0,0,0,Class_6
+34493,0,0,1,2,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,1,0,1,1,4,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,5,0,3,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,9,0,3,0,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,Class_6
+34494,2,0,0,1,0,0,1,2,2,0,1,0,0,1,0,4,4,1,0,13,0,3,0,5,5,0,0,0,0,0,0,12,0,1,0,1,1,5,3,0,0,3,0,2,0,0,1,10,0,15,0,0,0,10,2,1,0,0,1,4,0,0,7,10,0,5,6,0,1,13,0,0,5,4,1,0,0,1,13,1,0,0,0,0,3,8,15,2,0,0,0,0,2,Class_6
+34495,1,6,2,2,1,0,2,0,0,0,2,0,0,0,1,4,1,2,7,8,0,4,0,7,3,0,0,0,0,0,0,1,1,5,2,2,0,8,0,0,0,4,0,9,0,0,2,2,7,4,0,0,0,10,3,0,0,7,0,1,0,3,0,1,1,1,5,0,6,5,0,0,1,0,2,1,1,1,1,1,0,1,1,0,2,2,0,0,1,23,0,0,1,Class_6
+34496,0,0,10,10,0,0,0,1,0,0,7,0,0,0,0,6,0,2,1,1,4,2,0,1,0,4,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,5,0,0,1,0,0,0,0,9,0,0,1,0,0,0,16,1,0,1,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,2,0,0,0,1,0,0,0,0,0,0,Class_6
+34497,3,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,2,0,0,0,0,0,0,1,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,1,1,0,0,2,0,1,0,0,1,0,3,2,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34498,0,0,0,3,0,0,0,1,0,1,10,3,1,0,0,1,0,3,0,0,1,2,0,1,0,0,12,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,8,1,5,0,0,0,2,0,0,4,1,0,0,0,3,0,0,0,1,0,0,0,6,0,1,0,0,0,18,0,1,0,0,0,0,0,0,4,1,1,0,0,Class_6
+34499,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,4,0,0,0,2,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,0,0,4,1,0,0,0,0,13,0,1,0,0,2,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+34500,0,2,11,6,0,0,0,0,0,1,8,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,3,2,0,3,1,0,0,0,1,0,0,2,0,0,1,0,0,3,0,1,0,0,7,0,0,0,0,17,1,0,0,0,0,0,2,0,1,0,1,2,1,0,0,0,0,2,0,0,0,0,0,0,0,7,1,6,0,0,0,0,0,0,3,0,0,0,0,Class_6
+34501,1,0,1,0,0,0,0,0,0,0,20,0,0,0,0,1,0,0,0,0,2,0,0,7,0,0,5,1,0,6,0,2,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,1,1,1,0,2,0,3,0,5,0,1,1,0,6,1,0,1,1,0,2,0,2,0,0,0,0,9,0,7,0,0,0,12,0,4,7,0,0,0,0,0,3,0,0,0,0,Class_6
+34502,0,1,12,1,0,0,0,0,0,0,5,0,0,0,1,3,0,0,1,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,3,0,0,2,0,0,0,1,13,0,0,0,0,0,0,0,0,0,0,1,2,1,0,1,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34503,1,1,1,0,0,0,2,0,0,0,9,0,0,2,0,9,1,2,0,1,0,7,2,0,0,0,2,1,1,0,0,2,1,0,3,0,0,1,0,5,0,0,1,7,0,2,0,1,2,0,0,0,0,11,0,1,0,0,0,1,1,0,0,0,4,1,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34504,2,0,3,1,1,0,0,0,0,0,9,0,0,1,0,3,0,0,0,0,0,1,0,14,0,6,8,2,0,0,0,3,0,1,2,1,0,0,1,0,2,0,1,2,0,6,0,9,1,0,1,3,0,2,0,2,0,0,0,2,4,1,0,1,1,0,19,1,0,0,0,0,0,11,6,1,0,0,1,13,0,4,1,0,0,0,0,0,2,0,0,3,0,Class_6
+34505,2,0,0,0,0,0,0,5,0,0,3,0,0,0,0,2,2,1,0,5,0,5,0,3,2,0,0,0,1,0,0,7,0,0,2,1,2,3,8,0,0,6,0,1,0,0,0,3,0,1,0,0,0,2,0,0,0,0,1,10,0,3,0,1,0,0,9,0,0,6,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,8,0,1,0,1,0,0,Class_6
+34506,0,0,0,1,0,0,0,0,0,0,1,0,2,3,0,0,0,0,0,0,1,2,0,5,2,5,0,0,0,0,0,1,4,2,0,0,0,0,0,2,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,9,0,2,0,2,0,0,2,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,Class_6
+34507,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,2,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34508,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34509,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,2,0,0,0,0,0,0,0,2,1,0,2,0,0,4,0,0,3,0,2,0,0,7,0,1,1,0,0,1,2,1,0,1,0,0,3,0,0,0,1,0,2,0,7,0,1,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,3,1,0,0,1,0,0,Class_6
+34510,0,0,0,0,0,0,1,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,7,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+34511,2,1,11,4,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,2,0,2,0,0,0,0,2,1,0,0,0,0,0,0,2,1,0,1,0,0,1,0,0,2,0,3,0,1,5,0,0,0,1,11,1,1,0,0,0,0,4,1,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,5,0,2,0,0,0,0,1,0,3,0,0,0,0,Class_6
+34512,0,1,0,5,0,0,0,0,0,0,13,0,0,0,0,3,0,2,0,0,0,2,1,0,0,0,1,3,1,0,1,0,0,0,3,0,0,1,0,0,0,0,2,1,0,1,0,0,1,0,0,1,0,4,0,0,1,1,0,1,9,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,15,0,5,0,0,0,0,0,0,5,0,0,0,1,Class_6
+34513,0,7,8,4,0,0,0,0,0,0,8,0,2,0,0,4,0,6,0,3,1,9,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,3,0,0,0,1,0,10,0,1,0,0,30,0,0,0,0,8,2,0,0,1,0,0,1,4,4,0,0,5,1,0,0,0,0,3,0,0,0,0,1,3,0,2,0,5,0,0,0,0,0,0,3,0,0,0,1,Class_6
+34514,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,4,0,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34515,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,5,0,0,1,0,0,1,0,10,1,2,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,8,0,0,0,0,0,0,0,0,3,0,0,15,0,1,0,1,0,1,4,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,2,1,0,0,0,0,2,0,Class_6
+34516,0,0,7,4,0,0,0,0,0,0,10,0,1,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,2,1,0,1,0,0,0,0,1,1,0,2,0,0,0,2,0,0,0,4,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,6,1,0,0,0,0,0,2,0,0,0,0,Class_6
+34517,0,2,13,12,0,0,0,0,0,1,7,0,1,0,0,5,0,1,0,0,0,3,0,3,0,4,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,2,2,0,4,0,0,0,0,0,1,0,8,0,0,0,0,0,1,1,2,1,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,6,0,1,0,0,1,0,2,0,2,1,0,0,0,Class_6
+34518,0,5,25,3,0,1,0,0,0,0,8,0,1,1,0,1,0,1,0,0,2,0,0,0,0,0,3,5,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,3,1,18,1,2,0,1,0,0,0,24,0,0,1,1,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34519,0,4,3,9,0,0,0,0,0,0,12,0,1,0,1,1,0,0,0,0,0,2,1,1,0,1,3,2,0,1,1,2,0,0,1,1,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,10,0,0,0,1,0,1,2,3,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34520,0,0,8,8,0,1,0,0,0,0,12,0,1,0,0,1,0,0,0,0,0,0,0,7,0,2,2,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34521,0,0,1,1,1,0,0,7,0,0,0,0,13,1,0,1,9,2,0,2,0,2,0,19,2,1,0,0,0,0,0,1,2,1,0,22,0,0,0,3,0,2,0,2,0,0,0,3,0,0,0,0,0,0,3,0,0,0,5,1,0,6,0,3,0,2,45,1,0,1,1,3,0,0,1,0,0,0,9,0,0,0,1,0,0,0,12,0,3,0,4,2,0,Class_6
+34522,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34523,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,4,0,1,1,0,0,1,0,0,0,5,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,0,Class_6
+34524,0,0,5,4,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,1,0,2,0,0,0,2,1,1,0,0,1,2,0,2,2,0,0,2,0,0,0,1,1,0,0,4,0,0,1,0,0,0,0,5,2,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,Class_6
+34525,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34526,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,3,0,0,0,1,0,3,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34527,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34528,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,7,0,0,0,0,0,8,0,0,1,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34529,0,0,1,1,0,0,0,0,1,0,7,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,2,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34532,0,0,7,3,1,0,1,0,0,0,10,0,1,0,0,4,0,1,0,0,0,1,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,4,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34533,0,1,6,5,0,0,1,0,0,0,16,0,0,0,0,2,0,0,0,0,1,1,0,10,0,1,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,3,0,0,0,1,0,2,1,10,0,0,0,12,0,0,1,0,0,1,1,0,1,0,0,0,1,1,0,4,0,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34535,0,1,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,3,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34537,1,0,2,6,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,9,0,3,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,12,1,0,4,0,0,14,0,1,0,0,0,0,1,0,1,1,0,0,1,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+34538,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,1,0,0,1,2,2,0,0,0,0,7,0,1,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,1,0,Class_6
+34539,0,0,15,2,0,0,0,0,0,0,13,0,0,1,0,2,0,1,0,0,0,0,0,1,0,1,11,3,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,18,0,0,0,0,2,0,1,12,1,2,0,1,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+34540,2,0,0,0,1,0,0,2,0,1,2,0,0,0,0,4,2,2,0,1,0,2,0,3,1,1,0,0,0,1,0,1,0,1,0,0,1,0,1,3,1,5,0,1,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,1,0,2,1,0,0,0,9,0,0,0,2,0,2,0,2,11,0,0,1,1,0,0,3,0,1,0,3,0,0,0,24,0,0,Class_6
+34541,0,1,8,3,0,0,0,1,0,0,13,0,0,0,0,6,0,1,0,0,5,6,0,0,0,2,0,3,0,1,0,0,0,1,2,1,1,0,0,2,0,1,1,2,0,9,0,0,0,0,0,3,0,20,0,1,0,1,0,1,2,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,1,0,0,0,Class_6
+34542,0,0,10,5,0,0,0,0,0,0,8,0,0,0,0,4,0,0,0,0,0,4,0,0,0,0,5,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,2,0,8,2,1,0,0,0,0,0,14,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34543,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,6,0,4,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,4,0,0,0,0,0,1,0,0,0,0,0,5,0,0,5,0,0,12,0,0,0,1,0,0,5,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+34544,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,6,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,2,2,0,0,0,0,8,0,3,0,2,0,0,0,1,0,0,0,1,0,6,0,0,1,0,0,6,0,3,0,0,0,1,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+34545,0,0,7,1,0,0,0,0,0,1,7,0,0,2,0,0,0,2,0,1,0,1,0,1,0,2,2,1,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,1,0,12,0,0,0,0,0,0,1,13,1,0,0,0,0,1,3,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34546,1,0,1,0,2,0,0,0,0,0,8,0,2,0,0,2,0,1,0,0,0,3,0,1,1,1,0,2,1,0,0,0,0,0,1,0,0,0,0,1,1,1,1,1,0,1,0,0,3,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,2,1,1,0,0,1,0,2,2,0,1,1,0,0,3,5,1,3,0,0,0,0,0,0,3,0,0,1,0,Class_6
+34547,0,0,2,2,0,0,0,0,0,1,6,0,0,0,0,3,0,0,0,0,1,0,0,2,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,3,0,2,1,0,0,0,0,5,1,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,0,0,2,0,Class_6
+34548,0,0,5,5,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,0,1,0,0,0,2,7,2,0,0,0,1,0,1,1,0,0,1,0,0,0,8,0,1,0,1,0,0,4,0,0,0,0,13,1,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34549,0,1,13,0,0,0,0,1,0,0,9,0,1,0,0,2,0,1,0,0,0,4,0,0,0,0,11,0,0,1,0,2,0,0,1,1,0,0,0,0,0,1,0,1,0,7,3,0,1,0,0,0,0,18,1,2,0,1,0,1,4,0,0,0,0,3,0,0,0,0,0,1,2,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34550,0,0,1,2,0,0,1,0,0,0,1,0,1,0,0,0,0,10,0,0,0,1,1,1,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,2,0,0,3,1,0,4,0,0,9,0,0,0,0,0,0,0,0,2,2,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,1,1,1,Class_6
+34551,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,2,0,0,2,0,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,4,1,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,2,Class_6
+34552,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,10,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,0,0,3,0,Class_6
+34553,0,0,11,0,0,0,0,0,1,0,9,0,1,1,0,5,0,2,0,0,1,1,0,2,0,1,26,1,0,0,0,0,1,0,0,0,1,0,2,0,0,1,0,4,0,12,0,2,0,0,0,0,0,13,0,0,0,0,0,0,5,1,0,0,0,3,0,0,0,1,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+34554,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34555,0,0,4,0,0,0,0,0,0,0,8,0,0,0,0,1,1,1,0,0,1,2,0,0,0,0,23,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34556,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+34557,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,2,0,3,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,2,1,0,0,0,0,0,0,0,3,0,0,8,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+34558,0,0,14,7,0,0,0,0,0,1,9,1,0,1,0,2,0,2,0,1,2,5,0,0,0,1,18,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,1,0,18,0,0,0,0,0,0,4,1,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,2,0,0,Class_6
+34559,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+34560,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+34561,0,0,8,8,0,0,0,0,0,0,9,0,1,1,0,8,0,1,0,0,1,0,0,2,2,0,9,2,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,4,0,9,1,0,2,1,0,0,0,15,0,1,2,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+34562,1,0,0,1,0,0,0,1,0,0,3,0,5,0,0,0,0,4,0,1,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,9,0,0,0,0,1,1,0,4,0,0,0,1,1,0,2,0,2,0,1,1,1,0,1,4,0,0,2,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34563,1,0,6,1,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,14,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,1,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34564,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,1,1,0,1,0,2,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34565,0,0,1,1,0,0,0,0,0,0,13,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34566,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+34567,0,0,0,0,0,0,0,1,0,0,2,0,5,1,0,4,0,6,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,6,0,5,0,1,2,2,0,0,0,0,0,3,0,0,0,0,1,1,0,4,0,0,1,0,1,0,0,4,0,0,0,1,1,2,0,0,5,1,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_6
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+34569,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+34579,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,2,0,3,0,0,0,3,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+34585,0,1,4,1,0,0,0,0,0,1,10,0,0,0,1,3,0,0,0,0,0,2,0,0,1,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,11,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34586,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+34587,0,0,3,4,0,1,0,0,0,1,6,0,0,0,0,3,0,0,0,0,1,0,0,1,1,1,21,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,1,0,0,2,0,1,0,0,0,1,0,2,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,21,0,2,1,0,0,1,0,0,0,1,0,0,0,Class_6
+34588,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,6,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+34589,3,0,0,0,0,0,0,0,0,0,1,0,5,1,0,1,0,0,0,0,0,2,0,0,7,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,3,0,1,0,0,0,2,0,0,0,0,11,4,0,0,0,1,1,0,0,8,0,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,3,0,0,0,0,0,4,0,Class_6
+34590,0,1,15,3,0,0,0,0,1,1,12,0,1,0,0,3,0,1,0,1,0,3,0,0,0,0,3,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,11,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,2,0,1,0,0,Class_6
+34591,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34592,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34593,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,3,0,0,6,0,0,0,1,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,Class_6
+34594,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,1,1,1,0,0,0,0,0,3,1,3,0,0,0,0,0,1,0,2,0,1,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+34595,0,0,7,4,0,0,0,0,0,0,9,0,1,1,0,0,0,1,0,0,0,2,0,1,0,9,2,0,0,0,0,2,0,0,6,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,1,7,1,0,0,0,0,1,0,5,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,Class_6
+34596,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,3,0,2,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,Class_6
+34597,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,2,0,1,1,0,0,0,3,1,1,0,0,0,0,3,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,7,2,2,0,0,9,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,5,0,0,0,0,0,0,0,Class_6
+34598,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,1,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34599,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,3,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,9,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+34600,0,1,5,2,0,0,0,0,0,0,11,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,5,0,0,0,0,0,0,3,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34601,0,1,2,3,0,0,2,0,0,0,6,0,1,0,8,9,0,3,0,1,0,3,0,0,0,2,1,1,0,0,0,0,0,0,6,0,0,1,0,2,0,1,0,0,0,2,0,1,6,0,0,0,0,5,0,0,0,0,0,1,9,2,1,1,2,10,2,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,2,0,3,0,0,0,0,Class_6
+34602,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,4,0,1,0,2,0,1,0,0,1,0,1,0,0,0,0,0,6,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,11,0,0,3,0,0,12,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34603,0,0,11,9,0,0,1,0,0,1,7,0,0,1,0,4,0,0,0,0,1,3,0,2,0,2,2,6,0,1,0,0,0,1,5,0,0,1,0,1,0,0,0,2,0,4,0,0,0,1,0,0,0,14,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,7,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34604,0,0,2,2,1,0,0,1,1,0,11,1,0,1,0,6,0,2,0,1,0,4,0,1,0,3,0,4,0,0,0,0,2,0,1,0,0,3,0,1,0,1,0,4,0,3,1,2,5,0,0,0,1,15,0,1,0,0,0,0,10,5,0,0,0,6,0,1,0,1,0,1,0,0,0,2,0,0,0,12,0,2,0,2,0,0,1,0,3,0,0,0,0,Class_6
+34605,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+34606,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,3,1,2,0,0,1,0,0,7,1,2,0,2,1,0,0,2,1,1,0,0,0,2,0,2,0,7,0,2,0,0,0,3,1,0,0,0,0,4,0,0,4,0,0,13,0,2,0,0,6,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1,0,Class_6
+34607,0,0,0,1,0,0,0,0,2,0,0,0,1,0,1,0,0,2,0,0,0,3,0,4,0,3,0,1,0,0,4,0,0,1,7,0,0,0,0,0,0,1,0,3,0,1,0,2,0,0,0,0,0,1,0,0,2,0,0,4,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,Class_6
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+34631,0,0,10,2,0,0,0,0,0,0,9,3,0,0,0,3,0,0,0,0,2,1,0,0,0,2,24,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,4,0,1,0,0,0,2,0,2,2,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+34637,0,2,0,0,0,0,2,0,0,1,2,0,3,1,0,2,0,2,0,4,0,7,0,4,3,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,0,0,10,0,0,0,0,0,2,1,8,0,1,0,1,0,0,0,1,0,3,2,3,2,0,4,0,2,0,2,0,1,0,0,5,1,0,0,2,0,3,0,1,0,0,0,0,0,0,Class_6
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+34644,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34645,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,2,1,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,0,12,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34646,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34668,1,0,0,0,0,0,0,1,0,0,5,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,2,0,2,0,0,0,0,0,1,3,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,10,0,3,0,0,0,0,1,0,6,0,1,0,0,Class_6
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+34681,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34682,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,4,0,5,0,0,0,0,0,2,2,0,0,1,0,0,1,3,1,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,12,0,2,0,4,0,4,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,Class_6
+34683,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34686,0,1,5,1,0,0,1,0,0,0,18,0,1,0,0,0,0,0,0,1,0,0,0,5,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,6,0,0,0,7,0,0,2,0,0,1,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,8,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34687,0,0,0,2,1,1,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,4,0,4,2,4,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,2,15,0,3,0,0,9,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+34691,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,8,0,8,0,0,4,2,0,3,0,0,1,1,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,12,6,1,0,3,1,1,0,0,2,1,0,0,0,0,4,0,0,1,1,0,0,0,0,Class_6
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+34695,0,0,13,8,0,0,0,0,0,1,6,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,1,3,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,2,0,4,0,0,0,0,0,0,0,9,0,0,0,0,0,0,3,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34696,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,1,0,1,2,0,0,10,0,0,0,2,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34697,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,14,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34698,1,0,5,6,0,0,0,0,0,0,20,0,0,0,0,2,0,0,0,0,0,1,0,6,0,0,4,3,0,0,1,0,0,0,1,1,0,0,0,0,0,1,1,2,0,7,0,0,2,0,0,0,1,8,0,1,0,6,0,0,2,0,1,1,0,0,1,0,0,1,0,1,0,4,0,0,0,0,0,7,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34699,0,1,19,10,0,0,0,1,1,1,5,4,0,2,0,5,0,0,0,0,0,3,0,2,1,3,17,4,1,0,0,0,1,0,2,0,0,1,0,0,0,0,1,7,0,18,0,0,2,0,0,4,0,20,1,1,0,0,1,0,2,1,0,0,2,1,2,0,0,0,0,1,0,1,0,0,1,0,0,6,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+34700,0,2,7,2,0,0,0,0,1,0,17,0,0,0,0,5,0,0,0,0,0,2,0,3,0,2,4,1,0,0,0,5,0,0,2,0,0,2,0,0,0,0,0,1,0,3,0,1,2,0,0,0,1,9,0,0,0,7,0,1,2,3,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,6,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34701,0,0,0,3,0,0,0,1,0,0,0,0,0,1,2,2,0,0,0,0,0,0,0,2,3,3,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,3,0,2,0,0,0,4,0,0,0,0,0,3,0,0,0,0,2,10,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,Class_6
+34702,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+34703,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34704,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34705,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,2,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,6,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_6
+34706,1,0,1,0,0,0,1,1,0,0,1,0,3,1,1,1,2,0,0,1,0,10,0,2,2,0,0,1,10,0,0,0,1,1,0,0,0,0,0,0,0,2,0,2,0,0,0,4,0,2,0,0,0,5,0,0,0,0,0,1,0,2,1,0,0,0,13,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,1,1,0,0,1,2,Class_6
+34707,0,0,9,4,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+34708,0,3,3,3,1,0,1,0,0,0,10,0,0,0,2,5,0,9,0,0,1,11,2,7,1,4,0,3,1,0,1,1,1,0,8,0,0,0,1,0,0,1,0,5,1,1,1,2,4,0,0,0,0,15,0,0,5,0,0,10,0,0,3,1,0,3,0,2,1,4,0,1,0,0,1,2,0,1,2,3,0,1,9,0,0,0,1,1,0,0,3,0,0,Class_6
+34709,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,2,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,2,0,1,8,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,2,0,0,0,0,0,1,Class_6
+34710,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+34711,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34712,0,0,2,3,0,0,0,0,0,0,6,0,1,1,0,5,1,5,0,2,0,9,0,0,1,0,2,0,2,0,0,0,1,0,0,2,0,3,0,3,0,1,0,12,0,1,0,3,9,0,0,0,0,21,2,0,0,0,0,0,2,1,0,0,3,8,2,0,1,3,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,1,0,Class_6
+34713,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34714,0,0,10,3,0,0,0,0,2,0,9,2,2,0,1,4,0,0,0,0,1,1,0,0,0,1,12,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,14,0,0,2,0,0,0,0,25,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,1,1,0,Class_6
+34715,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,4,0,0,0,6,0,4,2,0,0,0,18,0,0,0,1,2,0,1,0,1,0,1,0,1,0,2,0,0,0,3,0,0,0,0,0,3,6,0,0,0,0,1,0,4,0,0,0,2,6,0,0,2,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,10,0,Class_6
+34716,0,0,15,0,0,0,0,0,0,0,10,1,0,0,0,6,0,0,0,0,0,2,0,2,0,0,18,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,17,0,0,2,0,0,0,0,8,0,1,0,0,0,1,1,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,1,0,0,2,0,0,1,0,Class_6
+34717,0,0,9,30,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,8,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,2,0,Class_6
+34718,1,0,0,0,0,0,0,0,0,1,2,0,1,2,0,5,0,0,0,0,0,2,0,5,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,2,0,0,15,0,1,2,0,0,1,4,2,0,1,0,1,2,0,0,0,0,0,4,1,4,0,2,3,1,0,1,0,1,0,0,2,1,0,0,1,0,0,1,0,0,0,0,0,2,0,Class_6
+34719,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,2,0,4,0,0,17,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+34720,0,0,2,0,0,1,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,2,0,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,Class_6
+34721,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,2,0,3,0,2,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,2,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,12,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,Class_6
+34722,0,0,7,15,0,0,0,0,0,0,9,0,0,0,0,2,1,0,0,0,0,1,0,3,1,2,7,2,0,0,0,0,0,1,3,0,0,0,0,0,2,2,0,0,0,7,0,0,0,0,0,0,2,5,0,1,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34723,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34724,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+34725,0,1,0,1,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34726,0,0,1,1,0,0,2,1,0,1,2,0,0,1,2,0,5,2,0,7,0,5,0,5,1,0,0,0,1,0,0,0,3,0,1,1,0,3,0,1,1,0,0,0,0,1,0,1,1,0,6,1,0,1,0,0,4,0,0,11,0,0,1,1,0,2,5,0,1,2,3,0,0,0,1,2,0,2,0,2,8,0,0,0,1,0,9,0,1,0,0,0,0,Class_6
+34727,1,3,2,2,1,0,4,0,0,0,8,0,1,2,0,15,0,3,0,2,1,10,0,0,1,0,1,0,1,1,0,0,0,0,1,0,1,3,0,3,1,0,0,5,0,0,29,2,7,1,1,0,0,17,4,0,0,0,0,1,1,5,1,1,7,7,0,0,1,3,0,0,0,0,0,0,0,1,0,4,1,0,0,6,0,1,1,0,7,0,0,4,1,Class_6
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+34729,0,0,13,3,0,0,0,0,0,0,8,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,1,0,0,0,1,10,1,1,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+34738,0,0,6,2,0,0,0,0,0,0,7,0,0,1,0,1,0,1,1,0,0,1,0,1,0,2,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,2,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34739,0,0,9,4,0,0,0,0,0,1,8,0,0,1,2,4,0,0,0,0,4,2,0,0,0,0,12,3,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,13,0,0,1,0,0,0,0,9,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34740,0,1,1,2,1,0,0,2,0,0,0,0,0,0,0,3,4,4,0,1,0,6,3,3,0,2,0,0,0,0,13,0,0,2,8,2,0,0,0,5,0,1,0,2,0,0,0,1,2,2,0,0,0,9,1,0,14,0,0,4,0,7,0,1,0,1,3,0,1,1,0,2,0,0,0,1,1,0,0,1,0,2,1,0,0,0,1,0,0,0,0,2,0,Class_6
+34741,0,0,2,0,0,0,4,1,0,0,0,0,0,0,0,1,0,1,0,1,0,6,0,4,0,0,3,1,0,0,0,0,0,0,1,0,3,1,0,1,0,3,0,2,0,0,0,0,2,1,0,0,0,3,1,0,5,0,0,16,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+34742,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,4,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,4,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,6,3,0,1,0,0,1,1,2,1,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+34743,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,3,0,0,0,1,0,10,0,3,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,0,3,1,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34744,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34745,3,0,3,1,0,0,1,0,0,0,0,0,1,7,1,3,1,0,0,2,0,4,0,3,4,1,0,0,0,0,0,4,0,0,2,0,1,0,0,0,0,8,0,15,0,0,0,1,1,0,0,0,0,9,4,0,1,0,0,1,0,1,0,1,2,1,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,15,2,0,0,0,0,1,Class_6
+34746,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,14,0,0,0,5,0,1,0,0,0,0,2,2,0,0,0,0,0,2,0,0,0,0,1,0,0,0,Class_6
+34747,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_6
+34748,4,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,3,0,0,14,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,0,Class_6
+34749,0,0,0,3,0,0,0,0,1,0,1,0,0,1,0,4,0,1,0,0,0,4,0,7,1,2,0,0,0,0,0,5,0,0,4,0,0,5,15,2,1,5,1,4,0,0,0,4,1,1,0,0,1,12,1,0,0,0,0,17,0,1,0,0,0,8,10,0,0,5,0,1,0,0,0,1,0,0,0,2,0,0,0,0,2,2,7,0,0,0,0,0,0,Class_6
+34750,0,0,6,0,0,0,0,0,0,1,6,0,0,0,0,2,0,2,0,0,2,1,0,2,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,4,0,0,0,0,0,0,0,21,0,0,0,0,0,1,2,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34751,0,1,3,2,0,0,0,0,0,0,2,0,0,1,0,1,0,1,0,0,0,1,0,0,2,4,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,5,0,0,0,1,0,7,0,0,0,0,0,1,2,0,0,0,1,4,0,7,0,4,0,1,9,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+34752,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,108,0,0,2,0,1,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34753,0,1,0,1,0,0,0,0,0,1,1,0,0,1,0,2,2,0,0,0,0,1,0,4,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,1,6,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,7,0,0,0,1,0,4,7,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,Class_6
+34754,0,4,0,0,0,0,0,1,1,0,1,0,2,1,2,3,2,9,5,4,0,7,0,20,0,0,0,0,0,0,0,2,0,0,1,0,0,2,1,2,1,0,0,4,0,0,0,1,5,1,0,0,1,2,2,0,1,0,0,10,0,0,1,2,0,2,0,1,11,1,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+34755,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34756,4,0,0,1,0,1,1,2,1,1,0,0,0,0,0,1,0,1,0,3,0,7,0,12,4,0,0,0,0,0,0,13,0,0,0,2,2,5,0,0,0,4,0,3,0,0,0,6,0,6,0,0,1,4,2,1,1,0,5,0,0,2,0,1,0,1,16,0,1,6,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,4,22,1,2,0,0,0,36,Class_6
+34757,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,1,0,0,1,0,2,2,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,4,0,1,0,0,1,1,2,0,0,0,0,3,2,0,3,0,0,9,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+34758,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34759,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,1,1,0,1,1,6,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,3,Class_6
+34760,0,0,8,9,0,0,0,0,0,0,8,0,1,0,0,3,0,2,0,0,0,2,0,0,0,8,3,2,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,1,0,8,0,0,0,0,0,1,0,15,0,0,0,1,0,2,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+34761,0,0,0,0,0,0,0,9,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,4,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34762,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,6,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,1,2,0,0,0,0,0,1,0,2,0,0,4,0,0,0,1,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+34763,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,2,0,1,2,8,0,0,0,0,0,0,3,3,0,0,1,3,0,1,3,0,0,0,0,1,2,0,0,4,1,0,0,0,0,3,0,0,3,0,0,14,0,1,0,1,0,0,5,0,5,0,0,0,0,0,0,1,0,3,0,1,0,0,8,0,0,0,5,1,0,0,0,0,6,Class_6
+34764,1,0,1,0,1,0,0,0,0,2,2,1,8,2,0,4,0,3,0,0,0,4,0,7,3,1,0,0,9,0,0,3,2,0,1,0,1,0,0,0,3,9,0,2,0,0,1,4,1,1,0,0,27,7,3,0,2,0,1,2,2,6,1,1,0,1,1,0,0,3,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,3,0,0,6,0,Class_6
+34765,0,0,4,1,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34766,0,1,2,3,0,0,1,0,1,0,6,0,0,0,8,8,0,2,0,1,1,3,0,0,0,1,2,1,0,0,0,0,0,1,5,0,0,2,0,2,0,2,0,0,0,0,0,2,8,0,0,0,0,6,0,0,0,0,0,1,1,3,1,1,2,10,1,1,0,0,0,0,0,1,0,0,0,0,0,9,0,2,0,0,1,0,1,0,1,0,0,0,0,Class_6
+34767,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,2,0,1,0,0,0,1,0,3,0,2,0,0,0,0,3,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,17,0,2,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,2,0,2,Class_6
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+34769,0,1,3,5,0,0,0,0,0,0,6,0,2,0,0,0,0,1,0,0,4,2,0,1,0,1,33,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,20,0,0,0,0,0,3,0,7,1,0,0,0,0,1,9,0,0,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+34781,0,0,1,4,2,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,1,3,0,2,0,0,1,1,0,0,2,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+34782,0,4,0,1,0,0,0,0,1,0,4,0,0,0,0,3,0,2,0,0,1,0,0,1,2,4,2,0,0,0,0,0,0,0,5,1,0,1,0,0,0,6,0,0,0,0,0,0,5,0,0,4,0,2,0,0,2,1,0,0,2,0,1,0,0,5,0,1,0,1,3,2,0,6,0,0,1,0,0,6,0,3,0,0,1,2,0,1,4,0,0,2,0,Class_6
+34783,0,0,7,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,8,1,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34785,2,0,1,3,0,0,0,0,0,0,1,0,0,1,0,4,0,1,0,0,0,1,0,0,5,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,23,0,2,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34786,0,0,9,3,0,0,0,0,0,1,6,0,0,0,0,4,0,0,0,0,0,2,0,0,0,2,8,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,8,0,0,1,0,0,1,1,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34787,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+34788,0,0,9,10,0,0,0,0,2,1,7,0,0,0,0,3,0,0,0,0,2,1,0,0,1,0,5,1,0,0,2,0,0,0,1,0,0,2,0,1,0,0,0,2,0,16,0,0,0,0,0,1,1,10,0,0,2,0,0,1,2,2,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,3,0,1,0,0,0,0,1,1,3,0,0,1,0,Class_6
+34789,0,0,0,3,0,0,0,0,0,0,3,0,4,1,1,0,0,3,0,1,0,1,0,0,2,5,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,24,0,1,0,0,0,3,0,0,0,0,0,1,0,0,2,0,2,2,0,2,0,1,0,1,2,0,1,3,0,0,0,3,0,0,0,0,2,1,0,0,0,0,0,1,3,0,0,0,0,3,4,Class_6
+34790,2,1,1,1,0,0,0,0,0,0,12,0,0,2,0,4,0,0,0,0,0,0,1,2,1,1,4,2,0,0,0,3,0,0,5,0,1,4,5,1,0,0,0,0,0,3,0,3,1,1,0,12,0,5,0,2,0,1,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,23,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+34791,0,2,3,0,0,0,0,0,1,0,7,0,0,1,0,4,0,2,0,0,1,2,0,0,0,0,5,1,0,2,1,1,0,0,3,1,0,0,0,1,0,1,0,0,1,2,0,0,2,0,0,1,0,6,0,0,0,0,0,0,4,0,0,1,1,1,0,0,1,0,0,0,1,1,0,0,0,0,0,8,0,2,0,0,1,0,0,0,0,0,1,0,0,Class_6
+34792,0,0,8,9,0,0,0,0,0,0,12,0,1,1,0,6,0,1,0,0,4,2,0,0,0,2,12,3,1,0,0,1,0,0,3,0,0,1,0,2,0,0,0,1,0,8,0,0,1,0,0,0,0,8,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,6,0,0,0,0,1,0,1,0,0,0,0,Class_6
+34793,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,14,0,2,1,0,0,0,0,2,1,0,0,1,2,0,0,1,0,0,0,0,0,2,0,0,1,0,0,3,1,0,3,0,1,13,0,0,0,0,0,0,2,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,Class_6
+34794,1,0,1,2,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,27,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,6,0,0,2,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+34795,0,0,7,2,0,0,1,0,0,0,11,0,0,1,0,2,0,1,0,0,1,1,0,8,0,2,12,5,0,1,0,1,1,0,1,0,0,0,1,0,0,2,0,1,0,15,0,0,0,0,0,1,1,17,1,1,1,0,0,2,1,3,0,0,0,1,0,0,0,1,0,0,0,8,0,0,0,0,0,3,1,5,0,0,0,0,1,0,2,0,0,0,1,Class_6
+34796,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,2,1,3,0,0,0,0,0,1,1,5,1,1,1,0,0,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+34797,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,1,0,5,0,1,0,2,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,0,8,1,0,2,0,1,12,0,0,1,0,1,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,6,0,Class_6
+34798,0,0,1,1,0,0,1,1,1,0,0,0,0,0,0,0,0,13,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,12,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,Class_6
+34799,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34800,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,4,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,6,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,2,0,0,0,1,0,Class_6
+34801,0,1,5,12,0,0,0,1,0,0,9,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,2,1,0,0,0,2,0,0,1,3,0,0,0,0,1,1,0,4,0,2,0,0,3,1,0,3,0,4,1,0,0,0,0,2,6,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,4,0,0,0,0,0,0,6,0,0,1,0,Class_6
+34802,0,0,0,3,1,0,0,0,1,0,10,0,0,0,1,0,0,1,0,0,3,0,1,0,0,1,17,0,0,0,5,2,0,0,4,0,0,0,0,1,0,0,1,1,0,3,0,0,0,0,0,2,0,2,0,0,0,2,0,1,4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34803,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,1,1,2,1,6,0,2,0,4,1,1,0,0,3,1,1,1,0,0,1,3,1,2,1,0,2,3,0,0,0,4,0,1,2,0,3,1,0,1,0,0,3,0,0,11,2,4,1,3,0,1,6,0,0,0,2,0,2,0,1,0,3,0,2,1,1,1,0,0,0,1,4,0,4,1,0,0,0,Class_6
+34804,0,0,0,1,0,0,0,0,0,0,8,0,1,2,0,4,0,2,0,2,0,1,0,0,1,1,14,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,1,1,1,2,0,0,0,0,8,0,0,0,2,0,0,0,2,1,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,19,0,6,1,0,0,1,0,1,3,0,0,1,0,Class_6
+34805,0,0,0,0,0,0,0,4,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,7,0,1,0,0,4,0,40,0,0,0,0,0,21,1,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,8,0,0,0,2,0,0,1,1,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,5,0,0,Class_6
+34806,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,4,0,1,1,0,1,9,0,0,0,3,3,0,0,0,0,0,0,0,1,1,0,3,0,0,0,4,0,9,0,0,6,1,14,0,0,0,0,3,0,0,0,0,0,2,2,0,2,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,2,1,1,0,0,2,0,0,0,0,3,0,0,2,0,Class_6
+34807,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,3,1,0,0,0,0,0,1,4,0,0,0,1,0,2,7,0,0,0,0,0,1,0,0,3,0,6,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,1,2,0,0,2,0,1,1,0,4,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,Class_6
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+34809,0,0,1,0,0,0,0,1,0,3,1,0,0,0,2,1,1,1,0,0,0,1,0,3,1,2,0,0,1,0,0,2,0,0,0,0,0,1,4,0,1,2,0,2,0,0,8,2,1,1,0,2,0,1,2,0,0,0,0,0,1,1,0,0,0,2,1,0,0,1,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+34818,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,8,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+34823,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,Class_6
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+34825,0,0,7,2,0,0,0,0,0,0,12,0,1,0,0,4,0,1,0,0,0,1,0,0,0,4,0,2,0,0,0,2,0,0,3,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,2,0,15,0,0,0,1,0,3,2,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,10,0,2,1,0,0,0,0,0,3,0,0,0,1,Class_6
+34826,0,1,2,10,0,0,0,0,0,0,3,0,3,0,0,4,1,2,0,0,0,1,0,5,1,9,0,0,1,0,0,1,0,0,22,0,0,1,0,1,0,0,0,1,0,0,0,1,4,0,0,0,0,14,1,0,3,0,0,12,2,3,3,1,0,0,1,2,1,1,0,0,1,0,1,0,0,0,0,1,2,2,0,0,0,0,2,0,5,0,0,0,4,Class_6
+34827,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,1,1,2,0,0,0,0,0,1,0,1,0,0,0,0,3,0,6,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+34828,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,1,1,0,5,0,9,0,6,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,2,0,0,12,0,1,0,1,0,5,0,0,0,3,2,1,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+34829,1,1,4,3,0,0,0,0,0,0,7,0,0,0,0,7,0,1,0,1,0,4,0,0,0,3,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,3,0,5,0,0,4,0,0,0,0,10,2,0,0,0,0,0,2,2,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,2,0,1,0,0,0,0,Class_6
+34830,0,0,3,1,0,0,0,1,1,0,12,0,0,0,0,3,0,0,0,0,1,1,0,8,1,0,24,1,0,0,0,2,0,0,2,0,1,0,0,0,0,0,1,2,0,3,0,1,2,0,0,0,0,9,0,1,1,6,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+34831,0,0,13,14,0,0,0,0,0,2,8,0,3,0,0,1,0,0,0,0,2,4,0,0,0,1,4,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,6,0,0,3,0,0,0,0,17,0,0,0,1,0,0,3,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,1,0,Class_6
+34832,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34833,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,15,0,0,4,1,1,1,9,0,1,0,0,0,0,0,1,0,0,6,1,0,1,0,0,0,0,2,0,2,0,1,3,3,0,0,0,0,0,1,0,2,0,1,2,0,1,0,2,0,1,15,0,0,1,3,0,0,4,2,0,0,0,7,0,0,0,0,0,0,0,3,1,0,0,1,0,0,Class_6
+34834,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,5,0,0,0,0,0,2,0,2,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,2,0,1,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,3,2,0,Class_6
+34835,0,0,2,1,0,0,0,0,0,0,7,1,1,1,1,6,1,0,0,0,1,3,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,2,1,1,0,0,1,0,16,0,0,0,0,0,0,1,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,1,1,0,Class_6
+34836,2,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,1,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,1,0,0,0,3,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,Class_6
+34837,0,0,2,1,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,2,0,4,0,0,0,1,2,3,0,0,2,0,0,0,3,0,0,1,0,0,0,1,0,0,0,2,0,0,3,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,Class_6
+34838,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34839,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+34840,0,0,0,1,0,0,0,0,3,1,1,0,0,4,3,5,0,0,0,1,0,3,0,7,2,2,0,0,0,1,0,3,0,1,0,1,1,2,16,0,0,5,0,4,4,1,0,0,0,1,0,0,0,12,2,1,0,0,0,5,0,0,0,1,0,1,3,0,7,3,2,0,0,0,2,0,0,0,0,2,1,0,0,0,1,0,10,0,4,1,0,0,2,Class_6
+34841,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,1,0,1,0,4,0,4,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,14,1,4,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,11,1,3,4,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,7,6,1,0,0,1,0,Class_6
+34842,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,2,3,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,10,0,0,0,0,1,0,1,1,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,12,0,1,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,2,0,0,0,Class_6
+34843,0,0,1,1,0,0,2,0,0,0,1,0,5,1,3,2,3,2,2,1,2,7,1,9,1,0,0,2,1,0,0,0,1,1,0,0,0,5,0,0,0,0,0,2,0,2,0,5,3,1,0,0,0,12,3,1,6,1,0,11,0,0,0,0,0,0,7,0,1,4,1,0,1,0,1,3,0,0,0,0,2,0,10,0,1,0,2,0,1,1,0,0,0,Class_6
+34844,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,12,0,0,0,0,0,0,5,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34845,0,0,0,0,0,0,0,1,0,0,0,1,3,5,1,0,3,5,0,0,0,3,0,6,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,1,0,1,2,1,0,6,0,0,0,0,2,0,0,0,2,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,Class_6
+34846,0,0,2,4,0,0,0,3,0,1,0,0,0,0,3,2,0,0,0,1,0,0,0,0,1,4,0,2,3,0,0,3,0,1,2,0,0,0,0,0,3,4,1,1,0,1,0,2,0,0,0,0,18,7,3,0,0,1,0,2,0,3,0,0,2,1,1,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,1,0,Class_6
+34847,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,2,1,1,0,0,0,0,0,1,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+34878,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,5,1,0,0,0,0,0,0,2,0,0,4,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,4,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,6,0,6,0,0,0,0,0,0,5,0,1,1,0,Class_6
+34879,0,1,8,0,0,0,0,1,0,0,11,0,2,0,0,10,0,2,0,0,1,1,0,0,0,2,3,0,0,0,1,0,1,0,0,1,0,0,1,1,0,0,0,5,0,6,0,0,3,0,0,1,0,31,3,1,0,0,0,1,3,1,0,0,1,1,0,0,1,0,0,0,1,2,0,0,0,0,0,2,0,1,0,0,0,1,0,0,1,0,0,0,0,Class_6
+34880,1,0,2,1,1,0,0,0,0,0,0,1,0,2,0,3,0,3,0,0,0,2,0,0,2,0,0,0,0,0,0,0,3,3,0,0,0,1,0,15,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34881,0,0,1,3,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,1,1,0,2,0,0,2,1,1,0,1,1,1,0,0,1,1,0,2,0,0,3,0,0,1,0,13,2,1,0,0,0,0,1,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,22,0,6,0,0,1,0,0,0,2,0,0,0,0,Class_6
+34882,2,0,2,1,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,0,2,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,15,0,0,0,1,0,1,0,0,0,3,0,1,1,0,0,0,0,0,0,1,0,0,0,2,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,Class_6
+34883,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34884,0,0,23,16,0,0,0,0,0,1,11,0,0,1,0,2,0,0,0,0,5,2,0,0,0,7,28,3,0,0,1,0,0,1,3,1,2,0,0,0,0,2,0,3,0,20,0,0,3,0,0,1,0,12,1,0,0,1,0,1,13,1,0,0,1,0,0,1,0,1,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,1,0,0,2,1,Class_6
+34885,0,0,9,5,0,0,0,0,0,0,7,0,1,0,0,2,0,4,0,2,0,2,0,0,0,9,0,6,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,5,0,12,0,0,3,0,0,0,0,13,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,1,0,Class_6
+34886,0,0,0,0,0,0,0,0,2,0,10,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34887,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+34898,0,3,3,1,0,0,0,0,0,1,6,0,0,0,0,3,0,0,1,0,0,2,0,0,1,0,0,2,1,0,0,0,0,0,3,0,1,1,0,0,1,0,0,3,0,0,0,1,10,0,0,0,0,21,0,1,0,0,0,0,1,0,0,0,2,3,1,0,1,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+34899,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34900,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,3,1,4,0,3,0,13,0,10,3,0,0,0,1,0,0,0,2,3,4,0,1,1,0,0,0,16,0,5,0,0,5,0,2,1,0,0,2,5,6,0,2,0,0,3,0,6,5,2,3,4,7,6,0,9,3,2,0,0,1,0,0,0,10,1,0,0,0,0,0,0,3,0,7,0,0,1,0,Class_6
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+34902,0,1,19,0,0,0,0,0,0,0,9,0,0,0,0,4,1,0,1,0,1,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,14,0,0,0,0,0,5,0,11,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,1,0,0,1,0,0,1,0,0,Class_6
+34903,3,0,2,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,0,3,0,1,0,2,0,0,0,0,0,5,0,0,0,0,3,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+34904,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,6,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,8,Class_6
+34905,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,1,0,2,0,1,0,1,0,3,0,1,0,0,7,0,0,0,0,0,3,1,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,3,0,1,1,0,0,0,1,0,1,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34906,0,0,2,2,0,0,0,0,0,0,5,0,0,0,0,2,0,1,0,1,0,1,0,7,0,6,1,0,0,0,0,1,0,0,5,0,0,0,0,0,1,9,0,0,0,4,0,1,2,0,0,0,0,9,0,0,2,0,0,8,3,1,0,0,0,4,4,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34907,1,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,4,0,0,0,5,0,2,0,3,0,0,1,0,1,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,4,1,0,2,0,0,17,0,2,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,5,1,0,Class_6
+34908,0,0,6,12,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,Class_6
+34909,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,4,0,3,0,0,1,0,0,0,1,1,10,0,0,0,0,1,1,0,1,1,0,0,0,0,2,0,0,0,0,3,1,0,5,0,0,12,0,3,0,0,0,0,0,0,0,1,0,1,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+34910,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,Class_6
+34911,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+34912,0,0,1,0,0,0,0,0,0,0,16,0,0,0,0,0,0,1,0,0,1,0,1,4,0,0,25,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,5,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+34913,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,5,0,1,0,1,0,0,0,0,0,0,Class_6
+34914,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34915,3,10,1,0,0,0,0,0,0,0,9,1,2,0,0,3,0,1,1,0,0,2,0,0,1,0,2,1,2,1,1,1,0,0,2,0,1,1,0,0,0,0,0,2,0,0,0,3,6,0,0,1,0,19,4,0,0,2,0,0,0,0,0,0,4,6,1,0,1,1,2,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,18,0,1,0,Class_6
+34916,0,0,3,0,0,0,0,0,0,1,1,0,1,0,0,0,0,6,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,2,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,4,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+34917,0,1,3,0,0,0,0,0,0,0,9,0,0,0,0,2,0,3,0,0,0,3,0,0,0,4,14,2,0,0,2,2,0,3,3,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,4,0,17,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,3,0,1,0,0,0,19,0,3,0,0,0,1,0,0,2,0,0,0,0,Class_6
+34918,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,5,1,2,0,0,1,0,0,1,0,1,1,0,0,2,1,0,2,0,0,1,0,0,0,1,0,2,0,0,7,2,6,0,0,0,0,6,0,0,0,1,0,0,3,0,0,0,0,2,1,0,0,2,0,0,1,0,0,1,2,0,0,2,1,0,0,2,0,0,1,0,0,1,0,1,0,Class_6
+34919,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,3,1,0,0,2,0,2,0,3,0,3,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,2,5,0,0,0,1,3,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,4,0,0,0,0,2,4,Class_6
+34920,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,5,1,0,0,0,1,0,Class_6
+34921,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,2,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+34922,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,4,0,0,0,1,0,2,0,1,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,1,0,1,0,0,2,0,4,0,0,6,0,0,8,0,1,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,Class_6
+34923,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+34924,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,5,0,1,0,0,1,3,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,1,0,8,0,0,2,0,2,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,1,0,0,0,0,0,0,Class_6
+34925,0,0,0,1,0,0,0,1,0,3,0,0,0,1,0,4,0,5,0,3,0,2,0,18,2,7,0,0,0,0,1,0,2,1,3,0,0,1,1,2,0,9,0,2,1,0,0,0,1,0,0,0,1,0,2,0,2,0,0,12,1,0,0,1,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+34926,0,2,0,0,0,0,0,0,0,0,8,0,0,1,0,3,0,1,0,1,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,4,0,0,0,5,0,0,0,0,0,1,1,2,1,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+34927,0,0,4,4,0,0,0,1,1,0,17,1,0,2,0,2,0,0,0,0,1,1,0,8,0,4,6,1,0,0,0,3,0,1,2,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,1,0,5,0,0,0,10,0,1,1,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34928,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34929,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,3,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,4,0,0,1,0,0,12,0,0,1,2,2,2,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+34930,0,0,4,7,0,0,0,0,0,0,9,0,1,0,0,4,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,7,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,1,0,Class_6
+34931,0,0,8,4,0,0,1,1,0,0,14,1,0,0,0,3,0,0,0,0,0,4,0,1,1,0,4,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,4,0,0,5,0,0,0,1,22,1,0,0,0,0,2,4,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,1,1,0,0,0,0,0,0,0,0,1,0,Class_6
+34932,0,0,3,10,0,0,0,0,0,0,5,0,0,0,0,3,1,0,0,0,0,3,0,1,0,5,3,4,0,0,0,1,0,0,4,0,4,0,0,0,0,0,0,0,0,1,1,0,3,0,1,1,0,13,3,0,0,0,0,2,0,0,0,2,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,3,0,Class_6
+34933,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34934,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,1,0,5,4,9,3,0,0,0,2,0,0,4,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,6,0,0,1,2,0,0,0,0,0,1,0,3,0,1,0,8,7,0,3,4,2,1,0,0,1,1,2,2,0,0,1,0,0,0,2,21,1,2,0,0,0,0,0,Class_6
+34935,0,0,4,9,0,0,0,0,0,0,6,0,1,0,0,2,0,0,1,1,0,2,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,7,0,0,0,0,0,5,0,15,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34936,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,7,0,2,0,0,0,1,0,3,0,9,0,2,0,0,0,0,1,0,3,0,0,0,2,1,0,9,0,1,0,2,0,0,0,0,0,0,0,14,0,0,0,1,0,13,0,8,0,0,0,2,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,9,0,Class_6
+34937,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,2,0,1,0,0,0,2,0,2,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,2,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+34938,1,0,5,5,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,18,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,6,0,0,1,0,1,2,0,5,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34939,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,8,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
+34940,0,0,6,8,0,0,0,0,1,0,8,0,0,0,0,4,0,0,0,0,3,2,0,3,0,1,8,4,0,0,1,0,0,0,2,1,0,3,1,0,0,0,0,1,0,4,0,0,0,0,1,3,1,12,1,1,0,0,0,0,2,1,0,0,0,3,0,1,0,1,1,0,1,6,0,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34941,0,0,2,3,0,0,0,0,2,0,0,0,2,2,1,3,0,2,0,0,0,2,0,2,2,5,0,0,0,0,1,0,1,0,2,0,0,0,0,4,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,3,0,0,11,0,0,0,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,2,0,0,0,0,0,1,0,Class_6
+34942,6,0,0,1,0,0,0,0,0,2,2,0,1,3,0,2,8,2,0,2,0,6,0,11,5,1,0,0,0,0,0,17,0,2,0,7,0,2,3,0,0,5,0,2,3,0,0,19,0,1,0,0,1,3,2,1,0,0,9,1,0,1,0,3,0,1,46,1,2,4,1,2,1,0,1,2,0,0,1,0,0,0,1,0,0,14,40,0,2,0,0,0,37,Class_6
+34943,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,1,0,1,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,3,0,0,1,0,0,14,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34944,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,3,0,0,0,0,1,0,1,4,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,Class_6
+34945,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,3,0,1,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,2,0,0,17,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34946,1,0,0,1,0,0,0,0,0,1,6,1,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34947,0,2,1,2,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,3,0,6,0,8,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,2,0,3,0,0,0,0,1,0,0,5,0,6,0,0,3,0,0,16,0,0,1,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,Class_6
+34948,0,0,4,6,0,0,0,0,1,0,7,0,0,0,0,2,0,0,0,0,0,1,0,0,0,2,5,1,0,1,0,0,0,0,2,0,0,1,0,0,0,3,0,2,1,1,0,0,0,0,0,1,0,7,0,0,0,0,0,0,3,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,4,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+34949,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,1,0,0,0,0,1,0,2,2,0,0,1,0,0,0,0,1,0,0,0,0,1,0,4,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,3,4,1,0,0,0,0,Class_6
+34950,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,5,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,5,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,12,0,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,Class_6
+34951,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,2,0,3,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,6,0,0,2,0,0,12,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+34952,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,3,1,0,0,1,0,1,0,3,0,0,0,0,0,0,0,5,1,0,0,0,1,0,33,0,0,1,0,1,28,2,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,3,1,1,1,1,0,1,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_6
+34953,0,1,2,1,1,0,0,0,0,0,1,1,2,0,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,4,1,0,0,0,0,0,3,0,0,0,0,1,2,2,0,1,3,0,6,3,0,0,0,0,0,1,1,0,0,0,2,3,0,1,0,0,0,0,0,1,1,0,0,0,2,0,1,0,8,0,1,0,0,1,0,0,0,0,Class_6
+34954,0,0,3,2,0,0,0,0,0,0,11,0,1,1,0,1,0,0,0,0,2,2,0,3,0,2,15,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,5,0,0,0,7,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,2,0,Class_6
+34955,0,1,4,2,0,0,0,0,0,0,8,0,0,0,2,1,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,8,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34956,0,0,6,13,0,0,0,0,0,0,18,1,0,0,0,1,0,0,0,0,0,0,1,9,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,8,1,0,0,0,0,12,0,5,0,0,0,17,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,3,0,2,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,1,1,Class_6
+34957,0,0,0,3,0,0,0,0,0,0,1,0,0,0,1,2,0,2,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,6,1,0,1,0,1,8,0,1,1,0,0,0,7,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34958,0,0,3,1,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,3,1,0,0,0,0,0,4,1,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34959,1,0,0,0,0,0,0,0,0,4,0,0,0,2,2,4,0,0,0,4,0,2,4,0,1,0,0,0,0,0,0,2,0,1,1,0,0,1,4,0,0,0,1,2,1,0,14,1,0,9,0,0,1,2,2,1,0,0,1,1,0,0,0,1,0,0,0,0,0,3,7,0,2,3,1,0,1,0,0,0,0,0,0,1,2,9,1,1,0,0,0,0,0,Class_6
+34960,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,4,0,3,0,2,0,2,0,2,0,8,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,13,0,5,0,0,0,0,1,0,0,0,0,1,1,5,0,0,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+34961,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34962,0,0,5,4,0,0,0,0,0,0,13,0,0,3,0,2,1,2,0,0,0,1,0,7,0,5,5,1,0,0,1,2,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,2,2,0,0,0,0,5,1,0,0,5,0,2,2,0,0,1,0,0,0,0,3,0,0,1,1,2,0,1,0,0,0,5,0,3,0,0,0,0,0,1,2,0,0,0,0,Class_6
+34963,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,1,0,1,14,0,0,2,0,0,0,0,4,0,0,1,0,0,0,1,0,0,0,2,0,0,2,0,2,0,0,0,3,0,1,0,1,0,0,0,0,2,0,1,0,0,0,3,0,0,0,1,0,0,37,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,Class_6
+34964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+34965,0,0,0,1,0,0,0,2,0,0,1,0,1,0,0,1,1,1,0,1,0,0,0,6,1,3,0,0,0,0,0,0,0,0,4,0,0,2,1,0,1,11,0,2,0,0,0,1,2,0,0,0,0,0,1,0,4,0,0,4,0,1,0,0,0,1,8,2,0,0,1,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+34966,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,16,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34967,0,1,4,1,0,0,0,0,0,1,7,0,0,1,0,6,0,0,0,0,0,2,0,1,0,0,8,1,0,0,0,0,2,0,2,1,0,1,0,1,0,1,0,2,0,4,0,0,6,0,0,0,1,3,0,0,0,0,0,0,0,3,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,1,1,2,3,1,0,0,0,Class_6
+34968,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34969,0,0,10,1,0,0,1,0,0,0,9,1,0,0,0,5,0,0,0,0,2,3,0,1,0,0,12,2,0,0,0,0,0,0,1,0,0,2,1,1,0,0,1,1,0,9,0,2,0,0,0,3,0,13,1,0,0,0,0,0,3,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,1,0,2,0,0,0,1,Class_6
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+34977,0,0,0,1,0,0,0,0,0,0,8,0,1,0,0,3,0,2,0,0,0,3,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,1,0,3,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+34978,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34979,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,19,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,4,0,0,0,1,0,0,7,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+34980,0,0,5,7,0,0,0,0,0,0,10,1,1,1,0,2,0,1,0,0,1,1,0,0,0,0,17,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,1,0,0,1,0,7,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34981,0,0,6,4,0,0,0,0,1,0,10,0,0,0,0,0,0,2,0,0,6,1,0,0,0,1,16,2,0,0,1,0,0,0,4,1,1,1,0,1,0,0,0,0,0,13,0,0,0,0,0,2,0,4,1,0,0,1,0,0,5,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,5,0,3,1,0,0,0,0,1,2,0,0,0,0,Class_6
+34982,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,1,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,2,1,1,0,0,1,0,2,0,0,0,0,2,0,0,5,0,0,5,0,0,0,1,0,3,0,1,0,2,1,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,Class_6
+34983,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34984,0,0,0,7,0,0,0,0,0,0,0,0,0,1,1,0,0,5,1,1,0,2,7,12,0,0,0,0,0,0,0,2,1,0,6,0,0,0,0,1,0,1,0,1,0,0,0,5,1,0,6,0,0,4,0,0,4,0,0,0,4,2,0,1,0,1,8,0,0,1,0,1,2,5,1,3,0,0,0,4,1,3,3,0,0,0,1,1,3,0,0,0,0,Class_6
+34985,0,0,1,1,0,0,0,0,0,0,7,0,0,0,1,3,0,0,0,0,1,1,0,1,0,6,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,2,1,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34986,0,1,6,5,0,0,1,0,0,0,11,0,0,0,0,5,2,0,0,2,2,2,0,1,0,0,0,1,1,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,3,0,0,4,0,2,8,0,6,0,0,0,3,0,0,8,2,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,8,1,0,0,0,0,0,2,0,0,1,0,Class_6
+34987,0,2,13,4,0,0,1,0,0,0,11,0,0,0,0,3,1,0,0,0,2,3,0,2,0,3,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,10,0,0,1,1,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34988,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,1,0,0,1,0,0,9,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,9,0,0,5,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34989,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34990,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,6,0,0,0,0,0,1,3,2,1,0,0,1,0,0,Class_6
+34991,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,4,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34992,0,0,15,9,0,1,2,1,0,2,6,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,10,0,0,0,0,0,0,0,11,0,0,0,1,0,0,4,1,1,1,0,1,0,0,0,1,0,1,1,0,0,0,0,0,1,10,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+34993,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,1,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,2,1,0,0,1,0,0,1,3,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,14,0,0,Class_6
+34994,0,0,3,12,0,0,0,0,0,0,0,0,1,1,0,2,0,1,0,0,0,3,0,1,1,1,0,2,0,0,2,0,0,0,11,0,0,1,0,0,1,1,0,0,0,1,0,0,3,0,0,0,0,2,0,1,0,0,0,12,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,3,0,Class_6
+34995,0,1,0,0,0,0,2,0,1,0,3,0,1,0,0,3,0,0,0,5,0,1,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,3,0,3,0,1,15,1,2,1,0,0,1,1,2,0,0,0,1,2,0,0,0,1,0,1,6,1,1,0,0,1,1,1,2,7,0,2,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+34996,0,0,10,4,0,0,0,0,0,1,11,0,0,1,0,7,0,0,0,0,4,0,0,0,0,1,5,2,0,0,0,0,0,0,5,0,0,1,0,0,0,0,2,3,0,10,0,0,2,0,0,1,0,10,1,4,0,0,0,0,2,1,0,0,1,1,0,0,0,1,2,0,0,1,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_6
+34997,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+34998,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,3,1,1,0,0,0,0,0,1,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+34999,2,0,0,0,0,0,0,0,0,3,0,0,0,3,0,1,5,1,0,2,0,2,3,13,1,0,0,0,0,0,0,19,0,1,0,6,2,5,4,0,0,4,0,2,2,0,0,7,0,3,0,0,0,3,0,0,0,0,4,0,0,2,0,3,0,2,53,3,0,0,1,2,0,4,1,1,0,0,0,0,0,0,0,0,0,4,32,0,0,0,0,0,15,Class_6
+35000,0,0,6,8,0,0,0,0,1,0,1,0,0,0,0,5,0,0,0,0,1,3,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,7,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,13,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35001,0,0,15,9,0,0,0,0,1,0,18,0,0,0,0,0,0,0,0,0,1,1,0,7,0,1,7,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,10,0,1,1,1,2,4,0,4,0,0,0,5,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35002,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,Class_6
+35003,0,3,0,0,1,0,0,0,0,0,6,0,0,0,0,6,0,1,2,0,1,9,0,1,0,0,3,1,0,0,0,0,1,0,3,0,0,1,1,1,0,0,0,0,0,1,9,1,4,0,4,0,0,8,2,0,1,1,0,0,0,1,0,1,1,1,1,2,0,1,1,0,0,0,0,0,0,0,0,1,0,4,0,4,0,1,0,0,4,1,0,2,0,Class_6
+35004,0,0,8,3,0,0,0,0,0,0,6,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,9,0,1,0,0,0,0,0,4,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35005,2,1,9,2,0,0,0,1,0,1,0,0,3,2,0,6,26,7,0,2,0,7,6,4,1,7,0,0,1,1,0,1,0,1,3,6,0,1,0,1,0,21,5,7,0,1,0,3,5,2,0,0,1,37,3,0,19,1,0,18,0,1,0,2,1,5,2,0,0,4,0,1,0,2,0,0,0,0,8,0,1,0,0,0,2,2,7,1,2,1,0,1,3,Class_6
+35006,1,0,10,2,0,0,0,0,0,0,10,0,2,0,0,6,0,0,0,0,2,1,0,0,0,5,7,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,17,0,0,0,0,0,0,0,18,1,0,0,0,1,4,0,4,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35007,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,0,2,0,4,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,12,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,1,0,Class_6
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+35009,0,0,11,8,1,0,0,0,0,3,8,0,0,1,0,5,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,17,0,0,0,0,0,0,0,6,2,0,0,0,0,1,5,0,0,0,3,0,0,0,0,1,0,3,1,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35010,0,0,0,0,0,0,0,2,1,0,0,0,0,4,0,0,1,1,0,1,0,0,0,1,2,0,0,0,0,0,0,1,1,1,0,4,0,0,0,1,0,1,0,0,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,42,0,0,10,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,3,6,1,4,0,0,0,0,0,Class_6
+35011,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,4,0,1,1,0,0,3,0,1,1,7,0,2,1,0,0,0,0,1,0,0,0,1,0,1,0,8,0,2,1,1,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35012,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,1,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,8,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+35013,2,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,4,0,0,2,0,2,0,8,1,0,0,0,1,0,0,14,0,0,0,6,1,2,1,0,1,9,0,0,3,0,0,4,0,2,0,0,0,0,1,0,0,0,8,1,0,2,0,3,0,2,44,2,0,4,1,1,0,3,1,1,0,0,7,1,0,0,0,0,0,4,34,0,0,0,0,1,18,Class_6
+35014,0,0,8,5,0,0,0,0,1,2,9,3,0,0,0,1,0,0,0,0,1,0,0,10,0,3,17,2,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,2,2,5,0,1,0,0,0,5,0,5,0,1,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,28,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35015,0,0,13,9,0,0,0,0,0,0,6,0,1,0,0,0,0,2,0,2,0,2,0,0,0,1,6,3,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,4,0,2,5,0,0,0,0,8,1,3,0,1,1,0,4,1,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,1,5,2,0,0,0,0,0,1,0,0,0,0,Class_6
+35016,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,10,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+35017,4,0,1,0,0,0,0,0,0,0,0,0,3,0,1,1,0,3,0,0,3,2,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,12,0,0,0,2,0,1,0,0,0,0,0,1,0,0,1,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+35018,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Class_6
+35019,0,0,11,1,0,0,0,0,0,1,11,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,23,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,3,0,9,0,0,0,2,0,1,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35020,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,4,0,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,2,0,1,0,0,0,4,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,4,0,Class_6
+35021,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35022,0,0,4,5,0,0,0,0,1,0,10,0,0,0,0,0,1,3,0,1,0,2,0,0,0,1,3,4,1,0,0,1,0,0,2,1,1,0,0,0,0,0,1,5,0,0,0,1,5,0,1,0,0,17,6,0,0,0,0,0,1,3,0,2,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,2,1,0,0,1,0,Class_6
+35023,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35024,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,5,0,3,0,2,0,3,0,0,2,1,2,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,5,0,0,0,2,7,0,0,0,0,2,0,0,1,3,0,0,0,1,0,0,1,1,1,0,0,0,0,2,0,1,0,0,0,0,0,3,0,5,0,0,0,0,0,0,2,0,1,0,0,Class_6
+35025,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,1,0,2,0,4,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,8,0,0,1,0,0,18,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35026,0,0,5,0,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,1,0,1,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,2,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35027,0,0,8,2,0,0,0,0,0,0,14,1,0,0,0,0,0,0,0,0,2,1,0,7,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,8,0,0,0,0,0,1,0,3,0,0,0,6,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,4,0,1,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35028,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35029,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,2,1,0,0,0,0,2,0,2,1,0,0,0,0,0,0,6,0,0,0,1,0,0,0,1,0,0,4,2,0,0,0,3,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,17,0,2,5,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,1,0,0,0,0,Class_6
+35030,1,0,20,14,0,0,2,0,0,1,10,0,0,0,0,2,0,1,1,2,0,1,0,0,0,0,16,2,0,0,0,0,0,1,2,0,0,2,1,0,1,0,0,3,0,14,0,0,3,1,0,1,0,15,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,0,6,0,0,1,0,0,0,6,0,0,0,0,Class_6
+35031,1,0,0,0,1,0,0,1,0,3,0,0,0,0,0,1,0,0,0,4,0,0,0,6,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,3,0,0,0,0,0,0,Class_6
+35032,0,0,6,2,0,0,0,0,0,0,10,1,0,1,0,5,0,0,0,0,6,0,0,0,0,0,15,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,1,0,19,1,1,0,0,0,2,0,10,0,0,0,0,0,0,3,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+35033,1,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,2,0,7,1,0,0,0,0,0,1,1,0,0,8,1,0,0,0,0,1,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,8,0,0,8,0,0,0,0,0,2,6,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_6
+35034,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35035,0,4,5,1,0,0,1,0,0,1,9,0,0,1,0,1,0,3,0,0,0,3,0,0,1,1,7,1,0,0,0,0,2,0,5,0,0,1,1,1,1,0,0,0,0,6,0,0,0,0,0,1,1,5,0,0,0,0,0,0,2,0,0,1,1,1,3,0,0,0,0,0,0,0,0,0,0,0,1,10,1,0,1,0,0,0,0,0,3,0,0,0,0,Class_6
+35036,0,0,10,10,0,0,0,0,0,1,5,0,0,0,1,2,0,0,0,0,1,1,0,1,0,5,1,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,1,13,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+35037,0,2,1,2,0,0,1,0,0,0,8,0,1,1,0,3,0,0,0,2,0,5,0,0,0,1,1,0,1,0,1,0,2,0,2,1,0,0,0,1,0,0,0,7,0,1,0,0,7,0,0,0,0,10,2,0,2,2,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_6
+35038,0,0,3,7,0,0,0,0,0,1,3,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,5,2,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,6,1,0,0,0,0,0,2,2,0,0,2,0,2,0,0,1,0,1,1,0,0,0,0,0,0,3,0,7,0,0,0,0,0,2,1,0,0,1,0,Class_6
+35039,0,0,16,10,0,0,0,0,0,1,8,0,4,0,0,3,2,2,0,0,3,1,0,2,0,1,9,4,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,7,2,0,3,0,0,0,0,17,0,1,1,2,0,0,1,2,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,0,0,1,0,2,0,0,0,0,Class_6
+35040,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,2,3,1,0,0,0,0,0,0,0,1,0,1,0,2,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,3,3,0,12,0,0,1,0,0,1,4,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35041,0,1,10,5,0,0,0,0,0,2,7,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35042,0,0,2,0,0,0,0,0,0,2,7,0,1,0,0,2,0,1,0,0,8,1,1,0,0,0,12,1,0,0,2,1,0,1,2,0,0,2,1,0,0,0,0,2,1,5,0,0,2,0,1,8,0,9,0,2,0,2,0,1,4,0,0,0,0,0,2,0,0,2,0,2,0,0,0,0,0,0,0,13,0,2,0,0,0,0,1,0,1,0,1,0,0,Class_6
+35043,0,0,0,0,1,0,0,1,0,0,0,1,1,1,0,2,0,1,0,0,0,1,0,5,0,2,0,1,1,0,0,0,0,3,1,0,0,0,0,0,1,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,30,0,1,0,0,0,0,1,0,3,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,4,0,Class_6
+35044,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35045,2,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,3,0,0,1,0,1,1,8,1,0,0,0,0,0,0,14,0,0,0,6,1,0,3,0,0,3,0,0,3,0,0,4,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,1,0,0,49,4,0,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,4,35,2,0,0,1,0,11,Class_6
+35046,0,0,12,1,0,0,0,0,0,0,10,1,0,0,0,5,0,0,0,0,0,3,0,5,0,2,15,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,14,0,0,1,1,1,0,1,11,0,0,0,0,0,1,5,1,0,0,1,1,0,0,0,1,0,0,1,1,1,2,0,0,0,1,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35047,0,3,10,8,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,3,0,0,1,0,0,0,8,0,0,3,0,1,1,2,0,1,0,9,0,1,1,0,0,0,0,9,0,5,0,0,0,0,5,2,1,0,0,2,0,0,1,2,0,0,0,0,1,1,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35048,0,1,11,4,0,0,0,0,0,0,8,0,3,0,1,5,0,0,0,0,5,3,0,2,0,0,26,2,0,0,0,0,0,3,1,1,1,1,0,1,0,1,0,1,0,13,0,0,0,0,1,0,0,23,2,1,1,0,0,0,2,4,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35049,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,1,0,4,0,9,0,1,0,0,0,0,1,1,0,1,0,1,0,0,0,13,0,1,0,2,0,0,0,0,0,0,0,5,0,0,2,0,0,15,0,1,1,0,1,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,Class_6
+35050,0,1,2,4,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,2,0,3,0,2,1,1,0,0,0,0,1,0,11,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,11,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+35051,0,1,0,0,0,0,0,2,0,0,0,0,1,1,1,0,0,2,0,0,0,2,0,3,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,7,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+35052,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35053,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35054,0,0,6,12,0,0,0,0,0,0,6,0,0,0,0,0,2,1,0,0,1,0,0,0,0,1,5,1,1,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,2,0,2,0,0,0,0,1,1,0,2,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,9,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35055,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,7,0,2,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,4,0,0,2,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35056,0,0,9,3,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,0,1,1,0,2,0,1,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,5,0,0,0,0,0,3,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35057,0,0,9,2,0,0,0,0,2,0,16,0,0,1,0,1,0,0,0,1,0,2,0,0,0,0,22,0,0,0,0,0,0,0,2,0,0,6,1,0,0,0,0,2,0,7,0,0,0,0,0,5,0,14,1,0,0,0,0,1,7,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35058,4,0,2,1,0,0,9,2,3,1,1,0,4,1,0,2,5,1,0,4,0,3,0,6,3,0,0,0,3,0,1,5,1,2,2,0,1,7,0,0,0,6,0,4,0,0,0,5,0,6,0,0,0,5,9,0,2,0,0,5,1,1,4,4,2,2,9,2,0,7,2,2,2,1,3,10,0,11,0,0,1,0,9,0,2,1,2,1,1,4,0,0,0,Class_6
+35059,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,3,0,5,0,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,5,0,0,2,0,0,12,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,4,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35060,0,0,13,1,0,0,0,1,0,0,17,0,0,0,1,4,0,0,0,1,0,3,0,4,0,1,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,12,1,0,1,0,0,1,1,5,0,2,0,9,0,2,0,0,0,1,0,1,2,0,0,1,0,0,0,3,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+35061,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,2,0,1,0,1,0,1,0,5,0,2,0,3,0,0,0,4,0,0,1,2,0,1,0,0,1,3,0,0,0,2,0,0,0,1,3,1,1,4,1,0,1,0,0,14,0,1,3,3,0,1,6,0,1,1,2,0,0,0,0,0,0,1,2,1,1,0,0,0,1,0,0,1,2,0,0,0,0,Class_6
+35062,0,0,8,12,0,0,0,0,0,0,8,1,0,0,0,2,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,3,0,0,1,0,Class_6
+35063,0,1,14,10,0,0,0,0,0,2,9,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,11,1,0,0,1,0,0,1,1,0,1,0,0,1,0,0,1,2,0,6,0,0,1,1,0,0,0,1,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,5,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35064,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35065,1,1,8,5,0,0,0,0,0,1,8,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,6,2,0,0,2,0,0,0,0,1,0,1,0,1,0,0,0,0,0,4,0,2,1,0,0,3,0,2,0,0,0,1,0,1,9,1,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,22,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35066,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,4,1,5,0,0,0,0,0,2,0,0,2,1,0,1,0,0,0,4,0,1,0,1,0,0,1,0,0,0,0,8,0,0,2,0,0,6,0,2,0,1,0,0,6,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,5,0,0,0,1,0,0,0,0,1,0,Class_6
+35067,0,0,3,4,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,0,0,0,2,0,1,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+35068,0,1,2,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,5,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,3,0,1,1,0,Class_6
+35069,0,0,12,2,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,1,2,0,0,0,1,14,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35070,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35071,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,2,0,0,0,0,0,1,0,2,0,2,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35072,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35073,0,0,1,2,0,0,0,0,0,0,7,1,0,0,1,1,0,0,0,0,2,2,0,0,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,4,0,3,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+35074,0,0,0,0,0,0,0,3,0,0,1,2,0,0,0,2,0,3,0,0,0,1,0,4,1,0,0,1,0,0,0,0,2,0,1,1,0,0,0,2,0,9,0,0,0,0,0,1,1,0,0,0,1,5,0,0,4,0,0,4,0,0,0,2,1,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35075,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,2,0,5,0,1,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35076,2,0,1,2,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,1,0,0,0,2,6,0,1,0,0,0,0,0,0,2,1,0,0,0,0,1,8,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35077,0,0,3,3,0,0,0,0,0,0,0,0,1,0,0,4,0,3,0,0,0,5,0,2,0,2,0,0,0,0,0,0,0,0,9,0,1,1,0,0,0,4,3,1,0,0,0,0,2,0,0,0,0,8,0,1,4,0,0,12,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,5,0,0,1,0,0,1,2,0,0,0,Class_6
+35078,0,1,12,8,0,1,0,0,0,0,9,0,0,1,0,3,0,1,0,1,3,3,0,2,1,1,8,6,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,6,0,0,1,1,0,1,0,22,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,0,0,0,0,3,0,0,1,0,Class_6
+35079,0,0,7,13,0,0,0,0,0,1,6,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,3,6,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,2,0,10,0,2,1,0,0,0,0,17,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35080,0,0,1,3,0,0,0,1,0,0,1,0,3,0,2,1,0,2,0,0,0,1,0,2,1,7,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,26,0,4,0,0,0,4,0,0,0,2,0,2,1,0,0,0,2,1,0,0,1,0,3,1,1,0,2,1,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+35081,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+35082,0,0,1,4,0,0,0,0,1,0,0,1,2,0,0,0,0,5,0,1,0,1,0,2,0,5,2,1,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,1,0,0,0,0,2,0,0,1,0,4,0,0,1,1,0,0,3,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35083,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35084,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,2,Class_6
+35085,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,0,1,0,2,5,0,1,0,1,0,0,0,1,0,0,0,0,2,0,0,0,4,1,3,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,6,1,0,0,0,0,0,7,Class_6
+35086,0,0,9,1,0,0,0,0,1,0,7,0,0,1,0,0,0,0,1,0,0,0,0,2,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,8,0,0,0,0,0,1,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35087,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
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+35089,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+35100,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,2,0,4,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,1,0,1,1,0,33,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,Class_6
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+35102,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,1,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35103,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,5,0,1,1,3,0,0,1,0,0,0,0,0,7,0,0,2,0,0,0,4,0,1,0,0,0,1,2,0,0,0,1,8,1,0,5,0,0,12,0,0,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35104,1,2,1,0,0,0,1,0,0,0,5,0,3,0,0,1,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,7,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,Class_6
+35105,0,2,9,5,0,0,0,0,0,0,5,0,0,2,1,2,0,1,0,0,0,3,0,0,0,0,1,1,0,1,1,0,2,0,0,0,0,1,0,0,0,3,0,2,0,0,0,1,1,1,0,0,0,29,1,0,0,0,0,2,0,0,0,1,0,3,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35106,0,0,0,5,0,0,0,0,1,0,7,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,2,1,0,0,0,2,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35107,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35108,6,1,1,0,0,0,0,0,1,1,0,0,2,1,0,2,2,1,1,4,0,3,2,11,3,0,0,0,0,0,0,11,0,0,0,1,2,2,0,1,1,4,0,6,0,0,0,4,0,0,0,0,0,0,1,1,0,0,2,2,0,0,0,2,0,4,6,0,0,9,0,1,0,5,0,0,0,0,0,0,1,0,2,0,3,4,13,2,1,2,0,0,20,Class_6
+35109,3,1,3,3,0,0,0,0,0,0,10,1,2,1,0,4,0,2,0,1,1,1,0,0,1,2,2,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,14,0,3,0,0,7,1,0,0,0,7,1,0,1,0,0,2,2,4,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,1,0,0,1,1,Class_6
+35110,1,0,0,0,0,0,0,0,0,0,6,0,0,0,1,9,0,3,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,3,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,Class_6
+35111,0,0,1,0,0,0,0,2,0,0,1,0,0,3,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,3,0,2,1,1,0,3,2,0,0,0,0,2,0,0,1,0,1,4,0,0,0,0,0,0,12,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,Class_6
+35112,0,0,9,3,0,0,0,0,0,0,16,0,0,1,0,1,0,0,0,0,0,2,0,7,0,0,6,2,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,0,0,8,0,0,3,2,0,1,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35113,0,0,0,3,0,0,1,0,0,0,1,0,1,0,0,1,4,0,0,7,0,2,0,3,1,1,0,0,3,0,0,0,0,1,2,2,0,1,1,0,1,0,2,4,0,1,0,2,2,0,0,1,0,3,5,0,6,0,0,13,0,2,0,1,0,1,3,0,0,2,0,0,0,0,1,1,0,2,1,1,0,1,0,0,0,0,13,1,2,1,0,0,0,Class_6
+35114,1,0,7,9,0,0,0,1,0,0,12,0,0,1,0,4,0,1,0,0,0,0,0,0,0,2,4,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,9,0,1,1,0,0,1,0,7,1,0,0,1,0,0,4,1,0,0,0,0,2,0,0,1,1,2,1,2,0,0,0,0,0,6,0,4,0,0,0,0,0,0,3,0,1,0,0,Class_6
+35115,0,0,8,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,2,0,0,2,0,0,0,0,21,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35116,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_6
+35117,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,0,0,0,5,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35118,0,0,6,7,0,0,0,0,0,0,9,0,0,0,0,5,0,6,0,0,0,5,0,1,1,7,6,0,0,0,1,1,1,0,4,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,13,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_6
+35119,0,1,6,8,0,0,0,0,1,0,15,1,0,0,3,5,0,2,0,0,1,4,0,1,0,0,5,1,0,1,0,0,0,0,3,0,1,2,0,0,0,0,0,2,0,1,0,0,5,0,0,20,0,12,0,1,0,1,0,0,6,0,0,0,0,2,2,0,0,2,0,0,0,3,0,0,0,0,0,18,0,1,1,0,0,0,0,0,2,2,0,0,0,Class_6
+35120,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+35121,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35122,1,2,2,1,0,0,0,0,0,1,6,0,4,0,0,6,0,3,0,1,1,5,0,0,0,2,3,2,3,0,1,0,1,1,0,0,0,2,1,1,1,5,0,22,0,5,0,2,14,0,1,0,0,24,6,0,0,0,0,0,0,3,0,1,4,2,2,0,2,3,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,1,0,1,0,0,Class_6
+35123,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,1,0,4,0,3,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
+35124,0,1,3,4,0,0,0,0,0,0,5,0,0,0,0,4,0,2,0,1,1,0,0,0,0,1,4,3,0,0,0,0,1,0,2,0,0,0,0,1,0,0,1,1,0,0,0,2,2,0,0,0,0,10,0,0,0,1,0,0,0,1,0,0,1,2,0,0,0,1,0,3,0,0,1,1,1,0,0,4,0,5,0,0,0,0,0,1,2,0,0,2,0,Class_6
+35125,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,6,0,0,0,0,0,4,0,3,0,7,0,0,0,0,1,0,1,0,1,0,1,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,9,0,1,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_6
+35126,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,3,1,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
+35127,1,0,5,11,0,0,1,0,0,2,7,0,0,1,0,0,0,2,0,0,1,1,0,0,0,3,10,2,1,0,2,0,0,0,7,0,1,2,1,0,0,3,2,0,0,1,0,2,0,0,0,0,0,9,1,0,0,0,0,2,0,1,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+35129,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,2,0,0,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+35138,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,2,0,1,0,10,0,1,0,3,0,0,1,4,0,0,1,0,2,0,1,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,9,0,0,7,0,0,11,0,0,0,0,2,1,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,0,2,0,Class_6
+35139,0,2,0,3,0,0,0,0,0,3,2,0,0,0,0,1,0,1,0,1,0,4,0,5,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,21,0,7,0,0,0,0,0,0,0,0,0,2,3,0,8,0,0,3,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,1,0,0,0,0,0,0,Class_6
+35140,0,0,8,12,0,0,0,0,0,1,22,0,0,0,0,2,0,0,0,0,1,1,0,5,1,0,12,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,0,2,0,0,0,9,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35141,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,4,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,12,0,0,0,2,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,Class_6
+35142,0,0,5,4,0,0,0,0,0,0,19,0,0,2,0,4,0,0,0,0,2,3,0,8,0,3,12,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,9,0,2,1,0,0,0,0,13,1,0,1,5,0,1,0,0,0,3,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,4,0,0,1,0,0,0,0,1,1,0,0,0,0,Class_6
+35143,0,0,0,1,0,0,0,0,0,0,5,0,0,0,1,2,0,3,0,1,1,1,0,1,0,1,5,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,1,2,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,1,0,Class_6
+35144,0,1,9,2,0,0,1,0,0,0,9,0,1,0,0,4,1,0,0,0,0,2,0,1,0,4,7,5,0,0,0,2,0,1,0,0,1,0,0,0,1,2,0,0,0,6,0,0,0,0,0,0,0,27,3,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,1,1,0,0,0,0,0,2,0,0,0,0,Class_6
+35145,0,0,10,3,0,0,0,0,0,0,16,1,0,0,3,4,0,1,0,1,1,1,0,9,0,1,16,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,0,0,0,5,0,0,0,10,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35146,0,0,0,0,0,0,0,1,1,0,0,0,2,3,0,4,0,3,0,0,0,1,0,1,1,2,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,5,0,2,6,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,Class_6
+35147,0,1,15,9,0,0,0,1,2,0,8,0,0,0,0,2,0,0,3,1,3,2,0,2,0,6,8,1,0,0,1,0,0,0,7,0,0,3,0,1,0,0,0,0,0,10,1,0,0,0,0,1,0,6,0,0,0,1,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35148,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,7,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,8,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35149,0,1,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,2,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,3,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35150,0,3,12,5,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,0,0,0,2,0,2,17,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,8,0,2,1,0,0,2,0,7,0,1,0,1,0,2,8,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35151,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35152,0,0,4,1,0,0,0,0,0,0,9,0,1,0,0,2,0,0,0,0,0,3,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35153,0,0,0,1,1,0,0,0,0,0,2,0,1,0,1,2,0,1,0,0,1,0,0,6,0,4,0,1,0,0,0,2,0,0,0,1,0,2,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,12,0,2,3,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,7,0,0,2,0,Class_6
+35154,0,1,11,7,0,0,0,0,2,1,9,0,1,1,0,2,0,0,0,0,0,3,0,0,0,1,4,1,0,0,0,1,0,3,1,0,2,0,0,0,0,0,0,1,0,7,0,0,1,0,0,0,0,16,1,0,0,0,1,1,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35155,0,0,3,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,1,1,0,5,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,2,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35156,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,2,0,0,0,0,1,1,15,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,4,0,1,0,0,1,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35157,0,0,9,3,0,0,0,0,0,0,12,0,0,0,0,2,0,1,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,0,12,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35158,0,0,9,9,0,0,0,0,0,0,10,0,0,1,0,0,0,0,0,0,0,4,0,2,0,3,5,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,1,0,4,0,0,0,2,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,7,0,3,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35159,0,0,10,6,0,0,0,0,0,0,16,0,0,1,1,2,0,0,0,0,0,1,0,0,1,1,3,4,0,0,3,2,0,1,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,9,2,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,1,1,Class_6
+35160,0,1,9,1,0,0,0,0,0,0,7,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,1,0,0,0,Class_6
+35161,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,6,0,1,1,3,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,9,0,3,0,1,0,2,0,0,0,0,1,21,0,0,0,0,0,3,0,0,0,1,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_6
+35162,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,1,Class_6
+35163,1,0,4,2,0,0,0,0,0,0,12,0,0,1,1,2,0,3,0,0,0,5,3,10,0,1,0,3,0,0,0,1,0,0,3,6,0,1,2,1,0,0,0,3,0,2,0,3,3,1,1,0,0,8,0,0,0,2,0,2,3,0,0,0,1,2,1,0,0,2,0,2,2,1,0,2,0,0,0,23,0,11,1,0,2,0,0,0,5,0,0,0,0,Class_6
+35164,0,0,8,4,0,0,0,0,0,0,5,0,1,0,0,8,0,0,0,0,2,4,0,0,0,0,7,0,0,4,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,15,0,0,0,0,0,0,0,0,0,4,2,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,4,0,Class_6
+35165,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,3,0,0,1,0,1,0,0,0,1,0,1,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+35166,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,0,1,0,1,0,0,0,3,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,13,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35167,0,0,5,12,0,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,3,0,0,1,2,0,2,2,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,4,0,4,0,0,0,0,0,1,1,1,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,9,0,4,0,0,0,0,0,1,0,0,0,2,0,Class_6
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+35178,0,1,3,3,0,0,0,0,0,0,8,1,0,1,0,7,0,2,0,0,2,2,1,0,0,2,8,2,0,1,0,2,0,1,0,0,0,0,0,0,0,4,0,1,0,8,1,0,0,0,0,2,1,9,0,0,0,0,0,0,6,3,0,0,0,1,0,1,0,2,0,2,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,5,0,0,2,0,Class_6
+35179,0,2,7,3,0,0,0,0,0,0,16,0,0,0,1,1,0,0,0,1,0,0,0,9,0,0,10,2,0,0,1,1,0,0,1,0,0,1,0,0,0,0,1,1,0,8,0,0,1,0,0,1,0,2,0,0,4,7,0,0,3,0,0,2,2,0,1,0,0,0,0,0,0,1,0,0,1,0,0,17,0,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
+35180,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+35181,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,2,3,2,0,4,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,2,0,1,0,2,0,1,1,0,0,4,0,1,0,0,0,2,0,2,0,1,0,0,1,0,4,0,2,0,0,3,0,0,0,Class_6
+35182,0,0,8,12,0,0,0,0,0,0,3,2,0,0,0,2,0,2,0,0,0,4,0,2,0,4,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,5,0,1,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
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+35185,2,0,0,0,2,0,0,0,3,1,0,3,0,0,0,2,0,6,0,0,0,0,0,2,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,8,8,0,1,0,0,1,1,0,0,0,0,1,2,0,0,1,0,0,19,0,2,0,1,0,0,2,1,0,2,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
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+35187,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,9,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+35188,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,0,2,0,2,0,0,1,0,1,0,5,0,0,0,0,0,0,9,0,0,0,0,0,0,3,2,0,0,Class_6
+35189,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35190,1,0,2,1,0,0,0,1,0,2,0,1,1,0,0,1,0,2,0,1,0,7,0,2,1,0,0,1,29,0,0,0,0,2,3,2,0,0,0,0,0,2,0,1,0,0,1,2,0,0,0,0,0,7,12,0,2,0,0,4,0,2,0,0,0,4,2,0,0,2,5,2,1,0,3,1,1,0,0,1,0,1,1,0,0,0,0,0,3,0,1,1,0,Class_6
+35191,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_6
+35192,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,3,0,1,0,4,1,1,0,0,0,0,0,0,6,0,0,1,2,2,0,1,0,1,0,0,0,0,2,0,1,0,0,6,0,0,1,0,0,12,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35193,0,0,0,2,0,0,0,0,2,0,10,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,5,0,2,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,12,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+35194,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,3,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_6
+35195,1,0,1,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,0,6,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,2,1,0,1,0,1,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35196,0,1,0,0,0,0,0,0,2,0,1,0,2,0,0,0,2,0,1,0,0,0,3,5,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,1,1,0,0,0,1,4,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,6,2,1,0,0,0,0,0,0,2,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,1,Class_6
+35197,0,0,0,3,0,0,0,0,1,0,0,1,2,0,1,0,0,4,0,1,0,1,0,2,0,11,0,2,0,0,0,0,0,0,7,3,0,1,0,0,0,2,1,2,0,0,0,0,5,0,0,0,0,6,1,0,4,0,0,13,0,1,0,0,1,0,1,2,0,1,0,1,0,0,2,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,1,0,0,Class_6
+35198,0,0,13,4,0,0,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35199,0,0,10,5,0,0,0,0,2,0,9,0,0,0,2,2,0,1,0,0,0,3,0,2,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,3,2,0,5,0,1,1,0,0,1,1,18,1,4,0,0,0,0,0,0,0,2,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35200,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,3,1,4,1,2,0,2,0,8,0,1,0,1,0,0,0,1,0,0,3,1,0,1,0,0,0,3,0,0,0,0,10,2,1,0,0,0,0,5,0,0,4,0,0,12,0,1,1,0,0,3,6,6,0,0,1,0,0,0,0,0,0,4,0,0,0,0,4,0,0,0,2,0,1,0,0,1,0,Class_6
+35201,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,3,0,0,0,1,1,0,0,6,6,0,0,1,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,1,2,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,Class_6
+35202,0,0,0,4,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,2,0,0,0,7,0,0,2,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,3,0,5,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35203,1,0,0,0,0,0,0,0,0,0,0,0,2,4,1,0,0,0,2,0,0,2,0,7,4,6,0,0,0,0,0,0,10,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,15,0,3,0,0,2,0,5,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,1,0,Class_6
+35204,0,0,12,2,0,0,0,0,0,0,11,1,0,1,0,1,0,0,0,0,2,0,0,1,0,3,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,6,0,0,0,0,0,0,0,10,0,0,0,0,0,1,1,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35205,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,4,0,3,0,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,3,2,0,0,0,2,1,1,0,0,0,0,1,1,0,3,0,0,10,0,0,0,1,0,4,4,1,0,0,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35206,3,0,2,1,0,0,0,0,0,0,14,0,0,1,0,1,0,0,0,0,1,2,0,1,1,6,10,0,0,0,2,1,0,0,1,1,0,0,0,0,1,0,0,3,0,1,0,1,1,0,0,4,0,8,0,0,1,0,0,2,2,2,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,16,1,2,1,0,0,0,0,1,3,0,0,0,0,Class_6
+35207,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,2,0,3,0,0,3,3,0,0,2,0,0,0,1,1,0,0,0,0,0,2,0,15,0,4,0,0,0,1,0,0,0,0,0,2,2,0,0,0,3,0,0,0,0,0,0,1,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,0,Class_6
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+35209,0,0,0,0,0,1,0,0,4,0,13,1,0,2,0,3,0,0,0,0,6,1,1,16,0,2,5,0,0,0,1,3,0,0,1,0,1,0,1,1,0,0,1,0,0,2,0,3,0,0,0,19,0,4,0,1,0,0,0,0,1,4,0,1,0,1,1,1,2,1,0,1,2,34,0,0,0,0,0,21,0,0,0,0,0,0,0,0,1,3,0,0,0,Class_6
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+35219,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,10,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_6
+35220,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,5,0,3,0,4,0,1,1,0,0,0,1,1,1,1,0,0,0,0,0,8,0,0,0,0,0,2,1,0,0,0,0,5,2,0,2,0,0,10,0,0,0,1,1,3,7,0,2,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,3,3,0,0,1,0,1,0,Class_6
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+35222,0,0,4,4,0,0,0,0,0,0,11,0,0,0,0,1,0,1,0,0,3,1,0,0,0,1,14,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,1,0,3,0,0,0,0,0,0,4,1,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,2,2,0,0,0,Class_6
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+35224,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,1,4,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,2,1,0,0,0,0,1,1,0,0,0,0,4,1,0,1,0,0,8,0,0,0,0,0,2,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35225,1,0,1,0,1,0,0,1,0,2,2,1,8,2,0,4,0,3,0,0,0,4,0,7,3,2,0,0,9,0,0,3,2,0,1,0,0,0,0,0,3,9,0,2,0,0,1,4,1,1,0,0,27,7,3,0,2,0,1,2,0,6,1,1,0,1,1,0,0,3,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,3,0,0,6,0,Class_6
+35226,0,0,0,11,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+35228,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,1,0,2,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35229,0,0,4,5,0,0,0,0,0,0,16,0,0,0,0,2,0,0,0,0,2,1,0,0,0,2,11,1,0,0,0,1,2,1,2,0,0,0,0,0,0,1,0,2,0,4,1,0,1,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,3,0,Class_6
+35230,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,2,0,5,0,5,1,0,0,0,0,0,0,1,6,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35231,0,0,15,4,0,0,0,0,0,0,7,1,1,2,0,4,0,2,0,1,0,3,0,2,0,4,2,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,13,0,0,1,0,0,0,0,22,0,0,0,0,0,2,6,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+35232,0,0,16,4,0,0,0,0,0,0,10,0,1,1,0,4,0,2,0,1,1,1,0,0,0,1,2,2,0,0,4,0,1,0,1,1,0,0,0,0,0,0,0,4,0,8,0,0,1,0,0,0,0,19,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35233,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35234,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,2,0,0,5,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,1,Class_6
+35235,0,1,1,8,0,0,0,0,2,0,10,0,0,0,0,0,0,2,0,0,3,3,0,1,1,2,3,2,0,0,4,0,0,1,2,1,0,3,0,1,0,0,0,5,1,2,1,0,0,0,1,11,0,7,0,0,0,2,0,0,2,0,0,0,0,1,4,1,0,0,0,1,0,4,0,1,0,0,0,17,0,2,1,0,0,0,0,0,0,1,0,0,0,Class_6
+35236,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,0,2,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,3,6,2,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,Class_6
+35237,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35238,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35239,1,0,8,3,0,0,0,0,0,0,16,0,1,0,0,3,0,0,1,0,0,1,0,6,0,6,6,1,0,0,1,2,0,0,1,1,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,13,0,1,1,8,0,1,2,2,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,3,0,1,0,1,0,1,1,0,0,0,0,0,0,Class_6
+35240,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35241,0,1,7,4,0,0,0,1,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,1,0,5,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,4,0,4,0,5,0,3,2,0,0,0,0,3,0,0,0,0,0,7,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,2,0,1,0,0,1,1,Class_6
+35242,0,0,1,2,0,0,0,0,0,0,11,1,0,0,1,0,0,1,0,0,0,0,0,6,0,0,4,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,4,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35243,0,0,10,1,0,0,0,0,0,0,7,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,4,0,2,1,0,0,0,1,22,2,3,0,2,0,0,1,2,0,0,0,1,1,0,0,2,0,1,1,0,1,0,0,0,0,6,1,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35244,0,1,6,8,0,0,0,0,1,0,8,0,0,1,0,2,0,0,0,0,0,4,0,0,0,4,0,2,0,0,0,2,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,6,0,0,0,0,0,1,5,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35245,0,0,7,7,0,0,0,0,0,0,8,0,0,0,1,5,0,1,0,1,0,3,0,0,0,4,5,4,1,0,0,0,0,0,5,0,0,1,2,1,0,0,2,0,0,0,0,1,3,0,0,3,1,4,0,0,0,1,0,0,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,1,2,1,1,4,0,0,2,0,Class_6
+35246,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,0,2,1,0,0,0,0,4,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35247,0,0,1,0,0,0,0,0,1,3,0,0,1,2,0,1,1,1,0,4,0,2,0,6,2,2,0,0,0,0,0,5,3,0,1,3,0,1,0,1,2,2,0,3,0,0,0,4,0,4,0,0,0,1,4,0,2,1,1,10,0,2,2,14,1,0,7,2,0,11,3,0,1,0,1,1,0,0,1,0,1,0,0,0,1,2,2,1,1,0,0,0,0,Class_6
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+35267,0,0,10,4,0,0,0,0,0,1,7,0,1,0,0,0,0,1,1,0,2,8,0,1,0,2,20,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,2,0,0,0,0,3,0,11,0,1,0,0,0,0,2,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,4,0,2,1,0,0,0,0,0,0,0,0,1,0,Class_6
+35268,0,0,9,1,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,2,1,0,0,0,2,28,1,0,1,1,0,0,0,0,0,1,2,0,0,0,0,0,1,0,19,0,2,0,1,0,0,0,6,0,0,0,1,0,0,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35269,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,1,0,1,0,4,0,12,1,2,0,1,2,0,0,2,6,0,0,1,0,0,0,0,0,2,0,3,0,0,2,0,0,1,0,7,0,0,2,0,1,12,0,0,0,0,0,1,1,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35270,0,1,0,0,0,0,0,1,1,0,0,0,2,0,1,0,2,7,0,0,0,3,2,2,0,0,0,1,1,0,1,1,3,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,1,1,0,5,1,0,10,0,1,1,0,0,1,2,1,0,2,0,2,2,0,0,0,4,1,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+35271,0,0,18,5,0,0,0,0,0,0,8,0,0,1,0,1,0,0,0,0,1,2,0,2,0,4,7,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,12,0,0,1,0,0,2,0,17,0,0,0,0,0,1,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35272,0,0,4,8,0,0,0,0,0,0,0,1,0,0,1,0,0,3,0,0,0,2,0,1,0,5,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+35274,0,0,0,0,0,0,0,0,0,0,10,0,0,0,1,2,0,3,1,0,1,1,0,0,0,3,4,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,2,0,0,3,0,0,0,1,2,0,0,0,0,0,0,2,2,2,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,1,Class_6
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+35277,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35278,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,3,1,0,0,1,0,0,0,0,2,0,0,0,0,0,3,8,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,13,0,0,0,0,0,0,4,1,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+35279,0,1,10,3,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,1,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,5,0,0,1,1,0,1,0,6,0,0,0,14,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35280,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,2,0,0,1,0,8,0,4,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,4,0,4,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,0,0,0,1,0,3,Class_6
+35281,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,2,0,2,0,0,0,1,0,1,0,5,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35282,7,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,1,5,0,0,0,8,2,0,0,1,1,0,0,1,0,0,0,14,0,0,0,0,0,0,3,0,1,1,1,0,3,0,Class_6
+35283,2,0,0,1,0,0,3,0,0,0,6,2,0,1,0,1,0,1,0,0,0,1,0,2,0,1,0,0,1,0,0,2,0,0,5,0,0,2,0,1,0,0,3,3,0,2,0,0,6,0,0,2,0,3,1,1,0,1,0,1,1,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,1,0,2,1,0,0,1,0,Class_6
+35284,0,0,3,9,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,14,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,2,0,0,0,4,0,0,0,1,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+35285,0,0,0,0,0,0,0,0,18,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,1,1,0,0,0,0,0,3,0,1,1,0,0,1,0,1,0,4,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,7,0,2,6,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,4,3,0,0,0,2,0,Class_6
+35286,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,2,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+35287,0,0,5,1,0,0,0,0,0,0,9,0,0,0,5,1,0,2,0,0,0,0,0,5,1,4,2,1,0,0,1,1,0,3,4,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,2,0,10,0,0,0,0,0,0,1,2,2,0,1,1,0,0,0,1,0,1,0,3,0,1,0,0,1,22,0,3,0,0,1,0,1,1,3,0,0,1,0,Class_6
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+35322,0,4,10,4,0,0,0,1,0,0,7,0,0,0,0,3,0,2,0,0,0,3,0,0,0,2,1,6,0,0,0,2,0,2,0,0,0,0,0,0,0,3,0,5,0,15,0,0,4,0,0,0,0,12,0,0,0,0,0,1,4,1,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+35325,0,0,2,3,0,0,0,1,1,0,11,0,0,0,0,3,0,2,0,0,1,0,2,2,0,3,3,1,0,0,3,0,0,0,5,0,0,0,0,0,0,0,0,4,0,2,0,1,3,0,0,0,0,13,0,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,1,7,1,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35326,0,1,4,3,0,0,1,0,0,0,9,0,2,0,1,3,0,1,0,0,1,0,0,0,1,0,8,0,2,4,2,0,1,2,2,0,0,5,0,0,0,0,0,1,0,0,1,0,10,0,0,0,0,7,0,1,1,3,1,0,2,1,0,0,0,4,4,0,0,0,0,0,0,2,0,1,0,1,1,6,0,9,0,0,0,0,1,0,3,0,0,0,0,Class_6
+35327,0,0,3,0,0,0,0,0,0,0,1,0,1,1,0,1,1,0,1,2,0,4,0,10,0,0,0,0,1,0,1,0,0,0,1,0,1,3,0,2,0,1,1,1,0,0,0,2,4,0,0,0,0,8,1,0,1,1,0,18,0,0,0,1,0,1,6,0,1,1,1,0,0,1,0,2,0,0,0,0,0,1,5,0,0,2,4,0,0,1,0,0,1,Class_6
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+35391,0,1,4,3,0,0,0,0,1,0,10,0,0,1,0,2,0,1,0,0,2,1,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,5,0,0,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
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+35393,0,1,1,3,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,2,1,4,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35394,0,0,13,2,0,0,0,0,0,0,7,0,1,1,0,3,0,1,0,0,0,1,0,0,0,1,3,1,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,1,0,26,0,0,0,0,0,0,1,14,0,3,0,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+35396,0,0,8,2,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,4,0,0,0,0,14,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,1,0,Class_6
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+35398,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+35401,0,0,1,0,0,0,0,4,0,1,0,0,0,1,1,1,0,0,0,1,1,1,0,1,4,1,0,0,3,0,0,3,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,29,7,2,1,3,0,0,1,0,6,0,0,2,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1,0,0,1,0,0,2,0,Class_6
+35402,0,0,11,5,0,0,0,0,0,0,8,0,1,0,0,5,0,0,0,0,1,2,0,0,0,3,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,12,0,0,0,0,0,0,0,17,2,0,1,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,2,0,0,2,1,Class_6
+35403,0,0,3,8,0,0,0,0,0,0,8,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,2,0,0,2,0,2,0,0,0,2,0,5,0,0,2,0,0,0,2,6,1,0,0,0,0,1,6,0,1,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,16,1,9,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35404,0,4,16,6,0,0,0,1,0,2,8,0,0,0,2,9,3,4,0,3,0,1,0,0,0,0,10,7,0,0,2,1,2,1,10,0,0,2,0,1,0,0,0,3,0,3,0,2,12,0,0,0,0,18,2,0,1,2,0,1,6,3,0,2,0,1,0,0,0,2,0,0,0,0,0,5,0,0,1,17,0,8,0,0,0,0,0,0,4,0,1,1,0,Class_6
+35405,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,0,2,0,0,2,0,0,4,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35406,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35407,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,13,0,0,0,0,0,0,2,1,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
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+35409,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,4,0,0,11,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,Class_6
+35410,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,6,1,0,0,0,1,0,2,0,1,0,0,1,0,0,5,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,3,14,0,1,0,0,0,1,13,0,0,1,4,0,0,0,0,1,0,0,3,2,0,0,0,0,0,1,4,1,0,0,0,0,0,Class_6
+35411,0,0,3,2,0,0,0,0,0,0,16,0,0,1,2,5,0,0,0,0,0,3,0,3,0,2,4,1,0,1,0,0,1,0,3,1,0,1,0,0,0,0,0,0,0,3,0,1,1,0,0,1,0,14,0,0,0,6,0,1,0,1,0,1,0,2,0,0,1,1,0,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35412,0,1,1,4,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,2,1,0,5,0,7,3,1,0,0,0,3,0,0,3,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,1,1,0,2,0,1,0,0,0,2,0,4,0,0,0,1,0,0,0,0,0,0,0,8,0,0,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35413,0,0,7,2,0,0,0,0,0,0,7,0,0,0,0,4,1,0,0,1,2,1,0,2,0,0,4,3,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,3,0,6,0,1,0,0,0,1,0,13,0,0,1,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,Class_6
+35414,5,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,1,0,0,2,0,0,3,0,0,0,1,1,0,0,0,0,1,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,2,0,0,0,0,2,8,0,0,0,0,0,0,3,0,0,1,0,0,0,4,0,0,0,0,0,3,0,1,1,0,0,1,0,0,0,0,0,0,0,Class_6
+35415,2,0,1,0,0,0,2,1,0,0,1,0,2,3,0,3,1,0,0,8,0,4,1,7,2,0,0,0,1,0,1,4,1,1,0,0,0,2,28,3,3,0,0,3,1,0,3,3,0,43,0,0,0,2,3,2,2,0,0,0,0,3,0,2,1,1,7,1,1,5,7,1,2,1,1,1,0,1,0,0,1,0,2,0,0,1,1,1,0,6,0,0,0,Class_6
+35416,0,0,4,8,0,0,0,0,1,0,8,0,0,0,1,3,0,3,0,0,1,3,0,1,0,2,7,1,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,2,0,3,0,2,0,0,0,1,0,21,0,0,1,0,0,0,2,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,3,0,0,0,0,0,0,0,0,0,Class_6
+35417,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,12,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35418,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,6,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35419,0,2,5,10,0,0,1,0,0,0,8,0,1,0,0,2,0,0,0,0,2,1,0,1,0,0,1,5,0,0,1,1,0,1,2,0,0,1,0,0,0,1,0,0,0,2,0,0,2,0,0,1,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,4,1,0,0,0,0,0,3,0,0,0,0,Class_6
+35420,0,0,4,2,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,1,1,0,1,5,0,1,14,0,0,0,0,1,0,1,1,0,0,2,0,0,0,0,0,4,0,6,0,0,1,0,0,2,0,8,0,1,0,4,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,2,0,0,0,Class_6
+35421,0,0,10,8,0,0,1,0,0,0,5,0,2,0,0,6,0,3,0,0,1,3,0,0,0,1,1,3,0,0,0,0,0,2,2,0,0,0,0,0,1,0,2,0,0,6,0,0,3,0,0,1,0,11,0,0,0,3,0,0,1,0,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,8,0,1,0,0,0,0,0,1,1,0,0,0,1,Class_6
+35422,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35423,3,0,1,0,0,0,0,0,2,1,2,0,0,1,1,1,1,0,0,3,0,3,0,5,2,1,0,0,0,0,0,8,1,1,0,5,1,2,0,0,1,3,1,2,0,0,0,3,0,0,0,0,0,4,2,4,0,0,3,5,0,0,0,4,0,3,22,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,15,0,1,1,0,0,8,Class_6
+35424,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35425,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,3,0,3,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,2,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,10,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35426,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35427,0,2,1,1,0,0,0,0,0,0,1,0,0,0,1,2,4,0,0,1,0,6,0,3,0,4,0,0,0,0,0,0,2,0,3,0,0,1,0,0,0,2,0,2,0,0,0,0,4,0,1,0,0,5,5,0,10,0,0,1,0,3,0,3,0,2,5,1,0,0,0,1,0,0,0,0,0,1,0,2,2,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+35428,0,0,0,0,0,0,0,1,0,3,2,0,5,1,0,0,0,6,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,1,1,0,0,5,0,1,2,0,0,1,1,2,1,0,0,1,3,0,0,0,1,1,0,1,0,0,0,0,0,0,1,2,2,0,0,2,1,0,0,1,0,0,0,1,0,1,1,0,0,0,Class_6
+35429,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,9,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+35430,0,0,15,4,0,0,0,0,0,0,9,0,0,0,1,4,1,1,0,1,3,2,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,23,0,0,0,0,0,0,0,13,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35431,0,1,1,0,0,0,0,0,0,0,9,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,1,0,0,4,0,0,0,0,0,1,0,2,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35432,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,3,1,4,0,1,0,0,0,2,2,2,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,14,0,4,0,0,0,3,1,0,0,0,0,5,1,0,3,0,0,6,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+35433,6,0,2,3,0,0,0,0,1,4,0,0,4,4,0,5,1,0,0,5,0,8,0,10,3,0,3,1,1,0,0,7,0,0,0,2,4,3,0,2,0,2,0,5,0,0,0,4,1,2,0,0,0,3,1,1,1,1,5,4,0,2,1,2,0,2,12,0,1,4,0,1,0,1,1,0,0,1,0,1,0,0,1,0,1,9,24,2,3,3,0,0,87,Class_6
+35434,1,1,19,4,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,2,2,0,0,0,1,5,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,7,0,0,3,0,0,1,1,7,1,0,0,0,0,0,5,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,11,0,2,0,0,0,0,0,0,2,1,0,1,0,Class_6
+35435,0,0,11,5,0,0,0,0,0,0,7,1,0,0,0,2,0,0,1,0,0,2,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35436,0,1,4,3,0,0,0,0,0,0,10,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,0,0,0,6,0,0,5,2,1,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,8,0,3,0,0,0,1,1,1,2,1,0,2,0,Class_6
+35437,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_6
+35438,0,1,2,14,0,0,0,0,0,0,9,0,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,1,1,0,1,2,0,1,4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,1,0,1,2,1,1,1,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,5,0,0,0,0,0,2,3,0,0,1,0,Class_6
+35439,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,3,0,2,0,1,0,0,0,0,1,1,4,0,0,2,0,0,0,2,1,1,0,1,0,0,4,0,0,0,0,6,1,1,0,0,0,4,0,0,0,0,0,2,0,0,1,2,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+35440,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,1,0,0,1,0,1,0,0,0,3,0,0,2,0,1,0,2,1,1,0,0,0,0,2,0,0,0,1,6,0,0,2,0,0,8,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35441,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,1,0,1,0,0,0,3,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,2,0,Class_6
+35442,0,0,7,10,1,0,1,0,0,0,0,0,2,0,0,4,0,2,1,0,0,6,0,3,0,2,0,0,0,0,0,0,1,1,13,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,17,1,0,2,0,1,12,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35443,0,2,0,0,0,0,0,0,0,0,0,0,4,0,1,0,1,5,0,0,0,0,0,7,0,1,4,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,5,2,0,10,0,0,0,0,0,0,8,0,0,3,0,5,0,0,0,0,8,2,0,0,0,1,1,0,0,0,2,0,2,0,1,0,0,Class_6
+35444,0,0,2,13,0,0,0,0,0,1,0,0,1,0,0,2,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35445,0,0,2,2,0,0,1,0,0,0,0,0,1,0,0,2,1,1,1,0,0,2,0,5,1,9,0,0,0,0,0,1,0,0,5,1,0,0,0,0,0,6,0,5,0,1,1,1,0,0,0,0,1,6,0,0,1,0,0,11,0,0,0,3,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+35446,0,0,2,1,0,0,0,0,2,1,9,0,0,0,0,7,0,1,0,1,0,0,0,0,0,0,9,0,0,0,1,3,0,5,2,1,3,1,1,0,0,0,1,0,0,0,0,0,2,0,0,7,0,14,0,0,0,1,0,3,0,1,0,1,0,2,1,0,0,0,0,0,1,14,0,0,0,1,0,18,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35447,2,0,1,0,0,0,0,0,1,2,1,0,0,2,0,0,0,3,1,0,0,6,4,12,1,1,0,0,0,0,0,13,0,0,1,2,1,2,0,0,1,7,0,6,0,0,0,5,0,1,0,0,0,3,1,1,0,0,5,4,0,2,1,1,1,1,12,0,0,4,0,0,0,1,2,0,0,0,1,0,0,0,0,0,2,3,24,0,0,0,0,0,37,Class_6
+35448,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,5,0,1,0,2,0,1,1,3,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,12,0,1,0,1,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35449,0,0,7,2,0,0,0,0,0,0,8,0,0,0,1,1,0,0,0,0,0,0,0,3,0,4,6,1,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,1,0,5,0,0,2,0,0,0,0,11,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
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+35467,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,2,0,4,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,3,0,0,15,0,0,0,1,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
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+35469,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35470,0,0,1,5,0,0,0,0,0,0,0,0,2,1,1,2,0,1,0,0,0,4,0,1,0,8,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,1,0,1,0,0,0,0,0,17,1,0,0,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35471,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35472,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35473,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,3,2,8,0,1,0,8,2,4,0,2,2,0,1,0,2,0,0,0,4,1,2,2,1,2,0,4,0,3,1,0,0,4,2,0,0,0,0,12,2,0,8,0,0,13,0,1,0,0,0,2,10,0,1,1,0,1,0,0,0,0,0,0,0,1,0,0,6,0,0,1,5,1,0,1,0,0,0,Class_6
+35474,0,0,2,2,0,0,0,0,0,0,7,1,0,0,1,3,0,1,0,1,0,0,0,0,0,2,8,1,1,0,2,2,0,0,2,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,1,0,5,0,1,0,3,1,1,2,0,1,0,0,0,0,0,1,1,0,0,0,10,0,0,0,0,0,19,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35475,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+35476,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,Class_6
+35477,0,0,0,0,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35478,0,1,0,0,0,0,2,0,0,1,2,0,1,0,0,1,1,0,0,0,0,5,0,2,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,20,0,0,0,0,0,0,3,8,0,0,0,1,0,0,0,0,1,4,1,2,0,0,3,1,1,0,1,0,1,0,0,6,1,0,0,1,0,0,0,1,1,0,0,0,0,1,Class_6
+35479,0,0,5,0,0,1,0,0,1,0,10,0,0,1,0,1,0,2,0,2,1,2,0,14,0,0,2,0,0,1,2,2,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,2,2,0,0,10,0,14,0,1,0,3,0,1,5,0,0,0,0,0,1,0,0,0,0,2,1,31,0,0,2,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_6
+35480,4,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,16,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,6,1,0,2,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,3,1,Class_6
+35481,0,0,8,3,0,0,0,0,0,0,10,0,1,1,0,1,0,0,0,0,3,2,0,0,0,2,10,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,9,0,0,0,0,0,1,0,4,0,0,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35482,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35483,0,0,8,4,0,0,0,0,0,0,5,1,0,0,0,0,0,1,0,0,1,1,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35484,0,0,9,4,0,0,0,0,0,2,9,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,1,0,0,2,0,3,3,1,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35485,0,0,1,2,0,0,0,0,0,0,10,0,3,0,0,1,0,0,1,0,2,3,0,7,0,0,3,1,0,0,0,0,0,0,5,0,0,1,0,0,0,1,0,3,0,0,0,2,12,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,1,2,0,0,0,2,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35486,0,0,31,4,1,0,0,0,0,0,8,0,0,0,0,3,0,0,0,1,0,1,0,0,0,1,6,4,0,0,0,0,1,0,1,0,4,0,0,0,0,1,0,2,0,11,0,0,1,0,0,0,1,8,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,3,0,0,1,5,0,0,0,0,0,12,0,4,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35487,0,1,18,4,0,0,0,0,0,0,10,0,0,0,0,3,0,0,0,0,1,3,0,0,0,1,6,3,0,0,0,2,0,0,2,1,0,0,1,0,0,0,2,1,1,9,0,1,2,0,0,1,0,15,0,1,0,0,0,3,8,1,0,1,0,0,0,0,0,2,0,0,1,1,1,2,0,0,0,11,0,1,1,0,0,0,0,0,1,0,0,3,0,Class_6
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+35489,0,0,24,11,0,0,0,0,0,1,10,0,0,1,0,7,0,1,0,0,1,2,0,1,0,4,7,2,0,0,0,0,1,0,1,0,0,1,0,0,0,2,0,3,0,7,0,0,0,0,0,0,0,25,1,1,0,0,0,0,1,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,1,1,3,0,0,2,0,Class_6
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+35491,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,3,1,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35492,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,3,0,0,1,0,0,7,Class_6
+35493,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35494,0,1,11,2,0,0,0,0,0,1,7,0,0,0,0,1,0,1,1,0,2,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,Class_6
+35495,0,0,6,2,0,0,0,0,0,0,7,1,1,1,0,2,0,1,1,0,1,2,0,2,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,3,1,4,0,0,0,0,0,0,0,17,1,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35496,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,4,1,0,0,0,2,0,3,0,2,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,5,0,2,0,0,0,3,0,0,0,0,0,5,0,0,4,0,0,13,0,0,0,1,0,3,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35497,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,0,6,0,3,0,8,0,9,0,10,0,0,2,0,0,0,0,0,6,0,0,6,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,2,1,0,9,1,0,9,0,1,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,1,1,0,Class_6
+35498,0,0,5,2,0,0,0,0,0,0,21,0,0,0,0,4,0,0,0,0,5,0,0,7,0,0,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,1,0,0,2,0,7,0,0,0,11,0,1,1,2,1,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+35499,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,4,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,5,1,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35500,2,2,1,0,2,0,0,2,0,0,0,12,2,0,1,3,1,4,0,1,0,8,0,8,4,1,0,0,12,0,1,0,1,3,1,0,2,0,1,0,10,3,0,3,0,0,1,1,3,0,0,0,0,9,3,0,2,1,0,14,0,2,0,0,1,1,2,0,0,1,0,0,0,0,7,0,1,0,1,0,2,0,0,0,0,1,0,0,3,2,0,6,0,Class_6
+35501,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,2,2,0,0,0,0,0,5,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,1,0,0,0,0,1,0,1,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,1,2,0,0,0,1,0,0,0,0,0,1,0,0,Class_6
+35502,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,8,0,0,1,4,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,10,0,6,0,1,0,4,0,0,0,0,1,30,0,0,0,0,0,5,0,0,0,1,0,1,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_6
+35503,0,0,5,4,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,1,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35504,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35505,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,0,3,0,2,0,0,0,0,0,0,0,5,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,13,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+35506,0,0,2,3,0,0,0,0,0,0,20,0,0,1,0,3,0,0,0,0,1,0,0,7,0,2,6,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,10,0,0,0,9,0,1,4,0,1,1,0,1,1,0,0,1,0,0,0,2,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35508,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,1,3,0,1,1,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,6,0,0,0,2,0,0,0,1,0,3,1,0,2,0,10,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,Class_6
+35509,0,0,8,4,0,0,0,0,0,0,9,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,5,0,8,0,0,1,0,0,0,0,15,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35510,0,1,0,0,0,0,0,1,0,0,8,2,0,0,0,0,0,0,0,0,2,1,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+35511,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,2,2,5,0,0,0,1,0,0,0,0,0,3,1,0,1,0,0,17,0,0,0,0,0,2,6,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35512,0,0,4,2,0,0,0,0,0,0,7,0,1,0,0,2,0,1,1,0,3,0,0,3,0,2,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,3,0,0,1,0,0,0,0,7,0,0,0,1,0,1,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35513,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,1,0,7,1,4,0,0,1,0,0,0,0,1,2,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,12,0,2,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+35514,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,12,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
+35515,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,1,0,0,0,2,0,1,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,2,6,0,0,1,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35516,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,8,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35517,0,0,2,1,0,0,0,0,0,0,1,1,0,0,0,1,0,4,0,1,0,3,0,1,0,10,0,0,0,0,0,0,1,2,1,2,0,0,0,0,0,25,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+35518,0,0,15,6,0,0,0,2,0,0,11,0,2,1,1,4,0,0,0,0,3,2,0,0,0,0,23,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,2,0,36,1,1,0,0,0,0,0,22,1,0,0,0,0,0,4,2,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35519,0,0,1,3,0,0,0,0,0,0,0,0,0,3,1,1,0,4,0,0,0,1,0,2,2,11,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,1,0,3,1,1,0,0,0,3,2,0,2,0,0,17,0,0,3,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35520,0,2,3,4,0,0,0,0,0,0,4,0,1,0,2,11,0,3,0,0,0,7,0,0,0,1,0,1,1,0,1,0,0,0,1,0,1,0,0,4,0,0,0,5,0,1,0,0,9,0,0,0,0,10,1,0,0,0,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,2,0,0,0,0,0,6,0,1,0,0,0,0,1,1,4,0,0,0,0,Class_6
+35521,0,0,0,7,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,5,0,2,1,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,4,0,0,1,1,0,14,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35522,0,0,13,6,0,0,0,1,1,1,10,0,0,1,0,2,0,0,0,0,1,1,0,0,0,2,10,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,13,1,0,1,1,0,0,0,13,0,0,0,0,0,0,2,2,0,0,1,0,2,0,0,1,0,1,0,1,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,0,0,0,0,Class_6
+35523,0,2,13,7,0,0,0,0,0,0,8,0,1,0,0,4,0,3,0,0,1,1,0,0,0,2,13,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,10,0,0,1,0,0,2,0,8,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35524,1,11,1,2,0,0,1,1,0,0,0,7,3,1,0,5,0,27,0,0,1,1,0,4,0,20,0,0,0,0,1,4,0,0,17,0,0,0,0,0,0,6,0,1,0,0,0,5,2,0,0,0,1,2,1,0,3,0,0,15,0,1,2,0,1,3,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,0,2,Class_6
+35525,0,0,11,13,0,0,0,0,0,0,12,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,17,0,0,0,0,0,0,0,8,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35526,1,0,0,0,1,0,0,1,0,1,0,0,1,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,6,0,0,0,0,1,0,0,0,0,1,1,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
+35527,0,0,12,4,0,0,0,3,0,1,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,1,0,2,0,0,1,0,0,8,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35528,0,0,23,6,0,0,0,0,0,1,9,0,0,3,0,1,1,1,0,0,0,6,0,2,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,10,0,0,0,0,0,1,0,17,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,Class_6
+35529,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
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+35534,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,9,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,14,0,0,0,2,0,1,4,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+35535,0,0,17,4,0,0,0,0,0,0,9,1,0,0,0,2,0,0,0,1,1,1,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,0,1,0,16,0,0,0,0,0,0,0,26,0,0,0,0,0,1,3,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35536,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35537,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,2,0,0,0,0,2,0,2,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,7,0,3,5,2,0,3,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,3,0,1,0,0,0,0,0,Class_6
+35538,0,1,2,6,0,0,0,0,0,0,9,0,0,0,0,3,0,2,0,0,6,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,6,0,1,0,0,0,0,0,3,0,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35539,0,1,8,5,0,0,0,0,0,1,11,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,15,0,0,0,3,1,0,0,2,0,0,0,0,1,0,0,0,1,0,4,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,3,0,0,0,0,1,8,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35540,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,10,0,0,0,0,3,0,0,1,4,0,0,0,0,0,0,2,0,0,0,6,0,0,1,0,0,0,0,3,0,0,1,0,0,4,0,0,0,2,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+35541,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,2,1,0,0,1,0,7,0,3,0,1,0,0,1,0,0,0,2,0,0,1,0,0,0,0,2,9,0,0,0,0,0,2,1,0,0,0,0,4,3,0,8,0,0,10,0,0,0,0,0,3,4,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,1,0,1,Class_6
+35542,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,0,2,0,0,0,0,0,7,0,0,11,0,0,0,0,0,0,4,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35543,1,0,0,0,0,0,0,1,0,4,0,0,1,0,1,1,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,5,0,7,0,0,0,0,0,1,0,0,1,1,0,0,0,0,2,1,0,1,1,1,1,1,1,0,0,4,0,1,0,1,0,0,0,0,9,0,0,0,0,0,0,7,3,1,0,0,0,1,0,Class_6
+35544,1,1,3,0,0,0,0,0,0,0,6,0,0,1,0,2,0,2,0,0,2,3,0,1,1,0,4,2,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,1,0,4,0,1,1,0,0,4,0,3,0,0,0,0,0,1,1,2,0,0,1,0,1,4,0,0,1,1,0,10,0,0,0,0,0,9,0,6,1,0,0,0,0,0,0,0,1,3,0,Class_6
+35545,0,0,10,2,0,0,0,0,0,0,6,0,0,0,0,4,0,1,0,0,0,2,0,1,0,0,3,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,11,0,0,0,0,0,0,1,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35546,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,4,1,0,0,0,0,0,0,4,0,9,2,0,0,0,0,2,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,4,0,1,0,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35547,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+35548,0,1,1,2,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,0,2,0,2,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,9,0,1,0,0,0,2,2,0,0,0,0,2,0,0,0,1,0,16,0,0,1,0,0,1,4,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35549,0,1,0,0,0,0,0,0,1,0,6,0,1,0,0,1,0,4,0,0,0,0,0,1,0,0,3,0,0,0,0,2,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,2,1,0,0,0,0,0,0,1,0,0,0,Class_6
+35550,0,0,6,2,0,0,0,0,0,0,11,1,0,0,0,3,0,3,0,0,1,1,0,2,0,1,14,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,8,0,0,0,0,0,1,0,11,0,0,0,1,0,0,7,2,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35551,0,0,9,4,0,0,0,0,0,0,6,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,3,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,7,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35552,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,8,0,0,1,4,4,1,1,0,0,0,0,2,1,0,0,0,0,0,0,10,0,6,0,1,0,3,0,0,0,0,1,32,0,0,0,0,0,4,0,0,0,1,0,1,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_6
+35553,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,1,0,3,0,0,0,0,0,0,1,0,1,0,2,0,2,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,0,2,0,0,17,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,Class_6
+35554,0,0,4,0,0,0,2,1,0,0,0,0,3,1,0,1,1,2,0,0,0,5,0,2,0,1,0,1,0,0,2,0,0,0,0,0,0,1,1,1,0,0,0,1,0,1,0,0,1,0,1,0,0,3,1,0,2,0,0,13,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,Class_6
+35555,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,5,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,13,0,0,0,0,0,0,4,1,0,3,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+35556,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,1,0,0,1,2,2,0,0,0,0,7,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,3,0,1,0,Class_6
+35557,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,4,1,0,0,1,0,2,0,2,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,2,0,0,6,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,4,1,0,0,0,0,0,0,Class_6
+35558,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,6,1,3,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35559,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,9,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,2,0,Class_6
+35560,0,2,3,5,0,0,0,1,0,0,4,0,1,1,0,3,0,0,0,4,0,2,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,15,0,0,0,0,0,0,0,2,0,0,1,2,1,0,0,3,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,2,0,1,0,0,0,0,Class_6
+35561,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,0,1,1,0,0,2,0,0,0,0,1,1,0,0,2,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,Class_6
+35562,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,0,1,7,2,2,1,0,0,0,0,0,2,0,0,0,0,0,0,10,0,2,0,5,0,0,1,0,0,0,0,6,0,0,0,0,0,1,0,1,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35563,0,1,0,2,0,0,3,1,0,0,0,0,0,0,0,0,1,17,0,1,0,1,2,3,0,0,0,0,0,0,2,1,0,0,3,1,0,0,0,2,0,2,6,0,0,0,0,1,3,2,0,0,0,4,3,0,2,0,0,12,0,2,0,0,0,1,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,1,2,0,0,0,0,0,1,Class_6
+35564,0,0,0,0,0,0,0,0,3,0,0,3,1,0,0,3,0,2,0,0,0,0,0,8,0,3,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,6,0,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,19,0,0,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,Class_6
+35565,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,4,0,1,0,0,9,1,0,0,0,1,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35566,0,0,1,0,0,0,0,0,2,0,11,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,6,0,4,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35567,0,0,13,10,0,0,0,0,0,3,8,1,0,2,0,5,0,5,0,2,1,4,1,4,2,10,5,3,0,2,0,2,2,2,6,3,0,0,0,3,2,2,1,8,0,21,0,2,17,0,1,13,0,27,1,1,0,2,1,3,13,7,2,0,10,0,0,6,0,2,0,1,18,3,0,1,0,1,2,9,2,22,0,0,0,0,2,1,8,1,0,0,0,Class_6
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+35585,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35586,0,0,15,6,0,0,0,0,0,0,10,1,1,0,0,2,0,0,0,2,2,2,0,0,0,4,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,4,0,2,0,0,0,0,0,19,0,0,0,0,0,1,6,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35587,0,15,1,4,0,0,0,0,0,0,9,0,1,0,0,2,2,0,0,5,0,1,0,11,1,10,0,1,0,0,1,0,1,0,4,2,1,1,0,0,0,7,0,2,0,1,0,1,2,0,0,0,0,8,2,0,5,0,0,12,0,0,0,1,1,3,3,0,3,2,0,1,0,0,0,6,0,0,2,1,0,0,7,0,0,2,1,0,1,11,0,0,0,Class_6
+35588,0,0,8,8,0,0,0,0,1,0,9,0,0,1,1,3,0,0,0,0,1,0,0,0,0,1,1,4,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,2,0,10,0,0,1,0,0,0,0,8,0,1,0,1,0,1,5,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35589,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,6,0,0,0,0,0,1,3,2,1,0,0,1,0,0,Class_6
+35590,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35591,0,1,15,5,0,0,0,0,0,0,18,0,1,1,0,3,0,0,0,0,0,1,0,5,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,14,0,1,1,0,0,3,0,11,0,0,0,10,0,0,3,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35592,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,12,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35593,0,0,4,3,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,3,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,10,0,0,0,0,1,4,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+35594,0,1,16,6,0,0,0,0,0,2,7,1,1,0,0,4,0,3,0,0,0,6,0,0,0,8,2,0,1,0,1,2,0,0,2,0,1,0,0,0,0,2,0,2,0,2,0,1,2,1,0,0,0,27,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35595,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,3,0,4,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,6,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,Class_6
+35596,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_6
+35597,0,0,1,0,0,0,0,1,0,0,9,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35598,0,1,0,0,0,0,0,4,0,0,4,0,0,3,0,2,2,4,1,0,0,1,0,9,0,3,0,0,0,0,0,4,2,1,3,0,1,1,2,0,1,8,1,4,0,0,0,14,1,0,1,4,0,5,0,0,4,2,0,11,0,0,0,0,0,2,8,3,0,0,0,0,0,0,5,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,0,1,0,Class_6
+35599,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35600,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,4,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+35601,0,0,3,1,0,0,0,0,1,0,1,0,0,0,0,2,0,1,0,0,0,1,0,1,0,9,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,7,0,0,2,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35602,0,0,0,1,0,0,0,2,0,0,1,0,1,0,0,2,0,1,0,1,0,7,0,3,0,1,0,0,1,0,0,0,2,0,1,1,0,1,0,0,2,9,0,0,0,0,0,2,1,0,0,0,0,8,0,0,10,0,0,10,0,1,0,1,0,6,4,1,1,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,1,Class_6
+35603,0,0,10,3,0,0,0,0,1,0,10,1,0,0,1,2,0,0,0,0,0,0,0,0,0,2,5,6,0,0,0,0,1,0,1,1,1,2,0,0,0,0,1,0,0,9,0,1,1,0,0,0,1,5,0,2,0,3,0,0,2,1,0,1,0,2,1,0,0,0,0,2,0,1,0,2,0,0,0,11,0,9,0,0,0,0,0,0,4,1,0,1,0,Class_6
+35604,0,0,12,8,0,0,0,1,0,1,8,0,0,1,0,2,0,0,0,0,1,3,0,2,0,2,4,3,0,0,0,0,1,0,2,0,0,0,0,1,0,2,0,2,0,0,0,0,2,0,0,0,0,16,0,0,0,0,0,2,6,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,9,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35605,0,0,9,4,0,0,0,0,2,0,8,0,1,0,0,0,0,1,0,0,0,1,0,1,0,2,4,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,6,0,2,1,0,0,0,0,10,0,0,0,1,0,2,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35606,0,1,8,8,0,0,2,0,0,0,5,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,1,0,1,0,2,0,2,0,0,0,2,3,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,14,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35607,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,3,1,0,0,0,0,0,1,2,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,3,0,0,8,0,0,0,2,0,0,5,0,0,0,1,0,0,0,0,0,0,1,2,1,0,0,1,0,1,0,6,0,0,0,0,1,0,Class_6
+35608,0,2,8,1,0,0,0,0,0,1,7,1,0,1,0,3,0,1,0,0,0,0,0,0,0,1,4,5,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,2,0,5,0,0,3,0,0,0,0,12,5,0,0,1,0,0,2,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35609,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,4,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,8,0,2,2,1,0,0,0,0,0,7,2,0,3,0,0,0,0,1,1,0,3,0,0,0,0,2,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35610,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,1,2,0,0,0,1,0,1,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,3,0,1,1,8,0,0,3,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,1,0,Class_6
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+35613,1,0,0,0,0,1,0,0,2,0,12,2,0,1,0,0,0,0,0,1,1,0,1,5,0,3,2,0,0,0,2,5,0,0,0,0,2,0,2,2,0,0,0,0,0,0,0,1,0,2,0,20,0,2,0,2,0,2,0,1,0,0,0,0,1,0,0,2,1,0,0,0,0,107,0,0,0,1,1,9,0,0,0,0,0,0,0,0,1,4,0,0,0,Class_6
+35614,0,1,6,2,0,0,0,0,1,0,5,0,0,0,0,3,0,1,0,0,1,1,0,0,0,1,6,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,5,1,1,0,0,0,0,0,12,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,2,0,0,1,1,Class_6
+35615,0,0,0,0,0,0,0,7,0,0,0,1,0,1,0,2,1,0,0,0,0,0,0,8,0,7,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,19,0,0,0,0,1,0,11,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+35616,0,0,11,17,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,16,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35617,0,0,10,9,0,0,0,0,1,0,8,0,0,1,0,2,0,0,0,0,1,3,0,0,0,2,10,3,1,0,0,1,0,1,1,0,0,2,0,1,0,1,0,4,0,2,0,1,3,0,0,1,0,25,0,0,2,0,0,2,5,1,0,0,1,1,0,1,0,0,0,1,1,2,0,1,0,0,0,4,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35618,0,0,10,10,0,0,1,0,0,0,16,0,1,0,0,1,0,1,1,1,0,1,0,7,0,0,6,1,0,0,3,1,0,0,0,0,0,0,0,0,0,1,0,3,0,2,0,0,2,0,0,0,0,8,1,1,0,11,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,3,1,1,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35619,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35620,0,0,9,2,0,1,0,0,0,0,8,0,2,1,0,2,0,0,0,0,0,2,0,1,0,2,12,0,0,1,0,1,0,0,2,0,0,0,0,0,0,1,0,1,0,2,0,0,3,0,0,0,1,18,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,2,1,0,0,0,0,Class_6
+35621,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,10,0,2,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,1,0,1,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+35622,0,0,7,8,0,0,0,0,0,0,9,1,0,0,0,3,0,0,0,0,2,4,1,1,0,2,10,3,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,1,0,4,1,1,0,1,0,0,6,0,0,1,0,1,0,1,1,0,0,2,1,0,1,1,1,0,0,4,1,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35623,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35624,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,1,0,0,0,2,0,1,1,9,0,1,0,0,0,0,0,1,14,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,8,0,0,1,0,0,12,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35625,0,0,0,3,0,1,0,0,0,0,1,2,1,0,1,1,0,1,0,0,0,2,0,1,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,2,0,2,0,0,0,1,0,3,0,1,0,0,2,8,0,1,0,1,1,0,2,0,0,0,0,0,0,6,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35626,0,0,4,3,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,5,4,0,0,0,0,9,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,5,1,6,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,Class_6
+35627,0,0,4,3,0,0,0,0,0,1,6,0,0,0,0,6,0,0,0,0,4,2,0,2,0,1,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,3,2,2,3,0,0,1,0,19,0,1,0,0,0,2,2,0,0,0,0,2,0,0,0,0,0,0,0,2,0,2,0,0,1,6,0,3,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35628,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35629,1,1,9,6,0,0,0,0,1,0,8,0,0,0,1,5,0,0,0,0,5,2,0,0,0,2,11,3,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,0,16,0,1,0,0,0,2,0,14,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,3,0,0,2,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,0,0,0,1,0,Class_6
+35630,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,1,1,0,4,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,16,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+35631,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35632,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,7,0,4,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,13,0,1,0,0,0,0,7,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,5,0,Class_6
+35633,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,1,1,0,1,0,0,13,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35634,0,0,0,0,0,0,0,0,0,0,7,1,0,0,1,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,45,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35635,0,0,1,6,0,0,0,0,3,0,15,0,0,1,0,0,1,0,0,0,3,0,0,11,0,2,11,0,0,0,0,1,0,0,5,0,0,1,0,0,0,0,0,0,1,5,0,0,0,0,0,3,0,1,0,0,1,8,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,5,0,2,0,0,0,5,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_6
+35636,0,1,0,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,2,0,1,0,6,1,1,0,0,0,1,1,2,0,0,0,0,0,0,0,5,0,3,0,1,0,3,2,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,5,1,Class_6
+35637,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,2,1,Class_6
+35638,1,2,0,8,0,0,0,0,0,1,1,1,0,0,1,3,0,0,0,1,1,4,0,16,2,6,4,2,0,0,1,2,0,0,8,0,0,1,1,1,2,1,5,0,1,2,0,1,0,0,2,7,0,11,0,0,1,0,0,1,3,1,1,0,1,0,1,1,0,0,0,0,2,5,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,2,0,1,0,Class_6
+35639,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,6,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,2,0,12,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35640,0,0,3,10,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,3,1,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,1,0,2,0,1,1,0,0,0,0,6,1,0,1,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35641,0,0,3,6,1,0,0,0,1,0,8,0,0,0,0,2,0,6,0,0,1,1,0,0,0,0,1,1,0,0,0,1,0,1,5,1,0,3,0,1,1,1,0,1,0,0,0,0,0,0,0,1,0,8,0,0,0,1,0,0,0,3,0,0,1,0,0,0,0,0,0,2,0,7,0,0,0,0,1,1,0,0,0,0,0,0,0,1,2,0,0,1,0,Class_6
+35642,0,0,0,0,0,0,0,1,1,0,2,0,0,1,0,1,0,1,0,0,0,1,0,1,1,0,1,0,0,0,0,3,1,0,0,0,1,0,0,0,1,4,0,2,0,0,1,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,5,1,5,5,1,0,3,0,1,0,1,0,1,0,1,0,0,0,0,0,0,2,2,0,3,0,0,0,0,0,Class_6
+35643,0,0,1,5,0,0,0,0,0,1,5,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,4,0,0,0,2,0,0,0,2,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35644,0,0,1,4,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,1,0,2,0,1,0,1,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,6,0,0,0,0,0,12,0,0,0,0,1,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35645,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,3,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+35646,0,0,1,18,0,0,0,1,0,0,6,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,4,3,0,0,1,0,0,0,5,0,0,1,0,2,0,0,0,1,0,1,0,0,3,0,0,0,0,16,0,0,0,0,1,0,2,2,1,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,3,0,0,1,0,Class_6
+35647,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35648,1,0,7,11,0,0,0,0,0,0,18,1,0,0,0,0,1,1,0,0,1,1,0,13,0,1,12,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,2,0,0,2,0,4,0,1,0,12,0,1,3,0,2,1,0,1,1,0,0,1,0,0,3,3,0,1,0,0,0,9,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+35649,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,8,0,0,0,0,0,1,1,0,0,0,0,0,1,1,2,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+35658,0,0,1,1,0,0,0,0,0,0,1,2,0,1,0,0,2,1,0,1,0,1,1,15,0,0,0,0,0,0,2,2,0,0,1,0,0,2,0,2,0,1,0,1,0,0,0,3,0,1,0,0,1,2,0,0,3,0,0,9,0,2,0,0,0,2,5,0,0,0,1,1,0,1,0,1,0,0,1,0,0,0,0,0,0,4,0,0,3,0,0,2,0,Class_6
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+35660,0,1,3,10,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,1,5,0,3,1,2,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35661,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,5,1,5,0,1,0,0,1,1,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,8,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35662,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,3,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,2,0,2,2,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,5,Class_6
+35663,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35664,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,3,0,0,8,1,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35665,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,6,0,0,0,0,0,0,1,1,0,1,2,1,0,0,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,3,0,0,3,0,0,16,0,1,0,0,0,0,5,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35666,0,0,18,10,0,0,0,0,0,0,6,0,0,1,0,4,0,1,0,0,0,3,0,0,0,7,4,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,1,0,0,0,13,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,0,0,0,Class_6
+35667,0,0,10,3,0,0,0,0,1,2,7,0,0,0,0,0,0,2,0,1,4,1,0,1,0,2,6,1,0,0,4,0,0,1,4,0,0,0,0,0,0,0,1,0,0,10,0,1,0,0,0,1,0,9,1,1,0,0,0,1,3,2,0,1,0,0,0,0,0,3,0,0,1,2,0,1,0,1,0,7,0,0,0,0,0,0,0,0,0,1,0,1,1,Class_6
+35668,0,1,3,4,0,0,0,0,0,0,7,0,0,1,0,0,1,2,0,0,0,1,0,1,0,0,1,0,0,0,1,1,0,1,5,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,1,1,0,0,4,1,1,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,22,1,4,0,0,1,0,0,0,4,0,0,0,0,Class_6
+35669,0,0,1,1,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,0,0,0,0,5,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,1,0,0,0,4,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35670,0,0,4,4,0,0,0,1,0,0,11,0,1,0,0,1,0,3,0,1,1,4,0,3,1,1,0,1,0,0,1,2,0,0,1,0,1,1,0,0,0,1,2,9,0,2,0,0,6,0,0,0,0,2,0,0,0,0,0,2,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,2,0,0,0,0,Class_6
+35671,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,1,0,0,0,3,0,3,0,7,0,0,0,0,1,1,0,3,3,1,1,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,3,0,1,1,0,0,1,6,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35672,0,0,18,6,0,0,0,0,2,0,7,0,0,0,0,9,0,1,1,1,0,3,0,1,0,1,16,0,0,0,0,0,0,1,0,2,0,0,0,0,1,3,0,4,0,8,0,1,1,1,0,0,0,17,0,1,1,0,0,1,1,2,0,2,2,1,1,1,0,0,0,0,0,1,0,0,0,0,0,10,0,2,0,0,1,0,0,0,2,0,0,0,0,Class_6
+35673,0,0,9,5,0,0,0,0,0,0,11,0,0,0,2,1,0,0,0,0,1,0,0,8,0,3,14,1,0,0,0,1,0,0,5,0,0,0,0,0,1,0,0,2,0,3,0,1,1,0,0,0,0,10,0,1,1,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35674,1,10,0,11,0,0,1,1,0,0,1,6,3,1,0,5,0,27,0,0,1,1,0,9,0,10,0,1,0,0,0,1,1,0,15,0,0,0,0,0,0,10,0,1,0,0,0,7,2,0,0,0,0,2,1,0,4,0,0,16,0,0,2,0,0,4,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,0,2,Class_6
+35675,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,1,1,0,2,0,2,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,0,1,8,0,5,0,0,0,0,3,1,0,0,0,6,1,0,0,0,0,13,0,2,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,3,0,0,1,0,Class_6
+35676,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35677,0,0,4,1,0,0,1,2,0,4,6,0,0,1,0,1,1,2,0,2,0,3,0,7,1,2,0,2,0,0,1,4,0,1,3,3,4,4,7,0,0,6,0,5,0,0,0,4,3,4,1,0,1,5,0,0,5,3,0,9,0,3,0,3,0,0,10,0,0,9,2,1,0,8,0,3,0,0,2,4,0,0,1,0,0,0,4,0,0,0,3,2,0,Class_6
+35678,0,0,4,3,0,0,0,0,0,0,8,1,1,0,0,2,0,0,0,1,1,2,0,0,1,2,8,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,2,0,4,3,0,0,0,0,0,0,7,0,0,0,0,0,1,6,0,0,0,1,2,0,0,0,0,0,0,0,1,0,2,0,0,0,9,0,2,0,0,0,0,1,0,2,0,1,0,0,Class_6
+35679,0,0,0,1,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35680,0,0,12,5,0,0,1,0,0,0,9,0,0,1,0,5,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,3,0,1,1,0,0,0,0,0,0,3,0,2,0,13,0,0,1,0,0,1,0,25,0,0,0,0,1,1,0,0,0,0,3,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35681,0,0,0,0,0,0,0,2,0,1,1,0,0,2,1,3,1,1,0,3,0,1,0,7,1,0,0,0,0,0,0,4,1,0,1,4,2,1,0,0,0,4,0,1,0,0,0,4,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,3,0,1,14,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,8,1,0,0,0,0,5,Class_6
+35682,0,1,3,11,0,0,1,0,0,0,13,0,0,0,0,4,1,4,0,1,0,3,0,2,1,2,0,2,0,0,0,0,0,1,11,1,0,3,0,0,1,2,0,1,0,0,0,0,12,1,0,0,0,9,0,0,0,1,0,1,0,3,0,1,0,2,1,0,0,4,0,1,0,0,2,0,0,1,1,4,0,3,0,0,0,1,0,0,3,0,0,1,0,Class_6
+35683,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,1,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,2,4,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35684,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,4,0,0,11,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35685,0,0,50,12,0,0,0,1,4,0,16,0,0,1,1,7,0,1,1,1,9,3,0,2,0,7,6,7,0,0,0,0,0,1,16,0,0,0,0,2,0,1,1,2,0,18,0,1,5,0,0,2,1,35,0,0,2,0,0,1,6,4,2,1,1,3,6,0,1,1,0,2,0,0,1,0,0,1,1,19,0,12,3,0,1,0,1,1,6,0,0,0,0,Class_6
+35686,0,0,6,0,0,0,0,1,0,0,10,1,0,0,0,3,1,0,0,0,1,1,0,0,0,0,8,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,1,0,1,0,1,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35687,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,2,0,10,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,3,0,1,0,0,14,0,2,0,1,0,2,0,1,0,1,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
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+35689,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
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+35691,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,3,0,2,0,0,0,4,1,4,0,2,0,0,0,0,4,0,0,0,7,0,0,1,0,1,0,2,1,0,0,0,0,0,3,0,0,0,0,4,0,0,4,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,Class_6
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+35693,0,0,2,2,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35694,0,0,1,1,0,0,0,0,0,1,1,0,3,0,0,1,0,2,0,1,0,2,0,1,2,0,0,0,25,0,0,0,0,0,0,3,0,0,1,1,1,3,0,2,0,0,0,0,1,0,0,0,0,3,6,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+35695,0,0,1,2,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,0,1,0,2,0,8,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,1,0,0,0,0,2,2,0,0,0,0,9,1,1,0,0,0,3,1,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35696,0,0,4,2,0,0,0,0,0,0,8,0,0,0,1,5,0,0,0,0,0,0,1,0,0,1,17,1,0,0,0,1,0,0,0,3,0,0,0,2,0,0,0,1,0,5,1,1,2,0,0,5,0,6,1,0,0,0,0,0,2,0,0,0,0,1,1,0,2,1,0,0,1,28,0,0,0,1,0,12,0,1,0,0,0,0,1,0,2,0,0,1,0,Class_6
+35697,0,0,14,5,0,0,0,0,0,1,6,0,1,0,0,5,0,1,0,0,1,0,0,1,0,0,4,2,0,0,3,1,0,1,1,0,1,0,2,0,0,1,1,2,0,9,0,1,1,0,0,0,0,2,1,0,0,0,0,0,9,3,1,0,0,1,0,1,0,0,0,1,1,0,0,0,0,0,0,10,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35698,0,0,3,4,0,0,0,0,0,0,5,1,1,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,2,0,0,3,0,0,0,0,1,0,2,1,0,0,1,0,0,0,0,1,0,1,9,0,2,0,0,0,1,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,Class_6
+35699,0,0,1,0,0,0,0,1,4,0,2,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0,0,0,2,0,9,1,0,0,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,Class_6
+35700,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35701,0,1,22,8,0,0,0,0,0,0,10,0,1,0,0,2,0,1,0,0,0,2,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,14,0,0,0,0,0,2,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35702,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,2,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,12,0,0,0,0,0,1,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,Class_6
+35703,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,0,0,1,0,0,2,0,8,0,3,0,0,0,0,0,0,1,0,2,0,0,1,0,1,0,1,0,0,0,0,0,6,0,0,0,0,0,5,0,0,3,0,0,14,0,2,0,1,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,3,0,Class_6
+35704,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,2,0,0,0,0,0,1,1,1,13,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,Class_6
+35705,0,0,9,11,0,0,0,0,0,0,12,0,0,0,0,2,0,0,1,0,0,1,0,0,0,4,9,0,0,0,2,0,2,0,4,0,0,0,0,1,0,2,0,2,0,4,1,0,0,0,0,2,0,13,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,2,0,0,Class_6
+35706,0,1,2,3,0,0,1,0,0,0,10,1,0,1,0,2,0,0,0,0,0,1,0,6,0,0,9,1,0,0,0,3,0,0,0,0,0,1,0,1,1,1,0,0,0,4,0,1,0,0,0,0,0,14,1,0,0,4,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35707,0,0,2,12,0,0,0,0,1,0,3,0,1,0,0,3,0,0,0,2,0,5,0,0,0,13,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,1,1,1,0,2,0,0,1,0,0,0,0,15,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,8,0,1,0,0,0,0,2,0,1,0,0,1,0,Class_6
+35708,0,1,0,0,0,0,0,0,0,0,11,0,0,0,0,1,1,5,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35709,0,4,3,1,0,0,1,0,0,0,10,0,2,0,0,7,0,3,0,1,0,1,0,3,0,5,0,0,1,0,1,0,0,1,1,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,23,0,0,0,2,0,0,4,1,1,0,0,0,6,0,0,0,0,0,0,4,0,4,0,0,0,17,0,0,4,0,0,0,0,0,0,0,0,1,0,Class_6
+35710,0,0,10,3,0,0,1,0,0,0,8,1,0,0,0,3,0,0,1,1,0,0,0,0,0,0,4,2,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,7,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35711,0,0,12,2,0,0,0,0,0,0,8,0,0,0,0,2,1,1,0,0,0,5,0,2,0,4,13,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,1,3,0,6,0,0,0,0,0,0,0,6,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,1,1,0,0,0,0,Class_6
+35712,2,0,12,9,0,0,0,0,0,0,10,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,11,0,0,1,0,0,0,0,24,0,0,0,0,0,0,3,3,0,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35713,0,0,0,0,0,0,0,0,0,0,5,0,0,2,0,3,0,0,0,2,0,2,0,5,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,3,18,0,0,0,0,0,0,0,0,9,0,9,0,0,0,0,0,1,3,0,1,0,0,0,0,10,0,0,0,2,0,0,7,0,0,1,Class_6
+35714,0,0,1,5,0,2,0,0,0,0,12,0,0,0,1,2,0,2,0,0,3,0,1,0,0,2,12,0,0,3,1,0,0,1,7,0,1,5,0,0,2,0,4,1,0,2,0,1,1,2,0,3,1,6,0,0,0,7,0,0,3,0,2,0,0,0,0,0,0,1,0,0,1,7,0,0,0,0,0,8,0,1,0,0,0,0,0,1,4,1,0,0,0,Class_6
+35715,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,1,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,9,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,11,0,1,0,0,0,3,2,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,Class_6
+35716,5,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,11,0,0,0,9,0,3,1,3,0,0,4,0,0,0,0,6,2,0,0,0,0,2,1,9,0,2,0,0,0,2,0,0,0,0,0,4,2,0,11,0,0,17,0,2,0,0,0,0,0,1,2,0,0,0,2,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,Class_6
+35717,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,10,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,2,0,Class_6
+35718,0,0,3,2,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,5,0,1,0,4,0,1,0,0,1,0,0,1,4,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,11,0,1,2,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+35719,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35720,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,4,0,0,0,0,0,10,2,1,0,0,0,0,0,0,0,4,0,1,0,0,2,1,0,2,0,0,0,0,7,1,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,0,15,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,Class_6
+35721,0,0,3,4,0,0,0,0,0,0,10,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+35722,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35723,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,1,0,4,0,3,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,5,2,3,0,0,0,0,0,0,0,0,0,10,0,2,0,0,0,0,1,2,0,2,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,5,4,0,0,0,0,Class_6
+35724,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,6,1,0,0,0,1,0,0,1,0,3,0,0,0,0,0,1,2,6,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,7,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35725,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,1,0,0,0,1,0,6,0,0,0,0,9,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,12,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35726,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,2,0,0,0,0,0,7,0,0,0,0,1,0,1,1,0,0,0,1,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,10,0,1,0,0,0,0,6,0,0,1,1,2,0,0,0,0,4,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+35727,0,0,0,2,0,0,1,0,3,0,15,0,1,0,0,4,2,1,1,0,2,0,1,0,0,0,9,1,0,0,2,2,1,0,2,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,1,6,1,1,0,0,0,1,0,1,3,0,0,1,3,0,0,0,0,0,0,0,0,5,0,0,0,0,0,12,0,2,0,0,0,1,0,1,2,1,1,0,0,Class_6
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+35729,0,0,8,2,0,0,0,0,0,0,6,0,0,1,0,5,1,1,3,0,2,1,0,0,0,4,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,22,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35730,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+35733,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,0,2,3,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35734,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,0,1,0,4,0,2,0,3,0,0,13,0,0,0,0,1,0,0,0,0,0,2,0,2,0,1,0,0,0,1,0,0,0,0,3,3,1,0,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35735,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,2,0,0,0,2,0,2,5,5,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,2,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,Class_6
+35736,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,4,5,0,0,3,0,0,0,0,3,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,37,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35737,0,0,6,0,0,0,1,0,0,1,12,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,4,0,0,0,0,9,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+35738,0,0,11,2,0,0,0,0,0,1,8,0,0,0,0,1,1,0,0,0,1,2,0,0,0,0,5,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,6,0,0,0,0,0,0,0,17,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35739,0,0,0,3,0,0,0,0,0,0,0,1,2,0,0,1,1,2,0,0,0,4,0,8,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,2,0,0,4,0,0,16,0,0,0,0,0,1,1,0,1,0,0,0,1,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,3,Class_6
+35740,0,0,2,5,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,1,1,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,4,0,0,0,10,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35741,1,0,18,5,0,0,0,0,0,1,10,1,0,0,1,2,0,0,0,0,1,0,0,0,0,2,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,21,0,0,1,0,0,7,0,12,1,1,0,3,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35742,0,0,2,7,0,0,0,0,3,0,12,0,0,0,0,2,0,1,0,0,2,1,0,3,0,4,3,0,0,0,0,1,0,0,4,0,0,0,0,0,1,1,0,3,0,8,0,0,2,0,0,2,0,9,0,0,2,0,0,2,1,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,17,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35743,0,0,8,15,0,0,0,1,0,0,7,1,1,0,0,5,0,1,0,0,1,0,0,0,0,1,0,1,0,2,0,3,0,2,1,0,0,0,0,0,1,0,0,1,0,2,0,0,1,0,0,12,0,6,1,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,11,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35744,0,0,16,11,0,0,0,0,0,0,7,0,0,1,3,6,0,0,0,0,4,2,0,0,0,5,2,4,0,0,0,2,0,0,5,0,0,0,0,0,0,2,0,0,0,6,0,1,1,0,0,0,0,10,0,0,0,0,0,2,3,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,4,0,0,2,0,Class_6
+35745,1,0,0,0,0,0,0,1,1,0,7,0,0,1,0,0,0,0,0,0,0,2,1,16,0,0,1,0,0,0,1,3,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,2,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,63,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+35746,0,0,7,2,0,0,0,1,0,0,11,0,0,1,0,2,0,0,0,0,1,2,0,10,0,2,7,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,1,0,0,0,0,9,0,0,0,4,0,1,0,1,0,1,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
+35747,0,1,10,5,0,0,1,0,0,0,10,0,0,2,0,4,0,0,0,0,3,4,0,2,0,2,23,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,18,0,0,0,0,0,0,2,2,0,1,0,0,1,0,0,2,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,Class_6
+35748,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,5,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35749,0,1,4,2,0,0,0,1,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,9,1,0,0,0,0,0,2,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35750,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,2,4,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+35751,0,0,6,12,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35752,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,5,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,5,0,0,1,0,0,2,0,19,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35753,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,2,0,3,3,7,1,0,0,0,0,0,1,8,0,2,0,0,0,1,1,0,1,0,0,0,0,0,0,2,1,2,0,0,1,3,0,0,0,0,0,1,0,1,0,4,0,3,5,0,3,6,0,0,4,0,0,1,1,2,0,1,0,0,0,0,3,16,2,3,0,0,0,0,0,Class_6
+35754,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,2,0,1,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,12,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Class_6
+35755,0,0,2,2,0,0,0,0,0,0,5,0,1,0,1,0,0,1,1,0,0,3,0,1,0,5,6,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,2,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35756,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35757,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,6,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,12,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35758,0,0,0,2,1,0,0,0,1,0,9,0,0,0,0,7,0,0,1,1,0,1,0,0,0,0,2,0,0,0,2,0,0,1,1,0,0,0,0,1,0,0,0,3,0,1,3,1,1,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,3,1,1,0,1,0,0,0,3,0,0,0,0,0,7,0,3,0,2,1,0,0,0,1,0,0,0,0,Class_6
+35759,0,0,7,9,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,6,3,0,0,2,0,0,0,1,0,0,1,1,0,0,0,2,0,0,8,0,0,2,0,0,1,0,6,0,0,1,0,0,1,4,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,7,0,6,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35760,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,3,3,5,0,0,0,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,3,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,3,4,0,0,0,0,1,1,0,0,3,0,0,0,0,0,1,0,0,1,31,1,1,0,0,0,0,0,Class_6
+35761,0,4,4,2,0,0,0,0,1,2,7,0,1,0,0,5,0,1,1,0,0,5,0,0,0,0,4,1,0,1,0,1,0,0,1,1,0,0,0,1,1,0,0,3,0,4,0,0,2,2,0,0,0,26,0,0,0,0,0,0,1,0,0,0,1,3,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,Class_6
+35762,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,2,0,5,1,0,0,0,0,0,1,3,1,3,0,1,0,1,0,0,0,6,0,0,0,0,0,7,1,1,0,0,0,4,0,0,2,0,0,20,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35763,4,1,4,1,0,0,1,0,0,0,1,0,1,0,2,0,0,0,0,0,0,2,0,0,1,0,1,3,0,0,0,0,0,1,3,0,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,6,0,0,0,0,2,8,1,2,0,0,1,3,4,0,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35764,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,1,2,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,4,1,0,0,0,0,7,0,0,0,0,1,0,1,2,2,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,2,2,0,0,0,Class_6
+35765,5,0,1,1,0,0,0,1,1,1,0,1,19,1,0,2,1,2,0,0,0,0,0,5,2,0,0,0,2,0,0,0,1,0,0,2,0,1,0,1,2,0,0,1,0,0,0,4,0,1,0,0,6,5,0,0,0,0,32,2,0,1,3,1,0,1,34,1,3,8,1,1,1,1,1,1,2,0,3,0,0,0,0,0,0,5,0,1,0,1,0,0,0,Class_6
+35766,0,0,14,1,0,0,0,1,2,0,8,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,2,0,7,0,1,0,0,0,0,0,9,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35767,0,0,0,0,0,0,1,0,0,1,1,0,2,1,0,2,5,0,0,2,0,3,0,7,0,0,0,0,0,0,0,4,0,0,1,0,0,0,2,0,0,1,0,2,1,0,1,2,1,3,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,1,4,0,0,2,0,0,2,1,2,0,1,0,1,0,1,0,1,0,2,2,0,1,1,0,0,0,0,Class_6
+35768,0,2,2,0,0,0,0,1,1,0,6,0,0,0,0,5,0,1,1,0,6,1,0,0,0,0,9,0,1,0,1,1,0,1,1,1,1,1,2,2,0,0,0,2,0,2,0,0,0,0,0,7,0,3,0,0,0,0,0,0,4,0,0,0,2,0,0,1,0,0,0,0,0,6,0,1,0,0,0,22,0,1,0,0,1,0,0,0,3,0,0,0,1,Class_6
+35769,0,0,5,6,0,0,0,2,0,0,0,0,1,1,0,2,0,2,0,0,0,2,0,2,0,12,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,2,0,7,1,2,0,0,0,0,1,12,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
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+35781,0,0,2,2,0,0,3,0,0,0,6,1,1,0,0,6,0,2,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,1,2,0,0,0,0,2,10,1,1,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35782,2,2,0,2,0,0,0,0,0,0,10,0,0,0,0,0,0,2,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,2,0,0,0,2,0,2,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,1,0,1,1,1,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35783,1,0,2,3,0,0,1,0,0,0,1,0,0,0,0,0,0,10,0,2,0,7,2,1,0,25,0,3,1,0,3,0,4,4,3,0,0,1,0,2,0,16,0,2,0,0,1,0,0,0,0,2,0,3,0,0,2,0,0,4,0,4,0,1,0,3,0,2,0,1,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,4,0,3,0,Class_6
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+35785,0,0,5,1,0,0,0,0,0,0,13,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,6,1,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,2,0,13,0,0,0,0,0,0,0,10,0,0,0,1,1,0,6,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,1,0,Class_6
+35786,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,1,0,0,3,0,1,0,0,0,0,2,0,0,0,0,0,8,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35787,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,8,0,0,1,1,0,0,2,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35788,0,2,21,11,0,0,0,0,0,0,6,1,0,1,0,4,0,3,1,0,0,2,0,0,1,4,5,4,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,3,0,8,0,0,2,0,0,0,0,26,1,4,0,0,0,2,0,2,0,1,0,4,0,0,0,0,0,0,1,1,0,1,0,0,1,6,0,0,0,0,0,0,0,1,4,0,0,0,0,Class_6
+35789,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35790,3,0,0,1,0,0,0,0,0,0,1,0,0,0,2,1,0,1,0,0,0,0,0,1,1,3,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,14,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,4,5,0,0,0,2,0,0,5,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_6
+35791,1,0,0,0,0,0,0,1,0,0,0,0,6,2,0,1,0,6,3,1,0,1,1,12,0,1,2,0,1,0,2,0,0,0,0,5,0,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,1,0,10,0,0,0,0,1,0,11,0,1,0,0,0,0,0,0,4,0,0,0,1,0,0,9,0,0,0,3,0,0,0,0,0,4,Class_6
+35792,0,1,3,1,0,0,0,0,0,1,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,5,0,0,2,0,0,0,0,5,0,0,0,0,0,5,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+35793,0,0,11,10,0,0,0,0,0,0,10,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,1,0,8,0,0,0,0,0,3,0,9,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35794,0,0,18,11,0,0,0,0,0,2,8,0,0,1,0,5,0,1,0,0,1,0,0,0,0,12,1,3,1,0,0,2,0,1,3,0,0,0,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,18,2,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35795,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,1,0,2,4,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35796,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,0,1,0,1,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,15,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35797,1,0,2,1,0,0,0,0,0,0,7,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,2,0,0,3,0,0,1,0,0,0,1,2,5,1,0,0,0,9,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,Class_6
+35798,0,1,6,7,0,0,0,0,1,1,9,0,0,2,0,4,0,1,0,0,1,2,0,0,0,11,0,9,0,0,0,2,0,0,1,0,0,0,0,0,0,3,0,5,0,5,0,0,2,0,0,0,0,9,0,0,0,0,0,2,1,2,0,2,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,6,1,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35799,0,0,16,1,0,0,0,0,0,0,13,0,0,0,0,2,0,0,0,0,1,3,0,6,0,1,1,2,1,1,1,1,0,1,2,0,0,0,0,0,0,1,0,1,0,6,0,0,1,0,1,0,0,13,0,0,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35800,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35801,0,0,0,0,1,0,0,1,1,0,2,0,0,0,0,3,2,0,0,1,0,3,0,5,1,4,0,0,0,0,0,2,0,1,1,0,0,2,2,3,0,12,0,1,1,1,0,2,1,1,0,0,2,2,0,1,0,0,0,21,0,3,0,0,0,2,6,0,0,6,2,4,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0,1,0,Class_6
+35802,0,0,4,2,0,0,0,0,0,0,9,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,3,1,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,1,0,1,0,0,2,0,0,3,0,5,0,0,0,1,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,2,0,Class_6
+35803,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,2,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,0,1,0,0,2,0,0,12,0,3,0,0,0,1,1,5,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,1,0,0,0,0,Class_6
+35804,0,0,2,2,0,0,0,0,0,0,11,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,22,1,0,0,2,1,0,0,1,0,1,0,0,3,0,0,0,0,0,3,0,0,0,0,0,3,0,1,0,0,0,1,0,1,4,0,0,1,0,0,0,1,0,0,0,0,0,14,0,0,0,0,0,8,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+35805,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35806,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35807,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,Class_6
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+35809,0,1,7,3,1,0,0,1,0,1,18,0,1,0,0,2,0,0,0,0,0,3,0,9,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,2,2,0,1,1,0,0,0,0,13,0,0,0,6,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,6,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
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+35818,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,1,0,0,0,0,3,1,0,2,0,0,4,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+35819,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,4,0,0,0,0,1,0,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35820,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,1,1,1,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,3,1,0,0,0,0,14,0,0,0,0,0,4,5,0,1,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,3,0,0,0,0,0,Class_6
+35821,0,0,2,1,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,24,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,1,0,27,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,Class_6
+35822,0,0,21,8,0,0,0,0,0,1,10,0,0,0,0,2,0,0,0,0,2,0,0,0,0,9,7,4,0,0,0,0,1,0,2,0,0,0,0,0,0,3,0,0,0,12,0,0,1,0,0,0,0,12,0,0,0,0,0,0,0,4,0,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,Class_6
+35823,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,2,0,2,0,1,0,2,2,0,0,1,0,0,0,4,0,0,0,2,2,0,1,0,0,3,0,2,0,0,0,0,1,0,0,0,1,1,0,0,0,0,2,4,0,1,0,0,0,0,12,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,1,0,0,2,5,0,1,0,0,0,8,Class_6
+35824,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,1,0,3,0,0,1,3,0,1,0,2,0,3,0,0,0,0,0,0,6,0,0,1,0,0,0,5,0,2,0,1,7,2,5,0,0,0,0,3,1,0,0,0,0,2,3,1,1,2,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,2,4,0,2,0,Class_6
+35825,0,1,7,10,0,0,0,0,1,0,10,0,0,0,2,2,0,3,0,0,0,1,0,3,0,1,9,3,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,2,3,0,0,1,0,0,0,0,10,0,0,0,2,0,0,4,1,0,0,1,0,0,0,2,0,0,0,4,0,0,0,0,0,0,4,0,1,1,0,0,0,0,1,0,0,0,0,0,Class_6
+35826,0,0,0,0,0,0,0,1,0,0,0,0,3,2,0,0,0,3,3,3,0,0,2,3,0,1,0,0,0,1,2,0,0,0,0,4,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,8,0,0,0,2,2,1,6,0,4,0,0,0,0,1,1,2,0,2,0,0,0,0,10,0,0,0,2,0,0,0,0,0,3,Class_6
+35827,3,0,1,2,0,0,0,0,0,0,0,0,1,0,1,2,0,1,0,0,1,2,0,1,1,1,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,2,1,0,0,1,0,9,1,0,0,0,2,8,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,Class_6
+35828,2,13,5,3,0,0,3,0,0,4,9,0,4,0,2,2,3,7,4,10,0,8,0,4,0,3,4,0,2,1,2,0,0,0,12,2,0,14,0,0,0,0,0,0,0,0,2,3,26,0,2,0,0,12,1,0,2,1,2,4,1,4,0,1,0,7,4,0,1,3,0,1,1,0,1,4,0,2,1,12,0,14,0,0,1,0,0,0,2,3,0,2,0,Class_6
+35829,7,1,0,0,0,0,0,0,1,0,1,0,0,2,0,0,1,0,0,2,0,0,3,7,1,0,0,0,0,0,0,9,0,0,0,2,0,2,0,0,0,3,0,0,0,1,0,4,0,3,0,3,0,2,0,1,0,1,3,3,0,0,0,3,0,0,13,0,0,6,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,2,12,2,2,0,0,0,36,Class_6
+35830,0,0,1,1,1,0,0,2,0,0,1,1,1,0,0,3,0,3,0,0,0,2,0,0,5,0,0,0,2,0,0,0,1,2,0,1,0,0,0,1,1,9,0,2,0,0,0,1,1,0,0,0,0,3,2,0,0,0,0,4,0,5,1,0,0,1,3,0,0,4,0,1,0,0,1,0,0,0,2,0,0,0,2,0,1,0,1,0,0,2,0,2,0,Class_6
+35831,1,0,2,4,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,0,3,0,0,9,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,2,0,0,0,6,0,1,4,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,10,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35832,0,0,10,4,0,0,0,0,0,1,12,0,1,0,0,1,0,0,1,0,0,2,0,0,0,1,13,1,1,1,0,1,0,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,9,0,0,0,2,0,0,2,1,3,1,0,2,0,0,0,0,0,0,1,1,0,0,0,1,0,14,0,2,0,0,0,0,0,0,7,0,0,0,0,Class_6
+35833,0,0,6,2,0,0,0,0,0,0,17,0,0,0,0,1,0,2,0,0,1,7,0,8,0,0,2,1,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,1,0,2,0,1,1,0,0,1,1,8,0,1,0,5,0,2,4,0,0,1,0,0,0,0,1,0,0,0,1,2,0,0,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35834,0,0,9,15,0,0,0,0,0,0,3,0,0,2,0,1,0,0,0,0,0,1,0,3,0,4,6,1,0,0,1,0,0,1,2,0,0,2,0,1,0,4,0,1,0,9,1,0,0,0,0,0,0,2,1,0,0,5,0,0,1,0,1,1,0,0,1,0,0,0,0,1,0,7,1,0,2,0,0,5,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+35835,0,0,10,2,0,0,0,1,0,0,6,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,3,1,1,0,0,1,1,1,5,0,0,0,0,0,0,2,0,2,0,5,0,0,0,0,0,0,1,33,0,0,0,0,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35836,0,0,7,4,0,0,0,0,0,0,10,0,0,1,0,2,0,0,0,0,3,1,0,0,0,0,7,2,0,0,0,3,0,1,0,0,0,1,0,1,0,0,1,2,0,7,0,0,0,0,0,2,0,1,0,0,0,1,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+35837,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,3,1,0,0,0,0,0,2,1,0,3,0,0,0,0,1,0,1,0,2,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35838,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,1,0,0,1,0,2,0,2,3,0,0,0,0,0,0,2,0,5,0,0,0,0,2,0,0,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,2,0,1,15,0,0,0,1,1,0,10,0,0,0,5,2,0,0,1,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35839,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,0,0,13,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35840,0,1,5,5,0,0,0,0,0,1,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,6,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35841,7,0,0,0,0,0,0,0,3,0,0,0,1,3,4,0,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,5,0,0,4,0,0,2,0,0,1,1,1,4,0,1,0,0,0,0,0,0,2,2,3,9,0,1,0,0,0,Class_6
+35842,0,1,1,4,0,0,1,0,0,0,7,0,0,0,0,1,0,5,1,0,0,5,0,0,0,1,4,6,0,1,0,0,0,0,6,0,0,0,0,0,0,1,0,3,0,0,0,2,3,0,0,0,1,5,0,1,0,0,0,0,2,0,1,0,3,7,1,0,0,2,0,0,1,0,1,3,0,0,1,4,0,3,0,1,0,0,1,0,1,0,0,0,0,Class_6
+35843,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,15,6,0,4,0,0,0,5,0,0,0,0,0,0,0,0,1,2,0,2,0,1,0,0,2,1,0,0,0,0,0,2,0,0,1,0,0,2,0,0,9,0,0,10,0,0,1,0,0,1,9,0,0,0,2,0,0,0,3,0,0,0,0,0,1,0,1,0,2,0,8,0,0,0,0,0,0,Class_6
+35844,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35845,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,6,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+35846,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35847,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35848,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,12,0,0,0,0,0,0,4,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,Class_6
+35849,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,2,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,8,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,Class_6
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+35858,1,0,0,0,0,0,0,1,0,0,3,0,0,2,0,0,1,0,1,2,0,0,0,3,1,0,0,0,0,0,0,5,0,1,0,0,4,0,36,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35859,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,1,0,0,0,Class_6
+35860,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,2,0,1,0,0,0,1,0,0,0,3,4,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,2,0,1,0,0,12,0,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+35861,0,0,1,1,0,0,0,2,0,0,0,0,4,2,0,3,1,4,0,0,0,2,0,13,1,0,0,0,2,0,0,2,1,0,1,0,3,4,50,2,0,1,1,0,41,2,0,4,0,3,0,0,0,5,2,0,1,1,0,5,0,2,0,0,0,1,4,0,0,0,2,0,0,0,0,3,1,1,1,1,0,0,0,0,1,0,3,0,1,0,2,0,1,Class_6
+35862,0,0,2,3,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,1,2,0,0,0,1,4,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,9,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35863,0,0,6,12,0,0,2,0,0,2,11,0,1,0,0,3,0,0,0,0,0,1,0,2,0,1,16,1,1,0,0,0,1,0,1,0,0,1,0,0,0,2,1,2,0,4,0,2,0,0,0,1,0,10,0,2,0,0,1,0,5,1,0,0,0,0,2,0,1,3,0,0,2,0,0,3,0,0,0,3,0,4,2,0,0,0,0,0,2,0,0,0,0,Class_6
+35864,0,0,12,10,0,0,0,0,0,0,8,0,1,0,0,4,0,1,0,1,0,2,0,10,0,2,5,0,0,0,5,0,0,2,11,0,0,2,1,1,1,0,2,2,0,17,0,0,1,0,0,3,0,10,0,2,0,1,0,0,5,0,0,0,7,3,0,1,0,1,0,0,0,2,0,0,0,0,0,34,0,8,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35865,0,0,12,18,1,0,0,0,0,1,9,0,0,0,0,6,0,1,0,0,0,2,0,1,0,3,6,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,11,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35866,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35867,0,3,0,4,0,0,1,0,0,0,0,1,4,0,3,1,0,5,0,1,0,0,0,4,0,1,0,0,0,0,8,0,0,0,14,0,0,2,0,2,1,0,0,1,0,0,0,0,2,0,0,0,0,5,0,1,0,1,2,9,0,0,1,1,0,0,0,1,6,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,0,0,3,Class_6
+35868,0,0,15,6,0,0,0,0,0,0,9,1,1,1,0,5,0,0,1,0,6,1,0,1,1,2,3,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,17,0,0,0,0,0,4,1,10,0,0,0,1,0,1,4,2,0,2,0,1,0,0,0,2,0,0,0,0,1,0,1,0,0,4,0,2,0,0,0,1,0,1,0,0,0,0,0,Class_6
+35869,0,0,8,4,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,1,0,0,0,8,7,1,0,0,0,2,0,3,0,0,0,0,0,1,0,0,0,2,0,4,1,0,0,0,0,1,0,5,2,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,1,0,Class_6
+35870,2,0,1,1,0,0,0,1,0,0,0,0,2,0,0,2,0,0,0,2,0,2,0,2,2,4,0,3,0,0,0,5,0,1,0,1,0,1,1,1,0,22,0,1,0,0,0,4,0,0,0,0,0,1,0,0,3,0,0,25,0,1,0,0,1,1,9,0,0,0,0,1,0,1,0,1,3,0,0,0,0,0,4,0,0,0,1,0,1,0,0,4,2,Class_6
+35871,0,0,0,0,0,0,1,0,0,3,1,0,1,0,0,1,0,0,0,1,0,1,0,1,1,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,1,0,0,0,2,2,7,0,0,0,1,8,0,0,0,1,0,1,1,0,0,5,0,1,0,0,1,0,1,1,0,1,0,0,0,0,1,0,2,2,0,0,0,0,0,Class_6
+35872,0,0,7,14,0,0,0,0,0,0,11,0,0,0,2,2,0,1,0,0,2,1,0,0,0,0,14,2,2,0,0,0,0,2,2,0,0,1,0,1,0,0,0,1,0,6,0,0,0,0,0,1,0,12,1,0,0,1,0,2,4,0,0,1,0,1,0,0,0,0,0,1,0,7,0,0,0,0,0,9,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35873,0,0,7,2,0,0,0,0,0,0,20,0,0,0,0,2,0,0,0,0,0,1,0,6,0,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,4,0,0,0,0,0,1,0,8,1,0,0,12,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,3,0,1,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35874,0,0,3,2,0,0,0,1,0,0,0,0,0,0,1,1,0,3,0,2,0,1,0,3,0,1,1,2,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,4,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+35875,0,0,1,2,0,0,0,0,0,0,0,0,2,0,2,4,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,4,0,0,1,0,0,1,2,0,0,2,1,2,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,Class_6
+35876,0,1,16,5,0,0,0,0,0,0,9,0,0,0,0,2,0,2,0,0,1,1,0,1,0,2,9,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,9,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35877,0,0,9,4,1,0,0,0,0,0,25,0,1,0,1,1,0,1,0,0,2,4,0,7,0,0,7,1,1,0,0,0,0,0,4,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,6,0,0,0,11,0,0,1,0,1,1,0,0,1,0,0,1,0,0,0,4,0,1,0,0,0,4,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+35878,0,0,5,4,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35879,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,5,0,0,0,1,3,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,4,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35880,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,5,0,0,0,7,0,2,0,0,0,1,1,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,1,0,1,1,0,12,0,0,0,0,0,0,10,0,0,0,0,1,0,0,2,1,0,0,0,1,2,0,10,0,1,0,1,0,0,0,0,3,9,Class_6
+35881,0,0,5,2,0,0,0,0,1,0,7,0,1,0,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,6,1,0,0,0,0,2,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35882,1,0,2,4,0,0,0,0,0,0,8,0,0,0,0,3,0,0,0,0,0,1,0,0,0,7,6,0,0,0,1,3,0,0,2,0,0,2,0,0,0,1,0,7,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,4,0,0,0,0,Class_6
+35883,0,0,0,1,0,0,0,0,0,0,10,0,0,0,1,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35884,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,Class_6
+35885,0,1,0,1,0,0,0,0,0,0,3,0,1,1,0,0,0,1,0,0,0,3,0,3,0,4,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+35886,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+35887,0,0,0,0,0,0,0,0,2,0,10,0,0,0,0,0,0,0,1,0,4,0,0,13,0,1,5,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,1,0,2,0,1,0,1,0,0,0,0,2,0,0,0,2,0,0,1,0,0,0,0,0,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35888,0,0,1,2,0,0,0,1,1,0,7,1,0,1,0,3,0,2,0,0,0,1,0,0,1,1,0,2,0,0,0,1,0,0,1,1,0,2,0,2,0,4,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,4,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_6
+35889,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,2,0,0,1,0,0,0,Class_6
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+35891,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,6,0,1,0,0,0,2,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,Class_6
+35892,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+35894,0,0,8,2,0,0,0,2,0,0,7,0,0,1,0,2,0,0,2,0,0,1,0,0,0,0,7,1,0,0,0,0,0,0,0,3,0,0,0,0,1,2,2,0,0,11,0,2,0,0,0,0,0,11,1,0,0,0,0,1,0,1,0,0,0,2,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+35896,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35897,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,1,2,1,0,1,0,0,12,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+35898,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,3,0,2,0,1,2,2,0,0,0,0,0,2,2,0,1,0,4,0,0,0,0,1,1,0,4,0,0,3,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
+35899,0,0,13,6,0,0,0,0,0,1,9,0,0,0,0,3,0,0,0,0,0,1,0,0,0,4,0,6,1,0,0,2,0,1,2,0,0,0,0,0,0,0,0,5,0,8,0,0,1,0,0,0,0,14,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,1,1,0,0,0,0,Class_6
+35900,0,0,0,0,0,0,0,4,0,1,0,0,3,2,0,0,0,3,3,1,0,0,1,2,0,1,0,0,0,1,1,0,0,0,0,4,1,0,5,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,8,1,1,0,1,1,0,6,0,4,6,0,0,0,0,1,2,0,2,0,0,0,0,9,0,2,0,3,0,0,0,0,0,3,Class_6
+35901,0,0,1,1,0,0,0,0,0,0,1,0,1,3,3,2,0,0,3,1,0,0,11,18,0,0,0,1,0,0,2,0,0,0,1,2,0,2,6,2,0,1,8,0,2,0,0,1,0,6,2,0,0,1,1,1,2,0,0,4,0,0,0,1,0,0,9,0,1,1,1,2,1,2,0,3,0,4,0,2,0,0,4,0,1,0,3,1,2,1,0,0,2,Class_6
+35902,0,0,1,0,0,0,0,4,0,0,2,0,0,0,0,3,0,3,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,2,4,0,0,1,0,0,0,6,0,1,0,0,0,3,1,0,0,0,0,13,1,1,5,0,0,11,0,2,0,1,0,3,2,1,0,1,1,0,3,0,2,0,0,0,4,4,0,0,0,0,0,0,2,0,14,0,0,2,0,Class_6
+35903,0,0,10,3,0,0,0,2,0,2,7,0,1,0,0,3,0,0,0,2,0,3,0,2,0,0,11,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,1,21,0,3,0,0,0,2,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35904,0,3,0,2,0,0,0,0,3,1,0,0,0,0,0,4,0,22,0,1,0,2,1,3,1,6,0,0,2,0,0,0,0,0,11,2,0,3,2,1,1,3,0,0,0,4,0,2,2,2,0,0,0,5,0,0,4,0,0,13,0,2,1,3,0,7,9,0,1,1,2,0,0,0,0,1,0,0,3,0,0,0,0,0,1,1,2,1,4,2,0,0,0,Class_6
+35905,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,3,0,0,0,0,0,3,0,5,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,8,0,3,0,0,0,0,8,2,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+35906,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,1,0,0,0,2,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,2,0,0,4,0,0,6,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,3,0,1,1,0,0,0,Class_6
+35907,0,0,20,4,0,0,1,0,0,0,8,0,1,1,1,6,0,2,0,0,3,5,0,0,0,2,4,2,0,0,1,0,1,1,1,0,1,1,2,0,0,1,1,7,0,8,0,0,2,0,0,0,1,33,2,1,0,1,0,0,0,0,0,0,1,1,0,1,0,2,0,2,0,0,0,2,0,0,0,3,0,6,0,0,2,0,1,0,0,0,0,1,0,Class_6
+35908,0,0,4,5,0,0,2,0,0,0,0,0,2,0,1,4,0,1,0,0,0,0,0,6,0,3,0,2,0,0,0,2,1,2,3,1,0,1,0,0,0,2,0,4,1,0,1,4,2,0,0,0,0,7,0,1,1,0,2,9,1,1,0,0,1,0,6,0,1,0,0,0,1,0,1,1,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_6
+35909,0,0,2,5,0,0,0,0,0,1,3,0,2,0,0,1,1,0,1,0,1,0,0,0,0,1,4,1,0,0,2,0,0,0,4,0,3,1,1,2,1,1,0,2,0,2,0,0,0,1,0,3,0,4,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,4,0,0,0,0,Class_6
+35910,0,0,14,12,0,0,0,0,0,1,9,1,0,0,0,4,0,0,0,0,3,1,1,0,0,1,14,0,0,0,1,0,0,0,1,0,2,3,1,1,0,0,1,1,0,6,0,0,2,0,0,2,0,12,0,0,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,1,Class_6
+35911,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,3,0,2,1,5,0,0,2,0,0,1,0,0,0,1,0,0,0,1,1,5,0,1,0,0,0,2,1,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,1,0,3,0,2,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,0,Class_6
+35912,0,0,4,4,0,0,0,0,0,0,7,0,0,0,0,2,0,2,0,0,1,2,0,0,0,3,5,1,1,0,0,2,0,0,1,0,0,1,0,0,0,1,0,2,0,5,0,0,2,0,0,1,0,9,1,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35913,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,4,0,0,0,0,0,1,0,1,1,3,0,0,0,0,0,6,0,2,0,0,0,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,16,0,7,4,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+35914,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,1,0,3,0,8,0,0,0,0,0,2,1,0,1,1,0,1,0,1,0,2,0,2,0,0,0,1,0,0,0,0,0,1,0,1,4,0,0,8,0,0,0,0,0,1,8,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35915,0,0,0,4,0,0,0,0,0,0,8,0,0,1,0,1,0,1,0,0,1,1,0,1,0,12,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,2,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,Class_6
+35916,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,1,0,1,4,1,0,0,0,7,0,8,0,1,0,0,0,1,0,0,0,0,0,4,1,0,1,0,0,9,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35917,0,0,7,27,2,0,0,0,0,1,7,0,1,0,0,5,1,1,0,0,1,2,0,0,0,33,7,4,1,0,1,3,1,2,9,0,0,1,0,0,0,8,0,0,1,9,0,0,1,0,0,0,1,24,0,1,0,0,0,1,1,5,0,2,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+35918,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,2,0,1,0,2,0,1,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,6,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35919,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,5,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,8,0,1,0,3,0,1,4,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+35920,0,1,4,5,0,0,2,0,0,0,6,0,0,1,0,6,0,1,0,0,2,5,0,0,0,0,6,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,2,1,6,1,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,16,0,5,0,0,0,0,0,0,3,0,0,0,0,Class_6
+35921,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35922,0,0,0,0,0,0,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35923,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,1,0,0,0,1,1,1,0,8,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35924,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,2,0,1,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,1,0,1,0,0,0,0,7,0,0,0,1,3,0,0,1,1,0,0,2,0,0,1,0,5,1,0,1,1,0,0,0,3,0,1,0,0,0,4,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35925,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,2,0,1,0,3,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+35926,2,0,0,1,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,2,0,0,0,3,0,1,0,1,1,0,0,0,0,0,5,0,0,0,0,1,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,Class_6
+35927,0,1,1,0,0,0,0,0,0,0,0,0,2,1,0,1,1,3,0,1,0,1,0,7,0,0,0,1,1,1,0,1,0,0,3,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,1,0,10,0,0,0,0,0,0,6,0,0,1,0,3,0,0,0,0,4,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+35928,0,0,1,2,0,0,0,0,0,0,7,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,3,1,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,6,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,2,0,Class_6
+35929,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,4,1,1,0,1,1,4,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,4,0,0,3,1,0,0,0,0,0,0,2,0,0,0,0,1,0,4,0,0,3,1,0,0,0,6,1,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,1,0,1,1,0,0,0,Class_6
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+35934,0,0,1,1,1,0,2,0,0,3,2,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,1,4,0,0,0,0,0,0,4,2,0,0,0,0,2,0,1,0,0,5,2,0,0,0,0,3,0,0,0,1,3,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_6
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+35936,0,1,12,10,0,0,0,0,0,1,15,0,0,1,0,2,0,1,0,0,4,1,0,20,0,5,3,1,0,0,0,2,0,0,2,0,0,1,1,0,0,0,0,1,0,21,0,1,0,0,0,0,0,16,1,2,1,0,0,0,5,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35937,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,8,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,8,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,2,Class_6
+35938,0,0,5,6,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,4,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,1,0,1,0,0,0,0,1,0,Class_6
+35939,0,0,1,4,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,1,1,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,1,0,3,0,0,1,0,0,1,0,4,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35940,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35941,0,1,3,8,0,0,1,0,1,1,22,0,0,0,0,4,0,0,0,0,2,3,1,7,0,3,7,2,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,1,0,8,0,0,0,6,0,1,1,0,2,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,7,0,5,0,1,0,0,0,0,1,0,0,0,0,Class_6
+35942,0,1,0,2,0,0,0,3,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,9,0,1,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,Class_6
+35943,0,0,2,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,3,0,7,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,1,0,1,0,0,3,1,0,2,0,0,13,0,0,0,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35944,0,4,35,1,0,0,0,1,0,0,12,1,0,2,0,8,0,4,0,2,2,4,0,2,0,3,22,6,0,1,0,1,0,1,1,0,0,3,2,0,0,2,0,4,0,40,0,0,5,0,0,0,0,27,0,2,0,1,0,0,13,0,2,1,3,3,2,0,1,0,0,0,0,1,0,0,0,0,0,6,0,5,0,0,0,0,0,0,9,0,0,1,0,Class_6
+35945,0,0,4,2,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,2,0,0,0,0,1,0,1,0,0,0,5,0,2,0,0,0,0,0,9,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35946,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,5,0,5,1,5,0,3,0,4,0,2,0,0,1,3,0,0,0,0,0,3,0,0,0,1,1,1,0,0,0,8,0,0,0,0,16,0,1,0,1,0,5,0,0,10,0,1,1,1,0,1,8,0,1,5,2,1,0,0,0,0,0,0,0,1,4,0,0,0,0,2,1,1,1,2,1,1,2,Class_6
+35947,0,1,6,0,0,0,0,0,0,0,0,0,1,1,0,6,0,1,0,0,0,4,0,3,0,4,0,1,1,0,0,0,0,0,2,0,0,0,0,1,0,4,0,4,0,5,0,1,0,0,0,0,0,15,0,0,1,0,0,16,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+35948,0,0,0,5,0,0,0,1,0,0,10,0,0,0,0,0,0,1,0,0,1,0,0,6,1,3,2,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35949,0,0,6,2,0,0,0,0,0,0,6,0,1,0,0,0,0,1,0,0,0,1,0,0,0,7,0,1,0,0,1,2,0,0,1,0,0,0,0,1,0,1,0,1,0,5,1,0,0,0,0,0,0,11,0,0,0,0,0,1,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,2,1,0,0,0,0,Class_6
+35950,0,0,4,5,0,0,0,0,0,0,7,2,0,0,0,3,0,1,0,0,0,0,0,2,0,2,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,1,0,0,0,0,25,0,1,0,0,0,0,5,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35951,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35952,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,3,0,0,0,0,0,3,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,1,0,1,0,0,0,6,0,0,12,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+35953,0,0,5,3,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,4,0,8,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,0,0,3,0,1,0,0,0,0,1,2,0,0,1,0,0,16,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35954,0,0,24,5,0,0,0,0,0,0,9,0,0,0,1,4,0,5,0,0,0,2,0,0,0,1,7,1,1,0,0,0,0,1,0,0,0,1,0,0,0,3,0,1,0,25,0,0,2,0,0,0,0,15,0,0,1,0,0,1,3,2,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+35955,0,5,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,2,0,2,0,5,0,1,3,0,0,0,1,0,0,0,5,0,0,1,1,0,0,0,0,1,0,0,0,2,11,0,1,0,0,0,0,0,0,1,1,0,2,1,0,2,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,3,0,2,0,0,0,0,1,0,1,0,0,0,0,Class_6
+35956,0,0,7,6,0,0,0,1,0,0,8,0,0,0,0,2,0,0,0,0,1,0,1,3,0,2,7,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,6,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35957,0,1,0,0,0,0,1,2,0,0,1,0,1,0,0,0,1,0,0,2,0,0,0,1,1,1,0,0,0,0,1,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,3,2,0,0,0,2,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+35958,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,5,3,0,1,0,0,4,6,2,0,0,0,0,0,0,3,1,0,1,2,0,0,1,1,0,1,0,0,1,0,0,2,0,0,5,0,0,1,1,0,2,0,0,10,0,1,0,1,0,0,7,3,0,0,2,0,0,0,3,2,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,1,0,Class_6
+35959,0,1,6,3,0,0,0,0,0,0,14,0,0,0,0,3,0,0,0,1,4,0,0,7,0,1,1,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,3,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+35960,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,3,0,2,0,1,1,0,0,3,2,0,0,2,0,1,0,1,0,2,0,2,0,1,0,1,1,0,0,0,2,1,0,0,0,0,0,8,0,0,0,0,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+35961,0,0,5,4,0,0,0,0,0,2,11,0,1,0,0,6,0,0,0,0,2,2,0,0,0,0,12,1,0,0,1,0,0,0,3,0,0,1,0,0,0,0,0,2,0,7,0,0,0,1,0,3,0,13,1,1,0,2,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35962,3,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35963,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,2,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,2,1,0,0,0,0,Class_6
+35964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+35965,0,1,1,1,0,0,0,0,0,0,0,0,1,1,0,2,2,2,0,0,0,3,0,9,0,8,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,3,0,0,6,0,0,12,0,0,0,1,2,1,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35966,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,3,0,6,0,4,0,2,0,0,0,3,0,0,2,1,0,0,0,0,0,3,0,1,0,0,0,2,0,0,0,0,0,5,0,0,2,0,0,8,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35967,0,1,6,9,0,0,1,0,0,0,7,0,1,0,0,1,0,0,0,0,0,3,0,0,0,1,6,1,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,0,2,1,0,1,0,0,1,0,16,0,0,0,0,0,1,0,1,0,0,0,4,0,0,1,0,1,0,0,1,0,0,0,0,1,3,0,2,0,0,0,0,0,0,1,1,0,1,0,Class_6
+35968,0,0,17,4,0,0,1,0,0,0,8,0,0,0,0,4,0,4,0,0,2,0,0,0,0,1,4,0,0,0,0,0,1,0,2,0,0,1,0,0,0,1,0,1,0,15,1,0,1,0,0,0,0,7,1,0,0,0,0,0,5,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,5,0,5,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35969,0,0,11,4,0,0,0,1,0,0,8,1,0,2,0,6,0,1,0,0,1,2,0,0,0,1,8,3,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,2,2,7,0,0,2,0,0,7,0,17,0,0,0,0,0,1,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,0,1,0,0,0,2,0,0,0,0,Class_6
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+35979,0,0,6,3,0,0,0,0,0,0,9,0,0,3,0,0,0,1,0,0,3,2,0,0,0,0,37,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,3,0,0,0,1,0,6,0,2,0,0,0,2,7,1,0,0,0,0,4,0,0,0,0,1,0,0,1,1,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35980,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35981,0,1,2,0,0,0,0,1,0,0,8,0,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,4,0,1,1,1,0,1,0,0,1,0,0,0,1,1,0,1,0,0,0,4,0,0,0,0,0,12,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+35982,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,2,0,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,Class_6
+35983,0,0,11,5,0,0,1,0,0,0,9,0,2,0,0,2,0,1,0,1,1,1,0,1,0,0,4,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,7,0,1,0,2,0,0,0,0,0,0,0,0,0,Class_6
+35984,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35985,0,0,4,2,0,0,0,0,0,0,13,0,0,1,0,2,0,0,0,3,0,6,0,0,0,3,10,3,0,0,0,1,0,1,3,0,0,1,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,17,0,1,0,1,0,1,5,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,1,0,0,2,1,0,0,0,0,Class_6
+35986,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,16,0,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35987,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,7,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,12,0,1,1,0,0,0,4,1,0,0,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+35988,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,3,0,1,1,2,0,0,1,0,0,1,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,Class_6
+35989,0,0,10,2,0,0,0,0,0,0,10,0,0,0,0,5,0,2,0,0,2,1,0,0,0,1,8,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,5,0,1,0,0,0,2,0,3,0,0,0,0,0,2,2,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35990,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,1,1,1,1,0,0,0,0,4,1,0,12,0,0,0,1,0,0,0,2,0,1,1,0,2,0,0,1,2,0,2,2,1,0,0,0,8,0,6,1,1,1,5,0,0,7,0,0,0,0,1,0,0,2,0,0,0,0,16,0,2,0,0,0,25,0,2,0,0,0,0,0,1,3,0,0,0,0,Class_6
+35991,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+35992,0,0,3,3,0,0,0,0,0,1,8,0,0,0,0,2,1,1,0,2,0,0,0,0,0,1,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,6,0,5,0,0,0,0,4,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,1,0,0,0,0,1,0,0,1,1,2,Class_6
+35993,0,0,0,2,0,0,0,0,0,0,5,0,0,0,1,3,0,2,0,0,1,0,0,5,0,2,3,0,0,0,2,1,0,0,3,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,1,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_6
+35994,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,2,0,2,0,0,0,0,0,2,0,15,0,1,0,0,2,0,0,0,2,0,0,0,0,3,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,2,0,0,16,0,2,0,0,1,0,0,2,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+35995,0,0,2,3,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,1,0,1,0,7,0,4,0,5,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,3,1,1,0,0,4,0,0,3,1,5,0,0,2,0,0,2,0,2,0,0,0,0,3,0,1,3,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,Class_6
+35996,0,0,14,3,1,0,1,0,0,0,10,0,0,0,0,5,0,0,0,0,1,2,0,0,0,1,10,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,15,0,0,1,0,0,2,1,20,2,0,0,0,0,1,7,0,0,0,2,0,0,0,0,1,0,1,0,1,0,5,0,0,0,7,0,1,0,0,0,0,0,0,0,0,1,0,0,Class_6
+35997,0,0,0,0,2,0,0,0,0,1,0,1,3,0,0,6,0,2,0,1,0,5,0,3,0,0,0,0,26,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,3,6,0,3,0,0,1,0,1,0,0,1,2,4,2,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+35998,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,10,0,0,0,1,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+35999,0,0,7,5,0,0,0,0,1,2,8,1,0,2,1,3,0,1,0,0,0,4,0,2,0,2,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,23,0,0,1,0,0,1,0,16,0,2,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,3,0,1,0,0,0,0,0,1,1,1,0,1,0,Class_6
+36000,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,2,0,4,0,0,0,4,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_6
+36001,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36002,0,1,16,0,0,0,0,0,0,1,10,1,0,0,0,3,0,0,0,0,2,2,0,0,0,0,14,1,0,0,0,2,2,1,0,0,0,1,0,1,0,1,0,0,0,24,0,0,0,0,0,0,0,13,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36003,0,2,9,11,0,0,0,0,0,0,10,1,1,0,0,3,0,1,0,1,0,1,0,1,0,0,13,3,1,0,0,0,0,0,4,0,0,2,0,1,0,0,1,3,0,10,0,0,1,0,0,3,0,13,0,0,0,0,0,2,1,2,1,0,0,1,0,0,1,1,0,0,0,10,0,0,0,0,1,19,0,5,2,0,0,0,0,0,1,3,0,0,0,Class_6
+36004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36005,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,2,0,2,0,0,0,1,1,0,0,0,0,2,0,0,1,0,0,12,0,0,0,1,3,4,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36006,0,1,18,1,0,0,0,0,0,0,10,1,1,0,0,4,0,1,0,0,1,4,0,0,0,1,15,3,0,0,0,0,0,0,1,2,0,0,0,0,0,3,0,1,1,14,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,1,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36007,0,0,7,4,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,8,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+36009,0,1,1,0,0,0,0,0,1,5,0,0,0,1,0,5,4,0,0,2,0,3,0,6,0,5,0,2,0,0,0,0,1,0,2,0,1,1,0,0,0,13,0,1,0,1,1,2,0,1,0,0,1,3,2,0,4,0,1,18,0,2,1,2,1,2,9,0,0,6,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,1,0,0,0,2,0,Class_6
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+36039,0,0,1,0,0,0,0,0,0,1,7,0,0,0,0,2,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,0,0,Class_6
+36040,0,1,2,2,0,0,0,5,0,0,1,0,1,0,0,0,0,2,0,1,0,0,0,4,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,7,0,2,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36043,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,1,0,0,0,0,0,0,3,3,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,10,0,0,0,0,1,1,1,0,0,0,0,6,0,0,0,0,1,0,0,1,0,0,0,3,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+36044,0,0,6,4,0,0,0,0,0,0,8,3,0,1,0,0,0,0,0,0,2,3,0,2,0,0,16,1,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,1,0,9,0,0,1,0,0,0,0,5,0,0,0,1,0,0,5,1,1,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,4,0,8,0,0,0,0,0,0,0,1,0,0,0,Class_6
+36045,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36046,1,1,6,2,0,0,0,0,0,1,9,0,0,0,0,2,0,1,0,0,1,1,0,2,0,1,11,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,5,0,1,2,1,0,1,0,5,2,0,0,1,0,1,6,0,0,0,0,0,0,0,0,0,0,2,0,3,1,0,0,0,0,17,0,1,0,0,1,0,0,0,6,0,0,2,0,Class_6
+36047,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36124,1,1,9,22,0,0,0,0,0,0,9,1,0,0,0,4,0,0,0,1,0,4,0,1,0,0,9,1,0,0,0,0,1,0,3,0,0,0,0,0,0,1,1,1,0,8,0,0,0,0,0,0,0,11,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,7,0,4,0,0,1,0,0,0,0,0,0,0,0,Class_6
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+36157,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36161,0,0,4,5,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,4,1,2,0,1,0,0,0,2,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,0,1,0,0,16,0,1,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
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+36164,0,0,8,9,0,0,0,0,1,0,9,0,0,1,0,2,0,0,0,0,2,3,0,0,0,1,11,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,7,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,1,0,0,1,0,Class_6
+36165,0,0,0,2,0,0,0,1,0,0,2,0,0,0,0,1,2,1,0,0,0,2,0,0,0,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36166,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,1,0,0,1,0,0,0,1,2,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+36167,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,15,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,7,0,2,0,1,0,0,0,0,1,1,0,5,0,0,0,0,0,8,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+36188,0,1,4,5,0,0,0,0,0,0,0,1,0,0,1,6,0,1,0,0,1,2,0,6,1,19,0,6,1,0,0,0,0,1,7,0,0,1,1,1,0,4,0,1,0,5,0,0,7,0,1,0,0,7,0,0,0,0,0,14,0,2,0,5,0,3,0,0,2,0,0,0,0,1,2,1,0,0,0,7,0,4,0,0,0,0,0,0,1,0,0,2,0,Class_6
+36189,3,0,1,3,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,3,1,1,0,1,0,0,0,1,0,4,0,0,0,0,3,0,0,2,0,0,1,1,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+36190,0,0,2,1,0,0,2,2,0,0,2,0,0,0,3,4,1,4,1,3,2,1,2,9,0,0,1,1,0,1,0,0,5,1,3,0,0,1,3,1,1,1,0,2,1,0,0,2,0,2,0,1,2,0,0,0,7,0,0,0,0,4,0,3,0,1,5,2,3,0,0,4,1,0,0,3,0,2,0,1,0,1,3,0,2,0,5,0,2,0,0,0,0,Class_6
+36191,1,1,9,7,0,0,0,0,1,0,9,0,0,0,1,3,0,6,0,1,0,0,0,0,0,1,11,2,0,1,0,2,0,0,1,0,1,1,0,2,0,1,0,0,0,2,0,1,3,0,0,2,1,5,0,0,0,1,0,1,0,2,0,0,0,0,0,1,1,0,0,1,0,3,0,0,0,0,1,5,0,2,1,0,0,0,0,0,2,0,0,0,0,Class_6
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+36193,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_6
+36194,0,0,3,2,0,0,0,0,0,0,8,0,0,1,0,2,0,0,0,0,0,1,0,5,0,1,21,0,0,1,0,3,1,0,2,0,2,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,4,0,6,0,1,0,1,1,0,0,0,1,0,6,0,0,0,0,0,0,10,0,2,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36195,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,1,0,1,1,0,0,1,0,0,1,2,1,0,1,1,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,1,5,0,0,3,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,Class_6
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+36197,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36198,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,2,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,3,4,0,1,0,2,0,0,0,0,0,3,0,0,0,0,0,12,0,1,0,0,0,1,2,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Class_6
+36199,0,0,9,2,0,0,1,0,0,1,12,0,0,1,0,3,0,0,0,0,0,0,0,4,0,2,7,0,0,1,0,0,0,1,3,1,2,0,1,0,1,3,2,0,0,10,0,1,0,0,0,1,0,18,0,0,0,6,0,0,4,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,2,1,4,0,1,2,0,0,1,1,0,1,0,0,2,0,Class_6
+36200,0,0,14,3,0,0,0,0,0,0,7,1,1,0,0,3,0,0,0,0,0,0,0,1,0,1,1,6,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,3,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36201,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,3,0,4,0,16,3,1,0,0,0,0,0,0,0,1,0,1,0,0,2,14,1,2,0,1,0,0,0,0,0,0,0,8,0,0,4,0,0,4,1,2,0,1,0,2,1,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,3,0,0,0,1,1,2,0,0,4,0,Class_6
+36202,0,0,2,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,4,0,1,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,6,0,0,4,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36203,0,1,0,0,0,0,1,0,0,0,3,1,0,0,0,1,0,1,0,0,0,1,0,5,1,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,2,0,0,5,0,0,14,0,1,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,2,0,1,0,0,0,0,0,1,0,2,0,1,0,Class_6
+36204,1,0,18,5,0,0,1,0,0,0,9,1,0,0,0,1,0,1,0,0,0,1,1,1,0,3,10,2,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,0,0,11,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,3,0,2,0,0,0,0,0,2,2,0,0,0,0,Class_6
+36205,0,1,7,0,0,0,0,0,0,0,9,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,12,2,0,0,0,2,0,0,1,1,0,0,0,1,0,1,0,2,0,11,0,1,4,0,0,1,1,10,0,0,0,1,0,2,2,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+36206,0,0,7,9,0,0,0,0,1,0,8,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,3,0,7,2,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,5,0,0,2,0,0,0,0,0,0,0,1,Class_6
+36207,6,0,0,2,0,0,0,0,0,0,0,0,1,3,0,4,0,0,0,0,0,2,0,8,1,0,0,0,0,1,0,7,0,0,0,3,4,3,2,0,0,2,1,1,0,1,0,7,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,4,0,1,33,0,0,1,0,0,0,0,1,3,0,0,0,1,0,0,1,0,1,3,42,1,0,0,0,0,8,Class_6
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+36268,2,1,2,3,0,0,0,0,0,0,9,0,0,0,0,7,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,4,0,16,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36269,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36274,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,10,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,10,0,0,0,3,0,1,2,2,0,1,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,Class_6
+36275,7,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,6,1,0,2,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,3,1,Class_6
+36276,0,0,8,5,0,0,0,0,0,0,9,0,0,0,0,7,1,0,0,1,2,1,0,3,0,8,0,3,0,0,0,2,1,0,2,0,0,0,0,0,0,5,0,2,0,3,0,0,0,0,0,0,1,13,0,2,0,0,0,3,6,6,0,0,1,2,0,1,0,0,0,1,0,5,0,0,0,0,0,2,0,0,0,0,1,0,1,1,1,0,0,3,0,Class_6
+36277,0,0,2,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,7,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,13,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+36278,0,0,1,2,0,0,0,0,0,0,8,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,4,0,0,0,0,1,1,0,5,0,0,0,0,0,1,1,0,0,0,1,0,0,3,0,0,1,0,6,0,0,0,0,0,0,4,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,9,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+36279,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,3,0,2,0,0,0,5,0,2,1,2,0,0,28,0,0,0,0,1,0,0,0,0,0,0,3,5,0,1,0,2,0,0,0,0,0,0,1,5,5,0,1,0,0,1,0,4,0,0,0,4,3,1,0,1,1,0,1,0,2,1,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,Class_6
+36280,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,1,0,1,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,9,0,1,1,2,1,0,0,0,0,5,2,0,0,0,7,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_6
+36281,0,0,8,7,0,0,4,0,0,0,8,0,2,1,1,5,0,11,3,0,0,1,0,7,0,3,1,0,0,0,2,0,2,0,7,1,0,3,0,0,0,0,1,2,0,2,0,2,11,0,4,1,2,23,0,0,6,7,0,2,8,2,2,0,1,1,0,0,2,1,0,2,0,0,0,2,2,1,0,33,0,3,4,0,0,0,0,0,4,0,0,0,0,Class_6
+36282,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,2,0,0,1,1,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,8,0,0,0,1,0,0,3,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,1,0,Class_6
+36283,0,0,3,2,0,0,0,0,0,0,6,0,0,1,1,5,0,0,0,0,1,0,0,0,0,2,5,2,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,1,0,8,0,0,1,0,0,0,0,11,0,0,0,0,0,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+36284,0,0,1,0,0,0,1,1,0,6,0,0,0,0,0,3,3,2,0,0,0,4,0,3,1,0,0,0,3,0,0,2,0,0,0,0,1,1,0,2,0,4,0,0,0,0,0,1,0,1,0,1,1,5,2,0,0,0,1,2,0,0,0,4,0,7,11,0,0,2,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,4,7,1,1,0,0,0,0,Class_6
+36285,0,1,0,2,0,0,0,0,0,0,8,0,0,0,1,5,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,5,0,1,0,1,0,0,0,0,0,0,0,0,3,6,0,2,0,0,0,1,0,1,0,0,0,2,0,Class_6
+36286,0,0,4,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36287,0,1,14,13,0,0,0,0,0,0,10,0,1,0,2,1,0,0,1,0,0,1,0,0,0,2,7,3,0,0,0,0,0,1,0,1,1,0,0,0,1,1,0,2,0,1,0,0,3,0,0,0,0,15,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
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+36385,1,0,7,3,0,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,2,0,0,13,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,6,0,1,2,0,0,5,0,8,0,1,0,8,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36387,0,0,5,8,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,8,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36388,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,2,4,2,1,0,0,0,0,0,Class_6
+36389,0,0,7,2,1,0,0,0,0,1,9,0,0,0,1,7,0,2,0,0,2,0,0,0,0,1,10,1,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,9,3,0,0,0,0,0,0,7,0,1,0,1,0,1,2,2,0,0,0,2,0,0,0,0,0,0,0,5,1,0,0,0,0,1,1,0,0,5,1,2,0,0,0,0,0,4,0,Class_6
+36390,0,0,2,1,0,0,0,0,1,0,2,0,1,2,0,0,1,1,0,0,0,1,0,9,0,5,0,0,3,0,2,0,0,1,5,0,0,1,0,2,0,1,0,0,0,3,0,0,1,0,0,6,0,2,0,1,5,0,0,14,0,0,4,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,5,0,1,0,0,0,1,2,0,3,0,1,0,0,Class_6
+36391,0,0,0,1,0,3,0,0,0,0,1,1,0,2,0,3,0,2,0,0,0,0,0,5,0,1,0,0,0,0,4,1,0,0,10,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,3,1,1,1,12,0,1,0,3,0,1,5,0,3,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,Class_6
+36392,0,0,0,1,0,0,0,0,1,0,9,0,0,0,0,2,1,0,0,0,2,2,1,2,1,0,10,1,0,0,1,1,0,0,7,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,3,1,12,0,0,1,3,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,16,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+36393,0,1,7,2,0,0,0,1,0,0,7,0,0,0,0,3,0,1,0,1,3,4,0,0,0,1,15,2,0,0,2,4,0,2,2,2,0,0,0,4,2,0,0,1,0,14,0,1,1,0,0,1,0,9,0,0,1,1,0,1,4,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,1,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36394,0,0,1,6,0,0,0,2,0,0,2,0,1,0,0,1,1,3,0,3,0,2,0,8,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,24,0,3,0,0,0,0,1,0,0,0,0,19,0,0,11,1,0,3,0,0,0,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,2,2,Class_6
+36395,0,2,1,9,0,0,0,0,0,0,7,1,0,1,0,1,0,5,0,0,0,3,0,4,0,2,2,0,0,0,2,2,0,0,6,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,2,0,2,1,0,0,3,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,6,0,1,2,0,0,0,0,0,7,0,0,0,0,Class_6
+36396,0,0,13,10,0,0,0,1,0,0,9,0,0,2,0,2,0,1,0,0,2,2,0,0,0,3,8,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,1,0,9,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36397,0,3,11,3,1,0,0,0,1,0,10,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,32,4,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,2,0,18,0,0,1,0,0,1,0,6,0,2,2,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,1,0,Class_6
+36398,0,1,2,0,0,0,0,0,2,0,10,0,0,0,1,0,0,0,1,0,5,0,0,2,0,0,16,1,0,0,0,2,1,0,0,0,0,1,2,1,0,1,1,0,0,1,0,0,0,1,0,19,0,0,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,107,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+36399,0,0,4,2,0,0,0,0,0,0,10,0,0,1,1,1,0,0,0,0,1,2,0,1,0,0,12,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,1,0,6,0,0,0,0,0,2,15,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,15,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36400,0,2,7,6,0,0,0,0,0,2,5,0,2,0,0,4,0,0,0,0,0,0,0,0,1,1,2,3,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,1,0,6,0,1,3,0,0,0,0,18,1,0,0,0,0,0,2,2,0,0,1,3,2,1,0,1,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,2,2,0,0,0,Class_6
+36401,0,1,0,0,0,1,1,0,0,0,7,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,6,0,1,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,8,0,4,0,1,0,1,0,1,5,0,0,1,0,1,0,0,0,0,0,2,1,5,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,1,0,0,Class_6
+36402,0,2,8,5,0,0,1,0,0,0,6,0,1,0,8,8,0,2,0,1,2,3,0,0,0,1,2,1,0,0,0,0,0,0,5,0,0,3,0,2,0,1,0,0,0,5,1,3,7,0,0,1,0,6,0,0,0,0,0,1,2,3,1,1,2,11,1,0,0,0,1,0,0,2,0,0,0,0,0,3,0,0,0,0,1,0,1,0,1,0,0,0,0,Class_6
+36403,0,0,10,1,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,13,0,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,0,1,0,1,2,0,0,1,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36404,0,1,0,2,0,0,0,0,4,0,0,0,2,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,5,2,0,0,1,0,1,0,1,1,0,0,1,0,0,0,4,0,1,0,0,0,2,1,0,0,0,1,0,0,0,0,13,2,2,7,1,0,2,0,1,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,3,0,1,0,0,0,Class_6
+36405,0,0,2,3,0,0,0,0,0,0,2,0,0,0,1,8,2,6,0,5,0,2,0,5,1,1,0,1,2,0,0,0,0,2,5,1,0,1,0,1,0,3,2,4,0,2,0,1,2,0,1,1,0,4,2,1,4,0,0,12,0,4,0,4,0,2,7,1,0,1,2,2,0,4,1,0,0,2,1,1,5,1,1,0,0,0,3,1,5,0,0,1,1,Class_6
+36406,0,0,0,1,0,0,0,3,0,0,0,0,2,0,0,3,0,0,1,0,0,3,0,8,1,10,0,1,0,0,0,0,0,2,1,0,0,1,0,3,0,4,0,1,0,0,0,5,0,0,0,0,0,0,1,0,3,0,1,14,0,0,0,1,0,1,3,1,0,1,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,2,0,Class_6
+36407,0,2,2,2,1,0,0,0,0,0,19,0,1,0,1,2,0,0,0,0,0,0,0,8,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,1,0,0,1,0,4,0,0,0,7,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36409,0,0,0,1,0,0,0,0,0,0,0,6,5,0,0,3,0,9,0,0,0,7,0,2,1,3,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,18,0,2,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
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+36422,0,0,11,4,0,0,0,0,0,0,10,0,0,0,0,5,0,2,0,0,4,0,0,0,0,0,2,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,9,0,0,3,0,0,0,0,21,0,0,0,0,0,2,0,2,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+36423,0,0,21,3,0,0,0,1,0,0,8,0,0,1,0,9,0,2,0,0,1,5,0,0,1,3,8,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,5,0,20,0,2,1,0,0,0,1,42,0,0,2,0,0,1,1,1,0,1,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,1,0,0,1,0,Class_6
+36424,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,4,0,3,0,0,0,11,0,5,2,6,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,2,0,5,0,0,1,0,0,12,0,0,0,1,0,2,3,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,Class_6
+36425,0,0,1,0,0,0,0,2,0,0,1,0,3,0,1,0,1,9,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,6,0,0,6,0,0,0,17,0,3,0,0,1,2,3,0,0,0,0,3,0,0,1,0,0,4,0,4,1,0,0,2,0,0,0,4,3,1,0,0,1,0,0,0,2,2,0,0,0,0,0,0,1,0,2,0,0,4,0,Class_6
+36426,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,2,0,2,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36427,0,0,0,0,4,0,1,0,0,0,0,0,0,1,0,2,1,7,0,0,0,0,0,2,1,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,3,0,0,13,0,1,1,0,1,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+36428,0,0,1,0,0,0,1,0,2,0,6,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,5,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,2,0,0,0,0,0,12,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,3,0,0,0,0,1,112,0,1,0,1,0,1,0,0,1,0,0,1,0,1,1,2,0,0,0,Class_6
+36429,0,0,18,5,0,0,0,0,1,1,11,0,0,0,1,4,0,1,0,0,2,1,0,0,0,0,3,6,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,12,0,0,0,0,0,1,0,12,0,0,0,0,0,0,5,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+36430,0,0,1,2,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,3,1,0,0,0,0,1,0,0,1,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,Class_6
+36431,0,0,7,5,0,0,0,0,1,1,11,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,2,1,0,0,4,0,0,1,0,0,0,1,1,1,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,6,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+36432,0,0,0,4,0,0,0,3,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,3,0,2,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,17,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_6
+36433,1,0,1,9,0,0,0,0,1,0,1,1,1,1,0,3,0,1,0,0,0,0,1,3,0,10,0,1,3,0,0,0,0,0,2,0,0,1,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,3,1,0,3,1,0,24,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+36434,1,1,0,1,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,1,0,1,0,1,1,0,0,0,1,0,0,0,1,1,1,1,0,2,0,4,2,2,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,2,1,3,4,0,0,0,2,2,0,1,0,0,0,0,1,0,0,0,0,0,1,4,0,1,0,0,0,0,0,Class_6
+36435,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36436,3,0,8,7,0,0,0,0,0,0,13,0,0,0,1,3,0,0,0,0,1,3,0,0,0,0,8,0,1,1,0,0,0,1,1,3,1,0,0,3,0,1,1,1,0,9,1,0,0,0,0,0,0,9,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+36437,2,0,3,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,1,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,2,0,7,0,1,2,0,0,2,0,9,0,0,0,0,3,8,0,0,0,0,1,1,2,0,0,2,0,0,1,4,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36438,0,1,6,3,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,1,0,8,1,0,5,1,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,2,0,6,0,1,1,0,0,0,0,22,0,0,0,5,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36439,0,0,1,2,0,0,0,0,0,0,10,0,0,1,0,5,0,0,0,0,0,1,0,12,0,13,0,4,0,0,0,2,0,0,2,0,0,1,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,7,1,0,0,0,0,2,1,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,2,0,0,0,0,0,1,1,0,2,0,0,Class_6
+36440,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,3,0,0,11,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36441,0,2,6,7,0,0,0,0,0,0,6,0,1,0,0,3,0,2,0,1,2,2,0,0,0,2,10,4,0,1,0,2,0,0,2,0,0,0,0,1,0,0,1,0,0,12,0,0,0,0,0,1,1,13,0,0,0,0,0,2,1,2,0,0,2,3,0,0,0,1,0,1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,2,Class_6
+36442,0,0,12,7,0,0,0,0,0,2,10,1,1,1,0,1,1,0,0,0,0,2,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,10,0,0,2,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,5,0,0,1,0,0,0,1,0,0,0,0,Class_6
+36443,0,0,0,1,0,0,0,4,1,0,11,0,0,0,0,1,0,0,0,2,4,0,1,10,0,0,16,1,0,0,0,2,0,3,2,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,6,0,3,0,0,0,1,0,0,8,0,0,1,2,1,0,0,0,3,0,1,1,26,0,0,0,0,0,14,0,3,0,0,0,1,0,0,2,0,0,0,0,Class_6
+36444,0,0,7,11,0,0,0,0,0,0,1,0,0,0,1,3,0,1,0,0,0,2,0,3,0,6,0,1,1,0,0,0,0,0,6,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,4,1,0,0,0,0,0,2,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36445,0,0,11,4,0,0,0,0,0,1,13,0,1,0,1,2,0,0,0,0,1,3,0,0,0,0,10,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,1,0,0,0,0,10,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,7,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36446,0,2,18,6,1,0,0,0,0,0,9,1,0,0,0,1,0,0,1,0,1,2,0,3,0,1,13,1,0,1,2,0,0,1,0,1,0,1,0,1,0,2,0,3,1,11,0,0,1,0,1,0,0,14,1,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,5,0,1,0,0,0,0,1,0,0,0,0,1,1,Class_6
+36447,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,1,2,3,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,Class_6
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+36449,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,1,0,0,0,0,0,0,2,0,0,1,0,0,0,8,0,0,0,1,1,2,0,4,1,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,Class_6
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+36461,1,2,0,2,0,0,1,0,0,0,12,0,0,0,0,2,1,3,1,1,0,3,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,3,8,0,0,0,0,11,0,0,0,0,7,0,2,1,0,0,1,0,3,1,0,0,0,2,0,0,2,0,0,0,1,1,1,1,0,0,3,0,6,1,0,2,0,0,0,4,0,0,0,2,Class_6
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+36463,0,0,0,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36466,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,3,1,3,0,1,0,3,0,2,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,1,0,0,15,0,1,0,0,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,Class_6
+36467,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,3,0,0,0,0,1,1,1,0,0,1,0,3,0,0,1,0,0,21,0,1,0,1,0,1,7,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,Class_6
+36468,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,1,2,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,3,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,4,0,1,0,0,9,0,0,0,1,0,0,3,1,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,8,1,0,Class_6
+36469,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,12,0,1,0,7,0,0,4,2,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,Class_6
+36470,0,0,3,7,0,0,0,0,0,0,6,1,0,0,0,4,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,0,2,0,0,0,0,0,0,2,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36471,0,1,1,1,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,2,0,2,0,1,1,2,1,0,0,1,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,2,1,0,0,2,1,0,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,5,0,1,0,0,2,0,0,0,1,2,0,0,0,Class_6
+36472,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,5,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,Class_6
+36473,0,1,1,4,0,0,0,0,0,0,7,1,0,0,0,2,0,0,0,0,3,3,0,2,0,1,6,0,0,0,3,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,1,7,0,1,0,1,0,1,5,2,1,0,1,1,1,1,0,0,0,2,0,0,0,1,0,0,1,8,1,0,0,0,0,0,0,0,5,1,0,0,0,Class_6
+36474,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,7,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36475,0,1,18,3,0,0,0,0,0,0,8,0,0,0,0,6,0,0,0,0,0,3,0,1,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,1,2,1,1,2,0,6,0,1,1,0,0,0,0,19,0,0,0,0,0,0,0,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36476,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,7,0,0,0,0,0,2,0,0,0,0,1,4,2,1,0,0,0,1,0,1,1,0,0,1,4,0,0,2,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,4,0,0,0,1,0,1,1,Class_6
+36477,0,2,3,2,0,0,0,0,0,1,11,0,0,0,1,5,1,2,0,0,3,0,0,0,0,1,25,0,1,0,1,2,0,0,3,0,2,1,0,1,0,0,0,2,0,6,0,0,0,0,0,9,0,4,0,0,0,3,0,1,9,0,0,1,2,0,1,0,0,0,0,0,0,16,0,0,2,0,0,20,0,4,0,0,0,1,0,0,0,0,0,0,0,Class_6
+36478,0,0,1,2,0,0,0,0,0,0,10,0,1,0,0,2,0,6,0,0,1,7,0,1,1,7,0,1,1,1,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,1,4,0,0,1,0,13,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36479,0,3,0,1,0,0,0,0,1,0,1,0,1,0,0,3,1,3,0,1,0,3,0,7,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,2,0,17,0,0,0,1,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,4,0,0,0,2,0,0,0,0,0,2,Class_6
+36480,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,3,0,0,1,0,0,12,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+36481,0,0,5,16,0,0,0,0,0,0,9,0,2,1,0,4,0,1,0,0,0,1,0,2,0,7,6,1,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,3,0,13,0,0,0,0,0,0,1,0,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36482,0,0,14,1,0,0,0,0,0,0,19,0,0,1,1,3,0,0,0,0,3,0,0,10,0,1,13,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,2,0,18,0,0,1,0,0,0,0,11,0,1,0,8,0,1,3,0,0,2,0,0,2,0,0,0,0,1,1,2,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36483,0,0,4,4,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,3,1,0,0,1,0,4,0,0,0,0,0,0,0,0,6,0,3,0,0,1,0,1,0,2,0,0,0,0,3,0,0,0,0,6,1,0,3,0,0,8,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,1,0,0,Class_6
+36484,3,0,1,1,1,0,2,2,0,0,4,0,2,1,8,7,21,2,1,2,0,1,13,24,1,3,0,0,0,5,3,5,0,1,4,1,0,7,1,0,2,2,3,0,2,4,0,9,3,0,7,0,1,1,2,0,12,2,0,26,1,1,0,2,0,3,6,2,1,1,2,1,3,2,1,6,0,0,0,1,2,0,17,0,0,1,0,0,1,1,5,1,0,Class_6
+36485,0,0,4,2,0,0,0,0,0,0,11,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,30,0,0,0,0,0,0,1,1,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,5,0,0,0,2,0,0,0,0,0,0,0,1,0,Class_6
+36486,0,0,9,3,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,8,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36487,0,0,2,6,0,0,0,0,0,1,9,1,1,0,0,3,0,1,0,0,0,1,0,2,0,0,8,2,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,12,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,2,2,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36507,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,9,0,0,1,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,1,2,Class_6
+36508,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36509,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,1,0,1,5,1,2,0,0,0,0,0,1,3,1,0,2,1,0,4,2,1,0,2,4,1,0,0,0,3,3,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+36510,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,1,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,11,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,8,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36511,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,1,0,1,4,1,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,4,0,1,2,0,1,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,2,1,0,0,0,0,1,Class_6
+36512,0,2,5,17,0,0,0,0,0,1,7,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,3,0,0,1,1,0,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,3,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,8,0,0,0,0,0,0,1,1,0,0,0,Class_6
+36513,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,11,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36514,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36515,0,0,0,2,0,0,0,0,1,0,9,0,0,0,0,2,0,1,0,0,2,1,0,5,0,0,2,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,5,0,2,2,0,0,1,0,0,0,0,4,1,0,0,0,4,0,0,0,1,0,8,0,1,0,0,0,0,0,0,1,1,0,2,0,Class_6
+36516,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,4,0,1,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,12,0,0,0,1,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36517,0,0,9,14,0,0,0,0,0,1,8,0,0,1,0,3,0,1,0,0,0,0,0,1,0,11,6,3,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,1,1,8,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36518,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,8,0,0,0,3,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+36519,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,1,0,0,2,0,3,0,9,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,13,0,4,0,0,0,0,5,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,Class_6
+36520,0,0,0,0,0,0,1,4,0,3,0,0,1,0,2,2,0,0,0,1,0,2,0,3,0,1,0,0,0,0,0,0,2,7,1,0,1,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,1,0,2,1,0,22,0,2,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,2,6,0,Class_6
+36521,1,0,1,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,3,0,1,0,4,1,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,0,2,1,0,0,0,0,6,0,5,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,2,0,0,5,0,5,0,0,0,0,0,0,3,0,0,0,0,0,4,0,1,1,0,0,0,0,0,Class_6
+36522,0,1,1,1,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,7,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,2,9,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,1,2,0,2,0,0,0,0,1,0,0,1,0,0,0,Class_6
+36523,7,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,1,5,0,0,0,9,2,0,0,1,1,0,0,1,0,0,0,14,0,0,0,0,0,0,2,0,1,1,1,0,3,0,Class_6
+36524,0,0,13,0,0,0,0,0,0,0,8,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,15,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+36525,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36526,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+36555,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,7,0,0,0,0,4,3,3,0,1,0,0,0,1,0,0,1,3,1,0,3,0,2,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,2,0,0,12,3,1,1,0,0,0,1,0,1,1,0,1,1,0,1,3,1,0,1,8,0,4,0,0,1,0,1,0,2,3,0,0,3,Class_6
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+36567,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,3,0,1,0,0,0,2,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,4,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+36589,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,3,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,12,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+36590,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,4,0,2,1,0,0,0,0,0,0,1,0,0,0,2,1,0,0,10,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_6
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+36592,0,0,12,7,0,0,0,0,3,0,8,0,0,1,0,2,0,0,0,1,1,0,0,0,0,0,6,3,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,13,0,0,0,0,0,0,0,13,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36594,0,1,4,4,0,0,0,0,2,0,14,0,0,0,0,1,0,0,0,1,0,0,0,4,0,1,11,1,1,0,0,3,0,0,4,0,2,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,6,0,0,0,7,0,1,2,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,5,0,0,0,0,0,0,4,0,0,1,0,Class_6
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+36596,0,0,0,0,0,0,0,8,0,0,0,1,1,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,1,20,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,Class_6
+36597,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36598,0,1,0,1,0,0,0,0,0,0,1,0,1,0,1,4,1,3,0,0,0,0,0,4,1,8,0,8,2,0,1,2,0,0,1,1,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,1,3,1,0,2,0,1,1,1,1,0,1,0,0,6,0,0,1,0,1,0,0,0,0,0,0,1,3,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36599,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,3,0,0,0,0,0,2,2,0,0,0,0,8,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,3,0,Class_6
+36600,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,1,0,2,0,9,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36601,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,2,0,1,0,5,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,1,0,1,1,3,0,1,1,0,0,0,0,1,0,0,0,4,0,0,0,2,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,1,0,0,1,1,1,0,0,Class_6
+36602,0,0,6,3,0,0,0,0,0,0,12,0,0,1,0,1,1,2,0,0,0,0,0,9,0,7,6,0,0,0,1,0,0,0,1,0,1,1,0,1,1,1,0,1,0,2,0,1,0,0,0,0,0,7,1,0,0,6,0,1,2,1,0,2,0,1,0,0,0,0,0,0,2,0,0,0,1,2,0,7,0,0,0,0,0,0,1,0,1,0,0,1,0,Class_6
+36603,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,2,0,2,0,2,0,0,0,0,0,0,1,2,1,0,0,0,0,1,0,10,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,12,0,2,0,0,1,1,3,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,1,0,Class_6
+36604,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36605,0,0,6,13,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,1,2,0,0,0,1,2,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,13,0,0,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,21,0,8,1,0,0,0,0,0,4,0,0,0,0,Class_6
+36606,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,2,1,0,0,0,0,3,0,2,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,5,0,0,0,0,1,2,0,0,0,0,0,4,1,0,0,0,1,1,7,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,Class_6
+36607,0,0,1,0,1,0,0,2,0,4,1,0,1,0,0,0,0,1,0,0,0,1,0,2,1,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,16,0,0,0,0,0,0,1,0,0,0,0,3,6,0,0,0,0,6,0,2,2,0,1,0,2,0,0,2,4,0,0,0,2,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
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+36622,0,0,21,8,0,0,0,0,0,0,9,0,1,0,0,7,0,0,0,0,5,3,0,2,0,2,10,1,0,1,1,1,1,1,4,1,0,0,0,2,1,1,3,1,0,12,0,3,3,0,0,4,0,17,1,4,0,0,0,0,0,1,0,1,5,0,0,1,0,0,0,0,1,6,0,1,0,0,0,4,0,6,0,0,1,0,1,0,3,0,0,0,0,Class_6
+36623,0,1,10,6,0,0,0,0,1,0,8,0,0,1,0,6,0,1,0,0,1,7,0,2,2,4,12,1,2,0,0,0,0,1,0,0,0,1,1,2,0,1,1,0,0,9,0,1,2,0,0,0,1,23,0,0,0,0,0,0,3,0,0,1,0,1,2,1,0,0,0,1,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36624,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,10,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+36625,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,6,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36626,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,12,0,0,0,1,0,1,0,0,0,0,2,0,0,0,1,0,0,9,0,0,0,0,0,0,3,1,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+36627,0,2,18,5,1,0,0,0,0,0,4,0,0,0,0,4,0,1,0,0,0,3,0,0,0,2,6,1,0,0,1,0,0,0,3,0,0,0,0,0,0,1,0,2,0,1,0,0,2,0,0,0,0,14,0,0,0,0,0,3,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36628,3,1,6,13,1,0,0,0,1,2,9,0,0,0,0,1,0,0,2,1,0,0,0,1,0,1,10,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,5,1,0,3,1,2,1,0,7,0,0,0,3,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,4,0,0,0,Class_6
+36629,0,0,3,3,0,0,0,0,0,0,5,1,0,0,0,4,0,1,0,0,2,2,0,0,0,1,2,4,0,0,0,1,1,3,0,0,3,0,0,1,0,1,0,3,0,8,0,0,0,0,0,0,0,7,0,1,1,1,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,3,0,0,1,0,1,1,0,6,0,Class_6
+36630,0,0,8,3,0,0,0,0,0,2,6,0,0,0,0,5,0,0,1,2,2,0,0,0,0,0,2,3,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,8,0,1,0,1,7,0,0,0,0,7,2,0,1,0,0,0,4,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36631,0,0,2,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36632,1,1,8,4,0,0,1,0,0,0,9,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,9,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,4,0,0,2,0,0,0,1,1,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36633,1,0,0,0,0,0,0,0,5,0,0,0,0,2,0,1,0,0,0,0,0,0,0,4,3,1,0,0,0,0,0,8,0,0,1,2,0,2,5,0,0,0,0,0,1,0,2,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,15,0,3,9,0,0,3,1,0,1,4,1,2,0,0,0,0,0,0,0,0,0,12,0,3,0,1,0,0,1,Class_6
+36634,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36635,0,0,1,0,1,0,0,0,0,2,8,0,2,0,0,6,0,5,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,9,0,0,1,0,2,0,0,0,1,6,1,0,1,0,0,0,0,3,0,0,2,4,2,0,0,1,1,1,0,2,1,0,0,0,0,1,4,2,0,0,0,0,0,0,1,0,0,3,0,Class_6
+36636,0,1,1,0,0,0,0,0,0,0,5,0,0,0,0,3,0,3,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,2,0,0,2,2,4,0,0,0,0,3,1,0,0,0,0,0,0,2,0,1,0,6,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,1,0,1,0,Class_6
+36637,0,0,9,3,0,0,0,0,0,0,10,0,1,0,0,5,0,0,0,0,1,5,0,0,0,0,11,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,11,0,0,2,0,0,0,0,12,0,0,0,0,0,0,1,2,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36638,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,2,1,0,0,2,0,3,0,0,0,0,0,0,2,0,1,4,11,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,8,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36639,0,0,5,6,0,0,0,0,0,0,9,0,1,0,1,0,0,0,0,0,2,2,0,0,0,0,6,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,2,0,4,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,7,1,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36640,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36641,0,1,11,4,0,0,0,3,0,0,8,1,1,0,0,2,0,1,0,0,1,2,0,0,0,0,2,4,2,0,0,1,1,0,0,0,0,0,0,6,0,0,2,2,0,8,0,2,0,0,0,0,0,16,0,0,1,0,0,0,1,2,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,2,2,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+36642,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,2,4,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,7,2,0,0,1,0,1,0,0,0,0,2,1,0,2,0,0,0,0,2,0,4,0,2,0,7,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,1,1,3,0,0,0,Class_6
+36643,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36644,1,0,1,0,1,0,0,0,0,2,2,1,8,2,0,4,0,3,0,0,0,4,0,8,3,1,0,0,9,0,0,3,2,2,1,0,0,0,0,0,3,9,0,2,0,0,1,4,1,1,0,0,27,7,3,0,2,0,1,2,0,6,1,1,0,1,1,0,0,3,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,0,0,3,0,0,6,0,Class_6
+36645,0,1,9,6,0,0,0,0,0,0,16,0,1,0,0,1,0,0,0,0,1,0,0,9,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,7,0,1,3,0,0,0,0,2,0,0,0,15,0,0,4,0,0,1,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36646,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,3,0,4,0,2,0,1,0,0,0,0,0,2,0,0,1,0,0,3,8,0,1,3,0,0,0,4,0,3,1,1,0,0,2,0,0,0,0,0,0,13,0,2,0,1,1,0,4,0,0,6,0,3,0,0,0,1,0,0,3,1,0,0,0,0,1,0,4,0,1,0,0,0,0,Class_6
+36647,0,1,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,1,3,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
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+36649,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,1,0,0,0,3,0,1,0,3,0,3,0,0,0,1,0,2,3,1,0,3,0,1,3,10,0,0,0,0,0,1,2,1,0,0,0,6,1,0,0,0,0,16,0,2,0,0,1,1,4,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,Class_6
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+36662,1,1,0,0,0,0,0,1,1,1,1,0,2,0,2,0,1,5,0,2,0,1,1,5,1,0,2,2,1,0,0,1,1,1,1,1,0,1,2,0,0,2,0,1,0,0,0,0,1,2,3,0,0,0,0,0,2,1,0,11,0,1,0,0,0,0,8,0,0,3,1,3,0,0,0,0,4,1,0,1,4,0,0,0,2,0,0,0,1,1,0,0,0,Class_6
+36663,0,1,5,1,0,0,2,0,0,0,10,0,0,0,0,3,0,2,0,0,1,1,0,0,0,2,7,2,0,0,1,1,0,0,4,2,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,13,0,16,0,0,0,0,0,0,2,0,0,2,1,2,0,0,0,1,0,0,0,2,0,0,1,0,0,14,0,4,0,0,1,0,1,0,0,0,0,1,0,Class_6
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+36665,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,8,1,0,0,0,1,0,0,0,0,0,0,8,0,0,1,0,0,14,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+36666,0,0,12,2,0,0,0,0,0,0,7,0,1,0,0,5,0,0,0,0,4,2,0,2,0,1,8,1,0,0,1,0,0,0,3,0,0,0,0,1,0,0,0,1,0,12,0,0,0,0,0,0,0,22,0,0,0,0,0,0,2,3,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36667,0,0,0,3,0,0,0,0,1,0,8,0,0,0,0,3,0,1,0,1,0,2,0,0,0,0,9,3,0,0,0,0,1,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,1,0,6,2,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,13,0,4,1,0,0,0,0,0,2,0,1,3,0,Class_6
+36668,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+36669,0,0,14,8,0,0,0,0,0,0,7,0,0,1,0,1,0,0,0,0,0,2,0,0,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,19,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,Class_6
+36670,0,0,7,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,2,1,0,0,0,3,3,3,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,4,0,1,1,0,0,0,0,7,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,1,1,0,0,0,0,Class_6
+36671,0,0,1,1,0,0,0,0,1,0,12,1,1,0,1,5,0,0,0,0,6,2,0,2,0,3,3,0,0,0,0,1,2,0,1,0,0,2,0,0,0,0,0,1,0,0,0,1,1,0,0,2,0,1,0,0,0,1,0,2,1,2,0,0,1,0,0,0,0,0,0,1,0,14,0,1,1,0,1,20,0,1,0,0,0,0,0,0,5,1,0,2,0,Class_6
+36672,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36673,0,2,13,4,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,0,2,0,8,0,1,8,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,5,0,4,0,7,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,2,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36674,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,1,0,0,0,4,2,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,0,9,0,1,0,0,0,5,0,0,0,0,0,0,1,0,0,0,5,3,0,0,0,3,0,0,9,0,0,1,0,1,0,1,0,0,0,0,7,0,0,0,0,0,0,4,11,0,0,0,0,1,2,Class_6
+36675,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,3,0,0,0,0,2,0,0,3,1,0,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,1,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36676,0,1,11,6,0,0,0,0,0,3,11,0,0,2,0,1,0,2,1,0,2,0,0,0,0,1,11,1,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,1,1,9,0,0,1,0,0,0,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,4,0,0,1,0,0,0,0,0,0,0,0,Class_6
+36677,0,4,0,0,0,0,0,0,0,0,7,0,1,0,0,6,0,4,1,1,0,1,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,4,1,1,0,0,0,1,0,0,4,3,2,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,4,0,0,0,2,0,0,0,1,0,1,0,0,0,3,0,0,0,1,0,0,0,0,2,0,0,3,0,Class_6
+36678,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,3,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36679,0,0,0,0,0,0,0,0,1,0,9,0,0,1,2,0,0,0,0,0,3,0,0,4,0,1,9,0,0,0,0,3,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,9,0,1,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,2,49,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36680,0,0,2,6,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,1,0,0,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,1,0,0,0,0,7,0,1,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36681,0,1,4,9,0,0,0,0,0,0,8,0,0,0,0,1,0,2,0,0,1,1,0,1,0,0,1,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,1,0,0,5,0,0,1,1,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,1,1,0,1,0,0,1,2,1,0,0,0,0,1,17,0,2,0,0,1,0,1,0,5,0,0,1,0,Class_6
+36682,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36683,0,0,0,2,0,0,0,0,0,6,0,0,1,0,0,3,1,0,0,2,0,2,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,5,0,1,0,1,0,2,1,0,0,0,0,5,1,0,0,0,2,1,0,1,0,2,0,3,6,0,0,5,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,0,Class_6
+36684,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,4,0,0,1,0,0,13,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36685,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,3,0,2,0,1,0,0,0,0,1,2,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,3,Class_6
+36686,0,0,11,3,3,0,0,0,0,0,9,1,1,0,0,7,0,0,0,1,5,0,0,0,0,0,14,2,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,3,0,8,0,0,1,0,0,1,0,15,1,0,1,1,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,6,0,0,1,0,Class_6
+36687,0,0,8,3,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,1,0,0,0,3,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,2,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36690,0,0,6,4,0,0,0,1,0,0,9,1,1,0,0,2,0,1,0,0,0,0,0,0,1,2,1,3,0,0,3,1,0,0,4,0,0,1,0,2,0,0,0,2,0,6,0,0,1,0,0,3,0,13,0,0,1,1,0,0,6,2,0,2,0,3,1,0,0,1,0,0,0,3,0,0,0,0,1,10,0,1,0,0,1,0,0,1,1,0,0,0,0,Class_6
+36691,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,5,0,0,1,3,0,0,16,0,3,0,0,0,1,3,0,0,0,0,2,0,0,2,0,0,7,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36692,0,1,5,9,0,0,2,0,0,0,19,0,0,0,0,2,0,0,0,0,0,2,0,7,0,1,12,1,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,1,0,3,1,0,0,0,0,2,0,5,0,0,0,7,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,2,1,0,0,1,0,9,0,1,1,0,0,0,0,1,2,2,0,0,0,Class_6
+36693,0,0,15,5,0,0,0,0,0,0,7,0,2,0,0,6,0,5,0,0,3,1,0,2,0,0,8,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,4,0,8,0,0,0,0,0,0,0,6,0,2,0,1,0,0,7,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36694,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+36695,0,1,5,8,0,0,1,0,0,0,6,0,1,3,0,1,0,0,0,1,0,1,0,2,0,7,3,6,0,0,3,0,0,1,3,0,0,0,0,0,0,2,0,0,0,10,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+36696,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,12,0,0,0,0,0,1,0,0,0,2,1,2,0,0,2,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,2,0,Class_6
+36697,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,3,0,1,0,0,0,3,1,2,0,8,3,0,0,0,0,1,0,0,3,0,0,1,0,0,0,4,0,0,0,0,2,1,2,0,0,0,1,10,0,0,1,0,0,5,0,0,0,1,1,2,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36698,0,2,13,3,0,0,0,1,0,0,7,0,0,0,0,3,1,0,0,0,1,1,0,1,0,0,17,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,8,1,0,2,0,0,0,1,18,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+36699,0,2,4,3,0,0,0,1,0,0,1,0,3,0,0,15,0,10,0,0,0,5,0,3,0,25,0,0,2,0,0,0,2,0,4,3,0,1,0,0,0,14,0,0,0,1,0,0,5,0,0,1,0,5,0,0,6,0,0,4,0,9,0,5,0,6,1,5,0,0,0,0,0,4,1,0,2,2,0,1,0,0,5,0,1,0,1,3,3,0,0,7,0,Class_6
+36700,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36701,0,0,10,3,0,0,0,0,0,0,7,0,6,2,0,3,0,1,0,1,1,4,0,1,0,2,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,19,0,2,2,0,0,0,2,10,0,0,0,0,0,0,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,1,0,0,0,1,1,1,0,Class_6
+36702,0,0,17,14,0,0,0,1,1,1,9,0,1,0,0,2,0,0,0,0,0,2,1,0,0,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,1,0,0,8,0,0,0,0,0,0,2,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36703,0,0,15,7,0,0,0,0,0,0,7,0,0,1,0,6,1,0,0,1,1,1,0,1,0,0,0,7,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,5,0,0,1,0,0,0,0,13,2,0,0,1,0,1,1,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,0,1,0,0,0,1,Class_6
+36704,0,0,14,1,0,0,0,0,0,0,7,1,1,0,0,2,0,0,0,0,1,0,1,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,5,0,1,0,0,0,0,0,9,1,2,0,0,0,1,2,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36705,0,0,0,1,0,0,0,0,2,0,0,0,0,3,0,2,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,3,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36706,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,14,0,0,0,2,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+36707,0,0,0,1,0,0,0,1,0,0,0,0,0,1,3,2,1,0,0,0,0,3,0,4,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,0,0,0,0,2,2,0,0,0,0,16,0,0,1,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+36708,0,0,1,4,0,0,0,0,0,0,9,0,1,0,0,2,0,0,0,0,0,2,0,4,1,1,4,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,1,5,1,0,0,1,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,0,0,0,0,0,2,0,0,0,0,Class_6
+36709,6,2,1,1,0,0,1,1,2,1,0,0,2,3,0,7,0,1,1,5,0,3,2,6,2,0,1,0,0,0,0,12,0,0,1,2,4,6,0,0,0,4,0,8,0,0,0,4,0,3,0,0,1,1,2,1,0,0,5,3,0,4,0,4,1,2,18,0,0,6,0,0,1,1,1,0,0,1,0,0,0,0,0,0,1,4,23,0,3,0,0,0,62,Class_6
+36710,0,0,10,2,0,0,0,0,0,0,9,0,1,0,0,2,0,1,0,0,2,2,1,0,0,1,2,1,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,1,0,11,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+36711,0,0,5,8,0,0,0,0,0,0,7,0,0,0,0,4,0,3,0,0,0,1,0,3,2,24,6,1,0,0,1,0,0,2,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36712,0,0,16,2,0,0,0,0,0,1,12,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,3,2,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,2,0,13,1,0,0,0,0,0,0,11,0,0,0,0,0,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,2,0,0,1,0,Class_6
+36713,0,0,2,1,0,0,0,1,0,0,0,0,1,0,1,2,0,1,0,0,0,1,0,13,0,9,0,0,0,0,0,0,0,8,4,0,0,0,1,2,0,1,0,0,0,0,0,4,0,0,0,0,5,3,0,0,6,0,0,12,0,2,0,0,0,0,9,1,2,1,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,2,0,Class_6
+36714,0,0,1,3,0,0,0,0,2,0,10,1,0,0,0,1,0,0,0,0,0,0,3,0,0,2,2,0,0,0,13,1,0,1,4,1,1,0,1,0,0,2,4,0,1,1,1,0,0,0,1,20,0,2,0,6,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,161,0,1,0,0,1,5,0,0,0,0,0,1,0,1,1,3,0,0,0,Class_6
+36715,0,0,1,1,0,0,0,1,0,0,0,0,2,1,0,4,2,4,0,0,0,3,0,1,3,4,0,0,0,0,0,1,2,0,1,1,0,0,2,0,0,4,0,1,0,1,0,1,1,2,1,0,0,1,0,0,4,0,0,4,0,2,0,2,0,1,6,0,0,1,1,0,0,0,0,0,0,0,5,5,0,0,0,0,2,0,0,0,1,0,1,0,0,Class_6
+36716,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,16,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,1,Class_6
+36717,0,0,3,6,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,3,0,2,0,0,0,0,0,2,1,0,0,0,0,12,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36718,9,0,0,0,1,1,0,0,1,0,1,0,1,0,0,5,2,1,0,0,0,1,0,1,2,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,6,0,2,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,1,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,1,5,1,0,0,0,0,0,2,2,0,0,0,0,0,2,Class_6
+36719,0,1,11,2,0,0,1,0,1,0,9,0,0,0,0,5,0,2,0,0,1,1,0,0,0,0,8,1,0,0,0,0,0,0,1,0,0,1,0,1,1,0,1,1,0,6,0,0,1,0,0,2,0,11,0,0,1,0,0,0,5,3,0,0,2,3,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+36720,0,0,2,5,0,0,2,0,0,1,1,0,1,0,3,1,3,2,1,1,0,4,10,16,0,6,0,0,1,1,9,3,1,1,3,5,1,2,3,4,0,0,2,0,1,1,0,10,3,3,0,0,2,7,2,0,4,0,0,22,1,0,0,0,0,1,14,0,1,3,0,0,0,3,0,8,0,2,0,10,0,6,18,0,0,1,1,0,2,2,1,0,3,Class_6
+36721,1,1,0,0,0,0,0,2,0,0,2,0,1,0,0,2,1,0,1,2,0,3,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,5,0,6,0,0,2,0,0,1,0,0,1,1,0,0,0,0,1,0,0,4,0,0,1,0,1,5,0,2,1,0,0,2,0,1,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,1,0,Class_6
+36722,1,1,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,2,1,0,0,0,2,8,1,1,0,0,1,0,0,1,2,0,0,1,0,2,1,1,0,1,0,1,0,0,0,0,0,0,3,0,0,2,0,0,2,1,0,10,0,0,0,1,0,2,7,0,0,1,0,5,0,0,0,0,4,1,0,0,4,0,0,0,1,0,5,1,1,0,0,1,0,Class_6
+36723,0,0,10,2,0,0,0,0,0,0,8,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,3,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36724,0,0,11,3,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,13,2,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,5,0,1,0,1,0,1,0,9,0,1,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,5,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36725,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,3,1,0,0,0,1,0,7,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,6,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,10,0,1,1,1,0,1,12,4,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,3,0,0,0,Class_6
+36726,0,0,4,2,0,0,0,0,1,0,9,0,0,2,0,1,0,1,2,1,0,1,0,0,0,1,12,1,0,0,0,1,0,0,1,0,0,3,0,3,0,1,0,1,0,5,0,0,0,0,0,3,1,5,0,1,0,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,1,6,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36727,0,0,2,2,0,0,0,0,0,0,7,0,1,1,0,3,0,0,0,0,0,4,0,1,0,5,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,24,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36728,0,0,0,1,0,0,0,0,0,0,7,0,0,0,1,3,0,1,0,0,2,1,0,2,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,1,1,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
+36729,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,2,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,4,0,0,0,0,0,0,2,0,0,1,0,1,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,Class_6
+36730,0,1,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,1,0,0,0,3,0,0,5,0,0,0,0,1,0,3,0,0,0,0,0,7,2,0,0,0,2,0,0,0,0,0,3,0,0,0,1,2,0,0,1,0,1,0,0,Class_6
+36731,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,5,1,1,1,0,1,1,11,1,0,0,0,0,0,0,5,1,1,2,1,0,2,1,2,0,1,0,0,1,0,0,2,0,0,2,0,0,1,1,0,6,0,0,10,0,1,0,0,0,0,9,3,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,Class_6
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+36734,0,0,8,4,0,2,0,0,1,0,7,0,0,0,0,6,1,1,0,0,2,3,0,0,0,5,4,1,1,0,1,2,0,1,2,0,3,0,0,0,0,2,2,2,0,11,0,1,3,0,0,0,0,16,0,1,0,2,0,2,1,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+36737,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,2,0,16,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+36738,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36739,0,1,8,3,0,0,0,0,0,0,18,0,0,0,0,2,0,0,0,0,5,1,1,8,0,1,12,1,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,1,0,11,0,1,1,0,0,3,0,5,0,0,0,12,0,0,5,0,0,1,0,0,1,0,0,1,0,1,0,3,0,0,0,1,0,7,0,0,0,1,0,0,0,1,1,1,0,1,0,Class_6
+36740,0,0,4,3,0,0,0,0,0,0,8,1,0,0,0,2,0,0,0,0,1,0,0,1,0,1,3,1,0,0,3,0,0,0,3,0,0,3,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,11,1,0,0,0,0,0,4,2,0,0,0,1,0,0,0,2,0,1,0,4,0,0,0,0,0,15,0,14,1,0,0,0,0,0,2,0,0,0,0,Class_6
+36741,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,3,0,2,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,7,0,0,4,2,0,0,0,0,0,1,0,1,1,0,2,0,0,0,0,0,0,0,0,1,1,5,0,0,0,0,0,0,1,1,0,0,Class_6
+36742,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,7,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,11,0,1,0,0,0,0,0,0,1,5,2,1,1,1,0,0,0,12,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_6
+36743,0,0,0,0,0,0,1,12,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,3,0,0,10,1,0,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+36744,0,0,9,6,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,5,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+36745,0,2,15,17,0,0,0,0,0,0,8,0,2,0,0,5,0,0,0,0,0,1,0,0,0,7,0,0,0,0,1,1,0,1,3,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,11,1,0,0,0,0,3,0,1,0,0,2,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,1,0,0,0,Class_6
+36746,0,2,1,1,2,0,0,1,0,0,7,0,0,0,0,8,0,0,1,0,1,0,0,4,0,3,4,3,0,0,0,1,1,0,2,0,0,0,0,1,0,1,0,2,0,2,3,2,2,0,1,2,0,4,0,0,1,1,0,0,0,3,0,0,2,2,1,1,0,2,0,1,0,2,0,1,0,1,0,4,0,6,2,2,0,0,0,0,3,0,0,2,0,Class_6
+36747,0,1,11,5,0,0,0,0,0,0,10,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,8,1,1,0,0,1,1,1,0,2,0,0,0,0,0,0,0,1,0,21,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+36748,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,3,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+36749,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,4,0,8,0,2,0,0,0,3,0,4,0,1,1,0,3,0,0,0,20,0,0,1,0,1,0,0,1,1,0,1,0,0,0,0,1,0,0,15,0,1,0,0,0,12,0,0,0,0,0,0,0,2,0,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36750,0,2,11,3,1,0,0,0,0,1,5,0,0,1,1,2,0,0,0,0,2,2,0,0,0,1,0,4,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,10,0,0,1,0,0,0,1,28,2,0,0,0,0,0,0,2,0,0,2,0,1,0,0,1,0,0,0,1,1,0,0,0,0,10,0,3,0,0,2,0,0,0,0,0,0,0,0,Class_6
+36751,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,2,0,1,0,0,0,1,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,2,0,0,12,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+36752,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,10,0,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,12,0,0,0,1,0,2,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36753,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,10,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,4,0,0,1,0,0,9,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+36754,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,1,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,6,0,3,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,3,0,0,0,2,0,1,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,4,0,Class_6
+36755,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,0,0,2,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36756,0,0,4,2,0,0,0,0,0,0,18,0,2,1,0,3,0,0,0,0,4,3,0,7,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,0,1,0,0,4,0,11,1,1,1,6,0,0,0,0,1,1,1,1,1,0,0,1,0,0,0,2,0,1,0,0,0,7,1,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+36757,0,0,7,2,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,4,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+36758,1,0,0,0,0,0,6,0,0,3,0,0,0,1,0,0,0,0,0,0,0,1,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,7,0,0,4,0,1,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,Class_6
+36759,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,2,0,3,0,2,0,3,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,2,0,0,0,2,0,0,0,0,0,9,1,0,1,1,1,9,0,0,0,1,1,2,1,1,0,3,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+36760,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,6,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+36761,0,0,11,20,0,0,0,0,0,0,8,0,0,0,0,3,0,1,0,0,0,1,0,0,0,12,5,4,1,0,0,2,0,0,4,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,3,0,2,1,2,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+36762,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,3,0,3,0,1,0,1,0,1,0,4,0,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,11,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,2,3,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_6
+36763,1,0,0,0,0,0,0,3,1,3,2,0,0,4,0,1,2,3,0,1,0,3,0,24,3,4,0,0,0,0,1,9,2,2,1,2,0,0,1,4,0,1,0,0,2,2,1,10,0,9,0,0,0,4,0,3,12,0,1,14,0,2,0,28,0,4,29,1,0,10,0,0,2,0,7,1,0,0,1,0,0,0,1,0,1,0,2,3,0,0,0,1,0,Class_6
+36764,0,0,5,6,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,5,0,2,0,0,0,2,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,1,0,2,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36765,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36766,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,12,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36767,0,0,3,3,0,0,0,0,0,0,8,1,1,0,1,3,0,0,0,0,0,1,0,0,1,1,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,18,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
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+36769,0,0,16,8,0,0,0,0,1,2,8,0,0,1,0,3,1,0,0,0,3,2,0,0,0,0,5,2,1,1,1,0,0,0,0,0,0,0,1,0,0,1,0,8,1,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,2,0,0,0,0,0,1,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
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+36782,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36783,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36787,0,0,14,7,0,0,0,0,1,0,8,0,1,1,2,1,0,0,0,0,0,1,0,0,0,2,8,1,0,0,0,0,0,0,2,0,0,9,0,1,0,0,0,0,0,2,0,0,1,0,0,1,0,8,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36788,0,0,4,4,0,0,0,0,1,0,8,0,0,0,0,1,0,0,0,0,1,1,0,1,0,1,8,1,0,0,1,2,0,1,4,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,1,4,0,1,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,10,0,1,0,0,0,1,0,1,2,0,0,0,0,Class_6
+36789,0,1,2,2,0,0,0,0,0,1,1,5,0,0,0,0,0,1,0,0,0,4,0,4,0,3,0,0,0,1,0,0,2,0,1,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,5,0,5,2,0,1,0,0,13,0,2,0,2,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,1,1,0,0,0,0,0,1,0,3,0,0,2,0,Class_6
+36790,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36791,0,0,6,2,0,0,0,0,0,0,19,0,0,0,0,1,0,1,0,0,0,0,0,8,0,1,10,3,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,5,0,0,0,9,0,1,1,1,0,1,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,2,1,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36792,0,1,8,1,0,0,2,0,0,1,7,0,3,1,0,8,0,2,0,0,0,5,0,1,0,0,4,1,1,0,0,1,0,0,5,0,0,2,0,0,0,0,1,11,0,0,0,2,15,0,0,1,1,19,1,0,0,0,0,0,2,0,1,0,2,1,2,0,0,5,0,2,0,0,0,0,0,0,0,2,1,1,1,0,1,0,0,1,3,1,0,0,0,Class_6
+36793,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36794,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,4,0,0,0,0,Class_6
+36795,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,10,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36796,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+36797,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,3,0,5,0,3,3,1,0,0,0,0,1,0,0,1,0,1,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,6,0,0,4,0,0,8,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+36798,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36799,0,1,5,9,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,7,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,2,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+36800,0,0,5,6,0,0,0,0,0,0,6,0,0,2,0,3,0,1,0,0,0,0,0,1,0,13,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,6,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36801,7,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+36802,0,1,5,3,0,0,0,0,0,0,8,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,6,2,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,4,0,0,3,0,0,0,0,1,0,1,0,0,0,0,7,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36803,0,2,1,1,0,0,0,0,0,0,18,0,1,1,0,4,1,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,7,0,1,0,0,0,2,5,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,11,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36804,7,0,0,1,0,0,0,0,0,0,1,0,4,0,0,2,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,4,1,0,0,1,0,0,0,9,2,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,2,0,1,0,0,1,2,0,Class_6
+36805,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,2,0,0,1,0,0,0,0,0,6,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,13,0,3,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,Class_6
+36806,0,0,11,0,0,0,0,0,0,0,7,0,2,0,0,6,0,0,0,0,2,1,0,0,0,2,13,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,13,0,0,1,1,0,0,0,18,0,2,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,5,0,Class_6
+36807,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,1,0,0,0,4,1,0,0,0,1,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,11,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
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+36809,0,0,3,4,0,0,0,0,0,0,7,3,0,0,0,3,0,1,0,0,1,0,0,1,0,2,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,1,12,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36810,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36811,0,0,2,1,0,0,0,0,1,0,0,0,2,0,0,2,0,14,0,0,0,3,1,1,1,1,3,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,2,0,0,1,0,0,0,1,0,0,0,9,1,0,1,1,0,10,2,1,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,1,0,Class_6
+36812,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,Class_6
+36813,0,0,13,6,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,16,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,5,0,0,0,0,0,0,11,0,0,0,2,1,1,0,0,0,0,1,0,0,0,1,0,0,0,12,0,4,0,0,0,0,0,0,3,1,0,0,0,Class_6
+36814,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,9,0,1,0,1,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,Class_6
+36815,0,1,13,11,0,0,0,0,0,1,0,0,0,0,0,5,0,2,0,0,0,2,0,0,0,7,4,2,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,1,0,7,0,0,4,0,0,0,0,5,0,1,0,1,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36816,4,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,2,0,2,0,3,0,9,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,1,5,0,0,1,0,0,1,0,1,1,0,0,1,0,0,0,5,0,0,1,0,0,0,0,2,0,0,Class_6
+36817,0,0,0,0,0,0,2,2,0,0,1,1,0,1,0,2,1,1,0,1,0,1,3,3,1,0,0,0,0,0,0,4,1,0,0,0,1,3,0,0,1,1,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,1,2,0,3,0,0,1,0,2,0,1,16,1,0,0,2,1,0,0,0,3,1,3,0,0,0,7,1,1,0,2,0,1,0,0,Class_6
+36818,0,0,2,0,5,0,1,0,1,0,6,0,0,1,0,5,0,4,0,0,1,2,0,0,0,1,6,2,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,2,0,0,2,0,0,0,0,2,0,5,0,0,0,0,0,0,0,2,0,0,0,3,0,1,0,1,0,2,1,2,0,0,0,0,0,2,0,3,0,3,0,0,0,0,0,0,0,2,0,Class_6
+36819,0,0,6,4,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,2,0,1,0,3,2,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36820,0,0,8,4,1,0,0,0,0,0,9,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,4,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36821,0,0,5,7,0,0,0,0,1,0,14,1,0,0,0,2,0,0,0,0,1,0,0,6,0,1,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,2,3,0,0,0,0,0,0,0,9,2,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36822,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,8,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,1,0,1,0,0,0,0,0,0,0,6,0,0,3,0,0,14,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,Class_6
+36823,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,1,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,5,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36824,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,0,0,2,1,4,0,0,0,0,0,5,1,0,0,1,0,1,0,0,0,5,0,0,0,0,0,5,0,0,0,0,0,2,0,0,2,0,0,11,0,1,0,0,1,1,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,2,0,Class_6
+36825,0,0,7,1,0,0,0,1,1,0,9,0,0,0,0,5,0,0,0,1,1,4,0,2,0,7,6,1,0,0,1,0,1,0,2,1,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,0,21,1,0,0,0,0,1,3,3,1,1,3,0,0,0,1,0,0,2,0,0,1,0,0,0,0,5,0,1,0,0,0,0,0,1,2,0,0,1,0,Class_6
+36826,0,1,13,2,0,0,1,0,1,0,9,0,0,0,0,5,0,2,0,0,2,1,0,0,0,0,9,1,0,0,0,0,0,0,1,0,0,2,0,1,1,0,1,1,0,7,0,3,1,0,0,2,0,12,0,0,3,0,0,0,6,3,0,0,2,3,1,0,0,1,0,0,0,0,0,2,0,0,0,4,0,2,0,0,0,0,0,1,1,0,0,1,0,Class_6
+36827,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,4,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36828,0,1,4,4,0,0,0,0,0,0,8,0,0,1,0,4,0,1,0,0,0,1,0,1,0,1,10,2,0,0,0,2,1,1,3,0,0,0,0,0,1,0,1,2,0,3,0,1,1,0,0,0,0,17,0,0,0,0,0,1,3,2,0,0,0,2,0,0,0,1,0,1,0,1,1,0,0,0,1,5,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+36829,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36830,0,0,3,9,0,0,0,0,0,0,16,0,2,0,0,3,0,1,0,0,0,0,0,8,0,2,17,1,0,0,2,4,0,1,5,2,0,0,0,0,0,1,0,2,0,3,2,5,4,0,0,0,0,7,1,2,4,1,0,1,6,0,1,0,0,1,2,1,0,0,0,0,0,0,1,3,0,0,0,34,0,6,2,1,0,0,0,0,4,0,0,1,0,Class_6
+36831,0,0,0,4,0,0,1,0,0,0,1,0,1,0,0,3,0,5,0,4,0,2,0,2,2,5,0,0,0,0,0,0,0,0,8,1,0,2,0,0,0,4,0,1,0,0,0,1,5,0,0,0,2,11,0,0,5,2,0,16,0,1,1,0,0,3,1,0,0,1,2,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36832,0,0,0,0,0,0,0,0,2,0,8,2,0,0,1,0,0,0,0,0,1,0,0,6,0,1,1,0,0,0,0,5,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,94,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36833,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36834,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,Class_6
+36835,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_6
+36836,0,1,8,0,0,0,0,0,1,0,16,0,0,0,0,1,0,2,0,0,4,2,0,0,0,0,33,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,10,0,1,1,0,0,5,1,1,0,0,0,1,0,1,6,2,0,1,0,0,1,0,1,0,0,0,0,9,0,1,1,0,1,12,0,4,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36837,0,2,1,4,0,0,0,0,0,0,1,1,0,2,1,6,0,3,0,0,0,7,0,13,0,7,0,4,0,0,1,2,1,0,6,0,0,0,0,0,0,10,0,0,0,2,1,0,2,0,0,1,0,8,1,0,0,1,0,2,2,0,0,2,1,0,2,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36838,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,4,1,7,0,2,0,5,0,2,2,4,0,0,0,0,0,0,0,0,4,1,0,2,0,0,0,15,0,1,0,0,7,0,1,0,0,0,0,3,1,0,19,0,1,8,0,3,0,1,0,5,0,2,0,2,0,1,0,0,2,0,0,3,0,1,1,0,0,1,0,0,0,0,1,8,0,2,0,Class_6
+36839,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,2,1,2,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,10,0,1,0,1,7,1,0,0,0,0,1,6,2,0,0,0,0,2,0,0,0,4,0,2,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,Class_6
+36840,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36841,0,0,5,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,14,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,1,0,0,0,0,Class_6
+36842,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,5,0,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,6,0,0,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+36843,3,0,1,5,0,0,0,0,0,0,0,1,0,0,0,3,0,2,0,1,0,0,0,5,0,3,0,0,14,0,0,0,0,0,1,4,2,0,0,0,0,5,0,0,0,0,1,5,0,0,0,0,0,1,1,0,1,0,0,0,0,2,0,0,0,4,14,0,0,1,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+36844,0,0,0,6,0,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,2,0,2,0,10,0,0,0,0,0,0,0,3,2,0,0,0,0,3,0,4,0,1,0,0,0,0,0,0,0,0,0,3,0,0,5,0,1,8,0,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,2,0,Class_6
+36845,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,1,0,3,1,6,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,7,0,0,0,0,0,2,0,4,0,0,0,2,1,0,1,0,1,12,1,1,0,0,0,5,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,Class_6
+36846,0,1,1,0,0,0,0,0,0,0,15,0,0,1,0,1,0,3,0,1,0,0,0,1,0,0,11,0,0,0,0,1,0,0,1,0,0,2,2,1,0,0,0,0,0,0,0,1,0,0,0,15,0,6,0,0,0,3,0,0,0,0,1,3,0,1,3,0,0,0,0,0,0,24,0,1,0,0,0,15,0,0,0,0,0,0,0,0,0,2,0,0,1,Class_6
+36847,0,3,4,3,0,0,0,0,0,0,16,1,0,0,0,3,0,0,0,0,1,0,0,1,0,0,37,3,0,1,2,1,0,0,2,1,2,0,0,3,0,1,2,1,1,4,0,1,0,0,0,0,0,13,0,1,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,Class_6
+36848,0,1,0,0,0,0,0,1,0,0,0,2,0,0,1,3,1,1,0,0,0,3,0,1,0,6,2,0,0,0,1,0,0,0,1,2,0,0,0,1,1,1,0,1,0,0,0,0,1,0,0,0,0,4,0,0,1,0,2,12,0,8,0,2,1,9,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,Class_6
+36849,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,1,0,0,0,1,0,4,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,1,6,0,2,0,0,0,3,0,0,0,0,0,3,0,0,0,0,1,10,0,0,0,1,0,1,11,4,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,5,1,0,0,0,0,0,0,1,Class_6
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+36859,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36860,0,1,2,5,0,0,0,1,0,0,8,0,2,3,1,1,1,2,1,0,1,5,0,0,1,0,3,2,0,0,2,0,1,2,0,0,0,1,0,2,0,0,0,1,0,4,0,0,1,0,0,3,0,10,0,0,0,1,0,0,5,1,1,0,0,3,1,0,0,0,0,0,0,3,0,0,0,0,1,23,0,2,0,0,0,0,0,1,2,0,0,0,0,Class_6
+36861,0,0,6,2,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,15,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36862,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,8,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+36863,2,0,3,2,0,0,2,0,0,1,8,0,1,4,0,3,0,0,0,1,0,1,0,0,2,1,6,0,0,0,0,2,0,2,3,0,0,2,0,1,0,0,0,11,0,1,0,2,10,0,0,0,0,17,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36864,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,6,0,1,0,1,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+36865,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,0,4,0,1,0,14,0,2,0,0,0,0,0,3,4,0,0,0,0,2,0,2,0,0,0,0,0,1,1,0,1,0,0,4,1,0,0,0,0,8,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+36866,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+36867,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,5,0,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,0,0,1,6,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,Class_6
+36868,0,0,6,4,0,0,0,0,0,0,9,0,0,1,0,5,0,1,0,0,0,3,0,2,0,5,6,4,0,0,0,1,0,0,1,0,0,1,0,0,1,3,0,1,0,8,0,0,2,0,0,0,0,11,0,0,0,0,0,1,0,0,1,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+36869,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,4,1,5,0,0,0,11,0,7,1,6,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,0,5,0,0,1,0,0,13,0,0,0,1,0,2,3,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,Class_6
+36870,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,Class_6
+36871,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36872,0,5,10,13,0,0,0,0,0,0,1,0,2,0,0,1,0,1,0,1,0,3,0,0,0,4,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,4,0,4,0,1,0,0,0,1,0,3,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36873,0,0,16,7,1,0,0,0,0,0,10,0,2,1,0,3,0,1,0,0,0,3,1,0,0,0,9,3,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,19,0,0,0,0,0,2,0,12,0,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,1,0,2,0,0,0,0,0,0,0,0,0,Class_6
+36874,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+36875,0,1,0,2,0,0,0,0,0,0,0,0,1,0,4,1,0,2,0,1,0,1,0,1,0,16,1,0,0,0,2,0,1,1,2,1,0,2,1,2,0,2,0,0,1,0,1,0,2,1,0,1,0,9,0,0,3,0,0,12,2,1,1,0,1,1,1,1,0,1,0,1,0,0,2,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,2,2,0,Class_6
+36876,0,0,17,10,0,0,0,0,0,1,21,0,2,2,0,9,0,2,0,0,8,5,1,8,0,5,10,8,0,0,2,0,1,0,12,1,0,1,0,2,2,1,0,0,1,7,0,2,2,0,0,3,0,34,0,7,0,21,0,0,4,0,0,0,0,1,0,0,0,3,0,2,0,10,0,0,0,0,0,6,0,2,0,0,0,0,1,2,0,2,0,0,0,Class_6
+36877,0,0,0,1,0,0,0,0,0,4,0,0,0,2,0,2,2,4,0,1,0,4,0,1,1,0,0,1,0,0,2,2,1,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,8,0,0,0,3,0,0,1,0,0,4,0,4,1,0,0,0,0,2,0,9,2,2,0,1,0,0,1,0,0,0,0,0,0,0,8,4,2,5,0,0,0,1,0,Class_6
+36878,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,3,2,0,0,0,0,0,0,0,2,1,1,0,0,0,3,0,3,0,1,0,0,1,2,0,0,0,0,0,3,0,0,0,0,1,2,0,0,0,3,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36879,0,0,8,4,0,0,0,3,0,1,7,0,1,0,0,7,0,0,0,0,0,2,0,0,1,2,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,2,0,0,0,0,3,0,0,0,0,10,0,0,0,0,0,0,3,0,2,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36880,0,0,12,1,0,0,0,0,0,0,8,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,1,0,5,0,0,0,0,0,1,2,1,2,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,8,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+36881,0,0,7,4,0,0,0,0,0,0,8,0,0,0,1,0,0,0,1,0,0,0,0,7,0,1,2,1,0,0,0,3,0,1,2,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,5,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36882,0,0,0,0,0,0,0,4,0,0,2,0,2,0,0,2,3,4,0,1,0,2,2,11,0,0,0,0,1,0,0,5,1,3,2,1,3,0,60,4,0,1,1,0,8,1,0,4,0,1,0,0,0,4,0,0,1,0,0,1,0,1,0,0,0,0,3,4,0,1,1,0,3,0,0,3,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Class_6
+36883,0,0,2,3,0,0,0,1,1,0,10,1,0,0,0,3,0,1,0,0,0,2,0,0,1,2,4,0,0,0,3,2,0,2,3,2,0,0,0,1,0,0,1,0,0,2,0,0,1,0,0,3,0,13,1,1,1,2,0,2,4,0,1,0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,0,27,0,5,0,0,0,0,0,0,1,1,0,1,0,Class_6
+36884,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,2,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,2,Class_6
+36885,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,7,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_6
+36886,1,0,8,32,0,0,0,0,0,0,7,0,0,2,0,4,0,1,0,0,3,4,5,0,0,6,0,0,0,0,0,2,2,0,2,0,0,3,0,0,0,0,0,0,0,4,0,2,3,0,2,0,0,23,4,0,0,1,0,1,14,5,2,0,0,0,0,0,0,0,0,4,2,0,0,6,0,0,0,25,0,3,0,0,2,4,0,0,4,0,2,4,0,Class_6
+36887,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
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+36889,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+36896,0,0,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,2,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,12,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+36897,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,1,0,0,2,1,0,0,4,2,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,4,0,0,7,1,0,0,0,0,0,5,3,0,2,0,1,3,0,1,0,2,2,1,5,0,0,2,2,0,0,0,1,1,0,0,3,0,0,0,0,0,7,0,1,1,0,0,0,1,0,Class_6
+36898,0,0,0,1,0,0,0,0,0,0,7,4,1,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36899,0,0,5,2,0,0,0,0,0,0,8,0,1,0,0,3,0,0,0,0,2,3,0,2,0,0,5,0,0,0,1,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,1,1,0,0,2,0,4,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,16,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36900,0,0,0,1,0,1,0,2,0,2,0,0,1,1,1,2,0,0,0,1,0,1,0,1,5,3,0,0,3,0,0,3,2,2,1,0,0,0,0,0,3,2,0,0,0,0,0,3,0,0,0,0,27,8,2,0,0,0,0,2,0,10,0,0,2,2,2,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,2,0,0,3,0,0,3,0,Class_6
+36901,0,1,0,1,0,0,0,0,0,0,10,0,1,2,0,3,0,1,0,0,0,4,0,11,0,8,0,2,0,0,0,1,0,0,3,0,1,0,0,1,1,2,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+36902,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36903,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36904,0,1,5,6,0,0,1,0,0,0,13,0,0,2,0,6,0,4,0,0,3,3,0,1,0,0,12,1,0,0,1,2,1,0,7,0,0,8,1,0,1,2,0,5,0,2,0,0,5,0,0,1,1,20,0,1,0,1,0,1,3,1,0,1,1,1,1,0,1,1,0,1,0,4,0,2,1,1,0,18,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36905,0,0,2,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,2,0,0,0,0,0,5,0,0,1,0,0,16,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36906,0,0,4,5,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,3,0,0,0,4,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,8,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36907,0,1,7,6,0,0,0,0,2,1,11,0,1,0,0,2,0,1,1,0,0,3,0,0,0,4,7,1,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,0,2,0,1,2,2,0,8,0,6,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,2,0,5,0,0,0,0,0,5,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36908,1,1,3,2,0,0,0,0,0,0,6,0,0,0,0,4,0,2,0,2,0,3,0,1,0,1,0,3,0,0,1,1,0,0,0,0,0,4,0,2,0,3,2,3,0,1,0,1,5,0,0,0,0,11,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+36909,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,1,0,1,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,15,0,0,0,0,0,2,0,0,0,0,0,7,0,2,3,0,0,11,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,2,0,Class_6
+36910,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,1,2,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,3,0,1,1,11,0,0,5,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,1,0,0,1,1,0,Class_6
+36911,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,Class_6
+36912,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,4,0,1,0,0,0,2,0,1,4,0,0,0,0,0,0,0,5,1,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,Class_6
+36913,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36914,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,10,0,4,0,0,1,6,0,0,0,3,1,3,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,0,3,2,3,0,1,0,0,9,1,0,0,0,0,0,0,2,0,0,2,2,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+36915,0,2,12,23,0,0,0,0,0,0,10,1,3,0,0,19,0,6,1,0,1,9,1,1,0,8,5,9,0,0,0,2,2,0,14,0,1,2,1,3,0,1,1,4,0,3,2,1,9,0,0,1,2,41,1,20,1,1,0,1,10,4,0,1,1,6,2,2,0,3,0,4,0,2,0,1,1,0,0,17,1,12,0,1,0,0,1,1,6,0,0,3,0,Class_6
+36916,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36917,0,0,5,13,0,0,0,0,1,0,7,0,1,0,0,0,0,1,0,0,3,1,0,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36918,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36919,0,0,0,1,0,0,0,0,1,1,2,0,0,4,0,3,1,0,0,2,0,6,0,0,0,4,0,0,1,0,0,2,0,0,0,0,0,6,1,0,0,11,0,2,1,0,0,4,0,0,0,1,0,4,0,0,0,0,0,6,0,2,0,3,0,1,4,0,0,10,0,1,0,4,0,1,0,0,0,0,0,0,0,0,1,0,16,0,1,0,0,0,2,Class_6
+36920,0,1,1,1,0,0,1,0,0,0,0,0,0,0,2,0,1,5,0,2,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,1,0,2,0,0,2,0,0,10,0,0,0,1,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,Class_6
+36921,6,0,0,2,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,4,5,3,0,0,1,0,0,0,1,2,2,0,1,1,0,1,0,3,0,0,0,0,0,2,1,0,0,0,0,3,1,0,1,0,0,1,0,11,1,0,0,0,11,0,0,0,0,0,0,1,1,0,0,0,9,0,0,0,1,0,0,4,1,0,0,0,1,2,0,Class_6
+36922,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,0,0,3,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,Class_6
+36923,0,1,2,1,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,3,1,0,0,0,5,5,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,2,0,0,2,0,0,3,0,1,0,0,1,0,0,0,5,5,1,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,22,0,8,0,0,0,0,1,0,4,0,0,0,0,Class_6
+36924,0,4,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,1,0,0,0,2,0,6,0,8,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,13,0,0,0,0,0,0,2,0,0,0,0,1,4,0,9,0,0,5,3,2,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,10,0,0,0,0,2,0,0,0,2,0,Class_6
+36925,0,0,13,3,0,0,0,0,0,0,13,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,4,0,0,0,0,0,3,1,8,0,0,0,0,0,1,1,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36926,0,1,1,6,0,0,1,1,0,0,0,0,3,0,0,5,3,7,0,0,0,3,0,6,0,14,7,1,0,0,0,0,0,0,3,1,0,1,0,0,0,7,0,5,0,1,0,0,0,0,1,0,0,9,1,0,5,0,1,4,1,1,0,5,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,1,0,0,0,1,0,Class_6
+36927,0,1,11,8,0,0,0,0,0,1,11,0,0,0,0,1,0,0,0,0,2,1,0,2,0,2,16,1,0,0,1,0,0,0,1,1,0,0,0,0,0,1,1,1,0,12,0,0,0,0,0,2,0,13,0,0,0,1,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36928,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,3,0,1,0,6,0,0,0,0,0,0,0,1,6,1,0,2,0,0,2,8,0,0,0,0,0,0,2,0,0,0,0,6,0,0,0,1,0,12,0,1,0,1,1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,3,0,Class_6
+36929,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,2,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,8,0,0,0,0,0,3,0,0,0,4,0,1,0,0,0,0,0,3,0,1,0,1,0,3,12,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,2,5,0,0,0,0,1,2,Class_6
+36930,0,1,0,0,0,0,1,3,0,3,1,0,3,0,0,0,20,3,0,0,0,0,0,6,0,0,0,0,0,0,0,3,1,3,0,0,0,1,0,0,1,0,0,1,0,0,0,5,0,0,0,0,0,1,2,0,0,0,0,5,0,1,0,0,1,0,18,2,0,0,1,1,3,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,1,1,0,Class_6
+36931,0,0,2,3,1,0,1,1,0,0,6,0,1,2,0,5,0,1,0,0,1,5,0,0,0,12,2,3,0,0,0,2,0,0,0,0,0,0,0,0,0,9,0,3,0,1,0,0,0,0,0,0,0,14,3,0,0,0,1,4,0,1,0,2,2,4,0,0,0,2,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+36932,0,0,7,17,0,0,3,0,0,1,15,0,0,1,0,5,0,0,0,0,3,2,0,2,0,10,9,3,0,0,2,0,2,0,2,0,0,0,0,1,0,0,0,2,0,4,0,2,2,0,0,2,1,13,0,0,1,2,0,2,9,0,2,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,14,0,7,0,0,0,0,0,0,4,0,0,1,0,Class_6
+36933,0,0,8,5,0,0,1,0,0,0,7,0,0,1,0,6,0,0,0,2,1,0,0,0,1,2,3,1,1,0,0,0,0,1,1,0,0,1,0,0,1,0,0,2,0,0,0,0,6,0,0,3,2,11,0,0,0,0,1,0,4,2,0,0,1,1,0,0,0,0,0,3,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+36934,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,3,0,5,1,0,0,0,0,0,0,2,1,0,0,0,0,3,0,0,0,1,0,1,0,0,0,2,0,1,0,2,1,0,0,0,0,0,1,0,0,0,0,1,0,4,5,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,4,1,0,0,0,0,0,Class_6
+36935,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+36936,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,5,0,6,0,0,0,5,0,3,4,1,0,1,0,0,0,1,1,0,0,3,0,0,0,0,0,5,0,2,0,2,3,1,0,0,0,0,2,4,2,0,3,0,5,0,0,3,0,5,0,4,9,2,0,1,0,1,1,0,2,1,0,0,0,0,0,0,0,0,0,8,4,1,1,0,1,3,0,Class_6
+36937,2,1,3,0,0,0,0,0,0,0,11,0,1,0,2,2,0,1,0,1,0,2,0,0,2,2,0,0,0,0,0,0,0,0,7,0,0,4,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,3,0,0,1,2,0,0,3,0,0,0,0,4,0,0,0,1,0,0,0,0,0,2,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+36938,0,3,0,1,0,0,1,0,0,0,7,0,0,0,0,6,0,1,0,0,1,0,0,3,0,4,5,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,5,0,0,0,0,10,1,1,0,1,0,0,4,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+36939,0,0,2,5,0,0,0,0,0,1,8,0,0,1,0,2,0,1,0,0,0,1,0,2,0,2,2,2,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2,0,3,0,0,0,1,0,0,1,0,0,1,0,Class_6
+36940,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,24,0,0,0,0,0,1,0,2,6,1,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,6,0,0,19,1,0,8,0,0,0,0,0,5,23,1,0,0,1,0,0,0,13,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36941,0,0,20,4,0,0,0,0,0,0,10,1,2,0,1,3,0,1,0,0,0,7,2,1,0,0,8,0,0,0,1,0,0,0,2,0,0,2,0,0,0,0,0,0,0,6,0,0,3,0,0,1,0,12,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,1,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36942,0,1,1,1,0,0,0,0,0,0,9,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,3,0,0,0,0,0,9,0,5,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,0,2,0,6,0,0,0,0,0,8,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+36943,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36944,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,2,0,3,1,0,0,2,0,0,2,1,1,0,0,0,1,0,0,0,2,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,1,1,1,1,0,0,5,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,3,0,0,0,0,Class_6
+36945,0,0,0,0,0,0,0,1,0,0,2,0,0,2,2,3,0,1,0,0,0,0,0,1,1,1,2,0,0,0,0,0,2,6,1,0,0,1,0,9,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,3,5,0,0,11,0,8,0,0,3,0,2,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,3,0,1,4,0,Class_6
+36946,0,0,13,1,0,0,0,0,0,2,9,0,0,1,0,3,0,0,0,0,1,1,0,2,0,1,6,2,0,0,2,0,0,1,1,1,0,1,1,0,0,0,0,2,0,11,0,0,1,0,1,1,0,16,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36947,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,1,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,3,0,0,0,0,3,0,0,2,0,0,0,1,0,0,1,0,0,7,0,0,0,0,0,1,3,6,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36948,0,3,8,6,0,0,0,0,4,0,12,0,0,0,0,3,0,0,0,2,0,0,0,0,0,2,6,1,0,1,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,6,0,0,2,0,0,1,1,14,0,1,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,10,0,1,0,0,0,1,0,0,1,0,0,0,1,Class_6
+36949,1,0,11,5,0,0,0,0,0,0,8,0,0,0,0,2,0,1,0,0,1,2,0,0,0,1,13,2,0,0,2,1,2,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,1,0,0,0,0,8,0,0,0,1,0,1,2,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36950,0,1,10,6,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,9,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,1,0,5,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+36951,1,0,8,1,0,0,0,0,0,1,9,1,0,0,0,3,0,0,0,0,4,2,0,4,0,2,4,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,4,0,2,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36952,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,2,0,2,1,1,0,0,0,0,0,3,0,0,0,0,3,3,27,1,0,1,0,1,17,1,0,1,1,1,0,0,0,6,1,0,0,0,0,0,0,0,0,1,1,0,2,0,1,0,0,0,0,2,0,5,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_6
+36953,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,1,5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+36954,0,0,1,4,0,0,0,0,0,1,9,0,0,0,0,4,0,0,0,0,0,2,0,1,0,1,5,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,9,0,2,0,1,0,1,2,1,0,0,0,4,2,0,0,0,0,0,2,0,1,1,0,0,0,8,0,5,0,0,0,0,0,0,2,1,0,0,0,Class_6
+36955,0,0,4,12,0,0,0,1,0,0,8,1,0,0,0,3,0,4,0,0,0,2,0,0,0,2,6,2,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,3,0,1,2,0,0,0,0,0,0,15,0,0,0,0,1,1,0,1,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Class_6
+36956,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,3,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,6,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36957,1,0,0,0,0,0,0,4,0,1,0,0,0,0,1,3,0,1,0,0,0,1,0,9,1,1,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,3,0,1,0,0,0,0,2,0,0,0,0,2,0,1,1,0,0,18,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,6,0,Class_6
+36958,0,0,2,5,0,0,0,0,6,0,2,0,0,2,0,3,0,1,1,0,1,1,0,12,0,7,2,0,0,1,0,1,0,1,2,0,0,0,0,0,0,1,0,3,0,1,0,0,1,0,0,0,0,4,1,2,1,1,0,3,3,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,1,11,0,1,1,0,0,0,0,0,2,0,0,0,0,Class_6
+36959,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+36960,0,2,8,20,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,3,3,0,3,0,9,5,2,0,0,0,0,0,2,0,0,0,3,0,3,0,0,0,0,0,10,0,0,0,0,0,1,0,9,3,0,0,0,0,1,2,0,2,1,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36961,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+36962,0,0,3,9,0,0,0,0,0,0,10,0,2,0,1,4,0,2,0,0,0,3,0,0,0,1,11,3,0,0,4,0,0,2,1,0,5,0,0,0,0,1,0,4,1,2,0,0,2,0,0,0,0,13,1,1,0,1,0,0,1,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,5,1,5,0,0,0,0,0,0,4,0,0,0,0,Class_6
+36963,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,4,1,0,1,0,0,0,7,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,3,0,0,4,0,0,6,0,0,0,0,0,0,8,1,0,0,2,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,4,0,0,1,0,2,0,Class_6
+36964,0,0,2,14,0,0,0,0,3,0,7,0,1,0,0,2,0,0,0,0,1,1,0,0,0,6,0,1,0,0,0,1,0,0,4,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,2,0,1,1,4,2,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,4,0,0,0,0,0,1,1,0,0,1,0,Class_6
+36965,0,0,7,11,0,0,0,0,0,1,10,1,1,0,0,5,0,0,0,0,2,3,0,0,0,0,7,0,0,0,0,0,0,0,3,1,0,1,0,2,0,1,0,0,0,1,0,0,3,0,0,1,0,10,1,0,1,0,0,0,3,1,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,8,0,4,0,0,0,1,0,0,8,0,0,0,1,Class_6
+36966,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,7,0,0,1,4,1,0,1,0,0,2,0,2,1,0,0,0,0,0,0,11,0,6,0,1,0,1,0,0,0,0,1,25,0,0,0,0,0,3,3,0,0,1,0,1,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_6
+36967,0,0,11,13,0,0,0,1,2,0,8,0,0,2,0,0,0,0,1,0,1,2,1,0,0,1,5,3,1,0,1,1,0,2,0,0,0,0,0,0,0,1,0,2,0,6,0,0,1,0,0,1,0,7,0,0,0,0,0,0,5,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,6,1,5,0,0,1,0,0,0,3,0,0,1,0,Class_6
+36968,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,4,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36969,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+36970,0,1,6,3,0,0,0,0,0,1,3,0,1,0,2,16,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,4,0,0,0,0,10,0,0,1,1,32,3,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36971,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,2,4,0,0,0,0,1,0,1,0,1,0,0,1,0,0,4,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,3,14,0,2,0,0,0,1,10,0,0,1,2,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36972,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,4,1,0,0,0,0,4,0,2,0,12,0,0,0,0,2,0,0,0,3,1,0,2,0,1,3,1,0,1,0,1,2,3,1,0,0,0,2,26,0,0,1,0,0,3,0,0,0,0,0,2,2,0,0,0,0,1,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+36973,1,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,2,0,20,0,0,0,0,0,18,1,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+36974,1,0,0,0,0,0,0,0,0,0,12,0,0,0,0,2,0,1,0,0,1,1,0,17,2,0,1,0,0,1,0,4,0,0,0,0,0,1,3,3,0,0,0,0,0,0,0,3,0,1,0,4,0,2,0,1,1,1,0,0,1,0,1,0,0,0,7,0,0,0,1,0,1,42,0,1,0,0,0,5,0,0,0,1,0,2,0,0,3,1,0,0,0,Class_6
+36975,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,2,0,0,0,2,0,6,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,Class_6
+36976,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,0,0,8,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36977,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,0,1,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,1,16,0,0,0,0,0,0,7,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,Class_6
+36978,0,0,3,6,0,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,0,0,6,1,2,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,5,0,0,0,0,0,0,2,9,0,0,0,4,0,1,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36979,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,13,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36980,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,3,1,5,0,0,0,3,0,1,0,8,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,27,0,3,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,4,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,2,Class_6
+36981,0,0,11,6,0,0,0,0,2,0,12,0,0,0,2,2,0,0,0,0,8,3,0,21,1,2,18,1,0,1,7,3,0,1,2,1,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,1,10,0,0,1,0,0,0,3,0,0,1,0,0,4,0,0,0,0,0,0,4,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36982,0,1,3,1,0,0,0,0,0,0,16,0,0,0,1,0,0,0,0,1,0,0,0,5,1,0,11,2,0,0,0,1,0,0,3,0,0,1,1,0,0,0,0,1,0,5,0,1,1,0,0,1,0,9,0,1,0,6,0,0,0,2,0,0,1,0,1,0,0,1,0,1,1,0,0,0,0,1,0,3,0,4,1,0,0,0,1,0,1,0,0,0,0,Class_6
+36983,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,2,0,4,0,0,0,0,2,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+36984,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+36985,0,0,3,1,0,0,0,2,0,0,0,1,0,1,0,3,0,1,0,0,0,1,0,3,2,8,0,1,0,0,2,3,1,0,1,4,0,1,0,1,0,6,0,1,0,0,0,1,1,0,0,0,0,5,0,0,0,0,0,5,0,3,0,0,0,3,6,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+36986,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,2,4,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,4,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+36987,0,0,14,6,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,0,1,1,0,0,0,15,2,2,0,0,0,2,0,3,1,0,0,0,0,0,0,2,0,2,0,3,0,0,0,0,0,0,0,10,0,0,0,0,0,2,2,0,0,2,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+36988,1,0,1,4,0,0,0,2,0,0,7,0,0,0,0,6,0,1,0,0,0,1,0,0,0,1,3,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,2,0,16,0,1,0,1,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,1,2,0,0,0,0,Class_6
+36989,1,1,0,2,0,0,1,0,0,0,1,1,0,0,1,3,0,0,0,1,0,6,0,11,0,2,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,12,0,0,7,0,0,10,0,0,0,3,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+36990,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36991,1,0,0,0,0,0,0,5,0,0,1,0,1,0,0,0,1,1,0,0,2,5,0,6,1,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,0,11,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,6,0,1,0,1,0,3,10,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36992,0,1,15,9,0,0,1,1,0,1,11,0,1,0,1,5,0,0,0,0,0,4,0,2,0,2,3,1,0,0,0,1,0,0,2,0,1,1,0,0,0,1,0,1,0,4,1,0,4,0,0,0,0,17,0,3,0,0,1,0,4,0,1,0,1,1,1,0,0,0,0,0,1,1,0,1,0,0,0,8,0,4,0,1,0,0,1,1,1,0,0,0,0,Class_6
+36993,0,0,11,5,0,0,0,0,0,0,10,0,0,0,0,1,0,1,1,1,1,0,0,0,0,0,3,3,0,0,0,0,1,1,0,1,0,0,0,1,0,1,1,2,0,15,0,0,1,0,0,0,0,8,0,2,0,1,1,0,0,1,0,0,2,1,1,0,1,1,0,2,0,0,0,0,0,0,0,7,0,3,1,0,0,0,0,0,1,2,0,0,0,Class_6
+36994,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+36995,0,0,0,0,0,0,0,0,0,1,1,0,1,6,7,2,0,0,0,2,0,0,0,16,3,0,0,0,1,0,1,2,0,0,1,2,5,5,58,0,0,0,0,1,21,0,0,3,0,2,0,0,0,3,0,0,2,0,0,1,0,0,0,0,0,2,0,4,0,2,1,3,2,1,3,5,0,0,1,1,0,0,1,0,2,0,2,0,0,1,3,0,0,Class_6
+36996,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+36997,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,2,1,0,0,0,0,0,3,0,5,0,0,0,0,0,0,1,3,0,0,0,0,0,3,0,13,0,2,0,0,0,5,0,0,0,0,0,5,0,2,3,0,0,11,0,5,0,0,1,1,2,0,0,0,0,2,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,2,0,Class_6
+36998,0,1,7,3,0,0,0,0,0,1,9,0,0,0,0,4,0,2,0,1,0,5,0,0,1,5,6,2,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,5,0,0,2,0,0,0,1,23,0,1,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+36999,0,1,10,12,0,0,0,0,0,0,9,1,0,0,0,2,0,0,0,0,1,2,0,2,0,3,13,8,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,10,0,1,0,0,0,2,0,12,0,1,0,0,0,0,3,1,1,2,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,5,0,0,1,0,0,0,0,0,1,0,0,1,0,Class_6
+37000,1,2,0,0,0,0,8,0,0,3,0,0,0,1,0,0,0,1,2,8,0,0,0,13,0,0,0,0,0,0,0,2,0,0,1,2,0,0,4,0,0,0,1,0,3,0,0,6,0,1,0,0,0,0,2,0,3,3,0,12,0,0,0,0,1,0,9,0,3,0,1,0,1,1,0,9,0,2,0,0,1,0,24,0,0,0,0,0,0,4,0,0,1,Class_6
+37001,0,1,22,8,0,0,0,0,0,0,19,1,3,2,0,10,1,3,0,0,8,5,0,0,0,1,15,1,0,0,0,0,1,1,0,0,0,5,0,1,0,0,2,4,1,19,0,1,1,0,0,0,0,21,1,0,0,0,0,0,15,1,0,1,1,1,1,0,0,0,0,0,0,1,0,2,1,0,3,12,0,1,0,0,0,1,0,1,5,0,0,0,0,Class_6
+37002,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,14,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+37003,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37004,0,0,1,0,0,1,0,0,0,0,7,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,9,2,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,4,0,1,1,0,1,0,0,1,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37005,0,0,16,7,0,0,0,5,0,1,8,0,0,3,0,1,0,0,0,1,0,3,0,0,0,6,13,3,0,0,1,0,0,1,2,0,0,1,1,0,0,0,0,1,0,18,0,0,0,0,0,1,0,14,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37006,0,0,5,3,0,0,0,0,1,2,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37007,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,2,0,0,17,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37008,0,0,4,7,1,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,1,0,13,0,5,1,3,0,0,0,0,0,1,5,0,0,1,0,0,0,0,0,1,0,3,0,1,2,0,0,4,0,9,0,0,4,0,0,12,0,1,0,0,0,2,3,0,0,1,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37009,0,0,14,5,0,0,0,0,0,0,9,0,0,0,0,5,1,1,0,0,3,0,0,1,0,3,4,4,0,0,1,0,1,0,2,1,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,3,0,18,2,8,0,0,0,1,1,1,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,1,0,0,0,0,1,2,1,0,0,0,0,Class_6
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+37017,0,0,13,1,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,1,0,10,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,7,0,1,4,1,0,1,0,1,1,0,0,1,0,0,1,3,0,0,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37018,0,0,2,1,0,0,0,0,4,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,2,1,0,1,1,0,0,0,1,2,0,0,0,0,0,1,0,2,0,1,0,6,1,6,0,1,0,1,0,0,2,0,1,0,1,1,0,0,1,0,0,0,0,28,0,0,0,0,0,15,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_6
+37019,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,2,0,0,0,1,0,3,0,3,1,1,1,0,0,0,1,1,0,0,0,0,1,1,1,3,0,0,0,1,0,2,0,0,0,0,1,4,2,0,1,0,1,7,0,1,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37020,0,1,17,9,0,0,0,0,0,1,7,0,1,0,0,1,0,0,0,0,3,0,0,0,0,1,7,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,2,1,0,9,0,0,0,0,0,3,0,8,0,1,0,0,0,0,2,1,0,4,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37021,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+37022,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,12,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,7,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,Class_6
+37023,0,0,1,0,0,0,0,0,0,2,0,2,0,5,0,8,0,3,0,0,1,3,0,1,1,3,0,0,0,0,0,1,1,0,1,0,1,3,0,3,0,6,0,5,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,3,0,6,0,1,2,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,1,0,0,1,1,Class_6
+37024,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,8,0,1,0,1,0,0,1,0,2,1,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,1,0,4,0,0,3,0,0,12,0,1,0,2,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
+37025,0,0,2,4,0,0,0,0,1,0,14,0,0,0,0,4,0,2,2,0,2,2,0,1,0,4,4,1,0,0,3,2,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,3,0,8,1,1,0,0,0,1,5,0,2,0,1,1,0,0,0,0,0,0,0,3,0,0,1,0,1,14,0,2,0,0,0,0,0,0,6,0,0,0,0,Class_6
+37026,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,19,0,0,0,0,0,0,0,0,4,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37027,0,0,13,5,0,0,0,0,0,0,10,0,1,1,0,1,0,2,0,0,2,4,0,0,0,1,3,5,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,7,0,0,1,0,0,0,0,22,0,0,0,0,0,0,1,1,1,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37028,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,Class_6
+37029,0,0,5,3,0,0,1,0,1,0,14,0,0,0,1,1,0,0,0,0,3,3,0,2,0,1,37,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,0,1,0,1,1,0,0,4,0,4,0,0,0,0,1,0,2,4,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37030,0,1,1,1,0,0,0,0,0,0,17,0,0,0,0,0,0,1,0,0,0,0,0,9,0,0,11,0,0,0,1,3,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,1,0,10,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37031,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37032,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+37033,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,0,0,12,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37034,0,1,15,3,0,1,2,0,0,0,9,0,1,1,0,3,0,0,0,3,0,6,0,0,0,2,0,5,0,0,0,0,0,0,2,1,0,3,0,0,0,1,0,3,0,7,0,0,0,0,0,0,0,18,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,2,0,3,0,1,Class_6
+37035,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37036,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,5,0,0,0,0,3,1,0,1,0,0,12,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,3,0,0,0,1,0,0,1,5,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37037,2,2,0,0,0,0,1,1,0,2,1,0,1,0,1,0,0,2,1,1,0,2,0,6,1,0,0,0,0,1,1,0,0,0,0,0,0,7,0,1,0,0,0,1,0,1,0,0,2,1,4,0,1,0,1,0,0,2,0,1,0,1,1,1,2,0,1,0,5,0,1,0,0,2,2,3,0,0,0,2,0,0,3,0,0,0,1,0,1,0,0,0,0,Class_6
+37038,0,0,9,7,0,0,0,0,0,0,4,0,0,0,0,3,0,1,1,0,3,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,2,0,2,4,0,0,0,0,8,1,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,2,0,0,1,1,0,0,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37039,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21,0,1,0,2,0,0,0,1,0,0,1,0,0,0,0,2,0,4,0,1,0,0,0,2,0,0,0,4,0,2,1,1,0,1,0,0,3,0,0,0,2,0,5,0,0,1,0,0,0,13,0,0,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37040,3,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,11,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+37041,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37042,0,0,1,0,0,0,2,0,0,0,6,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,Class_6
+37043,0,1,12,3,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,0,2,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37044,0,0,5,3,0,0,0,1,0,1,11,0,0,1,0,3,0,0,0,0,2,0,0,3,0,4,28,0,0,1,2,1,1,0,4,1,2,1,0,0,0,0,1,0,3,3,0,1,1,1,0,4,0,17,0,5,0,0,0,0,1,2,0,0,1,0,0,0,0,1,0,0,3,4,0,0,0,0,0,7,0,1,1,0,0,0,0,0,0,0,0,0,1,Class_6
+37045,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,8,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,Class_6
+37046,1,1,7,3,0,0,1,0,0,1,20,0,0,0,0,2,0,0,0,0,1,0,0,7,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,5,0,1,1,0,0,0,0,6,0,0,0,6,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,3,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,3,0,Class_6
+37048,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,2,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,4,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,6,0,0,0,0,0,0,Class_6
+37049,0,0,1,1,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,16,2,0,0,1,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37050,0,1,19,2,0,0,1,0,1,0,13,0,2,1,0,2,0,4,0,1,1,1,0,0,0,1,5,1,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,11,0,0,0,0,0,0,0,20,0,0,0,0,0,0,4,1,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_6
+37051,0,0,8,1,0,0,0,0,3,0,9,2,0,1,0,2,0,0,0,0,0,1,0,0,0,0,3,2,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,2,0,9,0,0,1,0,0,1,0,9,1,0,0,0,0,0,1,1,0,0,4,3,0,0,0,0,0,0,0,1,0,0,0,0,1,4,0,2,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37052,0,0,3,1,0,0,0,0,0,0,10,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37053,0,2,10,7,0,0,0,0,0,0,8,3,0,0,0,1,0,0,0,0,0,1,0,1,0,2,9,8,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,7,0,0,0,0,0,2,0,17,0,1,0,0,0,1,1,0,2,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,Class_6
+37054,0,0,8,4,0,0,0,0,1,0,12,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,35,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,1,8,0,0,0,1,0,0,0,7,0,2,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37055,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37056,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37057,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,39,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37058,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37059,0,0,2,3,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,1,9,0,13,0,0,0,0,2,5,1,2,5,4,0,0,0,1,0,0,0,2,0,2,0,1,0,0,0,0,1,4,2,0,2,0,0,4,0,1,0,0,1,0,22,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,Class_6
+37060,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37061,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,Class_6
+37062,0,0,8,3,0,0,0,0,0,1,6,0,1,0,0,2,0,0,0,0,1,3,0,1,0,1,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37063,0,0,0,3,0,0,0,0,1,1,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,5,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37064,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,2,4,0,0,0,2,0,4,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,7,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,5,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37065,0,0,18,13,0,0,0,0,1,0,10,0,0,1,1,2,1,1,0,0,3,5,0,0,1,6,25,0,0,0,0,2,0,1,2,0,0,0,0,1,0,2,0,6,0,14,0,1,1,0,0,1,0,22,0,0,0,1,0,2,0,0,0,1,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37066,3,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,5,5,6,0,0,0,0,0,0,2,1,0,1,0,0,0,5,0,5,0,2,0,0,0,2,1,0,0,0,1,3,0,0,1,0,1,0,0,5,0,0,1,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,Class_6
+37067,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37068,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,4,0,3,0,0,0,2,0,0,0,4,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,1,0,1,0,1,1,3,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+37069,0,0,15,0,0,0,0,1,0,0,10,0,0,2,0,1,0,0,0,0,0,3,0,3,1,1,7,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,3,1,16,0,0,2,0,1,0,0,24,1,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37070,0,1,4,7,0,0,0,0,0,0,8,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,15,3,0,0,0,2,1,1,0,0,0,1,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,17,0,0,0,0,0,1,8,1,2,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,12,0,4,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37071,0,0,4,6,0,0,1,0,0,0,1,1,0,0,0,2,0,2,0,0,0,2,0,8,0,6,1,2,0,0,0,0,0,1,2,1,0,0,0,0,0,3,0,2,0,4,0,0,0,0,0,0,1,6,0,0,4,0,0,13,2,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,0,Class_6
+37072,0,0,2,34,0,0,0,1,1,2,1,0,0,2,0,8,0,3,0,0,0,3,0,4,0,3,0,1,0,0,1,0,1,0,13,0,0,0,0,0,0,4,0,1,0,0,1,2,1,0,0,0,0,11,1,0,7,0,0,13,2,2,1,1,0,2,1,0,0,1,0,0,0,0,6,2,0,0,0,3,0,2,0,0,0,1,0,1,0,0,0,1,1,Class_6
+37073,0,0,0,1,1,0,0,0,0,4,0,0,0,1,0,1,0,0,1,2,0,3,0,1,3,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,2,0,8,0,0,2,0,1,4,0,0,0,5,1,0,0,0,0,0,0,1,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,5,0,0,1,0,0,0,0,Class_6
+37074,0,1,12,5,0,0,0,0,1,0,12,0,0,0,0,2,0,1,0,0,1,1,0,0,0,1,7,3,0,0,3,0,0,2,0,0,0,0,0,0,1,0,0,0,0,6,1,1,0,0,0,0,0,5,0,0,0,1,0,2,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,1,1,0,0,0,0,0,2,0,1,0,0,Class_6
+37075,0,0,2,1,0,0,0,0,0,0,7,0,0,0,1,4,0,1,0,0,1,2,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37076,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37077,0,1,1,4,0,0,1,0,0,0,0,0,0,0,2,3,0,5,2,7,0,5,1,11,1,2,5,0,3,0,1,0,0,2,3,2,0,5,0,2,0,0,0,2,0,0,0,2,3,1,1,1,0,7,3,1,2,0,0,14,0,1,0,4,0,1,2,1,0,7,0,1,0,6,0,0,0,0,0,0,1,3,2,0,1,0,0,1,2,0,1,1,0,Class_6
+37078,0,1,0,1,0,0,0,0,0,0,12,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37079,0,0,11,3,0,0,0,0,0,6,6,0,0,1,0,2,0,0,0,0,2,1,0,2,0,3,6,2,0,0,1,0,0,0,2,1,0,0,0,0,0,5,1,1,0,7,0,0,0,0,0,0,0,30,0,1,0,0,0,3,2,1,0,0,1,2,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37080,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,8,0,0,4,0,0,6,0,1,0,1,0,2,0,6,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,2,0,Class_6
+37081,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37082,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,4,0,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37083,0,0,6,5,0,0,0,0,0,0,6,0,3,1,0,1,0,1,0,0,1,2,0,1,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,2,0,2,1,0,1,0,0,0,0,7,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+37084,0,4,1,16,0,0,0,0,5,0,8,1,3,0,6,4,1,3,0,0,2,3,0,0,0,4,5,9,1,0,2,0,0,0,4,0,1,2,0,0,0,5,1,1,0,1,0,3,6,0,1,1,0,13,1,5,0,1,0,1,1,4,0,3,0,10,1,0,0,0,0,1,0,1,1,0,0,0,0,3,0,3,0,0,1,0,0,0,3,1,0,3,0,Class_6
+37085,0,0,12,2,0,0,1,0,0,2,7,0,0,1,0,0,0,1,0,0,1,6,0,2,1,1,13,2,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,4,0,5,0,0,1,0,0,2,0,25,1,1,1,1,0,0,3,1,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,2,0,0,1,0,Class_6
+37086,0,1,3,3,0,0,1,0,0,2,10,0,5,0,0,2,0,0,0,0,2,5,0,0,1,5,2,1,1,0,0,2,0,0,2,0,0,0,0,1,0,3,1,7,0,0,0,0,5,0,0,0,0,10,3,0,0,0,0,2,0,0,0,0,2,1,1,0,0,6,0,0,0,0,0,2,1,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+37087,0,0,1,0,0,0,0,8,0,0,1,0,1,1,1,2,1,5,0,0,0,1,0,4,1,1,0,0,0,0,0,0,0,0,0,4,0,1,0,3,0,3,0,1,0,0,0,3,3,0,0,0,0,4,2,0,3,0,0,5,0,4,0,1,0,4,9,0,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,1,0,Class_6
+37088,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+37089,1,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,1,0,0,1,0,3,0,1,0,0,0,0,16,0,0,0,1,1,0,0,0,0,1,0,2,6,0,1,0,0,0,1,1,0,0,0,0,5,2,0,1,1,2,1,0,2,0,0,0,0,6,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,2,1,Class_6
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+37097,0,0,4,3,0,0,0,0,0,1,16,0,0,0,0,0,0,0,1,0,1,0,0,6,0,2,9,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,3,0,1,1,0,0,0,0,0,0,1,0,4,0,0,8,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37098,0,1,11,9,0,0,1,0,0,0,9,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,7,1,0,0,1,2,0,1,1,0,0,3,1,0,0,0,0,2,0,3,0,0,1,0,0,0,0,7,0,0,0,0,0,1,1,1,0,0,1,0,1,0,1,1,0,0,0,0,0,2,0,1,0,12,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37099,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,3,0,0,0,6,0,0,0,0,0,0,6,0,0,0,1,0,0,2,0,0,3,1,0,2,0,0,0,0,1,0,0,0,0,4,1,0,2,0,0,16,0,1,0,1,1,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,Class_6
+37100,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,2,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,8,0,2,0,0,2,3,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37101,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,2,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,8,1,0,2,0,0,14,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37102,0,0,2,0,0,0,0,0,1,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,6,1,0,0,0,0,1,0,4,0,0,0,2,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37103,0,0,5,0,0,0,0,0,0,0,8,0,0,0,0,4,0,0,1,0,1,1,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,4,0,0,1,0,6,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37104,0,0,4,2,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,3,0,0,0,0,0,5,0,0,1,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37105,0,0,4,0,0,0,0,0,0,3,0,0,3,1,0,4,0,0,1,0,0,2,0,5,1,2,0,0,1,4,1,2,0,2,0,0,0,0,0,1,2,8,0,5,0,0,0,4,2,1,0,0,29,9,0,2,0,0,0,2,0,4,0,1,0,0,1,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,1,1,4,0,0,1,0,0,2,0,Class_6
+37106,0,0,4,6,0,0,0,0,2,0,6,1,1,0,0,6,1,2,0,0,1,4,0,0,0,3,1,2,0,0,0,0,0,0,1,0,0,0,0,3,0,0,3,0,0,7,0,0,2,0,0,2,1,25,0,2,0,1,0,0,5,0,0,0,0,1,1,0,0,1,0,1,1,2,0,1,0,0,1,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37107,0,1,7,1,0,0,0,0,0,0,13,0,0,0,0,3,1,0,0,0,0,2,0,6,0,0,3,1,1,0,1,2,0,1,1,0,0,1,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,13,0,0,0,6,0,1,1,0,0,1,0,0,1,2,1,0,0,0,0,3,1,0,0,1,0,5,0,3,0,0,0,1,1,0,0,0,0,1,0,Class_6
+37108,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,1,4,1,0,0,0,0,0,2,6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37109,0,0,17,14,1,0,0,0,0,0,8,0,1,0,0,0,1,1,0,0,0,3,0,2,0,1,4,4,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,1,6,0,0,3,0,0,0,0,7,2,0,0,0,0,0,0,2,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37110,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37111,0,1,12,3,0,0,0,0,0,0,7,0,0,0,0,6,1,0,0,0,0,1,1,3,0,1,4,1,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,2,0,0,2,0,0,0,0,18,1,0,0,0,0,1,9,2,1,0,0,2,0,0,0,0,0,1,0,1,0,1,0,0,0,15,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37112,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,2,0,1,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+37113,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,1,0,1,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,12,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37114,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,1,3,0,0,3,9,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,13,0,1,1,0,0,0,4,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37115,0,2,2,0,0,0,2,0,0,0,5,0,2,0,0,0,1,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,2,3,0,0,0,2,0,3,0,0,0,0,Class_6
+37116,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,2,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,2,6,0,2,0,2,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,2,0,0,0,1,0,Class_6
+37117,0,1,2,3,0,0,0,1,0,1,14,0,0,0,0,1,0,2,0,0,0,1,0,6,0,12,7,2,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,1,0,1,0,3,0,0,0,1,0,10,0,0,0,2,0,1,3,2,0,1,0,1,0,0,0,5,0,0,0,1,1,1,0,2,0,6,0,1,2,0,0,0,0,0,2,3,0,0,0,Class_6
+37118,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,1,0,0,6,3,0,0,0,0,0,0,0,1,1,0,2,0,0,0,7,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,11,0,0,0,0,0,0,7,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,Class_6
+37119,1,0,0,0,0,0,1,1,0,0,3,0,3,2,0,0,1,0,0,2,0,2,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,2,0,1,0,0,6,0,0,0,0,0,1,3,1,0,0,0,2,0,0,0,1,0,1,1,4,3,0,1,2,0,0,1,2,1,0,0,2,2,0,0,0,0,2,0,1,0,0,0,0,1,0,Class_6
+37120,0,0,6,5,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,3,0,2,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,11,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37121,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,1,0,1,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,8,2,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,2,0,0,4,0,0,2,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37122,0,1,8,6,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,14,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,Class_6
+37123,0,0,19,9,0,0,0,0,2,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,14,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,3,0,18,0,0,0,0,0,4,0,8,0,0,0,0,0,2,5,0,0,1,2,0,0,0,0,1,0,0,0,4,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,3,Class_6
+37124,0,0,6,3,0,0,2,0,0,0,8,0,0,1,0,3,0,0,0,0,5,0,0,0,0,1,9,1,0,0,0,0,0,0,3,0,0,1,1,0,0,0,1,0,0,4,0,0,0,0,0,2,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37125,3,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,1,1,0,0,0,2,2,6,1,2,0,0,0,0,0,8,0,0,0,1,0,0,0,1,1,5,0,1,0,0,0,5,0,0,0,0,0,1,0,1,0,0,2,3,0,2,0,2,0,1,4,0,0,5,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,3,9,0,0,0,0,1,18,Class_6
+37126,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37127,0,0,2,0,0,0,0,4,2,1,0,0,1,0,0,4,8,7,2,8,0,1,1,13,0,1,0,0,0,1,0,3,0,2,0,2,5,3,0,0,0,2,0,1,0,17,0,2,0,1,33,0,0,1,2,0,25,0,0,10,0,1,0,3,0,2,6,2,1,4,0,0,3,0,9,2,0,0,0,0,0,0,0,0,0,0,16,0,1,2,0,0,1,Class_6
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+37129,0,1,2,0,0,1,0,0,0,0,4,0,0,1,0,2,0,0,1,0,0,1,0,0,0,1,6,1,0,1,4,0,0,0,3,0,0,1,2,0,0,0,0,0,1,2,0,0,0,0,0,11,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,58,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,4,0,0,0,Class_6
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+37139,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37140,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37141,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,1,0,0,0,2,0,1,0,3,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37142,0,0,5,5,0,0,0,0,0,0,11,0,0,0,0,2,0,1,0,0,2,0,0,0,0,0,11,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,1,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,3,0,0,0,0,Class_6
+37143,1,0,10,24,0,0,0,0,0,1,6,0,0,0,0,2,0,1,0,0,0,0,1,2,0,4,0,1,0,0,3,2,0,0,4,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37144,0,0,2,3,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,4,1,0,0,0,2,0,0,3,0,0,0,0,0,0,3,0,3,0,0,0,3,4,0,0,0,0,5,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+37145,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,0,0,0,0,4,0,18,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37146,0,0,1,2,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,12,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_6
+37147,0,0,4,8,0,0,0,1,1,2,7,0,1,1,0,6,0,1,0,0,0,2,0,0,0,12,8,2,0,0,0,2,0,2,1,0,0,0,0,0,1,5,0,0,0,9,0,0,0,0,0,2,0,15,1,1,2,2,0,2,0,1,0,0,1,6,0,0,0,1,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,Class_6
+37148,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,1,0,6,0,0,0,4,0,5,0,4,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,4,0,0,12,0,2,0,0,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+37149,0,0,11,4,0,0,0,0,0,0,12,0,0,0,0,5,0,0,0,0,4,0,0,0,0,1,4,2,2,0,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,10,0,0,2,0,0,1,0,23,1,0,2,0,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37150,1,4,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,2,0,0,0,2,0,5,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,14,0,0,1,1,0,1,1,0,1,0,5,0,0,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,2,2,0,0,Class_6
+37151,0,0,5,14,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,1,6,0,0,0,1,0,1,0,0,0,2,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,2,0,9,0,0,0,0,0,1,3,5,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,Class_6
+37152,0,0,7,2,0,0,0,0,0,0,10,0,2,0,0,2,0,1,1,1,0,2,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,6,0,0,0,0,16,0,0,0,0,13,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,1,Class_6
+37153,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,11,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,Class_6
+37154,0,0,1,3,0,0,0,0,0,0,5,1,0,0,0,2,1,0,0,0,0,0,0,0,0,1,2,3,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,2,0,0,0,2,2,0,0,0,1,0,1,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37155,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,2,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37156,0,0,1,1,0,0,0,1,0,0,6,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,3,0,0,0,0,5,0,0,0,0,0,0,5,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,3,0,0,0,0,3,0,0,2,0,Class_6
+37157,0,0,2,0,0,0,2,3,0,4,0,0,1,0,0,3,2,0,0,0,0,0,0,1,2,4,0,0,0,0,0,1,0,0,1,2,0,0,0,1,0,3,0,5,0,2,0,0,2,1,1,0,2,3,1,0,2,0,0,2,0,3,0,0,0,4,1,0,0,3,2,1,0,0,4,0,1,1,0,1,5,0,0,0,2,0,1,0,1,0,0,0,1,Class_6
+37158,1,0,0,0,0,5,0,1,2,0,1,0,0,0,0,1,0,10,0,0,0,3,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,13,1,1,0,0,1,1,0,0,0,0,1,7,3,0,5,0,0,18,0,3,0,2,1,1,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37159,0,0,8,4,0,0,1,0,0,0,9,0,0,2,0,5,0,2,0,1,0,1,0,1,0,3,3,17,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,2,0,3,0,0,1,0,0,0,0,13,1,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,3,0,1,0,0,0,0,Class_6
+37160,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,11,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,1,2,0,0,0,0,1,0,0,4,1,0,10,0,1,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,1,0,Class_6
+37161,0,0,8,4,0,0,0,0,0,0,7,0,2,0,0,5,0,0,0,0,5,1,0,0,0,2,3,1,0,0,0,2,0,1,0,1,0,0,0,6,0,0,0,2,0,11,0,0,1,0,0,0,0,30,0,1,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+37162,1,11,0,0,0,0,0,1,0,1,4,0,0,0,0,0,5,3,4,8,0,1,0,12,0,0,0,0,0,0,1,4,1,0,4,0,0,0,1,1,5,8,0,3,0,0,0,3,0,0,0,0,0,0,0,0,2,28,0,12,0,0,0,3,0,0,7,1,10,3,0,0,7,0,2,2,0,0,0,0,0,1,3,0,3,0,1,0,0,11,0,2,0,Class_6
+37163,0,0,6,12,0,0,1,0,0,0,6,0,0,0,0,0,0,2,0,0,0,2,0,1,0,2,0,2,1,0,0,5,1,0,1,1,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,9,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,2,0,1,0,0,0,0,0,1,3,0,0,0,0,Class_6
+37164,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37165,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,1,1,0,7,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37166,0,0,10,10,0,0,0,0,0,0,8,0,1,0,0,4,0,1,0,0,0,2,0,0,0,1,4,2,0,0,1,0,1,0,3,0,0,0,0,0,0,1,0,0,0,2,0,2,5,0,0,0,0,10,0,0,1,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,1,0,1,0,1,0,0,0,0,Class_6
+37167,0,0,10,4,0,0,0,0,0,0,7,1,0,0,0,2,1,1,0,0,1,1,0,0,0,0,13,3,0,1,0,2,2,0,0,0,1,0,0,1,0,0,1,0,0,6,0,0,0,0,0,2,0,7,0,0,0,0,0,1,10,1,1,2,0,0,2,0,0,0,0,0,2,0,1,2,0,0,0,5,0,2,0,0,0,0,1,0,2,0,0,0,1,Class_6
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+37169,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+37178,0,0,13,9,0,0,0,0,1,1,9,0,2,0,0,3,0,0,1,0,1,4,0,4,0,0,14,1,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,1,0,12,0,0,1,0,0,1,0,8,0,0,0,0,0,0,2,2,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,5,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+37179,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,11,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,6,0,0,0,0,0,0,5,0,0,0,0,3,0,0,0,0,0,12,0,3,0,0,0,1,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37180,0,1,0,4,0,0,0,0,0,0,5,0,0,0,0,1,0,2,0,1,0,1,0,0,1,2,7,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,2,0,1,2,2,0,0,0,8,1,0,0,0,0,0,1,0,0,0,0,3,0,1,1,2,0,1,0,2,0,0,0,0,0,4,0,2,0,0,0,0,1,0,1,0,0,0,1,Class_6
+37181,0,0,8,16,0,0,0,0,0,1,10,0,1,0,0,3,0,2,0,0,0,5,0,1,0,0,2,3,0,0,2,0,2,2,1,0,0,1,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,2,1,1,1,1,0,0,0,1,0,1,1,0,0,0,1,0,0,2,0,2,0,0,0,0,1,1,1,0,0,0,0,Class_6
+37182,0,0,1,1,0,0,0,0,0,0,9,0,0,1,0,2,0,0,0,1,0,1,0,2,0,0,0,1,0,0,1,1,1,0,3,0,3,0,0,2,0,0,0,0,0,0,0,0,1,0,0,5,1,8,1,0,0,0,0,0,2,2,0,0,0,0,1,1,0,1,0,0,0,9,0,0,0,0,1,11,0,1,0,0,0,1,0,0,4,0,0,0,1,Class_6
+37183,6,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,10,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,2,6,0,0,0,0,0,10,Class_6
+37184,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,1,0,0,2,3,1,1,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,1,0,3,0,1,1,0,0,0,0,3,0,0,0,0,0,6,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,Class_6
+37185,0,0,9,10,0,0,0,0,1,1,10,0,1,1,0,4,0,2,0,0,3,1,0,0,0,2,7,3,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,4,0,5,0,0,2,0,0,0,8,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37186,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,8,0,2,0,0,0,3,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+37187,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,3,1,0,0,3,1,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_6
+37188,0,0,2,5,0,0,0,0,1,0,16,0,0,1,0,3,0,0,1,0,1,0,0,5,0,1,16,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,4,0,17,0,0,0,5,0,0,3,1,0,1,1,0,2,0,0,0,0,0,0,2,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37189,0,0,1,4,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,3,2,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37190,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,13,0,0,0,0,0,0,3,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37191,0,1,0,0,0,0,0,0,0,0,13,0,0,0,1,1,2,10,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,0,0,0,0,0,1,6,0,1,3,0,0,0,1,0,1,0,0,1,3,0,1,1,1,1,0,0,4,0,0,0,0,0,0,1,0,2,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37192,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37193,0,0,8,5,0,0,0,0,1,3,8,0,1,0,0,5,0,0,0,0,1,1,0,2,0,1,4,0,1,0,0,0,0,4,4,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,16,2,0,0,0,0,0,4,1,1,0,0,0,0,0,0,2,0,0,1,1,3,0,0,0,0,11,0,0,1,0,0,0,1,0,1,1,0,0,0,Class_6
+37194,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,3,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,1,3,2,0,1,0,0,1,0,1,0,2,3,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37195,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37196,0,1,12,7,0,0,0,0,0,2,6,0,1,0,0,5,0,0,0,0,0,1,0,0,1,0,2,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,4,0,0,2,0,0,0,0,10,1,1,0,1,0,0,0,0,1,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,1,7,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37197,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37198,0,0,6,6,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37199,0,1,1,5,0,0,0,0,0,0,2,1,0,0,0,3,4,1,1,1,0,2,3,9,0,3,0,1,0,0,4,0,1,0,3,2,0,0,0,1,0,2,0,0,1,0,0,5,2,0,2,0,0,2,0,0,2,1,0,20,0,1,0,0,0,1,6,0,0,0,0,1,1,1,0,5,0,0,0,2,0,5,15,0,1,0,0,0,0,0,1,0,1,Class_6
+37200,0,1,2,2,1,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,4,0,10,1,4,1,2,0,1,6,1,0,0,2,1,0,0,0,3,0,3,0,0,0,2,0,1,0,0,0,0,0,10,0,1,0,0,0,13,0,1,0,0,0,2,5,0,1,0,2,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,2,Class_6
+37201,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,1,4,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37202,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,1,0,2,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,2,Class_6
+37203,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,5,0,0,0,0,14,1,0,3,0,0,8,1,0,0,0,0,0,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,Class_6
+37204,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,3,0,5,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,4,0,10,0,2,0,1,2,1,1,0,0,0,0,1,0,0,3,0,0,12,0,1,0,0,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37205,0,0,1,1,0,0,0,0,3,3,0,0,0,0,0,5,1,0,0,0,0,6,0,1,1,4,0,1,0,0,0,0,1,0,0,2,0,0,0,1,0,2,0,5,0,0,0,0,1,0,0,1,1,6,1,0,0,0,0,8,0,0,0,5,3,1,7,1,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,4,5,2,2,0,0,0,0,0,Class_6
+37206,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37207,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,5,0,3,1,0,1,0,1,0,0,0,5,0,0,0,0,1,0,1,0,1,0,1,0,0,1,0,0,0,0,4,0,0,7,0,0,12,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
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+37209,0,0,11,1,0,0,0,0,0,0,7,2,0,0,0,1,0,1,0,0,0,2,0,0,0,2,5,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,4,1,13,0,0,1,0,0,0,1,16,0,1,0,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,6,0,0,0,0,Class_6
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+37211,0,0,6,5,0,0,0,0,1,0,7,0,0,0,1,2,0,2,1,0,0,0,0,2,0,2,3,2,0,0,0,2,0,0,5,0,0,0,0,1,0,0,0,1,0,4,0,0,2,0,0,0,1,9,0,0,3,0,0,1,1,1,1,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,3,0,1,0,0,0,0,1,2,0,0,0,0,0,Class_6
+37212,0,0,0,2,0,0,0,0,1,0,12,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,6,0,0,0,1,2,1,1,2,1,1,1,0,3,0,0,0,1,0,0,0,1,0,0,0,6,0,8,0,1,0,3,0,1,2,2,1,0,1,0,0,1,0,0,0,0,0,7,0,1,0,0,0,10,0,4,0,0,0,0,0,0,2,0,0,1,0,Class_6
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+37214,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,1,1,0,3,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,2,1,2,0,0,3,1,0,0,0,0,0,1,1,0,1,0,1,0,0,1,0,0,1,1,5,0,0,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,2,0,0,0,0,0,0,Class_6
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+37216,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37217,0,0,6,3,0,0,0,1,0,0,13,0,0,0,0,1,0,0,0,0,8,1,0,9,1,2,23,2,0,0,0,2,2,0,3,0,0,0,1,1,0,0,1,1,0,8,0,1,0,0,0,4,0,8,0,1,0,10,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37218,0,0,2,24,0,0,0,0,3,2,12,0,0,0,0,2,0,1,0,0,1,3,0,2,0,8,7,0,0,1,0,0,1,2,3,0,1,0,0,0,0,4,0,1,0,4,0,1,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,4,0,1,0,1,0,0,0,0,2,0,0,0,0,Class_6
+37219,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,1,0,0,0,0,0,8,2,0,0,0,0,0,0,1,0,3,0,1,0,0,1,0,0,1,0,0,0,0,4,3,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,2,8,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+37220,0,0,3,24,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,17,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,1,0,3,0,1,0,3,0,0,0,0,0,4,0,1,0,0,0,12,0,0,0,0,0,1,2,1,0,0,0,0,0,3,3,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,2,1,Class_6
+37221,0,0,1,0,0,0,0,2,1,1,0,1,4,0,0,0,6,0,1,3,0,1,0,2,2,3,0,0,0,0,0,3,0,0,0,4,1,1,0,0,1,7,0,1,0,0,0,3,0,0,0,0,0,4,5,0,0,0,8,2,0,1,0,2,0,0,22,0,0,1,0,1,0,0,1,0,1,0,7,0,0,0,0,0,0,0,2,0,0,0,2,3,0,Class_6
+37222,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,4,0,1,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,12,0,1,0,0,0,1,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,Class_6
+37223,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,4,0,3,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,5,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,3,Class_6
+37224,0,0,8,11,0,0,0,0,1,0,8,0,2,0,0,7,0,0,0,1,2,0,0,0,0,3,20,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,14,1,1,0,1,0,0,6,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37225,0,1,4,1,0,0,0,0,0,0,8,1,0,0,0,5,0,1,0,0,1,0,0,2,0,2,9,3,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,8,0,0,1,0,0,0,1,5,0,2,0,0,0,1,8,1,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,9,0,1,0,0,0,1,0,0,3,0,0,0,0,Class_6
+37226,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,16,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37227,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,1,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,10,0,1,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,0,Class_6
+37228,0,0,4,8,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,1,0,6,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,4,0,2,1,0,0,0,0,5,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37229,0,1,10,7,1,0,1,0,1,0,2,0,0,0,0,1,0,1,0,0,0,1,0,28,0,11,0,0,1,0,0,1,0,1,4,11,0,0,1,2,0,1,0,2,0,5,0,6,1,0,0,0,0,6,1,0,20,0,1,9,1,0,0,3,0,1,12,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,2,2,0,0,0,0,0,0,Class_6
+37230,4,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,1,0,1,4,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,5,0,4,0,0,0,3,0,0,0,0,0,1,0,0,0,0,3,0,0,3,1,1,0,0,4,0,0,0,0,0,1,0,0,0,0,0,11,0,0,0,0,0,0,4,2,0,0,1,0,0,0,Class_6
+37231,0,0,2,4,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,2,0,0,14,0,1,4,1,0,0,1,3,0,1,0,0,1,1,1,2,0,0,1,0,0,3,0,0,0,0,0,3,0,5,0,5,0,3,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37232,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,2,2,0,3,0,2,2,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,4,0,0,4,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+37233,0,0,1,0,0,0,0,2,0,0,3,0,1,0,0,1,0,0,0,0,0,0,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,0,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,2,1,0,0,2,1,2,0,0,5,1,0,0,0,0,2,0,0,0,2,0,0,0,0,Class_6
+37234,0,0,5,13,0,0,0,0,1,0,6,0,0,0,2,0,0,0,0,0,2,1,1,0,0,1,36,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,9,0,1,1,0,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37235,0,9,0,2,0,0,0,1,0,2,10,4,0,0,0,10,0,16,0,8,0,10,0,8,0,4,5,1,1,0,0,0,1,1,14,0,1,1,0,0,4,8,0,13,0,0,0,2,72,0,0,0,0,13,2,0,5,0,0,4,0,3,2,1,0,6,0,0,0,2,0,2,0,1,1,0,0,0,1,13,2,3,0,0,0,0,0,0,0,0,0,0,3,Class_6
+37236,0,0,7,1,0,0,0,0,0,0,10,0,0,0,1,8,0,0,0,0,0,1,0,0,0,4,0,1,0,0,0,1,0,1,3,0,1,3,0,0,0,1,0,1,0,2,0,0,1,0,0,0,0,9,0,0,2,0,0,1,0,1,1,0,2,3,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37237,0,0,24,5,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,1,0,3,2,0,0,1,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,10,0,0,0,0,0,0,0,25,1,1,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37238,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37239,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,1,1,2,0,7,0,0,0,2,0,2,2,0,0,0,3,5,1,0,0,0,3,0,2,1,0,4,1,1,3,0,0,1,1,1,0,2,0,2,0,0,1,0,0,18,0,0,0,1,0,0,9,0,4,4,0,0,0,3,0,1,0,1,0,1,0,0,0,0,0,0,10,1,1,0,0,0,0,Class_6
+37240,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,7,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,3,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37241,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37242,1,1,9,8,0,0,0,0,3,2,8,0,2,1,0,6,0,0,0,0,1,0,0,2,1,1,8,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,9,0,12,0,0,0,1,0,1,6,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,7,0,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37243,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,1,3,0,1,0,7,0,0,0,0,0,0,0,5,2,0,0,1,0,3,0,0,1,1,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37244,0,1,1,10,0,0,1,0,0,1,1,1,2,2,0,6,0,2,0,1,0,3,0,6,1,9,0,3,0,0,0,2,1,0,1,1,0,2,2,0,0,32,0,3,1,0,0,0,3,2,0,0,2,5,2,0,4,0,0,8,0,5,2,2,0,1,6,0,0,3,0,0,0,2,0,1,0,0,11,2,1,0,2,0,2,0,2,1,3,0,0,0,1,Class_6
+37245,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,2,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,11,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,10,0,1,0,0,0,0,2,1,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+37246,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,1,0,0,1,0,0,3,0,0,0,2,0,0,9,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37247,0,0,6,15,0,0,0,0,0,1,14,0,0,0,0,6,0,1,0,0,0,3,0,0,0,3,6,4,0,0,0,0,0,0,1,0,0,3,0,0,1,0,1,2,0,0,0,0,1,0,0,0,0,16,1,0,0,1,0,1,4,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,6,0,0,0,0,0,0,8,0,0,1,0,Class_6
+37248,0,0,2,7,0,0,0,0,1,1,12,0,1,0,0,2,1,2,0,0,0,0,0,3,0,1,9,0,0,0,3,3,1,0,1,0,0,1,0,1,0,1,1,0,0,1,0,1,0,0,0,0,0,4,0,0,0,6,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,3,0,0,0,0,0,0,1,1,0,0,0,Class_6
+37249,0,0,10,1,0,0,0,0,2,1,7,0,1,0,0,1,0,0,0,0,0,1,0,2,0,1,13,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,7,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+37258,1,10,0,0,0,0,1,0,0,1,1,0,3,0,0,2,4,0,1,4,0,3,0,4,0,1,0,0,1,0,0,1,0,2,1,0,1,3,0,0,0,7,0,4,0,0,0,0,4,0,0,0,0,6,2,0,6,2,0,1,0,0,0,1,0,0,0,0,4,5,0,0,0,0,0,4,0,0,0,0,7,0,21,0,0,0,3,0,0,3,0,0,0,Class_6
+37259,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,3,1,0,0,0,0,0,5,1,0,0,0,0,8,0,0,0,1,0,1,0,2,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37260,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,11,0,0,1,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,2,0,2,0,0,1,0,0,0,2,0,0,4,0,0,0,0,1,4,0,0,2,0,0,11,0,1,1,1,0,0,16,0,1,9,6,1,0,0,0,0,0,0,8,2,0,0,0,0,0,0,1,0,3,1,0,0,0,Class_6
+37261,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,1,0,3,0,3,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,0,6,0,0,2,0,0,0,0,1,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37262,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,1,1,0,2,0,0,3,5,0,1,0,0,0,3,1,0,0,0,1,1,0,0,0,0,0,14,0,1,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37263,0,0,1,1,0,0,1,0,1,0,0,0,1,0,2,2,1,0,4,0,0,1,7,15,0,2,0,0,0,0,3,0,0,0,1,0,3,1,2,1,0,2,0,0,1,0,0,1,0,1,0,2,0,4,2,0,5,0,0,4,0,1,0,0,0,0,4,0,1,0,1,0,2,0,0,2,0,2,0,3,0,0,3,1,2,0,2,0,0,1,0,0,1,Class_6
+37264,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,2,0,0,0,0,0,1,0,Class_6
+37265,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,5,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,13,0,0,0,0,0,4,1,1,0,0,0,0,2,0,1,0,0,7,0,1,0,0,3,0,12,0,0,2,1,1,0,2,0,0,0,0,3,0,0,0,0,0,1,1,0,1,0,0,0,1,0,Class_6
+37266,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37267,0,0,0,2,0,0,0,0,1,0,14,0,0,0,0,1,0,0,0,0,2,2,0,0,1,0,22,1,0,0,2,0,0,0,2,0,2,0,1,0,0,0,0,0,0,4,0,0,2,0,0,1,0,6,0,1,0,0,0,2,4,1,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,4,1,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37268,0,0,1,0,1,0,0,2,2,4,3,0,1,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,19,0,0,0,0,0,1,1,0,0,0,0,5,6,0,0,0,0,6,0,2,2,0,1,1,0,0,0,3,4,0,0,0,2,0,0,0,7,0,2,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+37269,0,0,13,4,0,0,0,0,0,0,7,0,0,1,0,2,0,1,0,1,2,3,0,0,0,0,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,2,0,0,0,0,8,1,0,0,0,0,1,10,2,0,1,0,3,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,1,1,Class_6
+37270,0,0,7,3,0,0,0,0,0,0,12,0,0,0,0,3,0,0,0,0,3,1,0,1,0,0,1,5,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,7,0,0,1,0,0,0,0,11,1,0,0,1,0,1,4,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,Class_6
+37271,0,0,3,2,0,0,0,0,1,1,9,0,0,2,0,3,0,2,0,0,1,1,0,0,1,0,17,2,0,0,0,0,0,1,1,0,0,1,0,0,0,2,0,2,0,6,0,0,0,0,1,2,0,2,0,1,0,1,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,2,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37272,0,1,17,5,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,1,0,0,0,2,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,3,0,2,0,10,0,0,2,0,0,1,0,13,1,0,0,0,0,0,4,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37273,0,0,4,1,0,0,0,2,0,0,7,0,0,0,0,3,0,3,0,2,0,1,0,3,0,2,7,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,7,1,0,0,1,0,0,2,0,0,0,0,1,1,1,0,0,0,0,0,4,0,1,0,0,0,3,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37274,0,0,5,6,0,0,0,0,0,0,11,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,15,0,0,0,2,0,0,0,1,0,1,0,1,1,0,1,0,0,0,6,0,0,0,0,0,3,0,7,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,2,0,0,7,0,1,0,0,0,0,0,0,1,0,1,0,0,Class_6
+37275,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
+37276,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,1,0,3,0,0,1,0,0,0,0,12,0,0,0,0,0,1,0,2,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+37277,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,0,12,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37278,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37279,0,0,15,9,0,0,0,0,0,0,6,0,3,0,0,5,0,0,0,0,1,2,0,0,0,0,5,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,1,0,16,0,3,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,1,0,0,1,0,Class_6
+37280,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,2,5,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,3,0,16,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,2,0,0,0,0,0,2,Class_6
+37281,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,1,1,0,0,3,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,5,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,Class_6
+37282,0,1,1,1,8,1,1,1,0,0,2,1,0,0,0,0,0,1,0,1,0,2,0,8,0,7,0,1,2,1,0,1,0,0,2,0,0,0,0,0,0,2,0,0,1,0,15,0,0,0,1,0,1,6,1,0,1,0,0,15,1,0,1,0,0,0,3,2,0,1,0,0,0,0,3,0,0,0,3,2,0,0,1,11,0,1,0,0,0,0,0,1,0,Class_6
+37283,0,0,3,4,0,0,0,0,0,0,2,0,0,0,0,3,0,0,1,0,0,0,1,4,1,8,0,0,0,0,1,1,0,0,2,2,0,1,0,1,0,2,0,1,0,4,0,1,0,0,0,0,0,5,0,1,1,0,0,8,1,3,0,1,1,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37284,0,0,7,2,0,0,0,0,0,0,11,0,0,0,0,4,0,4,0,1,0,1,0,1,1,1,16,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,3,0,0,0,0,0,1,4,2,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,8,0,3,0,0,0,0,0,0,5,0,0,0,0,Class_6
+37285,0,0,11,7,0,0,1,0,0,0,11,1,1,0,0,3,0,1,0,1,2,0,0,0,0,2,7,3,0,0,0,0,0,0,1,0,0,0,2,3,0,4,2,1,0,7,0,0,0,1,0,0,0,15,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,Class_6
+37286,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,1,0,0,1,1,0,8,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,4,9,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,1,0,Class_6
+37287,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,3,0,6,0,0,0,0,0,4,0,2,0,1,0,0,0,1,1,1,8,1,0,2,0,0,3,1,0,1,0,0,0,2,1,0,0,0,0,8,1,0,1,0,0,12,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+37289,0,0,3,10,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,1,0,0,0,0,9,3,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,1,0,1,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,1,0,0,1,0,0,1,0,Class_6
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+37307,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+37308,0,0,6,5,0,0,0,0,0,1,8,0,0,1,0,6,0,0,0,0,1,2,0,1,0,0,13,2,0,0,0,0,0,0,1,0,0,0,0,1,0,1,2,2,0,7,0,0,2,0,0,1,1,9,0,0,0,1,0,0,7,2,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+37309,0,0,2,3,0,0,0,0,2,0,1,0,1,0,0,4,0,4,0,0,0,2,0,12,0,3,0,1,0,0,0,0,0,0,12,0,0,2,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,5,0,0,5,0,0,8,1,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,8,0,0,0,0,0,1,0,0,1,0,Class_6
+37310,0,1,0,0,1,0,0,0,0,0,2,0,1,0,0,1,1,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,5,0,8,0,0,1,0,0,0,0,0,2,0,3,0,1,0,1,4,0,1,0,0,3,0,1,0,0,2,0,1,0,1,0,1,0,0,2,1,0,0,0,0,1,1,0,0,1,0,0,1,0,Class_6
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+37312,0,1,0,0,0,0,0,9,0,0,0,0,11,6,0,3,2,1,0,2,0,2,0,3,2,0,0,0,0,0,0,0,2,0,0,12,0,2,1,2,2,2,0,0,0,0,0,5,0,2,0,0,0,2,3,0,0,1,1,2,0,8,4,2,0,1,10,1,0,4,1,0,1,3,1,0,0,0,7,0,0,0,0,0,0,1,4,1,4,0,4,0,0,Class_6
+37313,1,0,16,3,0,0,0,0,0,0,17,0,0,0,0,3,0,0,0,0,2,1,0,6,1,0,6,3,0,0,0,2,0,0,2,1,0,1,0,1,1,0,0,1,0,16,0,1,1,0,0,0,0,6,0,2,0,11,0,1,0,1,0,1,0,2,1,0,0,0,0,0,0,2,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+37314,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,1,1,0,1,0,2,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,1,2,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,0,8,0,6,1,0,0,1,0,0,0,0,0,9,1,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_6
+37315,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37316,0,0,5,11,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,8,0,2,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,1,2,0,0,0,1,1,0,0,0,0,5,1,1,0,1,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,6,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37317,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,3,0,9,0,1,0,3,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,14,0,3,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,1,0,0,0,0,4,0,0,3,0,1,0,0,1,0,0,1,0,0,0,0,7,0,0,0,1,0,0,0,2,0,1,Class_6
+37318,1,0,3,2,0,0,0,0,0,0,7,0,1,1,0,3,0,1,1,1,0,3,1,1,0,0,1,3,0,1,0,0,0,0,3,2,0,1,0,0,0,5,0,4,0,4,0,1,6,0,0,2,1,6,0,0,0,10,0,2,3,2,0,0,1,4,1,0,0,1,0,1,0,1,0,0,0,0,1,3,2,4,0,0,0,0,0,0,1,3,0,1,0,Class_6
+37319,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37320,0,0,1,4,0,0,0,0,2,1,11,0,0,0,0,2,0,1,0,0,2,3,0,0,0,1,9,4,0,0,3,2,0,1,1,0,0,0,0,0,0,1,0,0,0,5,0,0,2,0,0,0,0,10,1,0,0,0,0,1,1,1,0,0,0,3,1,0,0,0,0,0,0,2,1,1,0,0,0,19,0,1,0,0,0,0,0,0,1,0,0,7,0,Class_6
+37321,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,6,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,4,7,0,2,0,0,2,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,2,0,Class_6
+37322,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_6
+37323,1,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,1,0,0,7,0,3,1,0,0,0,0,0,0,3,0,1,0,5,2,0,1,0,0,7,0,6,0,0,0,1,0,0,0,0,1,2,0,0,0,0,1,3,0,0,0,0,0,1,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,Class_6
+37324,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,1,1,0,1,0,2,0,0,1,3,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,3,0,3,1,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,1,2,0,9,1,0,Class_6
+37325,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,4,1,0,4,0,0,0,1,0,0,1,0,2,0,1,0,0,2,0,0,1,0,3,0,0,2,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,4,0,0,0,4,0,3,1,0,1,2,Class_6
+37326,0,1,17,10,0,0,0,0,1,0,8,1,1,0,1,2,0,2,1,0,3,1,0,0,0,0,12,1,0,0,0,1,0,1,2,0,1,0,0,0,1,0,0,2,0,15,0,1,4,0,0,2,0,13,0,0,0,0,0,0,5,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,3,0,1,0,0,0,0,1,0,1,0,0,3,0,Class_6
+37327,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,6,5,0,0,0,0,2,3,0,0,0,0,0,0,0,0,1,2,0,1,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,10,0,0,1,1,0,1,9,0,0,1,2,0,0,0,0,0,0,1,2,0,0,0,3,0,1,0,2,1,0,0,0,0,0,Class_6
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+37348,1,1,0,0,0,0,0,0,0,0,6,0,0,0,0,1,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,10,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,8,0,0,1,0,0,2,1,0,1,0,1,0,6,1,0,0,0,0,0,0,1,0,1,0,0,0,Class_6
+37349,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,2,0,1,0,4,0,0,0,4,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,0,0,1,0,Class_6
+37350,0,0,10,0,0,0,0,0,0,0,6,0,0,0,0,4,0,1,1,0,0,2,0,0,0,0,5,1,0,0,0,0,0,0,2,1,0,0,1,0,2,0,0,0,0,4,0,1,0,0,0,0,0,13,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37351,0,1,1,6,0,0,0,0,0,0,1,2,1,1,2,5,1,2,0,0,0,0,0,3,0,17,1,3,0,0,0,0,0,1,3,0,0,1,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,7,0,0,8,0,0,5,0,2,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,1,1,1,3,0,0,0,0,0,0,0,0,4,0,Class_6
+37352,0,3,2,2,0,0,0,0,0,0,10,1,0,1,0,3,0,4,0,0,0,0,1,0,0,0,5,0,0,0,2,1,2,0,1,0,0,0,0,1,1,0,0,0,0,3,0,1,1,0,0,3,0,8,0,0,0,2,0,0,6,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,1,2,0,0,0,1,Class_6
+37353,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,2,0,0,0,3,5,2,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,11,0,0,1,0,0,0,1,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37354,4,0,0,0,0,0,0,2,2,1,1,0,0,0,2,1,0,0,0,3,0,2,2,9,1,0,0,0,0,0,0,10,0,0,0,2,2,2,0,1,0,3,0,3,0,0,0,5,0,2,0,0,1,0,1,1,0,0,5,6,0,0,0,0,0,1,14,0,0,4,0,1,0,1,0,0,0,0,1,1,0,0,0,0,1,8,14,0,0,0,0,0,25,Class_6
+37355,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,4,1,1,0,0,0,1,0,2,0,0,0,1,0,0,0,1,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,7,0,0,0,1,0,0,4,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,11,2,0,Class_6
+37356,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,6,0,4,0,3,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,8,0,0,4,0,0,13,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,Class_6
+37357,0,0,7,15,0,0,0,0,0,2,8,0,0,0,1,2,0,1,0,1,1,0,1,0,0,2,8,0,0,2,2,3,0,0,1,1,1,0,0,0,0,0,1,1,0,1,0,2,1,0,0,3,0,19,1,2,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,2,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37358,0,0,3,9,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,3,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,9,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37359,0,2,13,4,0,0,0,2,0,0,6,0,0,0,0,6,0,0,0,0,0,3,0,0,0,2,1,5,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,16,0,1,2,0,0,0,1,22,2,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+37360,0,1,0,2,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,0,0,0,0,3,0,1,1,0,0,1,0,0,0,3,6,0,0,0,0,1,0,0,2,0,1,0,0,0,0,1,0,1,0,0,2,1,0,1,0,0,0,0,1,0,0,1,0,4,0,0,0,0,2,0,2,1,0,0,0,Class_6
+37361,0,0,6,2,0,0,0,0,0,0,7,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,2,0,0,0,0,0,1,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37362,0,0,8,3,0,0,0,0,0,0,14,0,0,0,1,2,0,0,0,0,1,0,0,7,0,0,23,1,0,0,3,1,0,0,1,1,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,1,0,5,0,2,0,4,0,0,2,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37363,0,0,2,2,0,0,0,0,0,0,8,0,0,1,0,1,0,1,0,0,1,1,0,0,1,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,2,0,0,0,0,5,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37364,0,0,4,2,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37365,0,0,5,18,0,0,0,0,1,0,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,4,3,0,0,0,3,0,0,0,9,0,0,0,0,0,0,1,1,2,0,3,0,0,0,0,0,1,0,4,1,0,2,1,0,1,0,1,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37366,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+37367,0,1,6,0,0,0,0,1,0,0,5,0,1,0,0,5,0,0,0,0,0,5,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,27,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+37388,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,1,1,3,1,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,4,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,9,0,0,0,0,0,1,3,0,1,2,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37389,0,1,11,6,0,0,0,0,0,0,11,0,1,0,1,2,0,0,0,0,2,2,0,0,0,1,13,1,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,13,0,0,1,0,0,5,0,5,0,0,0,0,0,1,3,0,1,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,7,1,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37390,0,1,3,7,0,0,0,0,0,0,8,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,6,1,0,0,0,4,0,0,2,0,0,1,0,0,0,0,0,1,0,5,3,0,1,0,0,4,0,2,0,5,0,1,0,1,10,2,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,8,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37391,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,5,0,4,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,8,0,0,0,1,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37392,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,9,0,0,0,1,0,0,0,0,1,2,2,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,3,2,3,0,0,0,0,1,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,Class_6
+37393,0,1,0,2,1,0,2,1,0,2,7,0,2,0,0,9,0,0,0,0,0,1,0,0,1,1,4,0,0,1,0,0,0,0,0,1,1,1,0,2,0,1,0,11,0,1,1,0,7,0,0,0,0,11,0,0,0,0,0,0,0,1,0,0,3,2,1,0,0,3,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,1,0,Class_6
+37394,0,2,2,2,0,0,1,2,2,0,3,0,3,3,1,5,1,1,0,1,0,7,4,2,0,0,0,1,3,0,3,4,0,6,1,2,0,3,0,1,0,3,0,2,3,0,0,4,5,0,0,0,0,13,3,0,0,0,0,18,0,2,0,5,0,8,6,0,0,6,0,1,0,4,3,2,0,3,1,2,0,0,0,0,0,0,10,0,2,0,0,1,7,Class_6
+37395,0,0,30,8,0,0,0,0,0,1,8,0,0,1,0,8,0,2,0,0,6,1,0,0,0,1,7,1,0,0,1,3,0,1,1,0,0,1,1,0,0,0,0,2,0,19,0,0,4,0,0,0,1,23,0,1,0,0,0,2,1,1,0,0,1,0,0,0,0,2,0,1,0,0,0,0,1,0,0,2,1,2,0,0,1,0,0,1,1,0,0,3,0,Class_6
+37396,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37397,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,6,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,4,1,1,0,0,0,2,1,0,0,0,0,6,0,0,0,2,0,1,0,0,0,1,0,1,0,0,1,0,2,0,2,0,0,0,0,0,1,1,1,2,0,0,0,0,0,Class_6
+37398,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,0,2,8,5,1,0,0,0,0,0,6,0,0,4,0,0,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,13,0,0,1,0,0,1,0,0,7,0,0,0,1,0,0,1,0,1,33,0,0,0,0,0,0,0,Class_6
+37399,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37400,0,0,2,9,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,7,1,4,0,3,2,0,0,2,1,1,2,1,1,2,0,0,0,6,0,2,0,0,1,1,3,0,0,0,2,6,1,0,1,0,0,9,0,1,0,0,1,0,8,0,0,1,0,1,0,0,0,0,0,0,0,5,0,1,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37401,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,13,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37402,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37403,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,2,7,0,0,0,1,2,7,0,4,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,4,0,0,9,0,0,12,0,0,0,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37404,0,0,2,10,0,0,0,0,0,0,8,0,0,0,0,1,0,2,0,0,0,1,0,3,1,4,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37405,0,0,1,3,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,4,0,3,0,0,0,2,0,0,0,2,1,0,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,4,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+37406,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,10,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,Class_6
+37407,0,1,16,8,0,0,0,0,0,0,6,1,0,0,1,3,0,0,0,0,1,3,0,0,0,1,2,1,0,0,0,2,0,1,0,0,0,1,0,1,0,0,1,0,0,5,0,0,2,0,0,1,0,11,0,0,0,0,0,1,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37408,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,1,0,0,2,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,0,0,Class_6
+37409,0,0,5,3,0,0,0,0,1,0,18,0,0,0,0,0,0,0,0,0,1,1,0,5,0,1,26,0,0,0,0,2,0,1,0,1,1,0,0,0,0,0,1,1,0,5,0,0,0,0,0,1,0,4,0,1,1,7,0,1,2,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,1,0,0,0,Class_6
+37410,0,0,6,3,0,0,0,0,0,0,11,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,1,0,0,0,0,11,0,0,0,0,0,1,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,5,0,0,0,0,Class_6
+37411,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,3,4,0,1,0,2,0,21,1,12,0,1,2,0,0,0,1,1,4,0,0,2,0,1,0,7,0,2,0,0,0,0,0,1,0,0,1,2,0,0,5,0,0,7,0,1,1,0,0,1,0,1,0,1,4,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,Class_6
+37412,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,3,0,3,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,10,0,0,0,0,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37413,0,0,8,5,0,0,1,0,0,0,6,0,0,0,0,2,1,0,0,0,0,5,0,0,0,1,3,6,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,1,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37414,0,1,1,0,0,0,1,0,0,0,0,0,1,1,0,1,0,2,0,0,0,1,0,4,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,8,0,1,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,12,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37415,0,2,1,4,0,0,0,0,0,0,10,0,0,0,0,4,1,1,0,0,0,5,0,0,0,2,0,2,0,0,0,1,1,0,2,0,0,0,0,0,0,2,0,3,0,0,0,0,1,0,0,0,0,5,2,0,0,1,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37416,0,2,0,3,0,0,1,0,0,0,6,0,0,0,0,5,0,1,0,0,0,5,0,1,0,2,4,0,0,0,0,0,1,0,1,0,0,3,0,3,0,2,0,3,0,0,6,2,1,0,0,0,1,4,3,1,0,0,0,0,0,0,0,2,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,1,0,0,1,0,Class_6
+37417,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,2,0,2,0,1,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37418,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,2,0,4,0,3,0,4,0,0,1,1,5,0,0,1,1,0,1,0,1,0,0,0,1,0,0,2,0,1,0,0,0,2,7,0,0,0,0,6,0,0,1,0,0,0,2,0,1,0,0,1,1,0,0,1,0,2,0,0,0,0,0,0,2,5,0,0,0,0,0,1,0,0,4,1,0,0,0,Class_6
+37419,0,2,2,0,0,0,0,1,0,0,7,5,0,0,0,10,0,3,0,1,0,5,0,1,0,1,2,0,0,0,0,0,1,0,2,0,0,1,1,5,0,0,0,6,2,1,0,0,6,0,0,0,0,9,0,2,0,2,0,2,0,5,0,1,5,5,2,0,0,1,0,0,0,3,0,0,0,0,0,2,0,1,2,0,0,0,0,0,4,0,0,0,0,Class_6
+37420,0,1,7,13,0,0,0,0,0,1,8,0,0,1,0,0,1,0,0,0,0,1,0,1,0,3,2,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,1,0,5,0,0,1,0,0,0,1,9,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37421,0,0,1,1,0,0,0,0,0,0,0,0,1,1,2,5,0,3,0,1,0,1,0,8,0,3,0,0,0,0,0,1,3,0,3,1,0,2,1,4,0,1,0,0,2,1,0,0,5,0,0,0,0,3,2,0,1,0,0,12,0,3,0,0,0,1,6,1,0,2,1,1,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,Class_6
+37422,0,0,9,3,0,0,0,2,1,0,0,1,1,0,0,5,0,1,0,0,1,3,0,6,0,5,2,1,1,0,0,0,0,0,2,2,0,0,0,0,0,5,0,2,0,5,0,0,0,0,0,1,0,15,0,0,6,2,0,0,4,0,0,2,0,3,1,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,2,0,0,0,0,0,1,0,0,5,0,Class_6
+37423,0,0,2,0,0,0,0,0,0,1,9,0,1,0,0,3,0,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,3,0,1,0,0,0,0,0,3,1,0,2,0,0,0,1,8,1,0,0,0,0,0,5,6,0,1,0,6,1,0,0,0,0,0,0,0,0,1,0,0,0,9,0,2,0,0,0,0,1,0,6,0,0,0,0,Class_6
+37424,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,5,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,7,0,0,0,1,0,3,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37425,0,0,0,2,1,0,0,0,1,0,11,1,0,0,0,2,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37426,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,1,0,4,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,2,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,Class_6
+37427,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,4,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+37428,0,0,1,0,0,0,0,0,0,0,13,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,1,0,5,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37429,0,0,3,3,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,5,0,2,0,0,0,0,1,17,0,0,2,0,0,0,3,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37430,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,5,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+37431,0,0,19,4,0,0,0,0,0,0,5,0,0,1,0,2,0,0,1,0,0,2,0,0,1,1,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,1,0,0,2,0,15,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37432,0,1,1,3,0,0,0,0,0,1,4,0,2,0,0,7,0,2,2,0,1,3,0,1,0,0,8,1,0,0,0,1,0,0,5,0,0,0,0,3,0,1,0,0,0,3,0,0,2,0,0,0,0,10,0,0,0,0,0,1,2,1,0,0,0,1,1,0,0,1,0,0,0,3,1,0,0,0,0,9,1,2,1,0,0,1,1,0,2,0,0,0,0,Class_6
+37433,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37434,0,0,8,5,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,4,1,0,0,0,0,0,1,1,0,0,1,0,0,0,2,0,2,0,1,0,0,1,0,0,0,0,13,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37435,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37436,0,0,2,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37437,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,1,0,3,1,0,0,0,0,2,0,0,2,0,0,13,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37438,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37439,0,0,4,1,0,0,1,0,0,0,0,1,0,0,0,2,1,0,0,0,0,2,0,0,0,4,0,0,0,0,1,0,0,3,6,0,0,2,0,0,0,13,0,3,0,0,0,0,3,0,0,0,1,6,7,0,3,0,0,2,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,0,Class_6
+37440,0,0,6,7,0,0,0,0,0,0,6,1,0,0,0,3,0,1,1,0,0,1,0,0,0,4,4,2,0,0,0,1,0,1,7,1,0,0,0,0,0,0,0,0,0,7,0,2,1,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37441,0,0,0,3,0,0,0,0,0,0,2,0,4,1,0,0,0,1,0,0,0,0,0,1,0,16,0,0,0,0,8,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,2,0,1,0,0,3,0,0,12,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,2,0,1,0,Class_6
+37442,0,1,1,1,0,0,0,1,1,0,0,0,3,1,2,0,0,4,0,1,0,2,1,2,0,2,0,0,1,0,0,1,1,0,1,1,0,1,1,1,0,1,0,0,0,0,0,2,0,0,0,2,0,1,1,0,2,1,0,10,0,3,0,0,0,1,7,0,0,2,1,5,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+37443,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,1,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,3,1,0,0,0,0,1,1,1,0,0,0,0,0,1,0,2,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+37444,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,2,0,0,0,5,0,1,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,6,0,1,0,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37445,0,0,18,4,0,0,0,0,0,0,7,0,0,0,0,1,0,4,0,0,0,2,0,1,0,1,7,2,0,0,1,2,1,0,1,0,0,0,0,0,1,0,0,3,0,10,0,0,0,1,0,0,0,6,0,0,0,0,0,1,1,2,0,1,6,1,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37446,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37447,0,1,10,5,0,0,0,1,0,0,10,0,0,0,0,4,0,0,0,0,1,3,0,0,0,2,31,2,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,10,0,1,2,0,0,1,0,17,0,0,0,0,0,0,5,3,0,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37448,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,2,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,1,1,0,0,0,0,0,0,6,0,0,1,1,0,10,0,1,0,2,0,0,2,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37449,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+37456,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,12,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,6,0,0,4,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+37457,0,1,0,0,0,0,0,0,1,0,0,0,2,0,1,1,0,4,0,0,0,0,2,7,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,1,1,0,10,0,0,1,0,0,0,7,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+37458,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,Class_6
+37459,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,4,0,4,0,0,0,2,0,0,1,1,2,1,0,1,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,13,0,0,0,8,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_6
+37460,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37461,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,1,1,0,0,0,0,0,1,2,0,0,1,0,1,0,0,1,3,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,12,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,Class_6
+37462,0,1,3,4,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,2,0,7,1,0,0,0,4,0,0,0,4,0,0,2,0,1,0,0,0,1,0,0,0,2,0,0,1,0,0,4,1,0,0,0,0,12,1,3,0,0,0,0,2,1,0,1,0,1,0,2,3,0,0,0,0,0,1,0,0,0,2,0,1,1,0,1,0,0,1,Class_6
+37463,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,3,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,3,0,Class_6
+37464,0,0,10,5,0,0,0,0,0,0,10,0,0,1,1,8,0,1,0,0,0,1,0,0,0,1,10,2,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,4,0,0,1,0,0,1,0,12,0,1,0,0,0,1,2,2,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+37465,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,1,9,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,1,0,0,1,2,0,0,7,0,2,0,0,0,0,5,0,0,0,0,0,1,0,5,0,0,14,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,1,0,0,0,0,1,0,Class_6
+37466,0,0,11,2,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,1,0,1,0,8,0,0,1,1,0,0,0,2,0,0,1,0,0,0,0,1,0,1,0,1,0,5,0,0,3,0,0,1,0,13,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37467,0,0,9,0,0,0,0,2,0,0,6,0,0,0,0,1,0,1,1,0,1,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,2,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37468,0,0,4,1,0,0,0,0,0,0,16,0,0,1,0,0,0,1,0,0,1,1,0,8,0,2,6,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,4,0,0,0,9,0,1,1,0,0,1,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37469,0,2,1,1,1,0,0,1,0,0,2,2,3,0,1,2,0,3,1,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,3,1,0,4,3,0,10,0,0,5,0,1,0,0,0,0,1,0,0,0,0,1,0,5,0,0,0,2,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+37470,0,0,2,11,0,0,0,0,0,0,8,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,12,1,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,8,0,0,2,0,0,0,0,4,0,0,0,1,0,0,6,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,13,2,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+37471,0,0,3,0,0,0,0,0,0,0,8,0,0,1,0,4,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,2,0,0,0,2,0,0,0,0,0,1,11,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37472,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,9,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37473,0,0,1,1,0,2,0,2,1,0,14,1,1,1,0,6,0,1,1,0,1,1,0,2,0,1,1,0,0,0,3,2,0,0,0,0,3,1,1,0,0,0,0,1,2,2,0,0,2,0,0,8,2,7,0,2,0,2,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,37,0,1,0,0,0,12,1,2,1,0,0,0,0,0,3,0,0,1,0,Class_6
+37474,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,7,1,2,0,0,0,2,0,2,0,4,2,0,0,0,2,0,0,1,3,1,0,3,0,0,2,12,0,0,0,0,0,1,2,1,0,1,0,5,1,3,2,0,0,13,0,1,0,0,1,2,2,1,0,0,2,0,0,0,2,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,3,0,Class_6
+37475,0,0,0,11,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,1,0,0,0,8,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+37476,0,0,0,0,0,0,0,1,0,1,2,0,5,1,0,3,0,6,0,1,0,0,0,4,4,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,10,0,9,0,0,2,1,0,0,0,0,0,5,1,0,0,0,1,2,0,3,0,2,0,2,1,0,0,4,1,0,0,1,1,2,0,0,5,1,0,0,0,0,1,1,1,2,1,0,0,0,0,Class_6
+37477,1,1,12,7,3,0,0,0,0,0,2,0,0,0,0,3,0,12,0,0,1,0,0,33,3,17,1,10,0,0,0,5,0,8,5,0,0,0,1,0,0,3,0,0,0,0,0,2,3,0,0,0,0,11,3,0,1,3,0,3,8,1,0,0,0,0,8,0,0,0,0,0,3,2,0,2,0,0,0,12,0,0,3,0,0,0,0,0,5,0,3,4,0,Class_6
+37478,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,1,0,2,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37479,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,1,1,0,1,0,0,2,2,1,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,10,0,0,0,0,0,0,2,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,9,0,0,1,0,0,1,0,0,0,3,Class_6
+37480,0,0,2,1,0,0,0,1,1,0,0,0,1,0,0,3,0,0,0,1,0,2,0,1,0,5,0,5,0,0,1,0,0,0,1,0,0,0,0,1,4,3,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,15,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,Class_6
+37481,0,0,7,0,0,0,0,0,0,0,13,1,0,1,0,0,0,0,0,0,0,1,0,5,0,0,3,0,0,0,0,1,1,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,4,0,0,3,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,9,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37482,0,0,1,4,0,0,0,0,0,0,2,0,0,0,2,1,0,1,0,0,0,1,0,1,0,7,0,4,0,0,1,0,1,0,10,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,12,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37483,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,5,1,2,0,0,0,0,0,1,0,0,1,1,0,1,0,1,0,9,0,0,0,0,0,2,1,0,0,0,0,0,0,0,2,1,0,12,0,1,0,0,0,0,7,0,0,0,1,0,0,2,0,0,0,0,0,2,0,1,5,0,0,0,0,0,0,3,0,0,0,Class_6
+37484,0,1,6,5,0,0,0,0,0,1,17,0,0,0,0,0,0,0,0,0,3,0,0,6,0,0,16,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,1,0,2,0,0,0,4,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,14,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37485,0,0,6,3,0,0,0,0,1,0,14,0,0,0,0,1,0,2,0,0,0,1,0,8,0,4,5,0,0,0,0,1,0,0,0,0,1,0,0,3,0,1,2,0,0,14,0,0,2,0,0,0,0,5,0,0,0,6,0,1,0,1,0,0,0,1,1,0,0,1,0,1,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37486,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37487,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,5,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,3,0,0,0,0,0,3,1,0,0,2,0,0,0,0,0,0,0,12,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
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+37609,0,0,5,5,0,0,0,0,0,1,7,0,1,0,0,7,0,0,0,1,0,5,0,0,1,5,0,1,1,0,0,2,1,0,1,0,0,0,0,0,0,1,0,3,0,1,0,0,1,0,0,0,1,16,6,0,1,0,0,2,1,1,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,5,0,3,0,0,1,0,0,0,1,0,0,0,0,Class_6
+37610,0,6,0,0,0,0,0,3,0,0,2,0,1,1,0,0,1,2,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,10,0,1,0,0,0,0,2,1,0,0,2,1,0,0,0,0,0,1,0,4,0,0,0,2,1,0,1,2,0,1,0,1,0,0,0,2,0,0,0,0,9,0,0,0,2,0,1,1,1,2,0,Class_6
+37611,0,0,2,6,0,1,0,1,1,0,15,0,0,0,1,2,0,2,1,0,3,2,0,7,0,2,12,2,0,0,2,2,0,0,1,0,0,1,0,1,0,0,0,1,0,9,0,0,0,0,0,2,0,15,0,0,1,5,0,1,1,1,0,1,1,0,1,1,0,0,0,0,0,3,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,1,0,Class_6
+37612,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,3,0,1,0,2,0,0,1,0,0,0,1,6,0,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,12,0,1,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,Class_6
+37613,7,0,1,1,0,0,0,1,0,0,1,1,19,0,0,3,0,3,2,1,0,0,0,7,3,0,0,0,2,1,0,6,2,0,0,3,0,0,0,1,1,1,0,1,0,0,0,7,0,0,0,0,2,5,0,1,0,0,43,4,0,1,2,3,0,1,40,1,3,7,1,1,1,0,1,2,2,1,3,0,0,0,0,0,4,5,2,3,0,0,0,0,0,Class_6
+37614,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,4,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37615,0,1,17,4,0,0,0,0,0,1,5,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,1,5,0,0,0,0,1,0,2,0,0,1,0,0,0,0,1,1,0,13,0,0,2,0,0,1,0,15,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,2,0,0,1,0,0,1,2,0,0,0,0,Class_6
+37616,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,7,0,0,0,0,0,0,0,2,0,5,1,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37617,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,6,3,8,0,2,0,0,2,5,0,0,3,0,0,0,0,3,0,0,0,4,2,0,1,0,1,1,0,0,0,12,0,0,0,0,44,1,0,0,2,0,8,0,0,12,0,0,1,1,0,2,8,0,0,1,2,0,1,1,1,1,0,2,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+37618,3,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37619,0,0,4,1,0,0,0,0,0,0,14,1,0,0,0,4,0,1,0,0,1,1,0,5,0,1,3,2,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,1,9,0,0,0,0,0,4,0,6,0,0,0,6,1,0,6,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37620,0,0,8,3,0,0,0,0,1,0,6,0,1,0,0,0,0,3,0,0,3,1,0,0,0,1,8,1,0,0,0,2,0,0,2,0,0,2,0,1,0,0,0,1,0,5,0,0,2,1,1,4,0,5,0,1,0,0,0,0,2,1,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,5,1,7,0,0,0,0,0,0,2,0,0,1,1,Class_6
+37621,0,1,1,0,0,0,0,0,0,0,14,0,0,0,1,2,0,0,0,0,0,2,0,3,0,0,3,1,0,0,0,1,0,0,1,1,0,1,1,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,13,1,0,0,0,0,0,0,1,4,0,0,0,0,Class_6
+37622,3,0,2,8,0,0,0,0,1,0,3,0,0,0,2,0,2,0,0,0,0,0,0,1,0,10,3,0,0,0,0,0,0,1,5,0,1,0,0,1,0,4,0,0,0,3,0,0,0,0,0,0,0,3,2,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,Class_6
+37623,0,1,0,0,0,0,0,1,0,0,0,0,2,2,0,5,0,7,0,0,0,1,0,1,1,7,0,0,0,0,0,0,0,0,2,0,0,2,0,4,0,9,0,1,0,0,0,0,0,0,0,0,1,7,1,0,0,0,0,13,0,0,0,1,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+37624,0,0,3,1,0,0,0,0,0,0,15,0,0,0,0,3,0,0,0,0,0,1,0,9,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,2,1,0,0,1,1,6,0,0,1,5,0,0,2,0,0,1,2,0,0,0,0,0,1,0,0,5,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37625,0,1,0,0,0,0,0,0,0,0,1,0,2,1,0,0,4,3,0,0,0,1,1,2,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,1,1,5,0,0,0,0,0,1,0,0,7,0,0,11,0,0,0,0,0,1,8,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37626,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37627,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,2,0,4,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,3,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,12,Class_6
+37628,0,0,8,4,0,0,0,0,0,0,9,0,2,0,0,3,0,1,0,0,4,4,0,0,0,0,6,1,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,26,0,0,2,0,0,1,0,16,0,0,0,0,0,1,3,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37629,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37630,0,1,12,4,0,0,0,0,0,0,10,0,0,0,0,5,0,0,0,1,0,1,0,0,0,4,11,3,0,0,0,1,0,1,0,0,2,1,0,0,0,1,0,3,0,10,0,0,1,0,0,0,0,10,1,0,0,0,0,2,3,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,5,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37631,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37632,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,5,2,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,8,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+37633,0,0,4,1,0,0,0,0,0,2,15,2,0,0,1,1,0,1,0,0,3,0,0,6,0,4,30,0,0,0,0,2,0,0,0,0,2,1,0,1,0,0,1,0,0,4,0,0,1,0,0,4,0,2,0,1,0,7,0,1,3,0,1,0,0,1,1,0,0,1,0,0,1,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37634,0,1,5,13,0,0,0,0,0,0,7,0,0,0,0,4,0,1,0,0,0,5,0,7,0,10,8,2,0,0,2,1,0,0,7,0,0,0,0,1,0,4,0,1,0,4,2,0,2,0,0,0,0,14,1,1,0,1,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,1,0,0,0,0,1,1,Class_6
+37635,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,5,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37636,0,1,7,3,0,0,0,0,0,0,9,1,1,0,0,1,0,1,0,0,2,0,0,2,1,1,6,1,0,0,1,1,0,1,0,0,0,0,2,0,1,3,0,1,0,3,0,1,1,0,0,9,1,2,0,0,0,7,0,1,8,0,1,0,0,0,0,0,2,0,0,1,0,10,0,0,0,0,0,13,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37637,0,0,9,9,1,0,0,0,0,0,6,2,0,0,0,4,0,1,0,0,1,0,0,2,0,0,7,4,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,13,0,0,2,0,0,0,0,6,0,2,0,0,0,2,5,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+37638,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,2,3,1,1,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,2,0,2,0,0,1,0,1,0,0,0,0,3,1,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+37639,0,0,7,6,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,2,4,0,1,1,5,5,2,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,11,0,1,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37640,0,0,16,2,0,0,0,0,0,0,8,2,0,0,0,2,0,0,0,0,4,1,0,0,0,0,1,3,0,0,0,0,1,3,0,0,0,2,0,0,1,0,0,0,0,4,0,0,1,1,0,5,0,14,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,5,0,1,1,0,0,0,0,0,1,0,0,4,0,Class_6
+37641,0,0,1,2,0,0,0,0,0,0,9,0,0,4,0,0,0,0,0,0,0,0,0,1,0,7,3,1,0,0,1,0,0,0,3,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37642,0,0,0,0,0,0,0,1,0,0,11,1,0,0,0,0,0,0,0,0,1,0,1,7,0,0,2,0,0,0,1,3,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,1,0,8,0,1,0,2,0,2,0,0,0,0,0,0,0,0,3,0,2,0,0,0,2,25,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+37643,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,5,0,0,2,0,0,1,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37644,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37645,0,0,5,9,0,0,0,0,0,0,12,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,12,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,7,0,1,0,0,0,0,0,3,0,0,0,0,0,0,8,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37646,0,0,6,5,0,0,0,0,0,1,21,0,0,1,0,0,0,0,0,0,3,1,0,5,0,0,10,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,8,0,0,5,0,0,1,1,1,1,0,0,0,0,0,0,3,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
+37647,2,1,2,4,0,0,1,0,0,0,6,0,0,0,0,3,0,0,0,0,2,0,2,0,0,3,7,0,0,0,1,1,0,1,2,0,0,2,0,1,0,0,0,0,0,2,0,0,1,0,0,3,0,14,2,0,0,1,0,0,8,1,0,0,0,3,0,1,0,0,0,0,1,2,0,2,0,0,0,11,0,3,0,0,0,1,0,0,1,0,0,0,0,Class_6
+37648,0,2,1,3,0,0,0,0,0,0,8,0,0,1,0,1,1,2,0,0,0,1,0,2,1,0,1,0,0,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,1,0,0,2,15,0,0,0,0,3,0,0,1,2,0,0,1,1,0,0,0,0,2,0,0,1,0,0,0,1,1,1,0,0,1,2,1,2,0,0,1,0,0,1,0,0,0,0,0,Class_6
+37649,0,0,2,7,0,0,0,0,0,0,14,0,0,0,0,2,0,0,0,0,1,3,0,0,1,0,33,0,0,0,1,1,2,1,1,0,0,1,0,1,0,0,0,3,0,4,0,1,0,0,0,0,0,8,1,0,2,1,1,0,2,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,Class_6
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+37652,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,1,0,2,0,1,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,19,0,1,0,1,0,1,5,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
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+37656,0,1,3,2,0,0,0,0,0,0,9,0,0,0,0,3,0,0,1,0,3,3,0,0,0,4,2,3,0,0,2,0,0,3,1,3,1,2,0,1,0,5,0,5,1,3,7,3,8,0,0,0,0,17,2,0,1,0,0,3,0,1,1,1,0,0,1,1,0,2,0,0,0,1,0,0,0,0,0,4,0,1,0,1,2,0,1,0,1,0,0,0,0,Class_6
+37657,0,0,17,3,1,0,0,0,2,0,10,0,0,0,1,4,0,1,0,0,2,2,0,0,0,1,47,0,0,0,1,0,1,0,2,0,1,1,0,1,0,0,0,1,1,14,1,0,1,1,0,1,0,9,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,1,2,0,0,0,0,0,12,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37658,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,6,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
+37659,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,1,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,2,0,0,1,0,2,2,0,3,0,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37660,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,1,0,6,0,0,4,0,0,1,0,0,0,0,2,0,1,0,0,2,0,0,0,0,2,0,0,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37661,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,2,4,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_6
+37662,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,3,0,1,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37663,0,0,5,5,0,0,0,0,0,0,19,0,0,1,0,1,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,2,0,1,0,6,0,0,1,0,0,1,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,5,0,2,0,0,0,0,0,0,2,1,0,0,0,Class_6
+37664,0,1,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37665,0,0,0,1,0,0,1,0,1,0,10,0,0,0,0,3,0,1,0,0,2,0,0,0,0,0,20,1,0,0,0,1,0,2,2,1,0,0,1,2,0,0,0,1,0,3,0,0,1,0,4,12,0,1,0,2,1,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,5,0,4,0,0,0,0,0,1,2,0,0,1,0,Class_6
+37666,0,0,5,3,0,0,0,0,1,0,5,0,0,0,0,5,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,3,0,0,0,0,Class_6
+37667,0,1,15,10,0,0,0,2,0,0,11,0,0,0,0,2,0,0,0,1,1,4,0,0,0,0,6,1,1,0,4,0,0,1,1,0,0,1,0,1,0,0,0,1,0,4,0,0,1,1,0,0,0,12,1,0,0,0,0,0,2,1,2,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,10,0,0,0,0,0,0,1,0,0,1,1,Class_6
+37668,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,1,0,7,0,4,0,0,2,0,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37669,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,2,0,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,1,0,3,0,12,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,2,0,Class_6
+37670,2,0,0,0,0,5,0,0,1,0,1,0,0,0,0,5,0,6,0,0,0,6,0,8,0,0,0,1,0,0,1,0,0,0,0,0,2,4,0,1,5,13,0,1,0,0,1,1,0,0,0,0,1,10,4,1,3,1,0,19,0,6,1,1,3,0,3,0,0,3,0,1,0,0,0,0,0,0,2,3,0,1,0,0,0,0,0,0,2,0,0,3,0,Class_6
+37671,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,2,0,1,0,1,0,1,3,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37672,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,5,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,8,0,0,0,0,0,0,6,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37673,0,0,10,3,0,0,0,0,0,0,13,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,5,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,3,0,1,0,0,0,0,0,15,0,0,0,5,0,1,1,0,1,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37674,0,2,0,0,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,0,1,1,2,0,1,0,0,0,0,2,2,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,1,0,0,2,0,1,0,3,0,4,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37675,0,0,7,3,0,0,0,1,0,0,5,2,1,0,0,12,0,3,0,0,1,3,1,0,0,1,2,3,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,5,0,2,1,1,0,0,0,0,0,29,0,0,1,0,0,0,0,0,0,0,1,3,2,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+37676,0,0,1,1,0,0,0,0,0,0,0,0,2,3,0,17,0,1,0,1,0,2,0,5,1,20,0,1,0,0,0,2,2,3,0,0,0,4,0,7,0,0,0,0,0,5,0,7,1,0,0,0,0,9,0,0,3,0,0,13,0,2,0,1,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,Class_6
+37677,0,0,4,3,0,0,0,0,0,0,0,0,3,1,0,2,0,0,1,0,1,2,0,6,2,10,0,2,0,0,1,0,1,0,1,0,0,0,0,1,0,2,0,0,0,1,0,4,2,0,0,0,0,3,1,0,5,0,1,13,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
+37678,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37679,0,0,9,3,0,0,0,0,0,0,6,0,0,1,0,2,0,0,0,0,4,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,5,0,0,0,0,0,0,0,8,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+37680,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,1,0,2,0,0,0,2,0,4,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,1,0,0,0,0,2,1,0,3,0,0,9,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,Class_6
+37681,1,0,3,4,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,2,0,8,0,9,4,1,0,0,0,0,0,1,8,1,0,0,0,0,0,3,0,1,0,1,0,1,1,0,0,1,0,6,0,0,3,0,0,14,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+37682,0,0,1,2,0,0,0,1,0,0,0,1,0,0,0,3,0,2,0,0,0,9,0,1,1,1,0,0,29,0,0,0,0,1,0,0,0,0,0,0,3,1,0,3,0,0,0,1,0,0,0,1,1,5,8,0,2,0,0,1,0,4,0,0,0,3,3,0,0,2,1,1,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,1,0,Class_6
+37683,0,0,11,3,0,0,0,0,0,0,8,0,1,0,0,4,0,2,0,0,3,3,0,0,0,1,14,6,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,2,2,0,0,2,0,14,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37684,0,0,8,2,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,2,0,7,0,4,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,9,0,0,0,5,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
+37685,0,0,1,1,0,0,0,0,0,0,0,0,2,1,0,4,0,2,0,1,0,7,0,10,0,1,0,0,0,0,2,0,1,1,4,0,1,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,4,0,0,5,0,0,12,0,0,0,0,0,0,1,0,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37686,0,0,11,4,0,0,0,0,0,0,10,0,3,0,0,1,0,1,1,1,1,2,0,6,0,0,1,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,13,1,0,0,4,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37687,0,0,6,3,0,0,0,0,0,1,21,0,0,0,0,3,0,0,0,0,0,0,0,6,0,0,3,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,5,0,0,0,13,0,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37688,0,0,17,2,0,0,0,0,0,2,7,1,2,0,0,7,0,2,0,2,3,1,0,1,0,0,6,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,32,0,0,0,0,0,0,0,14,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37689,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,6,0,0,0,0,1,0,4,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+37690,0,0,4,1,0,0,0,0,0,0,8,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,9,0,1,0,0,0,0,0,14,0,0,0,0,0,0,3,0,0,0,1,1,1,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37691,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37692,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37693,0,0,0,2,0,0,0,2,0,0,2,0,4,0,0,0,1,2,0,1,0,0,0,2,0,4,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,5,2,0,1,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37694,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37695,1,0,1,2,3,0,0,0,0,2,1,0,0,0,0,5,0,3,1,2,0,5,0,2,0,5,0,2,3,0,0,0,0,2,2,0,0,0,0,0,2,6,0,3,0,1,5,1,0,0,4,0,2,10,20,0,9,0,0,10,2,1,6,0,1,1,0,0,2,1,0,0,1,0,3,1,0,0,2,1,4,1,0,2,0,2,0,0,2,0,0,4,0,Class_6
+37696,0,0,3,1,0,0,0,0,0,0,1,0,2,1,0,2,0,0,0,0,0,4,0,4,1,5,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,5,0,0,0,0,0,2,0,0,4,0,0,9,0,0,0,1,0,1,2,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,Class_6
+37697,0,1,5,1,0,0,0,0,0,0,7,0,0,0,0,8,0,1,0,0,3,0,0,1,0,0,6,0,0,0,1,1,1,0,3,3,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,4,0,11,1,2,1,2,1,0,4,2,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,10,0,6,0,0,0,1,0,0,3,0,0,0,0,Class_6
+37698,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37699,0,0,6,4,0,0,0,0,0,0,9,0,0,1,0,2,0,0,0,0,6,1,0,2,0,0,14,1,0,0,0,2,0,2,0,0,0,0,0,2,1,0,0,0,0,13,0,2,0,0,0,1,1,7,0,0,1,2,0,2,5,0,0,0,1,1,0,0,0,1,0,0,0,10,0,2,0,0,0,6,0,1,0,0,0,0,0,0,3,0,0,3,0,Class_6
+37700,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37701,0,0,5,2,0,0,0,0,1,0,0,0,1,0,0,2,1,1,0,0,0,0,0,2,0,7,2,0,0,0,1,0,1,4,8,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,4,0,1,0,0,0,12,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,Class_6
+37702,0,0,3,3,0,0,1,0,0,0,4,0,0,0,1,2,1,2,0,0,0,1,0,0,0,1,5,3,0,0,0,1,1,1,2,0,1,0,0,0,0,1,0,1,0,3,0,0,0,0,0,2,0,7,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,3,0,1,0,0,Class_6
+37703,0,0,9,11,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,11,0,2,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,3,0,2,0,0,0,0,0,7,1,0,1,1,0,0,2,0,0,2,1,0,8,0,0,1,0,1,0,9,0,0,0,0,0,5,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37704,0,2,6,6,0,0,1,0,0,0,0,0,1,1,0,3,0,3,0,0,0,2,0,7,0,12,0,2,2,0,0,1,0,0,0,1,0,1,0,0,0,2,0,3,0,0,0,1,8,0,0,0,1,13,0,0,1,0,0,9,0,0,1,1,0,4,8,0,0,4,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,1,1,Class_6
+37705,0,0,13,1,0,0,0,0,0,0,8,0,0,0,0,2,1,0,0,0,3,1,0,0,0,0,19,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,11,0,0,1,0,0,1,1,13,0,1,0,3,0,0,6,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37706,0,0,5,3,0,0,0,0,0,0,10,0,0,0,2,1,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37707,0,0,0,0,0,0,0,1,0,0,2,0,7,0,0,1,0,3,0,1,0,0,0,4,1,0,0,0,1,0,0,0,1,1,0,1,1,0,0,1,0,2,0,0,0,0,10,2,0,2,0,0,2,0,1,0,0,0,1,3,0,1,0,1,0,2,3,4,0,0,0,0,0,1,0,1,0,1,2,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+37708,0,0,7,6,0,0,0,0,0,0,10,0,0,0,0,1,0,1,0,0,0,2,0,1,0,1,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37709,0,0,1,4,0,0,0,1,0,0,3,0,0,0,0,0,1,5,0,0,0,1,0,5,1,4,0,0,0,0,0,1,1,0,1,3,0,0,0,0,0,4,0,0,1,0,0,2,1,1,0,0,0,3,1,0,11,0,0,5,0,0,0,1,0,0,7,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,3,2,0,0,Class_6
+37710,0,4,2,2,0,0,0,0,0,0,10,0,2,0,0,3,0,5,0,2,0,7,0,0,2,2,0,0,1,0,0,0,0,0,13,0,0,0,0,0,1,5,0,11,0,0,0,2,46,0,0,0,0,8,0,0,2,0,0,3,0,1,0,2,0,9,0,0,0,2,0,0,0,0,0,0,0,0,0,7,1,0,0,2,0,0,1,2,4,0,0,0,0,Class_6
+37711,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37712,1,11,1,2,0,0,0,0,1,1,10,0,2,0,1,4,0,3,0,3,0,7,0,5,0,0,2,0,0,2,0,0,0,0,7,0,0,1,0,0,3,0,0,2,0,0,0,1,6,0,0,0,0,9,0,0,1,2,0,0,0,0,0,2,0,4,0,0,0,1,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,20,0,0,0,Class_6
+37713,1,0,10,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,1,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,4,0,4,0,0,1,0,0,0,0,13,0,2,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,1,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37714,0,0,0,2,0,3,0,0,0,0,1,1,0,0,0,0,0,6,0,1,0,9,0,13,0,2,0,0,0,0,0,0,1,1,2,0,0,1,0,0,2,3,0,2,0,1,0,0,0,0,0,0,0,4,1,0,10,0,0,10,0,3,0,1,1,2,2,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,0,2,0,0,4,0,Class_6
+37715,0,0,0,1,0,0,0,0,0,1,11,2,0,0,1,5,0,0,0,0,2,3,0,0,0,0,24,2,0,0,0,1,1,0,6,0,1,1,0,1,0,0,2,1,0,4,1,0,0,0,1,17,0,3,0,1,0,0,0,0,5,1,0,0,0,0,0,0,4,0,0,0,3,9,0,1,0,0,2,4,0,1,0,0,0,0,0,1,4,0,0,0,0,Class_6
+37716,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37717,0,1,0,0,0,0,0,0,0,0,1,0,2,0,2,0,2,3,0,0,0,1,1,6,0,0,0,0,1,0,0,3,1,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,10,0,1,0,1,0,0,6,0,0,1,0,3,0,0,0,0,4,1,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,Class_6
+37718,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37719,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37720,0,2,3,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,20,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,1,0,3,0,1,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37721,0,0,1,2,0,0,1,0,0,0,5,0,0,0,0,2,0,0,0,1,0,1,0,1,1,0,1,0,0,0,2,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,7,0,3,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37722,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,1,0,3,0,0,14,1,0,0,3,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37723,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,3,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37724,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,3,0,10,0,3,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,0,1,0,2,0,0,0,0,1,6,0,0,1,0,0,12,0,2,0,2,0,1,1,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37725,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,1,3,0,1,0,3,0,1,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,3,0,1,1,0,0,0,0,0,0,1,0,0,1,0,7,0,0,10,0,1,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+37726,0,2,6,0,0,0,0,0,0,0,10,0,1,0,0,2,0,0,0,0,1,3,0,1,0,5,4,1,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,2,0,3,0,2,0,0,0,0,0,16,0,0,0,0,0,2,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37727,0,0,2,3,0,0,0,0,0,0,12,0,1,1,0,1,0,1,0,1,0,0,0,5,0,0,4,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,2,0,0,0,6,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37728,1,0,12,3,0,0,1,1,1,0,11,0,0,0,0,3,0,1,0,2,3,0,0,1,0,2,3,4,0,0,0,1,1,1,1,0,0,1,1,1,0,1,1,1,0,21,0,1,0,0,0,5,0,5,0,0,1,0,0,1,8,1,0,1,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,2,0,2,0,0,1,0,0,0,2,0,1,0,0,Class_6
+37729,0,0,4,1,0,0,0,0,1,0,9,0,0,0,0,4,0,0,0,0,2,0,0,1,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,5,1,4,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37730,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,1,1,0,0,0,9,2,0,0,0,0,0,3,0,0,0,0,11,0,0,0,0,0,0,2,0,0,0,1,0,1,0,Class_6
+37731,0,0,6,2,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,1,6,1,0,0,1,7,0,3,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,9,0,0,1,0,0,1,0,18,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+37732,0,0,18,2,0,0,0,1,0,0,8,0,0,1,0,4,0,0,0,0,4,1,0,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,2,0,9,1,1,0,1,0,0,2,1,0,2,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,6,0,2,0,0,0,0,1,0,2,0,0,2,0,Class_6
+37733,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,8,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,0,2,0,1,2,0,0,0,0,0,1,1,0,2,0,0,16,0,1,0,0,0,3,5,0,0,3,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,4,3,0,0,0,0,0,0,Class_6
+37734,0,0,1,3,0,0,0,0,2,0,8,0,0,0,0,2,1,0,0,1,2,0,0,0,2,1,10,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,4,0,0,0,1,0,1,6,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37735,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,4,0,5,0,0,0,2,0,1,0,1,0,0,0,0,0,0,2,3,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,13,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,Class_6
+37736,0,0,0,0,0,0,0,2,1,0,1,0,3,1,0,0,5,0,0,1,0,0,0,14,0,0,0,0,0,0,0,5,0,1,0,2,0,1,0,0,0,3,0,1,0,3,0,5,0,0,0,0,0,1,0,0,10,0,0,18,0,0,0,0,0,3,22,1,0,2,1,0,1,0,0,2,0,0,5,2,0,0,2,0,0,0,0,0,0,0,1,0,1,Class_6
+37737,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,1,2,2,0,0,0,5,2,4,0,2,0,1,0,0,0,0,1,1,3,0,0,1,0,0,0,7,0,2,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37738,0,1,2,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,1,1,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,1,0,2,1,1,4,1,0,0,0,2,1,0,0,0,0,0,0,2,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37739,0,1,13,2,0,0,0,0,2,0,15,0,1,0,0,0,0,0,0,0,0,2,0,4,0,0,7,0,0,0,0,0,0,1,0,1,0,0,0,2,0,1,0,3,0,9,0,0,0,0,0,0,0,7,0,0,0,8,0,2,0,0,0,1,0,0,1,0,0,0,0,0,1,4,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+37740,0,1,7,2,0,0,0,0,0,0,7,0,2,0,0,3,0,4,0,0,2,2,0,1,0,3,2,2,1,0,1,1,0,0,4,1,0,3,0,0,0,3,0,4,0,0,0,1,2,0,1,1,0,18,2,0,0,0,0,0,1,0,1,1,1,2,0,0,0,1,0,1,0,0,0,1,0,0,0,10,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+37741,0,0,5,6,0,0,0,0,0,1,11,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,6,1,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,8,0,0,0,0,0,1,8,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,7,0,5,1,0,0,0,0,1,1,0,0,2,0,Class_6
+37742,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_6
+37743,0,0,21,16,0,0,0,0,0,0,11,0,0,2,0,2,0,0,0,0,0,3,0,0,0,3,4,0,0,2,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,5,0,0,1,0,0,0,0,12,0,0,0,1,0,0,0,0,1,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37744,0,0,1,4,0,0,0,2,0,0,0,0,0,0,0,2,1,5,0,1,0,4,0,2,1,2,0,1,0,0,0,0,0,3,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,13,0,0,0,1,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,Class_6
+37745,0,0,5,7,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,2,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,1,0,0,0,0,10,0,1,0,1,0,1,2,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37746,0,0,24,4,0,0,0,0,0,1,12,0,1,0,0,12,0,2,0,0,3,2,0,1,0,2,2,9,0,0,0,0,2,0,4,0,0,2,0,2,2,0,0,7,0,6,1,1,4,0,1,0,0,25,0,0,0,0,0,0,8,1,0,1,0,0,0,1,1,1,0,2,1,11,2,1,0,0,0,7,0,5,0,0,0,0,0,0,2,0,0,9,0,Class_6
+37747,0,9,1,0,0,0,1,0,0,0,0,1,0,0,0,3,2,5,0,1,0,7,0,3,0,17,0,0,0,0,3,0,0,0,2,0,0,2,0,4,1,7,0,2,0,0,0,0,2,0,0,0,0,5,0,0,2,4,0,12,0,1,0,0,0,1,1,0,8,1,0,0,0,4,0,2,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,1,6,Class_6
+37748,0,0,2,3,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,1,1,1,0,4,0,1,0,1,0,0,0,3,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,12,0,0,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37749,0,0,0,0,0,0,1,0,0,0,2,0,1,1,0,3,1,0,1,2,0,3,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,4,0,4,0,0,7,0,0,2,0,0,2,1,1,0,0,0,1,0,0,1,0,2,0,2,3,13,0,3,2,0,0,1,0,1,0,0,2,1,0,0,0,2,1,0,0,0,0,0,0,0,0,Class_6
+37750,0,0,14,1,0,0,0,1,0,0,12,2,0,0,0,3,1,1,0,0,2,0,0,2,0,0,31,2,0,0,0,1,0,0,0,1,0,1,0,3,0,2,1,3,0,16,0,2,0,0,0,0,0,12,1,0,0,1,0,0,7,0,1,2,1,2,0,0,0,1,0,2,0,3,0,1,0,1,1,6,0,1,0,0,0,0,0,1,0,0,1,0,0,Class_6
+37751,0,1,0,0,0,0,2,0,0,0,1,0,0,2,0,1,1,1,0,3,0,7,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,5,1,0,0,1,0,2,0,0,0,1,0,0,0,0,2,3,2,0,0,0,0,1,0,0,1,0,0,5,0,0,1,1,0,0,1,1,1,3,0,0,0,1,0,0,3,0,2,0,3,0,0,0,12,0,1,Class_6
+37752,0,0,5,1,0,0,0,0,0,0,14,0,0,1,0,0,0,0,0,0,1,3,0,6,0,0,17,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,12,0,0,1,0,0,2,0,9,0,1,0,4,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37753,2,1,1,3,0,0,1,0,0,0,6,0,0,0,3,10,0,2,0,1,0,4,0,0,0,0,1,0,0,0,0,1,0,0,3,1,1,2,0,2,0,1,0,4,0,2,0,0,3,0,0,0,1,12,2,2,0,0,0,0,0,0,0,0,2,1,3,0,0,2,0,1,0,0,0,2,0,0,0,2,0,0,1,1,0,0,1,0,0,1,0,0,0,Class_6
+37754,0,0,7,22,0,0,0,1,0,1,5,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,1,2,0,1,0,0,0,0,1,0,2,0,1,0,0,0,2,1,0,0,1,0,6,0,0,0,0,0,0,4,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37755,0,0,0,1,0,0,0,0,2,0,0,0,2,1,0,0,0,2,0,0,0,3,0,11,0,9,0,0,0,0,0,0,0,1,9,1,0,0,0,0,0,6,0,3,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,14,3,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37756,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,3,0,1,0,1,0,0,0,2,3,5,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,12,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
+37757,0,1,4,7,0,0,0,0,0,0,6,1,0,0,0,3,0,1,0,0,0,2,1,0,0,2,4,0,0,0,1,1,2,1,4,0,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,1,5,0,0,1,0,0,1,4,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,8,1,4,0,0,1,0,0,0,2,0,0,2,0,Class_6
+37758,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,1,0,1,0,4,0,1,0,0,0,0,43,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+37759,0,0,6,11,0,0,0,0,1,0,10,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,13,0,0,0,0,0,0,2,2,0,0,1,0,0,0,2,0,1,0,8,0,0,1,0,0,2,0,8,0,0,0,0,0,0,5,3,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37760,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37761,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,1,3,0,0,0,5,0,13,1,2,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,5,0,0,0,2,3,0,0,0,0,0,0,1,0,0,2,0,0,5,1,2,0,0,1,1,4,5,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,Class_6
+37762,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,3,0,0,0,1,5,0,0,0,0,0,0,2,2,0,1,1,0,0,0,0,0,0,0,0,7,0,0,0,1,0,1,0,1,0,0,0,0,1,0,4,0,0,0,0,0,0,83,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37763,0,0,1,1,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,1,1,0,0,0,1,2,2,3,0,0,1,1,0,0,3,0,0,1,0,2,0,0,1,1,0,1,0,0,0,0,0,2,0,8,0,0,0,0,0,0,1,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,4,0,0,3,0,Class_6
+37764,0,0,12,9,0,0,0,0,0,0,9,0,1,0,1,2,0,0,0,0,6,2,0,0,0,3,11,2,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,3,0,5,0,0,1,0,0,2,0,14,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,2,0,1,0,0,1,0,Class_6
+37765,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,3,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,7,0,0,0,0,0,2,0,0,0,1,0,0,1,0,1,0,0,13,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+37766,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,2,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,1,0,0,0,8,2,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,3,0,1,0,1,0,1,0,Class_6
+37767,0,0,1,1,0,0,0,0,0,0,1,0,1,0,1,7,0,1,0,0,0,2,0,6,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,2,0,1,0,0,0,0,5,0,0,5,0,0,10,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37768,1,0,4,3,0,0,0,0,0,0,8,0,0,0,1,3,1,0,0,0,1,0,0,0,0,1,15,0,0,0,0,0,1,0,2,0,1,0,1,0,2,1,0,1,0,2,0,0,2,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,0,0,0,0,0,2,1,0,0,0,Class_6
+37769,0,2,0,0,0,0,0,0,0,0,16,0,0,1,5,4,0,1,0,1,0,0,0,0,0,3,2,1,0,0,0,1,0,1,2,1,0,0,1,0,0,0,0,1,0,2,0,0,1,0,0,2,0,13,0,1,1,1,0,0,2,0,0,1,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,13,0,3,0,0,0,0,0,0,2,0,0,1,1,Class_6
+37770,0,2,11,7,0,0,0,0,0,0,9,0,0,0,0,2,0,1,0,0,0,2,0,0,0,3,8,4,0,0,0,0,1,0,5,1,0,1,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,12,0,2,0,2,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,2,0,0,0,1,0,0,0,0,0,0,Class_6
+37771,0,0,5,1,0,0,0,0,0,0,12,0,0,0,0,4,0,1,0,0,2,1,0,5,0,4,5,0,0,0,0,2,1,1,1,0,0,1,0,1,0,0,0,1,2,6,0,0,1,2,0,3,0,15,1,1,0,8,0,1,4,1,0,1,2,1,1,0,0,1,0,0,0,2,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+37772,0,4,0,1,0,0,3,0,0,2,2,0,3,0,0,2,1,1,1,3,0,7,0,8,2,0,1,0,0,0,0,1,0,0,0,1,1,1,0,0,0,5,0,9,0,0,14,0,0,0,0,0,1,3,6,0,1,0,1,0,0,0,0,2,1,2,10,0,0,1,0,0,0,1,0,1,0,1,3,3,0,0,1,0,1,0,0,0,1,0,0,0,1,Class_6
+37773,0,0,5,4,0,0,0,0,0,1,5,0,0,0,0,2,0,0,0,1,0,2,0,0,0,8,3,0,0,0,0,1,0,0,1,0,1,0,0,3,0,3,0,2,0,4,0,0,0,0,1,0,0,9,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37774,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,1,0,0,0,0,3,0,0,1,0,0,0,8,0,0,0,0,0,0,2,0,0,0,1,4,2,0,0,0,0,0,0,0,0,0,0,2,5,1,0,1,0,0,1,0,4,0,0,0,0,11,2,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+37775,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37776,0,0,0,3,0,0,0,0,2,0,15,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,4,0,0,0,1,1,0,0,5,0,0,0,0,0,0,0,2,0,0,2,2,1,1,1,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,1,1,Class_6
+37777,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,12,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+37778,0,0,7,5,0,0,0,1,2,0,0,0,0,0,1,1,1,0,0,0,3,1,0,2,1,6,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,1,4,1,0,4,2,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,3,1,Class_6
+37779,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,2,3,0,2,0,1,0,10,0,6,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,17,0,2,0,1,0,1,0,0,0,0,0,1,2,0,12,0,0,4,0,2,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,9,0,0,0,1,0,0,1,0,2,0,Class_6
+37780,0,1,4,0,0,0,0,0,2,0,9,0,0,1,2,1,0,0,0,1,1,0,1,5,1,1,5,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,11,0,2,1,3,0,2,0,0,3,0,0,1,1,0,1,0,0,1,0,0,0,61,1,0,0,0,0,14,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+37781,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,2,4,1,0,1,0,1,0,5,0,2,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,12,0,1,0,0,0,0,5,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+37782,0,1,2,3,0,0,0,0,1,0,0,0,1,0,0,4,0,3,0,0,0,4,0,5,0,11,0,0,0,0,0,1,0,1,4,0,0,0,0,1,2,1,1,0,0,3,0,0,0,0,0,1,0,12,0,0,3,0,0,12,0,1,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,1,1,0,1,0,2,2,1,Class_6
+37783,0,0,0,0,0,0,0,2,1,0,1,0,0,1,0,2,1,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,2,2,4,0,0,9,2,0,2,0,0,0,1,2,0,0,0,0,2,0,0,1,1,2,1,4,0,0,2,2,2,0,0,0,1,0,0,4,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+37784,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,4,0,3,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,5,0,4,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+37785,1,2,7,0,0,2,0,0,0,0,6,0,3,2,0,6,0,0,1,1,0,8,0,1,0,0,1,2,0,0,1,0,0,0,2,1,1,0,0,0,0,0,0,11,0,1,8,1,5,0,0,0,0,22,4,0,1,0,0,1,1,0,1,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,1,0,0,1,0,2,0,0,1,0,Class_6
+37786,0,0,18,12,0,0,0,0,0,0,12,0,0,2,0,2,0,0,0,0,0,2,0,0,0,1,8,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,0,0,19,0,0,0,0,0,4,4,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,0,3,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37787,0,0,19,4,0,0,0,0,0,1,8,0,0,0,0,0,0,1,0,1,1,0,0,3,0,3,8,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,9,0,0,0,0,0,0,0,12,0,0,0,0,0,1,2,3,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37788,0,0,0,0,0,0,4,0,0,0,0,1,3,1,0,1,0,10,0,0,0,3,0,2,1,1,0,0,2,0,0,0,1,0,0,0,0,2,0,0,1,2,0,2,0,0,0,0,1,0,1,0,0,7,7,0,4,0,0,8,0,6,0,6,0,2,0,0,0,3,1,3,0,0,2,0,0,1,2,0,2,0,0,0,2,1,0,1,0,0,0,0,0,Class_6
+37789,0,0,0,0,2,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,0,2,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,4,0,1,0,0,2,1,0,0,0,0,0,0,1,0,1,0,1,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,1,1,0,0,2,1,2,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37790,0,0,7,11,0,0,0,0,0,0,16,1,2,0,0,1,0,0,1,0,2,2,0,2,0,1,15,0,0,1,0,0,0,0,2,0,1,1,0,0,0,0,1,1,0,3,0,0,1,0,0,0,0,6,0,0,0,0,0,0,3,1,1,0,4,0,4,0,0,0,0,1,0,0,0,3,0,0,0,5,0,4,0,0,0,2,1,0,1,0,0,0,0,Class_6
+37791,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,6,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37792,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,1,9,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,12,0,0,0,0,3,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+37793,0,0,2,1,0,0,0,0,0,0,17,0,0,0,0,3,1,0,0,0,0,1,0,11,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,3,0,0,0,17,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37794,0,2,13,7,0,0,0,0,0,0,10,0,0,0,0,7,0,2,0,0,0,1,0,0,0,1,10,3,1,0,0,0,0,0,1,1,0,1,0,4,1,0,1,1,0,5,1,1,1,0,0,0,1,15,2,2,0,2,0,2,4,0,0,0,1,0,0,0,0,1,0,2,0,1,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37795,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,2,4,0,0,0,0,0,7,0,0,0,2,0,0,2,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,12,0,1,0,1,0,2,8,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+37796,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37797,0,0,6,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,5,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,8,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37798,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,3,0,2,0,1,0,0,0,0,0,0,10,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,10,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,2,0,1,0,0,0,2,2,0,0,0,Class_6
+37799,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37800,0,1,0,17,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,2,0,14,0,0,0,0,0,0,0,1,3,0,0,0,0,0,1,5,0,2,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,1,Class_6
+37801,0,0,0,0,0,0,0,0,4,0,12,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,4,0,0,0,0,2,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,2,0,3,0,2,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,103,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37802,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,1,0,9,0,0,0,0,0,0,0,1,1,1,0,2,1,0,0,2,0,2,0,4,0,0,1,2,0,0,0,3,4,0,5,0,0,13,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,1,0,Class_6
+37803,0,0,0,3,0,0,1,1,0,0,4,0,1,0,1,5,1,12,0,1,0,0,0,17,0,7,0,3,1,0,1,1,1,0,15,1,0,0,0,0,0,6,0,0,0,2,0,0,1,0,0,0,0,7,1,0,3,0,1,11,0,1,0,0,0,1,4,0,0,0,0,0,0,0,3,0,0,0,0,7,0,0,2,0,0,0,0,0,0,0,0,2,4,Class_6
+37804,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,8,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,7,0,1,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37805,0,0,0,3,0,0,0,0,4,0,0,0,0,1,0,1,0,1,0,0,0,2,0,3,0,0,1,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,Class_6
+37806,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37807,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,1,0,2,0,0,0,3,7,0,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,1,1,1,0,19,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,3,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,Class_6
+37808,0,0,0,0,0,0,0,1,7,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,1,0,3,0,0,0,0,0,0,0,5,1,5,3,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_6
+37809,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,4,0,1,0,0,0,5,0,0,0,0,1,0,1,2,0,0,1,2,0,2,2,0,0,0,0,1,0,1,0,0,0,0,1,0,1,1,1,0,0,1,0,10,0,0,1,0,0,0,6,0,0,7,0,2,0,0,0,0,4,1,2,2,0,0,0,0,1,0,2,0,4,0,0,1,0,Class_6
+37810,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37811,0,0,4,4,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,0,0,0,0,1,0,7,3,1,0,0,0,0,0,3,1,0,0,0,0,0,0,2,0,2,0,1,0,1,0,0,0,0,0,6,1,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37812,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37813,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,9,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,1,5,1,0,0,0,6,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37814,0,0,5,21,0,0,0,0,0,1,10,0,1,0,0,3,0,0,0,0,6,1,0,2,0,5,0,5,1,0,0,0,0,1,6,0,1,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,13,0,2,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37815,0,0,8,5,0,0,0,0,0,0,10,0,0,0,1,1,0,1,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37816,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,4,0,5,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,8,0,4,0,3,0,0,2,0,0,0,0,1,0,0,1,0,0,11,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+37817,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,3,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,12,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37818,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,12,4,0,0,0,0,0,2,0,3,2,0,0,2,1,0,0,0,1,3,2,0,1,0,0,15,0,0,0,0,0,1,4,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37819,0,0,0,0,0,0,1,2,0,0,2,0,0,0,0,1,0,0,0,1,0,2,0,2,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,21,0,2,0,0,0,2,0,0,0,1,0,2,0,0,3,1,0,0,0,0,0,0,2,1,6,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,5,0,1,0,1,0,1,0,2,0,0,Class_6
+37820,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,2,0,2,0,0,0,1,0,0,1,11,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,7,0,1,0,3,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,2,0,3,0,6,8,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
+37821,0,0,4,4,0,0,0,0,0,0,6,0,0,0,0,3,0,2,1,0,2,3,0,0,1,0,2,1,0,0,1,1,1,0,3,0,0,2,0,1,0,0,0,1,0,1,0,0,3,0,0,0,1,4,2,0,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,2,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,2,3,0,0,0,Class_6
+37822,4,0,2,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,1,0,0,0,2,7,1,0,1,0,0,0,0,10,0,0,0,1,3,4,0,0,0,3,0,7,0,0,0,5,0,0,0,0,0,1,0,1,0,0,2,5,0,0,1,3,0,0,6,0,0,3,0,1,0,1,0,1,0,0,0,1,0,0,0,0,3,3,14,0,1,0,0,0,34,Class_6
+37823,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37824,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37825,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,2,0,4,0,1,0,4,0,2,2,0,0,0,2,0,0,0,1,0,0,3,1,0,0,0,1,3,0,1,0,0,0,1,0,0,0,0,0,2,4,0,4,0,0,0,0,6,0,1,0,1,7,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,2,0,Class_6
+37826,0,0,6,2,0,0,0,0,0,0,6,0,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,4,1,3,0,0,0,0,1,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+37827,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+37828,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,4,0,7,0,5,0,4,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,13,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,Class_6
+37829,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37830,0,0,5,9,0,0,0,0,0,0,9,0,1,0,0,4,0,0,0,0,0,2,0,1,0,7,0,4,1,0,0,2,0,0,8,0,0,0,0,0,0,0,0,2,1,1,0,1,2,0,0,1,0,14,0,3,0,0,0,1,2,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,9,0,2,0,0,1,0,0,1,1,0,0,1,0,Class_6
+37831,1,0,4,5,0,0,0,0,0,0,20,0,3,0,1,3,0,0,0,0,0,0,0,14,0,3,8,1,0,0,0,0,0,0,2,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,14,0,0,0,1,0,1,0,0,2,0,0,0,0,0,1,3,1,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37832,0,0,14,8,0,0,0,0,0,0,10,1,2,0,0,4,1,1,0,0,0,6,0,4,0,8,3,1,1,0,6,0,0,0,1,0,0,0,0,0,0,5,0,3,0,7,0,1,0,0,0,1,0,15,1,2,1,0,0,1,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
+37833,3,0,0,1,1,0,0,1,0,0,0,0,4,0,2,0,0,6,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,1,0,0,2,0,2,1,0,1,0,3,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37834,0,0,1,0,0,0,1,0,0,2,0,0,1,1,0,1,7,0,1,4,0,2,0,0,1,0,0,1,1,0,0,0,1,1,0,0,1,0,0,1,0,4,0,1,0,0,0,2,0,0,0,0,1,2,9,0,0,0,0,0,0,0,0,0,1,1,7,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,2,0,0,0,0,0,0,0,Class_6
+37835,0,0,3,14,0,0,0,0,0,0,7,0,0,0,0,2,0,2,0,0,0,1,0,0,0,4,2,3,0,0,1,1,1,0,4,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37836,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,4,0,0,0,0,0,7,0,0,0,0,1,0,0,1,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,10,0,0,1,0,0,0,7,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+37837,0,1,2,5,0,0,0,0,0,0,5,0,3,0,1,1,1,1,0,0,1,0,0,0,0,2,0,3,0,0,1,2,1,1,0,0,0,2,0,1,0,1,0,1,0,2,0,5,1,0,0,0,0,6,0,0,0,0,0,0,7,3,1,3,0,3,1,0,0,0,0,1,4,0,1,2,1,0,0,4,0,6,0,0,1,0,0,0,0,0,0,1,0,Class_6
+37838,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37839,0,0,3,3,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,1,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,4,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37840,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,7,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,4,3,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37841,0,0,15,10,0,0,1,0,0,0,7,0,3,0,0,7,0,0,0,0,1,2,0,0,0,1,8,1,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,1,0,8,0,1,1,0,0,3,0,12,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37842,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37843,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,3,0,2,0,1,0,0,0,1,0,0,0,0,1,0,0,1,2,5,0,1,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,3,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37844,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,2,0,2,0,3,1,2,0,0,1,2,0,1,0,0,0,2,0,1,0,0,1,10,0,2,1,1,0,0,0,0,0,3,0,5,1,0,0,0,0,0,0,3,0,0,0,3,8,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,4,0,Class_6
+37845,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,1,0,0,0,0,3,0,0,0,0,6,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_6
+37846,0,0,14,7,0,0,0,0,1,0,11,0,0,1,0,3,0,1,0,0,1,2,0,0,0,0,8,2,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,12,0,0,1,0,0,1,0,11,0,0,0,1,0,0,3,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,2,Class_6
+37847,0,1,19,3,0,0,0,0,0,0,9,0,0,0,0,1,0,8,0,0,0,5,0,0,0,2,8,2,1,0,0,1,0,1,1,0,0,0,0,0,1,0,0,2,0,5,1,0,3,0,0,0,0,11,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,1,0,0,0,Class_6
+37848,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,1,0,2,0,6,0,8,0,2,0,0,0,1,0,0,13,0,0,0,0,1,1,12,0,1,0,0,0,2,0,0,0,0,1,4,0,0,1,0,0,10,0,1,0,3,0,0,6,1,0,1,0,0,0,0,0,2,0,1,0,2,0,0,4,0,0,0,0,0,0,2,0,1,0,Class_6
+37849,0,0,13,8,0,0,0,0,0,0,7,0,0,0,0,5,1,1,0,1,1,0,0,0,0,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,0,0,0,10,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,1,0,0,0,0,0,1,Class_6
+37850,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,4,0,0,0,0,0,6,0,0,0,0,1,0,0,2,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,10,0,0,1,0,0,0,7,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+37851,0,2,7,12,0,0,0,0,1,0,10,0,1,0,0,6,0,1,0,0,2,0,0,2,0,0,8,3,0,0,3,0,0,0,1,0,0,1,0,1,0,0,1,1,0,8,0,0,1,0,0,1,0,3,0,1,0,1,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,11,0,5,0,0,0,0,0,1,0,0,0,1,0,Class_6
+37852,0,0,9,11,0,0,0,0,0,0,12,2,0,0,0,3,0,0,0,0,0,1,0,2,0,3,1,8,0,0,0,2,0,0,0,0,0,0,0,0,0,6,2,1,0,3,0,0,1,0,0,0,0,2,0,0,0,1,0,2,2,0,0,2,0,1,0,0,0,0,0,1,0,1,0,1,0,0,1,11,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37853,0,0,0,1,0,0,0,0,11,0,0,0,0,0,1,4,0,0,0,0,0,2,0,1,2,2,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,3,0,2,0,0,1,3,0,2,0,0,1,11,0,0,0,0,0,1,0,0,0,16,0,5,5,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,Class_6
+37854,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,4,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37855,0,0,5,2,0,0,1,0,0,0,6,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,9,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37856,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,4,0,2,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,12,0,1,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+37857,0,0,4,4,0,0,0,0,1,0,8,0,0,0,0,1,0,0,1,0,3,1,0,2,0,1,28,1,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,8,0,2,1,0,0,0,0,6,0,0,0,0,0,0,3,0,1,0,2,2,0,0,0,0,0,0,0,2,0,0,0,0,0,16,0,2,0,0,0,0,0,1,0,0,0,1,0,Class_6
+37858,0,0,11,2,0,0,0,1,0,0,5,0,3,0,0,1,0,0,0,0,0,2,0,2,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+37859,0,0,14,4,0,0,0,3,0,0,7,0,0,0,0,1,0,2,0,0,2,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,4,0,0,0,0,0,1,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37860,0,1,8,2,0,0,0,0,0,1,6,0,0,1,0,6,1,0,0,0,1,1,0,1,0,2,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,9,0,2,0,0,0,0,0,10,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,Class_6
+37861,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,8,0,1,0,0,0,0,0,1,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,3,0,0,12,0,1,1,1,0,2,0,1,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,Class_6
+37862,0,1,4,2,1,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,1,1,0,1,1,1,1,0,0,0,0,1,0,0,1,0,0,3,1,0,0,1,0,0,0,1,0,1,0,0,0,7,0,4,0,0,0,1,0,1,1,0,0,1,0,2,0,0,0,1,0,0,1,7,0,1,0,0,0,7,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37863,0,2,1,0,1,0,1,0,1,0,4,0,0,0,0,4,0,3,0,3,0,4,0,4,0,1,1,1,0,0,0,0,0,1,0,0,0,5,0,0,0,1,0,5,1,0,2,2,4,0,0,0,0,6,4,0,2,1,0,0,2,1,2,0,0,8,1,0,0,1,0,1,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37864,1,0,5,27,0,0,0,0,0,1,6,0,0,0,2,3,1,0,1,1,0,2,1,3,0,5,8,1,0,0,0,3,0,0,5,0,0,0,0,1,0,1,1,2,0,6,0,0,0,0,0,0,0,7,1,0,0,8,0,0,3,1,1,1,0,0,1,0,3,0,0,0,6,1,0,0,0,0,0,4,0,0,1,0,0,0,1,0,1,0,0,0,0,Class_6
+37865,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37866,0,1,1,3,0,0,0,0,1,0,4,0,0,0,0,2,0,3,0,1,0,5,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,6,0,0,0,12,0,3,0,0,0,1,0,3,0,0,0,5,3,0,0,0,0,10,0,2,0,0,1,2,7,0,0,7,0,2,0,2,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,Class_6
+37867,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,5,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,2,0,10,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+37868,0,0,1,8,0,0,0,0,0,1,9,0,0,0,0,3,0,0,0,0,1,0,0,2,0,5,0,2,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37869,0,1,1,0,0,0,0,0,0,0,9,0,0,0,0,2,0,1,1,0,0,0,0,3,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,3,3,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37870,0,1,5,2,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,1,0,2,0,4,0,2,0,0,0,1,1,0,3,0,0,0,0,0,0,1,0,0,0,1,0,2,1,0,0,0,0,8,0,0,2,0,0,4,0,0,0,0,1,2,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37871,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,3,0,0,4,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,Class_6
+37872,0,0,6,2,0,0,0,0,2,0,8,0,1,0,1,3,0,0,0,0,3,2,0,0,0,2,2,2,0,0,1,0,0,1,4,0,0,1,0,0,0,1,1,2,0,23,0,0,0,0,0,0,0,14,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37873,0,0,26,9,0,0,0,0,1,0,17,1,1,1,0,3,0,4,0,0,3,5,1,0,0,5,18,2,0,0,1,0,0,0,1,2,0,1,0,3,0,3,0,2,0,20,0,0,0,0,1,2,0,19,0,1,0,0,0,0,2,3,0,0,2,1,0,0,0,1,0,1,0,3,0,1,0,0,0,7,0,1,0,0,0,0,0,2,0,0,0,0,0,Class_6
+37874,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37875,0,1,1,15,0,0,0,0,0,0,20,1,0,1,0,0,0,1,0,0,0,0,0,4,0,2,4,0,1,0,0,0,0,1,7,1,0,0,0,0,0,0,1,2,0,4,0,2,0,1,0,0,0,8,0,0,0,9,0,3,2,0,0,1,0,2,2,0,0,0,0,1,6,3,0,1,0,1,0,7,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_6
+37876,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,0,8,1,0,0,0,0,2,0,10,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37877,1,0,0,2,0,0,2,1,0,1,0,0,0,0,3,0,0,1,2,1,0,0,1,8,0,1,0,0,0,0,9,1,0,2,1,0,0,3,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,5,1,0,1,0,0,8,0,0,0,0,1,0,7,0,3,0,0,0,0,1,0,5,0,0,0,1,0,0,15,0,0,0,1,0,1,2,0,0,0,Class_6
+37878,0,0,10,4,0,1,0,0,0,0,10,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,12,0,1,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+37879,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37880,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37881,0,1,5,8,0,0,0,0,0,0,5,0,1,1,0,6,0,1,0,0,0,4,0,0,0,1,3,0,0,0,1,0,0,2,0,1,0,0,0,0,1,0,0,1,0,1,0,2,3,0,0,0,0,15,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+37882,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,5,1,0,5,0,0,11,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,1,0,Class_6
+37883,0,0,2,4,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,3,1,0,3,0,1,2,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,11,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37884,0,0,4,10,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,0,1,0,0,0,6,7,1,1,0,0,2,0,0,4,0,0,0,0,0,0,1,0,1,0,2,0,1,1,0,0,1,0,6,1,0,1,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37885,0,0,7,4,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,3,0,1,0,12,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,7,0,2,0,0,0,0,0,13,3,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37886,0,0,3,1,0,0,0,0,0,0,7,1,0,0,0,3,0,0,2,0,1,0,0,0,0,2,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,6,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37887,0,0,1,3,0,0,0,1,1,0,0,0,0,1,0,4,1,11,2,1,0,6,0,3,1,11,4,0,0,0,1,0,0,0,0,1,0,0,0,0,1,11,1,2,0,1,3,0,2,0,1,1,0,7,3,0,20,0,0,9,0,3,0,3,0,0,0,2,0,1,0,0,0,1,1,0,0,1,4,0,1,0,1,1,1,0,2,1,2,7,0,0,0,Class_6
+37888,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37889,0,0,9,3,0,0,0,0,0,1,9,0,0,0,0,5,0,0,0,0,0,3,0,0,0,1,12,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,20,0,0,1,0,0,0,0,16,0,1,0,1,0,2,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,1,2,0,0,0,0,Class_6
+37890,0,0,22,5,0,0,1,0,0,1,10,0,1,0,0,4,0,3,0,0,4,4,0,0,1,0,14,5,1,0,0,0,2,2,1,1,1,1,0,0,0,0,0,4,0,13,0,0,1,0,0,0,1,8,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,Class_6
+37891,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,9,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,0,3,6,0,2,0,0,4,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,1,1,0,5,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37892,0,1,2,1,0,0,0,1,0,0,2,2,2,3,0,0,3,1,3,5,0,15,0,4,1,1,0,1,2,0,3,7,0,0,0,2,0,1,3,0,1,3,0,3,1,0,0,3,5,2,0,0,0,5,4,0,1,0,0,11,0,1,0,2,0,1,11,0,1,2,0,0,0,1,0,8,0,2,1,0,0,0,18,0,0,0,5,0,2,1,0,2,4,Class_6
+37893,0,1,8,4,0,0,0,0,0,2,14,0,1,1,0,2,0,3,0,1,0,1,1,16,0,3,13,2,0,0,0,0,0,0,1,1,0,0,0,1,1,0,1,1,2,2,0,0,0,0,1,2,1,20,0,1,0,12,0,1,1,1,0,0,0,1,2,0,0,0,0,1,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37894,0,1,9,1,0,4,0,0,0,0,4,1,0,1,0,2,0,0,0,1,7,0,0,0,0,1,3,0,0,0,0,1,1,0,0,0,0,1,0,2,0,2,0,2,0,7,0,2,3,0,0,0,0,8,0,0,0,0,0,0,4,2,1,2,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,21,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+37895,0,0,0,2,0,0,1,0,0,0,8,0,1,0,0,3,0,2,1,0,0,1,1,0,0,4,0,0,1,1,0,1,0,0,5,0,0,1,0,0,0,3,0,0,0,1,0,4,1,0,0,0,0,4,0,2,1,1,0,0,0,2,1,1,1,1,1,0,0,2,0,1,1,6,1,0,0,0,0,4,0,0,0,0,0,0,1,1,1,2,0,1,0,Class_6
+37896,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,2,0,2,0,3,0,0,0,0,0,3,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+37897,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,7,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37898,0,2,14,3,0,0,0,0,0,2,9,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,8,3,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,6,0,0,2,0,0,1,0,8,0,0,0,0,0,1,6,0,0,0,0,1,0,2,0,0,0,0,0,1,3,0,0,0,0,4,0,7,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37899,0,4,1,2,0,0,0,0,0,0,7,1,0,1,0,0,0,5,0,0,0,0,0,2,1,0,2,1,0,0,1,0,0,0,2,2,0,1,0,2,0,1,0,0,0,1,0,3,17,0,0,2,0,6,2,0,1,1,0,0,1,1,0,0,1,7,1,0,0,1,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+37900,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37901,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,2,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,4,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,4,2,0,0,0,0,0,0,Class_6
+37902,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37903,0,1,8,6,0,0,0,0,0,0,10,0,1,0,0,8,0,0,0,0,2,0,0,0,0,0,6,1,0,0,0,0,1,1,1,0,0,0,0,2,0,1,0,3,0,16,0,1,3,0,0,2,0,15,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+37904,0,1,14,27,0,0,0,0,0,0,7,0,1,0,0,2,0,1,0,0,0,0,0,3,1,1,7,4,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,2,0,9,0,0,0,0,0,0,0,8,0,1,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37905,0,0,0,0,0,0,1,1,4,0,1,1,0,1,1,4,0,1,0,0,0,1,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37906,0,0,1,2,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,2,0,0,1,0,0,4,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,15,0,0,1,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+37907,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,1,9,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,3,0,0,3,0,0,4,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37908,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37909,0,0,7,3,0,0,0,0,0,0,7,0,0,1,2,4,0,0,0,0,0,0,0,2,0,2,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37910,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,2,0,7,0,2,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,3,1,0,1,0,1,0,0,0,1,3,5,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37911,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,Class_6
+37912,0,0,10,7,0,0,0,0,0,0,8,1,1,1,0,3,0,1,0,0,0,0,0,0,0,1,7,7,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,5,0,11,0,2,0,0,0,0,1,15,0,0,0,0,0,0,5,2,0,0,1,0,1,0,0,2,0,2,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,2,0,0,3,0,Class_6
+37913,0,0,20,4,0,0,0,0,0,1,5,0,0,0,0,4,0,0,1,1,0,2,0,1,0,0,1,0,1,0,0,0,0,0,3,0,0,1,0,0,0,1,1,2,0,18,0,0,1,0,0,0,0,5,0,3,0,0,0,2,1,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+37914,0,0,2,1,0,0,0,0,0,0,9,0,1,0,0,4,1,0,0,0,0,1,0,1,0,2,0,4,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,2,0,1,6,2,4,0,0,0,0,1,0,0,0,1,0,0,7,1,1,1,0,3,2,0,0,0,0,3,0,0,0,0,1,0,1,6,1,2,0,4,0,0,0,0,1,0,0,0,0,Class_6
+37915,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,1,1,0,7,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,Class_6
+37916,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37917,1,0,0,0,0,0,0,1,2,0,0,0,0,2,0,1,0,0,0,0,1,0,0,2,1,1,0,1,0,0,0,1,0,0,0,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,10,0,8,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,2,0,1,0,0,0,0,0,Class_6
+37918,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,Class_6
+37919,0,1,0,0,4,0,0,0,0,1,6,1,0,0,0,3,0,0,0,1,0,2,0,0,0,0,2,2,0,1,0,0,0,2,2,0,0,2,0,1,0,2,0,10,0,1,11,1,3,0,2,1,0,8,0,0,1,0,0,0,1,1,0,1,4,3,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,0,7,0,0,0,0,3,0,0,1,0,Class_6
+37920,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37921,0,0,8,4,0,0,0,0,0,0,15,0,1,0,0,1,0,0,0,0,0,1,0,12,0,5,4,4,0,0,0,1,1,0,2,0,0,2,0,0,0,1,0,1,0,6,0,1,2,0,0,0,0,7,0,0,0,7,0,1,2,1,1,0,0,2,1,0,0,0,0,1,0,1,0,1,2,0,0,9,0,2,0,0,0,0,1,0,2,0,0,0,0,Class_6
+37922,3,0,1,1,1,0,0,0,1,0,0,0,0,8,1,2,0,0,2,0,0,2,1,0,5,0,0,1,1,0,0,5,1,0,1,1,1,0,0,2,0,10,0,23,0,0,0,1,1,0,0,0,4,4,0,1,0,0,2,0,0,1,2,0,0,2,5,0,0,5,0,2,1,1,0,1,0,0,11,1,1,1,0,0,0,2,8,0,4,0,0,0,1,Class_6
+37923,0,0,0,0,0,0,0,4,0,0,1,3,0,0,0,0,0,2,0,0,0,0,0,6,0,4,0,1,0,0,0,0,0,0,1,2,1,0,0,0,0,3,0,2,0,0,2,0,0,0,0,0,0,2,0,0,4,0,0,20,0,4,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
+37924,1,0,6,8,0,0,0,0,0,0,9,0,1,1,0,2,0,1,0,0,1,3,0,0,0,0,2,5,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,1,0,12,0,0,1,0,1,0,0,19,1,2,0,1,0,1,8,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+37925,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37926,0,0,2,1,0,0,1,1,0,2,0,0,0,0,0,2,5,0,0,6,0,5,0,2,0,0,0,1,2,0,0,1,0,0,0,3,0,0,1,0,0,0,0,4,0,0,6,2,3,0,0,0,1,6,2,0,1,0,0,1,0,0,0,2,0,0,11,0,0,2,0,0,0,0,0,1,1,3,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,Class_6
+37927,1,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,13,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+37928,0,4,17,6,0,0,0,0,0,1,9,0,0,0,0,4,0,0,3,1,3,1,0,0,0,1,13,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,2,2,0,9,0,1,0,0,0,2,1,7,0,0,0,0,0,0,3,3,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37929,0,0,8,2,0,0,0,0,0,0,10,1,0,0,0,4,0,1,0,0,0,2,0,2,0,0,20,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,6,0,0,0,0,0,0,0,10,0,0,0,1,0,1,11,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37930,1,2,2,0,0,0,1,0,0,0,2,0,1,0,0,7,0,0,0,0,0,9,1,2,2,7,0,0,1,0,1,0,0,0,6,0,0,1,0,1,1,8,0,4,0,1,0,2,4,0,0,0,1,15,1,0,1,0,0,3,0,5,1,1,1,1,4,0,0,2,1,0,1,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,Class_6
+37931,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,1,0,0,1,5,1,0,0,0,0,0,0,8,0,0,0,5,1,1,0,0,0,2,0,0,3,0,0,7,0,0,0,0,0,1,1,0,0,0,5,0,0,0,0,3,1,2,28,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,2,33,1,0,1,0,0,4,Class_6
+37932,0,2,4,1,0,0,0,0,1,0,10,0,1,2,0,4,0,1,0,2,3,1,1,1,1,0,2,2,1,0,4,2,0,1,3,0,0,1,0,2,0,0,0,6,0,1,0,2,17,0,0,3,0,5,0,1,0,0,0,1,5,0,3,0,4,0,3,0,1,2,0,0,1,7,0,0,0,0,0,8,0,6,10,0,1,1,0,0,3,2,0,1,0,Class_6
+37933,3,1,4,1,0,0,0,0,1,0,0,0,0,0,0,5,0,2,0,0,6,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,6,0,2,0,0,0,1,1,0,0,0,0,6,1,0,1,0,0,8,2,1,0,0,0,2,1,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37934,0,0,15,6,0,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0,0,2,0,1,0,2,1,2,0,0,0,0,0,0,3,0,0,0,0,1,1,2,1,0,0,3,1,0,4,0,0,0,0,22,0,1,0,1,0,12,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37935,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,1,1,0,1,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+37936,0,4,12,5,1,0,0,1,0,0,10,0,0,0,0,5,0,1,0,1,0,4,0,1,0,1,4,2,0,0,0,0,2,0,4,0,0,1,0,0,0,1,0,4,0,2,1,2,1,1,0,0,0,16,0,0,0,0,0,1,2,2,0,0,0,2,0,0,0,1,0,0,0,1,1,0,0,0,1,4,0,0,1,0,0,0,0,0,3,0,0,0,0,Class_6
+37937,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,4,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,18,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+37938,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,4,0,3,0,0,0,1,0,5,0,8,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,4,0,1,0,1,0,1,0,0,0,0,0,11,0,0,3,0,0,13,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,5,0,0,1,0,0,0,0,0,0,Class_6
+37939,0,0,20,6,0,0,0,0,0,0,11,0,1,0,0,4,0,1,0,0,1,1,0,0,0,3,12,7,0,0,0,0,0,0,3,1,0,1,0,1,0,0,0,0,0,4,0,1,1,0,0,0,0,6,0,1,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,2,Class_6
+37940,0,0,10,1,0,0,0,0,0,0,7,1,1,0,0,2,0,1,1,0,0,6,0,0,0,2,2,2,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,9,0,0,0,0,0,2,1,14,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+37941,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37942,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,0,Class_6
+37943,0,0,1,1,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,2,1,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,3,0,0,5,0,0,1,0,0,1,0,6,0,0,0,0,0,0,8,1,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37944,0,0,0,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37945,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,3,0,2,0,7,4,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,4,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+37946,2,1,0,0,0,0,0,2,0,1,1,0,1,0,0,0,2,1,0,4,0,2,0,7,1,0,0,0,0,0,0,8,0,0,0,4,2,2,2,2,0,4,0,0,2,0,0,3,0,0,0,0,0,1,1,0,0,0,6,0,0,1,0,3,1,2,33,0,0,3,1,2,0,0,1,1,0,0,0,0,0,0,0,0,1,3,30,0,0,0,1,0,4,Class_6
+37947,0,0,0,0,0,0,0,9,1,0,0,0,0,1,0,0,2,1,0,0,0,0,0,9,0,0,0,0,0,0,0,1,1,1,0,11,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,1,0,3,0,0,0,0,1,0,0,23,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37948,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,1,0,7,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,12,0,0,0,1,1,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+37949,0,0,13,2,0,0,0,0,0,0,7,0,0,2,0,2,0,3,0,0,0,0,0,2,0,0,4,1,0,0,1,0,1,1,1,1,0,1,0,0,1,0,0,2,0,10,0,1,1,0,0,3,0,22,0,1,0,1,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,5,1,0,0,0,0,0,2,1,0,0,0,Class_6
+37950,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,2,0,1,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,5,0,1,1,0,0,0,0,1,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,5,4,0,1,0,0,0,1,0,Class_6
+37951,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37952,0,1,5,6,0,0,0,0,0,0,8,0,0,0,2,0,1,0,0,0,2,0,0,1,0,2,47,5,0,0,0,0,0,1,1,0,0,1,0,0,0,2,0,0,0,7,0,0,1,0,2,0,0,4,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,3,1,0,0,0,0,0,0,0,0,2,0,Class_6
+37953,0,0,1,1,0,0,0,1,0,0,4,0,0,0,0,4,0,1,0,1,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,3,1,0,0,0,1,11,3,0,0,1,2,0,5,0,0,0,0,0,0,0,3,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,Class_6
+37954,0,1,3,4,0,0,0,0,0,0,10,0,4,1,0,1,1,0,1,0,3,0,0,2,0,1,6,2,0,0,0,3,0,1,1,0,1,1,1,2,2,1,0,1,0,4,0,0,0,1,0,5,0,7,0,0,0,1,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,10,0,0,0,0,0,14,0,2,0,0,0,0,0,1,3,0,0,0,1,Class_6
+37955,0,1,12,5,0,0,0,0,3,0,9,0,0,0,0,2,0,0,0,0,3,0,0,2,0,1,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,17,1,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37956,0,0,0,1,0,0,0,1,0,1,0,0,2,1,0,6,2,7,0,0,0,1,0,0,3,0,0,1,2,0,0,3,0,0,0,1,1,1,0,0,0,2,0,5,0,1,0,2,0,0,0,3,7,5,1,0,0,0,3,1,0,0,1,2,3,1,6,1,0,2,1,0,1,0,1,1,0,2,4,2,2,0,0,0,0,2,0,1,1,0,0,0,0,Class_6
+37957,0,0,8,8,0,0,0,0,0,0,10,0,1,1,0,7,1,2,0,0,1,3,0,0,0,2,1,0,0,0,0,2,1,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,16,0,0,0,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37958,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,3,0,0,0,1,0,2,0,5,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,1,2,2,0,0,0,0,1,1,1,3,0,0,9,0,0,0,0,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,Class_6
+37959,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,30,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37960,2,0,0,2,0,0,0,2,0,0,3,1,0,1,0,10,3,2,1,1,4,4,1,8,1,11,0,0,0,0,0,4,0,0,3,1,0,0,0,0,0,29,0,4,0,0,0,5,2,0,0,0,0,7,0,0,3,0,1,13,0,1,0,0,0,3,6,0,0,2,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,1,4,0,1,0,0,1,0,Class_6
+37961,0,1,1,2,0,0,1,0,0,0,8,0,0,0,0,4,0,1,0,2,0,0,0,2,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,3,0,0,0,0,3,1,0,0,1,5,0,0,0,0,0,6,1,2,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37962,0,0,9,8,0,0,0,0,0,0,6,0,1,0,0,4,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,7,0,0,0,0,0,0,1,7,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+37963,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,2,0,1,0,0,0,5,0,0,3,0,0,0,0,0,0,11,1,5,8,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,3,0,0,0,0,0,Class_6
+37964,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,2,0,1,0,0,0,2,6,0,1,0,0,0,1,1,0,0,0,0,7,0,0,3,0,0,15,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37965,0,0,15,3,0,0,1,0,0,1,9,1,2,1,2,4,0,0,0,0,0,1,0,0,0,3,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,2,0,3,0,0,1,0,0,2,0,24,1,0,1,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,4,0,0,0,0,Class_6
+37966,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+37967,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+37968,0,1,0,0,0,0,0,2,0,0,0,0,2,0,3,0,4,9,0,0,0,0,0,9,0,2,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,10,0,0,0,2,0,0,5,0,0,1,2,2,0,0,0,0,4,1,0,0,0,0,2,0,0,0,6,0,1,0,0,0,0,Class_6
+37969,0,0,13,14,0,0,0,1,0,0,11,1,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,4,0,0,0,0,0,0,0,12,1,0,1,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37970,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,Class_6
+37971,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,5,0,0,0,1,0,0,0,1,2,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,4,0,0,13,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+37972,0,0,3,8,0,0,0,0,1,0,1,0,0,0,0,0,0,7,0,0,0,1,0,3,0,5,3,2,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,2,0,1,0,0,3,1,2,0,0,4,1,0,5,2,0,1,1,0,0,2,2,0,1,0,1,0,1,1,0,0,0,1,0,1,0,2,0,1,3,0,0,0,1,0,0,0,0,1,0,Class_6
+37973,0,1,5,1,0,0,0,0,0,0,2,0,0,0,2,2,0,5,0,0,0,2,0,41,2,23,0,0,1,0,1,0,1,2,6,1,0,0,0,1,0,15,0,2,0,2,0,0,0,0,0,0,1,10,0,0,7,0,0,7,0,1,1,0,0,2,0,1,1,0,4,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,2,1,Class_6
+37974,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+37975,1,0,0,2,0,0,0,1,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,13,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_6
+37976,1,3,12,1,1,0,0,0,0,0,13,0,0,0,0,6,1,0,0,1,0,2,0,4,2,2,5,1,0,2,0,0,3,0,0,0,0,1,0,0,0,0,0,4,0,8,0,0,0,0,0,0,0,18,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+37977,0,0,1,3,0,0,0,0,2,0,8,0,0,0,0,1,0,1,0,0,0,5,0,1,0,6,3,2,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,3,1,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,Class_6
+37978,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37979,0,1,11,3,0,0,0,0,0,0,9,1,0,0,0,2,0,0,0,0,4,4,0,0,0,2,10,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,1,0,0,0,0,18,1,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+37980,0,0,6,3,0,0,0,0,0,0,19,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,14,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37981,0,3,13,9,0,0,0,0,1,0,7,0,0,0,0,6,0,0,2,0,0,2,0,0,2,0,1,4,1,0,1,0,2,0,0,0,0,0,0,0,0,2,0,4,0,8,0,0,7,0,0,0,0,13,0,0,0,0,0,0,4,5,0,1,2,0,2,0,2,4,0,2,0,0,0,2,0,0,2,5,0,3,0,0,0,0,0,0,2,0,2,2,0,Class_6
+37982,0,0,0,4,0,0,0,0,1,1,0,1,0,1,0,2,0,0,0,1,0,2,0,3,0,4,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,9,0,4,0,0,0,3,0,0,0,0,0,6,0,0,0,0,0,12,0,1,0,1,0,2,6,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,2,1,Class_6
+37983,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,4,0,0,0,0,0,5,0,0,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,16,0,1,3,0,0,0,6,0,0,1,2,2,0,0,0,0,4,1,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,Class_6
+37984,0,1,10,8,0,0,0,0,0,0,3,0,0,1,0,2,0,0,0,0,3,0,0,0,0,4,2,1,0,0,2,0,2,0,5,0,0,1,0,0,0,0,0,0,0,2,0,0,4,0,0,1,0,9,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+37985,0,0,13,4,0,0,0,0,0,0,8,1,0,0,0,3,0,0,0,0,0,7,0,2,0,2,13,3,0,0,0,0,0,1,2,0,0,0,0,0,0,1,1,1,0,11,0,0,1,0,0,1,0,21,0,2,1,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37986,0,0,15,16,0,0,0,0,0,0,7,0,0,1,0,1,0,0,0,0,0,0,0,2,0,3,1,4,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,3,0,13,0,0,2,0,0,0,0,14,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+37987,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+37988,0,0,6,7,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37989,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,4,0,0,0,0,1,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37990,0,1,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,4,0,0,0,0,0,6,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,1,0,10,0,1,0,0,0,0,6,0,0,1,0,3,0,0,0,0,4,1,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+37991,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,2,0,0,0,0,4,0,0,4,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,9,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,11,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+37992,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+37993,0,2,7,2,0,0,0,0,3,0,9,0,0,2,0,6,0,1,0,0,1,8,0,2,0,1,6,1,0,1,0,1,0,2,4,0,0,1,0,0,0,0,0,1,0,4,0,0,1,0,0,1,0,18,2,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+37994,0,0,8,2,0,0,0,0,0,0,8,0,1,0,0,3,0,2,1,0,0,2,2,0,0,1,5,1,1,0,0,1,0,0,2,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,10,2,0,0,0,0,0,5,1,0,1,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+37995,0,1,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,2,0,1,0,0,12,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7,0,Class_6
+37996,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,0,0,0,9,2,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,2,0,1,0,1,0,3,0,Class_6
+37997,0,0,0,0,0,0,1,6,0,9,0,1,2,2,0,0,35,1,0,3,0,0,0,28,1,0,0,0,2,0,0,8,0,2,0,2,5,0,3,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,3,0,0,1,2,2,1,3,0,1,17,8,1,1,9,0,8,2,2,0,0,1,3,0,0,0,2,0,1,1,1,0,0,0,0,0,1,Class_6
+37998,0,1,1,3,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,1,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
+37999,0,0,3,7,0,0,0,0,1,0,10,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,23,3,0,0,0,2,0,1,2,0,0,0,0,0,0,4,0,3,0,1,0,1,5,0,0,0,0,6,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,1,1,4,0,0,0,0,0,3,1,1,0,0,0,0,1,0,1,2,0,1,0,Class_6
+38000,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,1,2,0,1,0,0,0,1,0,0,0,0,2,0,1,0,1,0,2,2,0,0,0,3,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
+38001,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,2,2,0,0,0,2,0,1,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,1,1,0,15,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,1,1,0,0,Class_6
+38002,0,0,0,1,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,4,2,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,2,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38003,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_6
+38004,0,0,4,11,0,0,0,0,0,0,5,0,0,0,0,1,0,3,0,0,0,4,0,0,0,3,6,2,0,0,1,2,0,2,4,0,1,1,0,0,0,0,0,2,0,1,0,0,3,0,0,0,0,15,0,0,0,1,0,2,2,4,0,0,0,1,0,0,0,1,0,1,0,1,0,3,0,0,0,4,0,3,0,0,0,0,0,0,4,0,0,1,0,Class_6
+38005,0,0,14,7,0,0,0,0,0,0,19,0,0,1,0,3,0,0,0,1,3,0,0,3,0,2,1,5,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,9,0,0,0,9,0,0,6,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,11,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38006,2,3,22,1,0,0,0,0,0,1,6,0,0,1,0,6,0,1,0,0,5,1,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,2,0,10,0,0,1,0,0,0,0,25,0,0,0,0,0,1,1,1,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38007,1,0,8,4,0,0,0,0,0,0,8,2,1,1,3,3,0,0,1,0,5,3,0,0,0,3,2,2,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,5,0,0,1,0,0,3,0,6,0,0,0,0,0,0,3,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,1,1,0,0,1,0,Class_6
+38008,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38009,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+38036,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38037,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38038,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,1,0,7,2,6,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,7,0,0,0,0,7,3,0,2,0,0,1,3,0,0,1,0,3,2,0,3,0,0,1,1,8,2,0,2,3,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,5,0,1,0,0,1,0,Class_6
+38039,0,1,10,4,0,0,0,0,0,1,10,0,6,0,0,3,0,1,0,0,0,0,0,0,0,1,13,2,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,4,2,5,0,0,0,0,0,0,0,17,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38040,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,6,0,8,0,2,0,3,0,1,1,6,0,1,0,0,0,0,1,0,4,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,13,0,2,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,2,0,Class_6
+38041,2,0,0,0,0,1,0,1,0,0,1,0,0,1,0,2,0,0,0,0,1,0,0,6,3,2,0,0,0,0,1,3,0,5,0,1,1,1,47,1,0,1,1,0,34,0,0,1,0,1,0,1,0,0,1,0,1,1,0,0,0,0,0,1,0,0,6,0,0,3,1,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38042,0,1,11,8,0,0,0,0,0,1,8,2,0,0,0,6,0,5,0,0,0,2,0,1,0,0,6,3,1,0,0,2,2,0,0,0,0,1,0,0,0,0,0,1,0,11,0,0,0,0,1,1,1,11,0,0,0,0,0,1,4,1,0,1,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,3,0,6,0,0,1,0,0,0,0,0,0,1,0,Class_6
+38043,0,3,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,6,1,0,0,1,0,0,0,4,0,0,1,0,0,0,0,1,5,1,0,0,0,0,0,5,0,2,0,0,0,0,5,0,0,0,0,4,0,0,1,0,0,3,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38044,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,4,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,Class_6
+38045,0,0,4,10,0,0,0,0,0,0,7,0,1,0,0,1,0,0,0,0,0,3,0,1,0,1,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38046,1,1,5,0,0,0,0,0,0,0,5,0,0,2,0,3,0,1,0,0,1,2,2,1,0,0,7,1,1,0,2,1,0,1,4,0,1,2,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,3,3,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,3,0,5,0,5,0,0,0,0,0,0,7,0,0,2,1,Class_6
+38047,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,1,0,3,1,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,2,1,0,0,0,11,1,0,1,0,0,1,0,3,0,0,0,1,1,0,0,0,1,0,1,4,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,Class_6
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+38073,2,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,6,1,0,0,0,0,0,0,8,0,0,0,3,2,1,5,0,0,3,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,8,0,0,0,1,2,0,2,25,0,0,1,0,0,0,0,1,1,0,0,7,1,0,0,0,0,0,4,41,3,0,0,0,0,7,Class_6
+38074,0,0,3,5,0,0,0,0,0,0,20,0,1,0,0,2,0,0,0,0,0,0,0,21,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,2,0,0,0,13,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38075,0,0,6,1,0,0,0,0,0,0,7,1,2,0,0,2,0,1,0,0,0,4,0,0,0,0,2,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,1,1,0,1,0,16,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,1,1,0,0,1,0,1,0,0,1,4,0,0,0,1,Class_6
+38076,0,1,1,8,0,0,0,0,0,1,6,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38077,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,2,2,0,0,0,0,0,7,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,3,0,0,9,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,Class_6
+38078,2,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,1,3,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,6,1,0,0,0,0,1,Class_6
+38079,6,0,1,1,0,0,0,3,0,0,0,1,19,1,0,2,0,4,0,1,0,0,0,8,1,0,0,0,3,1,0,1,0,0,0,2,0,0,0,0,2,0,0,1,0,0,1,7,0,1,0,0,3,6,0,0,0,0,40,2,0,2,2,1,0,1,36,1,3,12,1,1,0,0,0,2,2,0,5,0,0,0,0,0,4,4,1,1,0,1,0,0,0,Class_6
+38080,0,0,13,2,0,0,0,0,0,0,9,1,0,1,0,4,0,0,0,0,0,1,0,0,0,3,11,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,30,0,0,3,0,0,0,0,20,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38081,0,0,3,14,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,4,0,3,1,1,0,0,0,1,2,0,1,1,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,8,0,0,1,0,0,1,7,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_6
+38082,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,1,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38083,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38084,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,4,0,1,0,0,0,2,0,2,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,Class_6
+38085,0,0,0,2,1,0,1,2,4,1,0,1,5,0,0,5,0,2,0,0,0,4,0,6,2,7,0,2,27,0,0,1,0,1,3,0,0,1,0,1,3,2,0,0,0,0,0,1,0,0,0,0,0,3,6,0,1,0,0,2,1,3,1,0,2,0,6,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,1,Class_6
+38086,0,0,10,11,0,0,0,1,0,0,8,0,1,0,0,2,0,0,0,0,1,3,0,2,0,4,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,15,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,Class_6
+38087,0,0,11,2,0,0,0,2,0,0,8,0,0,0,0,3,0,0,0,0,1,0,0,3,0,1,2,3,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,1,0,0,0,0,6,0,1,1,0,0,1,1,0,0,1,1,1,0,0,0,0,0,0,0,6,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38107,1,0,12,5,0,0,1,0,0,1,10,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,16,2,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,3,0,13,0,0,2,0,0,3,2,6,0,0,0,0,0,0,2,2,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,9,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38108,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,4,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,17,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38109,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38110,0,1,1,1,0,0,1,0,0,0,7,0,2,0,0,3,0,1,1,1,0,3,2,0,0,1,2,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,2,0,0,0,0,3,0,0,2,0,17,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,2,0,0,0,0,0,0,10,0,0,0,0,Class_6
+38111,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,1,1,4,0,19,0,1,1,0,2,0,0,0,3,2,0,0,5,1,0,1,0,0,0,0,0,2,1,0,0,1,1,1,0,0,2,0,0,9,0,0,0,1,0,2,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38112,0,0,18,13,0,0,0,0,0,0,6,0,0,1,0,4,0,1,0,0,5,1,0,1,0,3,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,5,0,0,1,0,0,0,0,18,0,0,0,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,3,0,Class_6
+38113,0,0,11,0,0,0,0,0,0,1,6,0,0,0,0,2,0,1,0,0,6,2,0,1,0,3,5,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,12,0,2,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38114,0,0,1,2,0,0,0,1,0,0,6,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,2,7,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+38115,0,0,8,5,0,0,0,0,1,0,8,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,1,0,1,0,1,5,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38116,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,4,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,12,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+38117,0,0,11,4,0,1,0,1,0,0,17,1,1,0,0,5,0,0,0,0,2,1,0,9,0,2,11,4,0,0,1,0,0,0,2,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,15,0,0,0,6,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38118,0,0,13,6,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,0,0,2,4,3,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,0,9,0,0,2,0,0,2,0,14,2,0,0,0,0,1,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38119,4,1,1,8,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,1,0,3,0,0,2,2,2,4,0,0,0,0,0,1,1,0,0,1,0,0,0,6,0,2,0,4,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,0,0,1,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,1,5,1,Class_6
+38120,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,8,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,3,0,0,24,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+38121,0,0,0,0,1,0,0,1,0,1,7,0,2,0,0,4,0,2,0,0,1,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,4,0,0,0,0,8,0,1,2,0,0,1,1,0,1,0,0,2,0,0,1,0,0,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38122,0,0,2,0,0,0,0,0,0,0,22,0,1,0,0,0,0,0,0,0,2,1,0,9,1,1,26,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,6,0,0,0,0,0,3,1,3,0,0,0,11,0,1,2,0,0,2,0,0,1,0,0,0,0,0,1,5,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+38123,0,1,5,1,0,0,0,0,0,0,9,1,1,1,0,11,0,2,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,1,1,0,2,4,0,2,0,0,1,0,0,0,0,13,0,0,0,0,0,0,1,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38124,0,0,2,6,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,3,2,1,0,1,0,2,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,1,0,2,0,0,0,2,2,0,0,2,0,3,0,0,1,0,0,12,2,0,1,0,0,0,5,1,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,1,0,0,0,Class_6
+38125,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,2,0,0,0,1,1,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,15,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38126,0,1,0,0,0,0,1,1,2,0,1,0,4,0,0,1,3,9,1,3,0,1,1,6,0,0,0,0,2,2,0,1,0,4,2,3,0,0,0,0,0,1,0,1,1,0,0,5,0,0,1,0,0,6,1,0,11,1,0,10,0,1,0,0,1,2,6,0,5,4,0,3,0,0,0,1,4,3,0,2,0,0,13,0,0,0,1,0,5,5,2,2,0,Class_6
+38127,0,0,5,2,0,0,0,0,0,0,15,0,0,0,0,4,0,1,0,0,0,1,0,4,0,0,15,1,0,0,0,3,0,0,1,0,0,2,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,4,0,0,1,10,0,1,9,3,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+38161,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,19,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38164,0,0,3,14,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38165,0,0,12,4,0,0,0,0,0,1,1,0,0,0,0,1,0,0,3,0,0,2,0,6,0,5,0,0,0,0,0,0,0,4,2,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,1,3,1,0,6,0,0,12,0,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+38166,0,0,1,2,0,0,0,0,0,0,5,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,4,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,1,1,0,1,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+38167,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38197,0,0,15,2,0,0,0,0,1,0,8,1,0,0,0,4,0,1,0,0,1,2,0,3,0,1,0,2,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,24,1,0,0,0,0,1,0,19,0,1,0,1,0,0,9,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+38198,0,6,0,6,0,0,0,0,0,0,7,0,1,1,2,2,0,4,0,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,7,2,0,1,0,2,0,0,1,0,0,0,0,1,4,0,0,0,0,4,1,0,0,0,0,0,1,0,0,3,0,5,1,0,0,2,0,0,0,3,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+38199,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,5,0,4,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,3,0,0,2,0,0,0,0,0,0,0,4,0,0,0,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,1,2,1,0,0,6,1,0,0,0,0,0,0,1,0,0,2,0,0,0,Class_6
+38200,0,2,8,6,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,8,3,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,2,0,0,1,0,2,0,0,0,0,0,0,3,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,1,0,0,0,0,0,0,2,2,0,0,0,Class_6
+38201,0,1,3,1,0,0,0,1,0,0,7,0,0,0,0,1,0,0,0,1,0,2,0,0,2,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,2,2,0,0,0,0,11,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38202,0,1,4,2,2,0,0,0,0,0,6,0,0,1,0,10,0,2,0,0,0,2,0,1,0,4,7,2,0,0,1,0,1,0,1,0,0,0,0,2,0,0,0,4,0,0,7,0,5,0,0,0,0,11,0,0,0,0,0,1,1,0,0,0,0,4,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,2,0,0,0,1,0,0,9,1,Class_6
+38203,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,0,0,0,0,0,1,0,1,3,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,8,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,Class_6
+38204,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,3,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,5,0,0,0,0,0,0,1,1,1,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38205,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,4,0,2,0,0,0,5,0,9,0,3,0,0,2,0,0,4,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,24,0,1,0,0,0,0,15,4,8,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38206,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,11,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+38207,0,1,3,3,0,0,0,0,0,0,0,0,3,2,0,2,0,1,0,2,1,3,1,6,0,16,0,4,0,0,1,1,1,0,4,0,0,0,0,3,1,0,1,0,0,0,0,6,0,0,1,5,0,12,0,0,3,0,0,13,0,1,0,1,2,2,0,0,0,1,0,0,1,3,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,4,0,Class_6
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+38237,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,1,0,2,0,0,0,2,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,2,0,0,3,0,0,0,0,4,0,0,0,1,0,0,0,3,1,0,0,4,1,0,0,0,0,1,0,0,0,0,0,1,2,3,0,1,1,0,0,0,1,0,1,0,0,0,0,Class_6
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+38240,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,2,1,0,0,0,0,0,3,0,3,0,0,0,0,2,0,0,2,2,1,0,1,0,1,0,3,0,0,0,0,0,2,0,0,0,7,0,0,0,0,8,0,0,13,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,2,0,0,1,0,Class_6
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+38242,0,2,11,19,0,0,0,0,0,2,0,0,2,1,0,3,0,1,0,0,0,3,0,2,0,10,3,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,5,0,1,4,0,0,0,0,6,2,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
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+38244,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38247,0,0,3,20,0,0,0,0,0,1,11,1,0,0,1,1,0,1,0,0,0,2,0,3,0,8,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,1,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,11,0,0,0,0,1,0,0,0,1,0,0,1,0,Class_6
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+38269,0,0,21,2,0,0,0,0,0,0,9,1,1,0,0,1,0,1,0,0,0,4,0,0,0,2,7,1,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,1,0,7,0,1,1,0,0,0,0,14,1,0,0,0,0,1,1,4,0,2,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,10,0,1,0,0,0,0,0,0,1,0,1,0,0,Class_6
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+38279,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,1,0,2,0,3,5,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,6,0,2,10,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,7,2,0,0,0,0,0,Class_6
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+38281,0,0,2,1,0,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,0,2,0,7,1,8,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,7,0,1,0,1,0,0,0,0,0,0,0,14,0,0,3,0,0,4,1,1,0,0,0,1,3,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
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+38283,1,1,2,1,0,1,1,0,0,0,9,0,0,0,0,3,0,0,1,0,1,1,0,16,0,3,6,1,0,0,0,4,1,1,1,1,0,0,1,6,0,3,1,0,0,2,0,2,0,0,0,7,0,3,2,0,0,0,0,1,6,0,0,0,1,0,8,1,0,0,0,0,3,52,0,1,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,1,1,Class_6
+38284,0,7,0,1,0,0,0,1,0,0,2,1,1,0,0,2,2,4,0,2,0,0,1,1,0,1,0,0,0,1,1,0,2,0,1,2,1,4,1,3,0,2,0,0,1,0,1,4,0,1,0,0,0,3,0,0,1,10,0,0,1,2,0,0,1,0,5,0,8,0,0,0,1,5,0,1,0,4,1,1,0,0,4,0,0,1,2,0,0,18,0,0,0,Class_6
+38285,0,0,14,1,0,0,0,0,0,0,13,0,0,0,0,1,0,0,0,0,0,2,0,11,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,12,0,0,2,0,0,0,1,4,0,0,0,9,0,0,3,0,1,2,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,2,0,Class_6
+38286,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,3,1,2,0,0,0,2,0,3,1,1,0,1,0,0,0,0,0,3,0,1,0,0,0,1,0,1,0,0,0,1,0,0,2,0,0,2,0,3,1,0,0,1,8,0,0,2,4,0,2,3,0,4,0,0,1,1,0,0,1,0,0,0,0,0,0,0,7,2,0,Class_6
+38287,1,5,3,0,0,0,1,0,0,0,7,0,0,0,0,1,0,2,1,0,0,1,0,1,0,1,2,1,0,0,0,0,1,0,8,0,0,0,0,0,0,6,1,3,0,0,0,4,16,1,0,0,0,3,1,0,2,0,0,0,0,0,0,0,1,4,2,1,0,3,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,1,Class_6
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+38398,0,0,19,9,0,0,0,0,0,0,16,1,0,3,0,7,0,0,0,0,2,5,0,0,0,4,20,9,0,1,0,0,0,3,0,1,0,0,0,0,0,3,1,1,0,22,0,1,2,0,1,2,0,21,0,0,0,0,0,0,10,2,1,1,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,15,0,4,0,0,0,0,0,0,1,0,0,2,0,Class_6
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+38402,0,0,2,1,0,0,0,0,0,0,12,0,0,1,0,2,0,0,1,1,5,1,0,6,0,4,1,0,0,0,3,3,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,7,0,2,0,4,0,1,2,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38403,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,5,1,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,1,Class_6
+38404,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,3,1,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,13,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38405,0,0,11,7,1,0,0,0,0,0,10,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,10,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,14,0,0,0,0,0,0,1,11,1,0,0,0,0,2,4,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,Class_6
+38406,0,0,4,2,0,0,1,0,1,0,8,0,1,0,0,1,0,2,0,1,1,3,0,2,0,6,7,5,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,5,0,0,2,0,0,0,0,16,1,1,0,0,0,5,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,5,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+38407,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38409,0,0,4,7,0,0,0,1,0,1,9,0,0,0,0,3,0,0,0,0,3,0,0,2,0,1,9,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,2,0,11,0,0,1,0,0,3,0,3,0,0,0,0,0,1,9,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,24,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38410,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38411,0,0,9,4,0,0,0,0,0,1,10,0,0,1,2,2,0,0,0,0,3,2,0,0,0,1,20,2,0,1,0,2,1,1,2,4,0,0,2,0,0,0,0,0,0,9,0,0,1,0,1,2,0,7,0,0,0,4,0,0,6,0,1,0,0,0,1,0,0,1,0,0,5,3,0,0,0,0,2,2,0,1,0,1,0,0,0,0,2,0,0,0,1,Class_6
+38412,0,0,2,2,0,0,0,1,1,0,14,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,8,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,4,0,4,1,0,0,0,0,0,2,1,0,0,0,0,4,0,0,0,0,1,0,2,1,0,0,0,0,17,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+38413,0,0,0,0,0,0,0,4,0,0,8,0,0,0,0,3,1,3,0,0,0,0,0,2,0,9,1,0,0,0,0,0,0,0,5,2,2,1,0,0,0,4,0,0,0,0,0,2,7,0,0,0,0,8,0,1,0,0,0,2,0,6,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,3,0,Class_6
+38414,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,9,0,5,0,3,0,0,2,0,0,0,0,1,1,0,7,0,0,5,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,Class_6
+38415,0,0,7,1,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,2,2,0,9,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,0,0,3,2,1,0,5,0,0,5,2,0,1,0,1,1,0,0,0,0,0,0,3,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38416,0,0,5,2,0,0,0,1,0,0,7,0,0,1,0,0,0,4,0,0,0,2,0,0,0,1,6,1,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,3,0,0,0,0,Class_6
+38417,0,2,13,4,0,0,0,0,0,0,6,1,0,0,0,5,0,0,0,0,0,1,0,2,0,5,10,3,1,0,0,0,0,0,2,0,0,2,0,0,0,0,0,2,0,12,0,1,1,0,0,0,0,9,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38418,0,0,6,6,0,0,0,0,0,1,7,0,0,0,0,2,0,0,0,0,0,5,0,3,0,4,6,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,6,0,0,0,0,0,0,0,8,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38419,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,5,0,0,0,0,0,1,2,2,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,12,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+38420,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,3,1,0,1,0,4,0,7,1,1,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,14,0,0,0,1,0,0,4,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,3,1,0,0,0,0,0,Class_6
+38421,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,1,2,0,0,0,0,0,1,0,1,1,0,0,1,6,1,2,0,0,4,0,0,0,0,0,0,4,0,0,2,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,1,1,1,0,0,0,0,0,2,0,0,0,0,6,4,0,0,0,1,0,0,0,Class_6
+38422,0,2,8,0,0,0,1,0,0,1,7,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38423,0,0,1,0,0,0,1,1,2,0,0,0,1,2,2,2,12,0,1,3,0,4,2,17,1,0,0,0,0,0,0,3,0,3,4,3,1,3,1,1,0,2,3,1,0,0,0,3,0,2,0,0,1,3,1,4,2,1,0,3,0,4,0,5,0,3,27,0,1,3,1,1,0,4,0,1,0,0,5,1,0,0,1,0,0,2,9,0,0,0,2,0,0,Class_6
+38424,0,4,2,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,3,6,0,1,3,15,0,0,0,0,1,0,7,1,0,1,3,5,0,0,3,0,0,1,0,1,0,0,0,6,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,7,0,5,0,0,0,3,0,0,6,0,0,0,0,0,0,16,0,0,0,1,0,1,5,2,0,1,Class_6
+38425,1,1,3,1,0,0,0,0,0,0,10,0,0,0,0,7,0,0,0,1,0,3,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,3,2,0,0,0,0,1,0,2,0,2,0,0,0,0,1,1,0,0,0,1,3,0,0,0,0,0,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,6,0,Class_6
+38426,0,0,8,1,0,0,0,0,0,0,8,0,0,0,0,3,0,1,0,1,0,1,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,1,0,11,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38427,1,0,1,0,0,0,0,0,0,0,2,0,0,1,0,3,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,1,0,2,0,1,0,1,0,5,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,1,6,0,1,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,1,0,Class_6
+38428,0,0,0,7,0,0,0,0,0,0,9,0,3,0,0,3,0,0,1,0,0,1,0,0,0,1,2,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,1,1,1,0,0,0,0,11,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38429,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,12,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38430,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,16,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,1,1,1,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38431,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,3,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38432,0,1,11,3,0,0,0,0,0,0,8,0,0,0,0,1,0,1,1,0,4,1,0,1,0,0,4,0,0,0,0,1,0,0,1,1,0,1,0,0,0,1,0,1,0,6,0,1,1,0,0,4,1,4,0,0,0,0,0,0,3,1,0,0,0,2,1,0,0,0,0,0,0,2,1,0,0,0,0,7,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38433,0,1,6,10,0,0,4,1,0,1,10,0,0,0,0,3,1,1,0,0,0,4,0,2,0,1,7,1,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,2,0,8,0,0,1,1,0,2,0,16,1,1,0,1,0,1,2,0,0,0,0,3,0,0,0,1,0,0,1,0,0,1,0,0,0,5,0,3,1,0,0,0,0,0,0,0,0,0,0,Class_6
+38434,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,0,1,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,1,5,0,0,0,0,0,0,5,1,0,2,0,1,10,0,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_6
+38435,0,0,1,5,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,6,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38436,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,8,0,0,4,0,0,0,8,0,0,0,0,0,0,0,3,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38437,0,0,4,0,0,0,0,1,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,1,0,0,0,0,1,0,1,0,4,0,2,0,0,0,0,1,0,0,2,0,0,2,2,0,0,0,11,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38438,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,3,0,2,0,1,0,2,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,8,1,0,12,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,6,0,0,0,0,0,1,0,2,0,0,Class_6
+38439,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38440,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,Class_6
+38441,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,10,0,1,1,0,0,0,0,2,1,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,12,0,1,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+38442,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38443,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,1,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,Class_6
+38444,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,Class_6
+38445,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,2,0,2,0,0,0,0,1,2,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,Class_6
+38446,1,0,1,0,0,0,0,0,0,2,5,0,1,0,0,5,0,1,1,0,0,4,0,3,0,1,0,2,0,0,0,0,0,0,3,0,0,3,0,0,1,1,0,0,0,0,0,0,8,1,0,0,0,5,1,0,0,0,0,7,1,2,0,1,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,2,1,9,0,0,0,0,0,0,3,0,0,0,0,Class_6
+38447,0,0,1,2,0,0,0,1,0,4,1,0,0,0,0,1,1,3,2,0,0,2,0,1,0,3,0,1,2,0,0,2,0,0,0,0,0,0,0,0,1,2,0,2,0,0,0,3,0,0,0,1,1,2,0,0,3,0,2,8,0,0,0,1,0,1,10,0,0,3,1,2,0,1,0,0,0,0,0,1,1,1,0,0,1,0,0,0,1,0,0,0,0,Class_6
+38448,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_6
+38449,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,5,0,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,6,0,1,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+38467,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38468,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,4,0,0,0,2,0,4,0,6,0,0,0,0,0,0,0,0,4,2,0,1,0,1,1,18,0,0,0,0,0,1,1,0,0,0,1,1,1,0,1,0,0,12,0,0,0,0,0,0,3,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,Class_6
+38469,1,0,0,0,0,0,0,0,0,0,1,0,2,3,1,2,0,0,1,0,0,1,0,5,2,8,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,3,0,0,14,0,0,0,1,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,Class_6
+38470,0,0,6,4,0,0,1,0,0,1,5,0,0,0,1,6,0,1,0,0,0,5,0,1,0,1,1,1,0,0,0,1,0,0,4,1,0,5,0,0,0,0,0,1,0,1,0,0,2,1,0,0,1,3,0,0,0,0,0,0,0,2,2,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,9,0,1,4,0,0,0,0,1,0,0,0,3,0,Class_6
+38471,0,0,7,2,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,2,0,0,7,0,1,2,3,0,0,0,2,0,0,0,0,1,1,0,1,0,1,0,0,0,3,0,0,0,0,0,2,0,2,0,0,0,16,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,Class_6
+38472,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,3,0,0,1,2,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,4,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,Class_6
+38473,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,5,0,0,1,0,0,10,0,0,0,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38474,0,0,0,5,0,0,0,0,0,0,12,0,1,0,0,3,1,2,0,0,1,3,0,2,0,4,2,1,0,0,2,2,0,1,4,0,0,1,0,2,0,3,0,2,0,1,0,1,3,0,0,0,1,9,3,0,0,1,0,0,2,2,2,1,0,1,1,0,1,1,0,1,0,0,0,1,0,1,1,5,1,2,2,1,1,0,0,0,0,0,0,1,0,Class_6
+38475,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,4,1,5,0,2,0,0,1,0,2,1,0,0,2,2,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,3,2,0,7,0,0,12,0,0,0,1,1,0,8,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,1,0,0,4,1,0,1,1,0,0,Class_6
+38476,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,2,0,2,4,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,6,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+38477,0,0,0,1,0,0,3,0,0,0,6,2,0,1,0,4,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,3,0,0,5,0,0,1,0,4,0,1,1,6,0,1,0,0,2,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,Class_6
+38478,0,1,5,17,0,0,0,1,0,1,0,0,0,0,0,3,0,4,0,0,0,5,0,4,1,0,0,2,0,0,0,0,0,0,9,1,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,19,2,0,3,1,0,13,0,1,0,0,1,1,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,1,0,0,Class_6
+38479,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,1,0,2,1,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,9,0,0,0,1,0,4,10,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+38480,3,0,0,10,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,1,0,0,0,2,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,Class_6
+38481,0,1,2,2,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38482,0,0,8,5,0,0,0,0,0,0,8,0,1,1,0,3,0,0,0,0,2,1,0,0,0,1,6,1,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,1,7,0,0,7,0,0,0,0,17,0,0,0,0,1,1,8,4,0,0,0,2,0,0,0,1,0,0,1,0,0,1,0,0,0,5,0,2,0,0,0,0,0,0,2,1,0,0,0,Class_6
+38483,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,Class_6
+38484,0,1,11,4,0,0,0,0,0,1,8,0,0,0,0,5,0,2,0,0,0,4,0,2,0,1,7,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,5,0,0,0,0,0,0,0,18,0,0,0,0,0,0,2,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38485,0,0,8,2,0,0,0,0,0,0,9,1,1,0,0,1,0,0,0,0,0,0,0,2,0,7,3,5,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,1,1,13,0,1,0,0,0,0,0,12,0,0,1,0,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38486,0,1,6,1,0,0,0,0,0,0,15,1,0,1,0,0,0,1,0,0,0,1,0,6,0,0,5,1,0,0,0,1,2,0,2,0,0,0,0,0,0,0,0,0,0,7,0,1,1,0,1,1,0,3,0,0,0,6,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,1,1,0,0,0,1,Class_6
+38487,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38489,0,1,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,3,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+38498,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38499,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,12,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+38500,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+38501,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_6
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+38503,0,0,1,5,0,0,0,0,0,0,6,0,1,0,0,4,0,3,0,2,0,2,0,0,0,3,1,2,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,2,1,2,3,1,4,0,0,0,0,16,0,0,0,1,0,0,0,7,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,1,2,4,0,3,0,0,0,0,1,0,2,0,0,3,0,Class_6
+38504,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,Class_6
+38505,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,13,0,0,2,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,2,0,0,2,2,0,0,0,0,8,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,49,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,2,0,0,Class_6
+38506,0,4,2,2,0,1,0,0,0,0,8,0,0,0,0,2,0,1,0,1,0,0,0,1,0,0,9,1,0,0,1,0,0,0,2,0,0,3,0,3,0,0,0,0,0,0,0,2,6,0,0,0,0,4,1,0,1,0,0,0,3,2,3,2,0,1,0,1,0,2,0,1,0,0,0,0,0,0,1,5,0,3,0,0,0,0,0,0,7,0,0,0,0,Class_6
+38507,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,2,0,0,1,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+38508,0,2,3,1,0,0,1,0,0,0,2,0,1,0,0,6,0,0,0,0,2,6,1,3,0,8,2,3,2,0,0,0,0,0,6,0,0,2,0,0,1,9,0,6,0,4,0,3,4,0,0,0,1,15,1,0,1,0,0,3,0,4,1,1,1,1,4,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38509,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,Class_6
+38510,0,1,0,0,0,0,1,1,1,0,0,0,2,0,1,0,2,4,0,1,0,1,2,5,1,0,0,0,1,0,1,2,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,5,1,0,10,0,2,0,0,0,0,6,0,0,1,1,2,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+38511,0,0,15,6,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,24,0,0,2,0,0,1,0,13,1,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+38512,0,0,4,25,0,0,1,0,0,0,1,0,1,1,2,1,0,1,1,3,0,4,2,6,2,1,0,3,2,0,4,0,2,1,3,0,0,6,0,0,0,1,0,5,0,2,0,0,5,0,0,0,0,7,3,0,0,0,1,15,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,1,0,0,0,0,1,2,1,0,0,0,1,0,2,2,0,1,0,Class_6
+38513,4,0,0,0,0,0,0,1,0,0,2,0,2,0,0,1,3,1,0,0,0,1,0,3,2,0,0,0,0,0,0,2,0,0,0,1,2,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,2,3,0,0,0,0,5,2,1,0,0,2,0,2,12,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,1,0,2,Class_6
+38514,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,9,0,0,0,0,0,0,5,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+38515,0,2,17,8,1,0,0,0,0,2,7,1,1,0,0,1,0,0,0,0,5,1,0,2,0,0,14,7,0,0,0,0,2,3,1,0,1,1,2,0,0,1,0,3,0,26,0,0,1,0,0,0,0,17,0,0,1,0,0,1,3,2,0,0,0,1,0,0,0,2,0,0,1,0,1,0,0,0,0,4,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+38516,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,2,1,1,1,1,0,2,9,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,8,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,18,0,0,0,0,0,0,1,1,0,2,Class_6
+38517,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38518,0,0,3,1,0,0,0,0,0,0,9,0,1,0,0,2,0,2,0,3,0,3,2,1,0,1,1,1,0,0,0,1,0,0,4,0,0,2,0,0,1,0,0,1,0,1,3,0,10,0,0,0,0,4,0,0,2,0,0,0,0,1,0,0,0,1,3,0,0,1,0,2,0,0,0,0,0,0,0,8,1,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38519,0,1,9,8,0,0,0,0,1,0,7,0,0,2,0,6,0,0,2,0,1,6,1,0,1,4,3,7,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,4,0,3,2,1,10,3,0,0,0,44,4,0,0,1,0,10,4,4,1,0,1,1,0,0,0,1,0,0,0,2,2,0,0,0,0,6,0,2,1,0,0,0,0,0,5,0,0,0,0,Class_6
+38520,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38521,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,2,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,Class_6
+38522,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,2,0,1,0,0,0,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,12,0,2,0,1,0,0,2,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38523,0,1,4,8,0,0,0,0,0,0,7,0,0,0,0,8,0,1,2,0,0,2,0,2,0,2,7,1,0,1,1,1,0,0,2,1,0,0,1,0,1,0,0,3,0,1,0,1,1,0,0,4,1,14,0,2,0,0,0,0,10,1,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,2,1,0,0,0,0,0,2,0,1,1,0,Class_6
+38524,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,0,0,0,1,0,0,0,2,2,0,8,0,0,0,0,0,0,5,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+38525,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,13,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,Class_6
+38526,0,1,13,9,0,0,0,0,0,0,6,1,0,0,0,2,0,1,0,0,1,3,0,0,1,0,2,2,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,1,1,1,0,0,0,0,14,0,0,0,0,0,1,5,4,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,7,0,1,1,0,0,0,0,0,6,0,0,0,0,Class_6
+38527,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,2,0,9,0,0,0,2,0,1,1,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38528,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,1,0,4,0,1,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,24,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,1,1,1,0,Class_6
+38529,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,12,0,8,0,0,0,0,2,0,0,3,6,4,0,1,2,2,0,1,0,0,1,0,1,2,0,0,1,0,0,0,1,0,9,0,1,9,0,0,0,2,0,0,6,1,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+38530,1,0,0,1,0,0,0,0,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,21,0,0,0,0,0,23,2,7,0,0,1,0,0,0,5,0,0,0,26,0,0,0,0,6,0,0,0,28,0,0,0,7,0,5,0,1,1,0,0,0,1,0,10,0,0,0,0,0,0,66,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
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+38533,0,0,8,2,0,0,1,0,0,1,8,0,0,0,1,0,0,0,1,0,4,0,0,1,0,0,11,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,2,1,0,12,0,0,0,0,0,1,0,8,0,0,0,0,0,1,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38534,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,1,1,9,0,0,1,0,0,0,0,0,3,0,1,1,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,4,0,0,1,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38535,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,4,0,3,0,1,0,0,1,4,0,1,1,1,0,0,0,0,0,2,0,2,0,0,0,1,1,0,0,0,1,8,0,0,0,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
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+38537,0,0,11,2,0,0,0,0,0,0,7,0,1,0,0,2,1,1,2,0,0,2,0,1,0,2,12,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,13,0,0,1,0,0,0,0,20,0,0,0,1,0,2,1,4,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,2,0,0,0,0,1,0,Class_6
+38538,0,0,13,7,0,0,0,3,0,0,12,0,0,0,0,5,0,1,0,0,2,1,0,11,0,1,1,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,0,4,14,0,0,1,0,0,0,0,6,0,0,2,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,11,0,1,0,0,1,0,0,0,0,0,0,1,0,Class_6
+38539,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Class_6
+38540,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38541,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,2,0,1,0,0,0,0,0,4,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,4,0,0,3,0,0,0,0,0,2,4,0,0,0,2,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,1,Class_6
+38542,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+38543,0,0,5,3,1,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,3,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,Class_6
+38544,0,0,1,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,9,1,0,0,0,0,0,1,1,0,0,0,1,1,0,0,1,0,1,1,0,0,1,0,2,0,0,0,2,0,1,0,0,2,1,0,9,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+38545,0,0,4,1,0,0,1,0,0,0,0,0,3,0,0,3,0,0,0,0,0,1,0,5,0,14,0,1,2,0,0,0,0,2,6,0,0,0,0,2,0,0,0,2,0,3,1,0,0,0,0,0,0,3,0,0,4,0,0,13,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+38546,0,0,12,12,0,0,0,0,0,0,9,0,0,1,0,1,0,2,0,0,2,3,0,1,0,7,7,2,1,0,0,1,1,0,0,0,0,0,0,0,0,4,0,2,0,11,0,1,1,0,0,0,0,17,0,0,0,0,0,1,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,0,Class_6
+38547,0,0,5,2,0,0,0,0,0,0,6,0,2,0,0,3,0,1,0,0,0,2,0,0,0,5,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,1,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38548,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,4,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,Class_6
+38549,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38550,0,0,0,0,0,0,0,3,1,0,1,0,0,1,0,0,7,0,0,1,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,5,0,0,5,0,0,0,0,0,0,20,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38551,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,6,1,0,0,0,1,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,9,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38552,0,0,2,2,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38553,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,1,3,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,6,7,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,Class_6
+38554,0,1,10,10,0,0,0,0,0,0,7,0,1,0,0,4,0,0,0,0,1,1,0,2,0,2,1,0,0,0,1,0,0,0,12,0,0,0,0,0,0,0,1,1,0,14,0,0,1,0,0,0,0,16,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+38555,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38556,1,1,1,0,0,0,1,0,0,0,1,0,1,1,0,0,2,2,0,1,0,1,0,2,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,8,0,1,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,Class_6
+38557,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,1,0,4,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,2,0,0,0,1,1,0,0,0,0,5,0,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,3,0,0,0,Class_6
+38558,0,0,16,16,0,0,0,0,0,0,10,0,0,0,0,3,0,0,0,0,0,2,0,2,0,2,5,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,3,0,0,0,0,0,2,0,10,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38559,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38560,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,2,0,0,12,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38561,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,13,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,Class_6
+38562,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,4,0,4,3,0,0,0,0,0,0,0,0,4,0,1,4,0,2,13,0,0,0,0,2,3,0,0,3,0,0,2,1,0,2,0,1,12,0,0,1,2,0,1,9,3,1,3,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,Class_6
+38563,0,1,12,1,0,0,0,0,0,0,8,1,0,0,0,7,0,2,0,0,0,4,0,0,0,1,2,2,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,4,0,2,3,0,0,0,0,10,1,0,2,0,0,1,6,1,1,0,0,1,0,1,2,0,0,2,0,0,0,1,0,0,0,6,0,2,0,0,0,0,0,2,0,0,0,0,1,Class_6
+38564,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,13,0,0,0,1,0,2,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,Class_6
+38565,0,1,3,0,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38566,0,0,6,4,1,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,4,0,0,0,0,11,1,0,0,0,0,0,3,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38567,0,0,10,4,0,0,0,0,0,0,8,1,0,1,2,6,0,2,0,1,5,3,0,0,0,2,20,1,0,0,2,1,1,0,0,0,0,0,0,1,3,0,0,2,0,5,0,0,1,0,0,4,0,8,2,1,0,1,0,0,2,3,1,2,1,1,2,0,0,2,0,2,0,3,0,0,0,0,0,14,0,5,0,0,0,0,1,1,3,0,0,0,0,Class_6
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+38569,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,4,2,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,Class_6
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+38587,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38588,0,0,8,6,0,0,1,0,0,0,12,2,0,0,0,0,0,1,0,0,0,2,3,0,0,0,16,2,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,7,1,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,17,0,0,0,0,0,7,0,4,0,1,0,0,0,0,3,0,0,0,0,Class_6
+38589,0,0,10,0,0,0,0,0,1,1,8,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,13,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,2,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38590,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,1,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+38591,0,2,0,0,0,0,0,0,0,0,1,1,1,0,0,4,0,3,0,0,0,4,0,2,0,7,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,28,0,3,0,0,0,1,0,0,0,0,0,11,0,0,6,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,1,0,0,2,0,0,1,Class_6
+38592,1,1,1,3,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,27,0,1,0,0,0,2,0,0,0,0,0,8,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38593,0,0,8,2,0,1,1,0,0,0,3,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,6,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,3,0,0,0,Class_6
+38594,0,0,3,0,0,0,0,0,0,0,13,0,0,1,0,0,0,0,0,0,3,0,0,6,0,2,19,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,8,0,0,0,5,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38595,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+38596,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,8,4,0,6,0,1,1,5,1,4,0,0,0,0,0,0,0,1,1,4,0,4,2,3,1,3,4,1,1,4,0,0,1,1,13,0,0,2,0,1,7,0,0,13,0,3,1,1,0,4,2,2,0,1,4,1,1,3,0,0,0,0,2,0,0,0,0,0,2,2,20,2,0,1,0,1,3,Class_6
+38597,0,0,13,9,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,9,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,3,0,2,1,0,0,0,0,0,0,10,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38598,0,0,3,2,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,5,1,0,0,0,2,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38599,1,1,5,6,0,0,0,0,0,0,10,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38600,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,15,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+38601,0,1,0,6,0,0,0,0,0,0,1,0,1,1,0,3,0,1,0,1,0,2,0,3,2,10,0,2,1,0,0,0,0,2,6,0,0,0,0,1,1,14,0,2,0,0,0,0,3,0,0,0,1,7,1,0,1,0,0,5,0,0,0,0,0,1,0,2,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,Class_6
+38602,0,0,0,2,0,1,0,1,0,0,7,0,0,0,0,3,0,0,0,0,1,1,0,2,0,3,7,1,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,7,0,2,0,0,0,0,4,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,0,1,0,1,0,0,2,0,Class_6
+38603,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,4,0,0,0,0,0,0,0,4,0,0,0,1,1,0,0,0,1,6,0,0,0,0,9,3,0,1,0,0,1,3,1,0,1,0,2,2,0,3,0,0,0,1,6,2,0,0,3,1,0,1,1,0,0,1,0,0,0,0,0,0,2,0,4,0,0,0,0,1,0,Class_6
+38604,0,0,5,4,0,0,0,0,0,1,16,0,0,0,0,1,0,0,0,0,3,0,0,5,0,0,2,0,1,0,0,2,0,1,0,0,0,0,0,1,0,1,0,3,0,10,0,0,0,0,0,0,0,10,0,1,0,22,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,1,0,0,1,0,Class_6
+38605,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,2,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38606,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,7,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38607,1,0,1,0,0,0,0,0,0,0,7,0,0,0,0,2,0,4,0,2,0,1,0,0,0,2,1,0,2,0,0,0,0,0,4,0,0,0,0,0,0,2,1,1,0,3,0,2,2,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_6
+38608,0,1,8,1,0,0,0,0,2,0,9,0,0,1,1,4,0,2,0,0,1,4,0,4,0,3,16,1,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,4,0,3,0,0,1,0,0,1,0,8,1,1,1,1,0,1,0,1,0,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,1,0,0,1,1,0,0,0,Class_6
+38609,0,3,12,11,0,0,0,0,1,0,8,1,1,0,0,5,0,1,0,1,3,2,0,0,0,3,4,5,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,3,1,6,0,0,0,1,1,2,0,13,0,2,0,0,0,0,3,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38610,0,0,14,1,0,0,0,0,0,0,10,0,0,0,0,6,0,0,0,0,2,4,0,3,0,0,19,2,0,0,3,0,0,2,0,2,0,2,2,0,0,1,0,1,0,4,0,0,1,0,0,5,1,13,0,0,0,0,0,1,4,4,0,0,0,1,0,0,0,0,0,1,2,1,1,0,0,0,0,12,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+38611,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,Class_6
+38612,0,0,0,0,0,0,0,0,2,0,11,0,0,2,0,0,0,0,16,0,0,0,0,18,1,0,20,0,0,0,0,4,0,0,0,3,0,0,0,2,0,1,1,0,0,0,0,2,0,0,0,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,105,0,0,0,0,0,15,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+38613,0,0,1,7,0,1,0,0,0,0,8,0,0,0,1,3,0,0,0,0,2,2,0,0,0,11,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,3,0,0,0,4,0,0,1,0,0,0,0,7,0,1,0,1,0,2,1,2,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38614,0,0,0,0,0,0,0,0,11,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,6,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+38615,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,1,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,1,0,2,0,0,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,Class_6
+38616,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+38617,0,0,0,2,0,0,1,0,0,0,8,0,1,0,0,5,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,0,2,0,3,2,1,4,0,0,0,0,5,1,0,0,0,1,0,1,0,0,1,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,6,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+38618,0,0,8,7,0,0,0,0,1,0,8,0,0,1,0,1,0,1,0,0,4,3,0,0,0,1,14,0,0,0,1,4,0,0,0,2,1,0,0,0,0,0,0,3,0,6,0,0,1,0,0,0,1,14,0,0,1,0,0,1,4,0,1,0,0,0,1,0,0,0,0,0,2,1,0,1,0,0,0,3,0,3,0,0,0,0,0,0,2,1,1,1,0,Class_6
+38619,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,2,0,1,0,10,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,3,0,2,0,2,0,1,0,0,0,3,0,8,1,0,3,0,0,4,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+38620,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38621,1,0,0,0,0,5,0,0,0,0,1,0,0,1,0,1,1,11,0,0,0,3,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,12,2,1,0,0,1,1,0,0,0,0,1,7,3,0,5,0,0,18,0,3,0,2,1,0,2,1,0,4,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+38622,0,0,9,7,0,0,0,1,0,0,10,0,2,0,0,1,1,1,0,0,1,0,0,1,0,0,2,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,13,0,0,2,0,1,0,0,6,0,0,0,0,0,1,3,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38623,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,1,0,1,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,10,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,4,1,0,0,1,0,1,0,4,1,1,0,0,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38624,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38625,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,1,1,0,18,0,0,0,0,1,0,1,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,Class_6
+38626,0,1,2,14,0,0,0,0,0,12,11,0,0,0,0,7,0,0,0,0,0,0,0,2,0,1,13,8,0,0,1,1,0,2,2,0,0,0,0,0,0,0,0,2,0,7,0,0,4,0,0,2,0,9,2,0,0,0,0,1,4,0,1,0,2,4,0,0,0,4,0,0,0,7,0,0,0,0,4,8,0,4,0,0,0,0,0,0,4,0,0,0,0,Class_6
+38627,0,1,4,1,0,0,0,0,0,0,6,0,0,0,0,3,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,1,1,0,0,0,0,9,0,0,0,0,0,1,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,0,0,2,0,Class_6
+38628,1,0,9,1,0,0,0,0,0,0,4,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,0,0,4,0,0,2,0,1,0,3,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38629,4,0,0,1,0,0,0,1,0,1,2,0,0,1,2,0,0,0,0,3,0,1,0,2,2,0,0,0,1,0,0,0,0,0,0,0,1,3,0,4,0,3,0,3,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,9,0,1,1,2,0,0,0,0,1,0,0,0,0,0,3,4,8,0,1,0,0,0,5,Class_6
+38630,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,5,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,2,1,0,1,0,1,0,0,0,1,3,5,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38631,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38632,0,0,0,3,0,0,0,0,0,0,2,0,5,0,0,4,0,3,0,1,0,6,0,11,1,5,0,1,29,0,0,1,0,1,1,1,1,0,0,1,0,12,0,5,0,0,1,3,0,0,0,0,0,8,6,0,0,0,0,2,0,3,0,0,2,2,15,0,8,1,1,0,0,0,3,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,2,0,Class_6
+38633,0,1,2,5,0,0,0,0,0,0,7,0,0,0,1,1,0,0,0,0,0,1,0,4,0,2,4,0,0,0,2,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,6,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,Class_6
+38634,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38635,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38636,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38637,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,8,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38638,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,16,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+38639,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,14,0,0,0,0,0,0,2,0,0,0,1,0,0,0,Class_6
+38640,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+38641,0,1,8,1,0,0,0,1,0,2,10,0,0,0,1,0,0,0,0,0,3,4,0,2,0,1,31,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,2,2,0,16,0,0,0,0,0,1,0,11,2,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,1,1,0,0,0,Class_6
+38642,0,0,9,1,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,2,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,6,0,0,0,1,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38643,0,1,0,0,0,0,0,1,1,0,0,0,2,1,3,1,0,6,0,1,0,1,1,3,0,1,2,0,1,0,1,1,0,2,2,1,0,1,1,0,0,1,0,1,0,0,0,0,0,0,3,0,1,2,0,0,6,1,0,11,0,1,0,0,0,1,3,1,0,2,0,4,0,0,2,0,4,1,1,0,0,0,0,0,0,0,4,0,2,0,0,1,0,Class_6
+38644,0,0,7,2,0,0,0,0,0,0,8,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,4,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38645,0,0,2,1,0,0,0,1,1,1,9,0,0,0,2,0,0,3,0,1,1,0,0,1,0,0,12,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,4,0,2,1,0,0,12,2,8,0,0,0,0,0,0,7,1,4,0,2,1,0,0,0,0,0,0,0,10,1,1,0,0,0,15,0,1,0,0,0,1,0,0,6,0,0,1,0,Class_6
+38646,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,11,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,0,12,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,Class_6
+38647,0,0,20,9,0,0,0,2,0,0,7,0,0,0,0,3,0,0,0,1,1,3,0,0,0,2,2,1,0,0,0,2,0,1,2,0,0,1,0,0,0,4,0,1,0,23,0,0,2,0,0,0,0,12,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+38648,0,0,14,9,0,0,0,0,0,1,9,1,1,1,0,4,0,2,0,0,2,4,0,2,0,4,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,9,0,1,4,0,0,0,0,11,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,1,1,1,1,Class_6
+38649,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,4,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,6,0,0,1,0,0,10,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+38665,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38667,0,0,2,1,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38668,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,0,3,1,1,0,0,0,1,0,3,0,0,0,0,1,0,1,1,6,0,0,2,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,6,1,0,3,0,0,8,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38669,0,0,14,12,0,0,1,0,0,3,9,0,0,0,0,2,2,1,1,0,1,2,0,0,0,1,7,1,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,1,0,0,1,2,1,1,0,5,1,0,0,0,0,1,4,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,0,1,2,0,1,0,0,0,1,1,0,0,0,0,0,1,Class_6
+38670,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,2,0,1,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,5,0,2,0,0,0,0,0,0,0,0,0,8,1,0,0,0,0,14,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,Class_6
+38671,0,0,7,4,0,0,0,0,0,2,5,0,0,0,0,2,0,0,0,0,2,2,1,0,0,1,4,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,6,0,0,0,1,0,1,1,9,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,1,1,0,0,0,0,1,0,0,0,Class_6
+38672,2,0,9,4,0,0,0,0,0,2,10,0,0,0,0,11,0,0,0,0,1,5,0,0,1,5,6,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,18,0,0,0,0,0,1,0,20,0,0,0,2,2,0,3,2,0,0,1,0,1,0,0,1,0,0,6,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38673,1,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,3,0,1,0,2,0,1,0,2,0,2,0,0,3,0,0,0,3,0,0,0,1,0,0,4,0,3,0,0,0,1,2,0,0,0,0,6,1,0,0,0,0,12,0,2,0,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+38674,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38675,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,14,6,0,3,0,0,0,22,1,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,11,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,32,13,0,0,0,0,2,1,5,3,1,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38676,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38677,0,0,4,8,0,0,0,0,0,0,10,0,0,0,0,1,0,1,0,0,3,2,0,0,0,2,22,1,0,0,0,1,0,0,4,0,0,0,0,0,1,1,0,1,1,8,0,0,0,1,0,0,0,5,0,1,0,1,0,1,3,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,1,1,0,1,0,0,0,3,0,0,1,0,Class_6
+38678,0,0,0,1,0,0,0,0,0,0,10,0,0,0,2,0,0,1,0,0,0,0,0,5,0,0,2,1,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,2,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+38679,0,0,16,8,0,0,0,0,0,0,11,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,10,3,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,4,0,13,0,2,1,0,0,0,0,13,1,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38680,0,0,9,8,0,0,0,0,0,0,9,0,1,0,0,4,0,2,0,0,0,1,0,0,1,2,5,3,0,0,4,2,0,0,0,1,0,1,0,2,0,1,3,1,0,4,0,0,2,0,0,0,1,12,1,1,0,0,0,1,4,0,1,0,0,1,0,0,1,0,0,1,0,4,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,1,0,Class_6
+38681,0,0,5,16,1,0,0,0,0,0,9,1,0,2,0,6,0,1,0,0,1,4,0,0,0,9,1,4,0,0,0,3,1,0,3,0,0,2,0,0,0,1,1,4,0,3,0,0,4,0,0,0,2,22,0,0,0,2,0,1,2,1,1,1,1,2,0,0,0,0,0,1,0,0,0,0,1,0,0,14,0,5,0,0,0,0,0,0,2,0,0,1,0,Class_6
+38682,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38683,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,7,2,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,6,0,1,0,1,2,2,0,0,0,0,1,5,2,1,1,0,2,12,0,3,0,1,0,4,3,2,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,10,2,0,0,0,0,0,Class_6
+38684,0,0,1,1,0,0,0,0,0,0,6,1,0,0,0,2,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38685,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,3,0,0,0,4,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,2,0,3,0,0,0,0,3,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,5,0,Class_6
+38686,1,0,5,4,1,0,2,0,0,2,8,0,0,1,0,11,1,3,0,1,0,9,0,0,0,2,2,5,0,1,0,0,0,1,3,0,0,1,0,0,0,2,0,13,0,2,0,0,13,0,0,0,2,27,4,0,0,1,0,0,0,3,0,1,1,1,1,0,0,4,0,0,1,0,1,0,0,0,1,6,1,0,0,0,0,0,0,1,4,0,0,0,5,Class_6
+38687,0,0,22,18,0,0,0,0,0,0,8,0,0,0,0,2,0,1,1,1,1,1,0,0,0,5,0,0,0,0,1,2,0,2,2,0,0,1,0,0,1,0,0,1,0,9,0,0,0,0,0,0,0,11,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+38704,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,10,0,0,1,0,0,0,1,0,0,0,Class_6
+38705,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,5,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+38706,1,0,2,3,0,0,0,0,0,0,8,0,0,0,0,6,0,1,1,0,2,1,0,0,0,5,2,2,0,1,0,1,0,0,5,0,1,0,0,0,0,0,0,0,1,4,0,0,1,0,1,0,0,6,0,6,0,1,0,2,1,1,0,0,0,0,2,1,0,2,0,0,3,1,0,3,0,0,0,11,0,5,0,0,0,0,0,0,1,1,0,3,0,Class_6
+38707,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,5,0,0,0,0,2,0,2,0,1,0,0,0,1,2,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+38708,1,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,2,0,2,0,4,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,1,0,2,0,0,0,2,2,0,0,0,0,1,0,0,1,0,0,6,0,0,0,0,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,4,0,0,Class_6
+38709,5,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,2,0,6,2,2,0,0,0,1,0,5,0,0,2,2,0,0,0,0,0,4,0,0,0,0,0,5,0,0,0,0,0,5,2,0,2,1,5,2,0,0,0,2,0,0,17,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,4,16,0,0,0,0,1,2,Class_6
+38710,0,0,5,1,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,2,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,1,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38711,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+38712,0,0,9,7,0,0,0,0,0,0,8,0,0,0,1,1,0,0,0,0,1,2,0,0,1,0,9,2,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,1,1,12,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+38713,0,0,1,1,0,0,0,3,0,2,3,0,1,0,0,1,1,0,1,0,0,3,0,5,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,1,1,6,0,3,0,1,0,3,0,0,0,1,0,1,0,0,0,0,1,13,0,0,0,0,0,2,7,0,0,4,1,0,0,1,1,0,0,0,0,1,1,1,0,0,2,0,4,0,1,0,0,0,0,Class_6
+38714,0,0,0,1,0,0,0,1,0,0,1,2,3,1,0,0,0,4,2,0,0,1,0,1,0,1,0,0,0,0,5,0,0,0,7,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,0,2,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38715,0,1,7,1,0,0,0,0,0,1,8,1,0,1,1,2,0,0,0,1,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38716,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,2,0,0,0,1,0,1,0,3,0,0,0,0,1,0,0,0,1,1,0,0,0,2,0,0,0,0,1,1,0,0,9,1,0,0,0,0,1,2,4,0,0,0,1,0,0,0,0,0,3,1,4,0,0,4,3,1,1,1,0,1,0,0,2,1,0,0,0,0,1,3,0,1,0,0,0,0,0,Class_6
+38717,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,2,0,0,1,2,0,0,0,1,0,4,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,1,5,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+38718,0,0,0,0,0,0,0,0,0,0,7,1,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,3,0,0,1,0,0,1,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38719,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38720,0,0,1,4,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,9,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,2,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,4,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+38721,0,0,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,15,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38722,0,0,6,1,0,0,0,0,0,0,11,1,0,1,0,1,0,0,0,0,0,1,0,0,0,3,13,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,2,0,1,0,0,0,9,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38723,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38724,0,0,1,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,2,0,0,0,5,1,0,0,0,0,0,0,3,2,1,0,2,0,0,1,1,0,3,0,4,0,0,0,1,1,0,0,0,0,1,0,0,0,0,3,6,0,1,0,0,1,1,8,0,0,9,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,4,2,0,0,0,0,1,Class_6
+38725,0,0,15,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,2,0,2,0,2,3,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,15,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38726,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,1,0,2,0,2,1,0,0,0,0,0,0,2,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,2,1,0,0,0,0,10,0,0,0,1,0,1,9,0,1,2,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+38727,0,0,1,3,0,0,0,0,0,0,10,0,0,1,0,7,0,1,0,0,2,1,0,1,0,0,12,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,6,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+38740,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
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+38742,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,4,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38743,0,1,10,3,0,0,0,1,0,0,9,0,0,0,0,0,0,1,0,0,2,1,1,1,1,1,4,0,0,0,0,0,1,0,4,1,0,1,0,0,1,0,0,2,0,7,1,0,1,0,0,6,0,10,0,0,0,0,0,1,9,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38744,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,2,0,1,0,0,0,1,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38745,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,4,0,0,0,2,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+38746,0,1,17,2,0,0,0,0,0,0,17,0,0,0,0,2,0,1,0,0,3,2,0,5,0,2,8,1,0,0,0,0,0,0,3,1,1,0,0,1,0,0,0,4,0,14,0,0,1,0,0,1,1,15,0,1,0,6,0,0,1,1,0,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+38747,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38748,0,1,7,3,0,0,0,0,0,0,15,0,0,1,0,1,0,0,0,0,1,1,0,7,0,1,5,4,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,0,6,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,5,0,2,0,0,0,0,0,0,1,3,0,1,0,Class_6
+38749,1,0,5,2,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,7,0,0,0,0,1,13,2,0,0,1,0,0,0,6,0,1,2,0,0,0,0,0,2,0,11,0,0,1,0,0,0,0,6,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+38750,1,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,0,0,3,0,5,0,1,0,0,2,0,0,0,0,2,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,0,0,13,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+38751,0,1,2,2,0,0,0,1,2,0,13,0,0,0,1,5,1,1,0,0,0,2,1,0,0,8,15,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,1,2,0,4,0,1,0,1,0,0,3,0,0,0,2,0,0,0,0,0,0,0,1,2,0,2,1,0,0,12,0,1,0,0,0,0,1,0,1,0,1,0,0,Class_6
+38752,0,0,7,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,6,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,6,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38753,0,0,9,2,0,0,0,1,0,1,10,0,0,1,0,2,0,0,0,0,5,1,0,0,0,0,32,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,7,1,1,0,0,0,1,0,16,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,1,Class_6
+38754,1,4,3,6,0,0,2,0,0,1,1,0,1,0,1,6,1,2,3,7,0,8,7,21,0,1,2,0,0,0,1,1,0,1,5,9,0,8,2,0,0,1,0,8,3,1,0,5,0,2,1,0,2,7,5,0,4,2,0,19,0,2,0,2,1,4,4,3,4,4,2,1,0,2,2,3,0,0,0,1,0,0,19,0,0,2,0,2,0,2,0,0,3,Class_6
+38755,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,6,0,0,0,0,1,0,0,0,0,0,1,0,1,0,2,0,0,0,Class_6
+38756,0,9,2,2,0,0,1,0,0,0,7,0,0,0,0,1,2,7,1,2,0,4,1,2,1,1,0,2,0,1,2,0,1,0,7,0,0,2,0,1,0,0,0,1,0,3,1,0,26,0,0,0,0,6,1,0,0,1,0,0,4,1,1,0,0,5,0,0,3,3,0,0,0,0,0,0,0,0,0,6,0,11,0,0,0,0,0,0,3,2,0,0,0,Class_6
+38757,0,2,8,3,0,0,0,0,0,2,17,1,0,0,0,1,0,0,0,1,0,1,0,10,0,0,10,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,4,0,0,0,12,0,0,1,0,0,1,0,1,3,0,0,1,0,0,1,2,0,0,0,1,0,6,0,2,2,1,0,0,1,0,0,1,0,0,0,Class_6
+38758,0,0,0,0,0,0,2,2,0,0,1,0,0,2,0,5,2,6,0,3,0,3,1,1,0,2,0,0,0,1,4,1,1,0,6,1,0,3,1,1,2,8,0,5,0,2,0,0,4,1,0,9,1,3,0,0,0,0,0,10,0,7,2,3,0,1,1,0,0,9,0,0,0,8,0,0,0,1,8,4,1,0,0,0,3,0,5,0,5,0,0,1,8,Class_6
+38759,0,3,14,19,0,0,0,0,0,1,8,0,1,0,0,2,0,0,0,0,1,2,0,5,0,0,2,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,3,0,0,0,3,0,0,0,12,2,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38760,0,1,3,9,0,0,0,0,0,0,9,0,1,2,1,2,0,0,0,0,1,1,0,1,0,12,1,3,1,0,0,0,0,2,4,0,0,1,0,0,0,1,0,3,0,3,0,2,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38761,0,0,6,7,0,0,0,0,0,2,8,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,11,1,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,9,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,1,0,0,0,0,0,2,0,0,0,0,0,Class_6
+38762,0,0,9,5,0,0,0,0,1,4,11,0,0,1,0,1,0,1,0,0,4,0,0,0,0,3,18,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,12,0,0,1,0,0,3,0,5,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,11,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38763,0,0,8,1,0,0,0,0,0,0,5,0,0,0,0,3,0,2,0,0,1,0,0,0,0,1,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Class_6
+38764,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38765,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,17,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,1,2,0,2,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+38766,0,0,7,11,0,0,0,0,0,0,11,0,1,0,2,1,0,2,0,0,0,1,0,1,0,2,10,1,0,0,0,0,1,0,3,0,0,0,0,0,0,1,0,3,0,6,0,1,2,0,0,0,0,13,2,1,0,1,0,0,2,1,0,1,0,1,1,0,1,3,0,0,0,0,0,2,0,0,1,6,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38767,0,0,3,4,0,0,0,0,0,0,8,1,0,0,0,1,0,1,0,0,0,1,0,3,0,1,6,4,1,1,0,2,1,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,5,1,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38769,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,7,0,0,0,3,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,Class_6
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+38782,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,1,0,2,1,0,2,0,0,8,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38783,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,1,10,0,0,0,4,4,0,12,5,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+38785,0,0,7,2,1,0,0,1,0,1,8,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,0,3,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38786,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
+38787,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,2,1,1,0,12,0,3,2,5,0,2,2,0,0,0,3,5,1,0,1,0,3,0,1,2,0,5,3,3,6,0,0,2,0,1,0,2,0,2,1,0,1,0,0,18,0,1,0,2,0,1,8,0,1,8,1,1,0,3,0,1,0,1,0,1,1,0,0,0,1,0,11,1,1,0,0,0,0,Class_6
+38788,0,5,1,5,0,0,0,0,0,0,7,0,0,1,2,6,0,4,1,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,3,0,0,2,0,1,0,0,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,1,5,0,0,1,0,0,0,0,1,1,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+38789,0,0,1,2,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,2,0,6,0,12,0,1,0,2,0,0,0,0,3,1,0,2,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,14,1,0,0,0,0,2,0,1,0,2,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+38790,0,0,0,0,0,0,0,0,0,2,1,1,0,1,0,1,1,2,1,1,0,3,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,5,0,1,0,1,1,0,2,1,1,0,0,6,1,0,1,1,0,0,4,0,0,0,0,3,4,1,0,0,0,0,3,3,0,0,0,0,0,0,Class_6
+38791,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,1,3,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,2,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,9,0,0,0,0,0,1,3,0,0,2,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38792,0,2,8,8,0,0,0,0,0,0,8,0,2,0,0,4,0,0,0,0,0,2,0,0,1,2,10,3,2,0,0,2,0,1,2,0,0,0,0,0,0,1,0,2,0,8,0,0,0,0,0,1,0,10,1,1,1,0,0,1,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,2,0,Class_6
+38793,0,0,0,1,0,4,4,1,2,2,3,0,1,5,0,4,5,3,0,2,0,9,3,22,0,0,0,0,3,0,0,8,0,0,1,1,0,3,3,3,0,6,0,8,11,0,0,11,2,1,0,0,0,8,1,0,4,5,0,20,0,1,1,1,1,1,12,0,2,3,0,1,1,0,0,9,0,1,3,1,2,0,13,0,3,0,10,0,0,6,0,1,2,Class_6
+38794,0,0,19,4,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,3,4,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,2,0,18,0,0,2,0,0,0,0,12,0,1,0,2,0,1,1,1,1,1,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+38795,0,1,10,8,0,0,0,0,0,0,19,0,0,0,0,3,0,0,0,0,2,1,0,8,0,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,12,0,1,1,0,0,0,0,3,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,3,0,0,1,2,0,0,0,1,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38796,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,3,2,0,0,0,3,0,0,3,0,0,0,1,0,0,0,0,2,6,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,6,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,2,0,Class_6
+38797,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,2,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38798,0,0,14,6,0,0,0,0,0,1,9,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,4,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,14,0,0,0,0,0,0,0,16,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,4,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,Class_6
+38799,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,5,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38800,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38801,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,16,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+38802,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,3,1,6,0,1,0,2,0,6,2,4,0,2,0,1,0,0,0,0,2,0,0,4,0,0,0,10,0,3,0,0,0,0,1,0,0,0,1,2,1,0,9,0,0,12,0,1,3,0,1,1,0,3,0,4,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,1,Class_6
+38803,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,0,0,0,2,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,2,0,0,0,0,Class_6
+38804,0,1,3,1,0,0,0,0,0,0,10,0,0,1,0,5,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,2,0,0,3,0,0,0,0,13,0,1,0,0,0,0,3,3,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38805,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,4,0,4,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,1,1,0,0,0,0,1,0,0,5,1,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+38806,0,0,6,5,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,5,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38807,0,0,1,5,1,0,0,0,0,0,13,0,0,0,0,1,0,2,0,1,1,0,0,0,0,0,4,1,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,3,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,1,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38808,0,0,2,11,0,0,0,0,0,2,6,1,0,1,0,0,1,0,0,1,1,1,0,1,0,1,0,1,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,3,0,0,1,0,0,1,0,1,0,2,1,0,0,2,0,0,1,6,0,4,0,0,0,0,0,0,0,1,0,0,0,Class_6
+38809,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,11,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,5,0,0,2,0,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38810,0,1,3,2,0,0,0,0,0,1,9,0,3,0,0,3,0,1,0,1,1,3,0,1,1,0,5,2,0,0,2,0,0,0,1,0,0,2,0,0,0,0,0,1,0,5,0,1,12,0,1,0,0,6,1,1,0,0,1,1,0,0,0,0,0,4,1,0,0,0,0,0,0,1,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38811,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38812,2,0,1,0,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,8,0,0,0,0,0,8,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+38813,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,2,0,3,0,0,0,0,0,0,1,5,0,0,1,2,0,1,3,0,0,1,0,2,2,0,0,2,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,6,0,0,5,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,13,1,0,0,0,0,0,Class_6
+38814,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,6,0,0,0,0,1,5,1,2,0,0,1,0,1,0,0,0,4,1,0,4,0,1,1,6,0,3,0,1,2,0,0,0,1,0,0,4,1,0,6,0,1,13,0,0,1,2,1,3,0,1,0,0,3,0,0,0,2,0,0,1,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38815,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,3,0,0,12,0,2,0,0,0,0,7,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,Class_6
+38816,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,1,0,2,0,0,0,2,0,3,0,0,11,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,1,0,0,1,0,0,0,Class_6
+38817,0,0,0,11,0,0,1,0,1,1,2,0,1,3,0,2,0,0,0,1,0,2,0,16,0,9,0,0,0,0,3,0,0,1,5,0,0,1,0,2,0,1,0,1,0,3,0,1,1,0,0,3,0,1,1,1,9,1,0,14,0,0,3,2,0,2,6,0,0,0,0,0,0,0,3,1,0,0,1,4,0,0,1,0,0,1,1,0,3,0,0,0,0,Class_6
+38818,0,0,8,1,0,0,0,0,0,0,14,0,2,0,0,2,0,0,0,0,0,0,0,7,0,0,7,2,0,0,0,0,0,0,0,2,0,0,0,1,0,1,1,2,0,7,0,1,0,0,0,1,0,6,0,0,0,5,0,1,3,1,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,1,2,0,0,0,0,Class_6
+38819,0,0,0,16,0,0,0,0,0,0,3,0,1,0,0,2,1,3,0,1,0,0,0,0,0,23,1,4,0,0,0,0,0,0,3,0,0,0,0,0,0,7,1,1,0,3,0,1,4,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,0,0,1,0,0,1,0,1,2,0,0,0,Class_6
+38820,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,Class_6
+38821,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,4,0,2,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,12,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38822,0,1,2,2,0,0,1,0,0,0,19,0,6,1,0,3,0,0,0,1,1,3,0,2,0,1,6,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,6,0,3,0,0,0,0,0,1,0,44,0,2,0,2,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+38823,0,0,10,4,0,0,0,0,1,0,8,1,0,0,0,3,0,1,0,0,1,0,0,5,0,0,25,4,0,0,1,0,0,0,2,2,0,0,0,0,0,1,0,0,0,10,2,0,0,0,0,3,0,13,1,2,0,2,0,1,6,1,0,0,1,0,0,0,0,2,0,0,2,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38824,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,2,0,1,0,11,0,1,0,0,1,0,0,5,0,0,0,0,1,0,4,2,0,7,0,1,1,0,0,5,0,1,0,0,1,3,0,0,0,0,1,3,0,2,1,1,0,2,22,0,0,3,2,0,0,0,2,1,0,0,3,1,0,0,0,0,0,0,11,0,0,0,0,0,4,Class_6
+38825,0,2,0,3,0,0,0,0,0,0,5,0,1,0,0,1,0,2,0,1,2,1,0,1,0,1,1,1,0,0,0,0,1,0,6,0,0,3,0,1,0,0,0,0,0,0,0,2,3,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,0,3,0,0,2,0,0,0,0,0,1,1,0,1,0,3,0,0,0,0,0,0,2,0,1,2,0,1,0,Class_6
+38826,2,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,4,4,0,0,1,0,4,0,2,0,0,0,0,1,4,0,0,0,3,0,0,2,3,0,2,0,2,2,0,0,4,0,0,0,0,0,2,0,0,6,0,0,22,0,2,0,1,0,0,13,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,12,0,0,0,2,0,2,0,0,0,5,Class_6
+38827,0,0,13,1,0,0,0,1,0,0,10,0,0,0,1,4,0,0,0,1,2,3,0,0,0,1,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,1,0,0,1,1,5,0,2,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+38828,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,21,0,1,0,1,0,1,0,0,0,0,0,4,1,0,0,0,3,5,0,1,0,1,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
+38829,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38830,0,0,0,1,0,0,0,0,0,0,16,1,0,0,0,1,0,0,0,0,2,0,0,14,0,0,3,0,0,0,2,4,0,1,1,0,3,1,0,2,0,0,0,0,0,0,1,0,0,0,0,11,0,1,0,0,0,1,0,0,1,2,0,0,0,0,2,1,1,0,0,0,0,29,0,2,0,0,0,16,0,1,1,0,0,0,0,1,0,0,0,0,0,Class_6
+38831,0,1,14,9,0,0,0,0,0,0,6,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,3,0,10,0,1,0,0,0,0,0,7,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,4,0,3,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38832,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,4,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,Class_6
+38833,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,1,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,0,0,2,0,0,0,4,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,0,0,4,0,Class_6
+38834,1,0,9,8,0,0,0,0,0,0,15,0,0,0,0,2,0,0,0,1,0,1,0,7,0,0,9,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,7,1,0,1,0,0,4,0,4,1,0,0,6,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38835,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,1,1,0,1,1,0,2,0,0,0,0,1,8,0,1,0,1,0,0,1,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,4,0,0,Class_6
+38836,0,0,0,4,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,1,2,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,1,0,0,3,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+38837,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,1,0,1,2,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,0,0,6,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38838,0,1,13,2,0,0,0,0,0,0,7,0,0,0,0,1,1,1,0,0,1,2,0,0,0,2,13,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,1,0,0,8,0,0,0,0,0,1,0,9,2,3,0,0,0,1,3,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,1,0,0,0,2,0,Class_6
+38839,0,1,0,1,0,0,0,1,3,0,3,0,1,1,0,2,0,2,0,0,0,1,0,6,1,5,0,1,0,0,0,0,0,0,6,0,0,0,0,1,0,6,0,2,0,1,2,0,2,0,0,0,0,6,0,0,2,0,0,10,0,1,0,0,0,6,4,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_6
+38840,0,1,12,4,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,1,2,3,0,0,0,1,8,1,0,0,0,0,1,2,1,1,0,1,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,11,1,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,10,0,5,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38841,0,0,3,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,1,0,12,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38842,0,1,4,0,1,0,0,0,0,0,14,0,0,0,0,4,0,3,0,0,1,1,0,5,0,0,6,0,0,0,0,3,0,0,7,0,0,3,0,0,0,0,1,2,0,0,0,1,0,0,0,3,0,16,0,3,1,3,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,1,0,0,0,0,2,2,0,0,0,0,Class_6
+38843,0,0,11,13,0,0,0,0,1,1,10,0,0,0,0,2,0,1,1,0,3,1,0,0,0,2,17,1,0,0,5,0,0,0,2,0,0,0,0,0,0,1,2,0,0,10,0,0,0,0,0,4,0,8,0,0,0,1,0,0,3,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38844,0,1,11,3,0,0,0,0,0,1,16,0,0,0,0,3,0,0,0,0,0,0,0,7,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,6,0,1,0,0,0,1,0,14,0,0,0,8,0,1,0,0,0,2,1,1,1,1,0,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38845,0,0,0,4,0,0,0,0,0,1,7,0,0,1,0,1,0,1,0,1,0,1,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,1,2,0,0,0,0,4,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,Class_6
+38846,0,0,16,34,0,0,0,0,0,0,9,0,0,0,1,1,0,2,0,0,0,2,0,1,0,5,20,5,0,0,0,0,0,1,5,0,0,1,0,0,0,0,1,2,0,3,0,0,1,0,0,0,1,11,3,1,0,0,0,2,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+38847,0,0,3,6,0,0,0,0,0,0,7,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,2,0,0,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38848,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,3,0,7,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,0,0,2,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+38849,0,2,5,3,1,0,0,0,0,0,17,0,0,0,0,3,0,0,0,0,0,1,0,7,0,0,6,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,9,1,1,0,5,0,1,1,0,0,1,1,0,1,0,0,0,0,1,0,2,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+38852,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,1,1,0,3,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,4,0,0,24,0,1,0,0,0,0,2,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,1,0,1,0,Class_6
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+38854,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,14,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38855,0,1,3,5,0,0,0,0,2,0,2,0,0,0,0,1,0,4,0,0,0,3,0,22,1,12,0,0,1,0,3,0,1,0,4,0,0,0,0,1,0,8,0,2,0,1,0,2,2,0,0,0,1,2,1,0,2,0,0,8,0,1,1,2,0,2,0,1,0,0,4,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,Class_6
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+38857,0,0,4,0,0,0,0,0,0,0,9,0,0,0,1,8,0,3,0,0,3,0,2,0,0,4,1,1,1,0,1,3,1,0,5,0,0,1,0,2,0,0,0,3,0,0,0,2,3,0,0,0,0,7,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,9,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+38858,0,1,16,1,0,0,0,0,0,0,8,2,0,0,0,3,0,1,0,0,2,3,0,1,0,1,13,4,0,1,0,2,0,0,1,0,0,0,1,2,0,0,0,1,0,12,0,2,2,0,0,3,0,12,1,1,1,1,0,1,3,1,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,9,0,0,1,0,0,0,0,1,0,0,0,2,0,Class_6
+38859,1,1,41,33,1,0,0,0,0,2,13,0,2,0,0,9,0,4,0,2,6,4,0,0,0,4,19,3,0,0,2,0,0,0,6,2,2,2,0,1,0,0,2,0,0,27,0,0,4,0,4,9,0,37,4,1,0,1,0,0,3,3,0,0,0,0,0,0,1,0,0,2,2,5,0,0,0,0,0,7,2,21,0,0,0,0,0,2,6,2,0,3,0,Class_6
+38860,0,1,0,0,0,0,0,0,1,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+38861,0,0,10,2,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,2,0,0,3,0,1,5,0,0,0,1,1,0,2,1,0,0,0,0,0,0,0,0,2,0,4,0,0,1,0,0,0,0,3,0,1,0,0,0,2,9,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,4,0,0,1,0,0,0,5,0,0,0,0,Class_6
+38862,0,0,2,0,0,0,0,0,1,0,1,1,0,0,0,3,0,2,0,0,0,0,0,1,0,19,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,1,0,5,0,0,1,0,0,0,1,17,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38863,1,0,9,4,0,0,0,0,0,0,0,0,0,4,2,5,0,1,0,1,0,10,0,3,0,15,0,4,2,2,0,0,1,0,12,1,0,1,0,1,0,0,0,3,0,0,0,0,1,0,0,0,0,23,0,0,1,0,3,6,0,0,0,1,0,1,2,0,0,2,0,1,0,0,1,1,0,0,1,1,0,1,1,0,0,0,0,1,1,3,0,2,0,Class_6
+38864,1,0,0,1,1,0,0,0,5,0,0,0,0,0,0,3,0,2,0,0,0,3,0,2,4,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,2,15,0,5,0,0,0,2,0,0,0,0,1,1,0,0,1,0,3,4,0,1,6,0,0,0,4,0,0,1,2,2,1,0,0,0,0,0,7,1,0,0,0,0,0,3,4,0,1,0,0,3,0,Class_6
+38865,0,3,4,1,0,0,1,0,0,1,10,1,0,1,0,0,0,0,0,0,0,1,0,8,0,2,9,0,1,0,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,4,0,10,0,1,1,9,0,14,1,1,0,2,0,1,33,2,0,0,1,0,3,1,0,0,0,0,0,15,1,0,0,0,1,25,0,2,0,0,0,1,0,0,1,0,0,0,0,Class_6
+38866,0,0,3,1,0,0,0,0,0,0,1,1,1,2,0,5,0,3,0,1,0,3,0,5,1,0,0,1,0,0,0,0,0,0,2,1,0,3,0,0,0,2,0,9,0,1,2,1,1,0,0,0,0,9,0,1,3,0,0,0,0,1,0,0,1,0,1,2,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,Class_6
+38867,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,2,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38868,0,0,8,5,0,0,1,0,0,0,8,0,0,0,0,3,0,2,1,0,1,4,0,2,0,1,12,1,0,0,0,2,0,0,1,0,0,1,0,0,0,1,0,1,0,12,0,0,2,0,0,0,0,13,0,0,0,0,0,2,9,2,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38869,0,0,2,4,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,3,1,6,0,1,0,0,0,0,1,1,7,0,0,0,0,0,1,3,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,4,0,1,0,0,0,0,3,0,0,0,1,0,0,0,1,1,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+38870,0,1,12,7,0,0,0,0,0,0,7,0,0,1,1,1,0,0,0,2,0,2,0,0,0,0,1,2,0,0,0,0,0,0,11,0,0,1,0,0,0,1,0,2,0,4,0,2,3,0,1,2,0,19,2,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,5,0,1,0,0,0,0,0,0,3,3,0,1,1,Class_6
+38871,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,1,0,0,0,2,0,3,1,2,0,0,5,0,0,0,0,2,2,0,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,0,0,2,2,0,7,0,0,20,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38872,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,3,0,2,0,1,0,2,0,5,0,5,0,2,0,0,1,3,0,0,1,1,0,0,0,0,1,2,0,0,0,1,0,1,2,0,0,0,0,6,0,0,2,0,0,8,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38873,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,8,1,1,1,0,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38874,0,0,9,0,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,7,0,0,0,0,0,0,0,6,1,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,2,0,0,0,Class_6
+38875,0,0,9,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,1,2,1,0,0,0,0,0,0,4,1,0,0,0,1,0,1,0,0,0,3,0,0,2,0,0,0,0,13,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38876,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,4,0,1,0,8,0,3,2,0,0,0,1,0,0,0,2,1,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,0,1,0,0,2,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,2,0,0,0,0,2,0,1,0,0,1,0,0,0,3,1,0,0,0,2,0,0,2,0,Class_6
+38877,0,0,1,0,0,0,1,6,0,0,0,0,0,1,0,0,2,1,0,5,0,2,0,8,2,0,0,0,0,0,0,0,2,1,0,3,0,4,0,2,0,2,0,2,0,0,0,3,0,0,0,0,0,4,0,0,1,0,0,2,0,1,0,3,0,1,11,0,0,5,1,1,0,0,0,1,0,0,9,0,0,0,0,0,0,0,10,0,1,0,1,0,0,Class_6
+38878,0,0,6,3,0,0,0,0,0,0,8,1,0,0,0,4,1,0,0,2,4,1,1,0,0,3,21,2,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,8,0,0,0,1,0,1,0,15,0,0,0,1,0,1,3,3,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38879,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,1,4,5,0,10,0,2,0,2,0,3,0,0,1,1,1,2,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,2,0,0,2,0,0,12,0,0,0,4,0,5,6,3,0,1,0,0,0,0,4,0,0,0,0,0,1,0,2,0,0,0,8,1,1,2,0,0,2,Class_6
+38880,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,7,2,0,0,0,0,0,0,5,1,1,0,0,0,0,0,1,0,1,0,3,0,0,0,2,0,0,0,0,0,1,0,0,5,0,1,0,0,1,0,2,0,0,5,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38881,0,0,10,9,0,0,0,0,0,2,10,0,1,0,2,3,1,0,0,0,1,2,1,0,0,0,9,1,1,0,1,0,0,0,1,1,0,1,0,0,0,1,2,0,0,10,0,0,2,0,0,0,0,14,1,1,0,0,0,0,2,0,1,2,1,1,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38882,1,0,0,5,0,0,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,1,0,1,1,4,0,1,0,0,3,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,2,0,13,0,1,0,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38883,0,0,0,0,0,0,0,2,0,0,1,0,4,0,0,0,0,2,0,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,Class_6
+38884,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38885,0,3,24,67,1,0,0,0,0,4,10,0,1,3,1,7,0,2,0,0,0,1,1,4,0,3,4,3,0,0,2,0,0,2,3,1,0,0,1,1,0,0,1,5,0,17,0,0,2,0,0,4,0,24,1,2,0,2,0,3,1,2,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,4,0,4,0,0,0,1,1,1,2,1,1,0,1,Class_6
+38886,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,4,1,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,1,0,0,2,0,2,16,0,0,0,1,3,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,Class_6
+38887,0,1,6,1,0,0,0,1,1,0,13,0,0,0,0,5,0,0,0,0,0,2,1,7,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,1,1,2,0,0,0,0,3,0,0,0,1,13,1,1,0,4,0,1,1,3,1,1,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38888,0,0,0,2,0,0,1,0,0,0,0,0,0,1,3,2,0,1,0,1,0,4,0,3,0,6,0,1,0,0,0,0,0,0,12,0,0,3,1,1,0,6,0,0,0,2,2,0,0,0,0,0,0,4,0,0,8,0,0,11,0,0,0,0,0,2,0,1,0,1,4,1,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38889,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,1,11,0,1,0,1,0,1,0,0,0,1,1,1,0,0,1,9,0,1,0,3,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,3,0,3,6,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,3,0,Class_6
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+38898,0,1,0,2,0,0,0,0,1,0,21,0,0,0,0,0,0,1,0,0,1,0,0,6,0,1,5,0,0,0,4,2,0,0,4,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,2,0,2,0,0,0,12,0,1,3,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38899,0,0,4,3,0,0,0,0,0,0,8,0,0,0,0,6,0,3,0,0,0,4,1,0,0,2,6,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,3,0,0,0,0,0,1,0,15,1,1,0,1,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,1,3,0,Class_6
+38900,1,0,6,14,0,0,1,0,0,0,10,0,1,0,0,2,0,1,0,0,2,4,0,4,0,1,18,3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,3,2,15,0,0,0,0,1,0,0,8,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38901,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,9,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38902,0,0,3,5,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,3,0,6,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,1,0,0,0,0,6,4,0,1,0,0,8,2,0,0,1,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38903,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,12,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
+38904,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,4,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38905,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+38906,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,3,2,1,2,3,0,0,1,12,1,0,0,0,0,0,0,14,0,0,0,6,1,2,1,1,0,5,0,1,2,1,0,9,0,0,0,0,0,6,1,0,0,0,5,1,0,2,0,0,0,0,44,0,0,1,0,0,0,0,1,1,0,0,8,0,0,0,1,0,0,2,43,0,0,0,1,0,38,Class_6
+38907,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+38908,0,0,3,2,0,0,0,0,1,0,7,0,0,0,3,3,0,1,0,0,2,0,0,0,0,0,5,0,0,0,5,1,1,2,8,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,8,0,4,0,0,0,0,0,0,2,1,0,1,0,2,0,0,1,2,0,0,0,13,0,0,0,0,0,17,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+38909,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,3,0,1,0,0,0,11,0,2,0,0,0,0,0,0,0,0,0,1,1,0,2,0,1,6,0,1,0,0,0,1,7,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+38910,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,0,2,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,7,0,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38911,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,2,0,7,0,7,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,3,0,0,8,0,1,10,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38912,5,0,0,2,0,0,0,0,0,1,0,0,1,2,0,1,2,2,0,2,2,4,0,6,2,0,0,0,1,1,0,13,0,0,1,6,1,0,2,0,0,5,0,2,0,0,0,6,1,0,0,0,0,0,0,0,1,0,7,1,0,0,0,3,0,2,44,0,0,5,0,0,0,0,3,0,0,3,0,0,0,0,3,0,0,5,67,1,1,0,2,0,19,Class_6
+38913,1,1,14,2,0,0,0,0,0,0,8,0,1,0,0,2,0,0,0,0,2,0,0,0,0,3,11,3,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,1,13,0,1,2,0,0,3,0,7,0,0,1,1,0,0,3,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38914,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,4,0,1,0,3,0,4,0,11,1,2,0,0,0,0,0,3,2,2,0,1,4,0,36,1,0,2,1,1,37,0,0,2,0,0,0,0,0,2,2,0,0,0,1,0,0,1,0,7,1,1,1,0,1,5,2,1,2,1,0,7,0,0,2,0,0,0,0,0,1,0,2,0,0,0,3,1,0,Class_6
+38915,0,0,7,25,0,0,0,0,0,1,0,2,1,0,0,2,0,2,0,0,4,1,0,0,0,16,0,1,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,0,2,0,13,1,0,0,2,0,0,0,3,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+38916,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38917,0,1,2,25,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,2,5,0,4,0,1,0,0,4,1,0,1,2,1,0,0,1,0,1,2,0,0,0,1,0,1,0,0,0,1,0,3,0,0,0,0,0,12,0,0,1,1,0,0,6,2,1,0,0,0,0,0,3,0,0,0,0,4,0,2,0,0,0,1,0,0,0,2,0,0,0,Class_6
+38918,0,1,1,3,0,0,2,0,1,0,12,0,0,0,0,0,0,0,0,0,1,2,1,12,1,6,2,1,0,0,0,5,0,0,1,0,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,4,1,6,0,1,0,7,0,1,5,0,0,2,0,0,4,0,1,0,0,0,1,19,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_6
+38919,0,0,5,3,0,0,0,0,0,1,2,0,0,0,0,1,0,1,0,1,0,3,0,2,0,10,0,4,0,0,0,0,0,0,1,0,0,2,0,0,0,6,1,3,0,0,0,0,2,0,0,0,0,11,0,0,1,1,1,14,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,2,0,0,1,0,1,0,0,2,0,0,1,0,Class_6
+38920,0,2,6,33,0,0,0,0,0,0,8,0,0,0,2,0,0,0,0,0,1,0,0,1,0,2,5,2,0,0,0,0,0,0,7,0,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38921,0,2,4,2,0,0,0,1,0,0,6,2,1,1,0,4,0,1,0,0,5,2,0,1,0,5,35,4,0,0,1,1,0,1,0,0,0,1,2,1,0,1,0,1,0,3,0,1,2,0,0,0,1,15,0,1,0,2,0,0,7,2,0,0,0,2,1,0,0,2,0,0,0,1,1,0,0,0,0,6,0,1,0,0,1,0,1,0,0,0,1,0,0,Class_6
+38922,1,0,1,2,0,0,0,0,0,4,2,0,0,0,0,2,1,3,0,1,0,3,0,2,0,4,0,0,1,0,10,0,0,0,4,2,0,1,0,0,0,1,1,2,0,1,0,1,0,1,0,3,1,4,1,0,3,1,0,37,0,1,1,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+38923,0,0,5,2,0,0,0,0,0,0,14,0,0,1,1,1,0,0,0,0,2,1,0,7,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,2,0,6,0,0,0,0,0,1,0,2,0,1,0,4,0,1,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+38924,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,3,0,5,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,1,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38925,0,0,7,6,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,2,0,1,0,3,2,2,2,0,2,0,0,0,2,5,0,0,0,0,0,2,0,5,0,16,0,0,2,0,0,0,0,20,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38926,0,0,3,1,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+38927,2,1,0,1,0,0,0,1,0,0,0,0,1,1,0,7,2,15,0,1,0,3,0,10,1,13,0,2,0,0,0,0,0,2,3,2,0,0,0,1,0,35,0,1,0,0,0,2,1,0,0,1,1,8,0,0,7,0,0,7,0,9,1,1,0,5,5,5,0,1,3,1,0,3,0,0,1,0,0,0,0,0,1,0,0,0,2,1,2,1,0,1,1,Class_6
+38928,0,2,0,6,1,0,0,0,0,0,1,0,0,0,1,6,0,2,0,0,0,1,0,5,0,1,0,0,0,0,1,1,0,0,8,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,9,0,0,5,0,0,12,1,1,0,1,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,12,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38929,0,0,8,1,0,0,0,2,0,0,20,0,0,0,0,3,0,0,0,0,0,1,0,6,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,1,0,0,1,0,12,0,0,0,10,0,1,0,1,0,1,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
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+38936,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,Class_6
+38937,1,1,0,1,2,0,0,4,0,0,1,0,0,2,0,4,0,0,0,0,0,0,0,2,1,1,1,1,0,0,0,0,0,0,5,2,0,3,0,3,1,1,0,4,0,1,0,5,3,0,2,0,0,7,0,0,0,1,0,3,1,3,3,0,0,2,4,1,0,2,0,2,0,1,3,2,0,0,0,5,2,4,0,0,0,2,1,0,11,0,0,1,0,Class_6
+38938,0,0,0,4,0,0,0,0,0,0,4,0,0,0,0,5,0,2,0,0,0,1,0,0,0,6,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+38939,0,0,10,2,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,1,2,6,0,1,0,1,7,2,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,13,0,0,2,0,0,3,0,7,1,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38940,0,0,6,5,0,0,0,0,0,0,10,1,0,2,1,4,0,1,0,0,1,3,0,2,0,1,28,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,3,0,5,1,0,0,2,2,22,0,0,0,2,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38941,0,0,4,1,0,0,0,0,0,0,14,0,0,2,0,2,0,0,0,0,0,2,0,8,0,1,2,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,1,0,1,0,4,0,0,0,6,0,1,2,1,0,1,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+38942,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,1,0,1,3,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,5,0,4,0,0,0,3,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,1,0,0,4,0,0,0,0,0,1,0,0,0,0,0,11,0,0,0,0,0,0,4,2,0,0,1,0,0,0,Class_6
+38943,0,1,17,8,0,0,0,0,0,0,9,0,2,1,0,2,0,0,0,0,1,3,0,0,0,2,7,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,2,0,7,0,0,1,0,0,3,0,14,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+38944,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,Class_6
+38945,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38946,0,0,9,5,0,0,0,0,0,1,17,0,2,1,0,1,0,0,0,0,1,0,0,6,0,1,7,1,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,10,0,1,1,0,0,1,0,6,0,0,0,7,0,1,1,1,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,2,0,2,0,1,0,0,0,1,1,0,0,0,0,Class_6
+38947,1,0,11,8,0,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,7,0,0,2,0,0,0,0,18,1,0,0,0,0,1,2,1,0,0,0,1,0,1,1,0,0,0,1,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+38948,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38949,3,2,1,3,0,0,1,1,0,0,1,0,0,10,1,3,1,1,0,4,0,7,0,2,3,1,0,0,0,1,0,5,0,0,1,1,1,3,0,3,0,11,0,14,0,0,0,5,0,0,0,1,0,8,3,0,0,0,2,3,0,1,1,4,2,1,5,0,0,3,0,1,0,0,0,0,0,1,12,0,0,0,0,0,1,2,20,1,0,0,0,0,0,Class_6
+38950,0,0,0,0,0,0,0,1,0,0,8,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38951,0,1,14,3,0,0,0,0,0,0,8,0,1,1,0,5,1,1,0,0,1,3,0,1,0,2,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,23,0,0,1,0,0,0,0,25,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,1,0,0,1,0,Class_6
+38952,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38953,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,1,0,0,0,Class_6
+38954,1,3,2,2,0,0,0,1,0,0,2,0,2,3,1,5,1,0,0,1,0,6,2,2,0,3,0,1,3,0,0,6,1,4,0,2,0,3,0,0,0,7,0,2,3,0,0,3,5,0,0,0,0,14,3,0,0,0,0,14,0,2,0,4,1,8,9,0,0,2,1,2,0,0,3,1,0,2,1,0,0,0,0,0,0,0,9,0,0,0,0,2,6,Class_6
+38955,0,0,2,0,0,0,0,0,0,0,12,0,0,0,0,1,0,1,0,0,1,3,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,16,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,4,0,0,0,1,1,0,0,0,0,0,0,0,0,Class_6
+38956,0,0,1,2,1,0,0,0,0,0,8,0,0,0,2,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,2,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38957,1,0,8,5,1,0,0,1,7,0,9,0,0,0,0,5,0,0,0,1,4,2,0,3,0,2,1,6,1,1,0,3,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,4,0,0,0,0,15,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,3,0,4,1,0,0,1,0,0,2,0,0,1,0,Class_6
+38958,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,3,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,3,0,1,0,0,5,0,0,0,0,3,2,0,1,0,0,12,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38959,0,1,0,2,0,0,0,0,0,0,6,0,0,0,0,1,0,1,1,0,0,3,1,7,0,0,3,1,0,0,0,5,0,1,2,0,1,0,1,0,0,1,1,0,0,1,0,0,1,0,0,1,0,9,1,1,0,1,1,0,2,1,0,0,0,1,5,0,0,0,0,0,1,11,0,1,0,0,0,4,0,2,0,0,1,0,0,1,0,0,0,1,0,Class_6
+38960,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,11,0,0,0,1,0,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,1,1,0,12,0,4,0,2,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,54,0,4,0,0,0,6,0,0,0,0,0,2,0,0,0,2,0,0,2,Class_6
+38961,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,2,0,0,0,0,0,5,0,0,0,0,1,2,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,Class_6
+38962,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,2,1,1,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,1,0,0,0,0,3,0,2,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38963,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+38964,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,4,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,Class_6
+38965,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38966,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38967,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,4,1,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,2,0,0,12,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
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+38969,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,2,0,1,0,0,0,1,0,2,0,1,0,0,0,0,0,1,1,0,0,0,2,1,0,1,0,5,0,1,0,0,0,3,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
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+38988,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,3,1,0,0,0,0,0,2,7,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+38989,0,0,0,0,0,0,0,0,0,4,0,0,3,3,0,3,0,0,0,1,0,2,0,3,0,7,0,0,1,0,0,0,1,1,0,1,1,2,0,0,1,2,2,0,0,0,0,0,1,0,0,0,0,0,0,1,4,0,1,7,0,1,0,0,0,0,0,1,0,1,2,0,0,0,1,1,0,0,1,0,0,0,6,0,1,0,1,1,0,0,2,3,0,Class_6
+38990,0,0,3,5,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,0,0,0,9,0,3,9,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,1,0,6,0,0,1,0,0,0,1,2,0,0,0,7,0,0,2,0,0,1,0,1,1,0,0,0,0,0,0,2,0,0,0,1,0,4,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+38991,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38992,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38993,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,1,0,0,0,3,0,1,0,0,0,2,1,1,0,0,0,0,0,0,Class_6
+38994,4,0,1,1,0,0,0,1,0,0,1,1,25,0,1,3,0,2,0,0,0,1,0,7,3,0,0,0,2,1,0,0,0,0,0,2,0,3,0,0,1,1,0,1,0,0,0,10,0,1,0,0,4,7,1,0,0,0,34,2,0,2,3,2,0,0,41,1,3,18,0,1,0,1,0,1,2,0,3,0,1,0,0,0,0,6,1,2,0,3,0,0,0,Class_6
+38995,0,1,10,10,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,4,2,0,2,1,2,1,2,0,0,0,0,0,0,3,1,2,0,0,2,0,2,0,1,0,7,0,1,1,0,0,2,0,9,0,0,0,0,0,0,1,0,0,4,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,6,1,9,0,0,0,0,0,0,0,0,0,0,0,Class_6
+38996,1,0,3,0,0,0,0,0,0,0,5,0,1,0,0,1,0,1,0,0,2,2,0,0,0,2,4,3,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,3,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,1,2,0,0,0,1,0,0,0,8,0,1,1,0,0,0,0,0,0,0,0,Class_6
+38997,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+38998,5,1,0,0,0,0,5,0,0,0,0,0,0,0,0,3,1,3,0,3,0,1,0,6,2,2,0,0,0,0,0,5,0,1,0,2,0,1,0,0,0,9,0,3,0,0,0,3,0,0,0,0,0,1,0,0,3,0,4,6,0,4,0,1,4,0,12,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,3,0,1,2,16,2,0,0,0,0,2,Class_6
+38999,0,4,0,0,0,0,2,0,0,0,10,0,1,0,0,3,0,2,0,0,1,2,1,13,1,0,9,0,0,0,3,0,0,0,0,0,0,1,0,0,0,1,2,1,0,2,0,0,1,1,0,14,0,9,0,0,0,4,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,16,0,0,0,0,0,13,0,0,0,0,0,0,0,0,7,0,0,0,0,Class_6
+39000,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_6
+39001,0,0,3,11,0,0,0,0,0,0,8,0,0,0,0,1,0,3,0,0,2,0,0,2,0,1,5,2,0,0,1,1,0,0,3,0,0,1,0,0,0,1,2,2,0,2,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39002,0,0,1,1,0,0,0,0,0,0,7,0,1,0,0,3,0,1,0,0,1,1,0,2,0,7,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,1,0,0,0,0,3,4,0,0,0,0,2,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39003,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+39005,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,4,1,2,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,3,0,0,0,0,0,2,0,0,0,1,0,1,0,1,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,1,0,Class_6
+39006,5,2,1,0,0,0,1,0,1,1,1,0,3,0,0,2,1,2,0,1,0,2,2,11,1,0,2,0,1,0,0,7,1,0,1,2,1,1,0,0,0,7,2,2,1,0,0,5,1,3,0,0,0,6,0,2,1,0,3,3,0,1,0,8,0,1,13,1,0,6,0,0,0,7,0,0,0,0,1,0,0,0,1,0,1,2,16,1,0,1,0,0,41,Class_6
+39007,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,2,0,4,0,0,0,2,1,1,3,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
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+39029,0,0,4,3,0,0,1,1,0,0,13,0,0,0,2,0,0,0,0,0,0,2,0,7,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,4,0,0,1,0,0,0,0,8,0,0,0,4,0,1,1,1,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39034,0,1,3,1,0,0,0,1,0,0,17,1,0,0,3,0,0,0,0,0,3,2,0,6,0,0,21,1,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,1,0,0,0,3,0,8,0,1,0,5,0,2,0,0,1,1,0,0,0,0,0,0,0,0,1,6,0,0,1,1,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+39038,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+39039,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39040,0,1,1,2,0,0,0,0,0,0,9,1,0,0,0,4,0,3,0,1,1,1,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,1,0,2,0,0,0,1,0,1,0,0,3,0,0,2,0,8,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,2,5,0,0,0,0,0,7,0,3,0,0,0,0,0,0,5,0,0,0,0,Class_6
+39041,0,0,1,2,0,0,0,0,0,0,5,0,0,0,0,11,0,2,0,1,0,1,0,0,0,2,0,3,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,1,2,3,0,0,1,0,7,1,0,1,0,0,0,0,2,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,Class_6
+39042,0,0,7,2,0,0,0,1,0,1,6,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,10,3,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,1,1,6,0,0,0,0,0,1,0,14,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+39043,0,1,4,11,0,0,0,0,0,0,2,0,1,0,1,2,0,1,0,1,0,1,0,5,0,5,0,0,0,0,12,1,0,1,4,1,0,0,1,0,1,1,2,0,0,0,0,0,0,0,0,1,1,8,0,1,0,0,0,12,2,0,0,1,0,2,7,2,1,0,0,1,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39044,0,0,3,4,0,0,0,0,0,0,9,1,0,0,0,3,0,1,0,0,0,2,0,0,0,2,11,4,0,0,1,1,0,1,0,0,0,0,0,0,2,1,0,2,0,4,0,0,5,0,0,0,0,6,1,0,0,1,0,0,1,1,0,0,0,2,1,0,0,0,0,0,1,0,1,0,0,0,0,7,0,2,1,0,0,0,0,0,3,0,0,1,0,Class_6
+39045,0,0,6,3,0,0,0,0,0,0,14,0,0,0,0,1,0,1,0,0,0,0,0,11,0,1,0,1,0,0,0,3,0,0,1,1,2,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,3,1,0,0,10,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39046,0,0,4,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39047,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,1,0,1,3,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,1,0,2,0,1,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
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+39059,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+39062,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,3,0,1,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,2,0,0,0,0,0,1,3,0,4,0,0,9,0,1,0,1,0,1,13,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,0,Class_6
+39063,0,2,7,2,0,0,0,1,0,0,1,0,0,0,0,6,1,2,0,0,0,7,0,4,1,4,0,0,0,0,0,0,0,0,14,1,0,0,0,0,0,1,0,8,0,0,0,0,4,0,0,0,0,16,0,0,6,0,0,12,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,1,0,0,0,5,0,1,0,0,0,0,0,1,1,1,0,2,1,Class_6
+39064,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+39065,0,1,7,10,0,0,0,0,0,0,17,0,1,2,0,0,0,0,0,0,0,2,0,9,0,2,4,1,0,0,1,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,10,1,0,0,13,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,10,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+39066,0,0,6,4,0,0,0,0,0,0,8,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39067,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,1,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+39068,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,1,1,0,0,0,0,0,4,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_6
+39069,0,0,14,2,0,0,0,0,0,1,9,0,0,0,0,4,0,1,0,0,1,5,0,2,0,1,9,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,4,2,0,17,0,1,0,0,0,0,0,20,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,2,0,0,0,0,Class_6
+39070,0,0,11,7,0,0,0,0,0,0,9,0,0,0,0,0,0,5,0,1,2,1,0,1,0,2,7,0,0,0,0,2,0,1,0,1,0,1,0,1,0,0,1,0,2,5,0,0,0,0,0,2,0,18,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,16,0,1,0,0,0,0,0,1,1,0,0,0,1,Class_6
+39071,0,0,3,1,0,0,1,1,0,0,15,1,0,1,0,0,0,0,0,0,0,0,0,8,0,2,19,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,6,0,0,0,0,1,2,0,4,0,0,0,9,0,1,5,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,5,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39072,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,2,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,12,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,Class_6
+39073,0,1,0,0,0,0,0,1,1,0,0,0,2,0,1,2,1,5,0,1,0,2,0,2,0,1,0,1,1,0,3,1,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,2,0,0,0,1,0,3,1,0,10,0,3,0,0,0,1,1,1,0,2,0,2,0,0,1,0,4,1,0,0,1,2,0,0,0,0,3,0,2,0,0,2,0,Class_6
+39074,1,0,1,0,0,0,0,0,1,6,0,0,1,1,1,1,1,0,1,3,0,3,0,1,1,1,0,0,0,0,0,0,0,0,0,1,2,0,0,2,1,4,0,3,0,1,0,0,2,20,0,0,0,8,0,1,0,0,1,0,0,1,1,3,2,1,2,0,0,6,0,3,1,0,1,0,0,0,0,3,0,0,0,0,1,0,3,1,0,0,1,1,0,Class_6
+39075,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39076,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,2,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39077,0,1,3,6,0,0,0,0,0,0,6,0,0,1,0,7,0,1,1,0,0,4,0,0,1,1,1,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,3,0,0,0,0,25,2,0,0,0,0,0,3,1,0,1,1,1,1,0,0,1,0,0,0,0,0,0,1,0,0,4,0,3,0,0,0,0,0,0,0,2,0,1,0,Class_6
+39078,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,12,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,Class_6
+39079,0,1,8,0,0,0,0,1,0,0,16,0,1,0,0,2,0,1,0,0,0,3,0,7,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,3,2,1,1,0,0,0,0,9,0,0,0,4,0,0,1,0,1,1,0,2,0,0,0,0,0,0,0,1,0,1,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39080,0,3,3,2,0,0,0,0,0,1,5,0,0,0,0,2,1,0,0,6,0,5,0,0,0,0,2,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,7,1,0,0,0,5,2,1,0,0,1,0,0,1,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,0,0,0,Class_6
+39081,0,1,4,4,0,0,0,0,0,0,5,1,0,0,0,1,0,0,0,0,2,1,1,2,0,0,47,2,0,0,0,0,0,1,2,0,0,0,1,2,0,0,0,1,0,9,0,0,0,0,1,0,0,3,0,0,0,0,0,2,4,1,0,0,2,0,0,2,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39082,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,2,0,1,0,8,0,11,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,6,0,4,0,2,7,0,1,0,1,1,0,0,0,0,1,0,2,12,0,1,0,0,1,8,0,2,1,0,0,0,1,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,Class_6
+39083,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39084,1,0,0,0,0,0,0,6,0,0,0,1,0,0,0,1,0,2,0,0,0,2,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,3,0,2,0,0,0,1,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+39085,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39086,0,0,1,3,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39087,0,2,6,7,0,0,0,0,0,1,9,1,0,2,0,5,0,2,0,0,4,2,0,2,0,3,7,2,0,2,1,1,3,0,1,0,0,1,0,2,0,0,1,2,2,3,0,3,1,0,2,3,0,11,0,0,0,0,0,0,17,3,0,0,1,0,0,1,1,0,0,4,0,1,0,0,0,0,0,29,0,13,0,0,0,0,0,0,10,0,0,2,0,Class_6
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+39157,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39159,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39161,1,0,3,1,0,0,0,1,0,0,13,0,1,0,0,2,1,0,0,0,0,0,0,3,0,0,5,0,0,2,0,2,0,0,0,0,0,1,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,3,1,0,1,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,20,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39162,0,1,19,5,0,0,0,0,0,0,10,0,0,0,0,1,0,0,1,1,0,1,1,0,0,1,13,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,3,0,13,0,0,3,2,0,0,0,8,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,Class_6
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+39164,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39165,0,0,5,1,1,0,0,0,0,0,19,2,0,0,1,3,0,0,0,0,1,1,0,6,0,1,10,1,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,3,0,0,0,21,0,0,4,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,8,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+39166,0,0,11,2,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,2,0,9,0,1,2,4,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,24,1,0,0,7,0,2,3,0,0,1,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,6,0,1,1,0,0,0,1,0,2,0,0,0,0,Class_6
+39167,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39189,0,0,8,12,0,0,0,0,0,0,19,0,0,1,0,0,0,0,0,0,0,1,0,6,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,2,0,0,0,1,0,8,0,0,0,11,0,0,0,1,0,2,0,0,1,0,0,0,0,0,1,4,0,0,0,1,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+39193,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,1,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,9,0,1,0,0,1,0,1,1,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,1,0,0,1,0,Class_6
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+39195,0,1,12,6,0,0,0,0,0,3,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,4,0,0,4,0,0,3,0,15,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39196,0,0,9,1,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,8,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,0,6,1,0,1,0,0,0,0,3,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39197,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+39198,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39199,0,1,7,5,0,0,0,1,0,1,8,2,0,0,1,4,0,0,0,0,1,2,0,1,0,3,5,3,0,0,2,1,0,0,3,1,0,0,0,0,0,0,0,3,1,6,0,0,1,0,1,1,0,14,0,0,0,0,0,1,10,2,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,4,0,3,1,0,0,0,1,1,0,0,0,0,0,Class_6
+39200,4,0,1,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,1,4,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,2,0,5,0,0,0,0,0,0,0,3,0,1,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+39201,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,4,0,0,0,0,0,2,1,2,0,0,0,0,0,1,1,1,0,2,0,1,0,1,0,14,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,2,1,2,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+39202,1,0,3,2,0,0,0,0,0,0,7,1,1,0,0,1,0,1,0,0,0,0,0,10,0,0,20,1,0,0,1,2,1,0,0,0,0,0,0,0,1,0,1,0,0,3,1,0,1,0,0,3,0,0,0,1,0,1,0,1,9,0,0,0,0,1,4,1,0,0,0,0,0,15,0,4,0,0,0,20,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+39203,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,1,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,1,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,8,0,0,1,0,0,1,0,3,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+39204,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,2,0,2,1,0,0,2,0,2,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,6,0,1,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,5,0,2,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+39205,0,2,9,9,0,0,1,0,0,0,11,1,0,0,0,3,0,0,1,0,1,5,0,0,1,3,7,2,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,2,0,6,0,0,3,5,0,0,1,12,2,0,0,0,0,1,1,2,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+39206,5,1,1,3,0,0,0,2,2,0,0,0,0,0,1,3,0,2,0,0,0,3,0,0,4,3,3,1,0,0,0,0,1,1,0,0,0,1,0,0,0,23,0,2,0,0,0,3,0,0,0,0,1,5,0,1,0,0,3,0,0,2,0,1,2,1,3,1,0,1,1,1,0,1,0,3,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39207,0,0,0,7,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,2,0,0,0,2,2,3,0,0,0,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+39237,0,0,15,5,0,1,0,1,0,0,7,1,0,0,0,2,0,0,0,0,1,1,0,14,0,1,15,1,0,0,0,2,0,0,0,1,0,0,0,0,0,1,1,1,0,6,0,0,0,0,1,1,0,12,4,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,1,3,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39238,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39239,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,4,0,0,0,2,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39240,0,0,1,0,1,0,0,0,0,0,8,0,1,0,0,1,0,2,0,1,0,3,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,1,0,6,0,0,0,0,3,0,0,0,1,0,0,0,1,0,1,0,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,1,0,Class_6
+39241,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,3,0,0,0,1,0,3,1,0,0,0,2,0,0,0,0,0,0,8,1,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39242,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+39243,0,1,17,11,0,0,0,0,0,0,8,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,3,3,0,0,0,2,0,0,5,0,0,0,0,0,0,0,0,1,0,4,0,0,3,0,0,0,0,26,1,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,5,0,0,0,0,0,0,2,0,0,3,0,Class_6
+39244,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,4,0,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,1,1,0,0,0,2,0,0,0,3,0,0,0,2,0,1,0,0,10,0,0,1,1,0,1,8,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,1,0,0,1,0,0,Class_6
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+39246,0,1,16,22,0,0,1,0,2,0,12,1,0,0,0,11,0,1,1,0,12,2,0,3,0,7,20,3,2,0,1,0,0,0,1,0,3,1,0,2,0,3,1,4,0,20,0,1,0,0,0,3,0,14,1,1,1,2,0,0,9,0,0,3,1,2,0,0,0,0,1,0,0,3,0,1,0,0,0,11,0,0,0,0,0,0,0,2,1,0,0,4,0,Class_6
+39247,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,3,1,1,0,0,0,0,0,1,0,3,0,1,3,0,0,0,1,0,0,0,0,1,0,2,0,11,0,2,0,0,0,1,1,0,0,0,0,2,0,0,1,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
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+39287,0,0,5,2,0,0,1,0,0,0,6,0,1,0,0,1,0,0,0,1,1,2,0,0,0,0,5,5,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,4,0,0,1,0,12,0,0,0,1,0,0,1,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,4,0,3,0,0,0,0,0,0,2,1,0,0,0,Class_6
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+39426,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,5,0,3,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,2,16,0,0,1,0,0,10,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39427,0,0,0,1,0,0,0,0,0,8,0,0,0,2,2,2,2,0,0,2,0,1,2,1,1,0,0,0,0,0,0,1,0,0,1,1,1,1,0,0,2,1,3,2,0,1,0,0,0,33,0,0,1,2,1,0,0,0,0,1,0,1,0,0,4,1,0,0,1,4,1,0,0,0,1,0,0,0,0,1,2,0,0,0,3,0,3,1,2,0,0,1,0,Class_6
+39428,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,1,1,2,0,1,0,1,0,5,0,1,0,0,0,0,0,0,1,0,8,0,0,0,0,2,0,10,0,0,0,0,1,0,5,0,0,0,0,2,0,0,5,2,0,8,0,0,0,2,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,1,0,7,0,0,0,2,0,0,0,0,0,0,Class_6
+39429,0,1,13,7,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,2,0,0,0,1,14,0,0,0,0,0,1,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,1,Class_6
+39430,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+39431,0,0,2,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,4,0,3,0,1,0,0,0,4,0,1,0,1,0,0,0,0,1,1,0,1,0,1,0,1,1,0,0,0,0,6,2,0,0,0,0,8,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39432,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,5,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,17,0,0,0,0,1,5,0,0,0,0,0,0,2,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,Class_6
+39433,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,1,1,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,3,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,Class_6
+39434,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,1,0,3,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,0,0,1,0,3,0,0,0,0,1,1,2,0,1,0,1,0,0,1,0,0,0,6,0,0,1,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39435,0,0,13,7,0,0,1,0,0,0,5,0,0,2,0,0,0,0,1,0,0,0,0,1,0,3,3,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,8,0,0,3,0,0,0,0,6,0,1,0,1,0,0,7,2,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,2,0,2,0,Class_6
+39436,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,3,0,2,0,0,0,2,0,1,0,1,0,0,0,1,0,0,0,1,1,2,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39437,0,0,0,0,0,0,0,0,0,0,2,0,1,9,0,4,0,0,2,4,0,8,0,2,1,2,0,0,0,0,0,2,1,0,0,5,0,0,1,0,0,3,0,2,2,0,0,3,1,2,2,2,1,3,1,0,1,0,0,12,0,4,0,6,0,2,9,0,0,9,0,1,0,2,0,1,0,0,0,2,1,0,1,0,1,0,8,1,1,1,0,0,2,Class_6
+39438,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,1,0,0,2,0,0,12,0,0,0,0,0,1,5,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,Class_6
+39439,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,2,1,4,0,0,0,0,0,1,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+39440,1,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,3,0,4,1,1,0,0,0,0,0,2,1,0,0,0,1,0,3,0,0,3,1,1,0,0,0,2,1,3,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,1,5,0,1,2,0,0,0,0,1,0,0,0,8,0,0,0,1,0,1,3,15,2,0,0,0,0,3,Class_6
+39441,1,1,0,5,0,0,0,0,0,0,7,0,0,0,0,3,2,0,0,0,2,0,0,0,0,1,7,1,0,0,1,6,0,0,1,0,0,0,0,0,0,0,0,2,1,3,0,0,1,0,0,2,0,9,0,2,0,3,0,0,12,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+39442,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39443,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,4,0,3,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,8,0,2,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+39444,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,3,0,1,12,0,0,0,0,0,1,2,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,2,1,5,1,1,0,0,0,0,0,0,0,0,0,2,8,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39445,5,0,0,0,0,0,0,0,1,0,1,0,2,3,0,1,0,2,0,0,0,2,0,1,7,0,0,0,2,0,0,1,4,3,0,0,0,0,0,0,0,3,0,1,0,2,0,1,0,0,0,0,0,1,2,0,0,0,5,1,0,3,1,2,1,0,13,1,0,0,0,0,0,2,1,1,0,0,11,0,0,0,0,0,0,3,1,0,0,1,1,0,0,Class_6
+39446,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,12,0,0,0,0,0,0,3,2,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39447,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,2,0,5,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,0,2,0,0,17,0,0,0,2,0,1,1,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39482,0,1,11,10,0,0,0,2,0,0,13,0,0,0,0,1,0,0,0,1,3,1,0,0,0,0,7,2,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,1,0,25,0,0,0,0,0,0,0,6,0,1,0,0,0,0,5,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+39483,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,8,0,7,2,0,0,0,0,1,1,3,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,Class_6
+39484,0,0,15,15,0,0,0,0,0,1,7,0,0,0,0,0,0,1,0,0,0,0,0,1,0,9,10,4,0,0,0,2,0,1,4,1,0,0,0,0,0,0,1,1,0,4,0,0,1,0,0,2,1,14,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,2,0,Class_6
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+39486,0,0,3,3,0,0,0,0,0,0,2,0,0,3,0,1,0,0,0,0,4,1,0,9,0,5,1,2,0,0,1,2,0,0,5,1,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,0,10,1,0,3,0,0,13,2,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39487,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,10,0,1,0,0,1,1,14,0,0,0,0,0,17,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,1,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
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+39497,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,9,0,9,0,1,3,0,0,3,0,0,5,2,0,0,0,3,1,5,0,3,0,1,0,1,0,1,0,0,0,8,0,0,4,0,0,12,0,1,0,0,1,1,10,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+39498,0,0,2,2,0,0,0,0,0,0,8,0,2,2,0,4,0,2,1,0,7,1,0,1,0,1,16,2,0,0,0,1,0,0,0,0,0,2,3,1,0,0,0,1,0,2,0,1,1,0,0,9,1,11,0,0,0,1,0,3,1,0,0,0,4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,22,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+39501,0,0,6,3,1,0,0,0,0,0,8,0,0,0,0,2,0,2,0,0,0,2,1,0,0,5,1,1,0,0,1,2,0,0,1,0,1,3,0,0,0,1,0,1,0,1,0,0,5,0,0,0,0,9,0,0,2,0,0,2,6,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,9,0,2,0,0,0,1,0,0,2,0,0,0,0,Class_6
+39502,0,0,6,7,0,0,0,0,0,2,13,0,0,0,0,1,0,1,0,0,0,0,0,6,0,0,10,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,1,0,7,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39503,0,0,3,3,0,0,0,0,0,0,6,0,1,0,0,4,0,0,1,0,0,0,0,1,0,1,4,2,0,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,1,3,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,10,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+39504,0,2,8,6,0,0,0,0,0,0,7,0,0,0,0,9,0,0,0,0,2,2,0,0,0,13,4,3,0,0,0,2,0,1,2,1,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,0,10,1,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,0,Class_6
+39505,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,3,0,1,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,16,0,0,0,0,0,1,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+39506,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+39507,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39508,1,1,1,1,0,0,1,0,0,2,2,0,1,0,0,2,0,8,0,6,0,6,0,7,1,3,0,0,1,0,0,0,0,1,3,0,0,2,0,0,1,17,0,6,0,0,0,0,2,1,0,1,0,3,2,0,3,1,0,7,0,5,0,0,0,1,2,0,0,1,0,0,1,2,1,2,0,0,0,1,1,0,5,0,0,0,2,2,2,5,0,3,0,Class_6
+39509,0,0,0,3,0,0,0,0,0,0,1,0,1,1,0,0,0,2,0,0,0,2,0,2,0,8,0,0,0,0,2,0,0,0,2,0,0,3,0,1,0,4,0,0,0,0,0,2,0,1,0,0,0,4,0,0,0,2,0,9,0,0,0,1,0,1,4,0,0,0,0,0,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,2,0,Class_6
+39510,0,0,0,1,2,0,0,0,0,0,0,0,1,1,0,3,1,2,0,1,0,5,0,2,4,1,0,0,1,0,0,0,4,3,1,0,1,1,0,0,0,16,0,4,0,0,5,1,2,1,0,0,1,3,4,0,0,0,0,1,0,6,5,0,3,3,1,0,0,7,2,1,0,0,1,0,0,0,10,1,1,0,0,0,0,0,1,0,7,0,0,5,0,Class_6
+39511,0,0,3,2,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,2,0,10,0,13,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,3,0,0,0,1,0,4,1,0,0,1,0,0,1,1,0,0,0,0,5,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,Class_6
+39512,0,0,10,16,0,0,0,0,0,0,18,0,1,0,0,3,0,1,0,0,1,1,0,7,0,2,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,2,0,0,9,0,0,0,0,0,0,0,8,0,0,0,8,1,0,1,0,0,1,0,1,1,0,0,0,0,1,0,2,0,2,0,0,0,7,0,5,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39513,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,4,0,1,0,0,0,4,0,2,0,4,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,6,2,0,4,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,2,Class_6
+39514,0,0,3,1,0,0,1,0,0,0,9,0,0,0,0,2,0,0,0,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,4,0,3,0,1,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,0,0,0,0,Class_6
+39515,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,3,0,0,0,1,0,8,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,1,0,0,0,1,3,0,0,3,0,0,6,0,1,0,0,0,0,10,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,6,1,1,1,0,0,1,0,Class_6
+39516,0,0,3,2,0,0,0,1,1,0,9,2,0,1,0,3,0,3,0,1,7,0,0,0,0,0,1,0,0,1,1,3,0,2,1,0,0,1,1,1,0,0,0,3,1,0,0,1,0,1,1,5,0,9,0,0,0,0,1,1,2,1,1,0,0,0,1,1,0,1,0,1,0,45,0,0,0,0,0,23,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+39517,0,0,0,1,0,0,0,2,4,0,0,0,3,0,0,4,1,21,0,1,1,4,1,5,1,7,0,0,2,0,0,0,0,0,9,1,0,2,0,0,2,7,0,8,0,8,0,1,1,0,0,0,0,2,0,1,8,0,2,11,0,5,1,4,1,3,3,1,0,0,4,1,0,4,0,0,0,0,3,0,2,0,0,0,0,0,0,0,5,0,0,1,0,Class_6
+39518,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,2,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,Class_6
+39519,0,0,7,2,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,0,3,0,0,0,0,5,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,0,1,0,1,4,0,0,0,0,10,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39520,0,1,9,4,0,0,0,0,0,1,3,1,2,0,0,6,0,0,0,0,1,1,0,6,0,13,2,2,0,0,1,4,0,0,1,0,0,0,0,1,0,0,0,1,0,3,0,1,2,0,0,0,0,9,0,1,0,11,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+39521,0,0,5,5,0,0,0,0,1,0,6,2,1,0,0,2,0,0,1,0,0,0,0,0,0,0,5,1,0,0,0,1,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,7,0,0,0,3,0,0,2,1,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39522,0,0,22,7,0,0,0,0,0,0,7,0,0,2,1,2,0,0,0,0,0,1,0,2,0,9,2,2,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,16,0,0,0,0,0,0,0,18,0,0,0,1,0,2,2,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,3,0,Class_6
+39523,0,0,2,5,0,0,0,1,0,0,10,0,0,2,3,3,0,1,0,0,0,1,0,1,0,2,11,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,7,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39524,0,0,15,4,0,0,0,0,0,0,9,0,3,0,0,3,0,0,0,0,1,3,0,0,0,3,13,3,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,3,0,16,0,0,2,0,0,0,0,12,0,0,0,0,0,2,0,0,0,0,0,2,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,Class_6
+39525,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39526,0,0,6,8,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,1,1,0,0,0,5,3,4,0,0,0,2,0,1,2,0,0,1,0,0,0,0,0,0,0,18,0,0,1,0,0,0,0,11,0,0,0,0,0,1,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,2,0,Class_6
+39527,0,2,15,11,0,0,0,0,0,1,7,0,0,0,0,0,0,2,0,0,2,1,0,0,0,1,7,1,4,0,0,2,0,0,2,0,0,0,0,0,2,0,0,4,0,2,0,0,4,0,0,0,0,10,0,0,0,0,0,1,2,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,1,0,0,1,0,Class_6
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+39542,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39545,0,1,15,5,0,0,0,1,0,0,8,0,2,0,0,2,0,1,0,0,1,3,1,0,0,1,4,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,8,0,1,1,0,0,0,0,7,0,0,0,2,0,1,3,0,1,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,3,0,0,1,1,Class_6
+39546,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,4,0,0,0,0,0,2,0,6,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,14,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+39547,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,1,0,0,0,1,0,1,0,5,0,0,1,0,1,0,0,2,1,0,0,1,0,1,0,5,1,4,0,1,1,0,3,0,0,0,0,3,0,0,7,0,0,8,0,0,0,0,2,1,0,0,0,3,0,1,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,2,0,Class_6
+39548,1,0,7,3,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,2,0,0,0,0,3,2,3,0,0,0,1,0,0,11,0,0,1,0,1,0,0,0,0,0,7,0,1,4,0,0,0,0,14,0,2,0,0,0,0,0,3,1,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,1,13,0,3,0,0,0,0,0,0,4,0,0,2,0,Class_6
+39549,0,0,5,2,0,0,0,0,0,0,16,1,0,0,0,1,0,0,0,0,2,0,0,7,0,0,8,2,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,0,0,3,1,1,1,1,0,0,1,3,0,1,0,24,0,1,3,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,10,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+39550,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,1,5,0,1,0,3,2,0,0,0,0,0,2,0,0,0,0,0,2,1,0,2,0,0,6,0,0,1,1,0,1,8,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+39551,0,0,8,8,0,0,0,0,0,0,7,0,0,0,0,3,0,1,0,0,2,3,0,0,0,1,8,4,1,0,1,0,1,0,2,0,1,0,0,1,0,0,0,3,0,9,0,0,1,1,0,1,1,20,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+39552,1,0,1,0,0,0,0,8,0,0,1,0,0,1,0,4,0,1,0,1,0,7,0,3,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,4,0,2,0,2,4,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,1,0,0,0,3,0,Class_6
+39553,1,0,4,15,0,0,0,0,0,0,7,0,1,0,0,0,0,1,1,0,2,1,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39554,1,0,9,6,0,0,1,0,0,2,4,0,0,0,1,1,1,0,1,0,1,2,0,0,0,0,4,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,3,0,7,0,1,1,0,0,0,1,9,0,0,0,5,0,2,10,2,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,13,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39555,0,0,14,2,0,0,0,0,0,0,9,0,0,0,5,4,0,0,0,0,1,4,0,3,0,2,3,6,0,0,1,1,1,0,4,0,0,1,0,1,0,0,0,1,1,11,0,0,2,0,0,0,0,25,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39556,0,0,9,4,0,0,0,0,0,0,6,0,0,0,0,3,0,0,0,0,1,0,0,2,0,2,3,3,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,7,0,0,2,0,0,0,0,9,0,0,0,0,0,2,4,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,Class_6
+39557,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39558,0,0,6,4,0,0,0,0,1,1,9,0,0,0,5,3,0,0,0,0,0,2,0,1,0,1,1,1,0,0,0,2,0,1,3,0,0,0,0,0,0,2,1,2,0,9,0,0,3,0,0,1,0,13,0,1,0,1,0,1,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,1,0,0,0,Class_6
+39559,0,0,11,5,0,0,0,0,0,0,7,0,1,0,0,6,0,0,0,0,6,3,0,2,0,1,3,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,27,0,0,0,0,0,0,1,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,2,1,0,0,0,0,0,3,0,0,0,1,Class_6
+39560,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,5,0,1,0,0,0,1,0,3,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,9,0,0,5,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+39561,0,1,6,9,0,0,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,3,0,0,1,0,1,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39562,0,0,9,6,0,0,0,0,0,0,8,1,2,1,0,6,0,0,0,0,1,1,0,0,0,2,8,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,2,0,15,0,0,0,0,0,1,1,19,1,0,0,0,0,0,7,1,0,0,0,1,0,0,0,1,0,2,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39563,0,1,18,5,0,0,0,0,0,0,7,0,1,1,0,8,0,0,0,1,1,3,0,2,0,1,4,1,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,10,0,0,1,0,0,0,0,7,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,2,2,0,0,1,0,Class_6
+39564,0,1,14,4,0,0,1,0,0,1,7,0,1,1,0,0,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,12,0,2,0,1,0,0,3,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,0,0,Class_6
+39565,0,0,2,1,0,0,0,0,0,1,1,1,0,1,2,5,1,0,0,0,0,3,0,23,1,10,0,1,0,0,0,1,0,1,1,2,0,0,0,5,0,4,3,2,0,2,0,1,0,0,0,0,0,5,0,1,5,0,0,16,0,0,0,0,2,1,5,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,Class_6
+39566,0,0,5,2,0,0,0,0,0,0,11,0,0,1,0,6,0,0,0,0,0,1,0,0,0,0,10,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,14,0,0,0,0,0,2,0,9,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39567,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39589,0,2,0,1,0,0,1,2,0,0,12,0,1,1,0,1,1,2,0,2,0,2,0,1,2,3,5,1,1,0,1,0,0,1,3,1,0,0,0,0,0,1,0,1,0,1,0,0,8,0,0,1,0,4,0,0,0,0,0,2,2,3,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,1,Class_6
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+39592,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,4,1,0,15,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,1,Class_6
+39593,1,0,7,6,0,0,0,0,0,0,11,0,0,1,0,1,0,1,0,0,1,1,0,0,0,1,8,1,0,0,1,0,0,0,3,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39594,0,2,2,2,0,0,0,0,0,0,7,0,0,0,0,5,0,3,0,1,1,3,0,1,0,2,8,1,0,0,2,0,0,0,3,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,6,0,6,1,0,0,1,0,0,7,2,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,1,16,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39596,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,3,0,2,1,3,0,1,0,0,1,0,0,4,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,1,8,0,1,0,0,0,2,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,Class_6
+39597,4,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,0,8,0,2,2,1,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,4,4,0,0,0,1,1,0,14,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,9,0,0,0,0,0,1,Class_6
+39598,0,1,1,0,0,0,0,1,1,0,7,0,0,1,0,6,0,0,0,0,1,2,0,1,0,2,3,3,0,0,0,1,0,0,0,0,0,0,0,1,2,1,0,1,0,0,0,1,0,0,0,2,0,20,1,0,0,1,0,1,3,2,0,0,0,0,0,0,1,0,0,1,0,7,0,0,0,0,0,9,0,2,0,0,2,0,0,0,6,0,0,1,0,Class_6
+39599,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,1,1,2,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
+39600,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,1,3,2,0,0,0,1,0,0,0,4,1,1,0,0,1,0,0,11,0,1,0,0,0,0,1,0,0,0,0,5,1,0,3,0,0,4,0,0,0,1,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_6
+39601,0,1,18,1,0,0,0,0,0,0,12,1,1,0,0,4,0,1,0,0,1,2,0,0,0,1,25,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,9,0,0,0,0,0,3,0,14,0,0,0,0,0,0,2,1,0,0,0,1,2,0,0,0,0,0,0,1,1,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39602,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,12,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39603,0,0,1,0,1,0,0,0,3,0,1,0,0,3,2,3,3,0,2,3,0,1,4,14,1,0,0,0,0,0,0,6,4,1,0,0,0,0,0,3,1,4,0,0,0,0,4,1,0,9,0,1,0,0,0,0,15,1,2,2,0,3,0,32,0,1,27,8,1,4,2,2,4,1,5,1,0,1,0,1,1,0,0,0,5,1,1,5,0,0,0,0,0,Class_6
+39604,0,2,1,1,0,0,1,1,0,1,0,0,2,0,0,1,2,4,0,1,0,2,0,6,0,1,0,0,0,0,1,1,0,0,2,0,1,0,0,0,1,1,0,0,1,0,0,1,2,1,0,0,0,4,0,0,2,1,0,17,0,4,0,1,1,3,2,0,0,2,0,0,0,1,1,1,0,0,0,2,2,0,4,0,0,0,1,0,0,0,1,1,1,Class_6
+39605,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39606,0,0,4,10,0,0,0,0,0,0,16,0,0,0,0,0,0,1,0,0,3,4,0,10,0,7,6,5,0,0,1,0,1,3,1,0,0,0,0,0,0,0,1,1,0,11,0,0,0,0,1,1,0,6,1,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,3,0,Class_6
+39607,1,1,5,3,0,0,0,0,0,1,8,0,0,0,0,4,0,4,0,0,1,2,0,0,0,4,8,3,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,2,0,11,0,1,1,0,0,1,0,15,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39609,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,7,0,6,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,12,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
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+39627,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,1,0,1,0,1,1,0,0,0,4,0,0,1,0,0,0,2,0,1,0,0,0,0,1,2,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,3,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,4,Class_6
+39628,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,2,0,5,0,2,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,11,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39629,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,1,0,4,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,6,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,2,8,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,Class_6
+39630,0,1,1,0,1,0,0,2,1,4,1,0,1,1,0,0,0,1,0,1,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,22,0,1,0,0,0,1,1,0,0,0,0,4,6,0,0,0,0,6,0,3,2,2,1,0,1,0,2,2,4,1,0,0,2,0,0,0,1,0,2,0,0,0,0,1,0,0,1,0,1,0,0,Class_6
+39631,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39632,0,0,4,5,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,3,0,5,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,13,0,0,3,0,0,13,1,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39633,0,0,2,2,0,0,1,0,0,0,8,0,2,0,0,6,0,1,0,0,0,2,0,0,0,1,2,2,0,0,0,0,2,0,1,0,0,0,0,6,1,0,1,2,0,1,1,0,7,0,0,0,0,14,3,0,0,0,0,0,1,0,0,0,0,4,1,1,0,2,0,1,0,0,1,1,0,0,0,3,2,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+39634,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,4,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,2,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
+39635,0,0,8,7,0,0,0,0,0,1,7,0,0,0,0,3,0,0,0,0,0,4,0,0,0,6,3,8,0,0,0,2,0,1,1,0,0,0,0,2,0,1,0,2,1,6,0,0,2,0,0,1,0,15,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39636,0,0,7,7,0,0,0,0,0,0,7,3,0,1,0,0,0,0,0,0,6,0,0,13,0,0,8,2,0,0,0,1,0,0,5,0,4,0,0,0,0,2,0,1,2,3,0,0,0,0,0,3,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,13,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39637,0,0,1,1,0,0,0,0,0,0,10,1,0,0,1,0,0,1,0,0,0,1,0,7,0,1,8,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,3,0,0,0,5,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39638,0,0,0,2,0,0,0,0,0,0,11,1,0,0,0,3,0,0,0,0,5,4,0,3,1,2,0,1,0,0,1,1,0,0,3,0,0,3,0,1,0,0,0,1,0,0,0,0,1,0,0,7,0,3,0,1,1,1,0,0,2,1,0,1,1,0,0,0,0,0,0,1,1,6,0,0,0,0,0,28,0,5,0,0,0,1,0,0,4,0,0,1,0,Class_6
+39639,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,1,0,5,3,1,0,0,0,0,0,1,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39640,0,0,5,18,0,0,0,0,0,0,7,0,0,1,0,2,0,1,0,0,0,2,0,0,0,3,0,3,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,6,0,0,0,0,0,0,0,11,0,0,0,0,1,0,2,2,0,0,0,1,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,1,0,0,2,0,Class_6
+39641,0,0,2,5,0,0,1,0,0,0,1,0,1,0,1,3,0,0,0,0,0,1,1,1,0,3,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,2,0,1,0,3,0,0,0,0,0,0,1,7,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39642,0,0,1,2,0,0,0,0,0,1,10,0,0,1,1,3,0,0,0,0,1,0,0,5,0,0,0,1,0,0,3,4,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,5,0,1,1,2,0,1,0,0,2,1,0,0,0,1,3,0,0,0,0,0,0,8,1,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39643,0,0,3,6,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39644,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39645,0,0,5,2,0,0,0,0,0,0,5,0,0,0,0,1,0,2,1,0,0,3,0,0,0,1,2,2,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,1,0,3,0,0,2,0,0,1,1,9,0,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,3,0,Class_6
+39646,0,0,7,4,0,0,0,0,0,2,6,0,0,0,0,4,0,1,0,1,0,2,0,0,0,3,0,1,0,0,1,2,0,1,0,0,0,1,0,1,0,0,0,2,0,4,0,1,0,0,0,0,0,19,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39647,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,2,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,17,0,1,0,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
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+39682,0,0,11,4,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,8,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39684,0,1,5,2,0,0,0,0,0,1,5,0,2,0,0,1,0,3,0,0,0,2,0,1,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,4,0,1,0,1,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,2,3,0,0,0,Class_6
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+39686,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+39687,0,1,1,0,0,0,0,1,0,0,2,0,1,3,0,0,0,0,0,4,0,6,0,2,2,1,0,0,0,0,0,0,0,0,0,2,2,2,0,0,0,4,0,11,0,0,10,0,1,2,0,0,2,3,1,0,1,0,1,0,0,1,1,1,1,2,2,0,0,2,2,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,3,0,1,0,0,0,0,Class_6
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+39698,0,0,2,1,0,0,0,0,0,2,2,0,1,4,4,0,1,1,0,3,0,3,0,10,4,1,0,0,1,0,0,4,2,1,0,1,0,0,0,0,0,10,0,2,0,0,19,5,0,3,0,0,2,4,4,0,3,0,1,3,0,2,1,2,0,1,10,0,0,8,4,0,0,5,1,2,0,1,2,1,0,0,2,0,3,0,2,2,0,0,0,3,0,Class_6
+39699,0,0,0,0,0,0,2,0,1,0,1,2,0,0,0,4,1,0,0,2,0,1,0,2,3,1,0,0,0,0,0,0,1,1,0,0,1,2,0,0,0,6,0,10,0,0,8,0,1,1,0,0,0,8,4,1,1,0,1,1,0,1,0,2,2,1,7,0,0,3,4,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+39700,0,26,1,2,0,0,2,0,0,0,12,1,6,1,0,7,0,8,1,0,0,7,0,0,0,1,2,0,0,0,1,0,0,1,4,0,1,0,0,0,0,1,0,11,0,0,0,4,5,0,0,0,0,9,4,0,0,1,0,1,0,2,0,0,1,11,1,0,1,0,1,1,0,0,4,2,1,5,0,0,0,2,3,0,0,0,0,0,2,2,0,0,1,Class_6
+39701,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,Class_6
+39702,0,0,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39703,7,0,0,0,0,0,1,3,1,1,0,0,0,0,0,0,1,1,0,2,0,2,0,9,3,0,0,0,0,1,0,11,1,0,0,2,3,5,0,1,0,3,0,2,0,0,0,2,2,0,0,0,0,0,1,1,1,0,4,1,0,1,0,2,0,0,16,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,16,0,0,0,0,0,26,Class_6
+39704,4,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,0,Class_6
+39705,1,0,0,3,0,0,0,0,0,0,0,0,1,1,0,4,0,4,0,0,0,3,1,4,0,5,0,0,1,0,0,0,0,0,15,0,0,0,0,0,0,2,0,1,0,2,0,3,1,0,0,2,0,9,0,1,0,2,0,12,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39706,0,0,11,6,0,0,0,1,0,0,10,0,0,1,0,2,0,0,0,0,0,2,3,0,0,2,2,4,0,0,0,1,1,1,4,0,0,0,0,1,0,1,1,3,0,2,1,0,1,0,0,1,0,13,0,1,0,0,0,0,6,1,1,1,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,1,3,0,0,0,1,Class_6
+39707,0,0,1,0,1,0,1,0,1,0,1,0,1,0,0,4,0,3,0,0,0,3,0,3,1,9,0,2,0,0,0,0,0,0,1,0,2,1,0,1,0,6,0,0,0,0,0,0,2,0,0,1,0,9,1,0,2,0,0,17,1,2,0,1,0,2,2,1,0,1,0,0,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39708,0,0,7,4,0,0,0,0,0,0,13,0,1,0,0,4,0,7,0,0,3,2,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,1,0,3,0,1,2,0,0,0,0,10,0,0,0,4,0,0,4,0,0,1,0,2,0,0,0,2,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39709,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,0,2,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+39710,0,0,13,3,0,0,0,0,0,1,8,0,0,2,0,7,0,1,0,0,5,1,0,2,1,6,10,1,0,1,0,0,0,2,1,0,0,0,0,2,0,0,0,0,1,7,1,0,0,1,0,4,0,14,1,0,1,0,0,2,2,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,3,0,1,0,2,0,0,0,0,1,1,0,3,0,Class_6
+39711,0,9,0,0,0,0,0,1,0,0,8,0,0,0,0,1,0,5,2,1,0,1,1,4,0,0,3,1,0,0,1,0,0,0,8,0,0,0,0,1,0,0,0,1,0,0,0,0,11,0,11,1,0,1,0,0,0,3,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,2,1,1,0,0,0,5,0,3,0,0,0,0,0,0,3,3,0,0,0,Class_6
+39712,0,0,0,0,0,0,0,0,0,0,8,1,0,1,1,1,0,0,1,0,2,2,0,0,0,0,4,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,2,0,0,0,0,1,0,0,0,9,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,29,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39713,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,1,0,11,0,0,0,3,0,10,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,12,1,1,0,0,1,1,0,0,0,0,1,7,3,0,5,0,0,18,0,3,0,3,1,0,2,1,0,4,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39714,0,3,12,14,3,0,1,0,0,0,7,0,1,1,1,4,0,1,0,0,1,3,0,0,0,1,3,5,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,1,0,4,0,2,2,0,0,0,0,16,1,0,0,1,0,0,3,1,0,0,3,2,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39715,0,1,0,0,0,0,0,2,0,0,0,2,0,1,0,4,0,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,3,0,0,1,1,0,2,0,7,0,1,0,0,0,0,0,0,0,2,0,5,0,1,6,1,0,8,1,1,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,4,0,Class_6
+39716,0,0,11,7,0,0,0,0,3,0,8,0,0,0,0,2,0,3,2,0,1,1,0,0,0,8,13,0,0,0,0,2,1,2,7,0,0,1,0,0,0,0,0,2,0,14,0,0,2,0,0,2,0,12,0,0,0,0,0,1,6,3,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39717,0,0,1,4,0,0,0,0,0,0,0,0,1,1,1,3,0,2,0,0,0,1,0,1,0,2,0,1,0,0,2,0,0,0,2,0,1,0,0,1,0,1,0,0,0,0,0,2,3,0,0,0,0,4,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+39718,0,4,9,5,0,0,0,0,0,0,8,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,4,2,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,12,0,0,1,0,0,0,0,14,5,0,0,2,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39719,0,1,1,2,0,0,0,0,0,0,10,1,0,1,0,5,0,1,0,0,0,1,0,0,0,7,4,3,0,0,0,2,0,0,2,1,0,0,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,3,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,3,0,Class_6
+39720,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39721,0,0,2,2,0,0,0,1,0,0,10,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,11,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39722,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,0,0,2,0,0,0,0,0,2,1,0,0,0,0,1,1,0,4,0,0,12,0,0,0,0,0,1,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,1,Class_6
+39723,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,1,1,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+39724,0,0,5,4,0,0,0,0,0,1,13,0,0,1,0,3,0,1,0,0,2,0,0,2,0,1,9,3,0,0,2,0,0,1,1,0,0,0,1,0,0,2,1,1,0,10,0,0,1,0,0,1,0,8,0,0,0,1,0,0,1,2,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,9,0,6,0,0,2,0,0,0,0,0,0,0,0,Class_6
+39725,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39726,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,3,0,1,0,1,0,2,0,0,0,0,0,2,0,0,2,0,0,1,0,1,0,0,0,0,8,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,Class_6
+39727,0,0,9,6,0,0,0,0,0,1,5,0,0,0,0,2,0,1,0,0,1,2,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,1,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39728,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,5,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+39729,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39738,2,0,12,4,0,0,0,0,0,1,7,0,3,0,0,4,0,1,1,0,0,1,0,2,0,2,4,1,0,0,1,0,0,0,3,0,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0,5,0,10,0,0,1,0,0,0,3,2,0,1,1,2,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+39739,0,0,8,1,0,0,0,0,0,0,10,0,0,2,0,4,0,0,0,0,7,1,0,0,0,0,20,2,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,1,0,10,0,0,0,0,0,4,0,14,0,0,3,1,0,0,2,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39740,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,4,1,2,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,8,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39741,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,2,4,5,0,3,0,1,0,4,0,1,0,0,0,0,0,7,0,4,1,1,0,1,0,0,0,2,0,1,0,2,0,1,0,1,2,0,0,0,1,0,6,0,0,7,0,0,0,2,0,3,8,1,0,6,0,2,0,0,0,4,0,0,0,0,0,0,1,0,3,0,15,0,1,0,0,0,3,Class_6
+39742,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39743,0,0,1,3,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,1,0,0,0,5,3,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,2,0,0,9,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,2,0,Class_6
+39744,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+39745,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+39746,0,0,5,1,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,1,0,0,8,0,3,8,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,9,0,0,0,7,0,1,0,0,0,1,3,0,1,0,0,0,0,1,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39747,0,0,6,8,0,0,0,0,0,1,6,0,0,1,0,1,0,1,0,0,1,1,0,0,0,0,2,0,0,0,0,2,0,4,0,1,0,0,0,0,0,1,0,0,0,5,0,0,2,0,0,0,1,5,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,Class_6
+39748,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,1,0,8,0,1,0,0,0,0,0,0,2,0,0,1,0,0,1,13,0,0,0,0,0,1,0,0,0,0,1,2,0,0,1,0,0,14,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,Class_6
+39749,0,0,11,1,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,6,1,0,0,0,2,20,4,0,0,0,2,0,0,3,1,0,0,0,1,1,0,1,1,0,8,0,0,0,1,0,1,0,8,0,0,0,1,1,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+39750,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,3,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,1,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,1,Class_6
+39751,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,5,0,0,0,0,0,3,0,2,1,1,0,2,0,0,0,0,1,1,0,0,0,0,0,2,1,7,0,1,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,12,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+39752,2,0,0,0,1,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,2,0,2,3,1,0,0,1,0,0,0,2,1,1,0,0,0,0,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,2,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+39753,0,3,14,4,0,0,0,0,0,0,9,1,0,0,0,3,0,3,0,0,0,2,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,2,0,1,0,1,0,2,0,3,0,0,1,0,0,1,0,8,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,7,0,0,0,0,0,2,2,0,1,0,0,Class_6
+39754,0,0,4,1,0,0,0,0,0,0,15,0,0,1,0,1,0,1,0,0,2,1,0,7,0,1,6,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,9,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39755,0,0,7,14,0,0,0,0,0,0,3,0,0,0,0,3,0,3,0,0,0,2,0,15,0,2,5,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,3,0,3,1,1,1,0,0,2,0,11,0,1,0,1,1,0,4,0,1,0,0,0,10,0,0,0,0,0,0,12,0,0,0,0,0,1,0,2,1,0,0,1,0,0,0,0,0,0,0,Class_6
+39756,0,1,3,4,0,0,0,0,0,0,4,0,0,0,0,6,0,1,0,0,2,1,0,0,0,1,3,0,0,0,0,0,0,1,2,0,0,1,0,3,0,1,1,0,0,1,0,2,0,0,0,0,0,9,0,1,0,0,0,0,2,0,0,0,0,4,1,0,0,1,0,1,1,0,1,1,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39757,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,11,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,13,0,2,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+39758,0,1,1,2,0,0,0,0,0,0,12,0,0,0,1,3,0,1,1,0,0,3,0,0,0,1,2,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,2,1,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,11,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+39759,0,0,13,2,0,0,0,0,0,0,8,1,0,0,0,3,0,0,1,0,1,0,0,0,0,1,7,3,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,1,11,0,0,0,0,0,0,0,9,0,0,1,1,0,1,2,0,0,1,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39760,0,0,6,1,0,0,0,0,3,0,7,0,0,0,0,1,0,0,0,0,0,3,0,1,0,1,7,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,5,0,4,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+39761,0,0,13,7,0,0,0,0,0,0,16,0,0,0,0,3,0,0,0,1,7,2,0,0,0,0,11,1,0,0,1,0,1,1,2,1,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,1,9,0,0,0,0,0,0,1,1,0,0,1,1,0,2,0,1,0,0,0,1,0,0,0,0,0,2,0,4,0,0,0,0,0,0,1,0,0,2,0,Class_6
+39762,0,2,3,0,0,0,0,0,0,0,7,1,0,0,0,4,0,0,0,0,0,3,0,1,0,4,2,0,0,0,2,2,0,0,2,0,0,1,0,1,1,1,0,0,0,3,0,0,1,0,0,1,1,5,0,0,0,2,1,1,1,1,1,0,0,3,0,0,0,1,0,1,0,7,0,1,0,0,1,16,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39763,0,0,5,6,0,0,0,0,0,0,11,0,0,0,0,0,0,2,0,0,2,1,0,0,0,1,1,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,2,5,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+39764,3,0,1,0,2,0,1,5,0,0,4,2,11,1,0,3,1,1,0,1,0,5,0,6,0,0,0,1,13,0,2,0,2,0,1,2,4,0,0,0,1,4,0,7,0,0,2,0,1,0,0,0,1,5,6,0,5,0,1,14,0,4,1,0,2,7,3,1,0,1,1,0,0,0,2,0,1,0,2,2,0,0,3,0,0,1,0,0,2,0,0,1,0,Class_6
+39765,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,3,0,9,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,12,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+39766,0,0,5,5,0,0,0,0,0,0,9,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,6,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,6,0,0,1,0,0,2,0,10,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39767,1,0,1,2,0,0,0,1,0,0,0,1,0,0,0,6,0,2,0,0,0,5,0,1,1,0,1,0,29,0,0,0,0,1,0,1,0,0,0,1,3,1,0,1,0,2,0,0,0,0,0,0,1,7,5,0,1,0,0,1,0,4,0,0,0,2,3,0,0,1,1,0,1,0,2,0,0,0,1,0,0,0,0,0,1,0,1,0,2,0,0,0,0,Class_6
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+39796,0,0,6,2,0,0,0,0,0,0,19,0,0,0,0,4,0,0,0,0,3,1,0,6,0,1,5,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,2,0,0,4,0,0,1,0,0,2,0,7,0,0,0,8,0,0,0,0,1,1,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,7,0,1,2,0,1,0,0,0,0,0,0,0,0,Class_6
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+39802,0,0,10,7,0,0,0,0,0,0,18,0,0,1,0,0,0,0,0,0,1,1,4,6,0,2,10,1,0,0,0,0,1,0,5,0,1,1,0,3,1,0,0,1,0,6,0,3,1,0,0,0,1,5,0,0,0,5,0,0,6,1,0,2,0,2,2,0,1,0,0,0,0,0,0,1,0,2,0,7,0,0,2,0,0,0,0,0,0,0,0,1,0,Class_6
+39803,0,0,4,3,0,0,0,0,0,0,17,0,0,1,0,1,0,0,0,0,1,0,0,4,0,0,11,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,14,0,0,0,0,0,0,0,1,0,0,0,10,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,5,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+39804,0,0,0,1,0,0,0,0,4,0,9,1,0,0,0,0,0,0,0,0,1,0,2,0,0,1,3,0,0,0,5,1,0,0,2,1,0,0,1,4,0,0,2,0,0,0,0,2,0,1,0,12,0,0,0,2,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,81,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,8,0,0,0,Class_6
+39805,0,0,9,3,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,4,3,0,2,0,0,6,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,0,13,0,0,1,0,0,5,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,3,1,0,0,0,0,0,0,0,0,1,0,Class_6
+39806,0,1,0,0,0,0,0,0,2,0,0,0,2,0,0,1,0,4,0,0,0,0,0,6,0,0,0,0,1,0,0,3,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,2,1,0,10,0,0,1,0,0,0,6,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+39807,0,1,3,2,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,1,3,1,0,2,0,3,0,3,0,0,0,3,0,1,2,0,2,0,0,0,0,0,0,4,0,1,1,1,6,0,1,0,1,6,0,0,0,0,0,9,0,1,0,0,0,1,3,0,0,0,0,1,2,0,0,1,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,Class_6
+39808,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,3,0,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+39809,4,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,Class_6
+39810,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+39811,0,1,2,4,0,0,0,0,1,0,6,0,0,0,0,2,0,6,1,1,1,2,0,0,0,0,18,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,0,0,0,1,0,0,0,0,7,1,0,0,0,0,0,4,2,0,0,0,3,2,1,0,1,0,0,0,0,0,0,0,0,0,5,0,2,1,0,0,0,0,0,1,0,0,1,1,Class_6
+39812,0,0,2,1,0,1,0,0,0,0,7,0,1,0,0,2,0,1,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+39813,0,0,6,3,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,0,2,0,0,0,4,3,2,0,0,0,0,0,0,1,0,6,0,0,1,1,0,0,0,0,1,0,2,1,0,2,4,1,10,0,1,0,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39814,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39815,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,1,0,0,0,0,0,1,0,4,1,0,0,1,0,0,0,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,Class_6
+39816,4,0,0,0,0,0,0,1,0,0,2,0,1,1,1,1,0,1,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,5,0,0,0,1,0,4,0,0,0,0,0,0,0,0,1,0,0,10,0,0,1,0,0,2,7,2,0,0,0,0,1,0,0,0,0,0,5,2,0,0,0,0,0,0,1,2,3,0,0,0,0,Class_6
+39817,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+39818,0,1,6,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,3,0,1,0,2,0,1,0,0,0,0,0,7,1,1,0,0,2,0,1,1,0,0,0,1,2,0,0,1,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39819,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,9,0,0,2,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39820,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+39821,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,7,0,13,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,6,0,0,12,0,1,0,0,0,1,0,0,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39822,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+39823,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,11,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+39824,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,1,1,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,1,0,0,12,0,0,0,1,1,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+39825,0,0,11,12,0,0,0,0,0,2,15,0,0,2,0,1,0,3,0,0,0,1,0,5,0,0,9,0,0,0,1,0,0,1,3,0,0,1,0,2,0,0,0,0,0,10,0,3,0,1,1,6,0,7,0,0,0,2,0,0,1,2,0,1,0,0,0,2,0,0,0,0,0,4,1,3,0,0,0,12,0,2,2,0,0,0,0,0,1,0,0,0,0,Class_6
+39826,0,0,3,15,0,0,0,1,0,0,11,1,0,1,0,0,0,1,0,0,0,0,0,11,0,0,14,6,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,1,0,3,1,1,1,0,0,0,0,7,0,1,1,1,0,2,3,0,0,0,0,3,3,0,0,0,0,0,0,1,0,2,6,0,0,23,0,3,0,0,0,0,0,0,0,0,0,2,1,Class_6
+39827,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,3,0,3,2,0,0,0,0,0,0,0,5,0,0,0,0,0,4,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,10,0,1,0,0,0,0,3,1,0,3,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,Class_6
+39828,0,0,8,13,0,0,0,0,0,2,10,0,0,0,0,2,0,0,0,0,1,4,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,0,0,0,0,2,0,0,0,0,0,0,Class_6
+39829,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,2,0,7,1,1,0,0,1,0,0,9,0,0,0,3,1,0,4,0,0,2,0,1,2,0,0,9,1,1,0,0,0,1,1,1,0,0,4,0,0,0,0,4,0,1,23,0,0,1,0,0,0,1,0,2,1,0,7,0,0,0,0,0,3,3,33,2,0,0,0,0,10,Class_6
+39830,4,2,2,2,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,0,0,2,0,6,0,2,0,0,0,0,0,0,1,0,11,2,0,0,0,0,0,0,0,4,0,0,0,2,15,0,0,0,0,3,0,0,2,0,0,0,2,2,0,0,0,1,0,0,0,4,0,4,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39831,0,0,12,5,0,0,0,0,0,0,9,0,0,1,1,0,0,0,0,0,1,1,1,0,0,0,6,1,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,8,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,5,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39832,0,0,0,1,0,0,0,0,0,1,9,3,0,0,0,4,0,1,1,0,2,5,0,0,0,6,2,3,0,0,1,2,0,0,1,0,1,0,0,2,1,1,0,2,0,0,1,0,2,0,0,0,0,7,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,1,0,0,4,0,Class_6
+39833,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,11,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,9,0,0,0,0,1,1,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,Class_6
+39834,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+39835,0,0,12,27,0,0,0,0,0,0,8,0,0,0,0,2,1,1,0,0,0,0,0,3,0,2,5,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,7,0,0,2,0,0,0,0,5,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,1,0,1,1,1,0,0,8,0,4,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39836,0,1,14,4,0,0,0,0,0,1,10,1,0,1,1,5,0,0,0,0,0,1,0,1,0,1,11,3,0,0,0,2,0,1,0,2,0,0,0,1,0,0,0,1,0,9,1,0,2,0,1,0,0,12,0,1,0,1,0,1,2,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39837,0,0,16,2,0,0,1,1,0,2,9,0,1,0,0,1,0,1,0,0,2,1,0,2,0,0,9,4,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,2,0,12,0,0,0,0,0,2,1,17,1,1,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39838,2,1,0,1,0,0,0,2,0,0,1,0,1,0,2,1,1,11,0,1,0,5,0,6,2,0,0,0,0,0,0,6,0,0,1,4,1,1,0,1,0,2,0,2,0,0,0,1,0,0,0,0,0,12,0,0,4,0,0,1,0,2,0,1,0,14,12,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Class_6
+39839,0,0,8,1,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,0,2,0,1,0,1,6,2,0,0,0,2,0,0,3,0,0,2,0,0,0,0,0,2,0,6,0,0,0,0,0,0,0,10,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+39840,0,0,7,9,0,0,0,0,0,1,8,0,1,0,0,1,0,0,0,0,0,1,0,1,0,3,1,0,0,0,0,0,1,0,5,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39841,0,0,14,7,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,4,2,0,0,0,2,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,1,0,0,2,0,0,0,2,17,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,6,0,Class_6
+39842,0,0,0,2,0,1,0,0,2,0,6,2,0,0,0,0,0,0,1,0,2,0,1,15,0,0,0,0,0,0,2,3,0,0,1,0,3,1,1,3,0,0,1,0,0,0,0,0,0,1,0,6,0,1,0,2,0,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,1,64,0,2,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,Class_6
+39843,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,1,0,8,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,0,1,0,0,2,1,0,0,0,0,0,7,0,0,4,0,0,4,0,1,0,0,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,4,0,0,2,2,0,0,0,2,0,Class_6
+39844,0,0,2,4,0,1,0,2,0,0,7,0,0,1,0,5,0,3,1,0,2,5,0,0,0,0,3,0,0,0,0,1,2,0,1,1,0,0,0,0,0,0,0,0,0,3,1,0,1,0,0,4,0,5,0,0,0,0,0,1,2,0,0,0,2,0,0,0,0,1,0,0,0,1,0,1,1,0,0,2,0,3,0,0,0,0,1,0,3,0,0,0,2,Class_6
+39845,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,2,0,1,0,0,0,1,0,5,2,5,0,0,0,0,0,2,0,0,1,1,0,1,0,0,0,23,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,4,0,3,0,0,2,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,2,Class_6
+39846,0,1,9,1,0,0,0,0,0,0,13,1,0,0,0,3,0,0,0,1,0,2,0,9,1,0,2,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,2,1,8,0,0,0,0,0,2,0,7,0,0,0,6,0,1,2,0,0,2,0,1,1,0,0,1,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39847,0,0,1,16,0,0,0,0,1,0,7,0,0,1,0,1,1,0,0,0,0,0,0,0,0,2,3,1,0,0,0,1,0,1,2,0,0,0,0,0,1,0,0,0,0,4,0,0,3,0,0,1,0,3,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,0,6,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39848,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39849,0,0,3,4,0,0,0,1,0,0,20,2,0,0,0,0,0,1,0,0,1,0,0,4,0,2,12,1,0,0,0,1,0,0,4,0,0,0,0,2,0,0,0,0,0,2,0,0,1,0,0,0,0,2,1,0,0,5,0,0,3,0,0,1,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39850,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,2,1,1,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,5,2,0,2,0,0,4,0,0,0,2,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,2,1,0,0,0,0,0,Class_6
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+39855,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39858,1,1,8,1,0,0,0,0,0,0,18,0,0,1,0,0,0,0,0,0,1,0,0,8,0,0,8,2,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,11,0,0,1,0,0,0,1,9,0,1,0,9,0,1,0,0,0,1,1,1,1,0,0,1,0,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39859,0,0,4,9,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,6,0,1,2,1,0,0,0,0,0,0,10,0,0,0,0,2,0,1,0,2,0,2,0,1,0,0,0,0,0,11,1,1,2,0,0,12,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39860,0,0,9,2,0,0,0,0,0,0,14,0,0,3,0,5,0,0,0,0,0,1,0,2,0,1,15,2,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,14,0,0,1,0,0,1,0,15,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+39861,0,0,0,9,1,0,0,0,0,0,10,0,1,0,1,5,0,1,1,0,1,4,0,0,1,3,1,3,0,0,1,1,0,1,1,2,0,0,0,0,0,1,0,5,0,3,1,0,0,0,0,2,0,10,1,0,0,0,0,0,1,4,1,0,0,1,0,0,0,4,0,0,0,1,1,0,0,0,1,3,0,1,0,0,1,0,0,1,0,0,0,0,0,Class_6
+39862,0,0,0,0,0,0,0,0,0,4,0,0,3,3,0,3,0,0,0,1,0,2,0,3,0,9,0,0,1,0,0,0,1,1,0,1,1,2,0,0,1,1,2,0,0,0,0,0,1,0,0,3,0,0,0,1,5,0,1,7,0,0,0,0,0,0,0,1,0,1,2,0,0,0,1,1,0,0,1,0,0,0,6,0,1,0,1,1,0,0,2,3,0,Class_6
+39863,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,3,1,4,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39864,0,2,18,2,0,0,0,0,0,0,11,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,11,0,0,0,1,1,0,0,1,0,0,2,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,7,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+39865,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,3,0,4,2,2,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,19,0,2,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39866,0,0,11,3,0,0,0,1,0,0,10,0,0,4,0,3,0,0,0,1,0,2,0,0,0,4,9,2,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,2,1,7,0,0,3,0,0,0,0,16,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,5,0,0,0,0,0,0,0,2,1,0,0,1,0,Class_6
+39867,1,0,2,17,0,0,0,0,0,0,8,0,0,0,0,7,2,0,0,1,0,3,0,2,0,6,0,3,0,0,1,0,1,1,7,1,0,2,0,1,0,0,0,2,0,1,0,1,2,0,0,0,0,5,2,0,0,1,0,0,2,2,0,0,0,5,1,0,0,0,0,0,0,1,0,0,0,0,1,7,0,2,0,0,0,1,0,0,0,0,0,1,0,Class_6
+39868,0,0,0,0,1,0,1,0,0,3,2,0,1,1,3,1,0,0,0,0,0,1,0,9,3,0,0,0,0,0,0,2,2,0,0,0,0,3,0,0,1,4,0,5,0,0,13,3,0,0,0,0,1,0,3,0,0,0,1,2,0,0,0,1,1,0,5,0,0,3,1,1,0,4,0,2,0,1,2,1,2,0,1,0,2,0,0,0,0,0,0,1,1,Class_6
+39869,0,0,8,2,0,0,0,0,0,0,6,1,0,0,0,3,0,2,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,1,19,2,3,0,0,0,2,0,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,0,3,2,0,0,0,Class_6
+39870,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39871,0,0,20,4,1,0,1,0,0,1,11,0,0,0,0,4,0,1,0,0,3,2,0,1,0,2,5,6,1,0,0,0,1,1,2,0,0,0,0,0,1,0,0,4,0,15,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39872,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+39873,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,6,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,0,4,0,2,2,0,0,1,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,1,0,Class_6
+39874,0,2,2,2,0,0,0,0,0,0,0,0,1,0,0,1,5,0,0,0,0,3,0,1,0,3,0,1,0,0,1,4,0,0,1,1,0,0,0,0,0,2,0,1,0,3,0,3,0,1,2,0,0,9,2,0,2,0,0,11,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,Class_6
+39875,0,0,0,0,0,0,0,0,0,0,11,0,1,0,1,4,1,1,0,0,1,0,0,0,0,0,3,0,0,0,0,2,0,0,3,0,0,3,0,3,0,0,2,3,0,1,0,0,6,0,0,0,0,6,2,0,1,1,0,0,1,1,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,3,2,0,0,0,Class_6
+39876,0,3,1,0,0,0,0,0,1,0,9,0,0,0,0,2,0,1,0,1,2,3,0,0,0,0,13,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,7,1,1,0,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,2,0,Class_6
+39877,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39878,0,2,3,4,0,0,0,0,0,0,10,0,0,0,0,5,0,1,0,0,0,0,0,4,0,0,3,1,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,2,0,4,0,1,4,0,0,0,0,8,0,0,1,0,0,1,4,1,0,0,1,0,3,0,0,0,0,0,0,0,0,5,1,0,0,10,0,4,0,0,0,1,0,0,4,0,0,0,0,Class_6
+39879,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,1,0,0,0,0,0,0,3,1,3,0,0,0,0,0,1,0,4,0,1,1,0,0,1,0,3,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,5,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,Class_6
+39880,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+39881,0,0,4,0,0,0,0,2,0,0,0,0,4,2,0,3,0,1,1,0,0,2,0,7,1,1,0,0,1,4,1,2,0,2,0,0,0,0,0,1,1,3,0,4,0,0,0,4,2,1,0,0,23,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,1,1,3,0,0,0,0,0,3,0,Class_6
+39882,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,3,1,2,0,0,0,0,0,3,1,2,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,4,0,0,0,1,9,3,0,0,0,0,0,1,2,0,5,0,0,17,0,1,2,0,0,1,8,8,0,0,4,0,1,0,0,1,0,6,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+39883,3,0,0,0,0,0,0,2,4,0,2,1,0,2,0,1,1,1,0,0,0,3,0,13,2,1,0,0,0,0,0,7,0,0,0,2,2,4,0,0,0,4,0,1,0,0,0,7,0,1,0,0,0,3,0,1,0,0,3,4,0,0,2,2,0,0,15,0,0,8,0,1,2,1,1,0,0,0,1,0,0,0,0,0,4,2,22,0,1,0,0,0,24,Class_6
+39884,0,1,4,13,0,0,2,0,1,0,0,0,0,1,2,2,1,3,2,4,0,6,2,12,1,6,0,1,2,0,10,0,2,1,4,1,0,6,2,1,1,0,0,3,0,1,0,4,3,0,1,1,0,9,3,0,3,0,0,15,2,0,0,1,0,2,5,1,1,3,0,2,1,0,1,0,0,1,0,0,1,2,2,0,0,2,0,1,3,1,0,1,0,Class_6
+39885,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,7,0,0,1,0,0,0,0,0,0,2,2,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39886,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39887,0,0,1,1,2,0,1,1,0,0,8,0,0,0,0,2,0,2,0,0,1,0,1,0,1,0,4,0,0,0,2,0,0,1,0,0,1,1,1,0,0,1,1,1,0,3,0,2,2,0,0,1,1,4,1,0,1,0,0,0,5,1,0,1,0,1,2,0,0,0,0,1,0,1,0,0,1,0,0,5,1,1,1,0,0,0,1,1,2,0,0,0,1,Class_6
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+39889,0,0,0,0,0,0,0,0,0,0,7,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39906,0,2,7,5,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,1,1,0,0,0,2,6,1,0,0,1,2,0,0,1,0,0,1,0,0,0,0,0,1,0,9,0,0,0,0,0,2,0,4,0,1,0,0,0,1,8,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39907,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,5,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,4,0,0,0,0,0,5,0,0,0,0,0,6,0,0,0,1,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+39908,0,0,17,2,0,0,1,0,0,0,10,1,0,0,0,4,1,0,0,0,0,2,0,0,0,2,11,0,2,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,6,0,0,1,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,5,0,0,0,0,0,0,6,0,1,1,0,Class_6
+39909,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39910,0,1,0,0,0,0,0,1,0,0,10,0,0,1,0,0,1,0,0,0,1,1,0,3,0,0,1,0,0,0,0,1,0,4,2,0,2,1,0,1,0,0,0,0,0,3,0,0,0,0,0,7,0,2,1,1,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,12,0,1,0,1,1,7,0,1,0,0,0,1,0,0,0,1,0,0,0,Class_6
+39911,1,0,16,8,0,0,0,0,0,1,9,1,1,0,0,1,0,2,0,0,0,3,0,0,0,0,9,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,8,0,0,0,1,0,2,2,0,1,0,0,0,13,2,0,2,0,0,1,0,0,1,1,0,2,2,0,4,1,0,0,8,0,1,0,0,1,0,0,0,4,0,0,0,0,Class_6
+39912,0,0,5,2,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,1,1,0,2,0,3,1,3,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,4,0,0,1,0,0,1,1,12,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,0,Class_6
+39913,0,0,0,0,0,0,0,4,0,0,0,0,2,6,0,7,1,4,0,1,0,3,0,13,0,1,0,0,1,0,0,2,0,0,1,0,2,0,54,3,1,3,1,2,41,0,0,3,0,0,0,0,0,5,2,0,1,0,0,1,0,1,0,2,1,5,7,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,Class_6
+39914,1,0,0,0,0,0,0,2,0,0,1,0,0,4,0,1,3,0,0,1,0,0,0,6,1,0,0,0,0,0,0,12,1,0,0,7,1,0,2,1,0,3,0,0,3,0,0,10,0,1,0,0,0,6,1,0,0,0,4,0,0,0,0,3,0,0,42,2,0,0,0,1,0,3,1,3,0,0,0,0,0,0,1,0,0,3,43,1,0,0,0,0,8,Class_6
+39915,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39916,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,1,1,0,0,0,0,0,0,0,7,1,0,15,0,2,0,0,0,3,2,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+39917,0,0,5,3,0,0,0,0,1,1,11,0,0,1,0,2,0,0,0,0,2,0,2,0,0,1,26,0,0,0,0,2,0,3,1,0,0,1,0,0,1,0,0,1,0,6,0,0,0,0,0,1,0,7,0,0,0,1,0,2,7,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,10,0,2,0,0,0,0,0,0,2,0,0,1,0,Class_6
+39918,0,0,12,7,0,0,0,0,0,0,18,0,0,0,0,4,0,0,0,0,0,0,0,9,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,7,0,0,0,0,0,0,0,7,0,0,0,5,1,0,0,0,0,2,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39919,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+39920,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,Class_6
+39921,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+39922,0,0,10,4,1,0,0,0,1,0,9,0,1,0,0,3,0,0,0,0,3,0,0,2,0,0,4,1,0,0,0,0,0,1,2,0,0,1,1,0,0,0,1,5,0,4,0,0,2,0,0,0,1,4,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39923,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39924,0,1,0,2,0,0,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,2,0,0,1,0,1,0,1,0,1,0,0,1,0,3,0,0,0,0,4,1,0,0,0,0,0,2,3,0,0,0,3,0,0,0,0,0,2,0,0,0,1,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39925,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,Class_6
+39926,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39927,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,10,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39928,0,0,17,5,0,0,0,0,1,0,9,0,1,2,0,4,0,2,0,0,0,1,0,0,0,2,4,2,1,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,19,0,0,0,0,0,0,0,9,0,0,0,0,0,1,3,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39929,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,3,1,1,0,0,0,0,0,1,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,3,0,Class_6
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+39940,0,1,2,2,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,1,0,1,6,0,0,0,0,8,1,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+39941,0,0,0,1,0,0,3,0,0,0,0,0,1,1,1,1,1,0,0,4,0,4,1,14,1,0,0,0,1,0,0,3,0,0,0,3,0,4,2,0,0,6,0,1,0,0,0,0,1,1,0,0,0,8,0,0,4,0,0,18,0,0,1,2,0,0,5,0,4,0,1,0,3,1,0,7,0,2,0,0,1,0,19,0,1,0,3,0,0,0,2,1,2,Class_6
+39942,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39944,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,2,1,1,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,1,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,3,0,Class_6
+39945,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+39946,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,4,0,2,0,0,0,1,0,18,0,12,1,0,0,0,1,2,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,5,1,0,2,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,3,0,1,0,Class_6
+39947,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,Class_6
+39948,0,0,7,5,0,0,0,2,0,0,0,0,0,1,0,3,0,2,0,0,0,3,0,0,0,13,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,2,0,1,2,2,0,0,0,0,0,11,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,Class_6
+39949,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,3,0,0,8,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+39950,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,11,1,0,0,0,0,0,0,1,0,1,1,0,0,1,0,1,0,0,1,0,0,0,0,4,1,0,0,1,1,0,0,0,0,3,0,0,0,1,0,1,0,0,6,0,4,0,0,0,0,1,0,5,0,0,0,1,0,0,4,0,0,1,0,0,0,0,0,0,0,Class_6
+39951,6,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,10,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,2,7,0,0,0,0,0,13,Class_6
+39952,0,0,16,8,0,0,0,0,0,1,6,0,1,0,0,1,0,1,1,0,0,6,0,2,1,2,6,2,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,1,0,6,0,0,0,0,0,0,0,13,1,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,1,0,Class_6
+39953,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,1,0,0,1,0,6,0,4,0,0,0,0,0,0,2,1,1,0,0,1,0,1,0,2,0,0,0,0,0,5,0,0,0,0,0,1,0,0,5,0,0,14,0,0,0,1,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,4,0,Class_6
+39954,0,0,7,2,0,0,0,1,0,0,9,0,0,0,0,2,1,0,1,0,0,0,0,14,0,2,17,0,0,0,0,3,0,1,3,1,1,1,0,0,0,0,0,2,0,4,0,2,1,0,0,15,1,12,0,0,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,3,1,0,0,0,0,7,0,0,0,0,0,2,0,1,1,0,0,3,0,Class_6
+39955,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,8,0,0,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39956,0,1,8,5,0,0,0,0,0,0,6,1,0,0,0,1,0,1,5,0,4,1,0,0,0,1,10,1,0,0,0,2,0,1,1,1,0,1,0,0,0,1,1,4,0,3,0,0,3,0,0,3,0,12,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,13,1,2,0,0,0,0,0,0,5,0,0,0,0,Class_6
+39957,0,0,3,4,1,0,0,0,0,0,6,0,0,0,1,2,0,3,0,0,0,2,0,1,1,1,6,1,0,0,0,0,0,0,1,1,1,0,0,2,0,0,0,1,0,0,0,2,1,0,0,0,0,16,1,0,0,0,1,1,1,0,1,0,1,1,3,1,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,2,0,0,1,0,Class_6
+39958,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39959,0,0,0,0,0,0,0,1,0,0,2,0,7,1,0,2,0,3,0,0,0,1,0,3,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,1,0,0,4,1,2,0,0,0,1,0,2,1,2,0,0,2,1,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+39960,0,1,6,3,0,0,0,0,1,0,10,1,0,1,0,8,1,1,0,0,8,2,0,3,0,1,41,4,0,0,2,0,0,0,5,1,0,3,0,0,0,0,0,2,0,35,0,0,1,1,0,3,0,16,0,1,0,0,0,1,1,1,1,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,12,0,5,2,0,0,0,0,0,0,1,0,1,0,Class_6
+39961,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,7,0,1,0,0,0,0,0,0,4,3,0,0,0,0,0,0,2,0,0,2,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+39962,0,1,9,7,0,0,0,0,0,0,8,0,2,1,0,5,0,1,0,1,0,4,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,5,0,1,6,0,0,2,0,14,0,2,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+39963,0,0,6,2,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,3,0,0,0,1,0,1,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39964,0,0,15,4,0,0,0,0,0,1,8,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,25,0,0,0,0,0,0,0,11,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+39965,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,14,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+39966,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,3,0,0,0,4,0,3,0,3,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,8,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,11,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39967,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+39988,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,1,1,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,2,1,0,0,0,0,1,0,0,1,0,0,13,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+39989,0,0,2,0,0,0,0,0,0,0,4,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,4,0,0,0,0,0,0,1,1,0,0,0,5,0,1,3,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,8,0,1,0,0,0,0,0,0,1,1,0,0,1,Class_6
+39990,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,3,0,0,1,0,5,0,2,0,0,0,0,1,3,0,0,0,3,0,0,0,2,0,2,1,1,1,0,0,6,0,0,0,0,0,2,0,0,5,0,0,16,0,4,0,1,1,0,13,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,13,0,0,0,2,0,0,0,0,0,4,Class_6
+39991,0,0,5,2,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,3,1,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,12,0,0,0,0,0,0,0,5,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+39992,0,1,3,4,0,0,2,0,0,1,0,0,0,0,0,2,0,1,0,0,0,3,0,1,0,5,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,8,0,0,1,0,0,13,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39993,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,5,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,4,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,0,0,0,1,0,2,1,1,0,1,0,0,0,3,Class_6
+39994,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,5,0,6,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+39995,0,0,2,5,0,0,0,0,1,2,9,1,0,1,0,2,0,1,0,0,3,2,0,3,0,3,3,2,0,0,4,0,0,0,1,0,1,0,1,1,0,0,0,2,1,7,0,0,3,0,0,8,0,3,0,0,2,1,0,0,1,0,1,0,0,0,0,1,1,0,0,2,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+39996,0,0,10,5,0,0,0,0,0,0,6,1,1,0,1,5,0,0,0,0,0,3,0,2,0,2,5,1,1,0,0,0,0,0,1,0,0,1,0,2,0,0,1,0,0,9,0,0,1,0,0,1,0,23,2,0,0,0,0,0,3,2,0,0,1,3,1,0,0,2,0,1,0,3,0,0,0,0,0,2,0,4,0,0,0,0,0,0,1,0,0,2,0,Class_6
+39997,1,1,5,2,0,0,0,0,0,1,18,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,9,1,0,0,0,0,1,0,0,2,1,0,1,0,0,0,1,1,0,6,0,1,0,0,0,0,0,3,0,0,0,9,0,1,2,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,4,0,2,0,0,1,0,0,1,0,0,0,0,0,Class_6
+39998,0,0,8,7,0,0,0,0,0,1,8,0,0,0,0,6,0,0,0,1,3,1,0,2,0,1,11,2,0,0,1,0,0,1,0,0,0,3,0,3,0,0,0,0,0,7,0,0,2,0,0,1,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,7,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+39999,0,1,28,10,0,0,0,0,0,0,7,0,1,0,0,3,0,0,0,0,1,2,0,2,0,2,11,9,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,17,0,0,1,0,0,0,1,21,0,0,0,0,0,2,1,0,0,0,1,0,1,0,0,0,1,0,0,2,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40000,0,1,20,7,0,0,0,1,0,1,7,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,19,1,0,2,0,0,0,1,14,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,1,3,0,3,0,0,0,0,0,0,0,1,1,0,0,Class_6
+40001,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,0,0,4,0,2,0,1,0,3,3,0,0,0,0,0,0,1,0,1,0,0,0,3,0,0,0,4,0,2,0,0,0,2,0,0,0,0,0,4,0,0,1,0,0,0,0,5,0,1,2,0,1,0,1,0,2,0,0,0,0,2,0,0,0,0,0,0,8,1,0,0,1,0,0,0,6,0,0,Class_6
+40002,0,0,3,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,1,7,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+40003,1,1,0,0,0,0,1,1,0,1,0,0,0,0,0,4,0,1,0,0,0,3,0,4,1,1,0,0,1,1,0,0,0,0,0,1,0,1,0,2,0,4,0,2,0,0,0,1,0,0,0,0,0,3,1,0,1,0,1,0,0,0,0,0,1,0,8,0,0,1,0,0,0,3,1,5,0,0,0,0,0,0,4,0,0,0,0,0,0,0,12,4,0,Class_6
+40004,0,0,2,1,0,0,1,0,1,0,7,0,0,1,0,3,0,0,1,0,0,1,0,0,1,0,4,1,1,0,0,0,0,0,1,0,0,0,0,0,3,1,0,2,0,1,0,1,0,0,0,1,0,11,0,0,1,0,0,0,2,2,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,1,1,0,0,0,2,0,0,2,0,Class_6
+40005,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,4,1,0,4,0,2,1,7,2,0,0,0,0,0,0,12,0,0,0,5,1,1,1,1,0,4,0,1,3,0,0,9,0,0,0,0,0,0,2,0,0,0,3,3,0,6,0,1,0,1,38,0,1,2,0,0,0,0,1,1,0,0,0,0,0,0,1,0,2,3,36,0,0,0,0,1,41,Class_6
+40006,0,0,5,0,0,0,0,0,0,0,8,0,0,1,0,2,0,2,0,0,5,3,0,1,0,1,37,3,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,1,9,0,0,1,0,1,0,1,9,0,0,0,0,0,1,1,1,1,0,0,1,0,0,0,1,0,0,3,2,0,0,1,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40007,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,13,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40009,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,1,0,0,0,4,3,2,0,0,1,0,0,0,1,0,1,0,0,2,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,4,2,0,5,0,0,9,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40010,1,0,13,8,0,0,0,0,0,0,10,0,3,1,0,3,0,0,0,0,3,1,0,0,0,2,11,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,1,0,11,0,0,0,0,0,5,0,13,1,1,0,1,0,0,4,1,0,0,0,0,0,0,0,1,1,1,1,5,0,0,0,0,0,6,0,1,0,0,1,0,0,0,2,0,0,1,0,Class_6
+40011,0,1,1,0,0,0,2,4,0,1,1,0,0,0,0,5,4,0,1,1,0,1,0,5,0,2,1,0,1,0,2,7,0,0,0,1,2,6,5,1,2,2,3,1,0,0,1,4,0,2,1,0,1,5,0,0,3,2,2,21,0,5,2,1,2,2,9,0,1,12,4,1,0,0,0,1,0,0,5,3,3,1,0,0,4,1,1,2,2,0,0,0,0,Class_6
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+40013,0,0,1,1,0,0,0,0,1,0,6,5,0,0,0,2,0,0,1,0,1,0,0,0,0,0,14,7,1,0,0,2,1,0,3,0,0,1,0,1,0,0,0,1,0,2,0,1,1,0,0,15,1,3,0,2,0,3,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,9,0,1,0,0,0,1,1,0,2,0,0,0,0,Class_6
+40014,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,3,2,3,0,0,0,5,0,3,1,3,5,4,0,0,0,0,0,1,2,0,0,2,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,6,0,0,4,0,1,0,0,0,2,5,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,1,0,3,0,0,0,Class_6
+40015,0,2,10,5,0,0,0,0,0,0,10,0,0,1,2,1,1,0,0,0,4,1,0,0,0,0,4,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,1,1,0,4,0,1,0,0,0,0,0,11,2,0,0,1,0,0,5,0,1,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,2,1,0,0,0,0,0,1,0,0,0,0,Class_6
+40016,1,1,10,3,0,0,0,0,0,0,1,0,0,0,0,3,0,3,2,0,13,8,0,8,1,7,0,4,0,0,0,0,0,1,12,0,0,0,0,0,1,0,0,1,0,2,0,0,3,0,0,0,0,19,1,0,4,0,0,12,3,6,0,1,0,4,3,0,0,1,0,0,1,0,2,0,0,0,0,4,1,0,3,0,0,0,0,0,0,0,0,4,0,Class_6
+40017,0,0,2,6,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,3,0,4,0,2,0,0,0,0,0,3,1,0,0,0,0,16,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40018,0,0,4,3,0,0,0,0,0,0,6,1,0,0,0,3,0,0,0,0,0,2,0,1,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40019,0,0,1,3,0,0,0,0,0,0,3,1,0,0,1,2,1,1,0,0,0,1,0,10,0,5,0,0,0,0,0,1,0,1,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,1,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40020,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,3,0,0,7,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,2,0,0,3,0,0,0,0,0,0,1,0,0,0,Class_6
+40021,0,0,14,7,0,0,0,0,0,0,14,0,1,0,2,1,0,0,0,0,0,1,0,5,0,2,6,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,8,0,0,0,8,0,0,2,0,0,1,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40022,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,2,3,0,0,0,1,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,3,0,1,3,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,10,1,0,Class_6
+40023,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,2,0,3,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,4,0,0,0,2,6,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40024,0,3,4,0,0,0,0,0,0,0,8,0,0,0,0,6,0,1,0,1,1,0,0,0,0,2,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,1,2,0,0,0,0,2,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40025,0,0,5,0,0,0,0,0,0,0,7,0,0,0,0,4,0,1,0,0,1,0,0,0,0,1,4,0,1,0,1,2,0,0,1,2,0,2,0,0,0,1,0,2,0,4,0,0,0,0,0,0,0,13,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,3,1,0,0,0,Class_6
+40026,0,0,13,6,0,0,0,0,0,0,5,0,0,0,0,1,0,1,0,0,0,2,1,0,0,3,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,2,0,0,0,0,2,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,3,0,Class_6
+40027,1,0,1,0,0,0,0,0,0,0,1,1,1,0,0,3,0,2,0,1,0,4,0,3,0,4,0,1,1,0,0,0,0,2,0,0,0,0,0,2,1,23,0,2,0,0,1,3,1,0,0,0,2,15,0,3,3,0,0,7,0,2,0,1,0,1,3,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_6
+40028,0,1,19,6,0,0,2,0,0,1,8,0,3,1,0,8,0,3,0,0,0,11,0,0,0,3,3,1,0,1,0,0,0,0,4,0,0,0,0,0,0,0,2,6,0,2,0,1,4,0,0,0,0,52,0,0,0,0,1,0,0,1,0,2,2,3,2,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,2,1,Class_6
+40029,0,0,14,3,0,0,0,0,1,0,7,0,0,0,0,6,0,0,0,0,1,4,0,0,0,2,19,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,1,0,12,0,0,2,0,0,1,0,11,0,1,0,0,0,0,6,0,0,0,0,1,1,0,0,2,0,0,1,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40030,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40031,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,2,0,2,0,0,0,2,0,1,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,12,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40032,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,2,0,1,0,0,0,2,0,3,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,9,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,0,12,0,0,0,0,0,2,2,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
+40033,7,0,0,0,0,0,0,1,1,2,0,0,0,0,1,1,1,0,0,3,0,3,0,9,3,0,0,0,1,0,0,7,1,1,0,4,3,2,1,0,0,3,0,0,2,1,0,8,0,0,0,0,0,1,1,0,1,0,6,1,0,2,0,0,0,3,34,0,2,2,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,3,26,0,0,0,0,0,17,Class_6
+40034,0,2,1,1,0,0,1,0,0,0,0,1,0,0,0,1,0,3,1,1,0,0,0,9,1,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,10,0,0,0,0,0,0,2,0,0,2,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40035,0,4,1,0,0,0,0,0,0,0,2,0,1,0,0,3,0,1,0,3,0,1,0,9,0,4,0,0,0,0,0,0,0,1,4,0,0,1,0,0,0,23,0,1,0,0,0,0,0,0,0,0,0,2,0,0,10,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,0,0,2,0,Class_6
+40036,0,0,2,1,0,0,0,3,0,2,2,0,1,3,0,3,1,0,2,0,0,3,0,7,0,0,0,0,1,0,0,5,0,0,0,0,0,1,0,0,1,5,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,2,15,0,0,0,0,0,4,10,0,0,4,1,1,0,1,1,0,0,0,0,1,2,1,0,0,1,0,3,0,1,1,0,0,0,Class_6
+40037,0,0,0,0,2,0,0,1,0,0,0,1,1,1,0,2,1,2,0,1,0,5,0,4,3,0,0,0,1,0,0,1,3,1,2,2,1,2,0,2,0,15,0,4,0,0,7,1,2,2,0,0,1,3,4,0,1,0,0,1,0,7,5,1,3,2,5,0,0,9,3,3,0,0,1,0,0,0,10,1,2,0,0,0,0,1,2,0,9,0,0,0,2,Class_6
+40038,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40039,3,4,1,0,0,0,0,0,0,1,2,0,1,1,0,4,0,6,0,1,0,2,0,5,0,5,0,3,0,0,0,1,0,6,3,0,1,2,0,2,1,26,0,4,0,0,0,1,1,0,0,0,0,10,1,0,4,1,1,1,0,0,0,0,1,0,3,0,0,3,0,1,0,0,0,1,0,1,7,0,0,0,7,0,0,0,2,1,1,3,0,0,0,Class_6
+40040,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,21,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40041,0,0,0,0,0,0,0,3,0,0,11,0,0,1,0,3,2,5,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,2,6,0,1,0,1,3,0,0,4,0,2,0,0,5,1,0,0,1,11,1,0,1,2,0,1,1,2,0,0,0,1,1,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_6
+40042,0,0,12,6,0,0,0,0,0,0,16,0,0,0,0,1,0,2,0,0,0,0,0,8,0,3,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,12,0,1,0,1,0,1,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+40043,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40044,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,2,2,2,0,11,3,0,0,0,1,0,0,14,0,0,1,6,0,1,2,0,0,4,0,2,1,0,0,4,0,0,0,0,0,0,1,0,0,0,7,0,0,2,0,2,0,4,37,0,0,5,0,0,0,0,2,1,0,1,0,0,0,0,0,0,1,3,60,0,2,0,0,0,11,Class_6
+40045,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,2,0,2,0,2,0,2,0,0,0,1,0,0,1,0,0,1,3,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,7,0,1,0,0,0,0,0,4,0,0,Class_6
+40046,0,5,0,3,0,0,0,0,0,0,1,0,0,1,0,6,0,4,0,1,0,2,0,6,0,7,0,0,0,0,0,0,0,0,13,0,0,1,0,1,0,13,0,0,0,0,0,1,1,0,0,0,0,8,0,0,4,1,0,10,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,4,0,1,2,Class_6
+40047,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
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+40049,0,0,16,3,0,0,1,0,0,0,8,1,1,0,0,1,0,1,0,0,1,1,0,1,0,1,4,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,10,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,2,0,0,0,0,Class_6
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+40083,0,0,10,2,0,0,1,1,1,0,9,0,1,1,0,5,0,0,0,0,0,0,0,1,0,1,14,0,0,0,1,1,0,3,1,0,0,0,0,0,0,0,1,3,2,7,0,0,0,0,1,0,0,17,2,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,0,1,0,Class_6
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+40095,0,0,1,0,0,0,0,1,0,2,2,0,1,1,1,1,0,1,0,3,0,1,0,1,4,0,0,0,1,0,0,0,0,1,0,0,0,4,0,0,1,4,0,4,0,0,7,0,1,2,0,0,1,0,3,0,0,0,1,0,0,3,0,2,2,1,2,1,0,2,0,0,0,2,0,1,0,0,2,1,0,0,0,0,5,0,1,0,0,0,0,1,0,Class_6
+40096,0,1,2,3,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,2,0,3,4,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,1,0,1,3,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40097,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,2,0,1,0,0,0,2,0,1,2,6,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,36,0,3,0,1,0,2,0,0,0,0,0,10,1,2,0,0,0,5,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40098,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,5,0,0,0,0,0,0,4,0,0,1,0,0,8,0,1,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+40099,0,0,1,9,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,2,0,19,0,11,0,2,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,1,0,2,1,0,0,4,0,1,9,0,0,12,0,0,0,0,0,1,2,0,0,2,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+40100,0,1,9,0,0,0,0,0,0,0,15,0,0,0,0,4,0,0,0,0,1,2,0,6,0,0,32,0,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,2,1,6,1,0,0,4,0,0,3,0,0,1,2,0,1,0,0,2,0,0,0,1,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40101,1,0,10,1,0,0,0,1,0,0,4,0,1,0,0,3,0,3,1,0,0,2,0,0,0,0,2,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,3,0,1,0,3,2,0,0,0,0,19,2,0,0,0,0,0,1,0,0,0,4,0,1,0,0,1,0,0,1,1,0,0,1,1,0,13,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40102,1,0,1,2,0,0,1,2,0,1,0,0,0,0,2,2,2,4,0,1,0,1,3,3,1,0,0,1,0,0,1,1,2,0,1,2,1,5,0,0,0,2,0,3,1,0,0,1,3,0,4,0,0,0,1,0,1,0,0,12,0,1,1,0,1,0,7,0,2,3,0,0,0,0,1,2,0,0,0,2,2,2,0,0,1,0,3,0,0,0,0,0,1,Class_6
+40103,0,1,19,7,0,0,0,0,0,0,7,0,0,2,0,2,0,2,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,17,0,0,0,0,0,1,1,15,0,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,2,0,0,0,0,1,0,3,0,0,0,0,Class_6
+40104,0,2,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,4,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,6,0,0,13,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40105,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,3,0,5,1,0,0,0,4,0,0,0,2,0,0,0,0,1,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,0,0,2,3,0,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,Class_6
+40106,0,2,3,1,0,0,0,0,1,0,0,0,2,1,1,0,1,4,1,0,0,2,3,7,0,2,0,2,1,0,0,1,2,0,1,1,0,2,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,2,3,1,0,11,0,3,0,0,1,1,7,1,0,2,1,3,0,0,0,0,4,2,0,0,0,1,1,0,0,0,3,1,4,0,0,2,0,Class_6
+40107,0,0,0,1,0,0,0,1,2,0,1,0,0,2,0,1,0,0,0,0,1,3,0,1,0,4,0,0,1,0,0,1,2,0,0,0,0,3,11,0,0,4,0,1,2,0,0,1,0,0,0,0,0,2,0,0,1,0,0,12,0,2,0,1,0,1,10,0,0,4,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,0,2,0,Class_6
+40108,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40109,0,0,2,19,0,0,0,0,1,0,11,0,0,1,0,1,1,4,0,0,0,1,1,8,0,4,1,4,1,0,0,1,1,1,2,0,2,2,0,0,0,0,0,3,0,0,0,1,1,0,3,3,0,7,1,0,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,4,0,1,0,0,0,3,0,2,0,0,0,1,0,0,3,0,1,0,2,Class_6
+40110,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,20,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,7,0,3,0,0,0,0,0,0,0,0,0,5,1,0,2,0,0,10,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,0,0,3,0,0,0,0,Class_6
+40111,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,2,0,3,2,4,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,11,0,3,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,3,0,0,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,Class_6
+40112,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,1,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,12,0,1,0,0,0,0,5,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+40113,0,0,4,14,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,2,0,0,0,0,0,1,3,0,0,1,0,0,0,2,1,0,0,1,0,0,1,0,0,0,0,5,0,1,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,3,0,0,1,0,Class_6
+40114,0,0,13,0,0,0,0,0,0,0,7,0,0,0,0,2,0,0,1,0,3,1,0,0,0,0,5,4,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,1,0,6,0,0,0,0,0,1,0,8,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,1,0,2,0,0,0,0,1,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40115,0,0,36,19,0,1,0,0,0,2,10,1,2,1,0,7,0,1,0,3,0,5,0,0,0,1,3,2,0,0,0,0,3,3,2,1,0,0,1,0,0,1,0,0,0,20,0,0,5,0,0,2,0,19,0,0,0,0,0,3,1,1,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,5,0,0,0,0,2,1,2,0,0,2,0,Class_6
+40116,0,0,7,0,0,0,0,0,0,0,16,0,0,0,0,2,0,0,0,0,1,3,0,7,1,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,2,1,0,0,0,0,0,0,12,1,1,0,8,1,1,1,1,1,1,0,1,1,1,0,1,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40117,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,1,0,2,0,0,0,0,0,0,2,4,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,8,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+40118,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40119,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,1,0,10,0,0,0,2,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,3,12,0,4,0,0,0,1,2,0,0,0,1,1,1,0,0,0,1,14,0,1,0,0,0,0,2,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40120,0,2,4,5,0,0,0,0,0,0,13,1,0,0,0,2,0,0,0,1,2,2,0,1,0,3,2,3,0,0,0,1,0,0,3,0,1,0,0,2,1,5,0,0,0,0,0,0,2,0,0,5,0,5,0,0,0,1,0,0,4,2,0,0,0,2,1,2,0,1,0,0,1,0,0,0,0,0,0,15,0,1,0,0,0,0,0,0,3,0,1,0,0,Class_6
+40121,0,0,0,0,0,0,1,0,0,1,1,1,4,1,0,2,0,2,0,2,0,2,0,5,0,1,0,0,29,0,0,1,0,3,2,1,0,0,0,0,2,3,0,1,0,0,0,3,0,0,0,0,0,6,7,0,1,0,0,3,0,2,0,0,0,2,3,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,Class_6
+40122,0,0,5,0,0,0,0,0,0,0,14,0,0,1,1,1,0,0,0,0,0,0,0,6,0,0,50,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,10,0,1,0,1,0,0,0,7,0,0,0,5,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_6
+40123,0,1,7,2,0,0,1,0,0,1,5,0,0,0,0,4,0,1,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,6,0,0,1,0,1,0,0,0,1,0,6,0,0,4,0,0,0,0,10,0,0,0,0,0,0,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,4,1,0,0,0,Class_6
+40124,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,2,0,2,0,0,0,0,1,0,0,6,0,1,2,1,0,3,5,1,0,2,0,2,2,0,0,5,5,1,0,0,0,6,0,0,0,0,0,12,0,1,0,1,1,1,11,0,0,1,0,2,0,0,1,2,0,0,3,0,3,0,0,1,0,0,0,0,3,0,0,0,0,Class_6
+40125,1,0,12,4,0,0,0,0,0,0,10,0,0,1,0,2,0,0,0,0,0,2,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,7,0,0,1,0,0,0,0,8,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,1,0,0,0,0,0,1,Class_6
+40126,0,0,2,0,0,0,0,0,0,0,7,1,1,0,0,2,0,3,1,0,0,1,0,0,0,0,6,1,0,0,0,2,0,1,3,1,0,4,0,0,0,0,0,5,0,1,0,0,8,0,0,0,0,9,0,0,0,0,0,1,2,0,0,0,1,1,0,0,0,0,0,0,2,2,0,0,0,0,1,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40127,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,4,0,1,0,5,0,2,2,0,0,0,2,0,0,0,1,0,0,3,1,0,0,2,1,2,0,0,0,0,0,1,0,0,0,0,0,2,5,0,4,0,0,0,0,5,0,1,1,1,7,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,Class_6
+40128,0,0,7,4,0,0,0,1,0,2,8,0,0,0,0,3,0,1,1,0,0,2,0,1,0,1,13,1,0,0,0,0,1,1,1,0,0,1,0,1,0,0,0,1,0,12,0,0,0,0,0,5,0,10,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,3,0,0,0,1,Class_6
+40129,3,0,0,0,0,0,5,0,0,1,0,0,1,0,0,0,2,2,0,6,0,6,0,7,0,0,0,1,0,0,0,1,3,0,1,0,1,3,0,1,1,0,0,4,0,0,1,0,2,0,1,0,0,3,2,0,5,1,0,22,0,0,2,0,1,0,3,0,2,2,1,0,0,0,1,2,0,0,0,0,5,1,1,0,0,1,2,0,1,1,0,0,0,Class_6
+40130,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40131,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,1,2,0,0,0,2,0,3,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,0,1,0,1,1,2,0,1,0,0,12,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+40132,0,0,3,0,0,0,0,0,0,0,6,0,1,0,0,2,0,1,0,0,0,1,0,0,0,3,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40134,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,1,1,5,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,1,1,0,1,0,0,0,0,3,0,1,0,1,1,0,0,0,0,0,0,1,1,0,2,0,0,8,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40135,0,0,5,7,0,1,0,0,0,0,20,0,1,0,0,1,0,0,0,0,1,1,0,11,0,3,7,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,3,0,0,1,0,0,1,0,5,0,0,0,18,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40136,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,14,0,3,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,1,1,0,3,0,0,8,0,0,0,0,0,0,13,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40137,0,0,4,8,0,1,0,0,0,0,11,0,0,0,1,0,0,2,0,0,2,1,0,0,0,0,31,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,2,0,0,1,0,6,0,0,0,0,0,0,5,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,1,0,0,0,0,0,1,0,0,0,0,Class_6
+40138,0,1,11,27,0,0,3,0,1,0,9,0,1,0,0,9,0,2,0,0,1,3,0,1,0,8,3,4,1,1,0,2,0,0,5,0,0,1,0,0,0,1,0,8,0,6,0,0,5,0,0,4,0,37,3,0,0,0,0,1,5,4,2,1,1,3,1,0,0,1,0,1,0,4,0,2,0,0,0,8,0,4,0,0,0,0,0,0,4,0,0,5,0,Class_6
+40139,0,0,2,4,0,0,0,0,1,1,0,0,0,1,0,0,1,4,0,0,0,0,0,5,0,5,0,1,0,0,0,0,1,1,7,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,1,1,0,3,0,0,4,0,0,12,0,0,0,0,0,0,0,3,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40140,0,0,1,4,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,2,0,1,0,0,1,31,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,11,0,0,0,0,0,1,0,2,0,0,0,1,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40141,0,1,16,2,0,0,0,0,0,0,9,1,1,0,1,0,0,1,0,0,0,2,0,0,0,0,4,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,7,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40142,0,0,11,12,0,0,0,1,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,5,1,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,8,0,0,2,0,0,0,0,3,1,0,0,13,0,0,2,0,0,2,0,0,1,0,0,1,0,0,1,2,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40143,0,0,1,1,0,0,4,0,0,0,10,0,2,0,0,3,1,3,0,0,0,1,0,0,0,1,9,3,0,0,1,1,0,0,4,0,0,0,0,1,0,0,4,1,0,0,0,0,6,0,0,1,0,6,0,0,0,0,1,1,0,1,0,1,0,2,0,0,0,3,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,1,2,0,0,0,0,Class_6
+40144,7,0,0,0,0,0,0,0,0,2,2,0,3,0,0,2,0,0,0,1,0,3,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,7,0,7,0,0,0,2,0,0,0,0,0,2,0,0,0,0,2,0,0,1,1,0,1,0,3,0,0,2,0,2,0,1,0,0,0,0,13,1,0,0,0,0,0,8,0,0,1,1,0,1,0,Class_6
+40145,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,18,0,19,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,8,0,0,7,0,0,12,0,1,0,0,1,2,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,Class_6
+40146,3,0,0,0,0,0,0,6,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,2,1,0,1,0,0,0,0,2,0,1,1,0,2,0,3,0,0,1,1,2,0,0,0,2,0,0,0,1,2,0,0,1,0,1,0,0,0,0,0,1,1,1,0,0,0,0,Class_6
+40147,0,0,8,6,0,0,0,1,0,1,8,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,3,0,0,2,1,0,1,0,3,0,0,0,0,0,0,2,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40148,1,0,5,2,0,0,0,0,0,0,8,0,0,0,0,4,0,1,0,0,1,0,0,1,0,1,6,0,0,0,2,0,2,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,2,0,0,0,1,0,0,4,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,2,0,3,0,Class_6
+40149,0,0,1,6,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,4,2,0,2,0,8,0,2,0,0,0,0,0,1,2,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,13,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+40150,1,1,1,0,0,0,2,0,1,0,2,0,0,1,0,1,0,1,0,1,0,4,0,2,0,8,1,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,1,2,4,0,0,0,0,6,0,0,1,0,0,0,0,2,0,1,0,4,1,1,0,3,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,1,1,1,0,0,0,0,Class_6
+40151,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,5,0,1,0,0,0,2,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_6
+40152,4,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,2,0,0,0,0,3,0,0,3,0,0,0,0,0,0,2,0,0,0,3,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,4,0,3,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,5,Class_6
+40153,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40154,0,0,0,5,0,0,0,0,0,0,15,0,0,0,0,2,0,0,0,0,0,0,0,4,0,1,2,1,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,8,0,1,2,0,0,2,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40155,0,0,21,13,0,0,0,0,2,0,7,1,1,4,0,8,0,3,1,0,0,3,0,2,0,11,5,11,0,1,0,0,1,0,2,0,0,1,0,0,0,1,0,1,0,14,1,2,1,0,0,1,1,45,0,1,0,0,0,2,16,0,0,1,0,1,1,2,0,0,0,0,0,1,0,0,0,0,0,8,0,2,0,0,0,0,1,0,0,1,0,1,0,Class_6
+40156,0,1,5,3,2,0,0,0,1,0,9,0,1,1,0,3,0,1,0,1,0,1,1,2,1,4,6,4,1,0,0,0,1,3,3,0,0,0,0,0,1,2,0,0,0,9,1,0,1,0,0,1,0,17,0,1,0,0,0,0,2,1,0,0,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,3,0,Class_6
+40157,0,0,9,5,0,0,0,0,0,0,11,0,1,0,0,3,0,0,0,0,3,1,0,0,0,2,7,2,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,7,0,0,2,0,1,3,0,27,1,1,2,0,0,2,2,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+40158,0,0,15,3,0,0,1,0,0,1,15,0,0,0,0,1,0,0,0,0,0,2,0,0,0,3,4,1,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,10,0,2,2,0,0,0,0,5,2,1,0,1,0,0,10,0,0,0,0,1,1,1,1,0,0,0,2,0,0,3,0,0,0,8,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+40159,0,0,0,2,0,0,0,1,0,0,6,1,0,0,0,2,0,0,1,0,0,1,0,0,1,0,3,1,0,0,1,0,1,1,0,1,0,1,0,0,1,2,0,2,0,0,0,1,6,0,0,0,0,1,1,1,1,0,0,0,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0,1,1,3,0,0,0,0,Class_6
+40160,0,0,1,11,0,0,0,0,0,0,7,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,1,7,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,1,0,5,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40161,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,3,0,2,0,0,0,6,1,2,0,0,0,0,2,0,2,1,8,0,0,1,0,2,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,6,0,8,0,1,0,0,0,1,3,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,0,2,0,2,0,Class_6
+40162,0,2,13,11,0,0,0,0,0,1,1,0,2,1,0,4,0,0,0,0,1,4,0,1,0,20,3,2,1,0,0,0,1,1,1,1,0,0,0,2,0,3,0,3,0,7,1,2,1,0,0,3,0,31,0,0,0,0,0,0,0,1,0,2,0,2,1,0,0,0,0,0,2,0,7,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,2,0,Class_6
+40163,1,0,2,0,0,0,0,1,0,0,0,1,0,2,0,4,0,2,0,0,0,7,0,1,1,2,0,1,26,0,0,0,0,1,0,0,0,0,0,0,3,1,0,1,0,1,0,0,0,0,0,0,1,4,5,0,1,0,0,1,0,4,0,0,0,4,3,0,0,3,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,2,0,Class_6
+40164,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,3,0,1,0,4,4,0,0,0,0,0,0,2,0,0,0,0,1,6,15,0,1,0,0,4,1,0,1,0,0,7,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,2,1,4,2,0,2,1,0,1,0,1,0,0,1,0,0,0,2,0,2,1,1,0,0,0,0,Class_6
+40165,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,5,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,10,0,0,1,0,0,0,2,1,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+40166,0,1,10,4,0,0,0,0,0,0,1,0,0,1,0,5,0,1,0,0,0,3,0,23,0,11,0,5,0,0,0,2,0,1,0,8,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,11,0,0,4,0,0,12,0,0,1,2,0,2,16,1,0,0,0,1,0,0,4,0,1,1,0,0,1,0,1,1,0,1,1,0,0,0,0,0,0,Class_6
+40167,0,0,0,4,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,4,1,2,4,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,5,0,0,5,0,0,12,0,0,0,0,0,2,0,2,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40169,1,1,8,7,0,0,0,0,0,2,8,0,1,0,0,6,0,0,0,0,2,0,0,0,1,0,8,1,0,1,0,0,0,0,2,0,0,2,0,0,0,2,0,3,0,6,0,1,1,1,0,0,1,18,0,0,0,0,0,2,3,1,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40188,0,1,26,10,0,0,0,0,0,0,12,0,1,1,0,4,1,0,0,0,1,5,0,0,0,0,10,1,1,0,0,0,0,5,2,0,0,2,2,1,0,1,0,1,0,15,0,0,0,0,0,0,0,22,0,0,0,0,0,0,16,1,1,2,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,8,1,2,0,0,0,0,0,0,4,0,1,0,1,Class_6
+40189,0,1,6,10,0,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40190,0,0,9,13,0,0,0,0,0,1,10,0,0,1,0,2,0,0,1,0,0,1,0,3,0,1,6,0,0,0,4,0,0,0,6,0,0,1,0,0,0,0,0,4,0,3,0,0,1,0,0,0,0,6,0,2,0,0,0,2,1,0,2,0,0,0,6,0,0,0,0,0,5,9,0,0,1,0,1,2,0,0,0,0,2,0,1,0,1,1,0,0,0,Class_6
+40191,0,0,1,9,0,0,0,0,2,0,0,0,2,2,0,1,0,1,0,0,0,1,0,3,1,7,0,0,0,0,0,3,1,0,2,0,0,0,0,3,0,0,1,0,0,2,0,6,0,0,0,0,0,1,0,0,3,0,0,13,0,0,0,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,2,1,0,0,0,1,2,0,Class_6
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+40193,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,1,0,0,1,0,1,0,0,1,0,0,18,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,0,Class_6
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+40195,0,0,8,3,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,5,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40196,0,0,1,3,0,0,0,0,0,6,0,0,1,0,2,3,3,1,0,5,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,3,0,0,0,7,0,4,0,1,0,0,0,1,0,0,2,3,3,0,0,0,1,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,Class_6
+40197,0,0,9,3,0,0,0,0,0,0,9,2,0,0,0,2,0,0,0,0,3,2,0,0,0,1,4,1,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,2,0,5,0,1,0,0,0,2,0,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40198,0,2,0,1,0,0,0,0,0,0,9,1,1,2,1,2,0,1,0,0,0,3,0,3,0,5,4,1,0,1,0,2,0,0,3,0,0,0,0,1,0,1,0,1,0,0,0,0,3,0,2,0,0,3,0,0,0,0,0,1,8,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40199,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40200,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40201,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,1,0,0,0,3,0,2,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+40202,0,0,3,2,0,0,0,0,0,0,15,1,0,1,0,0,0,0,0,0,3,1,0,16,0,0,7,1,0,0,0,0,0,0,2,0,0,0,0,2,0,1,0,1,0,1,0,1,0,0,2,17,0,4,0,0,0,0,0,0,11,4,0,1,0,0,7,0,0,0,0,1,0,0,5,0,0,0,0,7,0,6,0,0,0,0,0,0,6,5,0,1,0,Class_6
+40203,0,0,0,0,0,0,0,0,8,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+40204,0,3,2,3,0,0,1,0,0,0,5,0,0,0,0,1,0,2,0,0,0,3,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,3,7,0,0,0,0,3,1,1,0,0,0,0,2,1,0,0,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40205,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,4,0,0,0,0,0,2,0,0,12,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,Class_6
+40206,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40207,1,0,5,2,0,0,0,0,0,0,8,0,1,1,0,2,0,0,0,0,0,2,0,1,0,0,9,3,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,3,0,7,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,1,0,0,0,0,0,1,0,0,1,0,Class_6
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+40409,0,1,15,5,0,0,0,0,0,0,7,1,0,1,0,2,0,0,0,0,2,5,0,0,0,4,1,5,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,8,0,1,1,0,0,0,0,15,0,0,1,0,1,1,4,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40410,0,0,14,2,0,0,0,0,0,0,12,0,0,1,0,3,0,2,0,0,0,1,0,1,0,3,3,2,0,2,0,0,0,0,0,0,1,0,2,0,0,0,1,4,0,16,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,8,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40411,0,2,8,7,0,0,0,3,0,0,3,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,1,0,9,0,1,0,0,0,1,0,16,0,2,0,2,0,0,5,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40412,0,0,2,1,0,0,1,0,0,1,4,0,0,0,0,0,0,0,0,0,0,2,0,1,2,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,8,0,0,0,2,8,0,0,0,0,1,2,0,0,0,1,0,1,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,3,0,0,0,0,Class_6
+40413,0,0,4,10,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,2,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,2,0,1,0,0,1,0,0,1,0,6,0,0,0,2,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40414,0,0,0,1,0,0,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,2,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40415,0,1,4,4,1,0,1,0,1,0,7,0,0,0,0,3,0,0,0,2,7,1,0,0,0,0,9,3,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,3,0,5,0,0,1,0,0,3,0,9,0,0,0,0,0,1,3,2,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,1,3,0,2,0,0,0,0,0,0,2,0,0,1,0,Class_6
+40416,1,0,2,4,0,0,0,0,0,0,12,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,39,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,0,1,0,9,0,0,1,0,0,1,0,5,0,2,0,2,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,15,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40417,0,0,4,6,0,0,0,0,0,1,7,0,0,0,1,0,0,0,0,2,0,2,0,0,1,1,4,1,0,0,1,2,0,1,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,1,14,3,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+40418,0,0,0,1,0,0,1,0,2,0,8,2,0,0,1,0,0,0,2,0,6,0,0,24,0,0,6,0,0,0,0,3,0,0,0,0,1,1,30,2,0,0,2,0,0,1,0,1,1,1,0,7,0,0,0,4,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,62,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+40419,1,0,1,2,0,0,1,0,0,1,1,0,6,0,2,0,1,2,1,1,0,2,1,9,0,0,0,0,0,0,0,0,0,1,1,4,1,2,0,2,0,2,0,2,0,0,0,3,2,0,0,0,0,3,1,1,1,0,0,22,0,1,1,0,0,0,9,0,2,2,0,0,0,2,1,5,0,1,0,0,2,0,11,0,0,0,0,0,1,0,0,0,3,Class_6
+40420,2,0,1,0,0,0,2,0,1,0,0,0,2,3,0,3,0,1,0,9,0,4,2,7,1,0,0,0,1,0,0,4,0,0,0,0,0,1,24,3,4,0,0,3,1,0,3,3,1,39,0,0,0,1,3,2,2,0,0,0,0,2,0,3,0,1,10,2,2,3,7,2,1,1,0,1,0,1,0,0,0,0,2,0,0,0,1,0,0,6,0,0,0,Class_6
+40421,0,0,8,4,0,0,0,1,0,1,4,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,2,0,5,0,1,2,0,0,0,1,10,0,0,0,0,0,1,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40422,0,0,1,0,0,0,0,0,1,1,2,0,0,1,0,0,4,1,0,0,0,1,3,17,5,0,1,0,0,0,0,8,1,1,1,0,0,2,3,0,0,2,0,1,1,4,0,2,0,4,0,0,0,4,0,0,0,1,0,0,0,1,0,3,0,10,10,0,0,4,4,2,1,0,0,2,0,0,0,0,0,0,1,0,1,44,1,2,1,0,0,0,0,Class_6
+40423,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,3,3,0,2,1,0,0,5,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,2,0,0,4,0,0,1,0,0,0,0,4,10,0,0,7,0,0,0,1,2,1,7,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+40424,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,1,0,0,0,6,0,0,0,0,0,1,3,2,1,0,0,1,0,1,Class_6
+40425,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+40426,0,0,6,5,0,0,0,0,0,0,18,1,0,0,0,1,0,0,0,0,2,0,0,7,0,1,16,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,1,2,0,0,0,0,1,0,0,0,10,0,0,2,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40427,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,2,4,0,0,2,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,3,4,0,12,0,1,0,0,0,1,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,Class_6
+40428,1,0,12,31,0,0,0,0,0,0,6,0,0,1,0,4,0,0,1,0,0,0,0,2,0,9,3,1,0,0,0,0,0,1,1,0,0,1,1,0,0,2,0,5,0,4,1,0,2,0,1,2,0,10,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,Class_6
+40429,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,5,7,0,1,0,2,0,3,0,4,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,3,1,0,1,0,0,16,0,3,0,0,1,3,6,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40430,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,1,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,6,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+40431,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,9,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,6,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+40432,0,0,0,13,0,0,0,0,0,0,0,0,1,0,0,4,0,3,1,2,0,1,0,2,0,2,0,2,0,0,2,0,0,0,13,0,0,0,0,0,0,1,0,2,0,0,0,0,4,0,0,0,0,6,0,0,7,0,0,9,0,2,1,3,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,5,0,1,3,0,0,0,0,1,0,1,0,0,0,Class_6
+40433,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,16,1,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40434,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,7,0,1,0,1,0,0,0,0,2,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,0,12,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40435,0,2,8,0,0,0,0,0,0,0,12,1,0,0,0,0,0,1,0,0,0,3,0,9,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,10,0,0,1,0,0,0,0,4,0,0,0,6,0,1,2,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40436,0,0,5,2,0,0,0,0,1,0,17,0,0,0,0,0,0,0,0,0,1,1,0,6,0,0,6,2,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,6,1,0,0,11,0,1,1,0,0,1,0,1,0,0,0,1,0,0,0,2,0,1,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40437,0,0,3,4,0,0,0,0,1,0,15,0,0,0,1,4,0,0,0,0,1,0,0,2,0,1,9,2,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,5,0,0,1,0,0,3,0,12,1,4,0,1,0,2,10,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,20,0,1,0,0,0,0,0,0,0,8,0,0,0,Class_6
+40438,0,1,12,3,0,0,0,0,0,0,9,0,1,1,0,4,0,1,0,0,1,4,0,1,0,6,5,4,0,0,0,1,0,0,0,2,0,0,0,2,0,0,0,1,0,3,0,0,0,0,0,0,1,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40439,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,2,0,3,0,2,0,1,0,2,0,0,0,4,0,1,1,0,0,4,4,1,0,2,0,2,0,0,0,2,1,0,0,0,0,4,1,0,0,0,1,15,0,1,0,0,0,7,5,0,0,3,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,1,0,0,1,Class_6
+40440,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40441,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,22,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,2,0,0,10,0,0,8,0,1,0,0,0,0,14,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40442,0,0,16,23,0,0,1,0,0,2,8,0,0,1,0,2,0,0,1,0,0,1,0,3,0,1,5,4,0,0,2,1,0,1,9,0,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,0,0,17,0,1,1,1,0,1,1,0,0,0,0,3,0,1,0,1,0,1,1,0,0,0,0,0,0,4,0,1,1,0,0,0,0,0,0,0,1,0,1,Class_6
+40443,0,0,5,12,0,0,0,0,0,0,10,2,0,0,3,2,3,2,0,0,1,0,0,0,0,1,5,0,0,0,2,0,0,1,4,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,10,0,0,0,1,0,0,4,1,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40444,0,0,0,0,0,0,0,0,0,0,1,1,1,2,0,0,0,1,0,0,0,2,0,3,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,7,0,1,1,1,0,1,5,1,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,4,0,Class_6
+40445,0,0,5,1,0,0,0,0,0,0,7,0,0,0,0,6,0,3,0,1,0,4,0,0,0,1,2,1,0,0,1,0,0,0,6,0,0,0,0,2,0,4,0,1,0,0,0,0,5,0,0,0,0,13,2,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40446,0,0,8,3,0,0,0,0,0,0,18,1,0,0,0,2,0,1,0,0,3,0,0,12,0,2,15,2,0,0,0,1,0,0,0,1,0,2,2,1,0,0,0,1,0,0,0,2,0,0,0,7,0,4,0,0,0,14,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,8,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40447,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,2,0,1,0,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_6
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+40449,0,0,1,0,0,0,0,0,0,0,12,0,2,0,0,3,0,1,0,1,0,2,0,4,0,2,0,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,1,0,4,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
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+40471,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,Class_6
+40472,1,0,8,1,0,0,0,0,0,0,5,0,0,1,0,0,0,2,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,5,0,0,0,1,0,0,0,7,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+40483,1,0,7,1,0,0,0,0,0,0,18,0,0,0,0,2,0,0,0,0,0,3,0,6,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,10,0,0,0,0,0,1,2,8,1,0,0,6,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40484,0,0,0,3,0,0,0,0,2,0,1,0,0,0,0,1,0,1,0,0,0,0,0,3,0,8,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,2,0,2,1,0,0,0,0,5,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+40486,0,0,8,6,0,0,0,1,0,0,11,0,1,0,0,1,0,2,0,1,0,4,0,0,0,0,14,3,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,1,0,8,0,0,1,0,0,0,0,15,0,1,1,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,13,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40487,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,6,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,5,0,0,0,5,1,1,0,0,0,0,0,12,0,0,5,0,0,12,0,0,0,1,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
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+40492,1,0,6,2,0,0,2,0,1,0,0,0,0,1,0,3,0,1,0,0,0,0,0,7,0,6,0,2,0,0,0,1,1,1,0,0,0,0,0,0,0,6,0,2,0,0,1,1,5,0,0,0,3,8,0,0,2,0,1,1,0,1,0,0,1,0,4,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_6
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+40496,1,0,8,21,0,0,0,0,0,0,10,0,0,1,0,2,0,1,1,0,2,1,0,2,0,4,11,1,1,0,6,0,0,2,0,0,1,0,1,0,0,0,0,1,0,2,0,0,2,0,0,2,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,1,3,0,2,0,Class_6
+40497,0,1,13,5,0,0,0,0,0,2,11,0,1,0,1,3,0,0,0,2,2,1,0,1,0,1,11,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,3,0,0,0,0,6,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,2,1,0,0,0,0,0,3,0,0,0,0,Class_6
+40498,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,5,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+40499,0,0,20,6,0,0,0,0,2,0,8,0,0,0,0,1,0,0,0,0,4,1,0,2,0,4,14,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,12,1,1,0,0,0,2,0,15,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+40500,0,1,9,3,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,3,2,0,0,0,1,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,8,0,1,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,1,Class_6
+40501,0,0,6,6,0,0,0,0,0,1,9,1,0,0,0,1,0,2,2,0,0,0,0,0,0,1,3,2,0,0,2,2,0,1,5,0,0,1,0,1,0,0,0,2,1,1,0,0,5,0,0,0,0,10,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,5,0,0,0,0,0,0,1,1,0,0,1,Class_6
+40502,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+40503,0,0,5,20,0,0,0,0,0,0,0,0,0,0,1,1,0,3,1,0,0,1,0,0,0,10,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,2,0,3,0,0,3,0,0,4,0,5,0,0,0,5,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+40504,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,4,0,5,0,2,0,2,0,3,0,10,1,0,0,0,0,0,2,1,2,1,0,2,0,3,0,3,0,0,0,2,0,0,0,0,0,0,0,2,0,0,4,0,0,13,0,3,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40505,0,0,8,4,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,3,1,0,0,0,4,9,3,0,0,1,0,0,4,6,0,0,1,0,0,0,1,0,3,0,12,0,1,0,0,0,1,0,14,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,4,0,Class_6
+40506,0,0,22,2,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,2,4,0,2,1,2,4,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,12,0,0,1,0,0,3,1,20,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40507,0,0,5,7,0,0,0,0,0,0,7,0,1,0,2,6,0,2,0,0,1,1,0,8,0,8,2,2,0,0,2,0,0,1,1,0,0,2,0,1,0,9,0,2,0,1,0,2,0,0,0,1,0,14,2,0,0,10,0,0,1,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,2,1,0,1,0,Class_6
+40508,1,0,9,7,0,0,0,0,0,0,6,0,0,1,0,3,0,0,1,0,0,1,0,0,0,2,3,0,1,0,0,0,0,0,2,1,0,0,0,0,0,1,1,1,0,6,0,0,1,0,0,1,0,14,0,0,0,0,0,0,1,3,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,1,Class_6
+40509,0,0,8,3,0,0,0,0,2,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,21,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40510,0,1,11,5,0,0,0,1,0,1,12,0,1,1,1,1,1,0,0,0,1,2,0,0,0,1,24,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,3,0,0,1,0,5,0,0,0,1,0,0,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40511,0,0,16,3,0,0,0,0,1,0,14,0,1,1,0,3,1,1,0,0,3,2,1,6,1,1,19,2,0,0,0,0,1,0,3,0,0,2,0,0,0,0,0,2,0,27,0,2,0,0,0,0,0,14,0,1,3,0,0,1,5,1,2,1,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,4,0,2,0,0,0,0,1,0,0,0,0,1,0,Class_6
+40512,0,1,2,2,1,0,2,0,0,0,8,1,0,0,0,4,0,1,1,1,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,9,0,1,0,1,5,0,0,0,0,10,0,1,1,0,0,0,2,0,0,0,5,2,2,0,0,1,0,1,0,1,0,0,0,0,0,2,0,2,0,0,0,2,0,2,2,0,0,0,0,Class_6
+40513,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40514,0,0,12,3,0,0,0,0,0,0,15,1,0,0,0,0,0,0,0,0,0,0,0,8,0,1,10,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,0,0,8,0,0,0,7,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40515,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40516,0,1,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,3,0,4,0,0,0,1,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,4,0,0,0,0,1,1,0,0,0,1,1,0,2,1,0,0,3,1,0,0,0,0,0,1,0,1,0,0,0,3,1,0,2,1,0,0,0,0,0,0,0,0,0,1,Class_6
+40517,2,3,1,3,0,0,1,3,0,0,0,0,1,0,0,2,1,3,0,4,0,2,2,16,2,0,0,0,0,0,6,2,0,0,1,1,0,5,4,4,0,1,2,4,0,2,0,8,2,3,0,0,1,1,1,0,2,2,0,10,0,2,0,1,0,2,8,0,3,3,1,3,0,2,0,2,0,0,0,0,0,0,32,0,1,0,0,0,2,2,1,0,0,Class_6
+40518,0,0,3,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40519,0,0,6,9,0,0,0,0,0,0,16,0,0,0,1,3,0,0,0,0,1,3,0,3,0,1,26,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,8,0,0,0,6,0,1,1,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40520,0,0,2,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,1,0,1,1,0,0,2,0,0,0,1,0,1,0,3,0,1,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,10,0,1,3,0,0,0,0,0,0,0,0,0,1,Class_6
+40521,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,9,0,1,0,0,0,0,0,1,2,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,14,0,1,0,2,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,4,0,Class_6
+40522,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,1,3,0,0,0,0,0,0,9,0,1,0,0,0,0,1,0,0,0,0,6,0,0,1,0,0,6,0,0,0,3,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+40523,0,1,3,2,0,0,0,0,0,0,9,0,1,0,0,6,1,4,0,0,0,3,0,1,0,3,1,4,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,3,0,0,0,0,1,0,0,1,0,7,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+40524,0,0,10,2,0,0,0,0,0,1,10,1,0,0,0,1,0,0,0,0,4,0,0,9,0,1,7,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,2,0,6,0,1,1,0,0,0,0,8,0,0,0,4,0,0,2,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40525,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,2,1,0,0,4,0,0,1,0,0,0,1,0,1,1,1,2,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,5,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,1,0,0,0,0,1,Class_6
+40526,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,7,0,0,0,0,0,3,0,6,0,3,0,3,0,0,0,4,0,0,1,1,1,0,2,0,0,1,2,2,0,2,1,3,2,0,0,0,1,7,1,0,0,0,0,1,0,0,0,0,0,1,10,0,0,2,0,2,1,0,0,1,0,0,0,1,0,0,0,3,0,0,0,1,1,0,0,3,0,Class_6
+40527,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,5,0,6,0,0,0,0,0,2,2,0,0,1,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,8,0,4,0,0,1,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40549,0,0,10,5,1,0,0,0,0,0,7,0,0,1,0,2,0,0,0,1,3,1,0,2,0,2,18,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,18,0,0,1,0,0,1,0,12,0,1,0,0,0,0,5,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,2,0,0,0,1,1,0,0,1,0,1,0,Class_6
+40550,0,0,12,4,0,0,0,0,0,3,9,1,0,0,1,2,0,0,0,0,1,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,2,0,0,0,0,5,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,1,0,0,0,0,0,0,4,0,0,1,0,Class_6
+40551,0,2,15,5,0,0,0,0,0,0,10,0,0,0,0,4,1,1,0,0,0,3,0,5,0,6,1,0,0,0,0,3,0,1,1,0,0,2,0,0,0,4,1,3,0,5,1,0,0,0,0,0,0,21,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,0,1,0,0,0,0,0,Class_6
+40552,1,0,1,1,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,15,0,0,7,1,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
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+40554,1,0,0,0,0,0,0,0,1,0,7,0,2,0,2,0,0,0,1,1,0,0,0,1,1,0,5,0,0,1,3,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,1,0,1,0,12,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,67,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,1,0,1,0,Class_6
+40555,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,4,1,2,0,0,2,2,0,0,0,9,3,0,0,0,0,2,0,0,1,0,0,2,0,1,0,2,1,3,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+40556,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,4,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,4,2,0,0,0,2,0,0,0,0,3,0,0,0,0,1,12,0,1,0,0,0,0,5,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40557,0,1,6,0,0,0,0,0,2,0,8,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,21,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40558,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40559,0,0,5,9,0,0,0,0,0,0,15,0,0,0,0,3,0,3,0,0,0,0,0,0,0,2,15,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,6,0,0,0,1,0,1,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,5,0,0,0,0,0,0,3,0,0,0,0,Class_6
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+40561,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40562,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,15,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,14,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+40563,0,0,10,7,0,0,0,0,0,0,5,0,0,0,0,4,0,1,0,0,0,2,0,0,0,1,4,5,0,0,0,0,0,0,3,1,0,1,0,0,1,0,0,2,0,7,0,1,1,0,0,1,0,10,0,0,0,0,0,3,6,0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40564,0,0,9,7,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,18,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40565,0,0,3,7,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,2,0,1,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,8,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40566,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,3,0,1,0,2,0,7,0,0,1,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,4,1,0,5,0,0,10,0,0,0,0,0,0,8,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,1,0,0,0,0,0,Class_6
+40567,0,0,6,4,0,0,1,0,0,0,10,0,0,1,0,4,1,1,0,1,0,6,0,0,0,2,7,3,0,0,1,1,0,0,1,1,0,0,1,0,0,0,0,3,0,10,0,0,1,0,1,0,0,22,2,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,1,0,1,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40611,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+40613,0,0,0,0,0,0,0,0,0,0,5,1,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,3,0,0,1,0,0,1,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,0,41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40616,0,0,8,9,1,0,1,0,0,0,14,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,5,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,1,1,0,8,0,0,1,0,0,2,0,6,0,0,1,0,0,0,2,1,0,0,0,2,0,0,0,0,0,1,0,3,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,2,2,Class_6
+40617,0,0,3,5,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+40618,0,0,10,19,0,0,0,0,1,1,9,1,0,0,0,1,0,0,0,0,2,3,0,0,0,0,11,2,0,1,2,0,0,0,2,0,0,0,0,0,0,1,0,1,0,14,0,1,1,0,0,0,0,9,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40619,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,2,0,6,0,0,0,0,1,0,0,0,0,1,0,1,1,3,0,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,3,0,2,0,0,0,0,0,0,1,0,1,8,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,Class_6
+40620,0,2,0,0,0,0,1,0,1,0,7,0,0,0,2,1,0,0,0,1,3,1,1,5,0,0,12,0,0,0,0,4,0,0,0,1,3,0,0,1,0,1,1,1,0,2,0,2,1,0,3,15,0,3,0,1,0,1,0,0,3,1,1,0,0,0,4,1,0,0,0,0,1,56,0,1,0,0,0,9,0,1,1,0,0,0,0,1,4,1,0,0,0,Class_6
+40621,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,3,0,2,1,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,Class_6
+40622,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,1,0,0,0,0,0,1,0,4,0,1,0,0,0,0,0,1,1,1,0,0,0,1,0,3,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,16,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40623,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,1,Class_6
+40624,0,1,7,6,0,0,0,0,0,1,8,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,16,0,0,0,0,0,1,0,2,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,2,0,0,0,0,0,0,3,0,1,0,Class_6
+40625,0,0,15,7,0,0,0,0,0,1,9,0,0,0,0,3,0,2,0,0,1,2,0,0,0,0,5,4,0,0,0,1,0,0,2,0,0,0,0,0,1,0,2,4,0,19,0,0,1,0,0,0,0,22,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40626,0,1,3,4,0,0,0,0,0,0,7,0,0,1,4,4,0,2,1,1,3,1,0,2,0,1,11,1,1,2,0,0,0,0,1,2,0,1,0,0,1,0,0,0,0,13,0,2,0,0,0,2,0,9,0,0,0,5,0,1,7,0,0,0,1,1,0,0,0,0,0,1,3,4,0,0,0,0,0,4,1,0,1,0,0,0,0,0,1,1,0,1,0,Class_6
+40627,0,0,6,13,0,0,0,0,0,0,9,0,1,0,0,7,1,4,1,0,0,2,0,0,1,3,3,2,0,0,0,0,0,1,10,1,0,3,0,1,0,4,0,2,0,3,0,0,13,0,0,1,1,17,1,0,1,0,0,1,6,4,0,3,0,0,2,1,0,1,0,1,0,2,0,2,0,0,1,7,0,8,2,0,0,0,0,0,6,0,0,0,0,Class_6
+40628,0,8,0,1,0,0,0,1,0,1,6,0,2,0,0,0,1,2,0,2,2,0,0,13,0,7,0,0,0,1,0,1,1,0,18,2,0,0,0,0,1,4,0,1,0,0,0,0,3,2,0,0,0,4,0,0,5,4,0,8,0,1,0,4,0,1,11,0,2,4,0,0,0,0,0,2,0,0,0,3,2,0,7,0,0,0,2,0,0,4,0,0,1,Class_6
+40629,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,10,1,1,0,0,0,0,1,0,0,0,0,0,0,2,0,6,0,1,0,1,0,1,0,0,0,0,0,5,1,0,0,0,0,12,0,1,0,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,Class_6
+40630,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40631,0,2,3,1,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,2,0,0,0,8,0,1,6,2,0,0,0,1,0,0,0,1,0,1,1,0,0,0,1,0,0,5,0,1,2,0,0,3,0,8,0,0,0,5,0,0,2,1,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,9,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+40632,0,1,2,11,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,2,0,1,0,0,0,2,0,1,0,0,0,1,0,1,5,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,10,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,0,0,2,0,Class_6
+40633,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,4,0,1,0,4,0,4,1,2,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,2,0,5,0,0,0,0,12,1,0,1,0,0,12,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40634,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40635,5,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,1,1,0,1,0,1,0,1,5,2,0,0,1,0,0,0,3,1,0,0,0,0,0,5,1,9,0,2,0,0,0,1,1,0,0,0,0,0,1,0,2,0,2,12,0,1,0,0,2,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+40636,0,1,9,2,0,0,0,0,0,0,10,0,0,1,0,8,1,2,0,0,0,5,2,0,1,3,4,2,0,0,0,0,0,0,0,1,0,1,0,1,0,2,4,1,0,5,0,0,0,0,0,0,0,18,0,0,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40637,0,1,2,3,0,0,0,0,1,0,10,1,0,0,0,1,2,0,1,0,2,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,3,0,0,0,0,0,6,0,1,1,0,0,2,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,7,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40638,0,2,2,0,0,0,0,0,0,0,16,0,0,0,0,2,0,0,0,0,1,1,0,3,0,0,5,1,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,2,0,11,0,0,0,10,0,1,5,0,0,1,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,8,0,3,0,0,0,0,0,0,0,1,1,0,0,Class_6
+40639,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,4,0,3,0,0,0,0,0,12,0,1,0,1,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,3,0,0,1,0,Class_6
+40640,0,0,8,0,0,0,0,0,0,0,6,0,0,0,0,2,0,2,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+40641,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40642,0,1,13,7,1,0,0,0,0,2,5,0,1,0,0,4,0,1,0,2,2,2,0,0,0,2,1,3,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,4,0,3,0,1,1,0,0,1,0,6,1,0,0,0,0,1,4,1,1,0,0,0,2,0,0,0,0,2,0,1,0,0,0,0,0,6,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40643,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,5,0,0,0,2,0,4,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,4,11,0,4,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,10,0,1,1,1,0,2,3,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40644,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,6,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,21,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+40645,0,0,10,1,0,0,0,1,0,0,9,0,0,0,2,3,0,0,0,0,1,3,0,1,0,3,11,3,1,0,0,2,0,1,2,0,0,0,1,1,0,1,1,0,0,4,1,2,1,0,0,1,0,8,0,1,0,0,0,3,1,0,0,1,1,0,0,0,2,1,0,0,0,4,0,1,0,0,0,4,1,0,2,0,0,0,1,0,1,0,0,0,0,Class_6
+40646,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,1,0,4,0,3,0,2,0,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40647,1,0,2,5,0,0,0,0,0,0,0,0,1,2,0,3,2,0,0,0,0,1,0,1,0,9,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,9,1,5,0,0,0,1,1,0,0,0,1,11,0,0,3,0,0,8,0,0,0,1,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,5,0,Class_6
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+40668,0,0,0,0,0,0,0,4,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,14,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+40672,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,1,0,0,1,1,0,8,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,0,Class_6
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+40674,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,7,2,2,1,0,4,0,3,4,0,0,0,0,0,0,1,2,0,2,0,0,2,0,1,1,3,0,2,0,0,1,1,0,1,0,0,0,5,5,0,3,0,0,8,0,2,0,0,1,4,13,0,0,11,4,1,0,0,1,0,0,0,7,1,1,0,0,0,2,0,4,1,1,0,0,0,0,Class_6
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+40676,0,1,8,4,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,2,1,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,0,0,0,2,0,0,1,0,Class_6
+40677,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,13,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,14,0,1,0,1,0,0,0,0,1,0,5,0,0,0,2,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,6,0,0,0,Class_6
+40678,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40679,0,0,3,1,0,0,0,0,0,0,8,0,0,1,0,7,0,0,0,0,0,0,0,1,0,0,2,1,0,0,2,0,0,0,1,3,0,3,0,0,3,1,0,0,0,0,0,0,2,0,0,2,0,9,0,0,0,0,0,1,1,3,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40680,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,1,0,5,0,1,1,2,0,1,1,0,0,1,0,0,1,4,0,0,0,0,0,2,0,2,0,1,10,3,1,1,0,0,0,9,3,0,2,0,0,13,0,1,0,0,0,8,12,1,0,1,0,1,0,0,0,0,1,4,1,0,0,0,2,0,0,0,1,0,0,1,0,2,0,Class_6
+40681,0,0,1,3,0,0,0,0,2,0,0,0,2,1,0,5,0,0,0,0,0,2,0,6,0,3,0,0,0,0,0,1,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,6,1,0,0,0,0,1,1,0,3,0,0,13,0,0,0,1,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,5,0,Class_6
+40682,0,0,6,7,1,0,0,0,0,1,18,0,1,0,0,2,0,0,0,0,0,0,0,10,0,1,4,1,0,0,3,1,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,0,0,0,0,9,0,2,0,13,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40683,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,2,0,4,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,2,0,1,0,1,0,3,1,0,0,0,18,0,0,0,0,0,0,1,0,15,0,0,0,1,0,0,0,1,0,1,0,3,0,0,0,0,1,0,0,0,0,0,1,1,1,0,2,0,0,2,0,Class_6
+40684,0,0,4,13,0,0,1,0,0,1,0,0,0,0,0,3,2,4,1,2,0,2,0,13,0,12,13,4,0,0,8,1,0,0,2,4,2,0,0,1,0,1,0,1,0,5,0,1,0,0,1,0,0,13,1,0,6,0,0,14,3,0,0,1,0,0,10,1,0,0,0,0,1,0,3,0,0,2,0,0,0,11,0,0,1,1,2,0,0,0,0,0,0,Class_6
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+40686,0,0,1,0,0,0,1,0,0,0,2,0,1,2,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,6,0,0,8,0,0,0,0,0,0,3,4,0,0,0,1,0,0,0,0,1,0,2,2,1,2,0,0,0,0,1,1,1,0,0,2,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40687,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,3,4,0,0,3,0,5,0,2,0,0,0,0,0,0,0,2,0,0,0,1,1,2,0,1,4,12,0,1,0,0,0,0,1,1,0,0,1,2,2,1,0,0,2,4,0,3,1,2,0,3,10,1,0,6,2,1,0,0,0,0,0,0,4,1,0,0,0,0,1,0,5,1,0,0,0,1,0,Class_6
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+40694,0,0,22,0,0,0,0,0,0,0,9,0,1,1,0,8,0,0,0,0,2,1,0,0,0,0,14,1,0,0,0,2,0,1,1,0,0,1,1,1,0,0,0,3,0,14,0,0,1,0,0,0,0,19,0,1,0,0,0,2,4,0,0,0,0,1,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40695,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,3,1,1,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,4,0,0,1,1,0,0,0,0,0,4,2,0,7,0,0,4,0,0,0,4,0,1,6,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,5,1,0,0,0,0,0,Class_6
+40696,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+40697,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,4,0,0,1,0,0,0,1,0,2,0,1,0,1,0,0,1,1,1,0,0,0,0,0,0,0,2,0,0,1,1,1,0,3,0,0,12,0,0,1,0,1,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40698,1,0,17,8,0,0,1,2,2,0,9,0,0,0,0,3,0,1,0,1,1,1,0,2,0,0,5,1,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,2,14,0,2,0,0,0,0,0,7,0,0,0,0,0,1,6,0,1,0,1,0,2,0,0,0,0,0,0,0,0,2,0,0,1,6,0,2,2,0,0,0,0,0,3,0,0,3,0,Class_6
+40699,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,3,0,2,0,0,0,6,0,1,2,4,0,0,28,0,0,0,0,2,0,0,0,0,0,1,3,3,0,1,0,0,0,1,0,0,0,0,1,3,5,3,1,0,0,1,0,3,0,0,0,2,6,0,0,1,1,0,1,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,1,0,Class_6
+40700,0,2,0,5,0,0,0,0,0,1,1,0,0,0,0,3,1,2,0,0,0,1,0,2,0,9,0,2,0,0,0,1,0,1,4,2,0,0,0,0,0,4,0,1,0,1,1,0,2,0,2,0,0,4,3,0,7,0,0,13,0,3,0,0,0,1,0,0,0,0,1,0,0,0,4,0,0,0,0,1,0,1,0,1,0,0,1,0,1,0,1,3,0,Class_6
+40701,0,1,0,0,1,0,2,0,0,1,3,0,1,0,0,1,1,0,0,1,0,1,3,7,2,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,3,0,7,0,2,13,0,2,0,0,0,1,1,3,0,0,0,1,1,1,0,0,2,3,2,2,0,0,3,0,0,0,1,2,1,0,1,2,1,0,0,0,0,0,0,1,0,0,2,0,0,0,Class_6
+40702,0,0,5,6,0,0,0,0,1,2,10,0,0,0,0,9,0,0,0,0,0,3,0,0,0,0,6,0,0,0,0,0,1,0,2,1,0,0,1,0,0,1,0,0,0,2,0,0,1,0,0,1,0,11,0,0,0,0,0,1,3,0,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40703,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,3,4,0,1,0,2,1,2,0,2,0,0,0,0,0,2,0,0,1,2,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,4,0,6,0,0,13,0,0,0,0,0,2,9,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+40704,0,0,6,5,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,6,1,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,1,1,0,0,1,0,0,0,1,0,0,0,0,Class_6
+40705,1,0,1,1,0,0,0,1,2,0,6,0,0,1,0,1,1,1,0,1,0,4,0,1,0,3,0,0,0,0,6,3,0,0,2,2,0,1,0,0,1,5,0,3,0,0,0,2,2,4,0,5,2,1,0,0,2,1,0,8,0,3,3,1,0,2,6,1,0,15,0,1,0,10,0,1,0,1,17,3,0,0,0,0,2,0,4,1,2,0,0,0,6,Class_6
+40706,0,0,15,3,0,0,0,0,0,0,9,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,6,2,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,6,1,0,1,0,0,0,0,11,0,0,0,0,1,2,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40707,0,0,0,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,1,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,0,0,7,0,1,0,5,0,1,6,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+40708,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40709,0,1,8,4,0,0,0,0,0,0,9,1,0,0,0,3,0,1,0,0,0,1,0,0,0,0,9,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,8,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,1,1,0,Class_6
+40710,0,0,6,4,0,0,0,1,0,0,6,0,0,1,0,1,0,3,0,2,0,2,0,0,0,0,3,6,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,1,0,5,0,0,2,0,1,0,0,6,0,2,0,0,0,1,2,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,2,0,Class_6
+40711,0,0,0,0,0,0,1,3,0,10,0,0,1,2,0,0,31,2,0,4,0,0,0,19,1,0,0,0,2,0,0,5,0,1,0,2,4,0,1,0,0,0,0,0,0,0,0,4,0,2,1,0,0,1,0,0,2,0,0,3,0,1,1,4,0,1,15,11,1,0,6,0,5,2,2,1,0,1,1,0,0,0,2,0,1,2,0,0,0,0,0,0,0,Class_6
+40712,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,1,5,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,2,1,Class_6
+40713,4,1,0,0,0,0,1,0,1,0,0,0,0,1,1,3,1,1,0,0,0,2,0,1,5,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,19,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+40714,0,0,1,4,0,0,0,1,0,0,8,1,1,0,1,6,0,8,0,0,0,4,0,0,0,3,4,0,1,0,0,0,0,0,2,1,0,3,1,1,0,0,0,5,0,1,5,0,6,0,0,0,0,7,1,0,0,0,0,0,1,2,2,0,0,5,2,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,2,0,0,0,0,3,0,0,8,1,Class_6
+40715,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,13,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40716,6,1,1,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,1,0,0,0,3,8,1,0,0,0,0,0,0,10,0,0,0,2,1,4,0,0,0,3,0,4,0,0,0,8,0,0,0,0,0,1,0,1,0,0,4,4,0,0,1,3,0,0,12,0,0,3,0,1,0,0,0,1,0,0,0,1,0,0,0,0,3,5,14,0,1,0,0,0,27,Class_6
+40717,0,0,12,5,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,4,1,0,1,0,2,8,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,11,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40718,1,0,14,3,0,0,0,0,1,0,12,2,1,0,0,3,0,3,0,0,1,1,0,2,0,5,1,3,1,0,0,3,1,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,13,0,0,0,2,0,1,4,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,6,0,1,0,0,0,0,1,1,1,0,0,0,0,Class_6
+40719,0,0,3,0,0,0,1,0,0,0,1,0,0,0,1,2,0,1,0,1,0,3,0,3,0,3,0,3,0,0,0,4,0,0,0,2,0,0,0,0,0,2,0,3,0,0,1,1,1,0,1,0,2,1,0,0,0,0,1,8,0,0,0,0,0,1,4,0,1,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40720,0,0,6,6,0,0,0,0,0,1,10,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,6,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40721,0,0,0,0,0,0,0,0,0,0,11,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+40722,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,11,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_6
+40723,0,0,37,6,0,0,0,2,0,0,9,0,0,2,0,6,0,1,0,0,6,3,0,2,0,5,4,3,1,0,0,0,0,1,2,2,0,1,1,1,0,1,0,8,0,25,0,2,2,0,0,6,1,30,1,2,0,0,0,0,6,0,0,1,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,3,0,0,0,1,1,0,2,0,0,2,0,Class_6
+40724,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,1,0,1,0,0,0,0,1,0,1,0,0,4,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
+40725,0,1,11,6,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,7,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,7,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+40726,0,0,13,4,0,0,0,0,0,0,5,0,0,1,0,3,0,0,0,0,1,2,0,0,0,3,2,2,0,0,0,0,0,0,1,1,0,0,0,1,0,1,1,2,0,8,0,0,0,0,0,1,0,3,0,1,0,0,0,0,2,1,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40727,0,0,4,6,0,0,0,0,0,1,11,0,0,0,0,3,0,0,0,0,0,5,0,0,1,0,8,1,1,0,0,2,0,1,0,1,0,0,0,0,1,2,0,1,0,2,0,0,1,0,0,0,0,9,0,0,0,1,0,1,4,0,0,0,0,1,0,0,0,0,0,1,2,2,1,0,1,0,1,16,0,3,0,0,0,0,0,0,4,0,0,0,0,Class_6
+40728,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,2,0,1,0,0,0,0,Class_6
+40729,6,0,0,0,0,0,1,2,0,4,1,0,0,2,0,3,2,2,0,11,0,2,0,7,3,0,0,0,1,0,0,11,1,0,4,7,1,2,1,1,1,3,5,2,1,0,0,10,0,1,0,0,0,3,0,0,0,0,5,2,0,0,0,1,3,2,39,0,0,14,1,1,0,0,1,2,0,1,0,0,0,0,2,0,7,3,45,0,1,0,0,1,15,Class_6
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+40733,0,0,3,2,0,0,1,0,0,0,2,0,0,0,1,0,0,0,1,0,0,1,1,12,0,2,1,0,0,1,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,1,5,1,0,9,2,0,1,0,0,1,1,1,1,2,0,0,2,0,0,1,0,0,0,8,0,2,1,0,0,0,0,0,0,0,0,0,0,Class_6
+40734,4,0,1,0,0,0,0,0,0,1,0,0,0,1,2,1,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,4,0,9,0,0,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,2,3,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,1,0,0,0,0,Class_6
+40735,4,0,0,0,0,0,0,0,1,0,1,0,0,4,0,1,0,1,0,4,0,3,2,10,1,1,0,0,0,0,0,8,0,0,0,3,3,4,0,0,0,3,0,9,0,0,0,5,0,0,0,1,0,4,3,1,1,1,4,5,0,0,0,0,0,3,15,0,0,6,0,1,1,2,2,0,0,1,0,0,0,0,1,0,1,4,19,0,1,0,0,0,28,Class_6
+40736,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,1,1,0,0,0,2,0,0,1,2,0,1,0,0,0,0,2,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,1,4,0,2,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+40737,0,0,16,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,1,3,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,10,0,1,0,0,0,1,0,12,0,0,0,1,2,0,0,0,0,0,2,4,0,2,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40738,0,0,3,2,0,0,0,0,0,0,0,1,1,0,0,3,0,4,3,3,0,3,0,6,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,1,0,1,0,1,0,0,0,0,2,0,0,1,0,1,0,0,1,0,0,12,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40739,0,0,8,7,0,0,0,1,0,0,18,0,0,1,0,5,0,2,0,0,0,2,0,8,0,1,14,3,0,0,1,2,0,0,1,0,2,0,0,0,1,0,1,0,0,5,0,1,0,0,0,0,0,4,0,0,0,3,0,2,5,1,0,1,0,0,2,0,0,0,0,0,1,4,0,1,0,2,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+40740,0,0,1,3,1,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,0,3,0,0,0,3,2,1,0,0,1,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,2,0,1,0,1,3,0,0,0,2,0,3,2,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40741,9,0,0,0,0,0,0,0,1,0,0,0,0,5,0,2,0,0,0,0,0,4,0,1,8,0,0,0,1,0,0,1,4,1,0,1,0,1,0,0,0,6,0,2,0,2,0,1,0,0,0,0,0,2,1,0,0,0,3,0,0,4,1,1,1,0,10,1,0,3,1,0,0,0,2,1,0,0,11,0,0,0,0,0,0,3,0,1,0,1,0,6,0,Class_6
+40742,0,0,0,0,0,0,2,0,0,0,10,2,1,1,0,1,0,0,1,0,0,1,2,1,0,0,1,0,0,0,1,3,1,1,0,0,0,2,0,1,0,0,0,5,0,0,0,0,2,0,0,0,0,5,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40743,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40744,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,3,0,4,0,1,0,2,0,1,0,0,0,0,0,0,0,2,2,0,0,1,0,0,0,3,0,0,0,0,2,0,2,0,0,0,0,3,2,0,5,0,0,17,0,1,0,3,1,0,0,0,4,0,0,0,0,0,2,0,1,0,0,0,0,0,4,0,0,0,1,0,0,2,2,0,2,Class_6
+40745,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,3,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,2,0,Class_6
+40746,1,2,1,6,0,0,0,0,0,0,10,0,0,0,0,6,0,2,0,0,3,2,0,3,0,2,8,1,0,0,3,0,0,0,4,0,1,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,6,1,3,0,13,0,0,1,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40747,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,1,1,4,0,0,0,3,0,4,1,3,0,0,0,0,0,0,0,0,3,2,0,2,0,1,0,3,0,0,0,1,0,2,2,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,1,1,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40748,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40749,0,0,4,5,0,0,0,0,0,1,6,1,0,0,0,1,0,2,0,0,1,2,0,0,0,1,1,0,0,0,0,1,1,0,3,0,0,1,0,0,0,0,0,3,0,1,0,0,2,0,0,1,1,4,0,3,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,1,0,2,0,0,0,3,Class_6
+40750,0,0,3,1,0,0,0,0,0,0,9,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,2,1,0,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,1,0,4,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40751,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,0,0,1,0,1,0,2,0,5,0,4,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+40752,4,0,0,0,0,0,0,1,1,2,0,0,0,2,1,2,0,1,0,3,0,4,0,13,1,0,0,0,0,0,0,7,1,0,0,2,2,1,0,0,0,3,0,2,0,0,0,6,0,4,0,0,1,1,1,1,0,0,2,2,0,0,0,0,1,1,14,0,0,2,0,0,0,2,0,0,1,0,1,1,0,0,0,0,0,6,13,1,0,3,0,0,30,Class_6
+40753,0,0,13,4,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,2,2,0,1,0,0,9,2,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,7,0,0,1,0,0,2,0,5,0,1,0,1,0,0,4,1,1,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,4,0,3,0,0,0,0,0,0,4,0,0,3,0,Class_6
+40754,0,0,5,1,0,0,0,0,0,0,9,0,0,0,0,1,0,2,0,2,0,0,0,2,0,0,5,2,0,1,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,4,0,1,0,0,0,3,0,1,0,0,0,0,0,0,4,0,0,1,0,Class_6
+40755,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,1,7,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,1,0,1,0,0,0,0,0,2,0,0,2,0,0,4,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,Class_6
+40756,0,1,1,0,0,0,0,0,2,0,7,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,2,0,0,0,2,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,58,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40757,0,0,13,6,0,0,0,0,0,0,8,0,0,0,0,4,0,0,0,0,2,6,0,1,0,2,7,6,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,12,2,0,2,0,0,1,0,12,1,0,0,0,0,1,1,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40758,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,1,3,0,1,0,0,1,1,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,8,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40759,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,3,0,3,0,1,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,1,0,2,0,0,0,2,1,0,0,0,1,7,0,0,0,0,1,8,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_6
+40760,0,0,10,2,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,4,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,6,0,0,1,0,0,0,0,8,2,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,4,0,0,1,0,Class_6
+40761,0,0,4,5,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,1,0,1,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40762,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,5,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40763,0,1,14,9,0,0,0,0,1,0,17,0,0,1,1,2,0,0,0,0,3,1,0,7,0,1,11,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,12,0,0,1,0,0,1,1,7,0,0,0,10,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40764,0,1,17,3,0,0,0,0,0,1,15,1,1,0,0,3,0,1,0,0,0,1,0,0,0,1,18,2,0,0,0,1,1,0,0,2,0,0,0,0,0,1,0,1,1,18,0,0,0,0,0,1,0,6,0,2,0,0,0,0,1,1,0,0,0,1,3,0,0,0,0,0,0,1,1,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40765,0,0,2,2,0,0,0,0,0,1,6,0,1,0,1,2,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,8,0,1,0,0,0,0,0,12,0,0,0,0,1,1,3,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40766,0,0,7,9,0,0,0,0,0,1,9,1,0,0,0,2,0,1,0,0,2,2,1,2,0,0,14,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,1,6,2,7,0,0,3,0,0,0,0,17,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,1,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,Class_6
+40767,0,0,9,0,0,0,1,0,0,1,6,0,1,0,0,2,1,0,0,1,1,5,0,1,0,1,6,1,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,3,0,0,0,1,2,0,0,0,0,23,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,7,0,2,0,0,0,0,0,1,4,0,0,0,0,Class_6
+40768,0,0,0,0,0,0,0,0,18,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,1,1,0,0,0,0,0,3,0,1,1,0,0,1,0,1,0,4,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,5,0,2,4,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,2,3,0,0,0,2,0,Class_6
+40769,0,0,0,1,0,0,0,1,0,0,7,0,0,1,0,2,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,3,0,3,0,0,0,0,0,0,3,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,Class_6
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+40781,0,0,3,5,0,0,0,0,0,1,1,0,0,0,0,3,0,1,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,13,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40782,1,1,0,0,0,0,0,0,0,0,5,0,0,1,0,3,0,0,0,0,0,0,0,2,0,3,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,2,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40783,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40785,0,0,2,2,0,0,0,0,0,0,1,0,1,0,0,3,0,4,0,1,0,2,0,4,0,5,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,4,0,1,0,0,0,2,0,0,0,0,0,7,2,0,2,0,0,10,0,0,1,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,1,0,0,Class_6
+40786,0,0,15,5,0,0,0,0,0,1,10,0,0,0,0,3,0,0,0,0,0,0,0,0,0,6,6,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,17,0,0,1,1,0,0,0,22,1,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,3,0,Class_6
+40787,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40788,0,0,7,0,0,0,0,2,0,0,10,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,11,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40789,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40790,0,0,3,1,0,0,1,0,0,0,7,0,0,0,0,2,0,0,1,0,0,2,0,2,0,0,3,2,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,2,0,6,0,0,0,0,0,0,0,7,0,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,24,0,3,0,0,0,0,0,0,2,0,0,1,0,Class_6
+40791,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40792,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40793,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40794,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40795,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,1,0,4,1,1,0,0,0,4,1,0,0,0,1,0,0,1,1,0,1,0,0,1,2,0,2,1,0,1,1,0,0,2,0,1,1,0,0,1,1,0,1,0,0,16,0,1,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,6,1,1,1,0,1,0,Class_6
+40796,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,0,0,0,0,2,0,0,0,7,0,2,0,0,0,0,3,0,1,0,1,0,1,1,0,0,0,0,5,0,0,2,0,0,5,0,1,0,1,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40797,0,0,6,5,0,0,0,1,0,0,9,0,0,0,0,5,0,0,0,0,0,0,0,0,3,2,11,4,0,0,0,0,0,2,2,0,0,0,1,2,0,0,0,2,0,13,0,0,1,0,0,0,0,3,0,1,0,1,0,0,2,1,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,11,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+40798,0,0,1,0,1,0,0,0,0,0,7,0,3,0,0,3,0,0,0,0,0,1,1,0,0,0,2,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,2,0,0,1,2,5,0,0,0,0,11,1,0,0,0,0,0,0,2,0,0,0,2,2,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,2,0,0,0,0,Class_6
+40799,0,0,6,0,0,0,1,0,0,0,10,1,2,0,0,5,0,0,0,0,0,2,0,1,0,1,1,1,1,0,1,0,0,0,2,1,0,1,0,0,1,1,0,6,0,1,0,0,7,0,0,0,0,11,1,0,0,0,0,0,0,1,2,0,2,0,1,0,0,1,0,0,1,0,0,1,0,0,0,3,0,1,0,1,0,0,1,0,2,0,0,0,0,Class_6
+40800,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+40801,0,0,10,4,0,0,0,0,0,0,8,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,10,0,0,0,0,0,0,0,9,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40802,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,3,0,0,1,0,3,0,1,0,1,0,5,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,1,3,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,6,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+40803,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,3,0,0,0,1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,3,2,0,1,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,5,0,1,0,0,0,1,1,0,0,2,2,5,1,2,0,0,0,0,0,0,0,0,0,0,5,2,2,1,0,0,0,0,0,Class_6
+40804,0,1,14,3,0,0,0,0,0,1,5,1,1,1,0,3,0,0,1,1,0,6,0,0,0,1,0,2,1,0,0,0,1,2,0,0,0,1,0,0,1,1,1,6,0,3,0,0,2,0,0,0,0,25,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,3,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40805,0,0,0,0,0,0,1,1,0,0,2,0,0,4,0,5,0,2,0,0,0,5,0,3,0,0,0,1,0,0,3,0,0,0,0,1,0,2,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,6,3,0,0,0,1,1,0,2,0,0,0,2,0,0,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,13,0,3,0,0,1,9,Class_6
+40806,2,3,37,9,0,0,0,0,0,1,7,0,0,0,0,7,0,0,0,0,6,5,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,5,1,0,1,1,2,3,28,0,0,0,0,0,0,0,34,1,1,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,2,0,0,0,0,0,1,1,0,0,1,0,Class_6
+40807,0,1,16,2,0,0,1,1,1,0,12,1,0,0,0,4,0,1,0,0,2,0,0,7,0,6,9,4,0,0,0,7,1,0,1,0,0,0,0,0,0,4,0,2,0,11,0,3,0,0,0,0,0,8,0,0,0,17,0,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+40809,0,0,0,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40810,0,0,9,1,0,0,0,1,0,1,5,1,0,0,0,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,14,0,2,0,0,0,0,1,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,Class_6
+40811,0,0,3,2,0,0,0,0,0,0,21,0,0,0,0,2,0,0,0,0,1,3,0,10,0,0,13,1,0,0,0,1,1,0,1,1,0,0,0,2,0,0,0,1,0,5,0,1,0,0,0,6,0,12,0,1,0,14,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,7,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40812,0,2,12,2,0,0,0,0,0,0,9,0,1,0,0,4,0,1,0,0,1,2,0,0,0,3,9,4,0,0,1,3,0,1,0,0,0,1,0,0,0,3,2,1,0,4,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+40813,0,0,8,8,0,0,0,0,0,0,11,0,0,0,1,0,0,0,1,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,7,0,0,0,0,0,1,1,10,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,6,0,2,1,0,0,1,0,0,0,0,0,0,0,Class_6
+40814,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,6,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40815,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,5,2,0,0,0,0,0,0,2,0,1,0,1,1,1,2,1,0,5,0,2,0,0,0,5,0,0,0,0,1,0,0,0,4,0,2,17,0,0,0,0,0,2,11,0,0,2,4,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,Class_6
+40816,0,0,0,1,0,0,0,1,1,0,0,0,0,2,0,2,0,1,1,1,0,0,0,4,0,0,0,1,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,6,0,0,3,1,0,8,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,Class_6
+40817,0,0,1,2,0,0,0,0,1,0,0,0,1,0,0,0,1,3,0,2,0,2,0,2,0,17,0,2,0,0,2,0,0,0,2,0,0,0,0,1,0,6,0,1,0,2,1,0,0,0,0,0,0,2,1,0,1,1,0,12,0,0,0,1,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,Class_6
+40818,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+40819,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40820,0,3,9,2,0,0,0,0,1,0,7,0,0,0,0,3,0,1,0,0,0,1,0,0,0,2,4,3,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,10,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,3,0,0,0,0,0,1,1,0,0,1,1,Class_6
+40821,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40822,0,2,4,2,0,0,0,0,1,0,9,0,0,0,0,5,0,1,0,0,0,2,0,2,0,2,10,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,4,0,1,0,0,2,0,0,0,0,13,0,2,0,0,0,0,10,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,15,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40823,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,5,1,0,0,0,0,0,1,0,1,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,1,0,0,0,0,5,2,0,3,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_6
+40824,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,5,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,1,1,0,6,0,0,0,1,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,7,0,0,0,0,0,0,Class_6
+40825,0,0,1,0,1,0,0,3,1,4,1,0,1,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,21,0,1,0,0,0,0,3,0,0,0,0,4,6,0,0,0,0,7,0,2,3,0,1,0,1,0,0,3,4,1,0,0,2,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+40826,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,8,0,0,0,3,1,5,2,2,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,2,1,0,0,1,0,2,0,2,0,0,1,6,1,0,2,1,0,9,3,1,0,1,0,0,3,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,1,0,Class_6
+40827,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40828,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40829,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40830,3,0,0,0,0,0,0,2,1,0,1,1,0,0,0,2,0,0,0,0,2,0,0,0,5,1,2,0,1,0,0,0,0,0,1,0,1,0,0,0,1,5,0,0,0,0,0,2,0,0,0,0,1,5,0,0,0,0,3,1,0,2,1,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+40831,0,0,0,0,0,0,0,0,5,0,0,0,0,3,0,0,0,1,0,0,0,2,0,3,1,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,6,0,0,0,0,1,2,0,0,2,0,0,3,8,0,3,0,0,12,0,0,0,5,0,6,1,0,1,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,1,0,Class_6
+40832,0,0,0,0,0,0,0,0,4,0,9,0,0,1,0,1,2,0,1,0,3,2,3,1,0,0,2,0,0,0,1,0,0,0,1,0,3,0,0,1,0,0,2,0,0,0,0,1,0,0,0,11,0,1,0,0,0,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,1,76,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,Class_6
+40833,0,0,1,1,1,0,0,0,0,0,8,0,0,1,0,3,0,2,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,2,10,0,5,0,0,0,2,6,0,0,0,0,0,0,2,0,1,0,0,2,2,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,2,Class_6
+40834,0,0,3,10,0,0,0,0,1,1,11,1,0,0,0,0,0,0,0,0,1,0,0,5,0,0,21,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,2,0,4,0,0,0,5,0,1,4,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40835,0,1,12,4,0,0,1,0,0,1,8,0,0,1,0,0,0,0,0,1,2,1,0,1,2,0,13,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,7,0,0,1,0,0,3,0,12,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,2,Class_6
+40836,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40837,1,0,0,0,0,5,0,0,2,0,1,0,0,0,0,5,0,6,0,0,1,6,0,8,0,0,0,1,0,0,1,0,0,0,0,0,2,4,0,1,5,13,0,1,0,0,1,1,0,0,0,0,1,10,4,1,3,1,0,19,0,6,1,1,2,0,4,0,0,3,0,1,0,1,0,0,0,0,2,3,0,1,0,0,0,0,0,0,2,0,0,2,0,Class_6
+40838,1,1,0,1,0,0,0,0,0,0,1,0,2,2,4,1,1,2,0,1,0,1,0,7,0,0,0,0,2,0,0,3,2,0,1,2,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,3,2,0,10,0,2,1,0,0,0,6,0,0,2,1,2,0,0,0,0,4,1,2,0,0,0,0,0,0,0,2,1,3,0,0,1,1,Class_6
+40839,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,Class_6
+40840,0,1,6,4,0,0,0,0,0,0,7,1,0,1,0,2,0,0,0,0,0,2,0,0,0,2,0,6,1,0,0,2,0,3,3,0,0,1,0,0,0,0,0,2,0,2,0,0,5,0,0,0,0,11,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40841,1,0,3,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,5,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,2,1,0,0,0,1,7,0,0,1,1,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+40842,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40843,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40844,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,3,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,12,0,1,0,2,0,3,2,0,0,0,0,1,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,Class_6
+40845,0,2,2,2,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,Class_6
+40846,0,0,6,0,0,0,0,1,0,0,11,0,1,1,0,0,0,0,0,0,1,0,0,2,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,2,0,0,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40847,0,0,4,0,0,0,0,1,0,0,8,0,0,0,1,2,0,1,0,0,0,0,0,0,0,8,1,2,0,0,0,2,0,0,0,0,0,1,0,1,0,7,0,3,0,9,0,0,0,0,0,0,0,12,1,0,0,0,0,1,1,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40848,0,0,9,3,0,0,0,0,1,0,8,0,2,0,0,4,0,0,0,0,0,2,0,3,0,0,15,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,9,0,0,1,0,0,3,0,21,1,0,1,0,0,1,1,2,0,0,1,1,0,0,0,2,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40849,0,0,4,4,0,0,0,0,1,0,7,0,0,0,1,1,0,0,0,0,1,2,0,5,0,1,12,1,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,1,0,6,0,1,0,0,0,1,0,8,0,0,0,2,0,1,11,2,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,14,0,2,0,0,0,0,0,0,2,1,0,0,2,Class_6
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+40859,0,0,15,1,0,2,0,0,0,0,7,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,9,0,1,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40860,0,0,11,1,0,0,0,0,0,0,14,0,0,0,1,0,0,1,0,0,0,1,0,8,0,0,9,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,6,0,0,1,0,0,1,0,3,1,2,0,7,0,0,1,0,0,3,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40861,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,2,5,0,1,0,1,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,2,0,4,0,0,6,0,0,0,1,0,1,6,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40862,0,0,7,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,3,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,1,1,0,0,0,0,11,1,0,0,0,0,1,1,1,0,1,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40863,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_6
+40864,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,12,0,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40865,0,0,44,3,1,0,0,0,0,1,11,2,0,0,8,6,0,1,0,0,14,1,0,0,0,2,3,3,1,0,0,0,0,2,1,0,2,1,0,0,0,3,0,7,0,34,1,0,2,0,0,1,0,37,0,1,0,0,0,1,4,4,0,1,2,1,2,0,0,0,0,0,1,0,1,0,0,0,0,12,0,0,0,0,0,1,2,0,1,0,1,1,0,Class_6
+40866,0,0,12,8,0,0,0,0,1,1,0,0,2,0,0,4,0,0,0,1,0,5,0,7,1,18,0,6,0,0,0,1,1,0,1,0,0,0,0,3,0,3,1,5,0,2,0,7,0,0,0,0,0,16,0,0,3,0,0,13,0,1,0,1,0,0,1,0,0,1,0,2,1,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,5,0,Class_6
+40867,0,1,0,17,1,0,0,1,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,14,0,0,0,0,0,0,0,1,3,0,0,0,0,0,1,5,0,2,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,3,1,Class_6
+40868,0,0,0,0,1,0,1,0,0,3,2,0,1,3,1,0,0,0,0,1,0,1,0,3,3,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,1,2,0,5,0,0,10,2,0,0,0,0,1,0,3,0,0,0,1,1,0,1,0,1,1,0,1,0,0,6,2,0,0,2,0,2,0,0,2,1,2,0,0,0,2,0,1,0,0,0,0,1,0,Class_6
+40869,1,1,64,12,0,0,0,0,1,2,12,1,3,1,0,6,2,4,0,0,0,0,0,8,0,9,12,10,0,0,0,3,0,0,3,0,0,2,0,0,1,1,4,8,0,40,1,0,2,1,0,0,0,35,2,0,2,4,0,1,6,0,0,0,0,1,1,0,1,0,0,2,0,0,0,0,0,0,0,17,0,15,0,0,0,0,0,1,1,0,0,0,0,Class_6
+40870,0,0,5,6,0,0,0,2,0,0,8,0,0,1,0,3,1,1,0,0,0,1,0,1,0,10,1,0,0,1,0,2,0,0,4,0,0,0,0,0,0,4,0,2,0,2,0,1,0,0,0,0,0,15,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40871,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
+40872,0,1,8,1,0,0,0,0,0,0,19,1,0,0,0,0,0,0,0,0,2,0,0,7,0,3,3,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,1,0,6,0,1,0,8,0,1,0,1,1,1,0,0,2,0,0,1,0,1,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40873,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,8,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+40874,0,1,7,5,0,0,0,0,0,1,11,0,0,1,0,2,0,0,0,0,1,3,0,0,0,1,5,7,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,11,0,0,0,0,0,0,0,18,1,1,1,0,0,0,2,1,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,4,0,0,0,0,Class_6
+40875,0,1,2,10,0,0,0,0,0,0,1,0,0,0,0,3,0,2,0,1,0,2,0,1,0,2,1,2,0,0,0,0,0,0,10,2,0,0,0,0,0,2,0,4,0,1,0,0,3,0,0,2,0,8,0,0,2,0,0,12,1,2,0,0,1,0,2,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+40876,1,3,1,2,0,0,2,0,1,0,0,0,0,0,0,4,0,0,1,3,0,2,0,0,1,2,0,1,0,0,0,0,0,0,3,0,0,4,0,0,1,7,0,9,0,1,0,0,7,3,0,0,0,1,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,12,4,1,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,0,1,0,Class_6
+40877,0,0,18,3,0,0,0,0,0,1,10,0,0,0,0,5,0,1,0,0,0,2,0,2,1,0,9,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,18,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,2,1,0,Class_6
+40878,0,0,0,0,0,0,0,0,0,0,11,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40879,0,0,2,4,0,0,0,0,0,1,11,0,0,0,0,4,0,2,0,0,2,0,0,2,0,6,7,3,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,1,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,10,0,0,0,1,0,0,0,0,3,2,0,1,0,Class_6
+40880,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,0,1,0,3,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,1,2,0,0,0,0,1,0,0,1,1,0,32,0,2,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+40881,0,0,4,1,0,0,0,0,0,0,5,0,0,0,0,4,0,2,0,0,0,2,0,1,0,2,2,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+40882,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,10,Class_6
+40883,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,2,1,0,1,0,1,1,0,1,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40884,0,0,0,2,0,0,0,5,1,0,0,0,2,0,0,2,9,3,1,1,0,7,0,9,3,2,0,0,1,0,0,1,2,0,1,2,0,1,0,0,0,2,0,0,0,0,1,3,1,0,0,0,0,2,1,0,1,0,3,0,0,5,0,0,0,5,9,4,0,2,1,0,1,0,1,0,0,0,2,0,0,0,3,0,3,3,11,1,0,0,1,0,0,Class_6
+40885,0,0,0,0,0,0,1,0,0,0,8,0,2,2,0,1,1,0,0,1,0,1,0,0,0,0,2,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,0,0,7,0,0,0,0,1,0,0,1,0,0,1,0,2,0,0,2,0,4,0,0,1,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+40886,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,1,1,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,12,0,1,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,Class_6
+40887,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,2,0,4,0,3,0,1,0,2,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,3,2,0,6,0,0,12,0,2,0,0,0,2,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,Class_6
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+40889,0,0,11,6,0,0,0,0,1,0,8,0,0,0,0,3,0,0,0,0,4,1,0,1,0,0,18,0,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,1,0,14,0,0,0,0,0,0,1,17,0,0,0,0,0,0,2,0,0,0,0,1,3,0,0,0,0,1,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+40891,0,0,2,5,0,0,0,0,0,0,8,0,1,0,0,5,0,0,0,0,1,2,0,1,0,1,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,6,0,0,1,0,0,0,0,12,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40892,0,1,13,7,0,0,0,0,0,0,8,0,0,0,0,4,0,0,0,0,3,2,0,0,0,1,22,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,9,0,0,2,0,0,4,0,11,0,0,0,1,0,0,3,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+40894,0,0,11,2,0,0,0,0,0,0,9,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,1,4,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40895,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,7,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,13,0,0,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40896,2,0,0,0,0,5,0,0,0,0,1,1,0,0,0,1,0,7,0,0,0,3,0,7,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,12,0,2,0,0,1,1,0,0,0,0,1,7,4,0,4,0,0,17,0,3,0,2,1,0,3,1,0,3,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+40897,0,1,0,0,0,0,0,0,1,0,1,0,1,1,0,3,1,2,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,4,0,0,0,1,8,5,0,0,0,0,0,1,2,0,6,0,0,17,0,1,2,0,0,1,6,6,0,0,4,0,1,0,1,1,0,7,4,2,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40898,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40899,0,0,16,9,0,0,0,1,0,1,7,0,1,0,0,1,0,0,0,0,0,0,0,2,0,2,2,2,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,14,0,1,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40900,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,2,1,0,0,0,0,1,0,1,0,0,0,0,0,3,1,0,0,0,2,0,1,2,0,0,0,0,1,0,0,0,0,1,0,3,0,0,16,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,7,0,0,0,2,0,0,Class_6
+40901,1,21,0,2,0,0,2,2,1,1,1,0,2,2,0,7,1,12,2,10,1,2,2,23,0,0,1,0,1,0,1,2,0,1,5,1,1,13,0,2,0,1,2,1,0,3,1,2,1,4,0,4,0,5,1,0,0,9,0,0,0,0,0,4,1,2,7,1,21,0,0,1,0,7,3,6,0,4,1,3,0,1,10,0,0,1,2,0,2,30,0,0,0,Class_6
+40902,0,1,6,1,0,0,0,1,0,0,14,0,0,1,0,4,0,0,0,0,2,2,0,7,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,3,2,0,1,9,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+40903,0,0,1,1,2,0,0,1,0,1,0,0,0,0,1,4,0,2,0,0,0,2,0,3,2,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,2,5,0,2,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,11,0,1,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,0,0,3,0,Class_6
+40904,0,2,2,0,0,0,1,2,0,0,0,1,1,1,0,6,3,3,0,2,0,4,0,7,0,1,0,0,0,0,0,3,0,2,4,1,0,1,0,1,10,3,0,3,0,0,1,2,1,1,0,0,0,7,2,0,3,2,0,13,0,3,1,0,1,2,2,6,0,2,0,1,6,0,3,1,1,0,0,0,1,0,0,0,1,0,1,0,5,11,0,2,0,Class_6
+40905,0,0,5,3,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,6,1,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,2,0,0,1,0,Class_6
+40906,0,0,1,2,0,0,1,0,0,0,5,0,1,0,0,3,0,1,0,0,1,2,0,0,0,0,4,0,0,0,1,1,2,0,5,0,1,0,1,3,1,0,0,2,0,2,0,0,1,0,0,4,0,11,0,0,0,1,0,1,3,1,0,1,0,0,0,0,0,2,0,1,2,3,1,2,0,1,0,27,0,1,0,0,0,0,0,0,5,0,0,0,0,Class_6
+40907,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,8,0,1,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,Class_6
+40908,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40909,3,0,0,0,0,0,0,1,0,1,0,0,0,1,0,4,0,2,0,2,0,3,0,1,0,2,0,0,1,0,0,0,1,2,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,2,6,0,1,0,0,1,0,1,0,0,0,2,1,5,0,1,2,1,0,8,2,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40910,0,0,2,3,0,0,0,1,3,0,0,0,2,0,0,0,0,5,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,4,0,0,2,0,0,8,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_6
+40911,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,4,0,0,0,0,0,0,7,0,0,1,3,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,3,0,1,0,0,5,0,0,16,0,2,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,2,0,Class_6
+40912,1,0,5,4,0,0,0,0,1,0,6,0,1,0,0,1,0,0,0,2,2,1,0,1,0,1,16,0,1,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,7,0,0,0,1,0,0,4,0,0,0,0,0,1,2,0,2,0,0,0,0,0,2,0,0,0,13,0,3,1,0,0,0,0,0,3,0,0,0,0,Class_6
+40913,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40914,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,12,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,Class_6
+40915,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40916,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,2,1,1,0,0,0,1,0,2,0,3,0,0,0,0,8,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+40917,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,1,0,11,0,0,0,3,0,10,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,4,12,1,1,0,0,1,1,0,0,0,0,1,7,3,0,5,0,0,18,0,3,0,3,1,0,2,1,0,4,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+40918,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,2,0,1,12,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,2,0,0,0,0,6,15,0,0,7,0,1,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,1,Class_6
+40919,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40920,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40921,0,0,8,10,0,0,0,1,0,0,10,0,0,0,0,5,0,0,0,0,0,1,0,1,0,11,1,0,0,1,0,2,0,0,5,0,0,0,0,0,0,3,0,5,0,9,0,0,0,0,0,0,0,17,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40922,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,7,0,0,0,1,0,0,0,0,1,0,8,0,0,2,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,2,0,1,2,4,0,12,0,0,0,0,0,1,8,0,4,1,0,0,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,0,4,0,1,0,Class_6
+40923,0,0,4,1,0,0,1,0,0,0,8,0,0,0,0,2,0,0,1,0,1,1,0,2,1,0,1,0,0,0,0,0,0,0,2,0,0,1,0,1,0,3,0,4,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40924,0,0,3,4,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,0,0,1,0,2,0,1,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,7,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+40925,3,0,0,0,0,0,1,1,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,1,4,0,0,0,1,0,0,2,1,7,0,3,0,0,0,0,1,2,0,0,4,0,0,0,0,0,11,1,0,1,0,0,0,0,8,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,Class_6
+40926,0,0,16,4,0,0,0,0,0,2,10,1,0,0,0,3,0,0,0,0,3,1,0,0,0,1,9,2,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,14,0,0,1,0,0,0,0,9,0,0,0,0,0,2,2,2,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,6,0,0,1,0,0,0,0,0,3,0,0,0,0,Class_6
+40927,1,0,5,0,0,0,0,0,0,0,6,0,1,0,0,2,1,0,0,0,1,0,0,8,0,1,2,3,0,0,0,3,0,0,1,1,0,0,1,1,0,1,0,0,0,3,0,1,0,0,0,3,0,12,0,2,0,0,0,0,3,0,1,1,0,0,6,0,0,0,0,0,0,15,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+40928,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,6,0,4,0,2,0,2,0,0,0,0,2,0,8,1,0,0,0,0,0,2,0,1,0,0,0,1,1,0,0,0,0,7,0,0,3,0,0,8,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,Class_6
+40929,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,3,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,16,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40930,0,0,10,5,0,0,0,0,0,0,9,0,2,0,0,3,0,0,0,0,1,0,0,1,0,5,29,2,0,0,0,1,0,1,1,1,0,0,0,0,0,1,0,1,1,12,0,0,1,0,0,3,0,16,0,1,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+40931,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,5,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,8,0,0,0,0,0,0,6,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40932,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,6,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+40933,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,8,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40934,0,1,1,6,0,0,0,0,0,0,7,3,2,0,1,1,1,0,0,0,0,1,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40935,0,1,10,4,0,0,0,0,2,0,15,0,0,0,0,2,0,3,0,0,1,1,0,0,0,1,8,1,0,0,0,0,1,0,3,0,0,3,0,0,0,0,2,0,0,9,0,1,2,0,0,0,0,4,0,1,0,0,0,0,10,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,11,0,2,0,0,0,0,0,1,2,0,0,0,0,Class_6
+40936,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,0,1,0,0,1,0,2,0,1,1,0,0,0,2,11,1,2,0,2,1,4,0,0,0,3,5,0,0,0,4,0,0,0,0,0,39,0,2,0,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,Class_6
+40937,0,1,13,3,0,0,0,0,0,0,18,0,1,0,0,3,0,1,0,0,0,1,0,8,0,0,4,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,17,1,0,0,6,0,3,5,0,0,1,0,0,1,0,1,0,0,0,0,2,0,1,0,1,0,7,0,2,1,0,0,0,0,0,0,2,0,0,0,Class_6
+40938,0,0,3,4,0,0,0,0,1,0,1,0,0,0,1,2,0,0,0,0,1,3,0,6,0,4,0,2,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,8,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+40939,0,0,13,2,0,0,0,0,0,0,8,1,0,0,0,6,0,0,0,0,0,1,0,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,6,0,0,0,0,0,1,0,7,1,0,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+40940,1,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,9,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,8,0,0,3,0,0,0,1,0,1,42,2,1,0,1,1,1,0,1,1,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,1,0,Class_6
+40941,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,1,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,2,5,3,0,1,0,0,8,0,0,0,0,0,1,3,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+40942,0,0,6,4,1,0,0,0,0,0,9,0,0,1,1,8,0,2,0,1,3,4,0,2,0,6,9,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,4,1,1,5,0,0,1,0,0,0,0,16,0,1,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,Class_6
+40943,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,1,0,1,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,Class_6
+40944,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40945,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,5,0,0,0,0,0,0,0,1,0,0,2,2,0,1,0,0,0,4,0,0,0,0,7,1,0,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,3,7,2,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+40946,1,0,0,2,0,0,0,0,0,0,8,0,1,0,0,5,1,6,0,1,0,3,0,0,2,1,2,2,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,2,0,1,0,2,3,1,0,0,0,3,0,0,0,0,0,2,1,1,2,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,Class_6
+40947,1,0,13,1,0,0,0,0,1,0,9,0,1,1,0,6,0,1,0,0,3,5,0,2,0,0,15,4,0,0,0,0,0,1,1,2,0,0,0,1,0,3,0,0,0,8,1,1,3,0,0,0,1,29,2,2,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40948,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,1,0,0,1,0,4,0,1,2,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,10,0,0,0,0,0,7,1,0,0,0,0,5,1,0,1,0,0,10,0,2,0,2,1,1,8,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,7,0,0,0,0,1,0,Class_6
+40949,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,4,12,2,0,2,0,2,0,6,0,1,0,0,1,0,0,6,0,0,0,0,0,3,0,0,0,5,0,0,2,0,1,1,0,2,0,0,0,2,1,0,2,0,1,12,0,0,0,0,0,0,9,0,0,2,2,0,0,0,0,3,0,4,7,2,0,0,0,0,1,0,8,0,0,0,0,1,0,Class_6
+40950,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,3,1,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+40951,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40952,0,0,0,0,0,0,0,1,1,0,1,0,0,1,1,1,1,3,0,0,0,0,2,9,0,0,0,0,0,0,1,1,1,0,2,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,1,0,0,2,0,0,17,0,0,1,0,0,0,7,2,0,1,1,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,2,0,3,0,0,0,0,Class_6
+40953,1,0,0,0,0,0,0,0,0,2,0,0,0,1,2,2,0,0,0,0,0,2,0,5,0,2,0,0,0,0,0,0,2,2,0,0,0,1,0,2,0,10,0,2,0,0,0,1,0,0,0,0,0,4,0,0,3,0,0,5,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,Class_6
+40954,1,0,3,0,0,0,0,0,0,0,8,0,0,0,0,3,0,0,0,1,1,1,0,0,0,1,8,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+40955,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,13,0,0,0,1,0,0,4,1,0,3,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+40956,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,5,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40957,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,2,0,0,0,1,2,0,0,4,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,6,0,0,0,0,0,0,1,0,2,0,0,14,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,Class_6
+40958,0,1,0,1,0,0,0,0,0,1,3,0,1,2,0,0,0,2,0,0,0,3,0,3,0,3,0,0,0,0,0,0,0,1,0,1,0,2,0,1,0,10,0,5,0,0,0,0,0,1,0,0,0,7,1,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+40959,0,2,0,1,0,0,0,1,0,0,4,0,0,0,0,5,1,2,0,0,0,1,0,1,2,0,0,0,0,0,0,2,3,2,5,0,0,0,0,2,0,1,0,0,0,0,0,3,1,0,0,0,0,4,0,0,1,6,0,7,0,0,0,0,1,1,2,2,1,2,0,0,0,0,0,0,0,1,1,1,3,0,3,0,1,2,1,0,1,0,3,2,0,Class_6
+40960,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,5,1,1,2,0,0,0,0,0,0,4,1,0,0,1,0,0,5,1,0,1,0,1,0,0,0,0,1,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,7,0,0,1,0,0,6,Class_6
+40961,0,1,8,1,0,0,0,0,0,0,9,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,13,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+40962,2,1,0,0,0,0,0,0,1,0,0,1,0,3,0,0,2,0,0,2,0,0,1,2,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,2,1,0,2,1,0,0,2,0,9,0,1,0,0,0,2,1,0,2,0,0,0,6,0,1,2,0,0,1,1,0,0,6,0,0,0,0,0,0,0,0,1,1,Class_6
+40963,0,2,10,4,0,0,0,2,0,0,13,1,0,0,0,0,0,1,0,0,2,1,0,0,0,0,6,4,0,0,0,0,1,0,0,0,0,0,0,4,0,1,2,1,0,8,0,3,0,1,0,2,0,12,0,0,0,1,0,0,2,2,0,1,0,0,1,0,0,0,0,0,0,14,0,0,1,0,1,1,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+40964,0,0,0,0,0,0,0,3,0,0,0,0,11,0,0,1,3,0,0,2,0,1,0,2,2,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,2,0,0,0,2,2,1,3,1,0,0,0,6,1,0,0,0,0,1,0,1,0,0,0,8,0,0,0,0,0,0,0,5,0,3,0,4,0,0,Class_6
+40965,0,1,0,4,0,0,0,0,0,0,0,3,1,0,0,1,0,5,0,2,0,1,0,6,0,3,0,0,0,0,1,0,0,0,8,0,0,0,1,0,3,21,0,1,0,0,0,0,1,0,0,0,0,0,1,0,9,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,3,0,Class_6
+40966,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,7,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,6,0,0,10,0,0,1,0,0,1,5,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+40967,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,5,0,0,0,0,0,1,0,1,1,0,0,0,0,4,0,22,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40968,0,1,15,6,0,0,0,1,0,0,9,0,0,0,0,2,0,0,0,0,1,1,0,0,0,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,12,0,0,0,0,0,2,0,24,1,0,0,0,0,1,3,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,1,2,1,0,0,2,1,Class_6
+40969,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
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+40977,0,0,9,2,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,16,0,0,1,0,0,0,0,8,0,0,0,0,0,1,4,3,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,5,1,3,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40978,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+40979,0,0,6,1,0,0,0,0,0,0,13,0,1,0,0,3,0,0,0,0,2,1,0,6,1,1,21,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,2,24,0,0,1,0,0,0,0,11,0,1,0,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,Class_6
+40980,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40981,0,0,18,2,0,0,0,0,0,0,8,0,1,0,1,5,1,1,1,0,0,1,0,2,1,0,9,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,10,1,0,1,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,1,0,0,Class_6
+40982,0,0,9,1,0,0,1,0,0,0,16,0,0,0,0,1,0,1,0,0,0,4,0,5,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,2,0,0,1,0,15,0,0,0,6,0,1,2,0,1,1,0,1,0,0,0,3,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40983,0,0,2,11,0,0,0,0,0,0,8,0,1,0,2,9,0,2,0,0,0,4,0,1,0,5,1,5,0,0,0,1,1,0,1,0,1,0,0,0,0,1,0,1,0,2,0,0,1,0,0,0,0,12,0,0,0,0,0,0,2,0,0,0,0,1,2,0,1,0,0,1,0,0,0,2,0,0,0,8,0,4,0,0,0,0,0,0,1,0,0,1,0,Class_6
+40984,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,3,0,5,0,0,0,3,0,6,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,2,0,0,12,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,Class_6
+40985,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40986,0,2,4,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,4,0,12,0,1,0,0,0,4,2,0,4,2,0,0,0,1,0,5,0,2,0,1,0,0,0,0,0,0,0,7,0,0,1,0,0,12,0,0,0,0,0,2,6,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,Class_6
+40987,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40988,0,0,0,1,0,0,0,0,1,0,6,0,1,1,0,1,0,0,0,0,1,1,0,0,0,1,8,1,0,0,1,1,0,0,2,0,1,0,0,0,0,0,0,1,0,1,0,1,1,0,0,7,0,1,0,1,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40989,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,1,1,2,0,5,0,0,0,0,1,0,0,1,2,0,1,0,0,2,0,3,3,1,0,1,0,0,2,1,0,0,0,3,0,0,4,0,0,8,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,Class_6
+40990,0,1,4,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,4,0,2,2,0,0,1,0,0,3,1,0,0,0,0,0,7,0,4,0,1,0,1,0,0,0,0,0,5,2,0,2,0,0,8,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,Class_6
+40991,0,0,13,2,0,0,0,1,0,1,12,1,2,0,0,2,0,0,0,1,1,4,0,1,0,0,3,1,0,0,0,0,1,1,0,0,1,2,0,0,0,0,0,1,0,13,1,0,2,0,0,0,0,28,1,0,1,0,0,3,2,0,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40992,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,1,1,0,2,1,3,0,0,0,0,0,0,3,0,0,4,0,1,4,1,1,0,1,4,0,0,0,0,3,1,2,0,0,0,1,0,1,4,0,0,0,0,0,1,1,0,1,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,Class_6
+40993,0,1,0,2,0,0,0,0,0,0,6,2,0,1,3,0,0,0,0,0,5,2,2,0,0,0,7,0,0,0,2,0,1,0,0,0,3,0,1,6,0,0,1,0,0,1,0,2,0,1,0,6,0,2,0,0,0,2,0,0,2,0,0,0,1,0,1,0,2,0,0,0,2,92,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,5,0,0,0,Class_6
+40994,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40995,2,2,16,1,0,0,0,0,0,0,11,1,0,0,0,6,0,0,0,0,2,1,0,2,0,1,10,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,15,0,0,1,0,0,1,0,19,0,1,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+40996,0,0,3,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,5,0,1,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,2,0,0,0,0,2,0,0,2,0,8,1,0,0,0,0,8,1,0,0,0,0,1,2,0,0,1,0,0,0,0,2,0,0,0,1,4,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+40997,0,0,4,11,0,0,0,0,0,0,10,0,0,1,0,3,0,1,0,0,2,3,0,0,0,1,5,5,0,0,1,2,0,0,1,0,1,0,1,3,0,0,0,3,1,3,0,0,3,4,0,0,0,18,1,0,0,0,0,1,4,0,0,0,3,0,3,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+40998,0,0,11,3,1,0,1,0,0,1,13,1,0,0,0,0,0,1,0,1,0,1,0,5,1,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,4,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_6
+40999,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41000,1,0,9,4,0,0,0,0,0,0,9,0,0,2,1,2,0,0,0,0,2,0,0,0,1,1,7,3,0,0,0,1,0,1,1,0,0,1,1,0,0,2,0,1,0,8,0,3,2,0,1,1,1,15,1,0,0,1,0,1,1,2,0,0,0,0,0,0,0,0,0,1,0,2,1,3,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,Class_6
+41001,0,0,0,0,0,0,0,0,0,1,2,1,7,1,0,0,0,1,0,1,0,3,0,2,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,7,0,0,3,1,1,0,0,0,0,3,3,0,1,1,1,0,0,0,0,0,0,1,1,4,0,0,0,0,0,1,0,1,1,0,3,1,0,0,1,0,1,0,0,0,0,0,0,0,0,Class_6
+41002,0,0,4,3,0,0,0,0,0,0,8,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,2,0,1,0,0,3,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,0,0,6,1,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41003,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,3,0,1,15,0,0,2,0,0,0,1,2,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,11,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41004,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,Class_6
+41005,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41006,0,0,1,1,0,0,0,1,0,0,6,0,0,0,0,4,0,0,0,0,0,5,0,1,1,4,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,3,0,1,1,0,0,0,0,0,0,14,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,3,0,0,1,0,0,0,0,0,1,0,Class_6
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+41008,0,0,4,9,0,0,0,0,0,1,10,0,0,0,0,1,0,0,0,0,0,0,0,3,0,3,4,3,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,0,1,0,2,0,1,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41018,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,4,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41019,0,0,3,2,0,0,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,1,2,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,14,0,0,1,0,0,0,2,1,0,0,1,2,1,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41020,0,1,0,10,0,0,0,0,1,0,11,0,0,0,0,3,0,2,0,0,0,2,0,4,0,16,0,0,3,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,2,0,1,5,0,4,0,0,1,1,0,7,2,3,0,3,0,1,0,0,0,0,0,0,0,2,2,2,0,0,0,6,2,0,0,0,0,0,1,0,1,1,0,1,0,Class_6
+41021,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41022,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,3,3,3,0,1,0,0,0,13,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,1,0,0,4,1,1,0,0,0,1,0,0,1,0,0,2,0,0,0,0,1,0,5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+41023,0,0,1,2,0,0,0,7,3,0,0,0,0,3,2,6,0,1,0,2,0,7,1,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,2,1,0,0,3,5,0,1,0,0,1,0,0,0,0,0,2,0,2,0,0,1,0,2,0,4,0,1,1,0,1,0,0,3,0,0,1,0,0,0,0,0,0,0,2,0,8,2,3,1,0,1,0,0,0,Class_6
+41024,0,0,4,6,0,0,0,0,1,0,6,0,0,0,0,4,0,0,0,0,0,4,0,2,0,7,2,1,0,1,0,0,2,2,1,0,0,0,1,0,0,11,0,0,0,2,0,3,1,1,0,0,0,7,1,0,1,0,0,6,3,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,1,1,0,1,0,0,Class_6
+41025,3,0,0,0,0,0,1,0,0,0,0,0,2,1,0,1,0,3,0,3,0,1,0,9,3,1,0,0,2,0,0,3,0,0,0,2,0,1,0,1,0,12,0,6,0,0,0,4,1,0,0,0,0,1,0,0,2,0,2,0,0,1,0,3,0,3,12,0,2,5,0,0,0,0,0,0,0,1,0,0,0,0,6,0,1,1,17,1,0,1,0,0,6,Class_6
+41026,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41027,0,1,1,0,0,0,0,0,0,0,9,0,1,0,1,4,0,2,0,0,4,3,0,0,0,2,27,2,0,0,1,0,0,0,1,1,0,0,0,2,0,0,1,0,0,10,0,0,1,0,0,5,0,5,0,0,0,1,0,0,1,1,1,0,0,1,0,0,0,2,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41028,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,7,0,1,0,1,0,7,0,0,0,0,0,0,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,3,0,0,10,0,3,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,Class_6
+41029,0,1,0,1,0,0,0,6,0,0,3,0,1,0,0,0,3,1,1,0,0,0,0,1,0,3,1,0,0,0,3,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,2,1,0,1,0,0,1,2,0,9,0,2,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41030,0,2,0,0,0,0,0,0,0,0,11,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41031,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,4,0,4,0,0,0,0,1,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,4,0,1,0,0,2,0,0,12,0,1,0,1,0,1,0,1,0,0,0,0,1,2,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,3,0,1,5,Class_6
+41032,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41033,0,0,0,2,0,0,0,0,0,0,7,0,0,1,1,3,0,0,1,0,0,0,0,4,0,2,2,1,0,0,2,1,0,0,1,1,0,0,0,0,0,2,0,1,0,2,0,0,2,0,0,0,0,3,0,0,0,0,0,0,4,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,6,0,3,1,0,0,0,0,0,2,0,0,0,0,Class_6
+41034,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,5,0,2,0,3,0,3,0,1,2,6,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,13,0,2,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,4,0,0,0,3,0,5,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,1,Class_6
+41035,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,0,0,9,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,1,0,2,0,0,17,2,0,0,0,0,0,1,3,0,1,0,1,0,0,1,1,0,0,3,1,0,1,0,0,0,0,5,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+41036,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,10,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,1,2,0,0,0,0,0,0,0,1,0,0,1,0,1,3,Class_6
+41037,0,0,1,0,1,0,0,0,4,1,0,1,4,0,0,5,0,1,0,0,0,7,0,2,0,0,0,0,30,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,8,0,2,0,0,1,0,2,0,0,1,0,5,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41038,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_6
+41039,0,1,0,2,0,0,0,0,1,0,10,2,0,0,0,1,0,2,0,0,0,1,0,7,0,2,2,0,0,0,2,1,1,0,1,2,0,5,1,2,0,0,0,1,1,3,0,0,0,0,0,8,0,3,0,0,0,2,0,0,2,2,0,1,1,0,0,1,0,1,0,1,0,19,0,0,0,0,1,20,1,1,0,0,0,0,1,0,1,0,0,0,1,Class_6
+41040,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41041,0,1,5,7,0,0,0,0,0,0,8,0,0,0,1,2,0,1,0,0,1,0,0,2,0,0,0,2,0,0,0,2,0,1,2,0,0,0,0,0,0,2,0,2,0,0,0,0,5,0,0,0,0,6,0,0,0,0,0,1,2,1,0,0,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,8,0,5,0,0,0,0,0,1,1,0,0,0,0,Class_6
+41042,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,2,0,1,0,3,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,4,1,0,0,0,0,12,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41043,0,0,6,3,0,0,0,0,0,1,9,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,18,1,0,0,0,0,1,3,1,0,0,0,0,0,0,0,1,1,0,4,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41044,0,1,3,11,0,0,1,0,0,1,2,0,0,2,0,7,0,4,0,0,1,5,0,16,0,4,1,0,0,0,1,2,1,2,3,0,4,2,0,0,0,5,20,3,0,0,0,0,1,0,0,0,0,17,1,0,0,3,0,1,3,3,1,1,0,2,2,0,0,1,0,0,0,1,1,2,0,0,1,3,0,0,3,0,0,4,0,0,2,0,0,0,0,Class_6
+41045,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,6,0,6,0,0,0,0,0,2,0,0,1,3,0,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,12,0,3,0,0,0,0,6,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+41046,0,0,2,11,0,0,0,0,0,0,0,0,2,0,0,3,0,1,0,0,0,0,0,7,0,2,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,3,0,0,3,0,0,12,1,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41047,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,2,0,1,1,4,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,3,0,0,1,2,0,13,0,1,1,0,0,0,3,0,0,0,0,0,0,5,0,0,1,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
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+41055,0,1,5,4,0,0,0,0,0,2,6,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,5,0,0,0,0,14,1,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,3,0,0,1,0,0,0,2,1,1,0,0,Class_6
+41056,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,1,0,5,1,1,0,4,0,0,0,1,0,0,4,0,0,0,0,0,0,6,0,1,0,0,0,1,0,0,0,0,0,2,1,0,1,0,1,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+41057,0,0,11,5,0,0,0,0,0,1,9,1,1,0,0,1,1,0,0,0,0,2,0,0,0,0,6,1,1,0,0,0,0,1,4,2,0,0,1,0,0,0,0,3,0,8,0,0,0,0,0,0,0,25,1,0,0,3,0,0,1,2,0,1,0,4,0,0,0,0,0,1,1,4,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41058,0,1,9,1,0,0,0,0,0,0,10,0,0,1,0,1,0,0,0,0,0,2,0,6,0,0,3,0,0,0,0,3,0,1,0,1,0,0,1,0,0,2,0,0,0,11,0,0,4,0,0,1,0,24,1,0,1,6,0,2,1,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,8,0,0,1,0,0,0,1,0,1,0,0,0,0,Class_6
+41059,0,0,0,0,0,0,1,2,0,2,0,0,1,0,0,4,0,2,0,0,0,3,0,3,0,0,0,4,1,1,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,2,2,0,0,0,0,0,3,0,0,2,0,0,9,0,1,2,0,0,0,2,0,0,2,1,0,1,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,0,2,0,Class_6
+41060,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+41061,0,0,8,2,0,0,0,0,0,0,13,1,0,1,0,0,0,0,0,0,0,1,0,6,0,2,3,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,11,0,1,0,0,0,0,0,6,0,0,0,4,1,1,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
+41062,0,0,0,4,1,0,0,0,0,1,0,0,0,0,1,1,0,2,0,2,0,1,0,2,0,1,0,1,0,0,0,0,0,0,9,0,0,0,1,1,0,5,0,2,1,0,2,0,0,0,0,0,0,2,3,0,9,1,0,12,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41063,0,0,7,4,0,0,0,0,0,0,8,0,0,0,0,4,1,2,0,0,2,1,0,2,0,2,5,3,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,17,0,0,0,0,0,1,0,15,1,0,1,0,1,0,2,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41064,0,1,4,3,0,0,0,1,3,0,6,0,0,1,1,0,0,3,0,0,1,1,0,10,0,0,3,2,0,0,4,3,0,0,1,0,2,0,0,3,0,0,0,1,2,2,2,0,0,0,0,8,0,5,1,1,0,1,0,0,1,0,2,0,2,0,2,1,0,0,0,0,0,34,0,2,0,0,0,21,0,1,0,0,0,2,0,0,0,2,0,0,0,Class_6
+41065,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,4,0,0,0,1,0,1,0,0,3,1,0,0,0,0,1,0,3,0,0,1,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,3,1,0,0,0,0,0,1,5,1,1,0,0,0,1,0,2,0,0,0,0,0,0,2,1,0,0,0,Class_6
+41066,0,0,1,4,0,0,0,0,0,0,0,0,1,1,0,3,0,1,1,0,0,2,0,4,1,2,0,0,0,0,0,0,0,0,5,0,1,0,0,1,0,5,0,1,0,0,0,0,5,0,0,0,0,3,0,0,5,2,0,13,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41067,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,1,2,0,1,1,0,2,2,1,0,0,4,0,0,0,0,0,2,2,0,0,0,0,11,0,0,0,0,0,1,7,0,0,3,0,1,0,1,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41068,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,9,0,0,0,3,2,4,0,1,0,0,0,0,1,0,0,0,3,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,3,0,0,12,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+41069,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,1,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,3,0,0,8,1,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+41070,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,4,0,0,0,3,0,2,0,1,0,0,0,0,5,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41071,0,2,2,1,0,0,0,0,1,0,6,0,0,1,0,0,0,0,0,0,0,2,0,0,0,3,5,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,6,0,2,0,0,0,1,5,3,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,15,0,9,0,0,0,0,0,0,3,0,1,0,0,Class_6
+41072,0,1,0,0,0,0,0,2,0,1,0,0,0,0,1,0,2,0,1,1,0,0,1,9,0,2,0,0,0,0,1,1,0,3,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,1,3,0,0,12,0,0,0,1,0,0,6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,Class_6
+41073,0,1,4,18,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,1,0,0,0,0,4,1,1,0,0,0,2,0,0,5,0,0,1,0,0,0,0,0,1,0,4,0,0,2,0,0,3,0,7,0,0,0,0,0,1,0,2,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,4,0,0,0,1,0,0,2,0,0,0,0,Class_6
+41074,0,0,3,3,0,0,0,0,0,0,0,0,0,0,2,3,0,4,0,0,0,4,0,4,0,1,0,0,0,0,0,2,1,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,5,1,0,2,0,0,13,0,2,1,0,0,4,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41075,0,0,1,1,0,0,0,0,0,1,7,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,2,3,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41076,0,1,6,9,0,0,0,0,0,2,21,1,1,0,0,2,0,1,0,0,0,1,0,16,0,1,1,4,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,4,0,1,0,15,0,0,0,1,1,2,0,0,1,0,0,2,0,0,0,3,0,0,0,0,0,4,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41077,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41078,0,0,0,2,0,0,1,0,0,0,6,1,0,0,0,7,0,1,1,1,0,6,0,2,1,0,3,1,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,5,0,1,0,0,2,0,0,0,0,14,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,2,1,0,0,0,0,Class_6
+41079,0,0,12,6,0,0,0,1,0,1,13,1,0,0,4,2,0,0,0,0,0,2,0,0,0,1,1,1,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,2,0,7,0,0,1,0,0,0,0,8,0,1,0,1,1,1,2,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,15,0,6,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41080,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,5,0,0,0,7,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41081,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,5,1,0,0,0,0,0,0,1,1,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41082,0,0,0,0,0,0,0,0,0,0,0,1,3,0,2,5,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,8,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41083,0,0,6,6,0,0,1,0,0,0,17,0,0,0,0,3,0,0,0,1,0,1,0,7,0,0,10,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,1,0,7,0,0,1,6,0,1,1,0,0,1,1,0,1,0,1,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41084,0,0,9,11,0,0,0,0,0,1,9,0,0,0,0,5,0,0,0,0,1,0,0,2,0,1,3,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,15,0,0,4,0,0,1,0,14,1,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,5,0,2,0,0,0,0,0,1,0,0,0,0,2,Class_6
+41085,1,0,3,1,0,0,0,1,0,0,13,0,0,0,0,4,0,1,0,0,0,0,0,16,0,1,6,1,0,0,0,3,1,1,0,0,0,1,0,0,0,0,0,1,0,7,0,0,0,0,0,5,0,8,0,1,0,1,0,0,9,1,0,1,0,2,2,0,0,0,0,0,0,6,1,2,0,0,1,6,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
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+41087,1,0,5,4,0,0,0,0,0,0,14,0,0,0,0,2,0,1,0,0,2,1,0,14,1,3,0,0,1,0,2,4,0,0,2,0,1,0,0,0,0,1,0,0,0,2,0,2,0,0,0,2,0,14,0,5,0,1,0,0,0,0,2,0,0,0,3,0,1,0,0,1,0,12,0,0,0,0,0,24,0,0,1,0,1,0,0,0,1,0,0,0,0,Class_6
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+41104,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41105,0,0,4,5,0,0,0,0,0,0,0,0,3,0,0,5,0,0,0,0,0,2,0,1,1,5,0,2,0,0,0,2,0,0,0,1,0,2,0,1,1,6,0,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,0,1,0,1,1,2,0,7,4,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,2,0,Class_6
+41106,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,3,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,13,0,3,0,1,1,1,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41107,0,0,1,1,0,0,1,0,0,0,1,2,2,0,0,2,0,0,0,0,0,4,0,1,0,6,0,0,0,0,0,0,0,0,8,0,0,1,0,4,0,14,0,5,0,0,0,0,10,0,0,0,0,9,0,0,0,0,0,13,0,5,0,0,0,3,0,0,0,2,0,1,0,0,0,1,0,1,2,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41108,0,0,1,7,0,0,0,0,0,0,1,0,1,0,1,2,0,3,0,0,0,0,0,9,1,3,2,4,1,0,0,0,1,0,7,1,0,2,0,0,0,1,0,2,0,1,0,0,1,1,0,0,0,7,0,0,3,0,0,19,0,0,1,0,0,1,1,0,1,0,5,0,0,3,4,0,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_6
+41109,1,1,4,13,0,0,0,0,0,0,0,0,1,1,0,2,3,0,1,0,0,3,0,2,0,9,0,1,1,0,4,0,2,1,1,0,0,0,6,1,0,0,0,1,0,1,0,3,2,0,1,0,0,6,1,0,5,0,0,12,0,1,0,2,0,1,2,1,0,0,0,0,0,1,5,0,0,2,0,0,0,1,1,2,0,1,2,0,0,4,0,0,0,Class_6
+41110,3,0,0,0,0,0,0,5,0,0,0,0,2,0,0,2,0,1,0,0,1,2,0,1,4,3,2,0,1,0,0,0,0,0,0,4,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,2,1,0,0,0,1,2,0,7,0,1,0,9,10,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,Class_6
+41111,0,0,4,10,0,0,0,0,0,0,6,0,1,0,0,3,0,4,0,1,0,7,0,0,0,17,2,8,0,0,0,2,0,0,3,0,0,1,0,0,0,8,0,4,0,1,0,1,5,0,0,0,0,4,0,0,0,0,0,1,0,1,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,2,0,0,0,Class_6
+41112,0,0,0,1,0,0,0,1,0,0,0,0,3,0,0,2,1,5,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41113,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41114,0,0,10,8,0,0,0,0,0,0,9,4,2,0,0,1,0,0,0,0,3,0,1,2,0,1,12,1,1,0,0,0,0,2,0,0,0,0,0,2,0,1,0,1,0,7,0,0,0,0,0,0,0,9,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41115,1,1,0,0,0,0,1,1,0,1,0,0,0,0,0,6,0,0,0,0,0,3,0,4,1,0,0,0,1,1,0,0,0,0,0,1,0,1,0,2,0,5,0,2,0,0,0,1,0,0,0,0,0,3,1,0,1,0,1,0,0,0,0,0,1,0,9,0,0,1,0,0,0,3,1,7,0,0,0,0,0,0,4,0,0,0,0,0,0,0,12,4,0,Class_6
+41116,0,0,0,0,0,0,0,1,4,0,38,0,0,1,1,0,0,0,0,0,5,0,0,1,1,0,5,1,0,0,0,1,0,0,2,1,1,6,0,1,0,0,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,15,0,3,0,0,0,0,2,0,1,0,0,0,0,Class_6
+41117,0,1,0,0,0,0,0,0,1,0,0,0,2,1,0,1,0,4,0,0,0,0,0,9,0,0,2,0,1,0,1,1,0,0,3,1,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,2,1,0,16,0,0,1,0,0,0,8,0,0,3,0,2,0,0,1,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+41118,3,0,1,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,2,5,0,1,0,0,0,0,0,0,2,1,0,0,0,0,1,10,0,0,0,3,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41119,0,0,4,1,0,0,0,0,0,2,6,0,1,1,0,2,0,1,0,0,0,2,0,0,0,1,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,2,0,4,0,1,9,0,0,0,0,17,0,0,0,0,0,2,2,2,0,1,0,1,1,0,1,2,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41120,0,0,4,2,0,0,0,0,0,0,6,1,0,0,1,3,0,0,0,0,2,2,0,0,0,2,20,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,8,0,0,1,0,0,0,0,10,1,0,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41121,0,1,20,4,0,0,0,0,0,2,16,0,2,2,0,16,0,4,0,2,13,8,0,0,0,10,14,9,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,6,0,17,1,0,5,0,0,2,0,42,2,0,0,0,0,0,3,1,0,0,2,2,0,0,0,0,0,3,0,3,1,0,0,0,1,13,0,3,0,0,0,0,1,1,3,0,0,0,0,Class_6
+41122,0,1,10,4,0,0,0,0,0,2,10,0,0,0,0,6,0,1,0,1,1,4,0,0,0,0,16,4,0,2,1,0,0,0,1,0,0,0,0,3,0,0,0,1,0,8,0,0,3,0,1,0,0,11,0,0,1,0,0,2,2,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41123,0,1,1,4,0,0,0,1,0,0,9,0,0,0,0,2,0,0,0,0,1,0,0,7,0,0,9,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,1,0,2,0,2,0,4,0,1,1,1,0,1,0,1,1,1,0,1,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41124,0,2,1,3,0,0,0,0,0,0,9,0,1,0,1,3,0,1,1,0,0,3,0,0,1,1,1,0,0,0,0,0,1,1,5,2,0,1,0,1,0,3,0,5,0,3,3,2,12,0,0,0,0,13,1,0,1,0,0,0,0,1,0,0,1,4,0,2,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41125,0,0,13,4,0,0,0,0,0,1,9,0,0,0,0,0,0,0,0,0,2,0,0,2,0,1,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,10,0,0,1,0,0,0,0,14,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,4,1,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+41127,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,3,1,6,0,1,0,2,0,6,2,6,0,0,0,1,0,0,0,0,2,0,0,4,0,0,0,9,0,3,0,0,0,0,1,0,0,0,1,2,1,0,9,0,0,12,0,1,4,0,1,1,0,3,0,4,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,1,Class_6
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+41144,0,0,12,3,0,0,0,0,0,1,8,0,1,0,1,4,0,2,0,0,2,1,0,0,1,0,1,1,0,0,0,2,0,2,2,0,0,0,0,0,0,1,2,0,0,10,1,1,0,0,0,1,1,19,0,3,0,0,0,1,3,0,0,0,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41145,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,1,1,0,0,0,1,1,0,1,0,2,1,4,1,0,0,0,0,0,2,0,1,0,0,1,0,2,0,3,0,0,4,3,1,0,0,0,0,4,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,1,Class_6
+41146,7,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,11,0,0,0,0,0,0,6,1,1,0,1,0,1,0,Class_6
+41147,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+41148,0,0,10,4,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,2,0,1,0,7,2,6,0,0,0,0,1,0,3,0,0,0,0,0,1,5,0,0,0,3,0,3,2,0,0,0,0,13,0,0,0,0,1,0,3,4,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,2,0,0,0,0,1,1,0,0,0,3,0,Class_6
+41149,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,3,0,0,2,0,0,13,0,0,0,1,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41150,0,0,1,0,0,0,0,0,0,0,10,0,0,0,1,1,0,1,0,0,2,0,0,6,0,1,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,2,0,0,0,1,0,1,3,0,0,0,0,5,0,0,0,5,0,1,1,1,0,1,0,0,3,0,0,0,0,0,0,1,0,2,0,0,0,12,0,1,0,0,0,0,0,0,0,0,0,1,1,Class_6
+41151,0,0,4,4,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,0,0,1,0,4,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41152,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41153,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,2,0,3,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,9,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41154,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+41155,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41156,0,0,2,6,0,0,0,0,2,0,10,1,0,0,0,0,0,4,0,0,4,0,0,1,0,2,17,0,0,0,0,0,0,1,4,0,1,0,1,0,0,0,0,2,0,4,0,0,1,0,0,2,0,6,0,0,0,1,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41157,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,17,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,1,0,Class_6
+41158,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,14,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41159,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,Class_6
+41160,0,1,0,1,0,0,0,0,2,0,0,0,2,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,1,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,17,0,2,0,3,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41161,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,2,4,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41162,0,0,13,2,0,0,0,0,0,0,4,1,0,2,0,5,0,0,0,0,0,0,3,0,0,0,4,1,0,0,0,0,0,0,1,2,0,1,0,0,0,0,1,1,0,4,0,0,2,0,0,0,1,11,1,0,0,0,1,1,6,1,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,10,1,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41163,0,1,2,1,0,0,0,0,0,0,10,0,0,0,0,6,0,1,0,0,0,1,0,2,0,1,0,0,0,0,1,1,1,0,3,1,0,4,0,0,0,1,0,0,0,4,0,0,1,0,0,3,0,10,0,0,0,4,0,1,6,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,20,0,2,0,0,3,0,0,0,3,1,0,0,0,Class_6
+41164,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+41165,0,0,0,1,0,0,0,3,2,0,0,0,0,0,0,1,2,4,0,0,0,2,0,3,1,1,0,0,0,0,0,1,1,1,1,0,0,0,0,1,0,4,0,0,0,0,0,2,0,0,0,0,0,1,1,0,3,0,0,4,1,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
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+41185,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41186,0,0,7,2,0,0,0,0,0,0,5,1,0,1,2,4,0,0,0,0,0,4,0,2,0,2,4,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,5,0,0,1,0,0,1,0,13,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,2,0,0,0,Class_6
+41187,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,5,0,0,0,0,0,6,1,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,4,0,0,3,0,0,4,0,1,1,0,0,3,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41188,0,0,5,4,0,0,0,0,1,0,7,1,0,0,0,2,0,1,1,0,0,2,0,1,0,1,4,1,0,0,0,0,1,0,0,0,0,0,3,1,1,1,0,1,0,0,0,1,2,1,0,0,0,10,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,1,1,0,1,0,Class_6
+41189,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,2,0,3,0,2,2,4,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,0,1,1,0,0,0,0,3,0,0,5,0,0,4,0,0,0,2,0,0,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+41190,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41191,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,3,0,2,0,3,0,6,1,0,1,0,0,0,2,0,1,0,0,2,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,2,1,13,0,1,0,0,0,0,5,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+41192,0,0,5,9,0,0,0,0,0,0,10,0,0,0,0,1,0,1,1,0,2,2,0,0,1,1,7,1,1,0,0,1,1,0,2,1,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,9,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41193,0,0,0,0,0,0,0,0,1,0,12,0,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,25,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+41194,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,2,0,10,0,5,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,2,0,0,4,0,0,12,0,0,0,0,0,0,4,0,6,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41195,0,0,1,2,0,0,0,1,0,0,8,0,1,0,0,2,0,2,1,0,0,0,0,2,0,5,0,0,0,0,1,0,2,0,2,0,0,0,0,0,1,1,0,2,0,1,0,2,0,0,0,1,0,4,0,3,0,1,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,2,0,Class_6
+41196,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+41197,2,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41198,0,0,0,0,0,0,1,1,1,0,0,0,2,0,0,0,1,0,1,2,0,0,0,28,0,0,0,0,0,0,0,8,0,0,2,1,0,3,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,51,0,0,0,1,0,4,0,0,4,0,14,0,0,1,3,1,0,1,0,2,0,0,0,0,11,0,0,0,0,0,1,13,0,0,2,Class_6
+41199,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41200,0,1,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,0,0,0,0,19,1,14,1,3,1,0,0,0,1,0,5,0,0,0,0,1,0,10,0,3,0,2,0,0,0,0,0,0,1,2,0,0,1,0,1,10,0,1,2,0,0,1,0,1,0,0,4,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,Class_6
+41201,1,0,0,3,0,0,0,7,2,0,0,0,2,2,0,1,8,3,0,3,0,6,0,5,3,1,0,1,0,0,0,8,3,0,0,5,1,5,2,1,2,2,0,2,0,1,0,2,0,0,0,0,0,1,4,0,1,0,1,12,0,1,0,9,0,5,16,1,0,13,0,2,0,3,0,1,0,0,1,0,4,0,0,0,3,1,17,2,1,0,0,0,1,Class_6
+41202,0,0,8,1,0,0,0,0,0,0,11,1,0,0,0,0,0,0,0,0,2,1,0,10,0,1,1,6,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,9,0,0,0,0,0,1,0,8,0,0,0,9,0,0,2,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41203,0,1,6,6,0,0,0,0,0,1,6,0,0,0,0,4,0,0,0,0,0,1,0,4,0,1,2,2,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,4,0,0,4,0,0,0,0,14,0,0,2,0,0,1,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41204,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,1,0,1,1,1,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,16,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+41205,0,0,0,0,0,0,0,3,0,0,2,0,2,4,0,2,2,0,0,0,0,0,0,5,1,0,0,0,0,0,1,2,0,2,1,1,2,0,19,1,0,1,0,1,3,0,0,2,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,0,0,0,0,0,0,1,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41206,0,0,2,2,0,0,0,0,0,0,1,1,1,0,0,0,0,2,0,0,0,7,0,10,2,5,0,1,18,0,0,0,0,0,0,1,0,1,0,1,0,3,0,2,0,0,0,0,1,0,0,0,0,3,7,0,0,0,0,1,0,3,0,1,0,3,17,0,0,2,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+41207,0,0,8,1,0,0,0,0,0,1,8,1,1,0,0,1,0,0,1,0,0,2,1,3,0,9,5,8,2,0,1,0,0,0,1,1,0,0,0,2,0,3,0,1,0,5,1,0,1,0,0,0,0,5,0,0,1,1,1,0,1,3,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,1,1,0,0,3,0,Class_6
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+41217,0,0,8,4,0,0,0,0,0,0,5,1,0,0,0,2,0,1,0,0,1,1,0,1,0,1,4,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,6,0,2,2,0,0,0,3,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+41219,0,0,3,5,0,0,0,0,0,0,11,0,1,0,1,1,0,0,0,1,2,2,0,0,0,1,5,4,1,0,0,0,0,1,4,0,0,0,0,0,1,1,2,1,0,7,0,0,4,0,0,3,0,8,0,1,0,2,1,1,1,1,0,1,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,10,0,3,0,0,0,0,0,2,3,1,1,0,0,Class_6
+41220,0,0,3,0,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,1,2,0,0,6,0,0,5,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,8,0,0,1,0,1,2,0,0,1,0,0,1,1,0,0,0,1,0,8,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41221,0,0,1,0,0,0,0,2,1,0,1,0,11,1,0,1,17,6,0,0,0,0,0,14,0,1,0,0,1,2,0,2,0,1,0,0,0,0,0,1,0,6,5,0,0,0,0,10,1,0,0,2,0,1,1,1,9,0,1,8,0,2,0,0,0,1,13,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,4,0,2,0,2,0,0,Class_6
+41222,0,1,8,18,0,0,0,0,0,1,7,0,0,0,1,2,0,1,0,0,3,0,0,2,0,2,1,3,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,4,1,1,1,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,3,0,0,0,0,0,Class_6
+41223,0,1,6,9,0,0,0,2,0,1,9,0,0,1,0,2,0,3,0,0,1,2,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,3,0,0,5,0,4,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,11,0,5,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41224,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,12,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+41225,0,0,1,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,4,0,0,0,3,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,1,Class_6
+41226,0,0,0,6,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,5,1,17,0,1,0,0,4,0,0,3,5,0,0,1,0,0,0,0,0,1,0,1,0,1,2,0,0,0,0,5,0,0,5,0,0,12,0,0,0,0,1,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,Class_6
+41227,1,0,10,1,0,0,1,0,0,0,9,0,0,1,0,4,0,0,0,0,0,2,0,0,0,1,6,2,0,0,0,0,0,2,0,1,0,0,0,0,0,2,1,1,1,14,0,0,1,0,0,0,0,15,1,0,0,0,0,0,3,2,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,2,1,0,0,0,0,0,0,0,0,0,0,Class_6
+41228,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41229,3,0,1,1,1,0,0,1,0,2,0,2,19,0,0,4,1,3,0,0,0,0,0,13,4,0,0,0,2,0,0,5,2,0,0,3,1,3,0,6,1,2,0,1,0,0,0,6,0,3,0,1,4,8,1,0,0,0,40,4,0,3,2,1,2,1,35,1,3,11,2,1,0,2,1,0,2,2,7,0,0,0,0,0,5,4,1,1,0,0,0,0,0,Class_6
+41230,1,0,0,5,0,0,1,0,0,0,0,0,2,0,1,9,0,1,0,0,0,5,0,10,0,7,3,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,1,0,1,0,1,3,0,0,0,0,22,0,0,10,1,0,12,0,3,0,2,0,6,1,0,0,0,0,0,1,4,1,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+41231,0,0,11,3,0,0,0,0,0,2,7,0,0,0,0,2,0,0,1,0,0,1,0,0,0,1,3,1,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,3,0,1,1,0,0,0,0,7,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,Class_6
+41232,0,0,16,5,0,0,0,0,0,1,10,0,0,1,0,2,0,0,1,0,2,1,0,0,1,1,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,1,0,1,0,0,0,0,7,1,1,0,0,0,0,5,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41233,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,3,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,0,0,0,2,0,0,10,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,5,0,1,1,0,0,0,Class_6
+41234,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,2,1,0,1,0,0,2,0,6,0,1,0,2,0,0,0,1,0,1,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,14,0,0,5,0,0,9,0,1,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41235,0,0,2,2,1,0,0,0,0,0,7,0,1,0,0,0,0,1,0,0,0,5,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,4,0,2,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,Class_6
+41236,0,0,2,0,0,0,0,0,0,0,10,1,1,0,0,4,0,1,0,0,0,1,0,0,0,0,2,2,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,18,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41237,0,0,3,5,1,0,0,0,0,0,7,0,0,0,0,4,0,0,0,0,0,4,1,2,1,6,18,5,0,0,0,1,0,0,0,2,1,0,0,0,1,2,1,1,0,2,0,0,5,0,0,0,0,12,0,1,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,Class_6
+41238,0,0,1,25,0,0,0,0,0,1,3,0,0,0,0,2,0,2,0,0,0,3,0,4,1,5,0,2,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,5,0,0,3,0,0,12,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,3,0,1,0,0,0,0,0,0,1,2,0,0,0,Class_6
+41239,0,2,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,1,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,3,0,0,4,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41240,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,2,0,0,0,0,0,0,1,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+41241,0,1,5,6,0,0,0,0,0,0,0,0,0,2,0,6,0,5,0,0,0,3,0,10,0,11,2,1,0,0,0,1,2,4,6,0,0,0,0,0,2,4,0,1,0,0,0,4,1,0,0,0,0,14,1,0,6,0,0,12,0,0,0,5,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41242,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,4,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,3,0,0,0,2,0,0,0,0,0,3,2,0,3,0,0,4,1,1,0,0,0,2,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,Class_6
+41243,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41244,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,3,0,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,10,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,10,0,0,0,2,0,2,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,Class_6
+41245,0,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,7,0,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41246,0,1,5,3,0,1,0,0,0,1,16,0,0,0,0,0,0,0,0,0,1,0,0,6,0,2,10,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,10,0,1,1,0,0,0,0,6,0,0,0,23,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,Class_6
+41247,0,0,6,0,0,0,0,0,0,0,15,0,1,0,0,3,0,1,0,0,3,1,0,4,0,4,13,3,1,0,0,2,0,0,0,0,1,0,0,0,0,2,0,1,0,6,0,0,2,0,1,1,0,6,0,1,0,6,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+41265,0,1,6,6,0,0,0,0,1,0,5,0,1,1,0,3,0,2,0,0,4,1,0,0,0,0,4,2,0,0,1,0,0,1,1,1,0,4,0,1,0,0,0,0,0,3,0,3,4,0,0,0,1,4,0,1,1,0,0,0,3,1,0,0,0,2,0,0,0,1,0,2,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,1,0,Class_6
+41266,0,1,1,1,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,8,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,5,0,1,4,0,0,1,0,0,1,0,0,0,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+41267,0,0,15,5,0,0,0,0,0,0,7,0,0,1,0,6,0,1,0,0,2,0,0,0,0,4,4,8,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,10,0,0,0,0,0,1,0,24,1,0,0,0,0,1,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+41268,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41269,0,1,2,14,0,0,1,0,0,0,0,0,0,2,2,1,0,1,1,5,1,6,0,11,1,2,0,1,3,0,5,1,0,0,4,0,0,2,0,1,0,0,0,0,0,1,0,3,3,0,0,2,0,5,3,0,3,1,0,14,0,2,0,1,0,5,4,0,1,1,0,0,2,1,0,1,0,0,0,1,2,0,1,0,0,1,0,0,1,0,0,1,0,Class_6
+41270,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,2,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,3,0,0,0,0,0,0,0,0,0,7,3,0,0,3,0,11,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
+41271,3,0,2,0,0,0,0,0,0,0,8,1,0,1,0,6,0,3,0,0,0,6,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,7,0,1,0,0,0,0,7,1,0,0,0,0,0,0,3,0,2,1,2,2,0,0,1,0,0,0,0,0,0,0,4,0,0,1,0,1,5,0,0,0,0,2,0,0,0,1,Class_6
+41272,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,2,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,3,0,3,0,0,4,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41273,1,0,4,0,0,0,0,4,0,0,0,0,0,5,0,2,3,1,0,2,0,2,2,9,0,1,0,0,0,0,0,3,1,0,0,3,1,3,1,2,0,7,0,3,0,0,0,7,0,0,0,0,0,6,0,0,0,0,4,1,0,2,0,8,0,3,20,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,32,4,0,0,1,0,8,Class_6
+41274,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,2,1,0,13,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,Class_6
+41275,0,0,10,9,0,0,0,0,0,0,9,0,1,0,0,1,0,0,0,0,0,4,0,0,0,0,4,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,7,0,0,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41276,0,3,14,5,0,0,0,1,0,1,7,0,0,0,0,3,0,1,0,0,0,1,0,0,1,0,14,2,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,1,0,5,0,0,1,0,0,3,1,14,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,1,0,0,1,1,Class_6
+41277,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,4,0,0,0,1,0,0,0,0,4,0,0,0,1,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,1,1,1,0,0,1,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,1,0,0,0,Class_6
+41278,0,0,0,1,0,0,0,0,0,0,0,0,2,3,0,1,0,0,0,0,0,1,0,4,1,5,0,1,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,0,3,0,0,2,0,0,0,1,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,0,0,0,1,0,Class_6
+41279,0,0,4,1,0,0,0,0,0,0,5,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41280,0,0,1,0,0,1,1,1,1,0,7,0,0,2,0,2,1,2,2,1,5,1,0,0,2,0,11,0,0,0,2,1,0,1,4,0,0,4,0,1,1,0,1,2,0,1,0,1,0,1,1,4,0,5,0,0,0,0,0,0,5,1,1,0,0,0,0,0,0,1,0,1,1,7,1,0,0,0,0,11,0,1,0,0,0,0,0,1,1,2,0,2,1,Class_6
+41281,0,0,15,2,0,0,0,0,0,1,9,0,0,0,0,7,0,0,0,0,1,1,0,3,0,1,17,3,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,2,0,23,0,0,1,0,0,0,0,15,1,0,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,1,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41282,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41283,0,0,6,2,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,1,3,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,7,0,0,2,0,0,1,0,3,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,Class_6
+41284,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,6,0,3,0,0,0,6,0,1,1,1,0,0,27,0,2,0,0,1,0,0,0,0,0,0,3,1,0,1,0,0,0,1,0,0,0,1,1,5,8,0,2,0,0,1,0,3,0,0,0,2,4,0,0,2,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,Class_6
+41285,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
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+41304,0,1,3,6,0,0,0,0,0,0,6,0,0,0,0,5,0,0,0,0,0,3,0,0,0,1,6,0,0,0,1,0,1,0,9,0,0,0,0,0,0,1,0,2,0,1,0,0,3,0,0,0,1,8,0,2,0,0,0,1,0,1,0,1,0,2,1,0,0,1,0,3,0,0,0,0,0,0,0,2,0,5,1,0,0,0,0,0,1,2,0,1,1,Class_6
+41305,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41306,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,5,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,Class_6
+41307,0,1,1,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,4,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,2,0,0,0,3,0,1,0,0,0,5,2,0,2,0,0,19,0,2,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,Class_6
+41308,3,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,2,0,1,0,7,2,0,0,1,0,0,0,4,0,0,0,1,0,1,0,1,0,8,0,1,0,0,0,2,0,0,0,0,0,1,0,0,4,0,3,6,0,2,0,5,4,1,8,0,0,3,0,1,0,0,0,1,0,0,0,0,0,0,3,0,0,2,14,1,0,0,0,0,1,Class_6
+41309,0,0,14,1,0,0,0,0,0,0,7,0,1,0,0,5,1,0,0,0,2,1,0,1,0,1,14,1,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,1,19,0,0,0,0,0,0,0,6,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+41310,0,0,2,5,0,0,0,1,5,0,7,0,0,0,0,2,1,2,0,0,0,0,1,1,0,2,0,0,0,0,3,1,0,0,15,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,7,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,0,0,0,1,0,1,0,0,0,0,0,Class_6
+41311,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,2,0,1,0,0,0,0,3,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,4,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,2,1,0,0,1,0,0,0,Class_6
+41312,0,0,5,2,0,0,0,0,0,0,6,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,10,0,0,0,1,2,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,1,0,0,1,2,3,0,0,0,0,0,3,8,1,0,0,1,0,0,0,0,0,0,1,0,14,0,0,0,0,0,10,0,2,1,0,0,0,0,0,4,0,0,0,1,Class_6
+41313,0,0,1,2,0,0,0,0,0,0,0,0,0,0,2,4,2,1,0,1,0,6,0,11,0,5,0,0,0,0,0,2,0,0,2,4,0,1,1,0,0,1,0,1,1,1,0,1,0,0,0,0,1,7,4,0,5,0,0,12,0,0,0,1,0,1,10,1,8,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,5,Class_6
+41314,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41315,0,0,0,2,0,0,0,0,0,0,6,0,0,0,0,7,0,0,0,0,0,2,0,0,2,2,5,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,7,1,0,3,0,0,0,1,16,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41316,0,0,2,1,0,0,0,0,0,0,2,1,0,3,0,5,1,0,0,0,0,1,0,0,2,3,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,26,0,2,0,3,0,1,0,0,0,0,0,5,1,0,0,0,0,6,0,1,0,0,0,1,1,0,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,Class_6
+41317,0,0,5,1,0,0,0,0,0,0,7,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,2,0,0,0,1,0,0,5,0,0,0,1,0,0,0,0,1,0,5,1,0,2,0,1,0,0,6,0,3,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_6
+41318,0,0,0,0,0,0,0,0,1,0,21,2,0,0,0,2,0,0,0,0,2,0,0,0,0,0,17,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,1,0,0,0,9,0,4,0,0,0,1,0,0,9,1,0,0,1,2,0,1,0,0,0,0,4,19,0,1,0,0,0,23,0,0,0,0,1,0,0,0,4,0,0,0,1,Class_6
+41319,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,4,0,1,0,0,0,3,0,2,1,0,0,0,0,0,0,0,0,0,2,0,1,2,0,1,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,12,0,4,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+41320,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41321,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,10,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,0,3,0,0,1,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+41322,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,22,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41323,0,2,17,4,0,0,1,0,1,0,13,0,1,1,0,6,0,3,0,0,7,3,0,2,0,2,4,3,0,0,0,0,2,1,1,0,0,4,0,1,1,0,0,1,0,9,0,1,3,0,0,4,0,16,0,1,0,0,0,0,6,0,0,0,3,1,0,0,0,1,0,1,0,5,0,0,0,0,1,8,0,2,0,0,0,0,0,0,4,0,0,0,0,Class_6
+41324,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41325,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,1,1,0,8,0,4,0,0,0,0,1,0,0,4,3,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,4,1,0,2,0,0,14,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
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+41327,0,0,5,3,0,0,0,0,0,0,6,0,1,0,0,2,0,0,0,0,1,0,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41344,0,0,5,3,0,0,1,0,0,0,6,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,2,3,0,0,0,0,1,0,1,0,0,2,0,0,0,2,0,0,0,3,0,0,0,0,0,1,1,10,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
+41345,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,6,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,3,1,0,0,1,0,5,1,0,3,0,0,0,0,4,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,3,0,Class_6
+41346,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41347,0,0,1,3,0,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,1,1,0,2,0,6,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,4,0,1,0,1,0,2,0,0,0,0,0,4,0,0,0,0,0,8,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41348,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,4,0,1,0,0,0,1,0,1,0,6,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41349,0,1,6,20,0,0,0,0,0,0,8,0,1,0,0,2,0,3,0,0,1,1,0,1,0,2,4,4,0,1,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,6,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+41350,0,0,1,3,0,0,0,2,0,0,0,0,1,0,0,2,0,3,0,0,0,2,0,1,1,6,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,4,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41351,1,0,1,0,0,0,0,2,0,0,0,0,0,3,0,4,0,0,0,1,0,1,0,3,0,1,0,2,0,0,0,1,1,1,0,1,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,22,0,1,0,3,0,4,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+41352,0,0,9,4,0,0,0,0,0,0,14,0,0,0,1,1,0,1,0,0,1,0,2,7,0,3,17,1,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,4,0,0,0,5,0,3,1,3,0,1,0,1,0,0,1,0,0,0,0,2,0,1,0,1,0,3,0,0,0,0,0,0,1,0,0,0,1,1,0,Class_6
+41353,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41354,0,0,2,0,0,0,0,2,0,0,0,0,0,1,1,2,1,0,0,0,0,3,0,9,0,0,0,0,0,0,0,3,3,0,0,6,2,1,3,3,0,6,0,2,1,4,0,6,0,0,0,0,0,4,0,0,1,0,2,0,0,4,0,7,0,3,16,0,0,2,1,1,2,0,1,2,1,0,0,0,1,0,0,0,3,1,31,2,0,0,0,0,5,Class_6
+41355,0,0,7,5,0,0,0,0,0,1,10,0,2,0,0,2,0,1,1,1,2,2,0,2,0,2,2,4,0,0,1,0,0,0,1,1,0,0,1,2,0,0,2,0,0,1,0,0,6,0,0,2,0,16,1,0,0,1,0,1,2,2,1,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,0,0,0,Class_6
+41356,0,0,6,8,0,0,1,1,0,0,8,0,0,0,0,4,0,0,0,0,0,2,0,1,0,0,7,3,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,3,0,9,1,0,0,0,0,1,3,1,0,0,1,1,2,0,0,2,0,0,0,0,0,0,0,0,1,4,0,5,0,0,0,0,1,1,2,0,0,0,0,Class_6
+41357,0,1,0,4,0,0,0,0,0,0,10,1,0,3,0,1,0,0,0,0,1,2,0,0,0,1,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41358,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41359,0,0,5,7,0,0,0,2,0,0,0,0,0,1,0,2,0,2,0,0,0,2,0,2,0,12,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,2,0,7,1,2,0,0,0,0,1,12,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+41360,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41361,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,1,0,0,0,1,0,Class_6
+41362,0,0,5,1,0,0,5,2,1,0,3,2,0,1,1,1,7,7,4,0,0,3,8,13,2,1,0,0,2,0,2,0,2,1,3,1,0,1,1,2,0,0,4,0,0,0,1,1,2,1,4,0,0,6,0,1,8,0,0,9,1,6,0,3,0,1,5,0,1,0,1,4,4,4,2,6,0,1,0,8,2,0,1,0,0,1,7,1,3,0,0,1,2,Class_6
+41363,0,0,0,9,0,0,0,0,0,1,10,2,1,2,0,3,0,2,0,0,0,0,0,0,0,1,12,2,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,1,0,2,0,0,0,0,0,1,0,2,0,1,2,1,0,2,9,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,18,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41364,1,0,1,2,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,2,1,0,2,0,2,8,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,3,0,2,1,1,0,0,0,2,0,4,0,0,0,1,0,0,3,2,1,0,0,1,1,0,0,0,0,2,0,5,0,0,0,0,0,6,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41365,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
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+41367,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,8,0,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,1,1,0,0,3,0,0,17,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41383,2,1,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,2,0,4,0,0,0,7,0,1,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,3,4,0,21,0,0,0,0,1,0,5,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,3,Class_6
+41384,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41385,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,3,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,5,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,Class_6
+41386,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41387,0,0,1,2,0,0,0,0,0,0,16,0,0,0,0,1,0,0,0,0,2,0,0,4,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,8,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+41388,2,0,4,2,0,0,0,0,2,0,18,0,0,0,0,4,0,0,0,0,2,2,0,6,0,2,3,2,0,0,0,0,0,0,2,0,0,1,1,1,0,0,0,2,0,6,0,1,1,0,0,0,0,18,0,1,0,7,0,1,1,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41389,0,0,0,0,0,0,4,0,0,1,1,0,2,0,0,0,3,0,0,2,0,1,0,3,1,0,0,0,1,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,7,0,1,0,0,0,0,1,5,0,3,0,1,0,0,0,1,2,1,5,2,1,0,2,1,0,1,0,2,5,0,5,3,0,0,0,2,0,0,0,1,0,0,3,0,0,0,Class_6
+41390,1,3,4,1,0,0,0,1,0,0,1,0,2,2,0,2,1,2,0,1,0,3,1,2,3,2,0,0,0,0,0,0,1,1,1,0,2,0,0,1,0,12,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,1,0,0,1,3,Class_6
+41391,0,0,0,0,0,0,0,0,0,1,6,0,0,1,1,5,0,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,3,0,2,2,1,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,1,0,1,0,0,0,4,1,0,0,4,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,0,0,Class_6
+41392,0,2,3,2,0,0,0,0,0,0,11,0,2,0,0,2,0,2,0,0,0,1,0,0,0,3,4,2,0,0,2,2,1,0,2,0,0,0,0,1,0,0,0,0,0,4,0,1,1,0,0,2,1,16,0,0,1,1,0,1,4,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,1,14,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41393,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,1,0,0,0,9,0,4,0,0,0,0,0,0,1,0,4,0,0,0,0,1,0,4,0,1,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,10,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41394,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,2,0,0,1,0,1,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41395,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,6,1,0,0,0,0,0,0,0,2,0,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,13,0,0,0,0,0,0,4,1,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+41396,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,3,1,3,0,0,0,3,0,1,0,5,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,2,0,5,0,1,0,0,7,0,0,0,0,2,1,0,0,0,0,10,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+41397,6,2,1,0,0,0,0,1,1,1,0,1,2,0,0,0,0,3,0,3,0,2,0,5,1,0,2,0,0,0,0,13,1,1,0,2,0,1,1,0,0,4,2,0,1,0,0,1,0,0,0,0,0,1,1,2,0,0,2,1,0,0,0,0,0,2,14,1,0,4,0,0,0,6,0,1,0,0,1,0,0,0,1,0,1,2,9,0,0,1,0,1,40,Class_6
+41398,1,0,0,2,0,0,0,0,0,0,7,0,1,0,1,2,0,1,0,1,0,4,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,Class_6
+41399,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+41400,0,1,0,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
+41401,0,0,8,1,0,0,0,0,1,0,9,1,0,0,0,3,0,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41402,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41403,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,2,0,8,0,0,0,2,0,3,0,11,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,1,0,1,0,0,1,0,0,0,0,12,0,0,1,0,0,13,2,2,0,1,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,Class_6
+41404,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,1,0,7,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,12,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,13,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+41405,0,0,10,2,0,0,0,0,0,2,11,0,1,0,0,2,0,1,0,0,6,3,0,1,0,1,5,1,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,16,0,0,0,0,0,0,0,7,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41406,0,1,14,3,0,0,0,0,0,0,11,0,0,0,0,4,1,0,0,0,3,6,1,0,2,1,38,3,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,0,13,0,0,4,0,0,3,0,15,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,2,0,1,1,3,0,2,0,0,0,0,0,0,2,2,0,2,0,Class_6
+41407,0,0,14,5,0,0,0,0,0,0,6,0,0,1,0,2,0,0,1,0,1,1,0,0,0,4,10,0,0,0,1,2,0,0,2,0,0,0,0,1,0,2,0,0,0,23,0,0,0,0,0,1,0,9,0,0,0,0,0,1,3,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,6,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41408,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,1,4,2,1,0,0,0,0,0,0,4,0,0,1,0,0,1,0,1,1,0,0,0,0,0,2,0,0,1,5,1,0,2,0,0,12,0,0,0,0,1,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41409,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41410,0,0,6,1,0,0,0,0,0,0,16,0,0,0,3,4,0,0,0,0,0,0,0,8,0,0,4,3,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,8,0,0,2,0,0,1,0,13,1,1,0,11,0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41411,0,0,9,4,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,6,0,0,1,0,8,1,2,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41412,0,1,4,3,1,0,0,0,1,0,20,2,0,0,1,0,0,0,0,0,0,0,0,7,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,11,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41413,0,0,3,7,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+41414,0,0,0,2,0,0,0,0,0,1,9,0,0,0,0,2,0,0,0,0,0,3,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41415,0,0,8,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,2,0,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,1,0,1,0,2,0,0,1,0,0,5,0,0,1,0,0,0,6,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41416,0,0,12,2,0,0,1,0,0,0,9,0,1,1,0,9,0,0,0,0,1,0,0,2,0,2,8,2,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,1,0,5,0,0,0,0,0,0,0,21,1,0,0,0,0,0,1,1,1,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41417,0,2,18,12,0,0,0,0,1,1,7,0,0,1,0,2,0,1,0,0,0,1,0,2,1,9,1,3,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,2,0,16,0,0,0,0,0,0,1,4,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41418,1,2,23,7,0,0,0,0,0,0,9,0,0,0,0,1,0,1,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,13,0,0,1,0,0,1,1,7,0,0,0,1,0,0,8,2,0,0,0,1,0,0,0,1,0,0,1,2,0,0,0,0,0,13,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41419,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,2,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,2,0,3,0,0,0,0,0,4,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41420,0,0,13,6,0,0,0,0,0,0,9,0,0,1,0,2,0,0,0,0,0,2,0,0,0,6,13,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,2,0,32,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+41421,0,0,0,7,0,0,0,0,0,0,19,0,0,0,0,2,0,1,0,0,2,0,0,5,0,0,6,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,1,0,6,0,0,0,0,0,1,0,0,2,0,1,0,0,1,0,1,0,2,0,0,0,8,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41422,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,5,0,0,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41423,0,0,13,6,0,0,0,0,0,0,8,0,1,0,0,2,0,2,0,1,0,6,0,1,0,3,3,1,0,0,0,1,2,0,2,0,0,0,0,0,0,2,0,5,0,13,0,0,2,0,0,0,0,12,0,0,0,0,2,0,0,2,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41424,0,0,3,1,0,0,0,0,0,0,8,0,0,1,0,3,0,0,2,0,1,0,0,0,0,0,4,1,0,0,2,0,0,0,4,0,0,1,0,0,0,0,0,2,0,2,0,2,5,0,0,0,0,14,0,0,0,0,0,1,2,0,0,0,0,1,0,2,0,1,0,1,0,0,0,0,0,0,0,7,0,3,0,0,0,0,0,0,7,0,0,0,0,Class_6
+41425,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+41426,0,0,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,4,0,3,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,2,0,1,0,1,1,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41427,0,3,1,1,0,0,0,0,0,0,0,1,0,0,0,3,0,5,0,0,0,7,0,2,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,18,0,1,0,0,0,1,0,0,0,0,0,8,0,0,1,0,0,13,0,2,0,2,0,8,2,1,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,2,0,Class_6
+41428,1,2,1,1,0,0,1,1,0,0,6,0,0,0,0,1,0,0,0,0,3,3,0,2,0,1,8,0,0,0,2,1,0,0,1,0,0,0,0,1,0,0,0,1,0,3,0,2,1,0,0,2,1,5,0,2,0,0,0,0,5,0,0,1,0,1,2,0,2,0,0,0,1,0,0,3,0,0,0,14,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+41429,0,1,9,9,0,0,0,0,0,0,1,2,0,1,0,2,0,1,0,0,0,1,0,0,0,10,1,0,0,0,0,0,0,1,1,1,0,0,0,2,0,0,0,0,0,5,0,1,0,0,0,1,0,5,0,0,0,1,0,0,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+41430,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,Class_6
+41431,2,0,9,11,0,0,0,0,0,1,13,3,0,0,0,2,0,2,0,0,2,2,0,3,0,3,3,0,0,0,4,1,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,2,0,5,0,2,0,0,0,1,5,0,1,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,5,0,7,4,0,0,0,0,0,1,1,0,0,0,Class_6
+41432,0,0,2,5,0,0,0,0,0,0,5,0,0,1,0,0,0,3,2,0,1,0,0,0,0,1,5,3,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,1,0,2,3,0,0,1,0,8,0,1,0,0,0,1,2,0,0,0,0,1,1,1,1,0,0,2,0,0,0,1,0,0,1,1,0,4,0,0,0,0,0,0,2,3,0,1,0,Class_6
+41433,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,0,0,1,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,3,13,1,0,1,3,2,2,0,0,2,0,9,0,0,0,0,0,0,0,0,2,4,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,2,0,0,0,0,0,0,Class_6
+41434,0,0,5,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,3,0,0,0,1,3,1,0,0,0,16,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41435,0,1,11,25,0,0,0,0,0,1,5,0,0,0,0,1,0,1,0,0,1,0,0,0,0,6,5,1,0,0,3,0,1,0,3,1,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,5,0,0,0,0,0,1,2,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41436,0,1,10,5,0,0,0,0,0,0,9,1,2,0,0,5,0,0,0,0,1,4,0,2,0,2,12,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,8,0,1,2,0,0,0,0,18,0,3,2,0,0,0,1,0,0,0,0,2,0,0,0,1,0,1,0,3,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41437,2,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,1,0,0,0,2,0,10,0,1,0,0,0,1,7,0,0,0,2,0,0,2,0,1,0,0,1,1,0,0,0,2,1,0,0,0,0,1,0,0,5,0,0,12,0,1,0,0,0,0,2,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,Class_6
+41438,0,0,4,3,0,0,0,0,0,0,15,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,18,1,2,0,0,0,1,1,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41439,0,0,13,3,0,0,0,0,0,0,8,0,0,0,0,4,0,0,0,1,1,1,0,2,0,4,7,4,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,1,10,0,0,1,0,0,0,0,8,0,0,1,0,0,0,5,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,4,0,2,0,1,0,0,0,0,3,1,0,0,0,Class_6
+41440,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41441,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,3,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,1,0,0,0,3,1,6,0,0,1,2,0,0,0,0,0,6,0,4,0,0,0,1,4,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,4,0,0,0,0,Class_6
+41442,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,1,2,0,0,1,2,0,0,0,0,0,0,1,0,1,0,1,0,4,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,3,0,12,1,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,3,Class_6
+41443,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,4,0,5,1,1,0,4,0,4,0,6,0,1,0,0,1,0,0,0,6,0,0,0,0,0,1,7,5,1,0,0,0,2,0,0,0,5,0,5,1,0,3,0,0,12,3,1,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+41444,0,2,13,2,0,0,0,0,1,0,10,1,0,0,0,2,0,1,0,0,1,0,0,0,0,1,6,1,0,0,0,1,0,0,0,1,0,2,0,1,0,0,0,0,0,9,0,1,0,0,0,2,0,7,0,0,0,0,0,1,3,0,0,0,0,2,1,0,0,1,0,0,1,0,1,0,0,0,0,13,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41445,0,0,14,5,2,0,0,1,0,0,9,0,0,1,0,1,0,0,0,2,0,4,0,1,0,2,3,10,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,2,0,7,1,0,0,0,0,0,0,12,1,0,0,0,0,0,2,1,1,2,0,0,0,2,0,1,0,1,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_6
+41446,0,2,7,1,0,0,0,0,0,0,7,0,1,0,0,7,0,1,0,0,2,1,0,1,0,2,8,2,1,0,0,1,0,0,3,0,0,0,0,1,1,1,0,2,0,3,0,0,1,0,0,0,0,13,0,0,0,0,0,0,1,1,2,1,2,1,0,0,0,0,0,0,0,2,0,2,0,0,0,4,0,5,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41447,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,3,1,3,0,1,0,0,0,0,1,2,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,13,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,2,0,0,1,1,Class_6
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+41464,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,2,0,4,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,1,1,1,0,0,1,0,0,1,0,0,8,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
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+41466,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,3,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+41467,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41468,0,1,0,0,0,0,0,2,0,0,0,0,2,0,1,0,2,4,0,0,1,1,0,8,1,1,1,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,1,0,10,0,1,0,0,0,1,4,0,0,1,0,2,0,0,0,0,4,1,0,0,0,0,1,0,0,0,2,2,1,0,0,1,0,Class_6
+41469,4,0,1,1,0,0,0,0,1,1,1,0,2,2,0,6,0,0,2,2,1,2,2,6,1,0,1,0,0,0,0,10,1,0,0,2,4,3,0,0,0,5,0,10,0,0,0,6,0,0,0,1,0,3,0,1,0,0,3,5,0,0,2,2,0,1,6,0,1,6,0,0,0,0,0,1,0,0,0,1,0,0,0,0,3,3,16,0,1,0,0,0,39,Class_6
+41470,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41471,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,11,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+41472,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,5,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41473,0,2,15,7,0,0,0,0,0,0,1,0,1,1,0,4,0,4,0,0,2,8,0,6,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,7,0,0,1,0,0,1,1,28,0,1,0,2,0,0,2,1,0,0,0,1,0,3,0,0,0,3,0,1,0,0,0,0,1,1,0,3,1,0,0,0,0,0,1,0,0,0,0,Class_6
+41474,0,0,12,6,0,0,0,1,0,0,6,0,0,1,0,2,0,1,1,0,0,1,0,0,0,0,4,8,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,3,0,14,0,0,1,0,0,0,0,13,1,2,0,0,0,0,3,0,1,1,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,1,Class_6
+41475,0,1,0,8,0,0,0,0,0,0,2,0,0,0,0,2,0,5,0,0,0,1,0,8,0,4,0,2,0,0,0,0,0,0,10,0,0,0,0,1,0,12,0,1,0,1,0,0,2,0,0,0,0,9,1,0,0,0,0,9,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_6
+41476,0,0,12,14,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,1,2,1,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,13,0,0,1,0,0,1,0,9,2,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,4,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41477,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,1,0,0,0,1,0,0,0,2,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,4,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,Class_6
+41478,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+41479,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,10,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+41480,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,12,0,0,0,0,1,1,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41481,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,0,0,2,1,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41482,1,17,0,1,0,0,0,1,0,1,1,0,0,0,0,2,2,2,3,4,0,4,0,10,0,0,0,0,1,0,1,0,0,0,8,0,1,0,0,0,1,8,0,2,0,0,0,2,1,0,0,0,0,5,0,0,2,20,0,11,0,1,0,1,0,2,1,0,7,0,0,0,1,0,0,2,0,0,0,0,0,0,4,0,1,0,1,0,0,7,0,0,3,Class_6
+41483,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41484,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+41485,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,4,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,12,0,1,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
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+41495,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41496,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,4,0,3,0,6,0,11,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,16,0,2,0,0,0,0,0,0,0,0,0,4,0,0,9,0,0,4,0,1,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,1,1,1,2,0,Class_6
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+41499,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,18,0,0,0,0,1,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+41500,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41501,0,1,3,8,0,0,0,1,1,0,12,0,0,0,0,3,0,1,0,2,0,4,0,4,1,1,5,2,0,0,2,0,0,0,6,0,1,2,0,0,0,1,2,3,2,3,0,0,0,0,0,4,0,9,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,7,0,1,0,0,1,0,0,0,0,0,0,1,2,Class_6
+41502,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41503,0,1,2,0,0,0,1,0,0,1,11,0,2,0,0,1,0,3,0,1,0,4,0,5,0,5,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,9,0,2,0,0,0,2,8,0,0,0,0,6,0,0,2,0,0,6,1,1,0,0,1,4,1,0,0,2,1,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,1,2,0,0,0,Class_6
+41504,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,1,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,3,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41505,0,1,14,5,0,0,0,0,0,0,9,0,1,0,0,3,0,2,0,0,2,2,0,0,0,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,1,0,7,0,0,5,0,2,0,0,8,0,0,0,0,0,0,3,1,1,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,0,9,0,2,0,0,0,0,2,4,2,0,0,0,0,Class_6
+41506,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+41507,0,0,3,4,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,1,0,5,0,3,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,3,0,0,1,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41508,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,8,0,1,1,4,4,1,1,0,0,0,0,2,1,0,0,0,0,0,0,10,0,6,0,1,0,3,0,0,0,0,1,32,0,0,0,0,0,4,0,0,0,1,0,1,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,Class_6
+41509,0,0,0,0,0,0,0,1,1,0,0,0,1,2,2,2,0,0,0,0,0,1,0,8,0,1,0,0,0,0,0,3,0,0,1,0,4,0,27,1,0,1,1,0,34,0,0,5,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,1,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,Class_6
+41510,0,1,0,0,0,0,0,0,0,2,0,0,0,1,1,4,1,1,0,3,0,0,0,3,2,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,1,1,0,0,0,0,0,1,4,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41511,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+41512,0,0,2,3,0,0,0,1,0,0,7,1,1,0,0,2,0,0,0,0,0,5,1,0,0,0,3,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,0,1,0,2,0,0,2,0,0,1,1,10,0,0,0,0,0,0,7,2,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,9,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41513,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,3,0,1,1,1,0,0,0,0,2,0,4,0,0,0,2,0,0,3,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,3,1,0,6,0,0,12,0,0,0,0,0,1,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,3,0,1,0,0,0,0,Class_6
+41514,0,0,0,3,0,0,0,0,2,0,0,0,1,1,0,0,0,1,1,0,1,2,0,4,0,0,0,0,0,0,0,2,1,0,23,0,0,0,0,0,1,4,2,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,16,0,0,1,4,0,0,4,0,0,1,1,0,6,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,Class_6
+41515,0,0,4,4,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,7,2,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,2,0,2,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41516,3,0,2,0,0,0,0,0,0,1,0,0,0,2,0,3,1,3,0,0,0,3,0,1,0,5,0,0,0,0,0,0,0,1,2,1,2,0,0,0,1,35,0,5,0,1,0,2,2,0,0,0,0,15,2,0,0,0,0,1,0,1,0,1,1,2,6,0,0,3,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,2,0,1,0,0,1,0,Class_6
+41517,2,0,3,2,0,0,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,2,0,1,1,3,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,13,1,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+41518,0,1,5,2,0,0,0,0,0,0,7,1,2,0,0,5,0,0,1,0,1,4,0,0,0,2,3,1,0,0,0,0,0,0,1,0,0,2,0,1,1,1,1,2,0,5,0,2,1,0,0,0,0,25,2,0,0,1,0,3,2,2,0,0,2,0,0,0,0,1,0,0,0,1,0,1,0,0,0,5,0,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41519,0,0,14,7,0,0,0,0,1,0,8,0,0,1,0,3,0,1,0,0,1,3,0,0,0,2,10,2,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,1,0,12,0,0,1,0,0,1,0,21,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41520,0,1,2,11,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,6,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41521,0,0,7,4,0,0,0,0,1,0,8,0,0,1,0,4,0,1,0,0,1,1,0,0,0,2,8,2,0,0,0,2,1,0,1,0,0,0,0,0,0,0,0,2,0,9,0,0,0,0,0,0,0,14,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,Class_6
+41522,0,0,9,7,0,0,0,0,0,1,7,0,0,1,0,2,0,0,0,0,1,0,0,0,0,9,10,2,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,1,0,4,0,0,0,0,1,3,0,11,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,1,0,0,3,0,Class_6
+41523,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41524,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,3,0,4,0,0,0,0,3,0,1,1,1,1,1,3,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,11,1,0,Class_6
+41525,0,6,4,6,0,0,0,0,0,0,7,0,1,0,0,2,1,1,0,5,0,0,1,2,0,0,3,0,0,2,1,0,1,0,7,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,1,1,0,0,0,1,1,0,0,0,3,3,0,0,0,0,0,1,3,1,2,0,0,0,6,0,4,1,0,0,0,0,0,5,1,0,0,0,Class_6
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+41527,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,7,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41537,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,22,0,10,0,1,0,0,0,0,0,2,1,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,14,0,1,0,0,0,2,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,4,0,Class_6
+41538,0,0,7,6,0,0,0,0,0,1,7,0,1,0,0,1,0,0,0,1,3,2,0,0,0,3,3,5,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,2,0,6,0,0,1,1,0,1,0,8,2,0,0,0,1,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,Class_6
+41539,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,Class_6
+41540,0,1,3,10,0,0,0,0,0,0,19,0,1,0,0,3,0,1,0,0,1,2,0,7,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,1,0,6,0,0,0,10,0,1,1,1,1,1,0,1,1,0,0,0,0,0,1,2,0,0,0,1,0,10,1,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41541,0,0,5,5,0,0,0,0,0,0,12,0,1,1,0,1,1,1,0,0,0,0,0,6,0,0,3,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,5,0,0,0,4,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+41542,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,3,0,0,1,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,9,1,0,0,2,0,0,3,2,5,0,0,0,0,0,0,1,0,0,0,1,0,0,0,8,2,0,Class_6
+41543,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41544,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,4,0,6,0,4,0,1,0,0,0,0,2,1,0,0,0,2,0,1,2,4,0,0,0,0,0,0,0,0,0,0,0,3,1,0,5,0,0,12,0,0,0,0,1,1,0,4,2,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,0,Class_6
+41545,0,1,9,4,0,0,0,0,0,0,5,0,0,0,0,2,0,1,0,0,2,1,0,0,0,1,8,0,0,0,0,0,0,0,3,0,0,1,0,0,0,2,0,0,0,11,0,0,0,0,0,1,0,10,0,2,0,0,0,0,2,0,0,0,0,1,2,0,0,1,0,0,0,0,1,0,0,0,0,6,0,2,0,0,0,0,0,0,3,0,0,1,0,Class_6
+41546,0,0,6,4,0,0,1,0,0,1,17,0,0,1,0,3,0,0,0,0,4,0,0,7,0,0,10,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,1,0,3,0,0,4,0,0,1,0,7,0,3,0,10,0,1,4,1,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41547,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,0,9,0,6,9,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,1,0,4,1,0,0,1,0,0,9,0,0,2,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41548,0,0,5,8,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,1,1,0,0,0,0,7,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41549,0,0,4,18,0,0,0,0,0,0,9,0,0,0,0,4,0,1,0,0,1,2,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,0,0,2,0,0,0,2,0,6,0,0,0,0,0,1,2,6,0,0,0,0,1,1,6,2,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,0,9,0,11,1,0,0,0,0,0,2,0,0,0,1,Class_6
+41550,0,0,6,3,0,0,0,0,1,0,9,1,1,0,0,4,0,1,0,0,1,3,0,0,2,0,7,2,0,0,1,0,0,0,4,0,0,2,0,0,0,0,0,3,0,7,0,0,1,0,0,0,0,13,0,4,1,0,0,0,5,0,0,0,2,1,0,0,0,0,0,0,0,4,0,1,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41551,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,3,0,0,0,3,0,1,0,3,0,0,2,0,0,0,0,1,1,1,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,1,5,0,0,12,0,0,0,0,6,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41552,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,3,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,8,0,0,0,1,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41553,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,2,2,0,0,0,2,2,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,1,0,7,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41554,0,1,1,0,0,0,0,0,0,0,10,0,1,1,0,1,0,0,0,0,0,1,0,1,0,6,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,3,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41555,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41556,0,0,5,5,0,0,0,0,0,0,15,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,5,0,0,0,4,0,1,2,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41557,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,2,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41558,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,3,0,8,1,0,0,0,0,0,0,0,3,3,0,1,0,0,2,9,0,0,0,0,2,0,0,0,0,0,0,3,1,0,1,0,1,12,0,0,1,3,0,1,4,3,0,3,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,2,1,0,0,1,0,Class_6
+41559,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,3,0,2,0,1,0,0,0,0,0,0,0,3,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,2,0,0,14,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+41560,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,1,0,0,2,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,12,0,3,0,0,0,0,1,0,0,0,0,3,0,0,5,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,2,0,1,0,0,3,0,Class_6
+41561,0,0,14,5,0,0,0,0,0,1,9,0,0,0,0,2,0,0,1,0,4,1,0,0,0,4,10,0,0,0,0,1,0,0,3,1,0,0,0,0,0,1,0,2,1,15,0,0,1,0,0,0,0,5,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+41562,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,18,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41563,0,0,0,1,0,0,0,0,0,0,0,1,2,1,0,0,3,0,1,0,0,1,0,1,0,2,0,0,0,0,0,4,1,0,2,0,0,1,0,0,1,7,0,2,0,2,0,2,1,0,0,0,2,4,3,0,1,1,0,17,0,0,0,2,0,1,6,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,2,0,0,0,1,0,0,Class_6
+41564,0,1,4,1,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,7,2,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,1,0,Class_6
+41565,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41586,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41587,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,14,0,0,0,2,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41588,0,0,1,6,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,1,0,2,0,1,0,8,4,1,0,0,0,1,0,0,2,0,0,0,0,0,0,2,0,1,0,1,0,2,1,0,0,0,0,12,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41589,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+41590,0,1,0,4,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,1,0,6,0,5,0,5,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,9,0,4,0,0,1,0,1,0,0,0,0,24,0,0,3,4,0,12,0,0,0,0,1,2,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,13,2,0,0,3,0,0,1,0,0,0,Class_6
+41591,0,0,15,3,0,0,0,1,0,0,10,0,0,1,1,7,0,1,0,0,1,4,0,0,0,0,15,2,0,0,0,0,1,0,0,0,0,3,1,0,0,0,1,0,0,11,0,0,0,0,0,0,0,23,0,0,0,0,0,1,1,1,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41592,0,0,12,2,0,0,0,0,1,0,7,0,0,0,0,1,0,0,0,0,0,2,0,2,0,1,2,3,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,4,0,4,0,0,4,0,0,0,0,11,0,1,0,0,0,0,0,4,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,4,0,0,2,0,Class_6
+41593,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+41594,0,0,2,2,1,0,3,0,0,0,6,1,0,0,1,14,0,5,0,0,0,12,0,1,0,0,0,1,1,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,4,1,3,0,0,0,1,20,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,3,1,1,0,3,0,1,0,0,0,2,0,0,1,2,0,0,0,0,0,0,1,1,0,Class_6
+41595,0,2,13,6,0,0,0,1,0,0,11,0,0,1,0,4,0,1,0,0,0,0,0,0,0,1,17,1,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,1,0,12,0,0,0,2,0,0,0,13,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,7,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_6
+41596,0,2,1,0,0,0,0,0,0,0,2,1,0,0,0,1,0,9,0,0,0,2,0,3,0,7,0,0,0,0,0,0,0,0,5,2,0,0,0,0,0,25,0,3,0,0,0,0,0,0,0,0,0,8,1,0,6,0,0,6,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,3,Class_6
+41597,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+41598,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,0,0,0,2,0,4,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,6,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41599,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,1,2,0,3,0,2,1,1,0,6,0,4,0,0,1,0,0,0,0,0,1,5,0,0,2,0,0,1,0,1,0,1,1,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0,0,0,2,Class_6
+41600,0,0,11,3,0,1,1,0,0,1,11,0,0,0,1,1,0,2,0,1,2,3,0,0,0,1,7,2,0,0,1,1,1,0,1,0,0,2,1,0,0,0,0,2,0,5,1,1,3,0,0,2,0,9,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,3,0,0,1,0,0,0,1,1,0,0,0,Class_6
+41601,0,0,0,0,0,0,0,8,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41602,0,0,1,6,0,0,0,0,0,0,0,0,1,0,1,12,0,1,0,1,0,1,0,2,0,5,0,2,1,0,2,0,1,1,0,0,0,0,1,5,0,5,0,2,0,0,0,1,0,0,0,2,0,15,1,0,0,0,1,12,0,0,0,1,1,0,1,0,3,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,1,0,1,3,0,Class_6
+41603,0,1,4,2,0,0,0,0,0,1,11,0,0,0,0,3,2,1,0,0,3,4,1,0,0,0,30,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,1,1,0,9,1,0,0,0,0,1,4,1,0,0,0,1,0,0,0,2,0,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41604,0,0,11,9,0,0,0,0,0,1,5,0,1,0,0,1,1,1,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,3,0,4,0,1,2,0,0,0,0,7,0,0,0,0,0,0,7,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,10,0,5,0,0,0,0,1,1,1,0,0,0,0,Class_6
+41605,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41635,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,1,8,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,3,0,3,0,0,2,0,0,0,0,5,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41636,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41637,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1,0,0,0,1,0,6,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,3,0,0,3,0,0,17,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_6
+41638,0,1,9,4,0,0,0,0,0,0,7,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,8,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,13,0,0,0,0,0,1,0,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41639,0,0,6,0,0,1,0,0,0,1,5,0,2,0,0,6,0,2,0,1,0,8,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,6,0,1,3,1,3,0,0,0,0,20,2,0,1,0,0,0,1,1,1,1,0,1,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,Class_6
+41640,0,1,14,10,0,0,0,0,0,0,7,0,0,1,0,0,0,1,0,0,5,2,0,2,0,0,29,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,14,0,0,1,0,0,3,0,6,1,1,0,1,0,0,0,0,0,1,2,0,0,1,0,0,0,0,2,1,0,3,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+41641,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,6,2,0,1,0,0,0,4,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,11,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,Class_6
+41642,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,1,0,5,0,0,0,1,0,4,1,2,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,1,0,0,8,0,0,5,1,0,0,1,0,0,5,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,1,0,1,Class_6
+41643,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,3,0,1,0,0,1,0,0,0,0,0,3,0,0,1,0,0,0,11,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,12,0,0,0,2,0,3,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41644,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,2,0,1,0,2,0,1,0,0,0,1,0,1,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,5,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,2,0,1,1,0,0,0,Class_6
+41645,0,1,7,11,0,0,1,0,0,1,7,0,0,0,0,3,1,2,0,0,5,3,0,0,0,8,1,9,0,0,0,2,0,1,5,0,0,0,0,1,0,2,0,1,1,2,0,0,5,0,0,3,0,14,0,1,1,1,0,1,0,4,1,0,0,2,1,1,2,6,0,2,0,6,0,1,1,1,0,13,1,10,0,0,0,0,0,1,4,0,0,1,0,Class_6
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+41676,0,0,0,4,1,0,1,0,0,1,7,0,2,1,1,8,1,2,0,0,0,6,0,21,13,4,0,2,4,0,1,1,0,0,0,0,0,1,0,0,0,11,0,9,0,0,5,1,1,0,0,0,1,10,11,0,0,0,1,5,1,3,1,0,12,3,1,1,0,7,3,0,0,1,0,1,0,0,2,1,0,0,1,0,3,0,1,0,0,0,0,0,0,Class_6
+41677,0,0,15,9,0,0,0,0,0,0,10,0,0,0,0,8,1,5,0,0,0,0,0,0,0,15,2,6,0,0,0,1,0,2,4,0,0,0,0,0,0,7,0,1,0,5,0,1,1,0,0,0,1,17,0,0,2,1,0,1,4,2,2,0,0,1,2,0,0,2,0,1,1,0,0,0,0,0,0,6,0,1,2,0,0,1,0,1,3,0,0,0,0,Class_6
+41678,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,1,0,0,0,1,0,3,0,0,0,1,1,0,0,5,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,3,1,0,6,0,1,15,0,0,0,2,0,0,2,1,0,0,1,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,3,1,1,0,0,0,1,Class_6
+41679,0,0,4,4,0,0,0,0,0,1,10,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,5,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,3,0,1,0,4,0,1,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41680,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,2,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41681,0,0,4,6,0,0,0,0,0,0,18,0,1,0,0,1,0,0,0,1,0,1,0,11,0,4,12,2,0,0,2,2,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,4,0,1,0,12,0,0,1,1,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41682,0,0,2,1,0,0,1,0,0,1,6,2,3,0,0,9,0,2,0,1,0,2,0,0,1,1,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,17,2,3,0,1,0,0,9,3,0,0,0,0,0,0,3,0,2,0,4,2,0,1,0,0,1,0,0,0,0,0,0,0,2,1,1,0,4,0,0,0,0,0,0,0,4,0,Class_6
+41683,0,1,23,17,0,1,0,0,0,0,5,0,1,2,0,4,0,1,0,0,0,0,0,0,0,1,1,1,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,3,0,6,0,0,2,1,0,0,0,16,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,1,0,3,0,1,1,0,Class_6
+41684,0,0,2,4,0,0,0,0,1,0,14,0,0,0,0,1,0,2,0,0,0,2,0,8,0,8,1,2,0,0,1,2,0,0,1,0,1,0,0,2,0,3,0,1,0,10,1,1,0,0,0,0,0,12,1,0,0,5,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+41685,0,0,12,2,0,0,0,0,0,0,7,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,0,8,0,1,1,0,0,0,0,10,0,2,0,0,0,0,1,4,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
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+41704,0,0,10,6,0,0,0,0,1,0,11,1,0,0,0,4,0,1,0,1,2,3,0,0,0,0,6,4,0,0,0,0,1,0,5,0,0,1,1,0,0,0,0,2,4,5,0,0,1,0,0,1,0,8,3,0,0,0,0,0,3,1,0,0,0,1,0,0,0,2,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+41705,0,0,24,5,0,0,0,0,0,1,8,0,0,0,0,4,1,2,0,0,0,2,0,2,0,0,14,2,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,5,0,3,1,0,0,0,0,19,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,4,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+41706,0,0,19,4,0,0,1,0,0,1,11,0,1,0,1,10,0,0,0,0,13,4,0,0,0,3,8,4,1,1,0,0,0,0,3,0,0,1,0,2,0,0,0,2,0,22,0,1,3,0,0,0,0,29,4,0,1,0,1,0,5,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,3,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41707,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+41708,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,0,2,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,2,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+41709,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,0,1,0,1,1,1,0,0,0,0,3,0,0,0,1,0,1,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+41710,0,0,1,3,0,0,0,0,0,0,5,0,0,0,0,2,0,7,2,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,6,0,1,0,1,9,0,0,0,0,3,1,0,0,0,0,0,6,2,1,0,1,4,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,4,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41711,0,0,7,3,0,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,2,1,1,7,0,0,2,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,4,0,1,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41712,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,Class_6
+41713,0,0,2,8,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,1,0,2,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41714,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,3,0,1,0,0,1,1,0,1,0,3,0,1,0,0,0,0,2,2,1,1,0,0,0,2,0,2,0,1,0,0,0,1,0,0,0,3,0,11,0,0,0,0,0,12,0,0,0,1,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41715,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,8,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41716,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41717,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,3,2,1,0,0,0,2,0,2,0,6,0,1,2,0,0,1,2,2,0,4,0,1,0,0,0,6,0,2,0,1,0,3,0,0,0,0,1,10,0,0,1,0,2,8,0,9,0,3,2,4,2,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,5,0,Class_6
+41718,1,1,0,0,0,0,1,0,0,0,0,0,1,1,0,2,0,3,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,8,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,12,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41719,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41720,0,0,3,2,0,0,0,0,0,0,7,2,0,0,0,0,0,0,0,0,3,0,0,1,0,3,17,2,0,0,1,0,0,0,1,0,1,1,0,0,0,1,2,0,0,3,0,0,1,1,1,4,0,5,0,0,0,0,0,0,3,1,0,1,0,0,0,0,0,1,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,1,Class_6
+41721,0,1,7,4,0,0,0,0,0,0,9,0,0,1,0,5,0,1,0,0,1,1,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,2,0,5,1,0,1,0,0,3,0,4,1,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41722,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,6,0,1,0,1,0,2,0,4,1,1,0,1,0,0,0,2,0,1,1,0,0,1,0,2,0,1,0,1,0,0,8,2,1,2,0,0,0,7,3,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,6,0,0,0,3,0,0,1,0,0,3,0,1,0,1,1,0,1,0,0,0,0,0,Class_6
+41723,0,0,4,7,0,0,0,0,0,0,9,0,0,0,0,5,0,1,0,0,3,2,0,1,0,4,3,4,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,0,0,7,1,0,0,0,0,5,0,8,0,0,0,0,1,2,5,0,0,0,1,2,0,1,0,0,0,0,0,7,0,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,0,1,0,Class_6
+41724,4,0,0,1,0,0,0,0,1,0,11,1,1,3,0,13,0,6,0,0,0,3,0,5,1,0,4,1,2,0,1,3,3,1,3,0,1,5,0,2,0,0,6,18,0,0,0,2,10,1,0,0,0,7,1,0,0,1,0,1,0,2,4,0,8,3,1,0,0,5,0,1,0,6,1,10,0,0,0,0,1,2,0,0,0,1,0,0,0,1,0,3,0,Class_6
+41725,0,0,8,1,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,12,0,0,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41745,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,4,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+41746,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,1,1,0,2,0,2,0,1,9,0,0,0,0,1,0,0,4,0,0,0,0,1,1,3,1,0,1,0,0,1,0,0,9,0,0,0,0,0,3,0,0,1,0,0,14,0,2,0,1,0,0,7,0,0,7,0,1,1,0,0,8,0,1,2,1,0,0,6,0,3,0,3,2,0,0,0,0,0,Class_6
+41747,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41748,1,3,1,1,0,0,2,0,1,0,13,0,2,0,1,4,1,6,0,1,0,1,0,5,0,0,0,0,0,0,0,0,0,2,5,1,0,2,0,0,0,1,0,0,0,0,1,1,15,0,2,1,1,3,0,2,0,1,0,1,3,1,0,2,0,3,1,0,0,3,0,0,0,0,1,3,1,1,0,17,0,7,1,0,0,0,0,1,2,1,1,0,0,Class_6
+41749,0,1,5,19,1,0,0,0,0,0,8,0,0,1,0,1,0,1,1,0,2,2,0,13,0,1,0,0,0,0,1,3,0,2,1,0,0,0,0,2,0,0,0,1,1,3,0,0,0,0,0,0,0,6,2,0,0,0,1,1,11,0,0,0,0,2,2,2,0,0,0,0,0,0,1,6,5,0,0,6,0,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+41750,0,0,1,2,0,0,2,7,0,0,1,0,9,1,1,1,2,4,1,0,0,2,1,12,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,3,0,1,0,2,2,0,0,0,0,4,1,1,0,0,0,18,0,2,0,0,2,0,6,0,3,2,0,0,0,3,1,3,0,0,0,0,2,1,14,0,0,0,1,0,0,0,0,0,0,Class_6
+41751,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,2,2,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,6,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41752,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,9,0,1,0,0,0,1,2,0,0,2,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,Class_6
+41753,0,0,4,11,0,0,0,0,0,0,7,0,0,0,0,1,0,0,0,0,0,1,0,0,0,5,1,1,0,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,0,4,0,1,1,0,0,0,0,15,2,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+41754,0,2,19,40,0,0,0,0,0,0,10,0,3,0,2,19,0,1,1,0,4,5,0,1,0,5,3,9,1,0,1,0,1,1,7,0,0,2,0,0,0,1,0,3,0,12,0,1,9,1,3,2,0,21,3,0,0,0,1,1,9,3,1,1,3,8,2,0,0,2,0,2,0,0,0,0,1,0,1,12,0,14,1,0,0,0,0,0,5,1,0,2,1,Class_6
+41755,0,1,10,4,0,0,0,0,0,0,7,0,0,0,0,5,0,0,0,0,0,7,0,0,0,2,4,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,5,0,0,3,0,0,0,0,19,2,0,0,0,0,2,3,2,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,2,0,0,0,0,1,0,2,0,0,0,0,Class_6
+41756,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,2,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+41757,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,1,0,6,0,7,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,0,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,Class_6
+41758,0,0,9,13,0,0,0,0,0,1,4,0,0,1,0,0,1,0,0,0,0,1,1,0,0,2,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,4,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41759,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,1,0,2,0,5,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,1,1,0,1,4,0,0,0,1,3,1,0,0,0,3,0,0,4,0,0,12,0,2,0,1,0,0,4,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,2,0,0,0,0,1,0,0,0,0,Class_6
+41760,0,0,12,8,0,0,0,0,0,2,7,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,9,1,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,1,0,0,1,0,Class_6
+41761,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,10,0,6,0,0,0,0,6,0,1,0,4,1,0,0,0,0,1,7,0,0,1,0,0,0,0,0,0,0,0,4,1,0,4,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41762,0,0,2,1,0,0,0,0,0,0,1,0,1,0,0,3,3,0,0,3,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,5,0,6,0,0,1,2,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,3,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,15,3,0,0,0,0,9,Class_6
+41763,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,11,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+41764,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,0,0,0,9,2,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,2,0,1,0,1,0,1,0,Class_6
+41765,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
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+41786,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41788,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41789,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,6,0,1,0,0,4,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,11,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41791,0,1,11,5,0,0,0,0,0,0,9,0,0,1,0,3,0,1,0,0,1,3,1,0,0,0,14,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,1,4,0,13,0,1,0,0,0,2,1,12,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+41793,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,5,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,3,2,0,0,0,0,0,0,3,0,1,4,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+41794,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,2,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+41795,0,1,5,2,0,0,0,0,0,0,11,0,1,1,0,3,0,4,0,1,0,1,0,0,0,6,7,2,0,0,0,2,0,0,4,0,0,0,0,0,0,3,0,1,0,2,0,2,2,0,0,0,0,10,0,0,0,1,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,20,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+41796,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,3,1,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41797,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,18,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41798,0,0,7,4,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,1,0,0,0,4,3,0,1,0,0,2,1,0,1,0,0,1,0,0,0,1,0,0,0,3,0,0,1,0,0,0,1,4,0,0,0,0,0,1,2,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41799,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41800,0,0,0,1,0,0,0,0,0,0,0,0,2,1,1,0,2,1,0,3,0,7,0,2,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,1,2,0,0,3,0,0,0,0,2,6,0,0,2,1,0,2,0,0,0,2,0,6,0,2,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,6,0,0,0,0,0,9,Class_6
+41801,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,6,0,1,0,1,0,3,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41802,0,4,3,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,9,0,2,2,2,0,2,0,0,0,0,1,3,1,1,0,3,0,1,0,23,0,4,0,1,0,0,0,0,0,0,0,18,1,0,3,0,1,13,0,1,0,1,0,0,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,3,0,0,6,1,0,0,1,0,2,0,Class_6
+41803,0,0,6,3,0,1,0,0,3,0,7,0,0,0,0,1,0,2,0,0,3,2,0,0,1,2,1,0,0,0,0,1,0,2,2,0,0,2,0,0,0,1,0,2,0,1,0,0,2,0,0,9,0,15,0,0,0,0,0,2,2,1,0,0,2,0,0,0,0,0,0,0,0,10,0,0,1,0,0,10,1,6,0,0,0,0,1,0,3,0,0,0,0,Class_6
+41804,0,0,1,1,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,5,1,8,0,5,0,0,0,0,4,0,0,0,3,5,0,0,2,0,0,1,0,2,0,0,0,2,2,1,0,0,0,4,1,0,10,0,2,9,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,0,Class_6
+41805,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,3,0,1,0,2,0,2,1,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,6,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,14,0,1,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,2,1,0,0,1,1,Class_6
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+41840,1,0,2,0,0,0,1,0,0,1,9,0,0,1,0,6,0,1,2,0,1,5,0,1,0,0,1,0,1,0,0,5,1,0,1,0,1,0,0,2,0,0,1,6,0,0,0,1,4,0,0,0,1,8,0,0,0,0,0,1,0,1,0,0,2,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,1,Class_6
+41841,0,0,3,0,0,0,0,0,0,0,6,0,1,0,0,3,0,2,0,0,0,1,0,2,0,9,2,1,0,0,0,0,1,0,3,0,0,0,0,0,0,3,0,0,0,2,0,3,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41842,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,12,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
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+41845,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,7,0,1,0,6,2,0,0,0,1,0,0,0,0,2,2,5,0,2,0,0,2,2,0,0,0,1,2,0,3,1,0,0,0,4,2,0,3,0,0,10,0,0,1,1,0,3,7,0,0,3,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,1,0,0,1,0,Class_6
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+41881,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,1,0,2,0,1,0,2,0,1,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,1,0,0,7,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
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+41883,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,8,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
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+41914,0,0,15,3,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,1,4,0,0,0,0,1,14,2,0,0,1,0,0,0,4,0,2,0,0,1,0,1,0,2,0,7,0,1,0,1,0,2,0,11,0,1,0,1,0,1,5,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,6,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41915,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,2,0,0,1,0,4,0,2,3,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,4,1,0,0,0,2,2,1,0,0,0,4,4,0,0,0,0,0,3,3,0,2,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,Class_6
+41916,2,0,1,0,0,0,0,0,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,1,5,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,7,0,1,0,1,1,2,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+41917,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,1,0,0,1,0,0,12,0,0,0,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+41918,0,0,1,2,0,0,2,0,0,0,8,0,0,1,0,5,0,1,0,0,1,1,0,0,0,0,7,1,1,0,1,1,1,0,2,0,0,1,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,11,0,0,0,1,0,0,3,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,9,0,4,0,0,0,0,1,0,4,0,0,0,1,Class_6
+41919,3,4,2,2,0,0,0,1,0,0,2,0,0,0,0,2,0,5,4,7,0,1,2,1,0,0,1,0,0,0,1,0,0,0,6,0,1,8,4,4,0,1,0,0,1,0,0,0,4,8,0,8,0,4,2,1,1,7,0,0,1,0,2,2,1,0,0,0,7,2,0,1,0,0,1,4,0,2,0,6,0,0,2,0,1,0,0,0,3,4,1,1,0,Class_6
+41920,0,1,1,3,0,0,0,0,0,0,17,0,0,5,0,3,0,3,1,0,0,0,0,2,0,9,6,1,0,0,2,3,2,0,3,1,0,0,0,0,1,1,0,0,1,7,0,1,0,0,0,0,0,10,0,5,5,1,0,1,8,0,3,2,0,4,1,0,0,1,0,0,1,3,1,0,0,0,0,16,0,3,0,0,0,0,0,0,2,0,0,1,0,Class_6
+41921,0,1,0,1,0,0,1,2,0,0,0,0,2,0,2,0,3,2,0,1,0,4,0,8,1,0,0,0,1,0,0,2,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,10,0,1,0,0,0,0,8,0,0,1,0,3,0,0,0,0,4,2,1,0,0,0,0,0,0,0,4,1,1,0,0,0,0,Class_6
+41922,0,0,1,0,0,0,0,5,0,0,0,0,11,0,0,1,11,0,0,1,0,1,0,7,0,0,0,2,0,0,0,3,0,0,1,0,0,0,0,1,1,4,0,1,0,1,0,2,0,0,0,0,2,2,1,0,3,0,1,15,0,2,1,0,0,1,8,1,0,6,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,3,0,2,0,0,Class_6
+41923,0,3,0,2,0,0,0,2,0,0,1,0,0,0,0,0,2,0,0,6,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,1,10,0,0,16,0,0,0,0,0,0,2,1,2,1,0,1,0,1,0,0,0,0,0,0,0,0,7,0,0,0,6,0,0,0,0,0,0,Class_6
+41924,0,0,6,0,0,0,0,0,0,0,11,0,0,0,1,12,0,0,0,0,0,3,0,0,0,6,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41925,0,1,7,7,0,0,0,2,0,0,9,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,4,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,2,0,4,0,0,0,0,0,0,0,11,0,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,5,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+41944,2,0,0,1,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,13,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+41945,0,0,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,14,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41946,0,0,0,4,0,0,1,0,0,0,0,0,0,0,1,2,2,1,0,1,0,2,0,3,0,5,2,0,0,0,0,0,1,0,1,0,0,1,1,1,0,4,1,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41947,0,0,3,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,6,0,1,2,1,0,0,1,0,1,0,8,1,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,1,0,5,1,0,2,1,0,13,0,0,0,2,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,3,0,0,0,0,2,0,3,0,0,0,Class_6
+41948,0,0,1,0,0,0,0,0,1,2,0,0,1,3,0,2,1,1,0,4,0,2,0,6,2,2,0,0,0,0,0,5,4,0,1,3,0,1,0,1,1,2,0,3,0,0,0,4,0,4,0,0,0,1,4,0,2,1,1,10,0,2,2,15,1,0,7,2,0,11,3,0,1,0,1,1,0,0,1,0,1,0,0,0,1,2,2,1,1,0,0,0,0,Class_6
+41949,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,1,0,0,1,0,2,0,1,0,2,1,0,0,1,0,0,1,1,0,0,1,0,0,2,0,3,0,0,3,2,2,0,0,0,0,9,1,0,0,2,0,0,0,0,0,0,1,2,3,1,0,0,0,0,0,1,2,0,1,1,0,2,0,1,0,2,0,0,0,0,1,0,0,1,0,Class_6
+41950,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,0,0,1,0,3,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,1,1,0,1,0,1,21,0,3,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41951,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41952,0,0,10,9,0,0,0,0,0,0,18,1,0,0,2,2,0,0,1,0,1,1,0,8,0,1,6,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,11,0,0,0,5,0,0,1,0,0,2,0,1,1,0,0,0,0,0,0,4,0,0,0,1,0,5,0,4,1,0,0,0,0,0,1,0,0,1,0,Class_6
+41953,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41954,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,1,0,5,0,0,0,0,3,1,0,0,0,0,0,0,1,1,2,3,0,0,0,2,0,1,1,0,0,1,0,6,0,0,3,0,0,6,1,4,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,3,0,Class_6
+41955,0,0,2,9,0,0,0,0,0,0,12,0,0,1,0,2,0,2,0,0,1,2,0,7,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,3,0,0,0,0,0,0,4,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41956,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,4,0,1,0,1,1,0,0,0,0,0,0,5,1,0,7,0,1,9,0,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+41957,0,0,2,2,0,0,0,0,2,0,8,0,0,0,0,0,0,3,0,0,2,0,0,3,0,4,2,2,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,1,0,2,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41958,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41959,0,0,2,3,0,0,0,1,0,0,0,0,1,0,1,3,1,4,0,0,0,1,0,3,1,2,0,0,0,0,0,0,0,0,2,2,0,3,0,0,0,5,0,0,0,0,0,2,5,0,0,0,0,7,0,0,1,0,0,13,0,0,0,0,1,3,7,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+41960,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,5,0,3,0,2,0,0,0,0,0,2,0,0,0,0,1,3,2,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,1,0,5,1,0,2,0,3,0,2,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,5,1,Class_6
+41961,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+41962,0,0,5,7,0,0,0,1,1,0,0,1,0,1,0,6,0,1,0,2,0,8,0,1,0,2,0,4,0,0,0,0,0,3,16,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,31,1,0,1,1,0,8,3,2,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41963,0,1,0,3,0,0,0,0,0,0,22,0,0,0,1,4,1,2,0,0,3,1,0,9,0,6,8,1,0,1,4,2,0,2,10,0,0,1,0,0,0,2,0,1,0,2,0,1,0,0,1,5,0,1,0,1,0,8,0,1,1,0,0,2,0,0,0,0,0,0,0,1,1,6,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41964,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,5,0,8,0,1,0,0,0,0,0,0,2,1,0,1,0,0,0,2,0,2,0,0,0,1,1,0,0,0,0,6,0,0,3,0,0,12,0,2,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,Class_6
+41965,0,0,7,14,0,0,0,1,0,1,7,1,0,3,1,1,0,0,0,0,0,0,0,0,0,4,8,3,0,0,2,0,0,2,1,1,0,0,0,1,0,0,1,2,0,24,0,0,2,0,0,3,0,13,0,0,0,2,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+41966,0,0,3,4,0,0,0,0,0,0,11,0,0,0,0,1,1,0,0,0,2,0,0,5,0,4,0,1,0,0,0,3,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,5,0,1,2,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,9,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+41967,0,2,15,7,0,0,0,0,0,0,9,0,0,0,0,5,0,0,0,0,3,3,0,0,0,5,3,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,16,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0,1,1,0,0,0,0,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41968,0,0,0,0,2,0,0,0,0,1,0,1,2,0,0,4,1,1,0,0,0,4,0,2,0,0,0,0,26,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,6,6,0,5,0,0,1,0,1,0,0,1,4,5,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+41969,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+41970,0,1,0,0,0,0,0,0,2,0,0,0,2,0,0,1,0,4,0,0,0,0,0,6,0,0,0,0,1,0,0,3,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,1,0,10,0,0,1,0,0,0,6,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+41971,0,4,1,0,0,0,0,0,0,0,5,0,0,0,1,0,0,3,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41972,0,0,9,2,0,0,0,0,0,0,6,0,2,1,0,3,0,0,0,0,0,0,0,1,0,4,0,2,0,0,0,0,0,1,0,1,0,0,2,0,0,2,0,1,0,4,0,0,1,0,0,0,0,8,0,2,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+41973,1,1,1,1,0,0,0,3,0,0,0,0,3,0,1,1,1,8,0,3,0,3,0,6,0,0,0,1,1,0,0,1,0,0,0,1,0,1,1,0,0,1,0,1,0,0,0,0,1,0,2,1,1,2,0,0,5,1,0,11,0,2,0,0,0,1,6,0,0,5,1,3,0,0,0,0,4,1,1,1,3,0,0,0,2,0,4,1,1,0,0,0,0,Class_6
+41974,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,2,0,0,1,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,2,0,5,0,0,12,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41975,0,0,12,2,0,0,0,1,0,0,6,0,0,0,0,3,0,0,1,0,2,1,0,2,0,3,15,1,0,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,7,0,0,0,3,0,0,9,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,19,0,2,0,0,0,0,0,0,1,0,0,0,1,Class_6
+41976,1,0,6,1,0,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+41977,0,0,2,0,0,0,0,0,0,0,12,0,1,0,0,0,0,1,0,0,1,1,0,3,0,0,10,0,0,0,0,2,1,0,1,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,1,0,0,2,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+41978,0,0,10,3,0,0,0,0,0,0,15,0,0,0,0,0,0,1,0,0,0,0,0,8,1,1,4,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,2,0,0,1,0,0,3,0,2,0,0,0,7,0,1,2,2,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41979,0,0,6,2,0,0,0,0,0,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,3,3,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,5,0,0,0,0,0,0,1,1,1,0,0,1,0,Class_6
+41980,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,1,4,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,11,0,0,0,0,0,2,0,0,0,0,2,0,0,0,1,0,0,8,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+41981,0,0,3,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,3,0,0,2,0,0,18,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41982,0,1,14,6,0,0,0,1,0,0,7,0,2,0,0,5,0,2,0,0,1,3,0,0,0,1,6,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,3,0,5,0,0,0,0,0,0,0,17,3,3,0,0,0,0,2,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,0,2,0,0,0,0,0,Class_6
+41983,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,2,1,0,0,0,0,1,0,0,2,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,2,0,0,0,0,1,2,Class_6
+41984,3,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,1,0,0,0,1,0,1,1,2,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,8,0,1,0,0,0,1,1,0,0,0,0,6,1,0,0,0,3,5,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+41985,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,1,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,7,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+41986,4,0,0,2,0,0,0,0,0,0,1,0,1,2,0,0,1,3,0,3,0,0,0,14,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,2,1,0,4,1,0,0,0,0,5,1,0,18,1,0,0,0,1,1,3,1,0,0,0,1,0,0,1,1,0,1,0,0,1,0,13,0,0,0,1,0,0,2,0,0,0,Class_6
+41987,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,2,1,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,1,0,0,3,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,2,0,Class_6
+41988,0,0,5,6,0,0,0,0,0,0,7,1,0,1,0,4,0,0,0,0,2,0,0,0,0,5,2,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,3,0,3,0,1,0,0,0,2,0,7,1,1,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,5,0,1,0,2,0,0,0,0,2,0,0,2,0,Class_6
+41989,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,3,0,4,1,0,1,1,1,0,2,7,0,0,0,3,2,1,2,1,0,1,0,2,0,0,0,5,0,0,0,0,0,6,1,0,0,0,3,0,0,0,0,3,0,0,29,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,4,24,1,0,0,0,0,15,Class_6
+41990,1,0,0,0,0,0,0,1,0,0,2,0,0,0,3,0,1,1,0,2,0,3,0,4,2,0,0,0,0,0,0,3,3,2,0,3,2,1,1,0,0,2,0,1,0,0,0,7,0,2,0,0,0,1,0,1,1,0,4,1,0,0,0,6,0,0,20,0,1,1,0,0,1,0,2,0,0,0,0,0,0,0,1,0,1,19,16,0,0,0,1,0,8,Class_6
+41991,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1,0,1,0,3,0,2,0,5,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,15,0,3,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,4,0,1,0,2,1,1,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,1,1,0,2,0,0,Class_6
+41992,3,0,1,1,0,0,0,1,0,3,0,0,3,0,0,2,3,2,0,1,0,4,0,4,0,1,0,0,2,0,0,0,1,1,5,2,0,1,0,0,2,3,0,1,1,1,1,0,2,0,0,0,0,10,5,0,1,0,0,2,0,0,0,0,1,2,6,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,2,0,0,Class_6
+41993,0,0,4,2,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,1,0,4,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,2,0,0,0,0,1,6,0,0,2,0,0,16,0,0,0,0,3,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+41994,1,1,2,3,0,0,0,0,1,1,9,0,0,0,1,0,0,0,0,0,1,2,0,1,0,0,25,0,0,0,1,0,0,1,1,0,0,0,0,0,0,2,0,0,0,2,0,0,1,0,1,1,0,4,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,5,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+41995,0,0,0,0,0,0,0,0,0,0,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+41996,0,0,0,0,0,0,1,3,0,0,1,0,0,2,0,0,2,5,0,3,0,0,3,4,2,0,0,0,0,0,1,4,1,0,0,1,1,3,0,0,0,2,0,0,0,0,1,0,2,3,0,0,0,0,0,0,0,0,0,6,0,3,0,0,2,1,3,0,0,3,3,0,0,1,0,0,0,0,0,1,1,0,0,0,4,1,6,0,2,0,0,0,2,Class_6
+41997,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,1,0,0,0,1,0,4,1,1,0,1,0,0,0,0,2,0,0,0,0,0,2,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,12,0,0,0,1,0,1,4,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+41998,0,0,2,2,0,0,0,0,0,0,0,0,0,0,5,3,0,2,1,0,1,4,0,1,0,5,2,0,0,0,1,0,0,0,8,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,15,0,0,2,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+41999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42000,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,5,1,0,21,0,1,0,0,1,0,2,0,2,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42001,0,0,3,2,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,5,0,8,0,1,3,0,1,0,0,0,0,1,0,2,3,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,2,0,0,5,0,0,0,3,0,2,1,0,1,0,0,1,1,0,0,0,2,1,3,0,0,0,0,1,0,1,0,0,0,0,Class_6
+42002,0,0,6,5,0,0,0,0,1,0,3,0,0,3,0,1,1,1,0,0,1,0,0,2,0,13,4,1,0,0,0,1,0,0,2,0,0,1,0,0,0,2,0,2,0,0,0,1,1,0,0,1,0,5,0,0,0,5,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,1,0,0,0,0,Class_6
+42003,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,4,0,9,1,0,0,0,1,0,6,3,0,0,0,3,2,2,1,0,0,3,0,1,0,0,0,5,0,0,0,0,1,0,1,0,0,0,1,1,0,1,0,5,0,4,15,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,1,0,1,1,16,1,0,0,0,0,13,Class_6
+42004,1,0,1,1,0,1,0,0,0,0,9,0,0,0,0,1,0,1,0,0,1,1,0,1,0,0,5,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,5,0,2,0,0,2,0,5,1,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,2,0,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,0,0,0,0,Class_6
+42005,1,3,2,3,0,0,0,0,0,0,16,0,1,1,0,0,0,2,0,1,1,1,0,2,0,0,5,0,0,0,2,3,2,0,3,1,1,5,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,1,0,0,1,0,1,0,4,0,0,0,0,0,1,0,0,3,0,0,1,1,0,0,0,0,1,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42006,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,1,1,1,0,0,3,0,0,0,1,0,0,2,0,0,1,1,0,1,0,0,1,0,0,2,0,0,0,0,0,1,0,0,2,2,1,1,0,4,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,Class_6
+42007,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,6,0,4,3,2,0,0,0,1,0,0,0,1,0,0,0,2,0,1,0,3,0,3,0,1,0,0,0,0,1,7,0,0,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_6
+42008,0,1,5,6,0,0,0,0,0,0,16,0,1,1,0,1,0,0,0,0,1,2,0,0,0,1,8,0,0,1,0,2,0,0,3,1,0,0,0,0,0,0,0,2,0,1,0,0,2,1,0,0,0,2,0,0,0,2,0,1,2,0,0,0,1,0,0,0,0,0,0,0,2,1,0,1,0,0,0,1,1,2,0,0,0,0,0,0,3,0,0,0,0,Class_6
+42009,0,0,13,3,0,0,0,0,1,0,9,0,0,0,0,8,0,1,0,0,0,0,0,0,0,2,13,1,1,0,1,0,0,1,2,0,0,0,0,0,0,0,0,1,0,12,1,0,0,0,0,0,1,15,1,1,0,0,0,1,3,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+42010,0,0,2,4,0,1,1,1,1,1,4,2,0,0,3,5,0,7,2,0,5,0,2,24,1,4,2,4,1,0,2,0,0,1,4,1,0,4,2,1,0,2,0,1,1,3,0,4,3,0,3,9,1,5,1,0,5,1,0,9,2,7,0,1,0,3,4,1,1,1,0,1,4,1,1,1,1,0,0,13,1,6,0,0,1,0,0,0,6,0,0,1,1,Class_6
+42011,0,0,2,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,7,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,12,0,1,2,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
+42012,0,0,15,5,0,0,0,0,0,0,7,0,0,0,2,5,0,1,0,0,5,1,1,2,0,1,11,5,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,1,11,0,0,1,0,1,1,0,16,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,Class_6
+42013,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,2,0,4,0,2,0,3,0,3,1,4,0,0,1,0,0,1,0,0,1,1,0,2,0,4,0,6,0,1,0,3,0,0,0,0,0,0,0,3,0,0,1,0,0,14,0,1,0,2,0,1,10,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,2,0,Class_6
+42014,0,0,2,5,0,0,0,0,0,0,9,0,0,0,0,2,1,1,0,0,0,0,2,4,1,0,5,1,0,0,1,1,0,0,1,1,4,2,0,1,2,1,0,1,0,2,0,0,0,0,0,4,1,6,0,0,0,3,0,1,2,2,1,1,0,3,1,0,1,1,0,2,0,2,0,0,0,0,0,15,0,4,1,0,1,0,1,0,1,0,0,0,0,Class_6
+42015,0,0,0,2,0,0,0,0,0,0,6,0,0,0,0,1,0,1,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42016,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,2,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42017,0,0,1,2,0,0,0,0,0,0,0,0,4,0,1,4,0,7,0,0,0,3,0,3,0,6,0,1,0,1,0,0,0,0,1,1,0,0,0,0,1,6,0,1,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,13,0,3,0,1,0,2,2,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,1,Class_6
+42018,1,0,10,7,0,0,0,0,0,0,14,0,2,0,0,1,0,1,0,0,1,1,0,6,0,1,6,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,1,0,8,0,1,1,15,0,1,0,0,0,2,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+42019,0,0,3,6,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,4,3,0,0,0,1,15,1,0,0,2,2,0,0,3,1,0,1,0,1,1,0,0,1,0,4,0,2,0,0,0,11,0,9,0,0,0,4,0,2,1,1,0,0,0,0,3,1,1,0,0,0,0,1,1,0,1,0,0,8,0,3,0,0,0,0,0,1,2,0,0,0,0,Class_6
+42020,0,0,11,4,0,0,0,0,0,0,11,0,0,2,1,4,0,0,0,2,3,3,0,0,1,1,2,1,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,15,0,0,1,0,0,1,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,7,0,0,1,0,0,0,0,1,1,2,0,1,0,Class_6
+42021,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,Class_6
+42022,0,0,31,4,0,0,0,0,0,1,10,0,0,0,0,2,0,2,0,0,2,1,0,2,0,3,1,2,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,2,0,6,0,0,1,0,1,0,0,9,1,0,0,0,0,3,3,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,4,0,1,0,0,0,0,1,0,2,0,0,1,1,Class_6
+42023,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,1,0,3,0,0,0,0,0,0,1,0,3,0,0,1,0,1,1,13,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,13,0,1,0,0,0,0,5,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,Class_6
+42024,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,16,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+42025,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+42026,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42027,2,0,11,2,0,0,0,0,0,1,7,0,0,0,1,4,0,1,0,0,0,3,0,0,0,3,1,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,3,0,6,0,0,2,0,0,0,0,12,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+42028,0,0,3,2,0,0,0,0,0,0,8,0,0,0,0,6,0,0,0,2,3,1,0,0,0,2,6,3,0,0,3,2,0,0,4,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,2,0,11,1,1,0,0,0,1,6,1,0,0,0,0,0,0,0,0,0,0,2,5,0,1,0,0,0,12,0,3,0,0,1,0,0,0,1,1,0,1,0,Class_6
+42029,0,0,1,12,0,0,0,0,0,0,5,0,1,0,0,1,0,2,0,0,0,2,0,0,0,20,6,2,1,0,2,1,0,1,7,0,0,0,0,0,0,7,0,2,1,1,0,0,3,0,0,0,0,4,0,0,0,0,0,0,2,2,1,1,0,4,0,0,0,1,0,1,0,1,0,2,0,1,1,9,0,0,0,1,0,0,0,0,1,2,0,0,0,Class_6
+42030,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,4,1,1,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,Class_6
+42031,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,8,0,3,4,1,0,2,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,4,0,0,0,0,0,Class_6
+42032,0,0,0,0,0,0,0,2,0,1,1,0,0,1,0,0,2,0,0,2,0,5,0,6,2,0,0,0,0,0,0,7,0,0,0,2,1,5,0,1,0,5,0,1,0,0,0,3,1,1,0,0,0,0,3,0,0,0,3,1,0,1,0,2,1,0,16,0,0,7,4,0,0,0,0,1,0,0,1,0,0,0,0,0,4,0,13,0,0,0,0,0,5,Class_6
+42033,0,0,1,2,0,0,0,1,0,0,0,0,4,0,0,10,2,15,0,1,0,4,0,1,0,1,0,6,6,0,0,0,2,1,6,3,0,0,0,5,4,2,0,1,0,0,0,0,2,0,0,0,0,10,4,0,1,0,0,9,0,13,0,0,0,3,0,2,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,1,1,2,0,1,1,0,Class_6
+42034,0,0,18,5,0,0,0,0,3,0,9,0,0,0,0,2,0,3,0,0,0,2,0,0,0,1,4,1,0,0,0,1,0,0,0,1,0,2,0,0,0,1,1,1,0,15,0,0,0,0,0,0,0,29,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42035,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,4,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,2,6,0,1,0,0,4,0,0,0,0,0,2,0,5,1,0,2,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42036,0,0,1,3,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,2,2,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,4,1,0,4,0,0,8,0,0,0,1,0,1,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,2,Class_6
+42037,0,0,19,5,0,0,0,0,2,1,8,0,0,2,0,7,0,0,0,0,2,3,0,2,0,9,13,8,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,10,0,0,0,0,0,0,0,14,0,0,0,0,0,1,7,0,0,1,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,4,0,2,2,0,0,0,0,0,0,0,0,1,0,Class_6
+42038,0,0,11,20,0,0,0,0,0,0,6,0,0,0,0,5,0,1,0,0,1,3,0,0,0,5,7,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,1,1,0,13,0,0,0,0,2,0,0,21,2,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+42039,0,0,3,10,0,0,0,0,0,0,7,0,0,1,0,5,0,1,0,0,0,2,0,2,0,13,7,3,0,0,1,0,0,0,5,0,0,1,0,0,0,2,0,0,0,2,0,0,1,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42040,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+42041,0,0,1,2,0,0,0,0,0,1,2,0,1,0,0,6,0,3,0,0,0,5,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,4,0,1,0,0,0,0,14,1,0,2,0,0,12,1,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,1,0,1,0,0,2,0,Class_6
+42042,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+42043,0,0,7,0,0,0,0,0,0,0,5,0,0,0,0,3,0,0,0,0,0,2,0,0,1,3,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42044,0,0,1,1,0,0,0,0,0,0,4,0,0,1,0,0,0,2,0,0,2,0,0,0,0,0,8,1,0,1,0,1,0,0,5,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,8,0,9,1,1,0,0,0,0,7,1,0,0,0,1,0,0,0,0,0,0,0,4,0,1,0,0,0,9,0,3,1,0,0,0,0,1,1,1,0,0,0,Class_6
+42045,0,0,2,5,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,1,1,0,0,0,6,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+42046,1,0,5,12,0,0,0,0,0,1,20,0,0,1,0,0,0,0,1,0,0,1,0,12,0,1,14,0,0,0,4,1,0,0,1,0,0,0,0,1,0,0,1,1,2,0,0,1,0,0,0,9,0,5,0,0,0,9,0,0,2,0,0,1,1,0,3,0,0,0,0,0,0,3,0,5,0,1,0,7,0,1,0,0,0,0,0,0,0,0,1,1,0,Class_6
+42047,8,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,1,5,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,1,0,0,0,0,0,Class_6
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+42067,0,1,1,3,0,0,0,0,0,0,8,0,0,2,1,5,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,2,0,1,0,0,0,0,0,0,3,0,1,0,1,0,0,1,0,0,0,1,8,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,1,0,0,0,0,Class_6
+42068,0,1,0,3,0,0,0,0,0,0,7,0,0,0,2,4,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,0,0,0,2,1,0,0,0,0,7,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
+42069,0,1,4,4,0,0,0,0,0,0,11,0,1,1,0,1,0,1,0,1,1,1,1,0,0,2,4,0,1,0,0,2,0,0,1,0,0,2,0,1,0,0,0,7,0,2,0,0,13,0,0,0,0,4,1,0,3,0,0,1,5,2,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,2,0,1,2,2,0,0,0,Class_6
+42070,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42071,0,0,0,0,0,0,0,0,1,0,3,0,3,0,0,0,0,8,1,0,0,0,0,2,0,11,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,3,0,0,5,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+42072,0,1,2,8,0,0,0,0,0,1,11,0,0,1,2,5,1,0,0,0,2,3,0,15,1,1,1,0,0,0,5,3,0,0,2,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,8,0,2,0,7,0,1,1,0,0,0,2,3,3,0,0,0,0,0,0,0,0,1,0,0,0,10,0,3,2,0,0,0,0,0,0,0,0,1,0,Class_6
+42073,0,1,7,4,0,0,0,0,0,0,12,1,2,1,1,1,0,2,0,0,1,2,0,0,0,1,17,1,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,11,0,0,1,0,0,0,0,9,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+42074,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,10,0,1,0,0,0,0,6,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42075,0,2,6,5,0,0,0,2,0,1,9,1,0,0,0,3,1,5,0,1,3,2,0,5,2,3,10,0,0,0,2,3,1,1,7,1,0,2,0,1,1,0,0,1,0,0,0,0,2,1,0,2,0,11,2,0,1,0,1,1,7,0,2,0,2,1,0,0,0,3,0,1,2,13,0,0,1,0,0,19,0,8,0,0,0,0,0,0,6,0,0,0,0,Class_6
+42076,0,1,17,3,0,0,0,0,0,0,8,0,1,1,1,5,0,0,0,0,0,2,0,2,0,4,9,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,2,0,14,0,0,0,0,0,0,0,20,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+42077,0,0,3,1,0,0,0,0,0,0,8,1,0,0,0,7,0,0,0,0,1,2,0,0,0,0,10,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,7,0,0,0,0,0,0,0,15,0,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+42078,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,2,1,4,0,0,0,0,0,14,0,6,0,0,0,0,0,5,0,5,2,0,0,0,8,2,0,2,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,13,0,0,0,0,0,1,10,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,Class_6
+42079,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,7,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42080,0,0,0,0,0,0,0,0,1,0,7,3,0,0,0,0,0,0,0,0,0,0,0,15,0,1,2,0,0,0,0,2,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42081,1,0,0,1,0,0,0,0,0,0,2,0,1,1,0,2,0,0,0,0,0,3,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,3,0,0,6,0,1,0,0,0,1,3,1,0,0,0,1,0,0,0,0,0,0,1,1,3,0,3,1,0,0,1,1,1,0,0,2,2,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+42082,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,2,0,1,0,0,0,2,0,1,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,9,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42083,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,6,0,0,2,0,0,0,0,0,1,7,8,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+42084,0,0,6,3,0,0,0,0,0,0,5,0,0,0,0,5,0,0,0,0,0,1,0,0,0,2,4,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,4,0,4,0,0,4,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,5,0,0,0,0,Class_6
+42085,1,0,0,1,0,0,0,3,1,1,2,0,0,3,2,2,0,0,0,1,0,0,2,9,1,0,0,0,0,0,0,4,0,0,1,0,1,7,21,5,0,0,0,1,4,0,0,4,0,6,0,0,0,4,0,0,0,1,0,1,0,1,0,2,0,0,4,0,2,3,2,1,0,0,0,1,0,0,0,3,1,0,0,0,3,3,0,0,0,2,0,0,0,Class_6
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+42122,0,0,17,8,0,0,0,0,0,0,8,1,1,1,0,4,0,0,0,0,0,2,0,2,0,4,0,3,0,0,0,0,1,1,0,1,0,0,0,1,0,3,0,0,0,19,0,0,0,0,0,0,0,21,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,2,0,0,0,0,1,0,0,0,2,0,Class_6
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+42125,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+42159,0,0,10,4,0,0,0,0,0,0,9,1,0,0,0,6,0,0,0,0,3,0,0,0,0,1,1,3,1,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,0,4,1,0,0,0,0,0,0,9,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+42161,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,3,0,0,0,0,0,1,1,0,0,0,0,11,0,0,0,0,0,0,3,0,0,0,0,0,1,0,Class_6
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+42164,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42165,0,0,17,3,0,0,0,0,0,1,11,0,0,0,0,3,0,0,0,0,0,2,0,0,0,2,6,4,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,4,0,0,1,0,0,1,0,15,2,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+42201,0,3,3,12,0,0,0,0,0,0,7,0,1,0,0,10,0,6,2,0,0,1,0,1,0,5,2,2,1,0,0,0,1,0,5,0,0,3,0,1,0,3,1,1,0,2,0,2,6,0,0,0,0,17,1,0,1,1,0,0,2,0,1,0,0,7,0,0,0,0,0,1,1,1,0,0,1,0,0,10,0,13,0,1,0,0,1,0,2,0,1,3,0,Class_6
+42202,0,0,6,2,0,0,0,0,3,0,6,0,1,0,0,7,0,2,0,0,3,1,0,0,0,3,3,2,1,1,0,0,0,2,2,1,0,1,0,0,0,0,0,4,0,3,0,0,2,0,0,0,1,26,0,2,0,0,0,6,0,2,0,3,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+42203,1,0,0,0,1,0,0,0,0,0,0,0,0,3,0,3,2,1,2,4,0,0,1,12,1,0,0,0,0,0,0,14,0,0,0,6,1,1,2,1,0,5,0,1,1,0,0,9,0,0,0,0,0,6,0,0,0,0,6,1,0,2,0,0,0,0,45,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,2,43,0,0,0,1,0,38,Class_6
+42204,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,0,4,0,0,0,0,1,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,8,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42205,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+42208,0,0,2,2,0,0,1,0,0,0,4,0,1,0,0,4,3,1,0,1,2,3,1,1,0,3,1,4,1,0,0,1,2,0,1,1,0,2,1,1,0,0,0,1,0,1,0,0,0,0,0,1,0,13,0,2,0,0,0,0,6,2,1,0,0,2,0,1,0,0,0,2,0,10,0,0,0,0,0,11,1,4,0,0,0,1,0,0,2,1,0,1,0,Class_6
+42209,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,2,0,6,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,10,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,3,Class_6
+42210,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,1,5,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42211,1,0,2,5,0,0,1,0,1,0,0,0,0,1,0,2,1,7,1,1,1,3,3,4,3,0,1,1,0,0,2,0,1,0,8,0,1,2,0,2,1,0,0,1,0,1,0,1,1,1,2,2,1,1,0,1,1,0,0,12,2,2,1,2,0,1,0,0,0,0,0,1,5,2,1,2,1,0,1,10,0,3,1,0,0,0,2,3,4,2,0,2,3,Class_6
+42212,0,0,0,1,0,0,0,1,1,0,0,0,2,0,1,1,0,3,0,1,1,2,0,2,0,1,0,1,1,0,4,3,1,0,3,1,1,0,0,1,0,1,3,3,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,12,0,3,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42213,0,0,1,13,0,0,0,5,0,1,1,0,0,0,0,2,0,2,0,1,0,2,0,14,0,9,3,1,1,0,0,0,0,0,6,0,0,0,0,0,0,4,1,1,0,0,0,0,2,0,0,0,0,9,1,0,5,0,0,12,1,1,0,1,0,2,0,1,0,1,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+42214,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,1,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,0,1,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,4,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+42215,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,3,1,0,0,0,0,7,0,0,3,1,1,0,1,1,1,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,1,0,6,0,0,1,0,0,4,1,0,10,0,3,0,1,0,0,8,2,0,2,0,2,0,1,0,0,4,1,0,1,0,0,2,0,0,0,1,0,1,0,0,0,0,Class_6
+42216,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,1,0,0,0,0,3,1,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,3,0,1,0,0,0,0,0,0,0,3,2,0,1,0,0,4,0,1,0,0,0,3,7,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+42217,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,5,0,1,0,0,0,0,0,2,4,0,0,0,0,0,0,2,0,0,1,2,0,1,2,0,0,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+42218,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,2,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,2,5,0,2,0,0,0,0,0,0,0,0,0,1,3,0,2,0,0,13,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42219,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,4,0,0,0,2,0,4,11,1,0,4,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,15,1,3,0,1,0,0,1,0,0,5,0,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,Class_6
+42220,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,1,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42221,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,2,0,8,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,6,0,2,0,0,0,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42222,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,5,1,0,0,3,0,4,0,2,0,5,0,0,1,0,0,2,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0,3,0,0,0,1,0,10,1,0,3,0,0,2,0,3,0,1,0,2,3,0,0,0,6,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,2,2,0,0,1,0,0,Class_6
+42223,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,10,0,3,0,0,0,0,1,1,0,0,0,0,0,1,0,4,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42224,0,0,9,6,0,0,0,0,0,0,10,0,2,1,0,1,0,0,0,0,0,2,0,0,0,0,3,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,6,0,0,4,0,0,0,0,12,0,0,2,0,0,0,2,0,1,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,2,13,0,6,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42225,0,0,1,0,2,0,1,2,1,1,0,0,0,1,1,1,10,0,1,1,0,8,0,9,1,0,0,0,0,0,0,5,0,0,0,4,1,3,0,1,1,1,3,0,0,0,0,3,0,1,0,0,1,3,2,2,1,1,0,1,0,2,0,6,0,2,18,0,0,2,0,2,0,3,0,0,0,0,0,1,0,0,1,0,0,6,8,0,0,1,1,1,0,Class_6
+42226,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,3,0,0,0,0,0,5,0,0,0,0,1,0,0,2,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,16,0,0,1,0,0,0,6,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+42227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,4,0,0,1,0,0,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,Class_6
+42228,0,0,6,8,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,2,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,1,0,0,0,1,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42229,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,0,1,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,18,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42230,0,0,6,6,0,0,1,0,0,0,10,0,0,0,0,1,0,0,0,2,1,1,0,0,0,2,8,3,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,2,0,10,0,0,1,0,0,0,0,11,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+42231,0,0,7,8,0,0,0,0,0,0,10,0,1,2,0,1,0,1,0,1,0,1,0,2,0,2,3,1,0,0,0,0,0,2,4,0,0,1,0,1,0,1,0,0,0,7,0,0,1,0,0,0,0,10,0,0,0,0,0,1,3,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+42232,0,0,8,0,0,0,0,0,0,0,9,0,1,1,0,4,0,1,0,0,0,2,0,0,0,0,1,4,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,12,1,0,0,0,0,0,2,0,0,0,0,3,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42233,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,3,1,6,0,1,0,0,0,0,0,2,4,0,0,0,1,0,0,3,0,3,0,1,0,0,0,0,0,0,0,6,1,0,2,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42234,0,0,1,1,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,1,0,4,0,2,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,6,0,2,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,4,2,0,0,0,1,0,Class_6
+42235,0,0,0,0,2,0,0,2,0,0,0,0,1,1,0,4,1,3,0,2,0,6,0,1,5,1,0,1,1,0,0,1,3,4,1,0,1,1,0,0,0,19,0,4,0,0,5,1,2,1,0,0,1,3,4,0,1,0,0,1,0,6,6,0,3,3,2,0,0,10,2,1,0,0,1,0,0,0,10,1,0,0,0,0,0,0,1,1,7,0,0,1,0,Class_6
+42236,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42237,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42238,0,0,0,1,0,0,0,0,0,0,14,3,0,1,0,4,0,0,0,0,1,4,3,3,0,0,2,0,1,1,0,3,0,0,0,0,0,4,1,1,0,0,2,1,0,1,0,1,1,0,0,6,0,5,0,1,0,3,0,2,1,0,0,0,0,0,0,1,0,0,0,1,0,60,0,0,1,0,0,10,0,0,0,0,0,0,0,0,4,0,0,1,0,Class_6
+42239,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,2,0,2,0,0,0,0,0,3,0,8,0,0,0,0,2,0,0,0,5,0,0,1,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,3,0,0,12,1,1,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,Class_6
+42240,1,0,2,1,0,0,1,0,0,0,0,0,0,0,2,1,1,1,1,2,0,0,3,7,1,3,0,0,0,0,0,2,0,0,1,0,0,2,2,0,0,0,0,0,0,0,0,5,0,2,0,0,0,3,0,0,0,4,1,13,0,0,0,0,0,0,2,2,1,0,0,1,1,0,1,2,0,0,0,0,0,0,9,0,1,0,0,1,0,0,0,0,0,Class_6
+42241,0,0,11,0,0,0,0,0,0,0,7,0,0,1,0,3,0,0,1,0,2,1,0,0,0,4,2,2,0,0,0,0,0,2,2,0,0,0,0,0,1,0,0,5,0,2,0,1,1,0,0,0,0,28,1,0,0,0,0,1,3,1,0,1,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,Class_6
+42242,0,1,17,16,0,0,0,0,0,0,8,0,0,0,0,0,0,2,0,0,1,0,0,2,0,5,2,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,3,0,17,0,0,2,0,0,0,0,13,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,2,0,1,0,0,1,0,Class_6
+42243,0,2,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,4,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,3,0,0,0,0,1,0,0,2,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+42244,0,2,0,0,0,0,0,0,0,0,1,1,1,0,0,4,0,3,0,1,0,2,0,2,0,7,0,0,0,0,0,0,0,3,5,0,0,0,0,1,0,30,0,3,0,0,0,1,0,0,0,0,0,10,0,0,6,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,1,0,0,0,0,0,4,Class_6
+42245,0,0,19,3,0,0,0,0,0,0,5,0,1,0,0,1,0,0,0,1,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,12,0,2,1,0,0,0,0,18,0,2,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_6
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+42267,0,0,7,1,0,0,0,0,0,1,12,1,0,0,0,3,0,0,0,0,4,2,0,0,0,0,7,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42268,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,1,2,0,0,0,0,0,3,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,2,0,0,10,0,0,6,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,2,0,0,0,0,0,0,Class_6
+42269,2,1,0,0,0,0,2,0,0,2,2,0,0,0,1,4,0,1,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,1,1,1,0,0,0,0,1,2,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+42270,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,2,0,4,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,4,1,0,0,0,0,9,0,1,2,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,Class_6
+42271,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42272,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42273,0,1,8,5,0,0,0,0,0,0,8,1,0,0,0,1,1,0,0,0,3,1,0,0,0,2,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,5,0,0,1,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+42274,1,0,4,3,0,0,0,0,0,0,8,0,0,1,1,7,0,0,2,0,0,2,0,1,1,7,2,0,0,0,0,0,1,0,2,1,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,2,4,1,0,1,1,2,0,0,0,1,0,2,0,1,0,0,0,0,1,9,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_6
+42275,0,0,10,6,0,0,0,0,0,0,12,0,0,0,0,2,1,0,0,0,1,0,0,0,0,1,1,3,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,2,0,7,0,0,1,0,0,2,0,10,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42276,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,12,0,1,0,1,0,1,4,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+42277,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,2,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42278,0,0,4,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,5,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42279,0,0,6,7,0,0,0,0,0,0,15,0,0,2,1,1,0,1,0,0,2,3,0,8,0,1,7,2,0,0,2,3,0,0,1,0,0,1,0,0,1,0,0,1,0,5,0,2,0,0,0,2,0,4,1,0,0,11,0,1,1,2,0,1,0,1,0,1,0,0,0,0,0,2,0,1,0,0,0,5,0,5,0,0,0,0,0,1,1,0,0,2,0,Class_6
+42280,1,1,0,1,0,0,0,1,0,1,0,0,0,0,0,2,8,5,1,7,0,8,0,6,2,1,0,0,1,0,2,1,0,0,7,3,0,6,0,0,0,0,0,1,0,1,0,4,2,3,0,0,1,12,2,0,4,1,0,13,0,0,0,0,2,1,8,1,3,3,3,1,0,0,0,6,0,0,0,1,0,1,19,0,1,1,1,0,2,1,1,1,2,Class_6
+42281,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,3,0,2,0,1,2,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,2,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,0,3,0,1,0,0,0,0,4,0,1,0,0,0,1,3,0,2,0,0,0,0,0,0,1,0,1,0,3,0,0,0,10,0,0,Class_6
+42282,0,0,9,3,0,0,0,0,0,0,5,0,1,0,0,2,0,2,0,0,0,1,0,0,0,2,7,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,6,0,0,0,0,0,2,1,8,0,0,0,0,0,1,2,1,1,1,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+42283,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42284,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,1,3,3,0,0,0,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,8,1,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42285,0,0,9,3,0,0,0,0,0,0,9,0,0,0,1,0,0,3,1,0,0,0,0,0,0,4,0,5,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,1,0,1,0,2,0,0,1,1,0,1,2,0,1,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,Class_6
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+42305,0,0,6,3,0,0,0,0,0,0,19,0,1,0,0,2,0,0,0,0,3,0,0,7,1,1,4,5,0,0,0,2,1,1,1,0,0,1,0,0,0,1,0,1,0,3,0,0,1,0,1,0,1,5,0,0,0,7,0,1,2,2,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,2,0,0,0,0,0,1,2,0,0,0,1,Class_6
+42306,0,0,0,2,0,0,0,1,1,0,1,0,0,1,0,0,1,3,1,0,0,0,1,9,0,0,0,1,0,0,0,0,0,0,9,0,0,1,0,0,0,2,0,2,1,0,0,4,0,0,0,0,0,1,0,0,4,0,0,16,0,0,0,0,0,0,9,2,1,2,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,Class_6
+42307,0,0,6,1,0,0,0,0,0,0,12,1,0,0,0,4,0,0,0,0,2,4,0,5,0,0,39,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,1,0,11,1,0,1,0,0,1,0,3,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42308,0,1,6,3,0,0,0,1,0,0,12,0,2,1,1,2,0,1,0,0,0,3,0,14,0,1,5,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,2,0,21,0,0,1,0,0,0,2,22,0,0,0,0,0,2,0,0,0,1,1,3,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42309,0,3,1,2,0,0,0,0,0,0,6,0,1,0,0,2,0,2,0,0,0,5,0,3,0,3,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,1,0,1,0,1,0,0,4,0,0,0,0,9,0,1,0,1,0,3,3,1,0,1,0,2,1,0,0,0,0,0,1,0,0,1,0,0,1,3,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+42310,0,1,9,0,1,0,0,0,1,0,10,0,0,0,0,1,0,1,0,0,2,1,0,0,0,2,8,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,7,0,0,0,0,0,0,0,7,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,1,1,Class_6
+42311,3,0,0,0,0,0,0,1,0,0,0,0,3,0,1,3,0,3,0,0,0,5,0,0,1,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,20,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,4,1,0,0,1,1,2,2,2,1,3,0,0,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,1,2,0,0,0,1,1,Class_6
+42312,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,3,2,0,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,12,0,0,0,0,0,1,0,0,5,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+42313,0,1,19,15,0,0,1,0,1,1,16,0,0,2,0,4,0,1,0,1,4,8,0,0,0,1,8,2,0,0,0,1,0,1,4,5,2,2,0,1,0,0,0,6,0,4,0,0,6,0,1,3,0,9,0,1,0,3,0,1,2,0,1,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,2,10,0,0,0,0,0,0,0,4,3,0,0,3,0,Class_6
+42314,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,8,0,0,0,0,0,2,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+42315,0,0,5,6,0,0,0,0,0,0,7,0,0,2,0,2,1,1,0,0,2,3,0,0,0,0,4,3,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,1,0,6,0,0,2,0,0,2,0,8,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,0,0,0,Class_6
+42316,1,0,32,25,0,0,0,0,0,0,8,0,0,0,0,6,1,7,0,0,0,2,0,0,0,4,10,0,0,0,0,0,0,2,6,2,0,3,0,2,0,0,0,6,0,28,0,0,2,0,0,1,2,29,4,4,2,0,2,0,8,2,1,0,2,2,1,0,0,2,0,0,0,0,0,0,0,0,0,6,0,16,0,0,0,0,0,0,7,2,0,0,0,Class_6
+42317,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,3,7,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42318,0,0,11,1,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,0,1,1,5,0,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,8,0,1,0,4,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42319,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42320,0,1,1,2,1,0,0,0,1,0,8,0,0,2,0,7,0,0,0,0,1,0,0,0,0,1,8,2,0,0,1,1,0,0,1,0,0,0,0,0,0,1,1,0,0,2,0,0,1,0,0,1,0,3,0,0,0,1,0,0,7,4,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,10,0,1,0,0,0,0,0,0,5,0,0,0,0,Class_6
+42321,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,1,0,1,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42322,0,1,12,8,0,0,1,1,0,0,8,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,3,0,0,4,0,0,0,0,4,0,0,0,0,0,0,0,2,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,8,0,0,0,0,0,1,0,0,0,1,0,Class_6
+42323,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42324,1,0,9,5,0,0,0,0,0,1,8,0,0,0,0,1,1,0,0,0,2,1,1,1,0,0,3,1,1,0,0,0,0,1,1,0,0,1,0,1,0,0,0,1,0,4,0,0,1,0,0,5,0,17,1,0,0,0,0,0,3,1,1,0,0,0,1,0,0,0,0,0,1,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42325,0,0,3,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,0,2,0,1,2,0,5,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,1,0,1,1,2,0,1,0,1,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+42360,0,0,13,3,0,0,0,0,0,1,11,0,0,0,0,2,0,0,0,0,1,0,2,0,0,1,10,2,0,0,2,0,0,0,0,1,0,1,0,0,0,0,1,0,0,9,0,0,0,0,0,1,0,12,0,0,0,0,0,2,8,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,2,0,1,0,0,0,0,0,0,0,0,1,Class_6
+42361,0,0,1,0,0,0,0,1,0,1,0,0,0,1,1,2,0,1,0,1,1,1,0,2,5,1,0,1,3,0,0,3,1,2,0,0,3,0,0,1,1,1,0,0,0,0,0,4,0,0,0,0,30,8,2,1,0,0,0,1,0,3,0,0,2,2,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,3,0,Class_6
+42362,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,2,1,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,Class_6
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+42387,0,1,0,1,0,0,0,0,0,0,7,1,2,2,0,2,0,3,0,1,0,3,0,4,1,6,0,0,0,0,0,0,1,0,7,0,0,0,1,1,0,3,0,3,0,0,0,0,13,0,0,0,0,5,0,0,2,1,0,2,0,3,0,0,0,6,0,0,0,2,0,1,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_6
+42388,0,1,7,3,0,0,0,0,0,0,10,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,2,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,1,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42389,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,5,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,3,9,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,Class_6
+42390,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,2,0,0,1,0,0,6,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42391,0,0,17,12,0,0,0,0,0,0,19,0,0,0,0,1,0,0,0,0,2,3,0,7,0,0,5,1,1,0,0,0,0,0,1,0,0,0,2,0,0,0,1,1,0,9,0,1,2,0,0,0,0,9,0,0,0,7,0,0,5,0,0,1,0,1,0,0,0,0,0,2,0,1,0,0,0,1,0,4,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+42394,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42395,0,0,3,6,1,0,0,0,0,0,8,5,0,0,0,1,0,2,0,0,3,0,0,1,1,7,7,11,0,0,2,2,0,0,3,1,0,0,0,0,1,0,0,6,0,4,0,2,1,0,0,1,0,8,0,0,0,1,0,1,2,1,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,5,0,1,0,0,0,0,1,0,0,1,0,Class_6
+42396,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,9,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_6
+42397,0,0,9,12,0,0,0,0,0,0,12,1,0,1,1,2,0,2,0,0,2,1,0,0,1,2,10,1,0,0,0,1,2,0,1,0,0,0,0,1,0,2,0,2,0,5,0,1,0,0,0,0,0,16,0,0,1,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42398,1,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+42399,0,0,7,4,0,0,1,0,0,1,18,0,1,0,0,1,0,0,0,0,0,1,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,7,0,0,1,0,1,1,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42400,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42401,0,0,3,0,0,0,0,0,0,0,6,1,0,0,0,5,0,5,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,3,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,Class_6
+42402,0,1,3,2,0,0,0,0,0,0,8,0,0,0,0,6,0,0,0,0,2,0,0,0,0,4,6,1,0,0,0,2,0,0,3,0,1,1,0,0,0,0,0,1,0,4,0,0,0,0,0,1,0,4,1,0,0,0,0,1,5,0,0,0,0,0,0,0,0,1,0,0,0,8,0,1,0,0,0,11,0,3,0,0,1,1,0,2,0,0,0,0,0,Class_6
+42403,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,5,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,2,0,0,17,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42404,1,0,2,0,0,0,0,4,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,4,1,0,1,2,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,11,0,3,0,1,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,3,0,Class_6
+42405,0,3,0,7,0,0,0,0,1,0,8,2,0,0,0,2,0,0,0,0,0,0,0,2,0,3,4,1,0,0,1,1,0,0,0,0,2,3,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,22,0,5,0,0,0,0,0,1,8,0,0,0,0,Class_6
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+42426,0,0,16,7,0,0,0,0,0,0,7,0,0,0,0,6,0,0,0,0,3,1,0,0,0,13,6,4,0,0,1,2,1,0,4,0,0,1,0,0,0,2,0,6,0,5,0,0,0,0,0,0,0,10,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42427,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,14,0,0,0,0,0,12,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42428,0,0,0,0,0,0,1,3,0,10,0,0,1,2,0,0,30,2,0,5,0,0,0,21,1,0,0,0,2,0,0,7,0,1,0,4,4,1,1,0,0,0,0,0,0,0,0,8,0,2,1,0,0,1,0,0,2,0,0,3,0,1,1,3,0,1,24,7,1,0,5,0,9,2,2,3,0,0,1,0,0,0,2,0,1,2,0,0,0,0,0,0,0,Class_6
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+42430,0,0,7,9,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,7,0,1,0,0,0,0,0,13,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42431,0,0,0,0,0,0,0,1,0,1,2,0,5,2,0,5,0,7,0,1,0,2,0,5,2,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,12,1,8,0,0,3,1,0,0,0,1,0,3,1,3,0,0,1,1,0,3,0,1,0,0,1,0,0,4,1,0,0,1,1,2,0,0,5,1,1,0,0,0,1,0,0,2,0,0,0,0,0,Class_6
+42432,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,13,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42433,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,1,0,4,0,2,0,5,0,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,5,1,0,3,0,0,13,0,1,0,1,0,2,4,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_6
+42434,0,0,5,3,0,0,0,0,0,0,8,0,0,0,0,5,0,0,0,0,0,3,0,2,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,4,0,1,0,1,3,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+42435,0,2,0,0,0,0,0,0,0,0,6,0,2,0,1,1,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,2,6,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,Class_6
+42436,0,0,0,0,1,0,1,0,0,2,4,0,1,0,0,1,1,1,0,0,0,1,0,9,4,0,0,0,1,0,0,2,2,1,0,0,0,1,1,0,3,5,2,11,0,0,21,0,0,3,0,0,3,1,4,0,2,0,2,3,0,4,1,0,1,0,6,2,0,3,4,0,0,1,3,2,0,1,2,1,0,0,0,0,2,0,1,0,4,3,0,0,0,Class_6
+42437,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,3,1,0,1,0,2,0,2,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42438,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,4,0,9,1,1,0,3,1,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,4,0,0,2,1,1,12,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,1,2,0,2,0,0,0,1,0,0,0,0,0,Class_6
+42439,0,1,1,3,0,0,0,0,0,0,7,0,0,1,0,2,0,0,0,0,0,1,0,0,0,3,1,3,0,0,0,0,0,0,4,1,0,1,0,2,0,0,0,1,0,1,0,0,2,0,0,0,0,11,1,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42440,3,1,11,8,0,1,8,0,0,1,10,1,6,3,0,17,0,11,1,4,0,6,2,4,0,1,0,1,0,0,2,1,3,1,10,0,1,10,0,1,0,2,1,29,0,2,0,0,34,4,0,1,0,35,0,0,0,0,0,0,9,9,1,1,14,5,0,2,0,6,0,3,0,1,1,4,0,0,0,5,0,1,6,0,1,0,2,1,2,1,0,0,0,Class_6
+42441,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42442,0,0,10,2,0,0,0,0,0,0,9,0,2,0,0,8,0,2,0,0,1,4,0,0,0,7,14,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,5,0,4,0,0,0,0,0,0,0,20,1,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,6,0,0,0,0,0,0,0,1,0,1,0,0,0,Class_6
+42443,0,1,3,3,1,0,0,0,0,1,8,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,4,2,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,1,0,4,0,0,1,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,5,0,1,0,0,0,0,1,0,0,0,0,2,0,Class_6
+42444,0,0,1,2,0,0,0,0,1,0,4,0,0,0,2,5,0,0,0,2,1,1,0,10,0,17,1,1,0,0,2,1,0,0,2,0,0,0,0,0,0,2,0,0,1,3,0,0,2,0,0,0,0,4,1,1,1,4,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,2,0,Class_6
+42445,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+42521,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,1,0,1,1,0,0,1,0,9,1,3,1,0,0,0,0,0,0,1,0,0,0,0,1,1,1,3,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,1,1,0,7,11,0,0,2,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,5,0,Class_6
+42522,1,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,2,1,0,0,0,0,2,0,4,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,2,0,0,0,0,5,0,0,12,2,1,0,0,0,1,0,0,0,1,0,0,0,0,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
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+42524,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,1,0,0,0,0,3,0,1,0,6,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,7,1,1,2,0,0,8,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,Class_6
+42525,1,0,5,2,1,0,0,0,0,0,8,0,0,0,0,11,0,8,0,0,2,0,0,0,0,5,2,1,0,0,0,2,1,1,4,0,0,2,0,1,0,2,0,1,0,1,0,0,3,0,0,0,0,9,0,0,0,0,1,2,4,1,0,1,2,1,2,0,0,0,0,1,0,0,0,1,0,0,0,10,0,1,2,0,1,1,1,0,0,0,0,0,0,Class_6
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+42703,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,7,0,0,0,1,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+42704,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,2,2,3,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,3,1,2,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,13,0,2,0,0,0,2,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_6
+42705,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,4,0,0,0,2,0,2,0,0,0,0,0,0,1,2,0,0,2,1,0,0,0,1,0,2,0,1,0,0,0,0,1,0,0,0,1,0,0,1,2,1,2,12,1,1,0,0,0,2,6,2,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,2,0,0,0,0,Class_6
+42706,0,0,12,3,0,0,1,0,1,1,9,1,0,0,0,6,0,1,0,0,0,4,0,1,0,3,2,3,0,0,0,0,0,0,3,0,0,4,0,0,1,2,0,1,0,6,0,0,0,0,1,0,0,16,1,0,0,0,0,0,3,1,0,0,1,1,0,0,0,0,0,1,0,2,1,1,0,0,0,5,1,1,0,0,0,0,0,0,2,0,0,2,0,Class_6
+42707,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,1,2,0,0,0,0,0,0,1,3,0,0,0,0,0,0,7,0,0,0,1,1,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,Class_6
+42708,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,1,1,0,0,1,0,0,12,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42709,1,0,1,1,0,0,0,1,1,0,2,1,19,1,0,2,0,2,0,1,0,1,0,4,2,0,0,0,2,1,0,0,2,0,0,2,0,0,0,0,1,0,0,1,0,0,0,6,0,1,0,0,6,5,1,0,0,0,32,2,0,1,2,2,0,0,36,1,4,3,0,0,0,0,2,0,2,0,3,0,0,0,0,0,1,6,1,0,0,0,0,0,0,Class_6
+42710,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42711,0,1,2,0,0,0,0,0,0,0,4,0,0,0,0,5,0,1,1,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,2,1,0,0,0,0,0,1,0,3,0,0,0,0,0,1,0,3,0,9,0,0,1,0,0,2,1,1,0,1,0,2,0,0,0,1,0,0,0,12,0,0,0,0,0,24,1,1,0,0,0,0,0,0,4,0,1,0,0,Class_6
+42712,0,0,8,7,0,2,0,0,0,0,14,0,0,0,0,2,0,2,4,0,0,2,0,25,0,0,11,2,1,0,0,1,0,1,10,0,0,1,0,0,0,0,1,0,0,2,0,3,0,0,0,10,0,2,0,0,2,0,0,0,8,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42713,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,1,0,5,2,3,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,14,0,0,0,1,0,1,0,0,0,0,1,3,2,0,0,0,0,0,0,0,0,0,1,0,5,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,2,0,2,0,Class_6
+42714,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42715,0,0,8,4,0,0,5,0,0,1,6,0,0,0,0,3,0,0,0,0,0,1,0,1,0,1,1,1,1,0,0,0,0,0,1,0,0,1,0,0,0,3,0,1,0,0,0,1,11,0,0,0,0,21,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,2,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,1,2,4,0,0,0,Class_6
+42716,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+42717,0,0,6,11,0,0,0,0,0,0,7,0,0,0,0,3,0,1,0,0,1,2,0,0,0,5,0,4,1,0,1,2,1,1,0,0,0,0,1,0,2,0,0,3,0,2,1,0,1,0,0,0,0,8,2,0,0,0,0,1,0,2,1,0,0,1,0,0,0,3,0,0,1,0,0,0,0,0,0,4,0,4,0,1,0,0,0,0,3,0,0,0,1,Class_6
+42718,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,13,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,1,0,0,0,Class_6
+42719,0,0,1,3,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,4,0,13,1,11,0,0,0,0,0,0,0,5,6,0,0,1,0,0,0,8,0,4,0,0,0,1,1,0,0,0,1,5,0,0,5,0,1,4,0,0,0,0,0,0,1,3,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42720,0,0,3,2,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,3,0,1,0,3,0,2,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,12,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
+42721,0,0,0,1,0,0,0,1,2,0,0,4,0,0,0,5,0,3,0,0,0,2,0,3,0,3,0,0,0,0,0,0,0,0,0,0,1,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,2,0,0,8,0,2,0,0,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,2,1,0,0,0,1,0,Class_6
+42722,0,1,2,2,0,0,4,3,0,0,4,0,2,0,2,3,12,2,1,12,0,1,8,34,0,0,0,0,1,3,9,6,0,2,3,6,0,5,2,3,1,2,1,3,1,1,1,8,1,0,2,2,0,3,2,0,18,0,0,28,0,2,0,0,0,1,8,0,1,0,2,0,2,1,0,8,0,3,0,1,0,0,30,0,1,2,1,0,1,2,0,0,6,Class_6
+42723,1,1,49,22,0,0,0,0,0,4,9,0,0,1,0,9,0,1,1,1,3,10,0,0,0,3,14,6,0,0,0,0,0,1,9,1,0,0,0,1,0,2,0,5,0,25,0,2,6,0,0,0,1,40,2,2,0,0,0,3,12,2,0,0,2,1,0,0,0,1,0,2,1,2,0,0,1,0,0,9,0,3,1,0,0,0,0,0,4,0,1,0,0,Class_6
+42724,0,10,1,0,0,0,0,1,1,0,3,1,1,0,0,1,2,4,1,6,0,1,1,2,0,1,0,0,0,1,1,0,2,0,3,2,1,3,1,4,0,1,1,0,1,0,1,5,1,1,0,1,1,2,0,0,1,9,0,0,1,1,0,0,1,0,5,0,9,0,0,0,1,5,1,1,0,3,2,1,0,0,5,0,0,0,2,0,0,23,0,0,0,Class_6
+42725,0,0,0,2,0,0,0,0,0,0,9,0,0,0,1,1,0,3,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,1,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+42875,0,0,21,5,0,0,0,0,0,0,10,0,1,0,0,1,0,0,0,1,1,3,0,2,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,8,0,0,0,0,0,0,0,21,0,2,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42876,0,1,2,1,0,0,0,1,1,0,1,0,5,0,0,2,0,3,0,0,0,0,0,21,0,14,0,1,0,1,0,1,1,0,1,2,0,0,1,0,0,7,0,3,0,8,0,0,0,0,0,0,0,7,1,2,3,0,1,12,0,3,1,3,0,3,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,1,Class_6
+42877,0,0,6,1,0,0,0,0,0,0,6,0,0,1,0,2,0,2,0,1,0,2,0,1,0,1,3,0,0,0,0,0,0,0,13,1,0,2,0,0,0,3,0,2,0,1,0,0,5,0,0,0,0,5,0,1,0,1,0,1,0,3,0,0,0,2,2,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,4,0,0,0,0,Class_6
+42878,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,4,0,0,0,0,0,6,0,0,0,0,1,0,0,1,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,10,0,0,1,0,0,0,6,0,0,2,0,2,0,0,0,0,4,1,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+42879,0,1,8,10,0,0,0,0,0,0,8,1,1,0,0,3,0,2,1,2,0,2,0,0,0,11,10,0,1,0,1,2,0,0,2,1,0,1,0,0,0,2,0,2,0,6,0,0,2,0,0,0,0,16,0,0,0,0,0,1,1,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+42880,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,4,0,2,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,2,10,3,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+42881,0,0,11,7,0,0,0,0,0,0,6,0,1,1,0,1,0,1,0,0,0,1,0,1,0,0,2,3,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,13,2,0,2,0,1,1,1,4,0,2,0,0,0,1,3,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,0,0,Class_6
+42882,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42883,1,0,1,0,0,0,0,13,0,0,0,0,8,0,0,3,4,1,0,3,0,0,0,10,2,2,0,0,1,0,0,0,1,0,0,12,0,0,0,1,1,3,0,0,0,1,0,3,1,0,0,0,0,2,2,0,0,0,1,1,0,11,1,2,0,2,20,1,0,1,2,1,1,0,0,0,0,0,9,0,0,0,0,0,1,0,1,0,1,0,4,2,1,Class_6
+42884,0,0,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,10,0,2,0,9,0,7,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,6,0,1,0,1,0,0,0,0,0,0,0,4,1,0,13,0,0,9,0,4,0,0,0,3,2,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,5,0,Class_6
+42885,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,4,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
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+42896,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42897,0,0,8,2,0,0,0,0,0,1,7,0,0,1,0,0,1,0,0,1,0,2,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,3,0,8,0,0,1,0,0,0,0,9,1,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,2,0,0,2,0,Class_6
+42898,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,13,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42899,0,0,3,2,0,0,0,0,0,0,16,0,0,0,0,1,0,1,0,0,0,2,0,5,0,1,12,2,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,0,4,0,0,0,0,5,0,1,0,5,1,0,4,0,1,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,7,0,3,0,0,0,0,0,0,3,0,0,0,0,Class_6
+42900,1,0,8,2,0,0,0,0,0,0,6,0,3,0,0,4,0,0,0,2,1,1,0,0,0,0,10,2,1,0,1,0,0,0,2,1,0,0,0,1,1,0,0,2,0,0,0,1,1,0,0,0,0,22,1,1,0,0,0,0,1,1,1,1,3,1,1,0,0,1,0,0,0,0,0,1,0,0,0,6,0,2,0,0,0,0,1,0,4,0,0,0,0,Class_6
+42901,0,0,13,5,0,0,1,0,0,1,13,2,1,1,2,6,0,0,1,0,0,3,0,1,0,2,13,0,0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,10,0,0,0,0,0,1,6,0,0,0,2,1,1,0,0,0,0,0,0,2,0,0,1,0,0,5,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+42902,2,0,0,0,1,0,0,0,2,0,1,0,0,0,0,4,0,2,0,0,0,4,0,0,1,1,0,0,0,0,0,1,1,0,2,2,0,0,0,1,0,3,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,4,0,0,0,0,0,0,5,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+42903,0,0,1,1,0,0,0,0,0,0,8,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,0,4,1,1,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,1,Class_6
+42904,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,1,0,4,0,1,0,0,0,0,0,0,3,0,0,1,3,0,0,3,0,0,0,0,0,2,3,0,0,2,0,6,0,0,0,0,0,12,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42905,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,5,1,6,0,2,0,0,0,0,0,0,0,5,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,9,0,1,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,Class_6
+42906,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,3,0,0,0,2,0,3,0,4,1,0,0,0,1,0,0,0,9,0,0,1,0,1,0,4,0,0,0,0,0,0,1,0,0,1,0,8,1,0,2,0,0,13,0,0,0,1,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,2,0,0,Class_6
+42907,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,2,0,0,1,1,0,1,3,2,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,11,0,1,0,2,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+42908,0,0,8,2,0,0,0,0,0,0,7,1,1,0,0,1,1,0,0,0,0,3,0,1,0,3,2,5,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,10,0,1,0,0,0,0,0,6,0,0,0,0,0,2,4,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+42909,0,0,10,0,1,0,0,0,0,0,7,0,0,0,0,1,1,2,1,0,1,4,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,6,0,1,0,0,0,0,0,20,0,0,0,0,0,0,3,1,1,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,1,0,1,0,2,0,0,2,0,Class_6
+42910,0,0,0,3,0,0,0,0,0,0,1,0,0,1,0,5,0,4,0,1,0,8,0,2,0,0,0,1,0,0,1,1,1,0,1,1,0,0,0,1,0,1,2,2,0,0,3,2,4,0,0,0,0,11,0,0,0,0,0,0,1,1,0,0,0,1,4,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+42911,0,0,0,3,0,0,0,1,0,0,12,1,0,0,0,1,1,0,0,0,0,0,0,5,0,1,10,1,0,0,0,1,2,2,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,6,0,0,4,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+42912,0,1,5,4,2,0,0,0,0,1,13,0,0,0,1,8,0,2,0,1,2,3,0,0,1,1,2,0,0,0,0,0,0,1,2,0,0,0,1,0,0,2,0,1,0,8,5,0,3,0,0,1,0,7,1,3,0,1,0,0,5,2,2,0,1,3,1,0,1,1,0,1,0,0,0,0,0,0,1,5,0,2,0,4,0,1,0,0,8,0,0,3,0,Class_6
+42913,0,0,0,5,0,0,0,0,0,0,10,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+42914,1,1,0,0,0,0,0,0,0,0,6,0,0,0,1,1,0,0,0,0,5,0,0,7,0,2,3,0,0,0,1,3,0,0,2,0,3,1,1,1,0,0,0,0,0,3,0,1,0,0,0,11,0,4,0,1,1,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,70,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+42915,1,0,4,1,0,0,0,0,0,1,9,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,12,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,0,0,Class_6
+42916,0,0,3,2,0,0,0,1,0,0,9,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,12,1,0,1,1,1,1,0,2,0,0,3,0,1,2,1,0,1,0,4,0,0,0,0,0,5,2,8,1,0,0,1,0,0,0,1,1,1,0,0,1,0,2,2,0,0,2,4,0,0,0,0,0,12,0,1,0,0,0,0,0,0,4,0,0,1,0,Class_6
+42917,0,0,3,0,0,0,0,0,0,0,6,0,0,0,0,1,0,2,1,0,3,1,0,1,0,1,11,1,0,0,0,1,0,0,2,0,0,0,1,0,2,0,1,0,0,2,0,0,0,0,0,6,1,4,0,0,0,0,0,2,7,4,0,0,0,1,0,0,0,1,0,1,1,0,1,0,0,0,1,21,0,1,0,0,0,0,1,0,4,0,0,0,0,Class_6
+42918,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,4,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42919,0,0,9,0,0,0,0,0,0,1,16,0,0,0,0,1,0,0,0,0,1,2,0,7,0,0,4,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,1,1,1,0,7,2,0,0,5,0,1,1,0,0,2,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+42920,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,2,0,0,0,0,2,0,1,0,0,0,0,3,0,0,3,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+42921,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42922,0,2,1,0,0,0,1,0,0,0,2,0,1,2,0,0,0,0,0,4,0,2,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,9,0,0,6,1,0,1,0,0,0,6,1,0,0,0,1,0,0,0,0,1,1,1,2,0,0,1,3,1,1,1,1,1,0,0,2,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+42923,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,1,0,2,0,4,1,13,0,1,0,0,0,1,2,0,3,2,0,0,0,2,0,4,0,0,0,2,0,1,0,0,0,0,0,8,0,0,1,0,0,12,0,0,0,0,1,0,5,1,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+42924,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42925,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,2,0,3,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
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+42927,0,0,1,3,0,0,0,1,0,0,0,0,0,0,1,3,2,0,0,0,0,1,0,0,0,3,0,1,12,0,0,0,0,0,2,2,0,0,0,0,5,0,0,0,0,3,0,2,0,0,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,1,10,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+42942,0,0,10,10,0,0,0,0,0,2,8,0,0,0,0,2,0,2,0,0,0,6,0,1,1,4,3,2,0,0,0,0,2,1,1,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,23,1,0,0,0,0,0,2,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42943,4,1,1,2,0,0,1,3,0,2,0,0,3,0,0,1,0,0,1,0,0,8,0,13,2,1,0,1,9,0,0,2,1,6,2,3,8,0,0,0,1,0,0,8,0,0,5,1,2,0,4,0,0,9,17,0,8,0,0,2,0,6,1,0,0,3,2,2,0,1,8,0,2,3,7,2,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,1,0,Class_6
+42944,0,1,0,3,1,0,0,0,0,0,7,0,1,0,0,4,0,2,0,0,0,1,0,0,0,1,3,3,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,3,0,0,0,3,3,0,1,0,1,4,0,0,0,0,0,0,2,3,0,1,0,4,0,0,0,1,0,1,0,0,0,1,0,0,0,2,1,2,0,0,0,0,0,0,2,0,0,2,0,Class_6
+42945,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,12,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42946,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42947,0,1,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,12,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,3,0,0,12,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42948,0,1,11,1,0,0,0,0,0,0,17,0,0,0,0,3,0,0,3,0,0,1,0,6,0,1,7,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,1,0,0,0,1,9,1,0,0,12,0,1,1,0,0,1,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+42949,0,0,9,11,0,0,0,0,0,0,7,0,0,1,0,4,1,1,0,0,1,4,0,1,0,6,5,1,0,0,0,1,1,1,0,1,0,1,0,0,0,0,1,4,1,7,0,0,0,0,0,3,0,21,0,0,0,0,0,0,4,2,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,5,0,0,1,0,0,1,0,0,0,2,0,Class_6
+42950,0,2,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42951,0,0,1,1,0,0,0,0,1,1,1,0,1,0,1,1,1,3,0,1,0,1,0,7,2,5,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,14,0,1,0,0,0,1,0,0,0,0,1,6,4,0,7,0,0,2,1,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+42952,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,3,3,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,7,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,2,Class_6
+42953,0,0,2,1,0,2,0,0,0,0,20,1,0,0,0,2,0,0,0,0,2,1,0,6,0,0,6,2,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,1,0,4,0,0,0,6,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,2,0,1,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42954,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,1,0,0,1,0,2,6,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+42955,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,6,1,2,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,2,6,0,1,0,0,12,0,0,0,0,2,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+42956,0,0,2,0,2,0,0,0,0,0,9,1,0,0,0,2,0,3,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,2,0,1,0,1,0,1,0,9,0,5,0,2,0,0,5,1,0,0,1,7,0,0,1,1,0,0,0,2,0,1,7,4,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,Class_6
+42957,0,1,3,0,0,0,0,0,0,2,9,0,1,0,0,2,0,3,0,2,0,0,0,5,1,0,2,2,0,0,0,0,0,1,7,0,2,2,0,0,0,0,0,1,0,0,0,2,8,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,0,7,0,0,0,0,0,2,0,0,0,0,Class_6
+42958,2,0,3,7,0,0,0,1,0,0,10,0,1,1,0,3,0,2,0,1,1,0,1,0,0,2,3,7,0,0,0,0,1,0,1,0,0,2,1,0,0,1,0,2,0,0,0,2,0,0,0,4,0,7,0,0,0,1,0,0,1,0,0,0,5,0,0,0,0,0,0,1,0,1,1,1,0,0,0,12,0,3,0,1,0,1,0,0,3,0,0,0,0,Class_6
+42959,0,0,5,0,3,1,2,0,0,1,7,0,6,0,0,11,0,6,0,1,0,6,0,1,0,0,4,2,2,0,0,1,0,0,2,3,0,2,0,0,0,0,0,20,0,0,2,1,11,0,0,0,0,20,1,0,1,0,1,0,2,1,0,0,1,2,1,0,0,5,0,0,0,0,1,0,0,1,0,2,0,5,0,3,0,1,0,0,1,0,0,0,1,Class_6
+42960,1,0,3,0,0,0,2,0,0,0,9,0,0,0,0,6,0,0,0,3,0,3,0,1,1,1,2,1,1,0,0,0,0,0,1,0,0,2,0,0,0,1,1,4,0,0,0,0,9,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,2,3,1,0,0,3,0,0,0,0,0,0,1,0,0,1,0,3,1,0,1,0,0,0,1,1,0,0,0,Class_6
+42961,0,1,2,3,0,0,0,0,0,0,12,0,0,1,0,1,0,1,0,0,1,0,0,15,0,2,8,0,0,0,2,2,1,0,3,0,0,1,0,1,1,0,0,1,0,5,0,5,0,0,0,2,0,10,0,0,0,7,0,0,1,0,0,1,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,3,0,0,0,0,0,2,0,1,0,0,Class_6
+42962,0,4,0,0,0,0,1,1,0,3,1,1,0,0,0,8,6,1,0,0,0,5,0,1,2,5,0,0,1,0,0,1,0,0,2,1,2,0,1,0,1,11,0,3,0,0,0,0,0,0,0,0,0,14,2,0,2,0,0,4,0,1,1,1,1,1,0,0,0,1,1,1,3,2,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,23,0,0,0,Class_6
+42963,0,0,7,2,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,2,3,0,1,0,3,9,3,0,0,0,0,0,1,0,1,0,0,0,4,0,0,1,0,0,5,0,0,0,0,0,4,0,9,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42964,0,0,14,0,0,0,0,1,0,0,13,0,0,1,0,5,0,0,1,0,2,1,0,0,0,1,16,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,9,0,0,2,0,0,2,0,21,0,1,0,2,0,0,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+42965,0,0,3,0,0,0,0,0,0,0,13,1,0,0,0,0,0,1,0,0,0,1,0,6,0,3,11,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,14,0,1,0,0,0,0,0,4,0,0,0,6,0,1,4,0,0,1,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+42995,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1,0,0,9,Class_6
+42996,0,1,8,13,0,0,0,0,2,0,7,0,0,0,0,4,0,5,0,0,0,0,0,0,1,0,1,3,0,0,0,1,0,0,5,0,2,0,0,1,1,1,0,1,2,3,0,0,0,0,0,3,1,9,0,1,1,0,1,3,4,0,2,1,0,2,1,0,1,4,0,1,0,0,0,1,0,0,0,8,0,8,0,0,0,1,0,1,2,1,0,1,0,Class_6
+42997,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,4,0,8,0,0,0,1,0,4,0,4,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,8,0,0,0,0,0,1,1,0,0,0,0,6,0,0,0,0,0,14,0,0,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,2,1,Class_6
+42998,7,0,1,0,0,0,0,0,0,4,1,0,2,0,0,1,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,2,0,0,0,0,4,0,0,0,0,0,14,2,0,1,2,1,2,0,3,1,0,4,0,0,0,0,0,0,0,0,3,1,0,0,0,0,3,4,2,1,0,0,0,0,0,Class_6
+42999,0,0,2,3,0,0,0,1,1,0,0,0,0,1,0,3,0,1,1,1,0,2,0,5,1,4,0,0,0,0,0,1,1,1,3,1,0,0,0,0,0,21,1,5,0,0,0,1,1,0,0,0,0,6,0,0,4,0,0,5,0,0,0,0,0,1,9,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43000,0,0,1,8,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,1,0,6,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43001,0,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,17,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43002,0,1,6,1,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,3,4,0,0,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,13,0,0,1,0,0,2,0,4,0,1,0,0,0,2,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+43003,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43004,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,3,0,15,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,2,0,1,0,0,1,0,0,12,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43005,0,1,7,5,0,0,0,0,0,0,5,0,1,0,0,4,0,1,0,1,0,5,1,0,0,4,8,2,0,0,0,0,1,1,1,0,0,0,0,3,0,0,1,0,0,5,0,0,1,0,0,0,0,23,0,0,0,0,0,2,1,2,0,1,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,6,0,1,0,1,0,0,0,0,1,3,0,1,0,Class_6
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+43014,0,0,2,9,0,0,0,0,1,0,6,0,0,0,0,2,0,0,0,0,1,1,0,0,0,5,4,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,4,1,0,2,0,0,0,0,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43015,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,1,1,0,1,0,0,1,0,6,1,0,0,0,0,0,0,2,1,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43016,0,0,7,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,5,0,1,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,0,0,2,1,1,0,1,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,6,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43017,0,0,11,1,0,0,0,0,0,0,8,0,0,0,0,2,0,1,0,1,3,1,0,0,0,10,2,2,0,0,1,2,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,15,1,0,0,1,0,2,2,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43018,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,3,0,0,1,2,0,0,0,0,1,0,0,2,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,2,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,1,0,Class_6
+43019,0,0,14,12,0,0,0,0,0,2,7,0,1,0,0,2,0,2,0,0,0,2,0,1,0,0,4,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,2,0,2,0,0,5,0,1,1,0,19,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43020,0,1,0,0,0,0,0,0,0,0,10,1,0,0,0,1,1,6,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,2,0,0,0,0,0,0,0,0,7,0,0,2,0,0,0,2,2,0,0,1,2,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43021,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,2,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,10,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_6
+43022,0,0,3,3,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,2,0,4,0,4,0,3,0,1,0,2,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,5,1,1,1,0,0,8,1,0,0,0,0,1,7,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43023,0,1,0,0,0,0,0,1,0,0,0,1,2,0,1,1,0,4,0,2,0,1,0,6,0,2,0,2,1,0,2,1,0,0,8,1,0,0,2,1,1,2,0,1,0,0,0,0,0,1,0,0,0,2,0,0,4,1,0,10,0,0,0,0,0,0,7,0,0,1,0,2,0,0,0,1,4,1,0,1,1,1,0,0,0,0,1,0,2,0,0,1,0,Class_6
+43024,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,20,0,1,0,0,0,0,5,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,Class_6
+43025,0,0,1,2,1,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,2,1,0,5,0,0,5,0,0,0,0,1,0,4,4,2,0,0,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,0,0,2,1,0,0,0,Class_6
+43026,0,0,22,4,0,0,0,2,0,0,6,1,0,0,0,3,0,0,0,0,1,1,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,22,0,0,1,0,0,0,0,18,0,1,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43027,0,0,7,3,0,0,0,0,0,0,9,1,0,0,0,3,0,1,0,0,2,2,1,2,1,1,4,2,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,3,0,8,0,0,0,0,0,1,0,11,0,0,0,0,0,1,3,1,0,0,0,1,1,1,0,0,0,0,0,2,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,Class_6
+43028,0,1,4,2,0,0,0,1,0,0,12,0,0,0,0,1,0,2,0,0,0,1,0,0,0,4,4,4,0,0,2,1,0,1,1,0,0,4,0,0,0,0,1,2,0,0,0,0,2,0,0,1,2,13,1,0,1,0,1,1,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,9,0,2,0,0,0,0,0,0,1,0,0,2,0,Class_6
+43029,0,1,16,1,0,0,0,0,0,2,10,0,1,0,0,4,0,1,0,0,0,2,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,14,0,0,0,1,0,0,0,22,0,1,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,0,1,0,0,0,0,Class_6
+43030,0,9,0,3,0,0,0,1,0,0,7,0,0,0,0,0,2,7,1,3,1,1,0,5,0,4,0,0,0,2,0,0,0,0,16,1,0,1,0,0,0,3,0,1,0,0,0,4,9,1,0,0,0,1,0,0,2,4,0,11,1,1,0,6,0,3,5,1,2,1,0,0,0,0,0,4,0,0,0,4,1,3,2,0,0,0,0,0,0,7,0,0,1,Class_6
+43031,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,9,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,0,0,2,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43032,2,0,1,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,2,5,2,1,0,0,0,0,0,0,2,1,0,0,0,0,1,10,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43033,0,0,14,4,0,0,0,0,0,1,11,0,1,0,0,5,0,0,1,1,1,1,0,2,0,0,2,3,0,0,2,0,1,1,3,1,0,0,0,0,0,0,0,3,0,4,0,0,3,0,0,1,1,11,1,0,0,0,0,1,0,0,1,1,0,3,0,0,0,1,0,1,0,0,1,0,0,0,0,10,0,4,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43034,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,8,2,0,0,0,0,0,0,1,1,0,0,3,0,0,0,0,0,2,0,0,0,0,3,2,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,Class_6
+43035,0,1,6,8,0,0,0,0,1,0,17,0,0,0,0,4,0,0,1,0,2,3,0,2,0,2,123,0,1,2,0,0,1,0,2,0,0,0,0,1,1,1,0,1,0,11,0,0,2,0,0,0,0,8,0,1,1,0,0,1,7,1,0,0,1,3,1,0,0,0,0,1,1,2,1,2,0,0,0,8,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43036,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,2,0,0,0,1,0,0,0,0,6,0,0,10,0,0,0,1,0,0,1,0,1,0,1,1,0,0,0,1,0,0,0,1,0,0,5,0,0,0,2,1,1,0,2,2,0,Class_6
+43037,0,1,6,1,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43038,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,2,0,1,0,2,0,2,4,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,0,1,0,0,2,3,Class_6
+43039,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,12,0,0,0,5,0,0,7,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+43040,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43041,0,0,16,3,0,0,0,0,0,1,12,0,1,1,0,1,0,0,0,0,1,2,0,0,0,1,4,2,0,0,0,0,0,1,0,1,0,1,0,1,1,0,0,3,0,8,0,0,1,0,0,0,0,18,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,Class_6
+43042,0,0,1,1,0,0,0,2,0,0,0,0,2,3,0,1,0,1,0,0,0,2,0,2,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,0,2,0,0,13,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43043,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43044,0,0,3,12,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,1,1,0,5,0,11,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+43045,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,0,3,0,0,1,0,0,0,2,0,1,0,0,0,1,0,2,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,1,0,0,0,0,4,Class_6
+43046,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,3,0,1,2,3,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,16,0,2,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43047,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,4,2,3,0,0,0,0,0,0,5,0,0,0,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,0,1,2,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,Class_6
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+43049,1,1,1,2,0,0,0,0,0,0,8,0,0,0,0,4,0,1,0,1,0,1,0,2,0,2,2,0,0,0,0,2,0,0,9,0,1,4,0,0,1,8,1,10,0,3,0,0,9,0,0,0,0,7,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,2,Class_6
+43050,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,2,0,7,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+43052,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43054,0,0,0,0,0,0,0,0,0,0,16,2,0,0,0,0,0,2,0,0,0,0,0,14,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,6,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,22,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,Class_6
+43055,6,0,2,1,0,0,0,0,1,0,0,0,1,0,0,2,1,0,0,3,0,7,5,11,1,1,0,0,0,0,0,9,0,0,0,2,1,4,0,0,0,12,0,6,0,0,0,2,0,2,0,0,0,4,1,1,0,0,4,0,0,3,1,5,1,0,12,0,0,7,0,0,1,0,2,0,0,0,0,3,0,0,2,0,0,4,20,0,0,0,0,0,50,Class_6
+43056,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,1,0,0,0,0,0,9,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43057,6,0,1,1,0,0,0,0,1,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,2,0,0,1,0,0,1,0,0,2,1,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,8,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+43058,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,14,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43059,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,14,0,0,0,3,0,1,4,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,Class_6
+43060,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,1,0,3,0,4,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,3,0,4,0,0,3,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,3,2,0,2,1,0,0,1,2,1,0,0,11,1,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_6
+43061,0,1,1,0,0,0,0,0,0,0,7,0,1,0,0,2,0,0,0,0,1,0,0,1,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,1,Class_6
+43062,0,0,11,4,0,0,0,0,0,0,7,0,0,0,0,4,0,0,0,0,7,5,0,2,0,4,2,1,0,0,0,2,1,2,1,0,0,0,0,0,0,1,4,2,0,6,0,0,3,0,0,1,1,14,1,2,0,1,0,1,3,1,0,1,1,2,0,0,0,0,0,0,0,2,0,3,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+43063,0,1,5,35,1,0,0,0,0,1,8,0,1,1,1,3,0,1,1,2,3,6,0,5,0,8,5,10,0,0,2,0,0,1,8,0,0,2,0,1,3,0,1,2,0,7,1,1,3,0,1,1,0,10,0,1,0,3,0,2,2,2,0,1,0,0,1,0,0,1,1,0,0,1,0,3,0,0,0,15,0,4,1,0,0,0,0,1,2,0,0,2,0,Class_6
+43064,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43065,0,1,16,7,0,0,0,1,1,0,10,0,0,0,0,8,0,1,0,0,1,4,0,0,0,5,5,0,0,0,0,2,0,0,0,1,0,0,0,0,0,3,0,0,0,7,0,0,2,0,0,0,0,19,0,0,0,0,0,1,3,1,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,3,1,0,0,0,Class_6
+43066,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,14,0,1,0,0,0,3,0,0,0,0,0,0,2,0,0,0,3,5,0,3,0,1,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,Class_6
+43067,0,1,3,1,0,0,0,0,0,1,10,1,2,0,0,5,0,1,1,0,3,1,0,0,0,2,22,2,0,0,0,2,1,0,0,0,0,1,0,0,0,1,0,0,0,4,0,0,1,0,0,1,0,10,0,1,0,0,0,1,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,3,0,Class_6
+43068,0,1,3,1,0,0,0,0,1,0,7,0,1,1,0,9,0,4,0,0,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,14,0,0,4,0,0,0,0,16,0,0,1,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,2,0,0,0,1,0,Class_6
+43069,0,2,0,3,0,0,0,0,0,0,11,0,0,0,0,3,0,1,0,0,1,1,1,2,0,0,0,1,0,0,1,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,2,1,3,0,1,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,2,0,0,0,0,0,10,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_6
+43070,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,17,0,2,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+43071,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43072,0,0,2,2,0,0,0,0,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43073,0,2,7,23,0,0,0,0,0,0,9,0,1,0,0,3,0,3,0,0,2,0,0,3,0,6,3,2,0,1,0,2,0,0,5,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,5,0,0,0,5,0,0,0,1,1,0,0,1,1,0,0,1,0,0,1,0,0,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43074,0,1,4,4,1,0,0,0,1,0,8,0,1,2,1,6,0,1,0,0,0,1,0,3,0,13,8,1,0,0,0,2,0,0,2,1,0,0,0,0,0,4,0,1,0,8,0,1,0,0,0,0,0,10,1,0,0,0,0,1,6,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,13,0,3,0,0,0,0,1,0,0,0,2,1,0,Class_6
+43075,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,1,0,3,0,0,0,0,0,2,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,8,0,0,0,0,0,0,0,1,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,Class_6
+43076,0,0,2,0,0,0,0,2,0,0,7,0,1,2,0,12,1,1,0,0,0,5,0,0,1,0,3,1,0,0,0,0,0,0,1,0,0,2,0,0,0,1,1,8,0,1,0,3,7,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,5,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,1,0,0,1,0,Class_6
+43077,0,0,5,5,0,0,1,0,0,1,8,0,1,0,0,3,0,0,0,0,0,5,0,1,0,2,1,4,1,0,1,1,1,0,0,0,0,0,0,0,0,1,1,2,0,6,0,0,0,0,0,0,0,18,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_6
+43078,0,0,14,1,0,0,0,0,5,0,13,0,0,0,0,3,0,1,0,0,6,4,0,0,0,1,6,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,2,0,5,0,0,1,0,0,1,0,21,5,0,0,0,0,1,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,1,0,1,0,0,0,0,2,0,0,0,0,Class_6
+43079,0,1,12,4,0,0,0,0,0,0,9,0,1,0,0,4,0,1,0,0,1,1,0,0,0,1,8,1,0,0,0,0,1,0,2,1,0,1,0,0,0,0,0,4,0,5,0,0,6,0,0,0,0,16,1,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,8,0,1,0,0,0,0,2,0,2,0,0,1,0,Class_6
+43080,0,1,1,4,0,0,0,1,0,0,9,1,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,2,0,0,3,1,0,0,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,5,1,0,0,3,0,1,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+43081,0,0,7,2,0,0,0,0,0,0,10,0,2,0,0,3,0,0,0,0,0,7,0,0,0,3,4,3,0,0,0,2,1,0,3,0,0,4,0,0,0,5,0,0,0,4,0,0,0,1,0,0,0,13,1,0,1,0,0,2,2,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,6,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43082,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,1,0,5,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+43083,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,1,0,2,0,10,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,1,1,0,0,0,0,3,3,0,1,0,0,12,0,1,0,0,2,1,2,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43084,0,0,0,0,0,0,0,1,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,5,0,0,4,0,0,0,0,4,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,2,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,11,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43085,0,0,1,1,0,0,0,10,0,0,1,0,1,0,0,1,11,0,1,0,1,4,0,6,0,4,0,0,2,0,0,4,1,1,1,3,0,0,0,0,0,7,0,0,0,0,0,3,0,0,0,0,1,3,1,0,1,0,0,18,0,3,0,1,0,3,10,0,1,0,0,1,0,1,0,0,1,0,5,3,1,0,1,0,0,0,0,0,3,0,1,1,0,Class_6
+43086,0,0,0,0,0,0,0,0,0,0,7,0,1,0,0,2,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,5,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,2,0,0,0,0,Class_6
+43087,0,0,9,2,0,0,0,0,0,0,5,0,3,0,0,4,0,0,1,0,2,1,0,0,0,1,12,2,0,0,1,0,2,0,1,0,0,0,0,0,0,0,4,2,0,8,0,0,1,0,0,2,0,14,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,7,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
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+43103,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,3,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,3,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43104,0,0,9,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,0,0,0,0,0,0,0,4,0,0,0,1,0,1,4,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,3,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43105,0,0,8,11,0,0,0,0,1,1,7,0,1,0,0,2,0,0,0,0,0,0,0,0,0,8,1,3,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,11,2,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43106,0,0,0,0,0,0,0,0,0,0,10,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43107,0,1,0,0,0,0,0,0,0,0,10,0,1,0,1,7,0,4,1,0,1,2,0,0,1,0,0,0,0,0,1,4,1,0,2,0,0,2,0,1,1,0,2,5,0,0,0,0,7,0,0,0,0,7,1,3,2,1,0,1,0,1,0,0,1,1,1,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,2,0,1,0,0,0,0,Class_6
+43108,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,2,1,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,Class_6
+43109,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43110,0,1,1,4,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,1,1,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,2,0,0,1,0,1,1,0,0,0,0,Class_6
+43111,0,2,0,1,0,0,0,1,0,0,0,0,3,0,0,1,0,3,0,0,0,3,0,6,1,10,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,5,1,1,0,0,0,0,0,0,0,0,1,3,0,0,1,0,0,9,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43112,0,1,1,6,0,0,0,0,1,0,6,0,0,1,0,7,0,3,0,0,2,2,1,2,0,3,4,4,0,0,2,0,1,0,3,0,0,1,0,1,0,2,1,0,0,1,0,2,0,0,0,2,0,8,0,0,0,0,0,0,2,4,0,0,0,1,1,0,0,2,0,0,0,1,0,2,1,1,0,7,0,6,0,0,0,0,0,0,0,0,1,1,0,Class_6
+43113,0,0,27,20,0,0,0,0,0,0,9,1,1,1,0,11,0,3,0,1,9,3,0,0,0,5,23,7,1,0,0,1,0,0,3,0,0,1,0,3,0,0,0,2,0,14,0,0,1,0,0,0,0,21,1,0,0,0,0,0,8,3,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,7,0,0,0,0,1,2,2,0,0,0,0,Class_6
+43114,0,1,11,10,0,0,0,0,0,0,8,0,0,0,0,2,0,0,1,0,1,1,0,3,0,1,14,3,0,0,1,1,0,4,1,0,0,0,1,1,0,0,0,2,0,7,0,0,0,0,0,2,0,8,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43115,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,3,2,0,0,0,0,1,0,7,0,4,0,0,1,0,0,0,2,2,0,0,1,1,0,1,0,9,0,1,0,0,0,1,0,0,0,0,0,3,0,0,4,0,0,10,0,0,1,2,0,1,5,4,1,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43116,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,1,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,4,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,Class_6
+43117,0,1,14,5,0,0,1,0,0,0,10,1,1,2,0,11,0,2,0,0,2,4,3,0,1,1,6,3,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,3,0,7,1,1,3,0,0,0,0,26,1,2,0,2,0,0,1,2,0,0,0,1,1,0,0,1,0,1,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,4,1,0,1,0,Class_6
+43118,0,0,5,3,0,0,0,0,0,0,9,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,2,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,2,Class_6
+43119,0,0,2,3,0,0,0,0,0,0,10,0,0,0,0,2,0,1,0,0,0,1,0,2,0,2,4,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,16,0,4,0,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,6,0,1,0,0,0,14,0,0,1,0,0,0,0,0,3,0,0,0,0,Class_6
+43120,0,0,6,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43121,0,0,25,10,0,0,0,0,0,0,9,0,1,0,0,1,0,0,0,0,0,4,0,0,0,2,4,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,13,0,0,0,0,0,0,0,10,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+43122,0,0,7,2,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,4,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,10,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43123,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43124,0,0,18,0,0,0,0,1,0,0,8,0,0,0,0,4,0,0,0,0,0,2,0,1,0,1,6,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,4,0,9,0,0,0,0,0,0,0,41,1,0,1,0,0,1,5,0,1,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,1,0,0,1,1,0,Class_6
+43125,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,2,2,1,1,0,0,0,0,0,4,1,0,1,1,0,0,0,0,0,4,0,1,1,1,0,4,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,1,9,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
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+43208,0,0,1,1,0,0,0,1,0,0,1,1,0,0,0,4,0,2,0,0,0,7,0,1,1,1,0,0,29,0,0,0,0,1,0,0,0,1,1,0,3,1,0,1,0,0,0,0,0,0,0,2,1,2,9,0,2,0,0,1,0,4,0,0,0,2,4,0,0,1,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,1,0,Class_6
+43209,0,0,7,4,0,0,0,1,0,0,10,0,0,0,0,2,0,0,0,0,0,0,0,5,0,2,5,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,5,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43210,1,0,0,3,0,0,1,0,1,0,0,0,1,1,0,5,2,1,0,1,0,3,0,7,1,3,0,0,0,0,0,7,2,1,1,0,0,0,0,0,0,11,0,11,0,0,0,6,0,0,0,0,0,8,2,3,9,0,1,28,0,1,0,2,0,5,13,0,1,3,1,2,2,0,0,10,0,7,5,2,0,0,1,0,1,7,3,3,0,0,1,1,0,Class_6
+43211,0,0,0,1,0,0,0,0,2,0,1,0,2,0,3,1,0,0,2,0,0,0,6,12,0,0,0,0,0,0,6,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,6,0,0,21,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,1,0,4,0,1,0,0,8,0,0,0,0,0,0,0,1,0,5,Class_6
+43212,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,1,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,1,0,0,1,0,0,2,0,0,0,0,9,0,0,0,0,0,12,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43213,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,2,1,1,0,0,0,1,0,1,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,8,0,0,0,1,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6,3,0,Class_6
+43214,0,0,11,5,0,0,0,0,0,1,5,0,0,0,1,2,0,0,0,0,1,4,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,9,0,2,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43215,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,4,2,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,14,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43216,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,2,1,0,1,0,1,0,4,0,1,0,0,0,0,1,0,0,0,4,0,1,0,0,1,0,2,0,0,1,0,0,0,1,1,0,0,0,4,0,0,9,1,0,13,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_6
+43217,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+43218,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,2,1,0,0,0,5,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,1,0,1,0,6,1,0,0,0,0,0,2,0,3,0,1,0,0,4,0,0,0,0,1,0,4,2,0,0,Class_6
+43219,0,1,7,15,0,0,0,0,0,0,11,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,10,0,0,0,1,0,0,1,2,0,1,0,0,0,0,0,1,0,0,2,1,0,1,0,0,0,0,8,0,1,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,4,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43220,0,1,0,0,0,0,1,4,0,0,0,1,0,0,1,2,2,2,0,3,0,1,0,5,0,1,0,0,0,0,0,0,0,1,0,2,1,2,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,3,1,1,0,1,0,1,0,1,0,0,0,0,4,0,1,0,1,0,2,0,0,0,1,0,0,1,1,0,0,0,0,Class_6
+43221,0,2,2,3,0,0,0,2,1,0,2,0,5,4,1,10,4,1,0,2,0,7,5,7,0,1,0,1,4,0,5,6,1,4,1,2,0,7,12,3,0,4,4,5,9,2,0,4,6,5,0,0,0,14,3,0,0,0,0,18,0,6,0,6,0,8,10,0,0,6,0,1,1,7,3,3,0,4,1,2,2,0,1,0,2,1,18,2,1,0,0,1,8,Class_6
+43222,2,1,8,2,0,0,0,1,0,0,10,0,0,0,0,1,0,1,0,0,4,0,0,0,0,0,1,3,0,0,2,0,1,0,0,0,0,0,3,1,0,0,0,1,0,9,0,0,1,0,0,0,0,3,0,0,0,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,11,0,5,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43223,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,3,0,0,1,1,0,1,0,0,0,9,0,3,1,0,0,2,0,0,0,3,0,0,0,0,0,3,0,0,1,0,1,4,2,0,8,0,0,8,0,0,0,1,0,3,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,3,0,0,0,0,0,0,Class_6
+43224,0,1,8,2,0,0,0,0,0,0,14,0,0,0,1,1,0,0,0,0,2,1,0,7,0,1,49,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,1,2,0,0,0,0,10,0,0,0,5,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43225,0,0,8,1,0,0,0,0,0,0,7,2,0,1,0,2,0,0,0,0,1,0,0,1,0,2,13,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,15,1,0,1,0,0,2,0,9,0,0,0,0,0,1,7,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43226,0,2,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,2,2,0,0,0,6,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,5,0,0,0,Class_6
+43227,0,0,0,2,0,0,3,0,0,0,1,1,1,0,3,1,0,1,2,2,1,1,2,13,0,1,1,0,0,2,0,3,0,0,0,0,0,1,0,0,0,1,2,1,0,0,0,9,1,0,0,0,0,2,1,0,1,0,0,6,0,0,0,0,0,1,9,0,2,1,0,0,0,1,1,4,0,2,0,1,0,0,13,0,0,0,0,0,0,2,0,0,0,Class_6
+43228,0,0,17,1,0,0,0,0,0,0,7,0,0,0,0,2,0,0,0,0,2,0,0,2,1,1,25,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,1,0,12,0,0,0,0,0,0,0,18,0,1,3,0,1,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+43229,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,1,0,9,3,3,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,3,1,6,0,0,0,6,1,1,0,0,0,2,0,0,0,0,0,2,0,1,1,0,1,0,2,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,3,1,3,0,0,0,0,Class_6
+43230,1,0,0,0,0,2,0,1,6,0,0,0,0,2,0,0,2,0,0,0,0,1,2,6,1,0,0,2,0,0,0,5,1,1,0,4,0,3,1,0,0,1,0,2,0,0,1,2,0,6,0,0,0,4,1,0,0,0,0,1,0,3,0,27,0,6,8,1,0,9,0,1,0,2,2,0,0,0,0,2,0,0,0,0,0,0,5,5,1,2,0,0,0,Class_6
+43231,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43232,0,0,3,2,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43233,0,0,5,2,0,0,0,0,0,0,6,0,0,0,0,1,0,4,1,0,1,1,0,0,0,1,2,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,16,0,0,2,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+43234,0,0,0,2,2,0,0,1,0,2,0,1,3,1,0,1,0,2,0,1,0,14,0,11,1,4,0,1,5,2,0,0,1,0,3,1,0,0,0,0,3,2,0,4,1,1,5,2,0,0,7,0,0,4,19,1,8,0,1,12,0,1,0,1,1,2,5,2,1,2,1,0,0,1,0,1,1,0,0,0,1,0,0,10,0,0,2,0,0,0,0,1,0,Class_6
+43235,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,3,0,1,0,0,0,0,1,0,0,1,3,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,12,0,3,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,Class_6
+43236,0,0,12,1,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,1,0,0,0,9,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,22,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,1,0,2,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43237,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,1,0,0,1,0,3,1,0,7,0,0,11,0,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,10,0,4,0,1,0,0,0,1,0,0,0,0,0,0,4,0,2,0,0,0,0,72,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,Class_6
+43238,1,0,3,1,0,0,0,0,1,0,6,1,0,1,0,7,0,1,0,1,0,2,1,0,0,0,3,1,0,0,0,1,0,0,1,0,0,0,0,4,0,1,0,1,0,0,3,2,8,0,0,0,0,8,1,0,0,0,0,0,2,2,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,3,1,1,0,7,0,0,1,0,2,0,0,0,1,Class_6
+43239,0,0,4,2,0,0,0,0,0,0,2,0,0,0,0,3,0,1,0,1,0,3,0,7,0,2,0,1,1,0,1,0,0,0,6,0,0,0,0,0,0,1,3,0,1,0,0,1,3,0,0,0,0,6,0,4,2,0,1,12,0,0,0,1,0,1,3,0,0,2,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,Class_6
+43240,0,1,1,1,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,1,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,3,0,Class_6
+43241,0,0,8,2,0,0,0,0,1,1,12,0,0,1,1,0,0,0,0,0,1,0,0,7,0,0,11,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,11,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43242,0,0,1,1,0,0,0,1,0,2,0,0,0,0,0,1,2,1,0,1,0,0,1,4,2,0,0,0,2,0,1,6,0,0,0,1,1,1,0,0,1,4,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,22,0,0,0,0,0,0,6,0,0,11,5,0,0,0,0,1,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43243,0,0,13,5,0,0,0,0,2,0,7,1,1,0,0,6,0,6,0,0,0,5,0,3,0,4,14,9,1,1,1,0,0,1,4,0,0,1,0,2,0,0,0,2,0,6,2,0,2,0,0,3,0,19,2,0,1,0,0,0,6,5,1,0,0,2,0,1,0,0,0,2,0,0,0,1,0,0,0,7,0,4,1,1,0,0,0,1,3,0,1,0,3,Class_6
+43244,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,2,0,0,0,3,0,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,8,0,4,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,Class_6
+43245,0,0,4,2,0,0,1,0,0,0,6,1,0,0,0,2,0,0,1,0,0,2,1,0,0,1,3,2,0,0,0,1,1,0,2,0,0,1,0,1,0,1,0,3,0,6,0,0,1,0,0,1,1,7,0,0,0,1,0,1,2,5,0,0,1,2,0,0,0,1,0,0,0,3,0,0,0,0,0,11,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43246,0,0,1,1,0,0,1,1,0,0,1,0,1,0,0,0,1,4,1,1,0,2,0,14,0,4,0,1,0,0,0,1,0,2,7,0,0,2,0,3,0,8,0,2,0,0,0,0,3,0,0,0,0,2,0,0,2,0,2,9,1,3,0,1,0,0,2,0,10,0,0,1,0,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,2,2,0,1,Class_6
+43247,1,1,5,5,0,0,0,0,0,0,17,0,0,0,1,0,0,2,0,0,1,0,1,9,0,4,2,0,1,0,0,1,0,0,3,2,0,2,0,0,0,0,0,1,0,3,0,1,0,0,0,1,0,7,0,0,0,9,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+43256,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,3,0,1,1,0,0,2,0,3,0,1,0,0,3,0,0,0,2,0,1,3,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,10,0,0,Class_6
+43257,0,0,6,8,0,0,0,0,0,1,20,0,1,1,0,2,0,1,0,0,1,1,0,12,0,1,2,1,1,2,0,0,0,2,1,3,1,0,0,0,0,0,1,0,1,2,0,2,0,0,0,1,0,7,0,0,0,12,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,3,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+43258,0,1,2,1,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,13,0,0,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43259,0,0,2,0,0,0,0,0,0,0,8,0,0,0,1,2,0,2,0,0,1,1,0,0,0,1,3,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,9,0,1,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,3,0,0,0,0,Class_6
+43260,0,7,0,0,0,0,0,0,1,0,3,0,0,0,0,0,4,2,4,2,0,1,0,7,1,1,0,0,0,0,1,3,0,0,5,2,0,4,0,0,2,1,0,0,0,1,0,5,1,0,0,3,1,1,0,0,0,3,0,1,1,2,0,1,0,0,6,1,15,1,1,0,1,0,0,6,0,3,1,2,0,0,5,0,0,1,0,0,1,8,0,0,0,Class_6
+43261,0,0,15,1,0,0,0,0,0,0,10,0,0,0,0,3,0,2,0,0,1,5,0,0,0,1,2,3,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,1,3,1,0,0,0,19,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,1,2,0,0,0,0,Class_6
+43262,0,0,3,0,0,0,0,0,0,0,8,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,2,0,0,0,3,0,0,0,1,0,3,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43263,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,5,0,0,0,1,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,7,10,0,2,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,14,0,2,0,1,0,2,3,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43264,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43265,0,0,1,0,0,0,0,3,0,0,0,0,9,0,0,2,12,2,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,1,0,6,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,1,0,5,0,0,3,0,1,0,0,0,1,18,0,0,1,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,2,0,0,Class_6
+43266,0,2,14,5,0,0,0,1,1,0,9,0,0,0,0,2,0,4,0,0,1,1,0,0,0,0,6,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,24,0,0,1,0,0,1,0,3,0,1,0,1,0,0,3,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,2,Class_6
+43267,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,1,2,2,1,0,0,0,0,8,0,1,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,2,0,0,2,0,0,10,0,0,1,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,4,0,0,0,1,0,0,0,1,0,0,Class_6
+43268,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,2,0,5,0,0,0,5,0,0,0,3,1,2,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,3,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,2,0,0,0,0,0,1,4,0,0,4,1,Class_6
+43269,4,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,0,1,0,3,0,2,0,3,0,2,1,0,0,2,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,3,0,0,0,0,1,8,3,0,1,0,0,6,0,0,1,1,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,0,0,0,0,0,Class_6
+43270,1,0,0,1,0,0,0,0,3,0,0,0,3,0,0,2,0,2,0,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,12,0,0,0,1,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43271,0,0,23,1,0,0,1,1,0,0,9,0,0,0,0,3,0,0,0,0,1,1,0,1,0,2,9,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,6,1,0,2,0,0,0,0,14,4,0,0,0,0,1,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+43272,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43273,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,12,0,0,0,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43274,2,0,1,1,0,0,0,0,0,0,0,0,0,1,0,3,2,4,0,0,0,2,0,8,0,11,0,3,0,0,0,2,1,5,2,5,0,0,0,0,0,29,0,1,0,1,0,1,0,0,0,0,1,5,0,0,4,0,2,6,0,8,0,0,0,2,3,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,4,0,Class_6
+43275,0,1,10,14,0,0,0,0,0,0,12,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,2,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,10,0,0,1,0,0,1,0,8,1,0,0,0,0,1,8,1,0,0,0,1,2,0,0,0,0,1,0,1,0,0,0,0,0,13,0,3,0,0,0,0,0,2,0,5,4,0,0,Class_6
+43276,0,2,1,1,0,0,0,0,1,0,10,0,1,0,1,2,0,1,0,0,1,2,0,8,0,1,9,1,0,0,1,4,0,0,2,2,0,1,0,2,1,0,1,0,1,1,1,6,0,0,0,7,0,3,0,2,0,2,0,0,0,1,0,0,0,1,2,0,0,0,0,1,0,82,1,2,0,1,0,7,0,1,0,0,0,0,0,0,2,1,0,0,0,Class_6
+43277,1,0,1,1,0,0,0,1,0,2,0,1,1,0,0,4,0,4,0,1,0,6,0,3,1,2,0,0,29,0,0,0,1,1,3,2,0,0,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,7,12,0,3,0,0,3,0,4,0,0,0,3,9,1,0,1,3,2,1,0,3,0,1,0,0,1,0,0,1,0,0,1,0,0,4,0,1,5,0,Class_6
+43278,0,0,15,4,0,0,0,0,0,0,9,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,8,0,0,0,0,0,0,0,7,0,0,0,0,0,1,5,0,0,1,2,0,0,0,0,1,0,1,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,1,Class_6
+43279,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,2,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43280,1,0,0,0,0,0,0,0,5,0,0,1,0,0,0,3,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,5,2,0,0,2,1,0,0,0,0,0,1,0,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,7,0,0,21,0,1,0,1,0,1,0,1,1,0,1,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,Class_6
+43281,0,0,4,2,0,0,0,1,0,1,13,0,0,0,0,0,0,1,0,0,0,2,0,7,0,0,24,2,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,2,0,1,0,4,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43282,0,0,1,3,0,1,0,0,0,2,1,0,1,1,0,2,0,0,0,1,0,1,0,1,5,7,0,0,3,0,0,3,2,4,1,0,0,0,0,0,4,1,0,0,0,1,0,3,0,0,0,0,27,7,2,0,0,0,0,2,0,7,0,0,2,1,2,0,1,0,1,1,0,1,0,0,0,0,0,1,1,0,1,0,1,2,0,0,3,0,0,4,0,Class_6
+43283,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,8,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,Class_6
+43284,0,0,13,5,0,0,0,0,0,1,10,0,0,0,1,4,0,2,0,0,1,2,0,2,0,1,13,4,0,0,0,0,0,1,1,1,0,0,0,1,0,3,1,2,0,13,0,0,0,0,0,0,0,6,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43285,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43287,0,0,14,1,0,0,0,0,1,1,9,1,0,0,0,3,0,1,0,0,3,1,0,3,0,0,21,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,2,0,17,0,0,1,0,0,0,0,10,0,0,1,0,0,1,2,2,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
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+43301,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,2,0,5,1,2,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,4,1,2,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,15,0,3,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
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+43304,8,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,2,0,Class_6
+43305,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43306,1,0,13,4,0,0,0,0,1,0,12,1,0,1,0,3,0,0,0,0,2,2,0,0,0,4,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,3,1,0,0,1,0,13,0,1,0,0,0,0,4,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43307,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43308,0,0,4,1,0,0,1,0,0,0,1,0,1,0,0,4,1,1,1,1,0,4,0,0,0,0,0,0,2,0,0,2,0,0,0,0,1,3,1,0,0,3,0,2,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,1,0,5,0,3,0,0,2,2,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,16,0,0,0,0,1,20,Class_6
+43309,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_6
+43310,0,2,10,4,0,0,1,0,0,0,8,0,3,1,0,1,0,0,0,0,1,3,0,1,0,3,5,3,0,0,2,0,0,1,1,0,0,1,0,0,1,1,0,0,0,2,0,0,2,1,1,0,0,18,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43311,0,0,18,3,0,0,0,0,0,0,7,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,8,0,1,4,0,0,0,0,7,1,0,1,0,0,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,1,1,1,0,0,0,Class_6
+43312,0,0,1,8,0,0,0,0,0,0,9,0,0,0,0,6,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43313,0,0,13,0,0,0,0,0,0,0,19,0,1,0,0,0,0,0,0,0,2,0,0,5,0,0,11,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,7,0,0,0,0,0,1,0,3,0,0,0,8,0,0,2,0,0,1,0,0,1,0,0,0,0,0,1,3,0,1,0,0,0,3,0,1,0,0,0,0,0,0,1,0,1,0,0,Class_6
+43314,5,0,0,0,0,0,0,0,0,0,0,0,2,1,0,3,3,1,0,0,0,2,0,7,3,0,0,0,1,0,0,0,2,3,1,6,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,3,2,0,0,0,4,0,0,1,0,1,0,3,14,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,2,0,0,1,1,3,0,0,1,0,1,Class_6
+43315,0,0,8,1,0,0,0,0,0,0,8,0,0,0,0,1,0,0,2,0,5,0,1,2,0,1,10,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,14,0,1,2,0,0,2,0,2,0,1,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,1,0,0,0,0,0,1,0,0,1,0,Class_6
+43316,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,3,0,1,0,0,0,1,0,1,0,2,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,1,0,0,3,0,Class_6
+43317,0,0,7,4,1,0,0,0,0,0,5,0,1,0,0,4,0,2,0,0,2,1,0,1,0,2,14,0,0,0,3,0,0,0,1,0,0,1,0,1,0,1,0,6,0,6,0,0,0,1,0,0,0,13,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43318,0,1,0,0,0,0,0,1,3,0,0,0,1,0,0,1,0,1,1,0,0,0,6,5,0,0,0,0,0,0,7,1,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,0,0,2,0,0,12,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
+43319,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,4,0,4,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,10,1,0,0,0,0,0,1,0,0,3,0,0,9,0,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+43320,0,1,10,1,0,0,0,1,0,0,9,0,0,0,0,4,0,0,0,0,0,2,0,0,0,0,12,1,0,0,0,0,0,1,2,1,0,0,0,0,0,0,0,2,0,8,0,0,3,0,0,4,0,15,0,0,1,1,0,0,2,1,0,0,0,2,0,0,0,1,0,1,6,0,0,0,0,0,0,4,0,1,2,0,0,0,0,1,1,1,0,0,0,Class_6
+43321,5,0,1,0,0,0,0,0,2,4,1,0,0,1,0,2,0,0,0,5,0,6,5,9,2,1,0,0,0,0,1,12,0,0,1,1,2,6,0,0,0,7,0,4,0,0,1,5,1,3,0,0,0,1,2,1,1,0,3,2,0,0,1,3,0,0,8,0,2,7,0,1,1,2,1,0,0,0,0,1,1,0,1,0,3,2,17,1,2,0,0,0,37,Class_6
+43322,1,3,1,0,0,0,0,0,0,0,7,2,0,1,2,0,0,0,0,1,2,0,5,1,0,1,3,0,0,2,0,0,0,0,5,0,2,2,2,5,0,0,1,0,0,0,0,3,0,1,0,5,0,0,1,1,1,0,0,0,1,0,0,0,4,0,0,0,2,0,0,1,2,101,0,0,0,0,1,5,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43323,0,0,0,1,0,0,0,1,1,1,1,0,0,2,0,1,2,1,0,2,0,0,2,5,1,2,1,0,0,0,0,4,0,0,1,0,1,4,0,0,0,3,1,1,1,0,0,2,1,2,0,0,0,0,0,0,0,0,1,3,0,2,0,0,1,3,2,0,0,10,1,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,9,2,1,0,0,0,0,Class_6
+43324,0,1,16,3,0,0,0,0,0,2,12,0,0,1,2,3,0,0,0,0,3,1,0,2,0,0,18,2,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,2,0,20,0,0,4,0,0,0,0,7,0,0,0,0,0,0,1,1,0,0,0,1,0,0,1,1,0,2,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43325,2,0,1,0,0,0,0,0,0,0,3,0,4,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,7,0,1,0,3,1,2,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,1,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
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+43327,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,10,0,2,0,3,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,2,0,0,1,1,1,0,0,2,0,0,8,1,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,Class_6
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+43344,0,0,2,4,1,0,0,0,0,0,9,1,0,0,1,4,0,0,0,0,1,0,0,1,0,1,40,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,7,0,0,0,0,0,0,0,5,0,1,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43345,0,0,4,0,0,0,0,0,0,0,6,1,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,14,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43346,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,2,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,Class_6
+43347,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,Class_6
+43348,0,3,13,4,0,0,0,0,0,0,8,0,2,0,0,5,0,1,0,0,0,1,0,5,0,2,4,1,0,0,1,0,1,0,4,0,1,2,0,0,0,0,1,0,0,4,0,2,2,0,0,0,1,22,2,0,0,1,0,2,1,0,0,1,2,0,0,0,0,1,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43349,2,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,3,3,0,0,0,0,0,0,3,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,1,6,0,0,0,0,2,0,0,8,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,1,0,0,0,0,0,3,Class_6
+43350,1,2,6,2,0,0,0,0,0,0,4,0,1,0,0,1,1,0,0,1,0,0,0,2,0,0,11,3,0,0,1,0,0,0,5,0,0,1,0,0,0,0,0,5,0,0,0,0,3,0,0,0,0,16,0,0,1,0,0,0,1,0,0,2,0,1,0,0,0,3,0,1,1,0,0,0,0,0,0,9,0,6,0,0,0,0,1,0,3,0,0,0,0,Class_6
+43351,1,0,0,0,0,0,0,3,1,0,0,0,1,0,1,1,3,0,0,0,0,0,0,2,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43352,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43353,1,1,1,0,0,0,0,0,1,0,8,1,0,0,0,1,0,0,1,1,1,0,0,4,0,1,5,1,0,0,1,3,0,0,3,2,1,1,1,0,0,0,0,0,0,2,0,4,0,0,0,36,0,1,0,4,1,1,0,0,2,0,0,0,0,1,2,0,0,1,0,0,0,58,0,0,0,0,0,8,0,0,0,1,0,0,0,0,1,3,0,1,0,Class_6
+43354,0,0,7,2,0,0,0,0,1,0,13,2,0,0,1,1,0,0,0,0,2,1,0,0,0,0,20,2,0,0,0,0,0,1,1,1,0,1,0,1,1,0,0,1,0,4,0,0,0,2,5,4,1,7,0,2,0,1,0,1,6,0,0,0,0,1,0,0,0,0,0,0,1,5,0,0,0,0,0,12,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43355,1,0,6,0,0,0,1,0,0,0,11,1,0,0,0,1,0,0,0,0,0,2,0,0,0,1,5,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,9,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43356,0,1,2,1,0,0,0,0,0,0,7,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,5,0,6,1,0,0,0,0,1,8,1,1,0,0,0,1,0,0,1,0,2,0,1,0,1,0,0,1,20,0,2,0,0,0,0,0,0,0,0,0,1,1,Class_6
+43357,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43358,1,0,3,5,0,0,0,0,1,0,21,0,0,0,0,0,0,0,0,0,1,0,0,9,0,0,11,1,0,0,1,0,0,0,1,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,8,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,3,0,1,1,0,0,5,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43359,0,0,1,1,0,0,0,0,1,0,1,0,0,2,0,5,0,0,0,0,0,4,0,1,0,2,0,0,1,0,0,1,1,0,0,0,0,1,11,0,0,4,0,1,2,0,0,1,0,0,0,0,0,5,0,0,1,0,0,12,0,0,0,1,0,1,10,0,0,4,0,1,0,0,0,0,0,0,7,0,1,0,0,0,0,0,3,0,0,0,0,1,1,Class_6
+43360,0,1,7,2,0,0,0,0,0,0,9,0,0,1,0,3,0,1,0,0,0,0,0,2,0,3,6,2,1,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,8,0,0,1,0,0,0,0,16,1,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43361,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,2,0,1,0,1,0,0,0,10,0,2,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,1,2,0,0,0,0,0,0,1,0,0,7,0,0,17,0,0,0,3,0,3,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,Class_6
+43362,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,3,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43363,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,1,1,0,0,0,0,1,2,0,0,0,0,0,0,5,0,0,1,2,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43364,0,0,4,3,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,3,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,1,0,1,0,1,2,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,8,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+43365,0,1,6,1,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,8,3,1,0,0,0,0,2,0,0,0,1,0,0,0,3,0,1,0,6,0,0,1,0,1,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,0,5,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,1,0,Class_6
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+43382,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+43384,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43385,0,0,0,0,0,0,0,1,0,0,2,0,5,1,0,0,0,4,0,2,0,0,0,5,3,0,0,0,0,0,0,0,0,1,0,0,1,3,0,0,1,8,1,11,0,1,2,1,1,1,0,0,0,2,0,0,0,0,1,3,0,2,0,2,6,0,1,0,0,4,3,2,1,1,0,1,0,0,2,1,2,0,1,0,3,1,0,2,2,0,0,0,0,Class_6
+43386,0,0,6,10,0,0,0,1,0,0,1,1,0,1,0,4,0,1,0,0,0,1,0,12,0,2,0,1,0,0,1,1,0,0,2,0,0,0,0,0,0,2,0,0,0,3,0,5,1,0,0,1,0,10,0,2,0,1,0,0,2,1,1,0,0,0,7,0,0,0,0,0,0,10,0,0,0,0,0,5,0,1,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43387,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+43388,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,14,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43389,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,7,0,1,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,1,1,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43390,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43391,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,5,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,5,0,Class_6
+43392,0,0,0,9,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,6,0,3,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,3,0,0,3,0,0,9,1,1,0,2,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43393,0,1,8,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,1,3,0,0,0,0,5,1,1,0,0,0,0,0,1,0,0,1,0,2,0,0,0,1,0,3,0,0,0,0,0,0,0,4,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43394,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,2,0,0,0,1,0,2,0,3,0,10,0,0,0,0,1,0,3,0,6,1,0,1,0,0,6,7,0,0,0,0,0,1,0,0,0,1,1,1,1,0,3,0,0,12,0,2,0,0,0,0,6,4,0,6,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,2,0,0,1,0,0,4,0,Class_6
+43395,0,0,4,2,0,0,0,1,1,1,10,0,0,1,0,1,0,2,0,0,0,0,0,2,0,0,3,1,0,0,1,0,0,0,2,0,0,0,1,0,1,1,0,0,0,8,0,0,2,0,0,1,1,8,0,0,0,0,0,2,11,0,0,0,2,1,2,0,0,1,0,0,1,0,0,0,0,0,0,20,0,1,0,0,0,0,0,1,8,0,0,1,0,Class_6
+43396,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,16,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,Class_6
+43397,0,1,7,6,0,0,0,0,0,0,12,0,0,0,0,2,0,0,0,1,2,0,0,0,0,0,3,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,3,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43398,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43399,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43400,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43401,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,2,3,1,1,0,0,0,0,0,4,1,1,0,0,2,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,6,0,1,0,1,0,0,4,0,1,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,3,0,Class_6
+43402,0,1,31,25,0,0,0,0,0,2,9,0,0,0,2,10,0,2,1,0,5,9,0,0,0,4,25,4,0,0,3,1,0,0,2,0,0,3,1,2,0,0,2,4,2,26,0,1,6,0,0,3,0,28,1,0,1,2,0,0,9,2,0,3,0,6,4,0,0,3,0,1,0,1,0,4,0,0,0,21,0,8,0,0,0,0,1,1,6,0,0,1,2,Class_6
+43403,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,2,1,4,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,6,0,0,0,1,1,4,0,0,0,0,0,1,0,0,3,0,2,10,0,0,0,0,1,1,10,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,1,0,Class_6
+43404,0,0,7,21,0,0,0,0,1,0,8,0,0,0,0,0,1,1,0,0,0,2,0,0,0,2,1,3,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,1,0,0,0,1,0,0,1,4,0,0,0,1,1,0,1,0,1,1,0,0,0,2,0,0,0,6,0,3,0,0,0,0,0,1,4,0,0,0,0,Class_6
+43405,2,0,2,2,0,0,0,0,0,2,1,0,1,0,1,0,1,1,0,1,0,1,0,1,2,2,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,10,0,1,0,2,0,6,2,0,0,0,0,3,2,0,0,0,3,7,0,2,0,0,1,1,4,1,0,3,0,0,0,3,0,1,0,0,2,2,0,0,1,0,0,0,1,0,0,1,0,0,1,Class_6
+43406,0,9,1,1,0,0,0,0,0,0,6,1,0,0,0,6,1,2,2,5,0,4,0,1,0,0,2,0,1,0,0,0,1,0,5,0,0,3,0,0,0,1,0,0,0,0,0,2,15,0,0,0,0,1,0,0,1,2,0,1,1,1,1,1,2,1,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,Class_6
+43407,0,0,1,4,0,0,0,0,0,0,0,0,1,0,1,4,2,2,0,1,0,2,2,2,0,2,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,2,0,2,0,0,0,0,2,0,0,0,0,7,0,0,7,2,0,33,0,0,0,0,1,2,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,Class_6
+43408,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,10,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43409,0,0,11,3,0,0,0,1,0,1,9,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,1,3,0,0,0,0,Class_6
+43410,0,0,10,4,1,0,0,0,0,0,10,1,0,1,0,1,0,0,0,1,1,2,0,1,0,0,1,4,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,1,10,0,1,0,0,2,0,0,13,0,0,2,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,1,0,3,0,1,0,0,5,0,1,2,0,0,0,0,0,2,0,0,1,0,Class_6
+43411,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,0,1,3,0,1,6,15,0,0,0,0,0,0,0,1,0,0,0,4,0,0,2,0,0,1,3,0,0,0,0,4,0,1,0,0,1,0,0,0,3,1,0,19,0,0,0,1,0,1,6,0,1,0,0,0,1,0,0,3,0,1,0,2,0,0,18,0,3,0,5,0,0,0,0,1,2,Class_6
+43412,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,2,1,2,0,0,2,0,0,1,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,6,0,0,0,1,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43413,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,9,0,11,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,14,0,0,0,1,0,1,4,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43414,0,2,17,3,0,0,0,0,1,0,11,0,0,0,0,3,0,0,0,1,1,1,0,0,0,0,10,1,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,1,0,14,0,0,1,0,0,2,0,7,0,0,0,0,0,0,3,1,0,1,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,3,0,0,2,0,Class_6
+43415,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,3,1,0,0,0,2,2,1,0,0,0,1,0,0,1,1,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,11,0,0,0,0,0,0,6,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,Class_6
+43416,0,0,5,2,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,2,0,1,0,0,8,1,0,0,2,0,1,6,0,0,0,1,0,1,0,1,2,0,1,1,0,0,1,0,0,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43417,0,0,9,3,0,0,0,0,0,0,10,0,0,0,3,4,0,1,0,0,1,2,0,1,0,4,11,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,4,0,0,4,0,0,0,0,9,0,0,0,1,0,0,1,1,0,0,1,2,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+43418,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43419,0,1,8,6,0,0,0,0,0,0,11,1,1,0,0,1,0,1,0,0,0,1,0,5,0,16,5,2,0,0,0,0,0,0,7,1,1,0,0,0,1,3,0,1,3,6,0,0,1,0,0,0,1,7,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,9,0,1,0,0,0,0,1,0,1,0,0,0,0,Class_6
+43420,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,1,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,4,0,0,7,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,1,Class_6
+43421,0,2,0,1,0,0,1,0,0,0,11,0,0,0,1,0,1,1,0,3,0,1,0,0,0,1,1,2,0,0,0,0,1,0,6,0,0,1,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,2,0,0,0,1,0,0,1,0,0,1,1,1,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43422,0,0,0,0,0,0,0,0,0,0,8,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,87,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43423,0,0,1,10,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,3,0,6,1,1,0,2,0,0,1,0,0,0,2,0,0,1,0,0,0,5,0,1,0,0,0,0,3,0,0,0,0,2,1,0,2,0,0,8,0,2,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43424,0,0,14,4,0,0,0,0,0,0,10,0,0,0,0,2,0,2,0,0,1,1,0,2,0,4,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,14,0,0,1,0,0,0,0,15,0,1,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43425,1,0,0,0,1,0,0,1,3,0,0,0,0,0,0,1,0,2,0,1,0,0,0,2,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,9,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,2,5,0,0,6,0,0,0,2,0,0,2,0,2,1,0,0,0,0,0,14,0,0,0,0,0,0,2,1,1,1,0,0,1,0,Class_6
+43426,0,0,1,0,0,2,7,1,0,1,3,1,2,2,3,1,4,8,2,2,0,3,3,35,0,0,0,0,1,2,2,3,4,1,2,3,0,4,4,1,0,1,4,0,1,1,1,1,1,3,1,3,1,3,0,0,9,0,0,13,0,2,1,2,0,1,9,4,1,1,0,2,6,2,0,6,0,3,0,6,1,1,1,0,0,1,8,0,0,0,0,0,1,Class_6
+43427,0,1,4,3,0,0,0,0,0,1,8,0,0,0,0,4,0,3,0,0,0,1,0,2,0,2,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43428,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,1,3,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,2,4,2,1,1,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,3,0,1,0,1,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+43429,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,3,0,4,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,5,0,0,9,0,0,0,0,0,0,4,4,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,Class_6
+43430,0,0,0,0,0,0,0,3,1,0,0,0,0,3,0,0,30,13,0,1,0,1,0,16,1,2,0,0,0,0,0,2,0,1,2,0,0,0,0,0,0,2,1,1,0,0,0,5,0,1,3,0,0,1,1,1,9,0,0,5,0,0,1,1,1,0,5,1,0,2,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,2,1,0,2,0,0,0,0,Class_6
+43431,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,3,2,0,0,1,0,5,0,2,0,0,0,0,1,0,0,0,2,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,14,0,2,0,2,0,3,2,0,0,0,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,Class_6
+43432,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,3,0,2,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,2,Class_6
+43433,0,0,3,1,0,0,0,0,0,0,7,0,0,1,1,1,0,2,0,0,0,1,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,2,6,0,0,0,0,4,0,0,0,0,0,0,2,1,1,0,1,1,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43434,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43435,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,5,0,1,0,0,0,0,2,0,0,0,2,1,0,0,0,1,0,0,0,5,0,2,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
+43436,0,1,11,0,0,0,0,0,0,1,7,0,0,0,0,5,0,0,0,0,4,5,0,1,0,1,3,1,0,0,2,0,0,0,1,1,0,0,0,1,2,2,0,2,0,10,0,0,1,0,0,0,0,12,0,0,0,0,0,0,0,1,0,0,1,0,0,1,2,0,0,0,1,1,0,0,0,0,0,5,0,1,0,0,1,0,0,0,0,2,0,2,0,Class_6
+43437,0,0,0,0,0,0,0,1,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,3,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43438,0,0,3,2,0,0,1,0,0,0,12,1,0,0,1,1,0,1,0,0,4,1,0,1,0,1,12,2,0,0,3,0,0,0,2,0,2,3,0,3,0,1,0,1,1,0,0,0,0,0,0,11,0,6,0,0,0,1,0,1,6,0,1,0,0,0,0,0,0,1,0,0,2,4,0,1,1,1,0,13,0,2,0,0,1,0,1,0,0,1,0,0,0,Class_6
+43439,0,0,0,2,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,18,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,6,0,1,0,0,0,2,0,3,0,0,0,10,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+43440,0,0,7,6,0,0,0,0,0,1,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,2,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43441,4,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_6
+43442,0,7,6,22,0,0,3,2,5,1,9,1,1,12,0,12,1,7,0,0,3,6,0,0,0,0,8,6,1,1,2,0,3,0,20,1,0,5,0,1,1,3,1,5,0,5,0,3,9,0,1,0,2,27,0,0,1,1,1,2,5,0,2,1,4,10,1,0,3,3,0,3,1,3,3,0,0,0,2,8,0,19,0,0,0,1,0,0,6,0,0,3,0,Class_6
+43443,0,1,12,7,0,0,1,0,0,1,6,0,0,1,0,2,0,0,0,0,2,1,0,0,0,2,20,1,1,0,0,2,0,2,2,0,0,0,0,0,0,0,0,4,0,12,1,1,0,0,0,0,0,16,0,1,0,0,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43444,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43445,0,2,3,12,2,1,0,0,0,1,8,0,1,0,1,1,0,0,0,0,1,3,0,0,0,4,14,2,0,0,2,0,0,0,2,0,0,1,0,0,1,1,0,4,1,12,0,0,2,0,0,4,0,7,1,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43446,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,3,0,0,0,0,0,1,4,0,3,0,2,8,0,1,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,Class_6
+43447,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,3,1,5,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,0,2,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+43448,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,1,0,1,0,3,0,1,0,1,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,2,1,0,3,0,0,8,0,1,0,0,0,2,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,Class_6
+43449,0,1,10,3,0,0,0,0,0,1,9,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,9,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,2,0,12,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,2,0,Class_6
+43450,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,3,0,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43451,0,0,2,15,0,0,0,0,3,0,7,0,0,0,0,1,1,0,0,0,4,1,0,0,0,1,5,2,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,13,0,1,1,0,0,0,0,1,0,0,0,0,0,0,2,2,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,1,4,0,2,0,0,0,1,0,0,0,0,0,0,0,Class_6
+43452,0,0,3,0,0,0,0,0,0,0,8,0,1,0,0,3,0,0,1,2,0,3,0,4,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,7,0,0,2,0,2,0,1,0,0,4,2,0,0,0,0,0,1,0,0,1,0,2,0,0,0,3,0,0,0,0,0,1,0,0,0,5,0,1,1,0,0,1,0,0,0,0,0,0,0,Class_6
+43453,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+43454,1,0,9,4,0,0,0,1,0,0,12,0,1,0,1,2,0,1,1,1,3,0,0,0,0,2,7,3,0,0,3,0,1,0,1,0,2,0,0,1,0,0,0,3,0,7,1,1,0,0,0,3,0,17,0,1,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,1,0,2,0,2,0,1,0,0,0,0,Class_6
+43455,0,0,12,3,0,0,0,0,0,0,8,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,14,2,1,0,0,1,0,0,1,1,0,0,0,0,0,2,0,4,0,5,0,0,0,0,2,1,0,10,0,0,0,0,0,1,2,0,0,0,1,2,1,0,1,0,0,0,1,0,0,0,0,0,0,6,1,5,0,0,0,0,0,0,1,3,0,0,0,Class_6
+43456,0,0,3,6,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,1,2,1,0,0,0,11,0,0,0,0,0,1,1,1,0,0,0,0,2,0,0,0,1,0,3,1,3,2,0,0,0,0,4,0,0,0,0,0,0,3,1,0,0,0,1,1,0,0,0,0,0,0,27,0,1,0,0,2,11,0,2,0,0,0,0,1,0,1,1,0,0,0,Class_6
+43457,0,0,1,0,0,0,0,0,0,0,7,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,4,0,0,1,0,0,1,0,2,0,0,0,0,0,0,1,0,2,0,0,5,2,1,0,0,0,0,4,0,0,0,0,0,0,1,3,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+43458,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,12,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Class_6
+43459,0,0,20,4,0,0,0,0,0,0,7,0,1,2,0,2,0,0,0,0,1,1,3,2,0,4,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,16,0,1,0,0,0,1,7,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43460,0,1,6,17,0,0,0,0,0,2,18,0,1,0,0,1,0,1,0,0,0,1,0,9,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,2,1,0,0,10,0,0,1,2,0,1,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_6
+43461,2,1,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,2,0,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,9,0,0,0,1,0,2,0,0,0,0,0,4,1,0,1,0,3,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,0,Class_6
+43462,0,0,0,0,0,0,0,0,0,0,0,0,2,9,1,1,0,0,2,0,0,2,0,7,4,6,0,0,1,0,0,0,7,2,0,1,0,0,0,4,0,1,0,1,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,15,0,1,0,1,1,0,4,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,0,0,4,0,Class_6
+43463,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_6
+43464,0,0,11,9,1,0,0,0,0,0,10,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,6,1,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,7,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,1,1,0,0,0,Class_6
+43465,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43466,0,0,9,5,0,0,0,0,0,0,6,0,0,0,0,2,0,4,0,0,5,4,1,0,0,3,12,3,1,0,1,2,0,0,0,0,0,1,0,1,0,0,0,1,0,22,0,0,1,0,0,0,0,15,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43467,0,0,1,3,1,0,0,0,0,1,11,0,0,0,4,2,0,2,0,0,1,1,0,0,0,4,9,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,9,0,1,1,0,0,2,0,7,0,2,0,0,1,0,8,0,0,1,0,2,0,0,0,1,0,0,0,1,0,0,0,0,1,15,0,0,1,0,0,0,1,0,4,0,0,0,0,Class_6
+43468,0,0,11,22,0,0,0,0,2,2,10,0,1,1,0,2,0,1,0,0,5,1,0,0,0,5,20,1,0,0,2,0,0,1,4,1,0,5,0,1,0,0,0,2,0,16,0,0,2,0,2,11,0,13,1,0,0,0,0,0,10,0,0,0,1,0,0,0,0,0,1,0,2,10,0,0,0,1,0,5,0,1,1,0,0,0,0,0,0,1,0,0,0,Class_6
+43469,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_6
+43470,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43471,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43472,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,3,2,5,0,0,0,0,0,1,2,0,0,1,0,0,0,2,1,3,0,1,0,0,0,2,0,0,0,1,0,1,0,0,1,0,1,1,0,0,0,0,0,2,6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,Class_6
+43473,0,1,2,1,0,0,0,1,0,0,1,1,0,3,1,3,0,6,1,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,12,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,1,Class_6
+43474,0,0,2,9,0,0,0,1,0,0,9,0,0,0,0,6,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,3,0,0,12,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43475,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3,1,1,0,0,0,0,4,1,2,1,1,0,0,1,0,1,0,1,0,0,0,0,0,0,3,0,0,0,2,0,2,1,0,0,0,0,4,1,0,1,0,0,11,0,0,0,1,1,2,5,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43476,0,0,5,1,0,0,0,0,0,2,0,0,3,1,0,4,0,1,1,0,0,2,0,5,0,2,0,0,1,4,3,2,0,2,1,0,0,0,0,1,1,5,0,5,0,0,0,4,2,1,0,0,31,11,0,1,0,0,0,1,0,2,0,0,0,1,1,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,1,1,5,0,0,0,0,0,0,0,Class_6
+43477,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43478,0,0,0,0,0,0,0,0,3,0,0,0,0,2,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,3,0,0,0,0,0,0,3,0,2,0,0,1,0,10,0,0,5,4,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,Class_6
+43479,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,6,0,1,0,0,2,0,0,1,0,1,0,0,0,0,0,0,2,2,1,0,0,1,0,7,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,3,5,0,0,11,0,8,0,0,3,0,2,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,3,0,2,3,0,Class_6
+43480,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,1,0,7,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,10,0,0,0,0,0,0,11,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_6
+43481,0,0,3,0,0,0,0,0,0,0,6,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,4,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43482,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43483,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43484,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43485,4,0,2,2,0,0,0,0,1,2,3,0,5,0,1,3,2,6,0,1,0,3,0,0,3,9,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,19,0,2,0,1,0,2,3,0,0,0,0,1,1,1,1,0,4,8,1,2,0,0,2,1,2,1,0,1,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,4,0,0,1,Class_6
+43486,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,8,0,0,0,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,Class_6
+43487,1,0,7,6,1,0,0,0,1,0,18,0,0,0,0,1,0,0,0,0,3,1,0,1,0,1,17,1,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,1,0,17,0,0,0,0,0,0,0,15,0,0,1,0,0,0,8,0,0,0,0,0,1,0,0,1,0,0,0,2,0,1,1,0,0,7,0,0,1,1,0,0,0,1,3,0,0,0,0,Class_6
+43488,0,1,7,5,1,0,0,0,1,0,12,0,0,1,0,6,0,0,1,2,0,1,0,7,0,0,3,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,10,0,1,0,6,0,1,1,1,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,10,0,2,1,0,0,0,0,0,0,2,0,1,0,Class_6
+43489,0,0,3,2,0,0,0,2,0,0,10,0,0,0,1,1,0,0,0,0,1,1,0,0,0,1,8,1,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,5,0,1,0,1,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,8,0,2,0,0,0,0,0,0,1,1,0,1,0,Class_6
+43490,0,1,19,11,0,0,0,0,0,0,12,0,1,1,0,4,0,0,0,0,2,2,0,0,0,5,10,13,0,0,0,0,1,0,1,0,0,1,0,0,1,1,0,2,0,13,0,0,0,0,0,0,0,15,0,0,1,0,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,1,5,0,0,0,0,0,1,1,0,0,3,0,Class_6
+43491,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43492,0,4,16,12,0,0,3,0,0,1,8,0,4,4,0,10,0,7,0,1,1,10,0,5,0,4,0,1,1,0,1,0,0,0,5,23,1,1,0,0,0,2,0,10,0,0,0,2,11,0,1,0,0,63,3,1,1,0,3,19,4,4,1,3,1,4,0,0,0,2,0,0,0,0,0,0,5,0,0,5,0,3,0,0,0,0,3,0,4,0,0,0,1,Class_6
+43493,0,0,4,1,0,0,0,4,0,0,0,0,0,0,0,4,1,2,0,0,0,3,0,3,1,0,0,0,1,0,0,0,0,0,2,1,0,0,1,1,1,11,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,6,0,8,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,3,0,Class_6
+43494,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43495,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,1,3,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,14,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,Class_6
+43496,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,0,1,0,0,2,1,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,3,1,0,4,0,0,12,0,0,0,1,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43497,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,8,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,Class_6
+43498,0,0,2,2,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,1,0,2,0,1,1,1,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,2,1,0,0,3,0,0,0,1,6,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43499,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,1,0,0,0,0,0,5,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,6,0,0,1,0,0,1,5,0,0,6,1,0,0,2,0,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+43500,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,1,1,0,1,0,1,0,1,0,0,0,0,0,1,3,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,5,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,1,1,0,0,0,0,0,1,2,0,0,0,Class_6
+43501,0,0,3,8,0,0,1,4,0,3,3,0,0,5,2,0,1,7,1,5,1,4,3,15,0,4,0,0,1,6,4,0,2,0,7,3,0,1,0,0,0,1,0,3,0,1,0,2,1,2,1,3,1,1,0,1,5,1,0,0,0,0,2,4,0,1,7,1,0,0,0,0,3,2,1,2,0,0,0,7,2,3,3,0,0,0,4,2,2,1,0,0,0,Class_6
+43502,0,0,15,5,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,0,1,2,0,12,0,5,3,5,0,0,1,3,0,0,0,0,0,0,0,1,0,2,0,2,0,3,0,7,0,0,0,6,0,12,0,0,0,0,0,2,7,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,6,0,2,6,0,0,0,0,0,2,0,0,1,0,Class_6
+43503,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43504,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+43505,3,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,8,0,0,0,1,0,1,0,0,0,0,0,8,0,0,0,0,3,4,0,0,0,1,1,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43506,0,1,9,4,0,0,0,0,0,0,6,0,0,0,0,2,0,3,0,0,0,2,0,1,0,1,1,1,0,0,0,1,0,2,1,0,0,0,0,1,1,1,1,3,0,4,0,0,5,0,0,0,0,11,1,0,0,1,0,1,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,7,0,4,1,0,1,0,0,0,1,1,0,0,0,Class_6
+43507,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,4,2,1,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+43508,0,0,1,2,0,0,0,1,0,4,0,0,0,0,0,3,3,1,0,0,0,2,0,0,2,1,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,4,0,3,0,1,6,0,0,0,0,1,0,6,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43509,0,1,8,1,0,0,0,0,0,0,9,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,5,0,1,0,0,1,0,0,3,0,2,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,0,8,0,0,0,2,0,0,5,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,18,0,4,0,0,0,0,0,0,2,0,0,1,0,Class_6
+43510,0,0,3,9,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,2,0,1,0,3,0,1,0,0,1,0,0,1,3,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,9,1,0,1,0,0,12,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43511,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,1,1,0,0,0,0,1,0,0,4,0,0,12,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,5,0,Class_6
+43512,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+43513,0,0,1,1,0,0,0,0,0,0,8,1,0,0,0,2,0,2,0,0,0,2,0,1,0,6,3,0,0,0,0,0,0,0,5,0,0,3,0,1,0,6,0,1,0,1,0,1,5,0,0,0,0,5,0,0,2,0,0,1,0,1,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+43514,1,0,0,0,0,0,8,0,0,0,2,0,0,0,0,1,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,8,1,0,0,0,1,1,0,3,3,0,9,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,2,1,0,0,1,2,0,0,0,0,0,0,0,2,0,0,1,0,1,0,2,0,0,1,Class_6
+43515,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43516,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,5,4,1,0,1,0,0,0,2,5,0,0,0,0,2,1,1,0,0,0,2,0,0,1,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43517,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43518,0,3,1,2,0,0,0,0,0,0,8,0,1,0,1,1,0,1,0,0,0,0,0,1,0,0,4,0,0,0,1,0,1,0,2,0,0,1,0,0,0,0,0,0,0,1,0,1,8,0,0,0,0,2,0,0,2,4,1,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,2,1,0,1,0,4,2,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43519,0,0,6,1,0,0,0,0,0,0,12,0,1,1,0,3,0,0,0,0,0,0,0,4,0,1,5,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,6,2,0,0,8,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43520,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43521,0,0,12,4,0,0,0,0,3,0,8,0,0,0,0,3,0,1,0,0,3,3,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,3,0,19,0,0,0,0,0,0,0,11,0,2,0,0,0,1,8,2,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,Class_6
+43522,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,Class_6
+43523,0,0,2,6,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,6,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,7,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43524,0,0,6,3,0,0,1,0,0,0,7,0,0,2,0,3,0,0,0,0,1,1,0,0,0,0,2,4,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,6,0,1,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,1,0,0,Class_6
+43525,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,3,0,2,0,3,0,0,0,0,2,0,0,1,2,2,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,7,0,0,3,1,0,8,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+43526,0,0,4,10,0,0,0,0,0,0,20,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,4,3,0,0,0,1,1,0,2,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,4,0,0,1,13,0,0,0,0,0,2,0,1,1,0,0,1,0,0,0,2,0,1,0,0,0,9,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43527,4,0,1,0,0,3,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,18,0,1,0,0,0,1,0,0,0,0,0,6,0,0,1,0,3,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43528,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,2,0,5,0,6,0,1,0,0,0,0,1,5,0,1,0,0,0,1,0,2,0,0,0,2,0,0,2,0,0,0,2,10,1,0,1,0,0,9,1,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,3,1,0,7,0,0,0,0,2,0,0,0,0,0,0,Class_6
+43529,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,4,0,0,1,1,0,0,2,0,1,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,2,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,Class_6
+43530,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,2,0,3,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43531,0,0,0,1,1,0,0,0,1,0,7,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,3,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43532,3,0,0,1,0,0,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,9,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,11,0,0,0,1,0,1,7,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,Class_6
+43533,1,0,1,0,0,0,0,1,1,0,0,0,0,0,1,3,0,0,0,0,0,0,0,1,2,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,21,0,2,0,0,0,3,1,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,Class_6
+43534,0,0,0,0,0,0,0,2,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,1,0,2,0,Class_6
+43535,0,0,8,3,0,0,0,0,0,0,10,1,0,2,0,1,0,3,1,0,2,3,0,2,1,0,6,2,1,0,1,0,1,0,4,0,0,2,0,0,1,0,0,3,0,6,0,0,1,0,0,1,1,14,0,1,0,0,0,0,1,2,0,1,0,1,0,0,0,1,0,0,0,1,0,4,0,0,0,11,0,3,0,0,0,0,0,0,7,0,0,0,0,Class_6
+43536,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,2,0,9,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,9,0,0,0,0,0,0,1,1,0,0,1,5,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43537,0,0,4,3,1,0,0,0,1,0,7,1,0,0,0,5,0,0,0,0,3,0,0,1,0,3,1,2,0,1,0,0,0,0,2,0,0,0,0,4,0,0,1,1,0,1,0,3,1,0,0,0,0,16,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,1,0,0,0,1,0,0,0,2,0,0,0,0,Class_6
+43538,0,1,10,7,0,0,0,0,1,2,7,2,2,2,0,14,0,3,5,0,0,5,0,0,0,3,7,3,1,0,2,1,0,1,4,1,0,1,0,8,0,2,1,3,0,3,2,1,5,0,1,3,0,30,0,0,3,1,1,4,6,2,2,2,1,4,2,2,0,1,0,1,5,16,1,0,0,0,1,30,1,3,0,0,0,1,1,0,7,1,0,0,0,Class_6
+43539,0,0,5,8,0,0,0,0,0,0,13,0,0,0,0,4,0,1,0,0,5,3,0,0,1,2,11,2,0,0,2,1,0,0,2,0,0,2,0,3,0,0,0,2,0,4,0,0,0,0,0,5,0,11,0,0,0,0,0,1,3,1,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43540,0,0,22,2,0,0,0,0,0,0,9,0,0,0,0,3,0,1,0,0,2,1,1,1,0,1,9,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,1,1,0,12,0,0,2,0,0,0,0,5,0,1,0,1,0,2,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,5,0,0,0,0,Class_6
+43541,0,0,3,22,0,0,0,0,0,1,5,0,0,1,1,1,0,3,1,0,1,0,1,9,0,6,6,1,0,0,1,3,0,0,7,0,0,0,0,0,0,1,0,0,0,8,0,0,0,0,0,0,1,8,0,0,0,14,0,0,2,2,1,0,0,1,1,0,0,1,0,1,0,0,1,1,0,0,0,1,0,2,1,0,0,0,0,1,0,1,0,0,0,Class_6
+43542,0,1,12,3,0,0,0,0,0,0,7,1,0,1,3,3,1,1,0,0,2,2,0,2,2,2,7,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,4,0,9,1,1,2,0,0,1,0,18,0,0,0,0,1,0,3,1,1,0,0,0,2,0,0,2,0,1,2,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,0,0,Class_6
+43543,0,0,3,1,0,0,0,1,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,6,0,5,1,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,1,4,2,1,0,0,0,0,8,1,0,4,0,0,12,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43544,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43545,0,0,0,1,0,0,0,0,2,0,0,1,1,1,2,9,0,3,0,0,1,5,0,6,0,5,1,1,1,0,0,0,0,4,2,0,0,1,0,7,0,2,0,0,0,0,0,0,0,0,0,0,0,8,0,0,2,0,0,8,0,2,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,Class_6
+43546,0,0,8,12,0,0,0,0,0,0,7,0,1,0,0,0,0,5,0,0,1,3,1,0,0,3,5,2,1,0,0,0,0,0,1,0,0,1,1,0,0,0,2,1,0,4,0,0,1,0,0,0,0,4,1,0,1,0,0,0,4,3,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,4,0,2,0,0,0,0,1,0,0,0,0,3,0,Class_6
+43547,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,4,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,9,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,Class_6
+43548,0,0,7,6,0,0,0,1,0,0,9,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,11,0,0,0,0,0,1,4,2,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43549,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,2,1,0,0,0,0,0,0,2,1,0,0,0,0,0,2,1,0,3,0,1,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,4,5,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,Class_6
+43550,0,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,8,0,0,0,3,0,0,4,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43551,0,0,15,8,0,0,0,0,1,2,11,1,1,0,0,2,0,0,0,0,3,4,0,0,0,1,6,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,5,0,1,1,0,1,1,0,18,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43552,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,2,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,Class_6
+43553,0,2,6,0,0,0,1,0,0,0,6,0,0,0,0,4,0,0,0,0,0,1,0,0,0,1,2,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,1,1,0,0,0,0,12,0,0,0,0,0,0,5,1,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43554,0,2,0,2,0,0,0,0,0,0,12,0,4,2,1,10,1,0,2,0,0,4,0,0,0,2,1,0,0,0,0,0,0,0,3,0,1,2,0,0,3,0,0,3,0,0,0,1,9,0,0,0,0,21,1,0,0,12,0,0,0,4,0,0,0,3,1,0,0,3,0,3,0,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,2,2,0,0,1,Class_6
+43555,0,0,7,1,0,0,0,1,0,0,10,0,0,0,0,2,0,0,0,0,0,2,0,1,0,1,24,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,1,0,16,0,0,0,0,0,1,0,15,0,0,0,1,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43556,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,4,0,2,0,0,0,2,0,0,1,0,0,0,2,0,0,1,0,2,0,1,0,2,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,6,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43557,0,0,11,3,0,0,0,0,2,0,7,0,0,0,0,1,0,2,0,0,0,2,0,2,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,8,0,0,0,0,0,0,0,14,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43558,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,2,0,0,0,0,0,2,1,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43559,1,1,0,4,0,0,0,0,0,0,8,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,33,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,4,0,1,0,1,0,0,2,2,0,0,0,1,0,0,0,2,0,0,3,2,0,0,0,0,0,3,0,1,0,0,0,1,0,1,0,0,0,0,0,Class_6
+43560,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43561,0,2,8,10,0,0,0,0,3,0,6,0,0,0,5,2,0,0,0,0,1,1,1,3,0,0,32,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,4,10,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,1,0,Class_6
+43562,0,0,3,7,0,0,0,0,0,0,5,0,0,1,0,0,0,1,1,0,1,0,0,0,0,2,7,0,1,0,2,0,0,0,7,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,5,0,3,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,Class_6
+43563,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43564,0,1,5,4,0,0,0,0,0,0,16,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,6,3,0,0,1,0,0,0,2,0,1,1,0,0,1,0,1,1,0,0,0,0,1,0,0,1,1,6,1,0,0,1,0,0,6,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43565,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,6,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43566,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43567,0,1,7,3,0,0,0,0,0,0,19,0,0,0,0,3,0,2,0,0,1,1,0,10,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,10,0,0,3,0,0,2,1,10,1,0,0,8,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+43569,0,0,8,2,0,0,0,0,0,0,14,0,0,0,0,5,0,1,0,0,1,0,0,10,0,2,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,1,0,0,0,0,12,0,0,0,10,0,0,1,0,0,1,0,1,1,0,1,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_6
+43570,0,0,12,1,0,0,0,0,0,0,22,0,0,1,0,3,0,0,0,0,1,2,0,7,0,4,3,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,6,0,0,0,0,0,1,0,15,0,0,0,7,0,0,1,0,0,1,0,1,1,0,0,1,0,0,0,1,0,1,0,0,0,9,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43571,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,2,0,0,1,1,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43572,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,1,0,0,12,0,0,0,0,1,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43573,1,0,1,6,0,0,1,0,1,0,9,0,1,2,2,3,0,1,0,0,0,2,0,0,0,2,27,1,0,0,0,2,0,0,1,1,0,0,0,1,0,0,0,2,0,7,0,1,0,0,0,0,1,13,0,0,0,0,0,1,2,0,0,1,0,3,1,0,0,0,0,0,1,1,1,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,1,0,0,Class_6
+43574,0,1,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,2,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,2,0,1,0,0,0,0,0,0,5,0,4,0,0,0,1,1,0,3,0,0,0,1,0,0,6,0,0,0,0,0,0,1,0,0,0,Class_6
+43575,0,0,13,4,0,0,0,0,0,0,7,1,1,1,0,2,0,0,0,0,1,1,0,2,0,0,15,4,0,1,0,0,0,0,0,1,0,1,0,0,0,2,1,4,0,22,0,0,1,0,0,0,0,12,1,1,1,0,0,0,4,2,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43576,0,0,0,2,0,0,0,0,0,0,3,0,1,1,0,2,0,0,0,0,2,4,0,1,0,7,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,8,2,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+43577,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_6
+43578,0,3,1,1,0,0,0,0,0,0,2,1,0,0,0,1,0,2,0,0,0,0,0,5,0,1,0,0,0,0,1,0,0,1,8,0,0,1,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,1,0,0,3,0,0,8,2,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43579,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,3,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,13,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
+43580,1,0,0,2,0,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,3,0,2,0,0,0,1,0,0,1,5,0,0,0,1,0,1,1,0,0,1,0,0,0,2,0,1,0,1,0,0,0,2,0,0,0,0,0,8,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43581,1,0,5,4,0,0,0,0,2,0,12,0,1,1,0,1,0,0,0,0,0,1,0,0,0,4,22,0,0,0,0,0,0,0,2,0,0,0,0,2,0,1,0,0,0,24,0,0,1,0,1,1,0,10,0,0,0,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43582,0,1,5,3,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,6,2,0,0,0,0,4,1,0,0,0,1,0,3,2,0,0,0,0,0,0,1,2,1,0,4,0,0,0,0,0,2,0,14,0,0,0,0,0,0,6,2,0,0,0,3,0,0,0,1,0,1,0,0,0,0,0,0,0,18,0,0,0,0,0,1,0,0,3,0,0,2,0,Class_6
+43583,0,0,11,4,0,0,0,0,1,0,10,0,0,0,1,1,0,0,0,0,2,0,1,0,0,1,13,0,0,1,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,9,0,0,1,0,0,0,0,14,0,0,0,1,0,1,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43584,0,0,2,0,0,0,0,0,0,2,4,0,0,0,0,2,0,2,0,0,0,2,1,2,0,0,23,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,2,2,2,0,0,2,0,0,0,0,0,0,0,2,1,0,1,0,0,0,4,0,13,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43585,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,15,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,2,10,2,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43586,0,2,4,1,0,0,0,0,0,0,21,0,0,0,0,3,0,2,0,0,1,1,0,8,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,3,0,0,0,0,0,1,0,11,0,1,1,13,0,0,1,1,0,1,1,2,1,0,0,1,0,1,0,3,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43587,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,4,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43588,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,4,0,3,0,0,0,8,0,1,1,3,0,0,28,0,0,1,0,1,0,0,0,0,0,0,3,3,0,1,0,0,0,1,0,0,0,0,1,2,8,0,2,0,0,1,0,4,0,0,0,2,3,0,0,2,1,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,1,0,2,0,0,2,0,Class_6
+43589,0,0,1,0,1,0,1,0,0,1,9,0,1,0,0,3,0,6,0,0,0,2,0,0,0,1,3,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,2,0,1,0,1,5,1,1,0,1,0,1,0,8,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,1,0,1,0,Class_6
+43590,0,3,0,5,0,0,0,0,3,0,0,1,0,0,0,2,0,1,0,1,0,3,0,11,2,1,0,1,0,0,3,2,0,1,3,3,0,3,3,2,0,1,1,0,0,0,0,2,0,2,0,0,1,1,1,0,5,0,0,9,0,2,0,0,0,1,7,0,6,1,0,0,1,1,1,3,0,0,1,0,0,0,19,0,0,0,2,1,1,0,2,0,1,Class_6
+43591,0,0,0,0,0,0,0,5,0,0,1,1,11,0,0,2,11,2,0,0,2,1,0,11,0,0,0,0,0,0,0,0,0,2,0,1,2,0,0,2,0,4,0,0,0,0,1,3,1,0,1,0,0,2,1,0,13,1,1,9,0,3,2,1,0,0,23,4,0,3,0,0,1,1,0,3,0,0,9,0,2,0,1,0,0,1,2,0,3,0,2,0,0,Class_6
+43592,0,0,16,15,0,0,0,1,1,1,13,0,0,0,0,7,0,1,0,0,1,3,0,0,0,1,29,5,0,0,1,0,1,2,2,0,0,1,0,0,0,1,0,3,0,22,1,0,2,0,2,0,0,22,0,0,0,1,0,0,1,2,0,1,0,3,0,0,0,0,0,4,0,0,0,1,0,0,0,5,0,8,0,0,0,2,1,0,0,0,0,1,0,Class_6
+43593,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43594,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,10,0,1,0,0,0,1,2,0,0,2,2,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_6
+43595,0,0,12,3,0,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,1,1,0,0,0,9,4,5,0,1,0,6,0,0,2,0,0,0,0,1,0,3,0,1,0,8,0,0,0,0,0,0,0,18,0,0,0,0,0,1,5,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43596,0,0,0,1,0,1,0,2,2,0,0,0,3,0,0,5,1,21,0,0,0,4,0,5,1,6,0,0,1,0,0,0,0,0,8,1,0,2,0,0,1,6,0,6,0,6,0,0,1,0,0,0,0,2,0,0,7,0,2,11,0,5,1,3,0,3,3,0,0,0,4,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,5,0,0,2,0,Class_6
+43597,0,0,8,8,0,0,0,0,0,0,7,0,0,1,0,2,0,0,0,0,0,2,0,0,0,7,2,0,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,1,0,2,0,1,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43598,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,7,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,0,3,5,0,0,0,1,0,7,0,2,0,1,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,8,0,3,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43599,0,0,21,2,0,0,0,0,0,0,6,0,0,3,0,1,0,2,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,0,0,0,0,2,1,1,14,0,0,0,0,0,4,5,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43600,0,0,11,6,0,0,0,0,0,1,12,1,1,0,0,5,2,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,6,0,0,1,0,0,1,0,0,1,0,6,0,0,1,0,0,0,1,9,0,0,0,0,0,0,0,0,2,1,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43601,0,0,16,15,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,19,1,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43602,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,2,0,0,0,1,0,Class_6
+43603,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,4,0,0,0,0,1,0,0,6,0,1,0,0,0,1,12,1,0,2,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,7,0,1,0,3,0,1,10,0,0,3,2,1,0,1,0,1,0,0,7,0,0,0,0,0,1,2,4,0,0,0,0,0,0,Class_6
+43604,0,0,2,6,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,2,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,2,0,0,0,2,9,1,0,0,3,1,0,0,1,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,2,0,Class_6
+43605,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,4,1,0,0,0,1,1,5,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43607,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,1,0,2,1,0,0,2,0,0,0,0,2,1,0,0,1,0,1,0,1,0,0,0,0,1,0,1,2,4,0,2,3,0,2,0,0,0,1,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
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+43639,0,1,16,2,0,0,0,0,0,1,12,0,0,0,0,2,0,1,0,0,0,1,0,1,0,3,28,4,0,0,2,1,0,0,4,0,2,0,0,1,0,2,0,3,0,10,0,1,0,0,0,0,0,21,0,4,0,3,0,0,2,1,0,0,0,1,1,0,0,0,0,0,9,0,0,0,0,0,2,6,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43640,0,0,9,5,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,3,0,3,0,1,0,0,0,1,0,9,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
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+43645,0,0,3,8,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,14,0,0,0,2,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+43705,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,5,0,1,0,0,0,2,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,1,0,0,0,0,1,1,Class_6
+43706,0,0,2,8,0,0,0,0,0,0,4,0,0,1,0,1,0,1,0,0,0,0,0,0,0,3,8,0,0,2,0,2,1,0,0,0,0,0,0,1,0,0,0,1,1,4,0,0,0,0,0,1,0,6,0,1,0,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,7,0,3,0,0,1,0,0,0,2,0,0,0,0,Class_6
+43707,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,2,0,1,0,3,0,1,0,0,0,0,0,1,0,0,1,1,1,4,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,1,12,0,0,0,0,0,1,10,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,2,0,Class_6
+43708,1,0,2,1,0,2,0,0,1,2,0,0,0,1,0,4,0,0,0,0,0,4,0,1,0,2,0,1,0,1,0,0,0,0,4,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,13,1,0,0,0,0,12,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,Class_6
+43709,0,0,8,1,0,0,0,0,0,0,9,1,0,1,1,2,0,0,0,0,1,2,0,3,0,2,19,0,0,0,0,0,0,0,1,1,1,2,1,1,0,0,0,1,0,7,0,0,2,0,1,1,1,11,1,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,1,Class_6
+43710,0,1,6,7,0,0,0,0,0,0,12,0,1,0,0,5,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,3,0,0,4,0,0,0,1,13,2,0,0,4,0,0,2,1,1,0,1,0,1,0,0,4,0,1,0,0,0,0,0,0,0,7,0,4,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43711,4,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,3,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_6
+43712,0,0,2,3,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,5,1,1,0,2,0,0,0,0,0,1,9,0,0,0,0,0,0,3,0,1,0,0,0,1,2,0,0,0,0,4,0,1,0,1,0,16,2,2,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43713,0,0,3,1,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,8,3,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,6,1,0,1,0,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,Class_6
+43714,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43715,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,2,1,0,2,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43716,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,1,1,1,0,2,0,0,0,4,1,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,2,0,0,3,0,0,12,0,0,0,1,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,Class_6
+43717,0,0,1,0,0,0,0,2,0,0,0,0,9,0,0,2,12,3,0,0,0,1,0,5,1,0,0,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,5,0,0,5,0,1,0,0,0,1,12,0,0,0,0,0,1,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,1,0,2,0,0,Class_6
+43718,0,0,9,3,1,0,0,0,0,0,8,0,0,0,0,1,0,3,0,0,3,0,0,3,0,0,31,1,0,0,1,0,0,0,1,0,3,1,0,0,0,0,1,1,0,4,0,2,0,1,1,1,0,8,0,1,1,1,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,5,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+43719,0,0,4,7,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,3,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,1,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,1,0,1,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,1,5,0,0,0,0,0,1,3,0,0,0,0,Class_6
+43720,1,0,9,2,0,0,0,0,0,0,18,0,0,0,0,1,0,3,0,0,1,1,0,8,1,3,7,2,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,2,0,4,0,0,0,0,0,0,0,9,0,1,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+43721,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,2,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,1,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43722,1,0,2,3,0,0,0,0,0,1,8,0,0,1,0,3,0,1,1,0,0,1,1,3,0,1,2,2,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,3,0,3,0,2,1,0,0,0,0,16,1,0,0,0,0,0,0,1,0,0,0,1,3,1,0,1,0,0,1,9,0,2,0,1,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43723,0,0,5,3,0,0,0,0,0,0,15,2,0,0,0,5,0,0,0,0,1,0,0,6,0,2,18,1,0,0,1,1,0,1,3,1,1,2,1,4,0,0,0,0,0,5,0,0,1,0,0,0,0,6,1,0,0,5,0,1,4,2,0,1,0,2,1,0,0,0,1,0,1,4,0,0,0,0,0,9,0,2,1,0,0,0,1,0,1,0,0,0,0,Class_6
+43724,3,1,6,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,4,0,2,0,0,0,0,0,5,0,0,1,0,2,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
+43725,0,1,0,1,0,0,0,1,0,0,13,0,0,0,0,1,1,7,0,1,0,1,0,2,1,0,1,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,2,2,1,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
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+43727,0,1,6,7,0,0,0,0,0,0,7,0,0,0,0,6,0,2,0,1,1,6,0,3,0,3,0,3,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,3,0,0,0,3,6,0,0,0,0,16,0,0,0,0,0,0,2,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,4,0,2,0,0,0,0,1,0,6,0,0,1,0,Class_6
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+43743,0,0,14,5,0,0,0,0,0,0,8,0,0,2,1,0,0,0,1,0,3,2,0,4,0,2,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,22,0,0,0,0,0,0,0,16,0,2,0,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,7,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43744,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43745,0,0,3,1,0,0,1,0,0,0,10,0,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,2,1,1,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,8,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,8,0,2,0,0,1,0,0,2,0,1,1,0,0,Class_6
+43746,0,2,3,0,0,0,0,0,0,0,2,0,1,0,0,2,0,4,0,0,0,0,0,10,0,11,0,0,0,0,0,1,1,0,5,0,0,1,0,0,0,22,0,0,0,1,0,1,0,0,0,1,0,0,0,0,8,0,0,4,0,1,0,2,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,2,0,Class_6
+43747,0,0,0,0,0,0,0,1,1,0,0,0,6,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,1,0,1,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43748,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,0,0,1,0,3,1,4,0,0,1,0,0,2,0,0,0,2,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,0,1,1,0,0,1,5,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,Class_6
+43749,0,1,3,0,0,0,0,0,1,0,9,0,0,1,1,3,0,0,1,0,0,1,0,4,1,0,0,0,0,0,2,2,0,3,1,1,0,0,0,0,0,1,0,2,0,2,0,0,0,0,0,4,0,12,0,4,0,2,0,0,6,2,1,0,0,1,0,0,0,1,0,0,0,6,0,0,0,0,2,19,1,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43750,0,0,10,6,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,12,1,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,2,0,Class_6
+43751,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,4,0,6,2,1,0,2,1,0,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,4,0,1,0,0,0,0,1,0,0,4,0,0,8,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+43752,0,1,10,10,0,0,0,1,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,1,0,2,2,0,0,0,0,1,0,0,3,0,0,1,0,0,0,1,2,1,0,10,0,1,1,0,0,0,0,28,0,1,0,0,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_6
+43753,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,5,0,2,0,0,2,11,0,1,0,2,0,1,0,0,0,0,0,6,6,1,0,1,0,3,0,0,0,3,0,0,0,1,3,0,0,0,1,16,3,0,0,0,0,12,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_6
+43754,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,0,2,0,Class_6
+43755,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,3,0,0,0,0,0,2,2,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,8,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,Class_6
+43756,0,0,0,1,0,0,0,1,0,0,0,0,2,3,0,5,0,1,2,0,0,3,0,10,1,8,0,1,0,0,0,1,7,0,1,0,0,0,0,4,0,1,0,0,0,0,1,6,0,0,0,0,0,8,0,0,5,0,0,10,0,2,1,1,1,0,7,1,0,0,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,4,0,0,1,0,0,2,0,Class_6
+43757,0,0,4,1,0,0,0,0,0,0,9,0,0,1,0,1,0,0,0,0,0,0,0,0,0,3,5,3,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,1,0,4,0,0,1,0,0,1,0,11,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43758,0,0,0,0,0,1,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,4,1,3,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,3,0,2,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,6,0,0,0,0,0,0,6,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,1,1,2,0,0,1,0,Class_6
+43759,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,3,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,1,7,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43760,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,2,4,0,0,0,0,2,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43761,0,0,21,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,1,0,8,0,0,0,0,0,0,0,19,0,3,0,0,0,0,1,0,0,1,2,2,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,1,0,Class_6
+43762,0,0,1,0,0,0,0,0,2,0,0,0,0,3,2,9,0,0,0,0,0,1,0,4,0,4,0,0,0,0,0,0,0,2,1,0,0,0,0,10,0,1,0,1,0,0,0,0,0,0,0,0,1,5,0,0,1,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43763,0,0,8,2,0,0,0,1,0,0,6,0,0,0,0,2,0,0,0,0,0,1,0,2,0,4,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,0,0,0,2,12,2,1,0,0,0,6,0,0,0,1,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43764,0,3,4,2,0,0,0,0,1,0,8,0,0,0,0,5,0,0,1,0,3,1,0,1,0,0,4,2,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,9,0,0,1,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43765,0,1,1,1,0,0,1,1,0,0,8,0,0,1,0,3,0,1,2,0,2,1,0,3,0,2,14,1,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,2,0,1,0,1,0,0,0,2,0,3,0,3,5,3,0,0,10,1,0,2,0,2,0,0,0,1,0,1,0,3,1,0,0,0,0,8,0,1,0,0,0,0,0,0,3,0,0,0,1,Class_6
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+43787,1,0,0,2,0,0,0,1,4,2,0,1,1,0,0,1,0,2,0,1,0,4,0,1,1,2,0,0,27,0,0,1,0,0,2,2,0,0,0,0,1,3,0,0,0,0,1,6,0,0,0,0,0,6,12,0,0,0,0,2,0,3,0,0,0,6,9,0,0,1,3,2,1,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,1,5,0,Class_6
+43788,0,0,6,2,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,11,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,7,1,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,0,0,2,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43789,0,0,1,8,0,0,0,0,0,0,7,0,0,1,0,0,0,1,0,0,0,1,0,0,0,4,2,4,0,0,0,2,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,0,3,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,3,0,Class_6
+43790,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43791,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,8,0,0,0,0,0,0,0,2,7,1,0,1,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,10,1,0,1,0,0,13,0,0,0,0,0,0,2,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43792,0,0,4,8,0,0,0,0,0,1,6,1,0,0,0,2,0,2,0,0,0,1,0,7,0,8,3,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,8,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,Class_6
+43793,0,0,0,1,0,0,0,0,0,0,16,0,0,1,0,0,0,1,0,0,4,0,0,7,0,0,18,0,0,0,0,2,0,1,0,0,1,3,3,1,1,0,0,0,0,1,0,0,0,0,0,8,0,2,0,1,0,10,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,1,0,14,0,1,0,0,0,1,1,0,0,0,0,0,0,Class_6
+43794,0,0,7,14,0,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,2,1,0,0,0,0,22,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,11,0,0,0,0,0,4,0,6,0,1,0,3,0,2,4,0,0,1,0,3,1,0,0,1,0,0,0,6,0,0,0,0,0,4,0,3,0,0,0,0,1,2,0,0,0,0,0,Class_6
+43795,0,0,0,1,2,0,0,0,0,0,14,0,1,0,0,2,0,0,0,0,0,1,0,3,0,0,2,0,1,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,2,0,4,0,0,0,7,0,1,4,0,1,0,2,0,1,0,0,0,0,0,0,2,1,1,0,0,0,9,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43796,0,0,14,3,0,0,0,0,0,1,5,0,0,0,0,4,0,1,0,0,0,0,0,0,0,3,6,3,0,0,0,0,1,1,4,0,0,0,0,0,0,1,3,1,0,19,0,0,2,0,0,1,0,10,0,3,1,1,0,0,10,1,0,1,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,7,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43797,0,0,1,1,0,0,0,1,0,2,0,0,2,0,0,1,2,3,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43798,1,1,3,3,0,0,0,0,2,0,14,0,0,0,0,0,0,3,0,0,1,1,0,0,0,3,26,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,5,0,0,0,0,0,1,9,0,0,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,0,20,0,0,0,0,1,0,0,0,2,0,0,4,0,Class_6
+43799,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,0,1,0,2,0,0,1,3,0,1,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,3,5,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,Class_6
+43800,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,3,0,1,0,0,1,0,2,0,1,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,17,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,Class_6
+43801,0,0,0,1,1,1,0,0,6,1,1,1,4,0,0,7,0,2,0,0,0,9,0,1,0,1,0,1,27,0,0,1,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,3,0,0,0,0,0,3,7,0,1,0,0,1,0,6,0,0,1,0,6,0,0,0,0,0,1,0,1,0,1,0,1,5,0,0,0,0,0,0,0,0,2,1,0,2,0,Class_6
+43802,0,0,10,1,0,0,0,0,1,0,11,1,0,1,0,10,1,0,0,0,0,3,1,0,1,1,2,4,0,0,0,0,0,0,3,3,0,0,0,0,0,2,0,1,0,0,0,0,2,0,0,0,0,18,0,0,0,1,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43803,0,0,6,2,0,0,0,1,1,1,10,0,0,1,0,3,0,1,1,0,0,0,0,0,1,0,5,1,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,12,0,1,0,1,0,1,4,0,0,0,0,0,2,0,0,1,0,0,0,3,0,1,1,0,0,11,0,3,2,0,0,0,0,0,0,0,1,1,0,Class_6
+43804,3,1,1,1,0,0,0,0,0,0,0,0,1,0,0,2,1,1,0,1,0,0,0,0,4,3,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,23,0,0,0,3,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43805,0,0,7,9,0,0,0,0,0,0,7,0,0,0,0,4,0,1,0,0,1,1,0,0,0,4,12,0,0,0,1,0,1,0,0,0,0,1,5,0,0,0,0,0,0,13,0,1,1,0,0,2,0,2,0,1,0,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,2,0,0,0,0,0,0,0,0,1,0,0,Class_6
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+43827,0,1,10,1,0,0,0,0,2,0,10,0,0,2,1,0,0,0,0,0,4,2,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,2,1,2,1,0,0,0,0,20,0,0,0,0,0,0,2,0,0,0,1,1,0,0,1,0,0,1,0,39,1,3,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43828,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43829,0,0,1,1,1,0,0,0,0,1,2,0,0,0,0,0,2,1,0,1,0,3,3,5,0,1,0,0,0,0,3,0,0,0,11,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,16,0,0,0,2,0,2,3,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_6
+43830,0,1,8,3,1,0,0,0,0,0,12,1,0,0,0,2,0,0,0,0,1,0,0,3,0,0,12,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,10,0,0,2,0,0,0,0,11,2,0,0,5,0,2,4,0,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43831,0,0,5,5,0,0,0,1,2,0,0,0,0,0,1,1,1,0,0,0,3,1,0,2,1,6,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,1,4,1,0,5,2,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,Class_6
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+43833,1,0,0,1,0,0,1,1,0,0,1,0,4,1,0,3,0,2,0,1,0,4,0,1,3,2,0,0,16,0,0,0,0,0,1,0,0,0,0,0,1,1,0,2,0,0,0,1,0,0,0,0,0,2,1,0,0,0,1,1,0,0,1,0,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+43834,0,0,3,8,0,0,0,0,0,0,10,0,0,0,0,0,1,0,0,0,1,2,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,7,0,0,2,0,0,0,0,11,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,9,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43835,0,0,0,5,0,0,0,0,0,0,21,0,1,0,0,4,0,2,0,1,0,0,0,10,1,1,11,2,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,2,0,1,0,3,0,0,0,4,1,13,0,1,0,7,0,2,0,0,1,1,0,0,0,0,0,1,0,0,0,9,0,1,0,0,0,14,0,0,1,0,0,2,0,0,2,1,0,0,0,Class_6
+43836,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,1,0,2,0,8,0,0,0,0,1,0,0,2,1,0,0,1,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,Class_6
+43837,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+43838,0,0,14,6,0,0,0,0,0,0,7,0,1,2,1,0,0,0,0,1,0,3,0,1,1,2,1,0,0,0,1,1,0,0,5,0,0,2,0,0,0,0,1,2,1,5,0,1,1,1,0,0,0,13,0,0,0,0,0,1,2,1,0,0,0,1,1,0,0,0,0,0,0,4,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43839,0,0,0,5,0,0,0,0,0,0,8,0,1,0,0,4,0,0,0,0,5,1,0,0,0,2,0,1,1,0,0,2,1,0,2,1,0,0,0,0,0,0,0,1,0,6,1,0,0,0,0,0,0,12,0,1,0,0,0,3,8,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_6
+43840,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,4,1,16,0,1,0,1,0,0,0,2,0,3,2,0,0,0,0,0,0,3,0,1,0,0,0,4,0,0,0,0,0,4,2,0,6,0,0,0,0,0,0,0,0,1,10,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
+43841,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,4,2,0,2,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+43842,0,0,16,13,0,0,0,0,0,2,11,0,0,0,1,2,0,2,0,0,0,2,0,0,0,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,3,0,13,0,0,1,0,0,1,0,4,0,0,0,1,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,10,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43843,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,3,0,0,1,0,0,12,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43844,0,0,13,9,0,0,0,0,0,0,5,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43845,0,0,8,3,0,0,0,0,0,0,11,0,0,1,0,2,0,0,0,0,0,1,0,5,0,3,13,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,3,0,0,0,4,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43864,0,1,4,5,0,0,1,1,0,1,6,0,0,1,1,0,0,2,1,0,1,0,0,0,0,0,3,4,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,8,0,0,0,0,19,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,2,1,0,5,0,0,0,0,0,0,4,1,0,0,0,Class_6
+43865,0,1,7,4,0,0,1,0,0,0,3,0,0,2,0,4,0,0,0,0,0,0,0,15,0,9,1,4,1,0,0,2,0,1,2,0,1,0,0,0,0,3,0,3,0,0,1,0,3,0,0,0,0,19,3,0,0,1,0,4,4,1,1,1,0,1,3,0,0,1,0,1,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,0,0,6,0,1,1,Class_6
+43866,0,0,1,1,0,0,0,0,0,0,0,0,5,0,2,2,0,0,0,1,0,3,0,4,1,2,0,4,2,0,1,0,0,0,3,0,0,1,1,0,0,1,0,5,0,0,0,1,2,1,0,0,0,5,4,0,6,0,0,12,0,1,0,2,0,0,2,2,0,0,0,0,0,0,0,0,0,1,0,1,1,2,0,3,0,0,1,0,0,0,0,0,0,Class_6
+43867,0,0,15,14,0,0,0,0,0,1,7,0,1,1,0,3,0,1,0,0,0,4,0,0,1,4,1,1,0,0,0,2,1,0,4,0,0,0,0,0,0,3,0,3,0,15,0,0,0,0,0,0,0,15,0,0,0,0,1,1,1,1,0,0,1,3,0,0,0,0,0,2,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43868,0,0,16,5,0,0,0,0,0,0,8,0,0,0,0,7,0,3,0,0,0,2,0,0,0,1,13,7,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,6,2,20,1,0,0,0,0,1,1,1,0,0,0,3,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43869,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,11,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43870,0,0,0,0,0,0,1,0,0,0,2,0,2,0,2,1,0,0,0,2,0,4,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,6,0,1,0,0,0,1,1,2,0,0,0,1,0,0,3,0,3,0,2,1,0,0,0,1,0,0,2,0,1,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43871,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,2,0,0,0,3,0,6,1,0,0,0,0,0,0,3,2,0,0,2,0,0,2,1,0,3,0,1,0,0,9,0,1,1,0,0,0,1,1,0,1,0,0,0,0,1,0,4,0,3,8,11,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,Class_6
+43872,0,0,4,3,0,0,0,0,0,0,13,1,1,2,0,2,0,0,0,0,2,0,0,7,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,11,0,1,0,0,0,1,0,9,0,0,0,4,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43873,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43874,0,2,0,5,0,0,0,0,0,0,7,0,0,0,0,2,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,1,7,0,1,1,0,0,0,0,0,3,0,0,0,0,Class_6
+43875,0,0,2,3,0,0,0,0,1,0,11,0,0,0,0,1,0,1,0,0,0,1,0,6,0,1,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,2,0,1,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+43876,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43877,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43878,0,3,2,3,0,0,0,0,0,0,9,0,0,0,0,3,0,0,0,0,1,2,3,0,0,0,5,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,2,2,1,1,0,0,0,1,0,1,1,0,0,0,25,0,0,0,0,0,28,0,2,0,0,0,0,0,0,2,0,0,0,0,Class_6
+43879,0,0,7,2,0,0,0,0,0,0,6,0,0,0,0,4,0,0,1,0,2,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,7,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43880,0,2,1,4,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,2,0,2,0,2,2,1,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,7,4,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+43881,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,2,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,4,0,2,0,0,0,3,0,1,0,0,0,0,1,0,0,0,4,1,0,0,2,0,1,2,4,0,0,0,1,1,0,0,1,0,1,0,11,1,0,0,0,0,0,5,1,0,0,1,0,0,0,Class_6
+43882,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,1,0,Class_6
+43883,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43884,0,0,7,1,0,0,0,0,0,0,11,0,0,3,0,4,1,1,0,0,1,1,0,0,0,15,18,6,0,0,0,2,0,2,0,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,0,0,1,9,1,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_6
+43885,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43903,0,0,4,2,0,0,0,0,0,0,14,1,0,0,0,3,0,0,0,0,1,1,0,8,0,2,0,2,0,0,0,1,0,2,1,1,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,9,0,0,0,6,0,0,0,0,1,1,0,1,1,0,0,0,0,0,1,4,0,0,0,2,0,4,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+43904,0,1,16,6,0,0,0,1,0,0,10,0,1,0,0,3,0,0,0,0,2,0,0,0,0,0,19,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,2,0,10,0,0,0,1,1,0,0,16,0,1,0,0,0,1,3,1,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43905,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43906,0,0,1,4,0,0,0,0,2,0,7,0,0,0,0,9,0,0,0,0,0,3,1,11,0,0,0,2,0,0,1,2,1,0,3,3,1,0,2,1,0,0,0,0,0,3,0,0,1,0,1,5,0,8,0,0,0,0,0,1,3,1,0,0,0,2,3,1,1,0,0,0,1,40,0,0,0,0,1,11,0,2,2,0,0,0,0,1,0,0,0,0,0,Class_6
+43907,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,4,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,5,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,1,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,Class_6
+43908,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,3,2,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+43909,0,0,3,4,0,0,0,0,1,0,15,0,0,0,1,4,0,0,0,0,1,0,0,0,0,1,9,2,0,0,0,2,0,0,1,0,0,1,1,0,0,0,0,1,0,5,0,0,1,0,0,3,0,12,1,4,0,1,0,3,10,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,19,0,1,0,0,0,0,0,0,0,8,0,0,0,Class_6
+43910,0,0,9,4,0,0,0,0,0,0,6,1,0,0,0,1,0,0,0,0,0,4,0,1,0,0,5,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,0,1,0,1,1,0,0,0,0,2,1,0,0,1,0,0,2,1,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,5,0,5,1,1,0,0,0,0,0,0,0,0,0,Class_6
+43911,0,0,3,3,0,0,0,0,0,0,0,2,0,0,0,0,0,7,0,0,0,3,0,1,0,2,0,0,1,0,0,0,0,0,2,0,0,1,1,1,1,2,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43912,0,0,6,8,0,0,1,0,0,1,1,0,0,0,0,1,1,0,0,0,2,2,0,0,0,4,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,3,0,0,1,0,0,0,0,9,0,0,0,0,0,1,1,1,2,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+43913,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,4,0,0,0,3,0,3,1,1,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,2,0,1,0,0,3,0,0,0,0,0,0,2,0,0,5,0,0,8,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+43914,0,0,1,2,0,0,0,0,0,0,16,0,0,0,0,3,0,0,0,0,1,0,0,7,0,0,2,1,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,1,0,8,0,0,0,7,0,1,0,0,0,1,1,2,1,0,0,0,1,0,0,1,0,0,0,0,0,4,0,0,1,0,0,0,0,0,4,0,0,0,0,Class_6
+43915,0,0,11,3,0,0,0,0,1,0,8,0,0,0,1,4,0,0,0,0,4,1,0,2,0,2,4,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,13,0,0,0,0,0,2,1,18,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,Class_6
+43916,0,0,11,6,0,0,0,1,0,0,8,0,1,2,0,1,0,2,0,0,10,1,0,1,0,1,17,1,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,5,0,7,0,0,1,0,0,3,0,17,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,2,0,0,0,0,1,0,0,1,1,0,0,2,0,Class_6
+43917,0,0,13,21,0,0,0,0,0,1,10,1,0,0,0,4,0,0,0,1,1,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,1,0,7,0,0,1,1,0,0,0,12,0,1,0,0,0,0,2,0,0,0,0,2,0,0,1,2,0,0,1,1,0,0,0,0,0,8,0,4,0,0,0,0,0,0,1,0,0,2,0,Class_6
+43918,0,0,13,5,0,0,0,1,0,0,7,0,1,0,0,2,0,2,0,0,2,1,0,1,0,2,5,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,9,1,0,1,0,0,1,0,31,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,1,0,0,0,0,1,1,0,0,0,0,0,Class_6
+43919,0,0,0,1,0,0,0,1,0,0,0,1,5,0,0,0,0,2,1,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,2,3,0,0,0,0,2,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,18,Class_6
+43920,0,1,2,3,0,0,0,0,0,0,11,0,0,1,0,2,0,1,0,1,2,0,0,3,0,0,33,1,0,0,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,3,0,0,2,0,1,1,0,10,1,0,0,1,0,0,1,2,1,0,0,1,0,1,0,0,0,2,0,3,0,1,1,0,0,16,0,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
+43921,0,0,0,0,0,0,0,0,2,1,0,1,0,0,0,2,1,0,0,1,0,1,0,5,1,0,0,0,0,0,2,0,0,4,2,1,0,3,6,1,0,5,0,1,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,5,0,1,0,1,0,0,1,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,1,0,0,1,0,0,Class_6
+43922,0,0,2,5,0,0,0,0,0,2,5,1,2,0,0,2,0,0,0,1,0,5,0,0,0,0,2,3,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,3,0,1,0,1,5,0,0,0,0,26,0,1,0,0,1,0,0,0,1,0,2,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+43923,0,0,1,1,0,0,0,1,2,0,14,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,34,2,0,1,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,1,0,1,0,0,0,3,0,1,4,0,0,1,0,1,1,0,1,0,0,0,0,1,1,0,0,1,0,13,0,3,0,0,0,0,0,0,1,0,0,4,0,Class_6
+43924,0,0,5,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,9,2,1,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43925,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,17,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+43946,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,3,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,2,1,0,0,1,0,4,1,0,0,0,0,0,0,1,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,5,0,Class_6
+43947,0,1,1,5,0,0,0,0,0,0,12,0,1,0,1,1,0,3,0,2,0,0,0,3,0,6,0,0,0,0,0,0,2,0,5,0,0,1,0,0,1,2,0,1,0,0,0,0,8,0,0,0,0,4,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,1,0,1,1,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43948,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,2,0,0,0,0,0,4,0,0,2,0,0,1,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,Class_6
+43949,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,1,2,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,5,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,2,1,0,0,0,0,0,0,0,2,1,0,0,2,0,0,0,1,0,0,0,4,1,0,0,0,0,3,Class_6
+43950,0,0,3,2,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,2,0,0,7,0,3,9,4,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,8,2,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,5,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,2,1,Class_6
+43951,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43952,0,0,2,3,0,0,0,0,0,0,12,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,3,0,0,1,1,1,0,6,2,0,1,3,0,1,0,0,0,0,0,2,0,0,0,0,0,8,0,2,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,12,1,1,1,0,1,0,0,0,2,0,0,0,0,Class_6
+43953,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,2,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,8,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,4,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43954,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43955,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43956,0,0,28,10,0,0,0,1,0,0,7,0,1,1,2,7,0,0,0,0,4,1,0,0,0,8,6,3,0,0,0,2,2,2,3,1,0,0,2,0,0,1,0,3,0,10,0,0,1,0,0,5,1,38,0,2,0,8,0,1,2,2,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,1,4,0,0,0,1,Class_6
+43957,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43958,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,7,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,2,0,1,0,0,0,3,0,3,0,0,0,2,0,3,0,0,4,0,0,17,0,1,0,0,0,1,9,0,0,0,0,0,0,4,0,3,0,0,0,1,0,0,10,0,0,0,0,0,0,0,1,1,1,Class_6
+43959,3,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+43960,0,0,4,1,0,0,0,1,0,0,0,1,1,0,0,1,1,4,0,0,0,0,0,5,0,12,0,3,0,0,0,0,1,1,1,3,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,1,13,0,0,5,1,1,13,0,3,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,1,7,1,Class_6
+43961,0,1,2,2,0,1,0,0,1,0,5,0,0,0,0,1,0,0,0,0,0,2,0,1,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43962,0,0,0,1,0,0,0,0,0,0,7,0,0,0,1,1,0,0,0,0,0,2,0,2,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,0,0,0,0,2,2,0,0,0,0,3,0,1,0,0,0,0,0,2,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+43963,0,0,1,0,0,0,0,0,0,1,2,0,1,0,0,2,0,1,0,0,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,7,0,0,0,0,0,0,3,2,0,5,0,1,2,0,2,0,2,2,0,1,0,1,1,0,0,1,1,0,1,0,1,2,1,1,0,4,0,0,1,0,1,0,0,0,0,0,Class_6
+43964,0,3,4,2,0,0,1,0,0,1,7,0,0,0,0,1,0,2,0,0,0,4,1,0,0,1,2,0,0,0,0,0,0,1,5,1,0,0,0,1,1,0,0,2,0,0,0,2,14,0,0,0,0,9,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+43965,0,1,5,2,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,1,0,2,1,1,0,0,0,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+44001,0,0,6,6,0,0,0,0,0,1,7,0,0,0,0,4,0,0,0,0,2,1,0,0,0,1,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,5,0,0,1,0,0,2,0,15,1,0,0,0,0,0,4,1,1,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,2,0,0,0,0,Class_6
+44002,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44003,0,0,14,1,0,0,0,0,0,0,9,0,0,0,0,5,0,0,0,0,1,2,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,0,0,11,1,0,1,1,0,0,2,1,0,0,0,1,0,0,1,0,0,0,2,3,0,1,0,0,0,4,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
+44004,0,0,0,1,0,0,0,0,0,0,7,0,0,0,0,6,0,0,1,0,0,1,0,0,1,1,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,Class_6
+44005,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,3,0,0,0,0,0,6,1,1,0,0,0,3,2,0,1,0,1,0,0,7,0,1,0,1,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,Class_6
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+44027,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,2,0,5,0,1,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,2,0,0,0,0,0,1,1,0,0,0,0,1,0,0,4,0,0,12,0,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+44035,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44038,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44039,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,12,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44040,0,0,11,3,0,0,0,0,0,0,19,0,0,0,0,2,0,1,0,0,0,0,0,5,0,0,3,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,9,0,0,0,10,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,1,0,0,0,0,Class_6
+44041,1,1,9,44,0,0,0,0,2,1,9,0,1,0,0,4,1,2,0,0,3,1,0,25,0,13,8,1,0,0,3,3,3,0,5,0,0,0,4,0,0,0,0,1,0,10,0,0,1,0,1,3,0,18,0,0,0,5,0,0,0,1,1,0,0,0,4,1,0,0,0,0,1,0,0,1,0,0,0,16,0,1,2,0,0,0,0,0,2,0,0,1,0,Class_6
+44042,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,3,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+44043,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,4,0,0,0,2,0,4,0,1,0,3,0,0,0,0,0,3,0,4,0,0,1,2,1,0,0,0,2,0,0,0,1,0,3,2,8,0,0,1,2,2,0,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44044,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,1,0,1,0,0,0,0,0,4,0,5,3,2,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,3,0,0,0,0,0,0,1,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44045,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,3,0,0,1,0,2,2,6,1,0,0,0,0,0,0,8,0,0,0,1,0,0,15,0,1,0,0,2,1,0,1,3,0,7,1,0,0,1,1,2,0,0,0,1,0,0,0,0,1,0,6,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,Class_6
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+44073,0,0,6,9,0,0,0,0,0,0,13,2,0,0,1,5,0,0,0,0,2,4,1,1,0,1,8,1,0,0,2,0,0,1,2,2,0,1,0,2,0,1,0,2,0,2,1,0,4,0,0,1,0,6,1,0,0,0,0,1,6,1,0,0,0,0,0,1,0,1,0,2,0,3,0,0,0,0,0,1,0,2,1,0,0,0,0,0,1,1,0,1,0,Class_6
+44074,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,1,0,3,0,3,1,3,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44075,0,0,0,0,0,0,0,0,1,0,8,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,2,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,1,7,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44076,0,1,6,1,0,0,0,0,0,0,6,0,1,0,0,3,0,3,0,1,3,1,0,2,0,0,12,4,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,14,1,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44077,0,0,0,0,0,0,0,1,0,0,0,0,11,0,0,1,6,0,0,0,0,0,0,5,0,1,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,9,0,2,0,0,0,0,9,1,0,2,1,0,0,0,1,1,0,0,7,0,0,0,0,0,0,0,2,0,2,0,2,0,0,Class_6
+44078,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,3,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,1,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,Class_6
+44079,0,0,0,1,0,0,0,6,0,0,1,0,0,0,0,1,6,0,0,0,0,3,0,5,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,13,0,5,0,1,0,2,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,1,0,Class_6
+44080,0,0,10,3,0,0,0,0,0,0,11,0,0,0,0,5,0,1,0,0,3,0,0,0,0,2,7,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,9,0,0,2,0,0,1,1,22,0,0,0,0,0,1,1,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,1,0,0,2,0,Class_6
+44081,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44082,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44083,6,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0,2,0,4,2,6,1,1,0,0,0,0,0,9,0,0,0,2,0,1,0,2,1,1,0,0,0,0,0,3,0,1,0,0,0,0,0,1,0,0,5,5,0,0,0,0,0,0,11,0,0,7,1,2,0,0,0,0,0,0,1,1,0,0,0,0,3,3,7,0,0,0,0,0,20,Class_6
+44084,1,0,8,18,0,0,0,0,0,0,10,0,1,0,0,2,0,1,0,1,1,4,0,0,0,1,9,2,0,1,2,0,0,0,3,0,0,0,0,1,0,0,0,1,0,9,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,8,0,4,0,0,0,1,0,0,2,0,0,1,0,Class_6
+44085,0,0,6,1,0,0,0,0,0,0,10,1,0,0,0,3,0,0,0,0,0,1,0,6,0,1,0,2,1,0,0,3,0,0,2,0,0,0,0,0,0,0,0,1,1,11,0,0,0,0,0,1,1,7,0,0,0,6,0,1,1,1,0,0,0,2,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44161,0,0,2,9,0,0,0,0,0,0,5,0,0,1,0,3,0,1,0,0,1,0,0,0,0,1,2,2,0,0,2,0,0,1,1,0,1,2,0,0,0,1,0,0,0,6,0,1,1,0,0,1,0,14,2,0,1,0,0,1,4,0,0,1,0,2,2,0,0,0,0,2,0,0,1,0,0,0,0,15,0,2,0,0,0,0,0,0,4,0,0,0,0,Class_6
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+44241,0,3,0,0,0,0,0,0,0,0,7,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,2,0,0,1,1,2,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,3,0,0,2,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44242,0,2,1,1,0,0,0,0,1,1,7,0,0,1,0,5,0,0,0,0,1,2,0,0,0,2,4,1,0,0,1,2,0,0,0,1,1,0,0,3,1,1,1,4,0,3,0,1,0,0,0,2,0,5,0,2,0,2,0,1,2,1,0,0,0,0,1,0,1,2,0,0,0,2,0,0,0,0,0,7,0,3,0,0,0,0,0,0,3,0,0,2,0,Class_6
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+44244,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44245,0,0,0,1,0,0,0,0,0,0,11,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,6,1,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,2,0,0,0,6,0,4,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44427,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,2,0,1,0,11,0,1,1,0,0,0,1,0,0,0,2,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0,0,1,0,1,0,0,3,0,1,12,0,0,0,1,0,0,3,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,4,0,0,0,1,0,0,0,1,1,0,Class_6
+44428,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44429,0,2,1,1,0,0,0,0,0,0,1,0,1,0,0,2,1,0,0,2,0,2,0,0,0,8,0,2,0,0,2,0,2,0,7,1,0,0,0,0,0,10,0,2,0,0,3,0,1,1,0,0,0,7,2,1,3,0,0,4,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,Class_6
+44430,0,1,0,1,0,0,1,1,0,0,7,0,2,0,1,1,0,2,0,0,0,4,0,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,0,5,2,5,0,0,0,0,7,0,2,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+44431,0,0,9,2,0,0,0,0,0,0,11,0,1,1,0,2,0,1,0,0,0,4,0,1,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,1,1,1,0,0,0,0,18,3,1,0,0,2,0,1,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44432,0,1,8,4,0,0,0,0,0,0,9,0,0,0,0,5,0,0,0,0,0,1,0,0,2,5,6,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,5,0,0,2,0,0,1,0,13,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,1,3,0,0,0,0,Class_6
+44433,0,0,5,5,0,0,0,1,2,0,0,0,0,1,1,1,1,0,0,0,3,1,0,5,1,6,4,0,0,0,0,1,1,2,2,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,4,1,0,6,2,0,7,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,1,Class_6
+44434,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44435,0,0,7,10,0,0,0,0,0,0,6,0,0,1,0,3,0,0,0,0,0,1,0,0,0,7,2,1,0,0,1,2,0,0,4,0,0,1,0,0,0,1,0,3,0,2,0,0,0,0,0,0,0,11,0,0,0,2,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+44436,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44437,0,1,9,5,0,0,0,0,0,0,11,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,8,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,5,0,6,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_6
+44438,2,6,1,0,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,5,0,0,2,14,0,0,0,0,0,0,1,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,8,1,5,0,0,0,1,0,0,5,0,0,0,0,0,0,12,0,0,0,0,0,0,15,2,0,0,Class_6
+44439,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,1,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,Class_6
+44440,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44441,0,0,8,11,0,0,0,0,0,0,5,1,0,0,0,1,0,0,0,0,1,1,0,0,0,2,4,0,0,0,1,1,1,0,1,0,0,0,0,1,0,1,0,0,0,2,0,0,1,0,0,0,0,9,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44442,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,1,0,Class_6
+44443,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,10,0,0,0,0,1,0,2,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,Class_6
+44444,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,1,0,0,0,0,3,0,0,3,0,0,8,0,1,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+44445,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,5,3,0,0,1,0,0,1,0,0,0,0,0,1,0,1,2,4,0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,0,0,1,7,0,0,1,0,1,0,0,0,0,0,0,11,0,0,0,0,0,0,4,0,0,0,1,0,1,0,Class_6
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+44514,0,1,10,4,1,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,3,1,0,0,0,1,17,3,1,0,0,2,1,0,4,1,0,1,0,1,0,0,0,1,0,7,0,2,0,0,0,0,0,7,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,Class_6
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+44516,0,0,5,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,5,0,2,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,15,0,0,1,0,0,16,0,0,0,0,0,1,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44517,0,0,0,0,0,0,0,2,0,0,5,3,1,0,0,3,0,1,0,1,0,1,0,2,0,2,0,0,2,0,2,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,4,0,0,12,0,0,0,0,3,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,4,0,Class_6
+44518,0,0,2,7,0,0,0,0,0,1,0,0,1,0,0,1,0,2,0,1,0,0,0,1,0,6,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,5,0,0,1,0,0,8,1,1,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,Class_6
+44519,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44520,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,6,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+44521,0,1,0,1,0,0,0,0,0,0,2,1,0,0,3,0,1,2,0,3,0,1,0,8,0,0,0,0,0,0,0,0,0,1,12,1,0,1,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,3,0,0,2,2,0,12,0,1,0,0,0,2,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,3,Class_6
+44522,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44523,0,1,4,2,0,0,0,0,0,0,11,0,0,2,1,5,0,1,0,0,0,1,0,0,0,0,11,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,10,1,0,0,0,0,0,0,15,1,0,1,1,0,2,4,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,1,0,0,0,1,0,0,0,1,0,0,Class_6
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+44548,0,0,10,5,0,0,0,0,0,0,14,0,1,0,2,2,0,0,0,0,2,0,0,4,0,1,10,1,0,0,0,0,0,0,5,0,0,1,0,0,0,0,1,1,0,8,0,0,1,0,0,1,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,1,1,0,0,0,1,3,0,1,1,0,0,0,0,0,3,0,0,0,0,Class_6
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+44550,0,0,9,2,0,0,0,0,1,0,10,0,0,2,0,2,0,0,0,0,2,1,0,1,1,0,40,1,0,2,0,2,0,0,0,2,0,2,0,1,0,1,0,2,0,14,0,2,0,0,0,3,0,10,0,0,0,2,0,0,3,1,0,0,0,2,1,1,0,0,0,1,5,1,0,1,0,0,1,4,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
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+44553,0,0,1,1,1,0,2,0,0,0,4,2,0,0,0,5,0,4,1,0,0,2,0,1,0,1,5,2,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,8,0,0,5,3,2,0,1,0,0,10,3,0,1,0,0,0,1,2,0,1,0,2,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,1,0,1,2,0,4,1,Class_6
+44554,0,1,3,1,0,0,0,0,0,0,14,0,0,1,0,2,0,0,0,0,1,0,0,19,0,6,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,1,0,5,0,0,0,0,0,1,0,5,0,0,0,5,0,1,1,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44555,0,0,17,10,0,0,0,0,0,0,11,0,1,0,0,4,1,1,0,0,2,4,0,0,0,1,4,2,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,0,12,0,0,3,0,0,3,0,26,1,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44556,1,0,2,0,0,0,0,1,0,0,0,1,0,0,0,3,6,3,0,0,0,1,1,9,2,0,0,0,0,0,0,1,1,1,0,6,0,1,2,1,0,1,0,0,2,0,0,1,0,1,5,0,0,3,1,0,9,1,0,11,0,1,0,0,0,0,7,1,2,0,2,0,0,0,0,1,0,3,0,0,0,0,8,0,1,1,2,0,1,0,0,0,1,Class_6
+44557,0,0,6,8,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,2,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44558,0,0,0,1,0,0,0,0,1,0,10,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,1,0,1,6,0,0,0,0,2,0,0,0,1,0,0,2,0,0,1,0,1,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,Class_6
+44559,0,0,4,7,0,0,0,1,0,0,7,0,0,0,0,2,0,0,0,0,2,0,0,0,0,3,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,1,1,0,0,0,0,16,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44560,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,16,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,1,Class_6
+44561,0,0,5,2,0,0,0,0,0,0,6,0,0,0,0,2,0,1,0,0,1,1,0,0,0,0,2,0,0,0,0,2,0,0,3,0,0,2,0,0,0,0,0,3,0,7,0,1,0,1,0,0,0,8,0,0,0,0,0,1,12,4,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,1,6,0,4,0,0,0,0,0,0,2,0,0,0,0,Class_6
+44562,0,0,12,6,0,0,0,0,1,0,8,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,0,2,0,0,0,1,10,1,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44563,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,5,0,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+44564,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+44565,0,13,6,3,0,0,0,0,0,0,19,0,0,1,0,1,0,14,0,3,0,5,0,0,0,4,2,1,0,0,0,0,2,0,6,1,0,4,0,0,1,5,0,7,0,0,1,0,47,0,4,0,0,7,0,0,3,3,0,0,5,3,0,2,4,3,6,2,1,6,0,2,0,0,2,1,0,0,6,6,0,15,0,0,0,1,0,2,2,4,0,0,0,Class_6
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+44588,0,0,5,8,0,0,0,0,0,1,2,0,0,2,0,4,0,1,0,1,1,0,0,3,0,13,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,1,0,0,0,1,2,0,0,0,0,13,1,0,0,0,0,10,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+44589,0,0,3,3,0,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,2,1,7,2,2,0,0,0,0,0,2,2,0,0,0,0,0,1,12,0,2,0,5,3,0,1,2,0,0,0,6,0,0,0,0,0,8,0,0,0,0,0,2,3,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44590,1,0,1,5,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,2,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,Class_6
+44591,0,0,5,8,0,0,1,0,0,1,7,0,1,0,0,3,0,1,0,0,1,5,0,0,0,11,1,7,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,2,0,4,0,0,1,0,0,0,0,15,0,0,0,1,0,1,1,1,0,1,0,3,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,2,0,Class_6
+44592,0,0,10,3,0,1,0,0,1,0,10,1,1,0,0,1,0,1,0,0,3,1,0,0,0,1,16,1,0,0,0,0,0,1,3,0,0,0,0,0,0,2,2,1,0,11,0,0,0,0,0,0,0,13,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,1,0,0,0,0,Class_6
+44593,0,0,10,6,0,0,0,0,0,0,7,2,0,0,0,4,0,1,1,1,1,2,0,0,0,3,6,3,0,1,0,1,0,0,2,0,0,0,0,1,0,1,0,2,0,9,0,0,0,0,0,0,0,8,1,1,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,9,0,0,0,0,0,1,1,0,0,0,0,Class_6
+44594,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44595,0,1,8,3,0,0,1,0,2,0,9,0,1,0,0,3,0,0,0,0,1,2,0,0,0,0,19,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,8,0,1,0,0,0,0,2,2,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,3,0,0,0,4,0,1,0,0,1,0,0,1,0,Class_6
+44596,0,0,0,0,0,1,0,0,1,0,9,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,72,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44597,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,1,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,2,0,0,0,0,0,0,2,0,0,2,0,0,0,1,1,0,4,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,Class_6
+44598,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,8,1,1,0,0,0,4,0,1,3,3,0,0,28,0,0,0,4,0,0,2,0,0,0,1,2,1,0,1,0,0,0,4,1,0,3,0,0,1,1,0,2,0,0,1,0,3,0,0,0,1,6,1,2,3,5,0,1,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+44599,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,3,0,6,0,0,0,1,0,4,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,2,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,Class_6
+44600,0,5,1,2,0,0,1,0,1,0,1,0,1,0,0,3,2,4,0,4,0,1,0,6,0,0,0,0,0,0,0,0,0,0,16,0,0,7,0,2,0,1,1,1,0,0,0,6,0,1,0,2,0,5,1,1,4,13,0,0,0,0,0,0,0,1,1,1,5,0,0,0,0,2,0,1,0,0,0,0,0,0,3,0,0,1,0,0,1,13,0,0,0,Class_6
+44601,0,0,6,5,0,0,0,0,0,0,8,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,1,0,0,9,0,0,1,0,0,0,1,5,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44602,0,0,17,1,0,0,0,0,0,0,7,1,0,1,4,3,0,0,0,0,7,3,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,12,0,0,2,0,0,0,0,13,0,2,0,0,0,3,2,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44603,3,0,0,0,0,0,0,1,0,0,1,0,1,0,1,1,0,3,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44604,0,0,3,2,0,0,0,1,0,0,1,1,0,0,2,3,0,0,0,2,0,4,0,4,0,5,0,1,1,0,0,0,0,0,3,0,0,1,0,0,1,3,0,1,0,0,0,3,0,1,0,0,0,4,3,0,2,2,0,10,2,1,0,2,0,1,6,1,0,1,3,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+44605,4,0,2,1,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,9,0,2,1,0,0,0,1,1,1,0,0,1,0,1,0,11,0,0,0,1,0,2,1,0,0,0,0,5,0,0,0,0,2,2,0,3,0,0,1,2,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44626,0,2,2,3,1,0,0,0,1,0,11,0,0,0,0,3,0,3,0,1,1,2,0,3,0,3,0,2,1,0,0,0,1,0,0,0,0,2,0,0,2,0,0,1,0,2,0,2,1,0,0,0,0,6,0,1,2,0,0,4,1,1,0,0,0,3,2,0,0,0,0,1,0,0,1,0,2,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,1,Class_6
+44627,0,0,3,12,0,0,0,0,0,1,8,0,0,1,0,4,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,2,2,1,1,1,0,0,0,0,2,3,0,5,0,0,1,0,1,1,0,5,0,0,0,0,0,1,4,1,1,0,1,0,1,0,0,1,0,0,0,0,0,3,0,0,0,13,0,6,3,0,1,1,0,0,1,0,0,1,0,Class_6
+44628,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44629,0,0,9,3,0,0,0,0,0,0,7,1,0,0,0,2,0,0,0,0,0,4,0,3,0,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,10,0,0,2,2,0,0,0,17,0,0,0,0,0,0,5,4,0,0,1,1,0,0,0,2,0,0,0,1,0,0,0,0,0,4,0,4,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44630,0,1,4,13,0,0,0,0,0,0,8,0,0,0,0,4,0,1,0,0,1,2,0,0,0,14,0,4,0,0,0,2,0,2,3,0,0,2,0,1,1,3,0,3,0,2,2,0,2,0,0,0,1,13,1,0,0,1,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,3,0,Class_6
+44631,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,3,0,1,0,2,0,0,1,0,0,0,1,8,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,3,0,0,3,1,0,12,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,Class_6
+44632,3,0,0,0,0,0,0,0,0,0,0,0,3,4,0,0,1,1,0,1,0,6,0,10,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,1,0,5,0,1,0,0,0,1,2,0,0,0,0,4,2,0,0,0,0,14,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,7,0,0,1,1,0,0,Class_6
+44633,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,3,0,0,0,0,1,0,5,1,0,0,0,0,1,1,0,0,1,0,1,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,7,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44634,0,1,8,10,0,0,0,0,3,0,5,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,7,1,0,0,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,7,0,1,1,0,0,0,0,7,0,0,1,0,1,0,5,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44635,0,2,0,2,0,0,0,0,0,1,7,0,2,0,0,1,0,2,1,6,0,2,0,0,0,0,4,1,0,0,3,0,3,1,1,0,1,0,0,1,0,0,0,1,0,3,0,1,10,0,1,0,0,2,0,0,0,0,0,1,0,2,0,0,1,2,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,1,0,1,2,1,0,0,Class_6
+44636,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,8,0,11,0,0,5,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,2,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+44637,0,0,6,6,0,3,0,0,0,0,11,0,0,0,0,2,0,0,0,1,1,3,0,0,0,4,6,0,0,0,0,2,0,1,2,0,0,1,0,0,0,4,0,2,0,3,0,0,0,0,0,1,0,15,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44638,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,9,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,12,0,1,0,0,0,2,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44639,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,6,1,3,0,0,0,0,2,0,0,0,0,5,0,2,0,0,0,10,0,0,0,1,0,1,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,Class_6
+44640,0,1,2,1,0,0,0,0,0,0,11,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44641,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,1,0,0,1,0,0,0,0,0,2,0,2,1,4,0,0,0,0,0,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,0,0,1,7,0,0,1,0,1,0,0,0,0,0,0,11,0,0,0,0,0,0,4,0,0,0,1,0,1,0,Class_6
+44642,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,0,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,1,1,5,0,0,3,0,0,4,0,0,0,1,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_6
+44643,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,7,1,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,0,7,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44644,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44645,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,5,0,6,0,1,0,0,0,1,0,2,0,0,0,0,0,0,3,0,3,0,0,1,0,0,1,1,0,3,0,0,0,0,3,0,0,0,0,4,1,0,4,1,0,17,0,1,0,3,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+44682,0,0,13,4,0,0,1,0,0,0,6,0,0,0,0,3,0,2,0,0,5,2,1,0,0,2,11,4,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,15,0,0,0,0,0,0,0,10,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_6
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+44698,0,0,1,2,0,0,0,1,0,0,8,0,0,0,0,1,0,0,0,1,0,2,0,2,0,7,3,1,0,0,0,0,1,1,0,0,0,1,0,0,1,9,0,1,0,2,0,1,0,0,0,0,1,6,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_6
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+44706,0,0,7,2,0,0,0,0,1,3,21,0,0,1,0,1,0,0,0,0,3,0,0,9,0,2,15,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,12,0,0,1,0,0,3,0,3,0,1,0,14,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44707,0,0,17,3,0,0,0,0,0,0,8,1,2,0,0,3,0,0,0,0,2,4,0,1,0,0,14,2,0,0,0,0,0,0,1,0,0,1,0,1,0,3,1,2,0,18,0,0,1,0,0,3,0,13,0,0,1,1,0,1,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,1,1,0,0,1,0,Class_6
+44708,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,3,0,0,1,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44709,0,1,2,0,0,0,0,2,0,1,0,0,1,0,0,0,6,1,0,0,0,3,0,7,2,0,1,0,2,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,7,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,9,0,1,1,1,0,0,3,1,1,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,Class_6
+44710,0,0,4,1,0,0,0,1,0,0,5,0,0,0,0,1,0,0,0,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,2,0,0,0,0,13,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44711,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44712,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44713,0,0,2,7,0,0,0,0,0,0,11,0,0,1,0,1,0,1,0,0,1,0,0,13,1,1,0,2,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,3,0,0,2,5,0,0,7,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1,0,1,2,0,Class_6
+44714,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,15,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44715,1,0,3,5,0,0,0,0,0,0,8,0,0,0,1,2,0,1,0,1,1,0,0,0,0,1,33,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,5,0,0,0,1,0,0,0,8,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44716,0,0,9,10,0,0,0,0,0,0,8,0,0,0,0,4,0,1,0,0,1,2,0,1,0,4,5,2,0,0,5,1,0,0,1,0,0,1,0,0,0,3,0,0,0,10,0,0,2,0,0,4,0,18,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,Class_6
+44717,1,1,6,4,0,0,0,0,0,0,10,0,0,0,0,3,0,0,0,0,2,3,0,0,0,0,7,2,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,8,0,0,1,0,0,0,0,10,0,0,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44718,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,1,0,0,0,3,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,3,2,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+44719,0,0,7,1,0,0,0,0,0,0,14,0,0,0,0,4,0,0,0,0,5,1,0,5,0,1,8,2,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,9,1,0,0,0,0,2,0,8,0,0,0,6,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44720,0,0,0,1,0,0,0,0,0,0,3,0,1,0,0,0,0,1,0,0,0,0,0,16,0,3,0,0,1,0,0,4,0,1,0,0,0,0,0,2,0,4,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,Class_6
+44721,0,0,25,5,0,1,0,0,0,0,5,0,1,0,0,2,0,0,0,0,1,1,0,0,0,0,9,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,9,0,0,1,0,0,0,0,23,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,0,Class_6
+44722,0,0,2,6,0,0,0,0,2,0,8,0,0,0,0,4,0,1,0,0,1,1,0,0,0,3,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,2,1,0,4,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,1,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44723,0,2,7,4,0,0,0,0,0,0,10,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,22,0,0,0,2,0,1,0,0,2,0,1,0,0,0,0,0,1,0,6,0,0,0,0,0,1,0,6,0,1,0,3,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,10,0,3,0,0,0,0,0,1,2,0,0,0,0,Class_6
+44724,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44725,0,2,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,4,1,2,0,0,0,0,10,0,2,1,6,0,0,2,0,9,0,1,0,1,0,0,0,1,0,0,0,3,1,6,1,1,0,0,0,10,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,1,0,0,0,1,0,1,0,0,1,0,Class_6
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+44740,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,1,3,0,1,0,0,0,14,0,7,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,8,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44741,0,0,1,1,0,0,2,0,1,0,1,1,1,3,2,5,2,2,3,1,0,4,5,22,0,4,0,1,0,0,1,2,2,2,2,0,0,1,2,3,0,6,0,0,1,0,0,4,0,3,0,0,0,3,2,0,7,0,1,4,0,0,0,1,0,1,10,3,3,1,0,1,4,0,1,3,0,2,0,2,1,0,3,1,4,0,2,0,0,0,0,0,1,Class_6
+44742,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_6
+44743,0,1,8,3,0,0,0,0,0,0,9,0,0,0,0,3,0,3,0,1,0,2,0,1,1,0,2,4,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,1,0,1,0,0,7,0,0,4,0,13,0,0,0,2,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,Class_6
+44744,0,1,2,2,0,0,0,0,0,0,10,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,5,1,0,0,0,0,1,0,2,0,0,0,0,1,0,2,0,0,0,3,0,0,3,0,0,0,0,4,0,10,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,2,10,0,1,0,0,0,0,1,0,1,0,1,0,0,Class_6
+44745,0,0,3,3,0,0,0,0,0,0,9,2,0,0,0,0,0,0,0,0,4,0,0,0,0,8,6,4,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,4,0,0,2,0,0,2,1,1,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44746,0,1,1,2,1,0,0,2,1,4,1,0,1,0,0,0,0,1,0,2,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,21,0,2,0,0,0,1,1,0,0,0,0,3,6,0,0,0,0,6,0,3,2,0,1,0,0,0,0,2,4,0,0,0,2,0,0,0,1,1,2,0,0,0,0,1,1,0,0,0,1,0,1,Class_6
+44747,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44748,0,0,7,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44749,0,0,3,4,0,0,0,0,1,0,0,0,0,3,0,2,0,1,0,0,0,1,0,5,1,5,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,8,0,0,1,0,0,0,0,0,0,5,0,0,2,0,0,0,0,0,0,0,1,1,3,0,0,1,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44750,0,0,18,11,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,0,0,1,0,6,0,11,5,7,1,0,0,2,0,2,3,0,0,0,0,1,0,3,1,0,0,9,1,0,0,0,0,0,0,12,0,0,0,1,0,1,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,1,0,0,3,0,Class_6
+44751,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,8,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,1,0,1,0,1,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,2,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,2,Class_6
+44752,0,0,2,2,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,1,2,2,0,0,0,0,9,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,8,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44753,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44754,0,0,1,0,1,0,0,0,0,6,1,0,0,1,0,3,0,9,0,2,0,3,0,0,2,0,0,1,1,0,0,1,0,3,2,1,1,1,0,0,0,9,0,3,0,2,0,1,0,1,0,1,0,0,0,0,2,0,0,4,0,2,0,1,1,1,4,3,0,2,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,2,0,1,Class_6
+44755,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,3,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,3,0,2,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,12,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44756,0,0,9,2,0,1,0,0,0,0,9,3,0,0,0,5,0,0,0,0,7,2,0,0,0,0,6,2,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,16,0,0,1,0,0,0,0,13,0,0,1,0,0,2,2,0,2,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,8,0,2,0,0,0,0,1,0,2,1,0,0,0,Class_6
+44757,0,0,1,3,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,8,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,2,1,0,0,0,1,0,0,0,0,1,3,0,2,1,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_6
+44758,0,0,2,5,1,0,0,0,0,1,10,0,0,0,0,3,0,0,0,0,1,1,0,0,0,2,34,2,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,2,0,18,0,1,0,0,0,2,0,1,0,0,0,0,0,1,6,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44759,0,1,2,1,0,0,0,1,0,0,0,0,5,1,0,0,0,2,0,0,0,0,0,16,0,10,0,1,1,1,0,0,2,0,1,0,0,0,0,0,0,12,0,0,0,1,0,0,0,0,0,0,1,2,1,0,4,0,0,12,0,4,1,1,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,2,1,Class_6
+44760,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44761,0,2,3,4,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,1,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,Class_6
+44762,0,2,9,8,0,1,0,0,0,0,8,0,0,1,0,0,0,0,0,0,1,0,0,2,0,3,1,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,4,1,0,0,1,1,10,0,0,0,0,0,1,3,2,0,1,0,2,0,0,0,1,0,0,0,4,0,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+44763,0,0,9,6,0,0,0,1,1,0,8,0,3,0,0,1,0,1,1,0,8,5,0,36,1,0,3,0,0,0,0,8,0,1,2,0,1,1,2,0,0,2,0,1,0,3,0,9,1,0,0,16,1,15,1,2,0,1,0,1,3,0,0,0,2,0,14,2,0,0,1,0,0,24,0,1,0,0,0,5,0,0,1,2,0,0,0,0,0,0,0,2,0,Class_6
+44764,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,10,0,0,0,0,0,0,0,2,1,0,0,5,0,2,4,6,0,0,0,0,0,1,0,0,0,2,1,3,0,0,1,0,0,12,0,1,1,0,0,0,5,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_6
+44765,0,0,0,0,0,0,2,0,0,3,4,0,1,0,0,2,0,1,0,2,0,3,1,16,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,1,0,0,20,1,0,0,0,0,1,3,6,0,3,0,1,1,0,0,0,1,1,3,10,1,0,0,0,0,2,1,1,9,0,0,2,2,0,0,6,0,0,0,0,1,0,0,0,0,0,Class_6
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+44782,0,1,13,4,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,1,0,0,1,0,2,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,3,0,11,0,1,0,0,0,0,0,8,0,2,0,1,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+44791,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,3,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,4,0,0,0,0,0,0,0,5,0,0,0,0,0,4,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44792,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,2,0,1,0,0,1,1,0,1,0,0,5,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,3,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,4,0,0,1,0,Class_6
+44793,0,0,2,1,0,0,0,0,0,0,6,0,0,0,0,1,0,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,1,0,2,0,3,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_6
+44794,0,0,9,7,0,0,0,0,0,1,25,0,0,1,0,0,0,1,0,0,1,1,0,9,0,0,6,2,0,0,1,0,0,0,2,0,1,1,0,3,0,0,0,0,0,1,0,0,1,0,0,1,0,10,0,0,0,7,0,0,3,0,0,1,0,0,2,0,0,0,0,0,0,4,0,1,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44795,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,2,0,11,0,0,0,0,0,0,0,1,0,0,0,2,0,1,1,0,0,6,0,0,0,0,3,3,2,0,0,0,1,2,0,0,3,0,0,8,0,1,0,0,0,2,13,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,0,0,0,1,0,Class_6
+44796,0,1,2,7,0,0,0,0,1,0,0,0,0,0,0,9,0,1,0,0,0,2,0,5,0,11,0,1,0,0,2,0,0,0,5,0,0,0,0,0,0,1,0,3,0,10,0,0,0,0,0,3,0,1,1,2,0,2,0,0,1,0,0,0,0,1,3,3,0,0,0,0,0,3,0,0,0,0,0,5,0,2,2,0,0,0,0,0,0,0,0,0,0,Class_6
+44797,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+44799,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,1,0,2,0,2,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,9,0,2,0,0,0,1,1,0,0,0,0,0,1,0,2,0,2,4,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,Class_6
+44800,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,3,2,2,0,1,0,6,0,2,0,0,2,1,0,0,0,6,0,0,0,1,0,4,3,2,0,1,0,2,0,1,0,0,0,1,0,0,2,5,2,0,0,0,2,0,1,0,0,1,0,2,7,0,0,3,0,1,0,8,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,Class_6
+44801,0,0,15,18,0,0,0,0,0,2,9,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,4,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,11,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44802,0,0,6,3,0,0,1,1,0,0,5,0,1,0,0,5,0,2,0,0,1,3,0,0,0,1,2,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,1,4,0,0,0,0,15,2,0,0,0,0,0,9,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,6,0,6,0,0,0,0,1,0,2,0,0,1,0,Class_6
+44803,1,0,0,1,0,0,1,0,0,0,1,0,0,4,0,1,0,0,0,0,0,1,0,4,1,0,0,0,0,0,0,9,0,0,0,4,0,0,2,0,0,2,0,1,3,0,0,6,0,0,0,0,0,3,2,0,0,0,2,1,0,0,0,3,0,2,33,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,0,2,2,15,1,0,0,0,0,4,Class_6
+44804,0,2,5,1,0,0,0,0,0,0,9,0,0,0,0,4,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,2,0,0,3,0,0,2,0,0,0,0,1,2,0,4,0,0,2,1,0,2,0,2,0,1,0,1,0,0,1,1,2,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,13,0,3,0,0,0,0,0,1,1,1,0,0,0,Class_6
+44805,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+44806,0,0,2,4,0,0,0,0,0,0,8,0,2,0,0,5,0,3,0,0,3,1,0,2,0,1,17,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,0,0,6,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,1,0,0,0,3,0,Class_6
+44807,0,0,8,7,0,0,0,0,0,0,12,0,0,0,0,1,0,2,0,0,0,2,0,2,0,8,0,2,0,0,0,0,0,1,0,0,0,1,0,2,0,2,0,2,0,4,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44808,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44809,0,0,0,1,0,1,0,2,4,0,0,0,3,0,0,4,1,21,0,1,1,4,1,5,1,7,0,0,2,0,0,0,0,0,9,1,0,2,0,0,2,7,0,8,0,8,0,1,1,0,0,0,0,2,0,1,8,0,2,11,0,5,1,4,1,3,3,1,0,0,4,1,0,4,0,0,0,0,3,0,2,0,0,0,0,0,0,0,5,0,0,1,0,Class_6
+44810,0,0,4,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,4,0,1,0,4,2,6,0,0,0,0,1,0,4,0,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,11,1,0,0,0,0,12,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44811,0,0,12,20,0,0,0,0,0,0,9,0,1,0,0,4,0,1,0,0,0,3,0,0,0,10,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,12,0,0,0,0,0,1,0,2,1,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44812,0,1,0,0,0,0,0,0,2,0,9,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,4,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,1,0,1,0,2,0,0,0,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44813,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,5,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,11,0,5,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,9,0,2,0,0,0,2,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,Class_6
+44814,0,0,1,1,0,0,0,0,0,0,7,0,0,0,0,3,0,1,0,1,2,2,0,0,0,1,5,2,1,1,1,0,1,0,2,0,0,1,0,1,0,0,0,3,0,0,5,0,4,0,0,0,0,11,0,0,0,1,0,0,0,1,0,0,0,3,1,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44815,0,0,8,2,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,3,6,7,5,0,0,0,0,0,0,2,0,0,0,0,2,0,1,2,1,0,3,0,0,0,0,1,0,0,15,0,1,0,0,0,13,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+44816,0,0,1,1,0,0,0,0,0,0,1,0,2,1,0,6,0,0,0,0,0,3,0,0,1,3,0,2,1,0,0,0,1,0,0,0,0,1,0,0,0,8,0,5,1,1,0,2,0,0,0,0,0,5,0,1,0,0,1,4,0,2,0,1,0,0,8,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,2,1,2,0,0,2,0,Class_6
+44817,1,0,7,4,0,0,0,0,1,1,18,0,1,1,0,0,0,0,0,0,1,0,0,7,0,1,5,0,0,0,0,1,0,0,1,1,0,1,1,0,0,1,0,0,0,2,0,1,2,0,0,1,0,1,0,1,0,11,0,0,5,0,0,1,0,1,2,0,0,0,0,0,1,3,0,0,0,0,0,5,0,3,1,0,0,0,0,0,1,0,0,0,0,Class_6
+44818,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44819,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,5,0,3,0,0,0,1,0,0,0,12,0,1,0,0,1,0,2,4,3,1,0,0,0,2,0,11,0,2,0,0,0,0,0,1,0,0,0,2,3,0,2,0,0,10,0,2,0,0,1,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,Class_6
+44820,0,0,0,2,0,0,0,1,0,0,0,0,1,1,1,0,0,3,0,0,0,2,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44821,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,1,4,0,0,0,0,1,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44822,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,2,1,1,0,0,0,3,1,1,0,1,0,1,0,0,1,0,0,0,10,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,4,0,1,0,0,0,12,0,0,1,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44823,0,0,2,14,1,0,0,0,0,0,9,0,1,0,0,3,1,0,0,0,0,0,0,1,0,0,6,2,0,0,2,1,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,1,0,1,0,0,0,0,1,0,0,9,0,8,0,0,1,0,0,1,2,1,1,0,0,Class_6
+44824,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,3,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,1,0,0,13,0,0,0,0,0,0,3,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44825,0,0,2,1,0,0,0,0,1,0,16,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,1,0,0,0,1,0,0,0,0,0,2,0,0,0,2,0,1,0,0,0,1,0,7,0,0,0,0,0,1,3,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,2,0,0,1,0,0,0,2,0,0,0,0,Class_6
+44826,0,0,4,4,0,0,0,0,1,0,0,0,0,0,1,4,0,2,0,0,0,1,0,6,0,14,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,7,0,3,0,5,0,3,1,0,0,0,0,7,0,0,0,0,0,9,0,2,0,1,1,1,6,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,Class_6
+44827,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,Class_6
+44828,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,12,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44829,0,4,0,3,0,0,1,0,0,0,7,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,6,2,0,1,0,2,0,0,1,0,0,0,0,2,2,0,0,0,0,1,0,0,0,1,0,1,0,0,0,2,0,4,1,0,0,2,0,0,0,3,1,0,0,0,1,1,0,2,0,0,1,0,0,0,0,1,1,0,0,Class_6
+44830,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44831,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,1,0,0,2,0,0,12,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44832,0,0,1,0,0,0,0,0,0,0,17,0,0,0,2,5,0,1,0,0,2,0,0,6,0,0,7,2,0,0,0,1,0,1,2,0,1,1,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,3,0,0,0,4,0,0,1,1,0,1,0,1,1,0,0,1,0,0,0,2,0,0,0,0,0,12,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44833,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,2,1,0,2,1,4,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,6,0,2,0,0,6,1,1,0,0,0,1,2,1,1,0,0,0,2,0,0,0,7,0,2,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,Class_6
+44834,0,1,15,14,0,0,0,0,0,1,11,1,0,0,0,2,0,0,0,0,4,1,0,0,0,3,18,5,0,0,3,0,0,0,1,0,0,0,0,2,0,0,1,0,0,11,0,0,1,0,0,3,2,7,0,0,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,3,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_6
+44835,0,0,8,4,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,6,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,Class_6
+44836,0,0,4,4,0,0,0,0,0,0,10,0,0,0,0,4,0,0,0,0,0,2,0,1,0,9,4,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0,0,6,0,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44837,0,0,1,0,0,1,0,0,0,1,3,0,0,1,0,1,8,1,1,1,0,1,10,12,10,0,0,0,0,0,1,7,1,0,0,2,0,0,4,0,0,0,0,0,0,0,0,3,0,4,0,0,0,1,1,0,1,0,1,0,0,2,1,3,2,7,14,0,2,3,0,0,1,0,0,2,1,0,0,0,0,0,1,0,0,29,0,4,0,1,0,0,0,Class_6
+44838,0,0,5,7,0,0,0,0,0,3,13,0,0,0,0,4,0,1,0,0,0,0,0,0,0,3,5,0,0,0,0,0,1,0,2,1,0,1,0,0,0,0,1,1,0,6,0,0,0,0,1,0,0,6,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,0,0,0,1,0,0,0,0,Class_6
+44839,1,0,14,5,0,0,0,0,0,0,10,0,0,1,0,3,0,0,0,0,2,3,0,0,0,3,7,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,21,0,0,1,0,0,0,0,18,0,1,2,1,0,1,1,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44840,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,2,3,0,0,0,0,0,0,1,1,0,0,1,0,0,2,0,0,3,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,4,0,2,0,2,0,0,6,0,0,1,1,0,0,0,0,1,0,0,2,2,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_6
+44841,0,1,12,1,1,0,0,0,0,0,5,0,1,0,0,5,0,0,0,2,0,2,0,0,0,2,3,4,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,2,0,1,3,0,0,0,1,15,1,0,0,0,0,2,0,1,1,2,0,5,1,0,0,5,0,2,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,2,0,0,1,0,Class_6
+44842,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,1,2,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,14,0,0,0,0,1,0,4,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,Class_6
+44843,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,Class_6
+44844,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44845,0,0,2,3,0,0,0,0,0,1,9,0,0,1,0,2,0,2,0,1,0,2,0,1,0,1,2,0,0,0,2,1,0,0,2,0,0,0,0,0,0,0,0,2,0,2,0,0,1,0,0,1,0,10,0,0,0,0,0,0,2,3,0,0,1,0,1,0,0,3,0,1,0,0,0,0,0,0,0,9,0,6,0,0,0,0,0,0,2,0,0,0,0,Class_6
+44846,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44847,3,0,6,5,0,0,0,0,1,0,13,2,0,0,0,4,0,0,1,0,2,2,1,5,0,2,9,1,0,0,1,0,0,0,1,0,0,2,0,2,1,1,0,2,0,2,0,0,1,0,0,3,1,3,0,0,0,1,0,3,1,2,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,1,19,0,1,1,0,0,0,0,0,2,1,0,0,0,Class_6
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+44860,0,0,7,6,0,0,0,0,1,0,8,0,1,2,0,0,1,0,0,0,0,0,2,0,0,0,25,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,9,0,1,1,0,0,0,0,6,0,0,0,1,0,1,4,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,1,0,0,0,0,0,0,5,0,1,1,0,Class_6
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+44862,0,1,6,2,0,0,1,0,0,0,0,0,0,0,0,3,2,0,0,1,0,0,0,1,0,8,0,0,1,0,0,0,1,1,2,0,0,1,0,0,0,3,0,0,0,7,0,0,0,0,1,0,0,9,0,0,6,0,0,4,0,0,0,3,1,2,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+44863,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,5,0,0,0,0,2,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_6
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+44865,0,2,8,13,0,0,0,0,1,0,7,0,1,0,0,2,0,4,0,5,0,3,2,0,0,2,1,2,0,0,1,0,0,0,5,0,0,1,0,0,0,0,0,1,0,1,0,2,4,0,0,1,0,7,0,0,0,0,0,0,4,0,0,0,0,1,0,0,1,0,0,0,0,2,1,0,0,0,0,4,0,8,1,0,0,0,0,2,1,1,0,0,0,Class_6
+44866,0,1,0,1,0,0,0,0,0,0,0,0,2,1,0,1,4,4,0,4,0,1,0,11,0,0,0,0,1,0,0,1,0,0,1,1,0,1,5,0,0,0,0,0,0,1,0,1,3,0,2,0,2,2,0,0,2,3,0,16,0,1,1,0,0,0,6,0,0,3,0,2,0,1,0,0,4,1,2,2,0,1,0,0,0,0,4,2,4,0,0,0,0,Class_6
+44867,0,0,0,0,0,0,3,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,9,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,4,0,0,2,0,6,0,0,3,0,0,0,0,0,0,0,Class_6
+44868,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44869,0,0,5,2,0,0,0,0,0,0,8,0,0,1,0,0,0,1,0,0,0,3,1,14,0,2,1,0,0,0,0,3,0,1,2,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,0,16,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,1,0,Class_6
+44870,0,0,3,1,0,0,0,0,0,0,6,0,0,0,0,2,0,1,0,1,0,0,0,2,0,4,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,1,0,0,0,2,1,0,0,0,0,15,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44871,0,0,13,12,0,0,0,1,0,0,7,1,0,1,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,2,1,0,0,0,0,0,0,0,1,0,15,0,1,1,0,1,0,0,9,0,0,0,0,0,0,3,2,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,4,0,2,0,0,0,0,1,0,2,0,0,0,0,Class_6
+44872,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,1,2,0,1,0,2,0,1,1,6,0,0,1,0,0,0,1,0,3,0,0,1,0,3,0,20,0,7,0,1,0,0,1,0,0,0,0,4,0,0,2,0,0,6,0,0,0,0,2,0,3,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_6
+44873,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,0,1,0,2,0,2,0,1,0,0,3,2,0,3,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,9,0,0,0,1,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,1,0,0,0,Class_6
+44874,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44875,0,0,2,0,0,0,0,1,0,1,0,0,2,0,0,3,1,0,0,0,0,1,0,3,1,0,0,1,0,0,0,5,0,0,0,1,0,0,0,0,0,6,0,1,1,0,0,2,1,0,0,0,0,3,1,0,0,0,0,8,0,1,0,0,1,1,16,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+44876,0,0,5,5,0,0,0,0,0,1,0,0,1,1,1,2,0,3,0,1,0,3,0,1,1,10,0,2,0,0,0,0,0,2,1,0,0,0,0,2,0,5,0,1,0,2,0,0,1,0,0,0,0,2,0,0,4,0,0,14,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,Class_6
+44877,0,0,22,6,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,14,0,0,0,0,0,0,0,14,0,0,0,2,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44878,1,2,1,3,0,0,0,0,0,0,2,0,0,0,2,4,6,4,0,4,0,1,0,9,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,1,1,0,1,2,0,4,0,0,1,1,0,0,0,3,0,0,4,0,0,13,0,1,0,1,0,1,3,0,0,1,3,1,1,0,0,0,0,2,3,6,1,0,0,0,0,0,4,2,2,0,0,0,2,Class_6
+44879,0,0,1,0,0,0,0,1,1,0,0,0,0,1,1,1,0,1,0,0,0,3,0,0,1,0,2,2,0,0,0,0,2,2,1,1,0,0,0,0,1,10,0,1,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+44880,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,1,2,0,1,0,2,0,2,3,4,0,1,2,0,0,0,2,0,0,0,0,0,0,1,0,17,0,2,0,0,1,1,1,0,0,0,0,9,1,2,5,0,0,14,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,1,0,0,Class_6
+44881,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44882,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44883,0,1,1,2,0,0,2,0,0,1,10,0,0,0,0,3,0,4,0,1,0,0,0,0,0,1,7,1,0,0,0,0,0,0,3,0,0,1,0,0,0,3,0,9,0,2,0,0,9,0,0,0,0,14,0,0,2,0,0,0,1,0,0,1,1,2,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,1,Class_6
+44884,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,1,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,8,0,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44885,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44896,0,0,15,21,0,0,0,0,0,0,10,0,1,0,0,3,0,0,0,0,0,2,0,2,0,4,1,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,1,13,0,0,1,0,0,0,0,9,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,0,Class_6
+44897,0,0,5,4,0,0,0,0,0,2,6,0,0,0,0,3,0,0,0,0,2,3,0,0,0,1,2,2,1,0,0,2,0,0,1,0,0,0,0,0,0,1,0,1,0,7,0,0,1,0,0,0,0,20,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,3,0,0,0,0,Class_6
+44898,0,0,6,3,0,0,0,0,0,0,10,0,0,0,0,2,0,0,0,0,0,0,1,5,0,0,7,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,1,1,0,1,0,3,0,1,0,4,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+44899,0,1,9,2,0,0,0,0,0,3,9,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,2,0,7,0,0,0,0,0,0,1,10,0,0,0,0,0,1,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44900,0,1,4,0,0,0,0,0,0,0,11,0,3,0,0,9,0,1,0,0,2,1,0,0,1,5,0,1,0,0,0,2,0,0,2,0,1,0,0,1,0,1,0,1,0,2,0,0,7,0,0,0,0,25,0,0,0,1,0,1,0,2,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44901,0,0,1,10,1,0,0,0,0,0,18,0,0,0,0,0,0,0,1,0,2,3,0,7,0,0,26,0,0,0,0,1,0,0,0,2,0,0,0,2,0,0,0,0,0,5,0,0,1,0,0,1,0,8,1,1,0,8,0,0,1,1,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,1,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44902,0,1,7,7,0,0,0,0,0,0,8,0,1,0,0,4,0,2,0,0,3,5,0,0,0,4,1,1,0,0,0,2,0,1,2,0,0,1,0,0,0,1,0,2,0,12,1,0,0,0,0,0,0,16,0,0,1,0,0,2,3,1,0,0,0,1,0,0,0,2,0,2,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,1,0,0,0,0,Class_6
+44903,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+44904,0,0,0,0,1,0,1,0,0,9,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,2,0,0,1,1,0,1,0,2,1,0,0,0,1,0,5,0,0,2,1,1,1,0,0,0,4,1,0,1,0,0,0,0,0,0,2,1,1,2,0,0,11,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,1,0,2,0,0,1,0,0,Class_6
+44905,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,16,1,0,0,0,0,0,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44906,0,0,4,2,0,0,0,0,0,0,11,0,1,1,0,7,1,1,0,0,6,4,0,3,1,4,2,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,5,0,0,2,0,0,0,1,16,1,0,0,0,0,0,2,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,4,0,2,1,0,0,0,1,0,0,0,0,0,0,Class_6
+44907,4,0,0,5,0,0,0,0,0,1,0,1,0,1,1,2,0,0,0,1,0,2,0,0,5,2,2,4,0,0,0,0,0,1,1,0,0,0,0,0,0,6,0,1,0,3,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,2,0,0,0,0,4,0,Class_6
+44908,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44909,0,0,15,1,0,0,0,0,0,1,9,1,2,0,0,0,0,2,2,0,3,1,0,0,0,0,5,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,11,0,0,0,0,0,1,1,7,0,0,0,0,0,0,2,0,1,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44910,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,6,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44911,0,2,4,9,0,0,0,0,0,1,6,0,1,0,0,3,0,1,0,0,1,1,0,2,0,6,0,5,0,0,2,2,1,0,5,0,0,2,0,2,0,6,0,2,0,3,0,0,0,1,0,0,1,13,0,0,0,1,0,1,3,3,0,3,0,2,1,0,0,3,0,0,0,0,0,1,0,0,0,5,0,2,0,0,0,0,0,1,1,0,0,2,0,Class_6
+44912,0,0,12,5,0,0,0,0,0,0,10,0,0,0,0,2,0,1,0,0,2,1,0,0,1,5,1,3,0,0,1,2,0,1,1,0,0,2,0,0,0,0,0,1,0,4,0,0,1,0,0,0,0,12,0,0,0,0,0,3,1,4,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,1,0,0,0,0,0,1,3,0,0,4,0,Class_6
+44913,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,13,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_6
+44914,0,2,0,3,0,0,0,0,0,0,1,0,1,0,1,0,0,5,0,1,0,2,0,1,0,2,0,0,0,0,0,0,0,0,8,1,0,1,0,0,0,7,0,4,0,0,0,0,1,0,0,0,0,7,0,0,0,1,0,8,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44915,0,1,3,0,0,0,0,4,1,0,5,0,0,0,0,4,1,3,0,0,0,2,0,2,1,3,0,0,3,1,0,3,0,0,3,1,1,1,0,1,0,15,0,1,0,0,0,1,1,0,0,0,1,15,3,1,5,0,1,25,0,1,0,0,0,5,7,0,1,2,3,0,0,0,3,3,0,0,4,5,0,0,0,0,0,0,1,0,15,0,0,1,0,Class_6
+44916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,2,1,0,2,0,0,12,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+44917,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,2,0,7,0,4,1,0,0,0,1,0,0,0,10,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,2,0,7,0,0,2,0,0,8,1,0,1,0,0,0,1,0,0,0,0,0,0,4,0,2,0,0,0,1,0,0,0,0,0,0,1,0,1,3,0,4,0,Class_6
+44918,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_6
+44919,0,0,10,8,1,0,0,0,1,0,12,0,1,0,0,3,0,1,0,0,0,1,0,1,0,1,6,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,19,0,1,0,0,0,0,1,19,0,2,0,0,0,0,4,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_6
+44920,0,0,8,11,0,0,0,0,0,1,13,0,0,0,1,3,0,0,2,1,0,0,1,8,0,0,8,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,1,0,5,0,0,0,0,0,4,0,3,0,0,1,5,0,0,1,1,0,0,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44921,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,2,4,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,12,0,2,0,0,0,2,0,2,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,Class_6
+44922,0,1,4,5,0,0,0,0,0,0,0,0,0,1,2,0,2,0,5,0,1,1,1,2,0,7,1,4,2,0,1,0,1,2,2,0,4,0,0,0,0,1,0,3,0,3,0,2,0,0,0,0,0,6,0,0,3,0,0,12,0,0,0,0,0,1,4,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44923,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Class_6
+44924,0,0,1,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,1,5,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,6,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,4,0,Class_6
+44925,0,0,11,1,0,0,0,0,0,0,7,0,0,1,0,5,0,0,1,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,0,0,1,0,0,0,0,15,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+44927,0,0,6,4,0,0,0,0,0,0,8,0,0,1,0,3,1,4,1,0,0,2,0,0,0,2,8,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,2,0,3,0,0,1,0,0,1,0,16,0,0,0,0,1,1,1,1,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
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+44937,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_6
+44938,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+44939,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,3,1,3,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,2,0,2,0,2,0,1,0,0,0,1,0,1,0,0,1,0,0,4,0,1,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+44940,0,2,0,1,0,0,0,0,0,0,8,0,0,1,0,1,0,2,0,1,0,0,0,0,2,1,0,0,1,0,0,2,0,0,1,0,1,1,0,0,0,3,1,2,0,1,0,0,3,0,1,0,0,3,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,1,0,0,0,0,Class_6
+44941,0,0,4,3,0,0,0,1,0,0,3,1,2,2,0,2,0,6,0,3,0,8,0,1,1,5,0,4,0,0,0,0,0,1,1,0,0,3,3,1,1,31,0,2,2,1,0,0,1,3,2,0,1,4,0,0,6,0,0,10,0,4,1,2,0,5,1,0,0,8,0,0,0,2,0,2,0,0,11,2,1,0,2,0,0,0,4,1,3,0,1,1,0,Class_6
+44942,1,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,1,2,0,0,0,1,0,1,2,2,3,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,0,12,0,1,0,0,0,0,2,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44943,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,3,10,5,0,0,0,1,0,0,7,1,0,1,0,0,4,1,2,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,3,0,2,3,0,1,2,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,26,0,0,0,0,0,0,0,Class_6
+44944,0,0,0,1,0,0,0,0,2,0,7,0,0,0,0,0,0,0,0,0,2,0,0,14,0,0,4,0,0,0,0,5,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,52,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+44945,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,3,0,3,3,1,0,0,0,1,0,0,1,4,2,1,0,0,0,0,1,0,1,1,0,1,0,1,0,0,0,0,0,3,3,0,4,0,0,12,0,0,0,0,0,1,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+44946,0,1,2,3,0,0,0,0,0,0,2,0,0,0,0,2,0,4,0,0,2,1,1,18,1,24,0,0,1,0,0,0,1,1,5,0,0,0,0,1,0,12,0,5,0,3,0,0,0,0,0,0,1,2,0,0,2,0,0,7,0,1,2,0,0,1,0,1,0,1,4,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44947,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,2,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,2,2,0,0,0,0,4,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44948,0,1,1,1,0,0,1,0,0,0,2,0,0,1,0,3,0,1,0,2,0,2,0,2,2,3,0,1,0,0,1,2,0,1,0,0,0,2,0,0,0,15,0,2,0,0,0,1,1,0,0,0,0,6,0,0,4,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,4,1,1,0,2,0,0,Class_6
+44949,0,0,24,1,0,0,0,0,0,0,9,0,2,0,0,6,0,0,0,2,0,3,0,2,0,2,19,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,11,0,0,2,0,0,0,0,21,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44950,0,0,5,1,0,0,0,0,0,0,19,0,2,0,0,1,0,0,0,0,4,1,0,9,2,1,6,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,6,0,0,2,0,0,0,0,19,0,0,2,15,0,1,0,2,0,1,0,0,1,0,0,0,1,1,0,2,0,0,0,0,0,4,0,1,0,0,0,0,1,0,0,0,0,0,0,Class_6
+44951,0,0,1,1,1,0,0,0,0,1,0,1,2,0,0,8,0,3,0,0,0,7,0,3,0,5,0,0,25,0,0,0,0,0,2,1,0,1,0,0,1,3,0,0,0,0,0,1,0,0,0,0,0,4,6,0,2,0,0,2,0,2,0,0,1,0,4,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,Class_6
+44952,1,0,0,1,0,0,0,3,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,4,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,10,0,3,0,0,1,0,1,0,0,0,0,5,1,0,0,0,0,5,0,1,0,0,0,0,5,11,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,Class_6
+44953,0,0,11,5,0,0,0,0,3,1,9,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,2,0,12,0,0,0,0,0,0,0,8,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,1,0,Class_6
+44954,4,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,0,4,0,3,0,3,0,0,5,0,0,0,0,0,0,0,2,0,0,1,1,2,0,2,0,4,0,5,0,0,0,1,0,0,0,0,1,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,6,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,7,2,1,0,0,0,2,Class_6
+44955,0,3,11,3,0,0,2,0,0,0,16,0,0,0,0,0,0,1,0,0,0,0,0,2,0,1,14,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,6,1,3,3,0,0,0,0,4,1,0,0,0,0,2,9,0,0,0,0,0,2,1,0,1,0,0,1,1,1,5,0,0,0,12,0,0,0,0,0,0,0,0,1,0,0,0,1,Class_6
+44956,0,0,10,2,0,0,0,0,0,0,10,0,0,0,0,5,0,2,0,0,2,1,0,0,0,1,8,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,5,0,1,0,0,0,2,0,3,0,0,0,0,0,1,2,3,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44957,0,1,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1,0,0,0,3,0,7,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,3,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,5,0,0,0,0,2,9,0,1,0,0,0,0,0,0,1,4,0,0,0,Class_6
+44958,0,1,1,0,0,0,0,0,0,0,0,0,1,4,1,3,0,1,0,1,0,2,0,2,0,7,1,1,0,0,1,0,0,0,2,0,0,1,0,9,0,4,0,1,0,4,0,3,0,0,0,0,0,13,0,0,3,0,0,8,0,0,1,4,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44959,0,0,5,13,0,0,0,0,0,0,6,0,0,0,0,4,0,0,1,0,2,1,0,2,0,7,2,0,0,0,1,0,0,1,5,0,0,1,0,0,0,1,1,1,0,2,0,0,2,0,0,1,0,8,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+44960,0,0,0,1,0,0,0,1,2,0,1,0,1,0,0,2,3,1,0,1,0,1,0,4,0,0,0,0,1,0,1,0,1,0,0,0,0,3,16,0,2,1,2,2,1,0,0,1,0,12,0,0,0,3,2,1,0,0,0,2,0,0,0,2,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,Class_6
+44961,0,0,6,2,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,0,0,0,0,9,1,2,15,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,2,2,1,0,1,0,6,1,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,1,0,1,0,Class_6
+44962,0,0,9,16,0,0,0,1,0,0,18,0,0,0,0,1,0,1,0,0,1,1,0,5,0,2,8,3,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,3,0,7,0,0,2,0,0,0,0,7,0,0,0,9,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,3,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_6
+44963,0,0,3,2,0,0,0,0,0,0,8,0,0,0,1,4,0,0,0,0,2,2,0,0,0,1,6,3,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0,0,0,0,8,0,0,0,0,0,0,6,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,15,0,7,0,0,0,0,0,0,3,0,0,0,0,Class_6
+44964,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,5,0,2,0,0,1,0,2,0,0,0,3,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,5,0,0,1,0,0,12,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,Class_6
+44965,0,0,0,3,0,0,0,0,0,0,12,0,0,0,1,3,0,1,1,0,0,2,0,8,1,0,2,2,0,0,1,2,0,1,0,0,0,0,0,1,0,0,0,3,0,2,0,0,2,0,0,1,0,5,0,0,0,6,0,1,2,2,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,7,0,5,0,0,0,0,0,0,4,0,0,2,1,Class_6
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+44967,0,0,0,0,0,0,0,2,0,0,1,0,0,0,2,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_6
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+44985,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,1,0,0,3,1,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44987,1,1,0,1,0,0,2,0,0,0,7,0,1,0,0,5,0,4,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,3,0,0,1,0,0,0,0,0,1,0,0,10,0,4,0,0,0,0,7,1,0,0,0,0,0,0,2,0,0,2,3,1,5,0,1,0,0,0,0,0,0,0,1,1,0,1,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+44988,1,0,0,2,0,0,2,0,0,2,1,0,5,1,0,6,0,0,0,6,0,1,0,2,0,2,0,0,0,0,0,3,0,0,0,0,0,6,0,0,1,6,0,7,0,0,2,2,0,0,0,0,0,9,4,1,4,3,0,0,0,5,0,2,1,4,7,0,1,2,0,1,0,1,0,0,1,0,0,0,0,0,12,0,0,0,0,0,2,2,0,0,1,Class_6
+44989,0,0,8,1,0,0,1,1,0,0,12,1,2,0,0,5,0,0,0,0,1,2,0,0,0,3,5,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,9,0,0,2,0,0,1,0,10,2,0,0,0,0,1,4,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44991,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,10,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,1,1,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+44993,1,0,1,0,0,0,1,0,0,1,2,0,0,6,1,1,5,1,2,6,0,8,0,1,4,2,0,0,1,0,0,5,3,3,3,2,0,6,3,2,4,19,1,4,0,0,1,1,1,3,0,11,1,5,2,0,4,1,0,10,0,9,5,3,0,1,8,0,0,10,1,0,0,12,0,1,0,0,11,4,2,0,0,0,2,2,10,2,1,0,0,1,7,Class_6
+44994,0,0,8,9,0,0,0,0,0,0,9,1,0,2,1,1,0,0,0,0,1,3,0,0,0,0,7,2,0,0,0,0,1,0,4,0,1,0,1,0,1,1,0,1,0,11,1,0,1,0,0,0,0,8,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,1,0,0,2,0,Class_6
+44995,0,0,14,4,0,0,0,0,0,0,9,0,1,0,0,5,0,0,0,1,4,1,0,0,0,1,3,2,0,0,0,0,0,0,0,0,0,2,0,0,0,3,2,0,0,17,0,0,0,0,0,0,2,13,0,0,0,1,1,0,3,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,6,0,Class_6
+44996,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,8,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_6
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+44999,0,0,10,6,0,0,0,0,0,0,9,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,6,0,0,0,0,Class_6
+45000,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,1,0,0,1,0,0,6,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,1,0,0,3,0,0,4,2,0,0,0,0,0,4,3,0,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45001,0,0,1,2,0,0,0,0,0,0,7,0,0,1,2,3,0,4,0,0,1,3,0,0,0,1,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,4,0,0,9,0,6,0,0,0,0,4,0,0,0,1,0,0,2,0,1,0,1,1,1,0,0,3,1,0,1,2,1,1,0,4,0,3,0,1,0,6,0,0,0,0,0,0,0,0,0,Class_6
+45002,1,1,2,3,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,2,0,0,0,0,2,0,0,8,3,0,0,0,1,0,0,0,4,0,1,2,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,3,1,0,0,0,0,0,0,Class_6
+45003,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,2,0,5,0,0,0,1,0,4,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,4,0,0,0,0,0,8,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,Class_6
+45004,0,2,4,1,0,0,0,0,1,0,7,0,0,0,0,2,0,0,0,0,2,2,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,1,1,0,3,1,10,0,0,0,3,0,0,2,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45005,0,0,4,5,0,0,0,0,0,0,13,1,1,0,0,4,0,0,0,0,2,2,0,5,1,1,5,0,1,0,3,1,0,0,1,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,11,0,0,0,0,0,0,2,0,5,0,0,3,1,0,0,1,0,0,2,19,0,0,0,0,0,21,0,1,0,0,0,0,0,0,1,1,0,0,0,Class_6
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+45035,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,2,0,Class_6
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+45038,0,0,0,2,0,0,0,5,0,0,2,0,3,0,0,1,9,1,0,1,0,4,0,8,0,1,0,0,1,0,0,5,1,0,0,1,0,0,0,0,0,3,0,1,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,18,0,1,1,1,0,7,24,0,0,3,0,0,0,0,0,1,1,0,4,1,1,0,1,1,0,0,0,0,1,1,0,0,3,Class_6
+45039,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
+45040,0,1,0,2,0,0,0,0,2,1,4,0,0,0,0,1,0,3,0,0,1,0,0,1,0,0,10,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,1,0,0,3,0,2,0,1,2,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,3,0,0,0,0,0,1,1,0,0,0,0,Class_6
+45041,0,1,15,1,0,0,0,0,0,0,17,0,2,0,0,4,0,0,0,1,7,3,0,9,0,0,9,3,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,12,0,0,0,0,0,0,0,7,0,0,0,5,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,2,0,1,0,1,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45042,0,0,1,4,0,0,0,0,0,0,9,0,1,0,0,3,0,1,1,0,0,0,0,0,0,2,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,0,1,0,1,0,4,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_6
+45043,1,2,4,0,0,0,0,0,0,0,22,0,0,2,0,0,0,0,0,0,3,0,0,4,0,1,7,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,2,0,0,2,0,8,0,1,0,9,0,0,0,1,1,1,0,1,1,0,0,2,1,1,0,2,0,1,0,0,1,6,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
+45044,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,4,0,0,0,0,0,2,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,1,1,0,10,0,0,1,0,0,0,3,0,0,2,0,2,1,0,0,0,4,2,2,2,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_6
+45045,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,11,0,3,1,0,0,0,0,0,0,0,2,1,0,0,1,0,1,7,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,12,0,0,0,0,0,0,5,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,Class_6
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+45077,0,0,11,2,0,0,0,0,1,0,7,0,2,0,1,9,0,2,0,0,1,3,0,0,0,1,12,1,0,0,0,2,0,1,2,1,0,0,0,0,0,1,0,2,0,14,0,1,0,0,0,1,0,37,0,0,0,0,1,1,1,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,Class_6
+45078,0,0,0,0,0,0,0,0,1,0,16,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,1,1,0,0,0,1,1,0,3,0,0,0,0,0,7,0,0,0,0,1,1,0,0,3,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,1,0,0,0,Class_6
+45079,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,7,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_6
+45080,0,1,1,0,0,0,0,0,0,0,9,0,0,1,0,1,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,2,1,0,0,0,0,Class_6
+45081,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45082,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,11,1,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45083,0,0,0,0,0,0,0,1,0,1,0,0,1,3,0,0,0,0,0,3,0,2,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,1,1,0,1,3,0,1,0,0,1,0,0,1,0,0,1,1,3,0,0,0,2,0,0,0,1,1,0,1,10,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+45084,0,1,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,1,3,1,1,0,2,0,7,6,0,0,0,0,1,0,0,4,0,0,1,0,0,0,1,0,1,0,0,0,2,6,0,0,0,0,4,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,0,1,4,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45085,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,9,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,7,0,0,0,0,0,0,0,3,0,0,0,0,0,8,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+45147,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45150,0,1,5,2,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,2,1,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+45155,2,3,0,0,0,0,0,0,0,0,5,0,1,0,0,0,1,1,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,0,1,2,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,1,2,0,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+45157,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,3,0,0,0,0,0,4,0,0,1,0,0,0,0,1,0,0,0,13,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45159,0,0,9,9,0,0,0,0,0,0,10,0,1,1,0,2,0,0,0,0,1,1,0,0,0,1,10,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,21,0,0,0,0,0,1,0,14,0,0,0,0,0,2,7,0,0,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_6
+45160,0,0,0,0,0,0,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45161,0,0,14,11,0,0,0,0,0,0,8,0,0,0,1,6,0,3,0,0,0,5,0,2,0,4,16,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,0,3,0,9,0,0,2,0,0,3,0,6,0,0,0,0,0,0,1,4,0,0,0,1,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_6
+45162,0,10,2,1,0,0,0,1,0,0,9,0,3,0,1,6,0,0,1,1,0,5,2,0,0,2,0,0,1,0,0,0,1,0,2,0,0,1,0,4,4,1,0,5,0,1,0,1,11,0,0,0,0,14,1,0,1,4,0,0,1,0,0,0,0,8,0,0,2,2,0,0,0,1,1,0,0,0,1,3,0,0,0,0,0,0,1,0,1,1,0,0,0,Class_6
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+45164,0,1,6,3,0,0,0,0,0,0,8,0,0,0,0,2,0,1,0,0,0,2,0,1,0,0,5,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,0,12,0,0,0,0,0,1,6,1,0,0,0,1,0,0,0,1,0,0,0,3,0,1,0,0,0,4,0,3,0,0,1,0,0,0,2,1,0,0,0,Class_6
+45165,0,0,6,2,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,9,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45232,0,2,13,8,0,0,0,1,1,1,20,0,0,0,5,5,0,0,0,0,6,2,0,0,1,3,28,3,0,5,1,0,1,0,2,1,0,2,2,3,0,1,1,5,0,16,1,1,1,0,4,17,1,22,2,7,0,3,0,0,7,1,0,0,0,3,23,0,1,3,0,0,6,56,0,0,0,0,0,2,0,1,1,1,0,0,1,0,1,3,0,0,0,Class_6
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+45235,0,2,4,4,0,0,0,0,0,0,8,0,0,1,0,4,0,0,0,0,0,2,0,1,0,1,3,1,0,0,1,1,0,1,1,0,0,0,0,1,0,2,0,3,0,5,1,0,0,0,0,0,0,20,0,0,0,1,0,1,0,2,0,2,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,Class_6
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+45240,0,1,10,1,0,0,0,0,1,1,19,0,0,0,0,0,0,0,0,0,0,1,0,7,0,1,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,14,0,0,0,0,0,2,0,2,0,0,0,9,0,0,9,1,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+45242,1,0,6,5,0,0,0,1,0,0,18,0,0,0,0,2,0,0,0,0,2,1,0,8,0,1,5,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,5,1,0,0,11,0,1,2,0,0,0,0,0,1,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45244,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,1,0,0,0,0,4,1,0,0,0,0,10,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+45409,0,0,11,2,0,0,0,0,0,0,18,1,1,0,0,1,0,0,0,0,3,0,0,6,0,1,3,0,0,0,0,2,0,0,1,2,0,0,0,1,0,0,0,2,0,9,0,0,0,0,0,0,0,7,0,1,0,13,0,2,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45414,0,0,9,3,0,0,0,1,0,0,17,0,0,0,0,4,0,1,0,0,0,3,0,4,0,2,3,2,0,0,2,1,0,0,0,1,0,0,0,0,0,2,0,2,0,6,0,0,3,0,0,1,0,7,1,0,0,13,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,0,0,0,0,1,0,Class_6
+45415,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45416,0,0,20,9,0,0,0,0,0,0,6,0,0,0,0,2,0,2,2,0,1,2,0,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,21,0,0,2,0,0,0,0,9,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,2,0,0,2,1,Class_6
+45417,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45418,0,0,4,2,0,0,0,0,0,1,11,0,0,1,0,0,0,0,0,0,1,1,0,0,0,2,1,2,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,4,0,0,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,Class_6
+45419,1,0,4,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,2,0,3,1,3,0,1,0,0,1,0,0,0,8,1,0,0,1,0,1,0,0,3,0,0,0,1,2,0,0,0,0,5,0,0,0,0,0,10,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,2,0,0,0,1,0,Class_6
+45420,1,1,0,1,0,0,0,0,1,0,0,1,1,1,2,1,0,0,0,1,0,2,0,0,3,7,0,1,0,0,0,0,0,1,2,0,0,4,0,0,0,3,0,1,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,2,0,0,0,1,0,1,0,1,0,1,1,0,0,1,2,0,0,0,1,2,0,1,0,0,1,0,1,0,1,0,0,0,0,Class_6
+45421,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,0,0,0,0,10,0,0,0,0,0,0,0,4,2,5,0,0,0,0,1,0,0,6,0,0,0,0,3,5,0,0,0,0,1,0,1,0,0,0,0,12,0,2,0,0,0,2,6,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,Class_6
+45422,0,0,1,1,0,0,0,0,0,1,7,1,1,0,1,0,0,1,0,0,0,4,0,0,0,2,0,1,0,0,0,2,0,0,2,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,1,5,3,0,0,1,0,1,0,4,0,0,2,1,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_6
+45423,0,0,1,3,0,0,0,0,0,0,12,0,0,1,0,0,0,0,0,1,0,3,0,0,0,0,3,2,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,5,0,0,1,0,0,0,0,6,1,1,0,0,0,0,4,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,7,0,5,0,0,0,0,0,0,1,0,0,1,0,Class_6
+45424,0,0,2,0,0,0,0,2,0,0,0,0,0,3,0,1,0,7,0,0,0,4,0,1,1,2,0,3,0,0,0,0,3,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,0,0,0,6,0,0,1,0,0,10,1,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,Class_6
+45425,0,2,0,1,0,0,0,0,0,0,3,0,1,0,0,4,0,11,0,0,0,1,0,2,0,2,0,2,1,0,0,0,2,0,5,1,0,2,0,0,0,26,0,1,0,0,0,0,1,0,0,0,0,1,0,0,4,0,0,4,0,3,0,0,0,2,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,Class_6
+45426,0,0,0,2,0,0,1,0,1,0,0,0,1,2,0,0,3,0,3,2,0,0,0,7,0,0,0,0,0,0,0,2,1,0,2,0,1,3,2,1,0,3,0,1,2,0,0,2,0,0,0,0,0,1,0,0,2,0,0,21,0,2,1,0,0,0,9,0,1,7,0,3,0,0,1,4,0,3,0,0,0,0,8,0,4,0,1,0,2,2,4,1,1,Class_6
+45427,0,1,2,4,0,0,2,0,0,0,1,0,3,0,1,1,0,2,1,7,0,1,2,16,0,0,0,0,0,0,3,3,0,0,1,0,1,0,2,0,0,0,0,1,0,1,0,8,1,1,0,0,1,0,1,0,4,0,1,8,0,2,0,1,0,0,6,0,7,1,1,1,0,0,0,4,0,3,0,2,0,1,27,0,0,0,0,0,0,1,1,0,0,Class_6
+45428,0,0,0,0,0,1,1,0,0,0,1,0,1,0,0,1,6,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,2,1,0,1,0,0,3,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,1,2,6,1,0,0,2,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_6
+45429,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45430,0,0,7,3,0,0,0,0,0,0,6,0,0,1,0,0,0,1,0,1,2,1,0,0,0,1,0,5,0,0,2,1,1,0,2,0,0,1,0,0,0,1,0,4,0,2,0,0,1,0,0,0,0,7,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,4,0,2,0,0,0,0,0,0,1,0,0,0,0,Class_6
+45431,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,1,4,0,0,0,2,0,14,0,0,1,0,1,0,0,1,0,1,1,3,0,1,0,0,0,4,0,2,0,1,2,2,3,0,0,0,0,4,0,0,10,0,0,12,0,2,0,0,0,1,7,0,0,0,1,0,0,0,2,0,0,2,0,0,0,0,7,2,0,0,4,0,0,0,0,2,0,Class_6
+45432,0,0,0,2,0,0,0,0,1,0,4,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,10,1,0,1,1,1,0,0,3,0,0,2,0,0,0,1,0,0,0,2,0,0,0,0,2,3,0,2,0,1,0,2,0,0,4,5,0,0,0,0,2,2,0,1,0,0,1,8,1,0,0,0,0,6,0,2,0,0,0,0,0,2,2,0,0,2,1,Class_6
+45433,0,0,3,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,6,2,0,0,0,4,1,2,1,4,1,0,0,1,1,0,1,0,2,1,0,0,0,0,0,2,0,0,3,1,0,3,0,0,4,0,0,0,0,0,0,3,1,0,1,0,0,0,1,3,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,Class_6
+45434,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,1,0,5,0,2,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,3,0,2,0,0,4,0,0,0,0,0,0,1,1,3,0,0,0,0,0,2,0,0,0,1,0,0,0,1,2,0,0,0,2,0,0,0,4,1,0,0,0,0,2,1,2,0,0,0,0,0,0,Class_6
+45435,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,2,0,1,0,4,2,0,0,0,0,0,0,1,2,1,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,5,0,0,0,0,0,12,2,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_6
+45436,0,0,6,10,0,0,0,0,0,0,8,0,0,0,0,2,0,0,0,0,0,2,0,0,0,3,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,1,0,0,0,0,14,0,0,0,0,0,2,4,1,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,1,1,0,0,1,0,Class_6
+45437,0,0,1,7,0,0,0,1,0,0,1,0,1,0,0,3,0,2,0,1,0,1,0,2,0,13,1,2,1,0,0,0,0,3,0,0,0,0,0,0,0,10,0,1,2,1,0,0,0,0,1,0,0,2,0,0,8,0,0,4,0,0,0,0,0,0,1,0,0,2,0,0,0,6,0,0,0,1,0,1,0,1,3,1,0,0,2,0,1,1,0,1,1,Class_6
+45438,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,6,0,0,0,4,0,2,0,2,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,2,0,1,1,0,2,0,Class_6
+45439,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,Class_6
+45440,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,6,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45441,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,1,2,0,0,0,0,0,0,4,0,0,0,2,0,3,0,1,0,4,0,3,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,3,0,3,4,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,5,1,0,0,0,0,2,Class_6
+45442,0,0,21,7,0,0,0,1,1,1,8,0,0,0,0,4,1,1,0,1,0,1,0,0,0,0,7,3,0,0,0,0,0,1,1,0,0,0,0,1,0,1,1,0,4,14,0,0,2,0,0,0,0,12,0,0,1,0,0,0,1,0,2,0,2,1,0,0,1,0,0,1,0,0,0,0,0,0,1,4,0,8,0,0,0,0,0,0,4,0,0,0,0,Class_6
+45443,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,4,0,1,0,2,0,3,0,3,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,1,0,0,2,0,0,12,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,Class_6
+45444,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,7,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,18,0,0,0,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
+45445,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,18,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_6
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+45507,0,0,7,4,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,1,0,4,2,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_6
+45508,0,1,0,0,0,0,0,0,0,0,6,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,26,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+45509,0,0,13,4,0,0,0,0,0,1,9,0,0,2,1,2,0,1,0,0,1,4,2,0,0,1,6,0,0,0,0,0,0,1,3,0,1,0,0,1,0,0,0,1,0,6,0,0,0,0,0,0,2,34,1,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,1,0,0,0,0,Class_6
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+45512,0,0,3,2,0,0,0,0,0,0,8,0,0,1,5,3,0,1,0,0,1,7,0,0,0,2,0,1,1,0,0,0,0,0,2,0,0,1,0,0,0,2,0,3,0,4,5,0,3,0,1,3,0,10,0,0,0,0,0,1,2,3,0,0,3,5,1,1,0,2,0,0,0,1,0,0,0,0,0,3,0,5,0,1,0,0,0,0,3,1,0,0,0,Class_6
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+45514,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45515,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45516,0,1,6,4,0,0,0,1,0,0,11,3,0,0,0,6,0,0,0,0,0,2,0,47,0,2,3,1,0,1,0,8,0,2,4,0,0,0,2,0,0,0,0,0,0,0,0,7,0,0,0,1,0,13,1,0,0,0,1,0,1,0,0,2,0,0,13,0,0,0,0,0,0,12,2,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45517,0,1,0,2,0,0,0,2,4,0,1,0,1,0,0,0,0,0,0,1,0,3,3,5,0,0,0,0,2,0,0,5,1,0,2,1,1,3,4,1,0,2,1,4,0,0,0,3,2,1,0,0,1,3,0,0,1,0,0,9,0,4,0,1,0,0,6,0,0,7,2,1,0,0,1,1,0,0,0,1,1,0,1,0,5,1,1,0,0,1,1,0,0,Class_6
+45518,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
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+45521,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,0,2,0,1,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,1,2,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,7,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45522,0,0,4,5,0,0,0,0,0,1,8,0,0,0,0,3,0,0,0,0,2,2,0,0,0,2,2,4,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,2,0,2,0,0,6,2,0,1,0,10,3,0,1,0,0,1,0,3,1,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,8,5,3,1,3,0,0,0,0,1,1,0,1,0,Class_6
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+45524,2,0,0,0,0,0,0,1,0,2,0,0,0,2,1,1,0,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,1,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,2,1,0,0,0,0,0,Class_6
+45525,0,0,12,5,0,0,0,0,0,0,9,0,0,0,1,6,0,4,0,0,0,2,0,2,0,0,13,5,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,0,12,1,1,0,0,0,0,1,25,0,1,1,0,0,0,1,1,0,1,0,1,0,0,0,1,0,0,1,1,1,0,0,0,0,6,1,2,0,0,0,0,0,1,1,1,0,0,0,Class_6
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+45547,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,1,1,1,0,2,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,4,0,0,0,0,1,0,3,0,0,0,0,8,1,0,0,0,0,7,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_6
+45548,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_6
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+45554,0,0,10,4,0,0,1,0,0,0,16,1,0,1,0,1,0,0,0,0,0,2,0,13,0,2,7,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,0,0,0,1,1,1,0,13,0,0,1,1,0,1,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,7,0,0,0,0,0,1,1,1,2,0,0,0,0,Class_6
+45555,5,0,2,3,0,0,1,1,1,2,4,0,0,3,0,3,0,0,0,3,0,5,3,13,1,0,0,0,1,0,0,11,0,1,0,2,3,4,0,1,0,6,0,11,0,0,0,7,0,4,0,0,0,2,1,1,0,0,5,5,0,3,0,5,1,2,17,1,0,9,0,1,0,4,1,0,0,0,0,4,0,0,1,0,3,3,25,1,2,0,0,0,63,Class_6
+45556,0,1,15,5,1,0,0,0,0,0,11,0,1,0,0,2,0,1,0,0,1,1,0,2,0,3,4,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,2,0,2,0,0,0,0,0,0,0,15,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45557,0,0,13,10,0,0,0,0,0,1,8,1,2,1,0,5,0,1,1,0,0,3,0,0,0,2,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,3,0,1,0,0,0,1,0,28,2,0,0,0,1,0,1,1,0,0,0,3,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,1,1,0,Class_6
+45558,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45559,0,0,1,12,0,0,0,1,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,2,0,0,0,2,0,0,1,0,0,0,0,8,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+45560,0,0,3,8,0,0,0,2,0,0,1,0,0,0,2,1,1,3,0,1,2,2,3,8,1,3,0,2,1,2,1,0,1,1,2,1,1,3,0,1,1,0,1,1,0,3,0,2,1,0,14,3,0,7,1,1,14,2,0,1,10,2,2,0,0,1,7,0,0,0,1,1,5,0,1,1,0,1,2,10,0,11,1,0,0,1,1,2,7,0,0,0,0,Class_6
+45561,0,0,5,5,1,0,0,0,1,1,14,0,0,0,0,2,1,0,0,0,1,1,0,4,0,0,4,1,0,0,0,1,0,1,1,1,0,0,0,3,0,0,0,2,0,4,0,1,1,0,0,7,1,8,0,1,0,13,0,1,2,1,0,1,0,1,0,0,0,0,0,2,2,1,0,0,0,1,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_6
+45562,0,0,6,3,0,0,0,0,0,0,11,0,0,0,0,1,0,2,1,0,0,0,0,7,0,1,6,2,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,0,0,0,0,0,4,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_6
+45563,0,0,8,10,0,0,0,0,0,0,14,0,0,0,1,4,0,0,0,0,0,1,0,6,0,1,2,1,0,0,0,1,0,1,1,0,0,0,0,1,0,1,0,1,0,5,0,0,1,0,0,0,0,2,0,0,0,5,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45564,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,11,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,2,4,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,Class_6
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+45591,0,0,1,13,0,0,0,0,0,0,8,1,1,0,0,0,0,1,0,0,1,0,0,0,0,1,4,1,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_6
+45592,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,5,0,0,1,0,3,0,6,1,1,0,0,1,0,1,3,0,0,1,0,0,1,0,0,0,4,1,0,0,0,0,1,0,0,0,0,0,2,2,0,2,0,1,15,0,1,1,0,0,1,10,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,0,0,1,0,0,0,0,0,Class_6
+45593,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45594,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45595,1,1,4,4,1,0,0,0,1,0,5,0,0,1,0,3,1,1,0,0,0,1,0,0,0,1,1,2,0,0,1,1,0,1,4,1,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,5,0,12,0,0,0,0,1,1,2,3,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,0,4,0,3,2,0,0,0,0,1,0,0,0,0,1,Class_6
+45596,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_6
+45597,0,0,3,0,0,0,1,0,0,0,8,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,3,0,0,1,0,1,0,0,1,0,0,2,0,0,0,1,0,5,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_6
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+45600,0,0,3,4,0,0,0,0,0,0,8,0,1,0,0,3,0,1,1,3,2,2,0,0,0,0,2,1,0,0,0,2,0,0,1,0,0,2,0,0,0,3,0,12,0,2,0,1,7,0,0,1,1,7,0,0,0,2,0,1,0,4,0,0,1,0,0,1,1,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,1,2,Class_6
+45601,0,0,4,0,0,0,0,0,0,0,0,0,1,2,1,5,0,1,0,1,3,2,0,8,0,0,0,1,0,0,0,3,0,0,0,6,1,1,2,4,0,5,0,0,1,1,0,5,0,3,0,0,0,3,0,0,0,0,1,0,0,0,0,5,0,4,21,0,0,4,0,0,1,0,0,2,1,0,0,0,0,0,0,0,1,0,42,4,0,0,1,2,6,Class_6
+45602,0,0,14,1,0,0,0,0,0,0,12,1,0,0,2,4,0,0,0,0,0,1,0,7,0,1,5,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,2,0,0,15,0,0,0,0,0,0,0,13,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,0,0,0,0,0,0,0,1,3,0,1,1,0,Class_6
+45603,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,3,0,1,0,0,0,1,2,0,1,4,2,0,0,1,0,0,0,0,0,4,0,0,0,0,0,2,1,0,0,0,0,2,0,0,1,0,1,11,0,3,0,2,0,2,8,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,Class_6
+45604,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_6
+45605,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,4,2,2,0,0,0,0,0,4,0,0,0,0,0,2,0,0,0,6,1,0,0,1,0,1,1,0,1,1,2,4,1,0,0,0,0,15,0,1,0,0,0,2,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,0,Class_6
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+45624,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,2,3,0,0,2,0,0,2,9,0,0,0,0,0,0,0,5,0,0,0,1,1,1,0,0,0,0,1,0,0,0,1,5,1,0,0,0,1,2,0,0,0,0,0,1,0,2,0,3,0,0,5,1,2,0,0,0,1,0,1,8,0,3,4,0,0,0,16,0,0,0,1,0,0,2,0,0,0,Class_7
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+45626,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_7
+45627,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_7
+45628,1,0,0,1,0,0,0,1,1,0,0,0,1,3,1,1,2,1,0,2,0,2,0,4,5,0,0,0,0,0,0,11,0,0,1,1,1,2,3,1,1,0,0,2,1,0,0,8,2,10,0,0,0,5,2,1,0,0,0,0,0,1,0,21,0,2,12,1,2,12,0,0,6,4,0,0,0,0,0,0,0,0,0,0,2,13,3,6,1,0,0,0,0,Class_7
+45629,1,0,0,1,0,0,0,4,0,0,0,0,0,0,0,6,0,3,0,0,0,1,1,9,0,0,1,0,0,0,1,4,0,7,0,1,6,2,66,1,0,3,2,2,57,0,0,6,1,0,0,0,0,2,1,0,0,2,0,1,0,0,0,1,0,0,9,0,1,0,0,3,2,1,0,0,6,1,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_7
+45630,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_7
+45631,1,0,2,0,0,0,0,0,0,0,1,3,0,0,1,0,4,1,0,0,0,1,0,7,0,0,0,0,2,0,0,8,0,1,0,2,0,0,6,0,0,0,0,0,2,0,0,3,0,5,0,0,1,2,0,1,2,0,0,0,0,1,0,10,0,0,9,0,6,3,1,1,1,0,1,4,0,0,0,1,1,0,2,0,0,0,5,4,0,0,0,0,1,Class_7
+45632,2,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_7
+45633,0,0,0,3,0,0,0,0,2,0,1,0,0,0,0,1,4,3,0,1,0,2,1,9,5,1,0,0,0,0,0,5,1,0,0,0,0,1,0,1,0,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,1,0,4,5,0,1,1,0,1,1,1,0,5,0,0,0,0,0,0,1,0,1,20,2,0,0,0,0,0,0,Class_7
+45634,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,1,0,0,0,0,Class_7
+45635,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,4,16,6,0,0,0,0,0,0,9,0,2,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,2,0,2,3,0,0,1,2,1,1,0,0,2,0,0,0,0,0,0,1,0,4,36,0,2,2,0,0,0,0,Class_7
+45636,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,1,0,0,0,2,0,0,1,0,1,0,15,5,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_7
+45637,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_7
+45638,0,1,0,0,0,0,0,0,0,0,1,0,0,5,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,3,2,1,0,0,0,0,0,0,0,1,1,4,0,0,0,1,0,0,0,0,0,0,0,2,2,1,0,0,0,0,0,3,0,2,0,0,1,1,0,1,0,2,2,1,1,0,0,6,0,1,3,0,0,0,0,Class_7
+45639,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,5,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+45640,0,0,0,0,0,0,1,0,0,0,0,0,0,1,5,0,0,2,1,1,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,5,3,1,0,0,0,0,1,0,0,4,0,8,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,1,2,0,0,0,0,0,0,0,0,0,0,17,9,0,2,1,0,0,0,1,Class_7
+45641,0,0,0,0,0,0,0,4,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,3,4,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,1,1,0,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,8,1,1,1,0,0,0,0,Class_7
+45642,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,2,2,0,0,0,0,0,3,0,0,1,0,1,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,2,3,0,0,2,0,1,Class_7
+45643,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,7,0,0,0,2,0,1,2,1,0,0,0,0,0,0,0,3,0,0,1,0,0,5,1,2,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,4,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,Class_7
+45644,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,6,0,1,5,0,0,0,0,Class_7
+45645,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
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+45945,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,0,0,0,2,0,26,0,0,1,1,0,14,0,0,3,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,2,0,3,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_7
+45946,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+45947,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,Class_7
+45948,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,11,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,Class_7
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+45961,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,1,3,1,0,0,0,2,1,1,0,4,0,13,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,10,0,0,3,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_7
+45962,0,0,1,1,0,0,0,0,0,1,0,0,3,1,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,4,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,Class_7
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+45964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
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+46066,4,0,1,4,0,0,2,0,0,9,2,1,4,1,0,4,0,5,0,0,0,4,1,17,2,1,0,0,2,0,1,6,1,2,0,0,0,6,7,0,5,1,0,3,1,0,0,4,7,2,0,2,5,6,2,1,0,1,2,11,0,1,7,0,6,4,1,0,2,18,1,0,0,1,5,1,0,0,7,2,5,1,0,0,1,23,0,0,7,0,0,0,1,Class_7
+46067,5,4,0,1,0,0,2,1,0,0,1,0,0,4,1,1,6,1,2,8,0,2,1,6,2,1,0,0,0,0,0,11,1,0,1,0,0,4,10,0,0,0,2,2,3,0,0,0,0,15,0,0,1,3,1,1,1,0,1,0,0,3,0,1,0,1,9,1,7,9,2,1,3,0,2,9,1,0,0,0,1,0,30,0,0,1,2,0,1,5,12,1,0,Class_7
+46068,1,2,1,7,0,0,2,0,1,0,6,1,0,0,2,9,1,1,0,1,1,1,9,8,0,0,0,0,0,0,24,0,0,0,5,0,0,1,0,0,0,2,9,2,0,0,0,9,1,0,0,7,0,3,0,4,0,5,1,0,0,1,0,0,0,1,1,2,2,0,0,0,0,4,0,1,0,0,0,8,0,1,4,0,0,1,0,1,0,0,0,0,0,Class_7
+46069,0,2,0,0,0,0,2,0,0,1,0,0,0,1,0,0,1,0,0,5,0,0,0,5,0,0,0,0,0,0,0,4,0,1,0,0,1,0,3,0,0,0,0,2,4,0,0,2,0,1,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,5,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,25,0,1,1,0,0,0,1,6,1,1,Class_7
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+46071,2,1,0,1,1,2,0,0,0,0,3,1,0,1,0,1,0,0,0,0,0,0,0,9,2,0,0,0,0,0,0,2,0,4,0,1,4,3,17,1,0,1,0,1,0,0,0,4,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,8,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,1,Class_7
+46072,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,8,0,0,0,0,0,0,0,Class_7
+46073,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_7
+46074,0,0,0,0,0,0,0,1,0,0,2,0,0,4,0,0,1,0,0,1,0,0,0,9,2,0,0,0,0,0,0,7,0,2,0,1,3,0,29,0,0,0,0,0,17,0,1,0,0,2,0,0,0,0,3,0,1,0,0,1,0,0,0,3,0,1,8,0,0,0,2,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,1,1,0,0,2,0,0,Class_7
+46075,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,5,0,5,0,0,0,0,0,Class_7
+46076,0,0,1,0,0,0,1,1,0,13,3,0,0,1,0,1,0,2,1,2,0,0,0,5,1,0,0,0,0,2,0,0,2,0,0,0,6,5,3,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,1,0,1,0,1,0,1,2,2,1,1,2,0,0,0,1,1,0,0,1,1,3,0,0,0,1,2,1,1,0,0,0,0,0,Class_7
+46077,0,0,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,2,0,1,0,1,0,2,17,1,1,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,1,0,1,0,0,0,0,1,1,0,0,1,1,0,0,0,0,1,0,0,0,Class_7
+46078,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,2,2,0,0,0,1,1,0,0,0,1,0,0,2,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,2,1,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,Class_7
+46079,1,0,0,0,0,0,0,0,2,0,0,0,0,0,2,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,7,0,2,0,0,0,0,0,Class_7
+46080,1,0,1,0,0,0,4,3,0,0,1,0,4,7,0,3,4,2,2,0,0,0,0,21,0,0,0,0,1,0,0,5,0,1,0,2,1,1,1,0,1,0,1,0,10,0,0,8,1,1,0,1,1,1,0,0,2,0,0,0,0,1,1,2,2,0,13,0,3,4,0,0,0,0,0,11,0,2,1,0,0,0,0,0,0,0,3,0,1,0,1,0,1,Class_7
+46081,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,Class_7
+46082,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,3,1,0,1,0,1,6,8,0,0,0,0,0,0,0,3,0,1,0,0,0,0,2,0,0,1,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,0,0,0,0,0,0,0,Class_7
+46083,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_7
+46084,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,2,0,0,0,0,0,0,0,3,0,0,0,0,1,2,0,2,1,2,1,1,0,0,25,0,1,0,0,0,0,0,0,1,0,6,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,1,5,0,1,4,0,2,0,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_7
+46085,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,2,1,0,2,2,0,0,0,5,3,0,0,0,0,0,0,8,0,0,0,1,2,1,4,0,1,0,0,1,1,0,1,5,0,6,0,0,2,0,0,1,0,1,0,0,0,0,0,11,0,1,8,0,0,6,0,0,1,1,0,1,0,0,0,0,0,0,1,0,3,8,0,0,0,0,0,0,0,Class_7
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+46841,0,0,1,0,0,0,1,0,0,14,0,0,0,8,0,8,0,1,0,0,0,0,0,4,5,0,0,0,0,0,0,0,0,2,0,0,2,1,7,6,0,0,1,3,1,1,0,5,0,2,0,0,0,1,0,2,0,0,0,0,0,1,3,0,0,0,5,0,0,2,0,1,0,0,0,0,1,0,2,0,0,0,0,0,0,10,2,7,1,0,0,2,0,Class_7
+46842,3,0,0,1,0,0,8,0,0,1,4,0,0,0,0,0,1,0,0,3,0,1,5,2,0,0,0,0,0,0,0,2,0,0,0,3,0,1,0,0,1,1,0,1,0,0,10,2,0,0,0,0,1,2,1,0,0,0,0,1,0,0,0,1,0,7,2,0,5,5,1,0,0,0,0,9,0,13,0,0,0,0,5,0,0,4,1,0,0,0,0,0,1,Class_7
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+46844,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
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+46899,0,0,0,2,0,0,1,0,0,0,1,0,0,1,0,0,1,3,0,0,0,2,0,14,2,0,0,0,0,0,0,3,0,0,0,0,0,3,20,0,0,0,0,1,0,0,0,1,2,13,0,0,0,0,2,1,0,0,0,1,0,2,3,0,0,0,1,0,1,8,14,3,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,3,2,0,0,0,Class_7
+46900,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46901,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_7
+46902,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,3,0,0,0,0,0,4,0,2,0,5,0,0,0,1,0,0,0,1,0,1,1,0,0,4,0,0,0,0,0,7,1,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,2,0,17,2,0,3,0,0,0,0,0,3,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_7
+46903,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,31,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,Class_7
+46904,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,1,0,1,2,0,0,6,6,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,2,8,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,16,0,0,0,0,0,0,0,Class_7
+46905,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,4,1,1,2,0,1,0,5,4,0,0,0,0,0,0,7,0,0,0,3,0,1,2,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,1,1,1,0,0,0,0,2,0,6,0,0,7,0,4,0,1,1,0,0,0,1,0,0,0,0,0,0,2,0,1,1,3,1,0,0,0,1,2,Class_7
+46906,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,5,0,0,0,0,0,Class_7
+46907,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,1,4,0,0,0,0,2,0,0,0,0,0,0,0,21,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,1,0,0,0,0,0,Class_7
+46908,0,0,1,1,0,0,11,1,0,1,1,0,0,1,0,3,4,2,0,8,0,5,0,4,2,0,0,0,2,0,0,2,0,0,0,0,0,2,16,1,1,0,0,1,0,0,2,0,0,22,0,0,0,2,1,0,0,0,1,0,1,0,0,0,0,1,1,1,3,6,1,0,2,0,7,0,0,5,0,0,0,0,1,0,5,3,1,0,0,2,1,0,2,Class_7
+46909,0,0,0,0,0,0,0,1,1,0,0,0,1,2,1,1,2,0,0,2,0,2,0,4,4,0,0,0,0,0,1,12,0,0,1,1,1,2,3,1,1,1,0,2,1,0,0,9,1,10,0,0,0,9,2,1,0,0,0,0,0,1,0,17,0,2,11,1,2,16,0,0,6,4,0,0,0,0,0,0,0,0,0,0,2,14,4,5,1,0,0,0,0,Class_7
+46910,0,0,0,0,0,0,2,0,0,0,1,0,0,3,0,4,0,1,1,0,0,0,0,4,2,0,5,0,2,0,0,6,1,0,0,2,0,1,14,1,1,0,0,6,0,0,0,1,1,9,0,0,1,8,0,0,0,0,3,0,1,0,0,0,0,5,12,0,0,6,0,0,3,1,0,0,0,0,1,1,4,1,0,0,0,0,8,0,1,0,0,0,0,Class_7
+46911,0,0,0,5,0,0,1,0,1,1,3,0,1,0,0,1,2,4,0,2,0,0,0,14,2,2,0,0,0,0,0,7,0,1,0,2,0,0,3,0,0,1,0,1,1,0,1,2,5,1,1,1,1,5,1,0,8,0,0,0,0,0,0,6,1,1,9,2,1,0,4,0,0,1,0,0,0,2,0,0,1,0,2,0,1,3,0,0,1,0,2,1,0,Class_7
+46912,0,0,0,0,1,1,0,1,0,3,0,0,0,2,0,0,0,0,0,5,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,Class_7
+46913,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,7,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,Class_7
+46914,0,0,0,1,1,0,0,0,0,2,1,0,0,6,0,1,0,2,0,4,0,0,5,4,9,0,0,0,2,0,0,0,0,0,1,0,0,12,2,4,3,0,1,4,1,0,0,3,0,3,0,0,0,2,0,0,0,0,1,0,0,0,1,0,1,2,2,0,0,17,0,3,0,0,1,1,0,0,0,1,0,0,0,0,2,7,0,8,3,0,0,0,0,Class_7
+46915,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,2,0,0,0,1,1,4,1,0,0,0,0,0,1,2,0,0,0,0,0,0,11,2,0,1,0,0,0,0,0,2,1,3,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0,0,6,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,Class_7
+46916,0,2,2,0,0,0,0,1,0,0,2,0,0,1,0,4,3,6,1,4,0,4,0,5,0,0,0,0,3,0,0,7,0,0,1,1,0,2,4,3,1,1,0,0,3,0,2,3,3,3,0,0,0,2,2,1,3,1,1,0,0,1,0,15,0,1,7,0,4,1,0,2,1,0,0,0,0,0,0,1,0,0,2,0,0,1,16,4,0,0,0,1,1,Class_7
+46917,0,0,1,1,0,0,0,0,0,2,0,0,0,0,2,0,5,0,0,0,0,0,2,8,8,0,0,0,0,0,0,4,1,0,2,0,0,1,2,1,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,1,1,0,0,0,0,1,0,1,0,9,15,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,1,0,1,33,0,1,0,0,0,0,0,Class_7
+46918,0,0,1,2,0,0,1,0,1,2,0,0,0,1,0,0,8,2,0,2,0,0,8,13,7,0,1,1,0,0,0,5,0,2,1,1,2,3,1,0,0,0,0,5,0,0,0,0,0,0,0,0,0,4,1,1,0,0,0,0,0,3,0,5,0,5,15,0,1,5,3,3,3,0,0,6,2,0,0,0,0,0,1,0,6,45,4,5,1,0,0,0,0,Class_7
+46919,0,0,0,0,0,0,2,0,2,5,1,0,0,0,0,2,0,2,0,1,0,0,0,6,0,0,0,1,0,0,1,4,1,2,0,0,1,1,26,3,2,0,0,1,1,0,0,1,0,16,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,9,0,0,0,0,0,2,0,1,2,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,Class_7
+46920,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,2,3,0,1,0,1,5,0,0,0,0,2,0,3,0,0,0,0,0,12,0,0,0,0,0,1,0,0,0,0,1,0,0,9,0,1,0,0,0,0,0,0,0,0,0,0,0,0,10,1,1,0,1,0,0,0,0,Class_7
+46921,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,9,4,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,2,0,2,5,0,0,8,0,0,0,0,4,2,0,0,0,0,0,2,0,0,0,0,0,0,5,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46922,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,0,0,5,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,5,1,3,0,0,5,0,0,0,0,1,0,0,0,0,0,1,0,5,0,0,0,0,0,4,0,7,0,4,7,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,3,1,4,5,1,0,0,0,Class_7
+46923,0,0,0,0,0,0,0,3,7,0,1,0,0,0,1,4,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,2,2,0,1,3,0,15,2,0,0,2,0,18,3,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,3,1,0,0,1,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,Class_7
+46924,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,Class_7
+46925,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,6,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,2,0,0,0,1,0,Class_7
+46926,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,3,2,0,19,0,0,0,1,0,1,0,0,0,3,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_7
+46927,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,1,0,1,1,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,1,9,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_7
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+46937,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,3,4,1,0,0,0,0,0,1,1,1,0,0,1,0,0,2,0,0,2,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,14,0,0,0,0,0,2,1,1,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_7
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+46939,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46940,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,3,0,2,0,3,4,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,1,1,2,0,0,0,0,1,0,0,0,4,1,0,0,0,0,7,1,5,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,1,0,0,0,0,0,0,Class_7
+46941,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,1,3,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,Class_7
+46942,0,2,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,1,0,0,0,3,0,4,0,1,0,0,1,0,0,1,0,0,1,0,0,0,2,0,0,0,1,3,1,0,3,0,0,1,0,0,0,1,11,1,1,0,1,1,0,0,0,0,1,2,0,1,0,0,0,0,0,0,2,1,0,2,0,0,0,0,1,0,1,0,1,0,1,0,2,0,0,Class_7
+46943,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,2,0,0,3,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,1,1,0,0,Class_7
+46944,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,10,0,12,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,13,0,0,1,3,0,4,0,3,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,Class_7
+46945,0,7,0,0,0,0,2,1,0,0,0,0,1,0,0,3,0,0,0,6,0,2,0,2,1,1,0,0,0,0,0,2,0,0,3,0,1,1,0,1,0,0,0,0,0,0,0,0,3,2,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,2,0,0,2,1,1,3,0,0,0,0,0,2,0,0,0,0,11,0,1,0,0,0,0,4,1,0,2,Class_7
+46946,0,0,0,0,0,0,0,1,0,0,0,0,0,4,5,1,1,1,0,0,0,1,7,1,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,2,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,6,2,0,0,0,0,0,0,0,0,2,0,0,0,1,4,0,0,1,0,1,0,0,Class_7
+46947,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,8,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,Class_7
+46948,1,0,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,1,3,0,1,0,0,0,0,0,Class_7
+46949,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46950,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,6,0,0,0,0,1,4,8,10,0,1,0,0,0,0,6,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,1,0,0,0,0,1,0,2,0,1,6,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,1,23,1,0,0,0,0,0,0,Class_7
+46951,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,2,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46952,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,Class_7
+46953,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,Class_7
+46954,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,11,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_7
+46955,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46956,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,1,0,2,0,0,2,2,0,0,1,0,0,0,3,3,0,2,0,0,0,8,0,1,0,0,1,0,0,0,1,2,3,0,0,1,1,1,0,0,0,3,0,0,0,0,0,0,0,4,0,0,4,0,1,1,0,1,0,0,0,0,1,2,0,0,0,8,2,4,0,0,0,0,0,0,Class_7
+46957,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+46958,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,3,0,0,0,1,0,1,0,3,0,2,0,0,0,0,0,5,0,0,2,1,3,1,35,0,0,1,1,1,24,0,0,3,1,0,0,0,0,7,2,0,0,0,0,2,0,0,0,1,0,0,5,0,0,2,0,0,0,0,0,10,0,2,0,0,0,0,9,0,0,0,0,0,0,0,4,1,0,Class_7
+46959,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,4,0,0,0,0,3,0,0,0,0,1,5,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,1,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,Class_7
+46960,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,2,0,0,2,3,0,0,0,0,1,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,2,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,0,Class_7
+46961,2,0,0,2,0,0,1,0,0,6,2,0,0,13,0,0,1,0,0,2,0,1,0,9,2,0,0,1,2,0,0,2,4,5,1,0,0,2,4,0,0,0,0,1,0,0,0,13,0,8,0,0,1,2,1,0,0,0,0,0,0,0,1,0,1,0,9,0,0,12,1,1,1,0,0,1,0,0,0,0,0,0,1,0,4,12,0,3,0,0,0,0,0,Class_7
+46962,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_7
+46963,0,0,0,0,0,0,0,2,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,14,0,7,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,9,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_7
+46964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_7
+46965,0,1,1,0,0,0,2,0,0,1,0,0,1,0,1,8,2,0,0,4,0,5,0,9,2,0,0,0,0,0,0,3,0,0,0,2,0,6,1,0,0,0,0,5,2,0,0,8,0,2,0,0,0,5,0,2,0,0,0,2,0,0,0,2,1,0,10,0,1,4,2,2,0,0,1,3,0,1,0,1,0,0,8,0,0,2,0,0,0,2,7,1,3,Class_7
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+47001,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_7
+47002,4,0,0,2,0,0,1,2,2,19,1,0,0,9,1,5,0,2,1,1,0,4,0,2,8,0,0,1,5,0,0,0,1,2,1,1,3,10,0,4,1,0,2,9,0,0,0,4,0,15,0,0,0,5,1,0,0,1,1,0,0,2,3,4,1,3,0,0,1,25,0,2,0,1,0,0,0,1,7,0,3,0,2,0,10,19,2,10,3,1,0,0,0,Class_7
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+47004,0,1,2,0,0,0,0,1,2,0,1,0,0,0,0,4,2,6,2,4,0,4,0,2,1,0,0,0,2,0,0,5,0,0,2,2,0,1,4,1,1,0,0,0,3,0,0,1,3,3,0,0,0,3,2,0,4,0,0,0,0,1,0,9,0,0,10,0,4,2,1,2,0,0,0,1,0,0,0,1,0,0,2,0,1,3,16,2,0,0,0,1,1,Class_7
+47005,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,9,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,1,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,Class_7
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+47720,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,Class_7
+47721,0,0,0,0,0,0,0,2,0,0,1,0,0,7,2,6,2,1,0,1,0,2,0,4,0,0,0,0,0,0,0,2,0,1,1,0,2,2,24,1,0,3,1,1,19,0,0,1,1,0,0,0,1,1,0,0,1,1,0,2,0,1,0,0,1,2,6,0,0,1,1,0,0,2,0,1,0,0,0,0,0,0,1,0,2,0,4,2,2,0,1,0,2,Class_7
+47722,7,0,0,0,0,0,0,1,0,0,0,0,0,5,0,2,0,0,0,0,0,1,0,21,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,15,0,0,0,2,0,2,1,0,0,0,0,2,0,0,0,0,0,0,10,0,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,Class_7
+47723,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47724,1,1,0,1,0,0,4,1,0,0,3,0,0,3,0,4,0,1,0,0,1,2,1,27,0,0,0,0,0,1,0,8,2,1,3,1,1,3,5,1,0,1,0,0,8,0,0,3,1,0,0,0,0,3,0,0,2,0,0,2,0,0,0,1,0,1,10,0,2,1,0,1,2,1,4,13,0,10,3,1,0,0,5,0,1,1,4,1,1,0,1,1,1,Class_7
+47725,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,5,0,0,0,0,0,0,8,1,1,0,0,1,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,12,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,9,6,0,0,0,0,0,0,Class_7
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+47741,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,1,1,10,4,0,0,0,1,0,0,6,0,2,0,0,0,1,0,1,0,0,0,1,0,0,0,2,1,0,0,0,0,2,1,1,0,0,0,0,0,0,0,1,0,2,10,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,1,0,3,22,0,0,0,0,0,0,0,Class_7
+47742,0,1,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,1,0,0,0,1,0,6,2,0,0,0,0,1,0,4,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,6,0,0,0,0,2,1,1,0,0,0,2,1,0,3,0,3,0,4,13,0,0,3,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,4,2,0,0,0,1,0,0,Class_7
+47743,0,1,1,4,0,0,0,0,0,3,1,2,1,0,0,4,6,2,0,4,0,0,9,11,7,0,0,0,0,0,0,5,1,1,1,3,0,2,2,0,0,0,0,5,0,0,0,0,0,3,0,0,0,2,0,1,0,1,0,0,0,0,0,4,0,11,15,0,0,3,1,0,6,0,0,2,1,0,0,0,0,0,1,0,6,45,2,5,0,0,1,0,0,Class_7
+47744,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,2,2,1,0,0,0,0,4,2,0,0,0,0,0,0,7,0,0,0,1,1,0,3,2,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,1,1,0,0,1,0,0,0,10,0,1,7,0,1,0,0,1,0,0,0,2,0,0,0,1,0,0,2,0,0,2,6,1,0,0,0,0,1,Class_7
+47745,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,Class_7
+47746,0,0,2,0,0,0,0,0,2,0,0,1,2,0,0,0,2,1,0,2,0,0,2,11,1,0,0,0,1,0,0,4,0,1,1,2,0,6,2,0,0,2,0,0,0,1,0,2,1,0,0,0,0,0,1,0,0,0,1,0,0,5,0,3,1,7,27,0,0,4,1,1,2,1,0,5,1,0,0,1,0,0,0,0,3,22,2,1,0,1,0,0,0,Class_7
+47747,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,Class_7
+47748,0,3,0,2,0,0,0,0,0,0,0,0,2,0,0,3,0,2,0,0,0,1,0,2,2,0,3,0,0,3,0,1,0,0,6,0,3,1,1,0,0,1,0,1,0,0,0,2,5,0,0,0,0,6,2,33,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,4,0,0,1,0,0,0,0,Class_7
+47749,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_7
+47750,0,0,1,1,0,0,0,5,0,1,0,0,1,1,1,1,1,2,0,2,0,0,0,21,1,0,0,1,1,0,0,3,1,2,1,0,2,0,51,2,0,1,0,0,42,0,0,5,0,0,0,0,0,5,0,0,0,0,0,0,0,4,0,5,0,0,6,0,1,1,0,0,0,1,0,2,0,1,2,1,0,0,0,0,2,0,2,0,0,0,0,0,0,Class_7
+47751,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,4,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,2,1,0,1,0,0,0,0,1,0,0,1,0,0,0,2,0,1,1,0,0,0,0,0,Class_7
+47752,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,4,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,9,0,0,1,0,0,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,Class_7
+47753,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,2,0,0,0,0,2,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47754,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,5,0,1,0,0,1,0,6,0,0,1,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,3,1,0,0,0,0,4,Class_7
+47755,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,3,0,0,0,0,0,Class_7
+47756,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47757,0,3,0,2,0,0,3,0,0,1,0,0,0,0,1,5,1,4,0,10,0,5,4,1,0,1,0,0,1,0,2,8,0,3,4,1,0,13,3,7,2,1,11,4,1,2,0,1,5,1,0,0,0,4,5,5,0,2,0,1,0,2,3,2,1,3,12,0,0,4,0,1,0,0,1,1,1,2,0,6,0,0,6,0,0,0,1,0,2,2,0,0,0,Class_7
+47758,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,2,1,0,1,0,0,1,4,1,0,0,0,1,0,1,3,1,1,2,1,0,1,0,1,0,1,0,1,0,0,0,1,0,0,3,2,1,2,1,0,0,2,0,0,1,2,2,0,0,0,3,1,0,0,0,2,0,0,1,0,1,0,1,2,1,1,0,0,0,0,0,0,1,0,1,0,0,Class_7
+47759,0,3,0,0,0,2,5,2,0,0,0,0,0,0,2,1,1,1,1,3,0,0,0,12,1,0,1,0,0,0,0,9,0,0,1,2,1,0,21,1,0,1,0,0,18,0,0,7,0,2,0,0,0,0,1,0,0,1,0,1,0,1,0,2,0,2,13,0,1,3,0,0,0,0,1,8,0,2,0,0,0,0,23,0,1,1,1,0,0,3,5,0,0,Class_7
+47760,0,0,0,0,0,0,0,0,1,0,0,0,3,1,0,2,4,0,0,0,0,4,0,3,3,2,0,3,0,0,0,2,0,1,4,1,2,0,1,0,0,3,0,0,0,1,0,1,2,0,0,0,0,3,0,2,0,0,0,9,0,0,2,3,0,6,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,Class_7
+47761,5,20,0,0,1,0,1,2,0,1,3,1,1,2,1,2,4,5,0,5,0,1,4,12,0,1,0,0,0,1,2,12,1,2,8,1,3,10,11,10,1,2,15,3,5,1,0,5,1,1,0,2,0,5,3,0,2,7,1,0,0,3,1,4,1,4,17,0,4,6,0,2,1,12,0,2,0,4,0,4,1,1,22,0,3,0,2,1,3,31,0,1,0,Class_7
+47762,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,3,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,2,0,2,0,0,0,1,27,1,1,0,0,2,0,1,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,10,0,0,4,0,0,1,0,3,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_7
+47763,0,0,0,0,0,0,0,0,0,1,1,1,0,1,1,0,1,1,0,1,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,3,0,14,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,0,10,0,2,0,0,0,4,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_7
+47764,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,6,7,5,0,1,0,0,0,0,6,0,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,1,1,0,0,0,0,0,0,1,0,5,9,0,1,1,0,0,5,4,0,2,0,0,0,0,0,0,1,0,3,31,1,1,0,2,0,0,0,Class_7
+47765,0,0,2,4,1,0,2,0,1,4,0,0,0,2,0,2,6,1,0,0,0,2,10,10,5,0,1,0,0,0,0,5,0,1,2,1,0,2,0,0,0,0,0,4,0,0,0,3,2,3,0,0,0,5,0,1,1,0,0,0,0,4,0,4,0,12,16,0,1,7,0,0,6,0,0,6,1,0,3,0,1,1,1,0,2,34,3,2,1,1,1,0,0,Class_7
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+47767,0,0,0,0,0,0,0,1,0,3,0,0,0,2,0,2,0,1,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,1,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,3,0,0,0,0,1,0,0,0,1,0,0,0,0,0,6,2,0,8,0,0,0,0,0,Class_7
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+47787,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,1,0,1,4,11,6,0,0,0,0,0,0,5,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,6,17,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,1,36,0,1,0,0,0,0,0,Class_7
+47788,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,2,3,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,Class_7
+47789,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,0,0,0,2,9,5,0,0,0,0,0,0,5,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,28,1,0,0,0,0,0,0,Class_7
+47790,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,7,3,1,0,0,0,0,0,1,0,1,0,0,1,0,1,1,0,4,0,4,0,0,0,2,1,0,0,1,0,1,0,0,0,0,3,1,0,2,0,0,0,2,3,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,1,1,0,0,1,0,Class_7
+47791,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,2,0,0,1,0,0,3,4,1,0,0,0,0,0,0,8,0,0,0,1,0,0,12,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,14,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_7
+47792,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,0,7,3,0,0,0,0,0,0,4,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,6,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,1,9,0,1,0,0,0,0,0,Class_7
+47793,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,2,2,2,0,4,0,0,1,6,0,0,0,0,0,0,0,1,0,0,6,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,3,0,0,1,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,3,0,3,2,0,0,0,2,0,0,1,1,0,0,1,2,0,0,Class_7
+47794,0,1,0,0,0,0,0,3,1,0,0,0,1,6,9,1,0,0,0,0,0,0,0,22,7,0,0,0,0,0,0,2,2,4,0,0,1,0,59,2,0,0,0,0,45,0,0,6,0,3,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,12,0,0,1,4,0,1,0,2,3,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,1,0,Class_7
+47795,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,3,0,1,1,0,1,4,0,2,0,1,0,0,0,0,0,2,0,10,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_7
+47796,1,0,0,1,0,0,0,0,0,4,0,0,0,2,0,0,5,0,0,5,0,3,0,0,2,0,0,0,3,0,0,6,0,0,2,0,0,5,3,0,0,0,0,0,5,0,0,1,1,39,0,0,1,1,1,1,0,0,0,0,0,5,2,1,0,0,2,0,1,15,0,1,0,0,2,1,1,4,4,0,1,0,0,0,1,1,2,3,1,1,0,0,0,Class_7
+47797,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,Class_7
+47798,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,5,0,0,0,0,0,Class_7
+47799,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_7
+47800,1,0,1,1,0,0,0,1,2,0,2,0,0,1,0,0,0,1,0,0,0,1,0,33,1,0,0,0,0,0,0,3,0,1,0,0,0,1,1,0,1,1,0,0,3,0,0,3,1,0,0,0,0,1,0,0,4,1,0,0,0,0,0,1,0,0,7,3,1,0,6,0,1,1,1,1,0,0,1,0,0,0,9,0,0,0,0,0,0,1,5,0,0,Class_7
+47801,0,0,0,0,0,0,0,3,2,0,0,0,0,2,1,1,0,1,0,2,0,1,0,11,0,0,0,0,0,0,0,2,1,1,0,0,2,2,50,2,0,0,1,1,42,1,0,2,0,1,0,0,0,3,0,0,1,0,0,0,0,5,0,1,0,0,3,0,0,6,4,0,0,2,0,2,0,0,1,1,0,0,0,0,1,0,2,0,1,0,2,1,1,Class_7
+47802,0,0,0,0,0,0,0,8,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,9,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,0,1,Class_7
+47803,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_7
+47804,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,1,0,1,1,2,1,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,Class_7
+47805,1,2,2,0,0,0,5,0,2,4,1,0,1,2,1,5,4,0,1,13,0,1,16,25,0,0,0,0,0,0,0,3,1,0,3,1,1,7,1,0,0,1,1,9,6,1,0,7,5,6,2,0,2,9,2,0,0,3,0,2,0,5,0,6,0,0,34,0,4,11,0,3,1,0,3,10,0,1,1,1,4,0,16,0,1,0,11,4,2,2,0,0,0,Class_7
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+47824,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,3,0,0,0,0,0,0,14,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47825,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,Class_7
+47826,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,5,2,0,0,1,0,6,0,4,1,0,0,0,0,0,0,5,0,1,0,0,0,4,7,0,0,5,1,5,0,0,0,3,0,2,0,0,0,5,0,0,0,0,0,14,0,2,0,1,3,5,6,0,0,2,3,0,0,3,0,0,0,0,0,1,0,0,0,0,2,0,5,0,0,0,0,0,0,Class_7
+47827,8,1,2,1,0,0,1,1,0,1,4,0,0,0,1,5,1,1,0,19,0,7,3,28,1,0,0,0,0,0,0,9,1,0,5,1,2,7,2,1,0,1,3,4,1,0,0,5,4,3,0,0,1,11,3,0,1,6,0,0,0,1,3,5,1,3,7,0,5,9,0,1,6,0,6,10,1,14,0,1,0,0,30,0,2,0,7,0,0,3,0,1,1,Class_7
+47828,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47829,2,1,0,0,0,0,8,0,0,0,1,0,0,0,0,0,1,0,1,5,0,0,2,4,0,0,0,0,0,0,0,4,0,1,0,2,0,0,1,0,1,1,0,2,0,0,1,8,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,2,5,1,3,0,0,1,1,0,0,5,0,2,0,0,0,0,6,0,1,0,0,1,0,0,5,0,1,Class_7
+47830,8,0,0,3,0,0,1,0,9,6,1,0,0,9,0,4,0,2,0,3,0,1,2,3,3,0,0,0,1,0,0,3,0,0,3,0,3,3,1,3,0,0,2,1,0,0,0,10,1,37,0,0,3,8,0,0,0,1,0,1,0,1,2,4,1,2,4,0,1,23,1,2,0,2,1,1,0,0,7,3,1,1,1,0,20,9,1,12,5,2,0,0,1,Class_7
+47831,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,6,0,0,0,0,1,0,0,3,0,0,0,0,0,2,16,0,1,0,0,0,0,0,0,1,0,5,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_7
+47832,0,0,0,1,0,0,1,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,3,1,0,0,0,0,1,14,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47833,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,1,0,0,0,6,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,Class_7
+47834,0,0,0,3,0,0,0,0,0,1,2,0,0,0,0,2,1,0,0,1,1,1,0,5,0,0,0,0,0,0,0,0,0,2,0,0,0,2,21,0,1,0,0,2,0,0,0,1,1,3,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,Class_7
+47835,1,0,0,1,1,0,3,0,0,2,0,0,0,2,1,4,2,1,0,2,0,2,1,3,4,0,0,0,0,0,0,3,0,4,1,0,1,4,38,2,1,0,0,3,0,0,1,0,0,27,0,0,3,4,1,1,0,0,0,0,0,0,2,2,0,0,9,0,0,10,0,1,1,0,1,0,0,1,0,6,1,0,0,0,7,1,1,0,1,0,0,0,0,Class_7
+47836,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,7,0,0,0,0,0,3,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,Class_7
+47837,0,0,1,1,0,0,2,0,0,4,2,2,0,3,0,7,3,1,0,6,1,8,6,4,2,0,0,1,2,0,0,2,3,0,1,1,3,13,46,2,2,1,1,5,5,0,1,2,4,18,0,0,2,8,1,0,0,1,0,0,0,3,0,1,6,1,2,0,0,19,0,2,1,2,0,0,0,0,0,0,4,1,0,0,5,1,7,0,3,3,0,0,1,Class_7
+47838,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,4,2,1,4,1,0,0,0,0,1,0,0,2,1,0,0,2,0,0,0,3,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,3,0,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_7
+47839,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,1,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,2,0,0,0,0,0,8,0,0,1,0,2,0,0,0,1,0,0,0,0,3,0,0,0,0,0,5,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0,3,4,1,0,0,0,0,1,Class_7
+47840,0,0,0,0,0,0,3,2,0,0,2,0,0,3,0,0,0,0,1,0,0,0,0,21,0,0,0,0,0,2,0,6,1,0,0,0,0,0,4,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,9,0,1,0,0,0,0,0,1,7,0,9,2,0,0,0,3,0,1,1,1,0,0,0,0,0,0,Class_7
+47841,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,3,1,3,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,8,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,6,0,0,1,0,0,0,0,Class_7
+47842,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,1,0,0,0,0,1,0,1,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,Class_7
+47843,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_7
+47844,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,Class_7
+47845,0,1,1,1,0,0,3,0,0,0,0,1,0,1,0,2,3,2,0,2,0,0,2,24,2,0,0,0,0,0,0,4,1,2,0,0,2,2,22,1,0,0,0,3,3,0,0,0,0,2,0,0,0,5,1,1,0,0,0,1,0,0,0,2,1,2,1,0,2,2,20,1,0,0,2,4,0,0,0,0,2,0,0,0,0,2,0,0,0,1,1,0,0,Class_7
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+47946,0,0,4,0,1,0,2,0,0,3,1,0,0,0,0,2,3,3,0,5,0,1,0,20,5,0,0,0,1,0,0,7,0,2,0,3,0,1,3,1,0,0,0,2,6,0,0,0,1,5,0,0,1,6,5,0,0,1,0,0,0,2,0,7,0,5,22,1,5,2,0,2,2,0,5,5,0,0,0,0,1,0,0,0,7,54,3,4,2,0,0,0,1,Class_7
+47947,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,1,0,14,1,0,0,0,0,5,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_7
+47948,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,Class_7
+47949,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
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+47955,0,0,1,0,0,0,0,3,7,0,1,0,0,0,2,4,0,0,0,0,3,0,0,1,0,0,0,0,0,0,1,0,2,2,0,1,3,0,15,2,0,0,2,0,17,2,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,3,1,0,0,1,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,Class_7
+47956,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,Class_7
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+47958,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,Class_7
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+47960,0,0,1,0,0,1,0,0,0,1,3,0,0,3,0,3,7,1,0,2,0,2,6,11,9,0,0,0,0,0,2,8,3,0,1,1,0,0,3,3,0,0,0,1,0,0,0,4,0,4,0,0,1,1,1,0,1,0,1,0,0,8,1,3,3,11,10,0,2,6,1,1,0,0,0,0,2,1,0,0,0,0,0,0,0,28,0,2,1,1,0,0,0,Class_7
+47961,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,Class_7
+47962,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,0,0,Class_7
+47963,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+47964,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,Class_7
+47965,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,2,1,0,0,0,1,0,0,2,0,0,0,0,0,1,4,0,0,0,0,0,1,0,0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_7
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+48002,1,0,0,0,0,0,0,0,0,2,0,0,0,1,1,5,2,3,1,1,0,2,0,2,4,0,0,0,0,0,0,2,1,2,0,0,0,3,22,2,0,0,0,2,0,0,1,4,2,20,0,0,3,1,1,1,0,0,0,0,0,0,1,1,0,1,4,0,1,1,1,0,2,0,1,3,0,1,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,Class_7
+48003,11,0,4,1,1,0,4,2,0,3,1,0,4,2,1,3,1,0,0,2,0,3,0,7,5,0,0,0,1,0,0,4,4,1,0,2,0,6,4,3,0,2,1,9,0,3,0,0,0,4,0,0,1,14,2,0,0,1,3,0,0,1,0,2,0,3,11,0,3,12,3,6,1,0,4,0,0,0,0,0,0,1,0,0,8,33,1,6,2,0,1,0,0,Class_7
+48004,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,3,0,0,0,0,0,1,0,7,1,0,0,0,0,0,1,2,0,0,1,0,1,2,15,1,0,0,0,2,0,0,0,1,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,2,2,0,0,4,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_7
+48005,0,0,0,1,0,0,0,5,0,0,3,0,3,4,1,8,2,8,0,0,0,3,0,13,1,0,0,0,1,0,0,3,4,1,1,0,3,0,61,2,0,2,1,3,43,0,0,4,0,2,0,0,0,5,7,0,1,0,0,1,0,4,0,0,1,1,8,0,0,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,2,0,Class_7
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+48044,0,0,0,0,0,0,0,1,1,0,2,0,0,0,2,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,1,2,0,0,0,0,4,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,2,0,0,6,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,Class_7
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+48082,0,0,0,2,0,0,1,0,0,0,2,0,0,1,1,2,0,0,0,0,0,3,0,4,1,2,0,0,0,0,0,3,0,4,0,1,0,6,25,0,0,0,0,1,0,0,0,0,0,16,0,0,1,2,0,1,0,0,0,0,0,2,0,1,0,3,7,0,0,3,0,0,3,0,1,0,1,1,0,2,0,0,0,0,2,1,1,0,2,0,0,0,0,Class_7
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+48242,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,2,1,0,0,2,1,3,0,0,0,1,0,2,0,0,0,4,0,1,0,0,1,1,0,0,0,0,1,2,0,1,0,3,0,1,8,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,2,11,0,6,0,0,0,0,1,Class_7
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+48429,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,1,0,0,0,0,Class_7
+48430,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,4,4,1,0,0,1,1,1,0,0,1,0,10,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,1,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,Class_7
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+48432,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,1,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,1,3,0,33,0,0,0,0,0,1,0,0,0,0,5,1,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,7,0,Class_7
+48433,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,0,0,2,0,11,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,2,1,0,1,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_7
+48434,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_7
+48435,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,3,1,3,0,2,0,0,0,7,1,0,0,0,0,0,0,3,0,0,1,1,0,0,4,0,0,1,0,0,1,0,0,3,1,1,0,0,0,3,0,0,0,0,0,2,0,0,0,3,0,4,12,0,1,0,0,0,0,0,1,2,0,1,0,0,0,0,9,0,1,2,0,0,0,0,4,0,0,Class_7
+48436,0,0,0,0,1,1,0,1,0,7,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_7
+48437,0,1,0,0,0,0,1,3,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,3,1,1,0,0,0,0,1,2,0,1,0,0,0,3,20,1,0,0,0,0,18,0,0,1,0,4,0,0,0,0,0,0,0,1,0,0,0,1,0,7,0,0,1,0,0,1,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,Class_7
+48438,0,1,5,2,0,0,3,0,0,0,1,0,2,0,0,5,0,2,1,3,1,10,14,7,4,1,0,1,3,0,0,6,1,2,5,2,2,10,3,2,1,3,5,17,0,1,1,2,8,3,0,0,1,12,1,0,4,0,0,6,2,6,1,0,14,5,5,0,0,10,1,0,0,0,0,2,0,0,0,2,3,0,0,0,3,0,7,0,3,1,0,1,0,Class_7
+48439,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,Class_7
+48440,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,2,10,7,0,0,0,0,0,0,6,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,4,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,25,0,0,0,0,0,0,0,Class_7
+48441,1,0,2,3,1,0,3,0,2,7,0,0,1,0,0,3,1,3,1,5,1,3,2,1,0,0,0,0,2,0,1,3,0,0,0,3,0,25,5,6,4,2,0,3,0,0,0,3,4,48,3,0,1,9,2,0,0,0,2,2,0,3,3,0,1,0,2,0,3,18,4,1,2,0,1,0,0,0,10,3,13,0,0,0,5,7,4,2,5,2,0,0,0,Class_7
+48442,0,0,0,0,0,0,0,6,2,1,1,0,1,3,1,1,0,0,1,1,0,1,0,9,0,0,0,0,0,0,0,5,0,2,0,0,3,0,41,0,0,0,1,0,34,0,0,5,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,3,1,1,6,0,0,1,0,1,1,2,0,2,0,0,2,0,0,0,0,0,2,0,4,0,0,0,2,0,0,Class_7
+48443,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,2,0,3,0,1,0,0,0,0,0,2,0,11,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,Class_7
+48444,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,Class_7
+48445,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,1,3,1,0,3,0,1,0,5,0,1,0,0,0,0,0,0,0,0,0,1,1,0,3,0,0,0,0,1,0,0,0,2,0,1,0,0,0,4,1,0,1,0,4,0,0,0,0,1,0,8,2,2,0,2,1,1,0,0,0,0,1,0,0,0,1,0,1,0,0,1,3,1,0,0,1,0,0,Class_7
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+48747,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,2,0,0,0,0,0,0,3,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,0,0,0,2,11,0,0,3,0,0,0,1,0,0,5,0,0,0,0,0,0,1,0,1,1,0,0,1,2,0,Class_8
+48748,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,20,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,Class_8
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+48755,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+48758,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,4,11,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+48759,1,15,0,0,0,0,0,0,0,0,5,2,12,0,0,11,0,1,0,2,0,5,4,2,1,1,0,1,0,0,1,0,0,0,4,1,0,15,4,7,3,5,0,6,1,0,0,2,2,0,0,0,1,15,1,0,1,19,0,0,0,6,0,3,0,3,8,0,7,3,0,1,0,0,0,18,1,1,0,1,0,1,7,0,0,0,1,0,2,13,1,1,0,Class_8
+48760,0,2,2,4,0,0,0,0,0,0,0,0,0,0,0,10,0,14,0,2,0,6,0,0,0,2,0,0,0,0,10,0,0,0,70,4,0,4,0,2,0,4,0,4,0,0,0,0,10,0,0,0,0,10,4,0,4,0,0,0,0,2,0,2,0,6,0,0,0,8,0,0,6,2,2,0,2,0,0,0,0,2,0,0,4,0,0,2,12,2,0,0,0,Class_8
+48761,0,7,1,0,0,0,3,0,0,0,4,0,10,1,2,3,0,2,3,5,0,3,0,17,1,1,0,0,0,0,1,4,0,0,2,2,0,8,2,1,0,0,0,0,1,0,0,1,2,0,0,0,0,5,1,0,1,45,0,0,0,5,1,2,0,1,14,0,12,1,0,1,4,5,1,28,0,0,0,1,0,0,4,0,0,0,1,1,3,17,0,0,0,Class_8
+48762,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,8,0,2,0,0,0,2,0,4,0,2,0,2,0,0,0,0,0,0,36,0,0,4,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_8
+48763,0,0,0,0,0,0,1,0,0,9,4,0,0,1,0,1,0,1,5,1,0,0,5,25,0,0,0,0,0,0,0,3,0,1,2,0,2,4,2,0,0,0,0,1,1,0,0,1,0,4,0,1,0,1,0,0,0,1,0,0,0,1,0,2,1,0,9,0,27,3,0,0,0,1,0,35,0,2,4,0,0,0,1,0,0,0,0,0,3,7,0,0,0,Class_8
+48764,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,2,3,0,0,0,1,1,0,0,0,1,1,0,0,2,0,0,0,0,0,3,0,0,2,0,1,0,0,0,1,3,1,0,0,0,0,1,0,7,0,0,1,0,1,13,1,1,0,1,0,0,1,0,0,0,2,0,1,0,0,1,1,0,0,0,4,0,0,4,0,Class_8
+48765,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,12,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,1,1,0,1,14,0,0,1,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,Class_8
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+48805,0,4,0,1,0,0,1,3,0,0,0,0,1,1,0,0,1,0,1,5,0,3,0,23,1,0,0,0,0,0,0,0,0,0,3,1,2,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,2,6,0,2,0,1,0,2,0,1,0,0,40,3,0,2,0,2,1,4,0,0,0,0,0,0,2,0,1,0,0,0,0,15,3,0,1,Class_8
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+48962,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,2,1,0,0,0,0,1,0,1,1,1,0,0,0,2,1,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,2,10,0,3,1,1,0,0,0,0,0,1,3,0,2,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
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+49039,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,3,16,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+49040,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,2,0,4,0,1,0,2,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,1,0,0,0,0,3,2,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,2,2,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+49041,0,0,4,0,0,0,0,1,1,1,1,0,0,2,0,6,1,0,0,1,0,3,1,2,1,4,2,0,1,0,0,0,2,2,0,0,0,5,1,2,1,0,0,2,0,0,17,1,3,0,0,0,1,6,1,0,0,0,0,0,0,13,1,0,0,7,4,12,0,0,0,0,1,0,1,0,0,1,0,3,1,0,1,0,12,0,1,0,17,0,0,1,0,Class_8
+49042,0,0,1,2,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,3,0,0,3,1,1,0,1,0,0,0,7,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+49043,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,10,0,0,0,0,1,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,9,0,2,0,0,0,1,0,0,5,0,0,0,0,0,0,18,0,0,0,0,0,0,1,0,0,0,Class_8
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+49113,1,1,1,1,0,0,4,6,0,0,2,1,0,2,0,0,0,0,2,4,0,2,0,31,0,0,0,0,0,1,0,4,2,0,1,1,1,4,0,0,8,0,0,2,2,0,0,7,1,1,0,0,0,1,1,0,1,46,0,0,0,1,3,1,0,2,23,0,35,1,0,0,5,1,1,39,0,2,0,0,0,0,1,0,0,2,0,0,0,3,6,0,0,Class_8
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+49115,0,0,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,4,5,0,2,6,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
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+49125,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,2,2,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,Class_8
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+49557,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,3,0,0,0,3,0,1,0,0,0,0,0,0,0,0,4,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,5,0,1,0,10,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_8
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+49560,0,0,0,0,0,0,0,2,5,2,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,2,0,0,1,0,0,1,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,10,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_8
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+49562,1,0,0,1,1,0,0,5,1,1,1,0,4,0,0,3,0,6,0,0,0,2,0,6,1,0,4,3,0,0,4,5,0,1,0,2,1,0,0,0,2,3,0,1,5,1,0,5,1,0,0,0,0,6,1,0,0,0,1,2,0,12,0,1,2,2,5,9,0,2,0,1,0,0,1,1,0,0,0,2,0,1,0,3,0,0,0,0,4,0,0,5,0,Class_8
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+49565,0,1,1,0,0,0,0,1,0,0,0,0,1,2,0,2,2,0,5,1,0,0,1,15,1,0,0,0,1,0,1,3,0,0,0,1,1,3,3,1,1,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,7,0,49,0,0,1,0,2,1,4,0,0,2,0,0,0,1,0,0,0,1,3,0,8,0,0,0,Class_8
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+49592,0,0,0,1,0,0,0,2,0,0,1,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,2,3,0,2,0,0,0,0,0,0,0,0,0,22,0,0,0,1,0,38,0,9,4,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+49593,2,5,1,0,1,0,2,0,0,0,2,1,1,6,0,4,7,5,0,8,0,8,1,31,3,0,1,0,0,0,0,3,2,0,3,2,0,2,0,1,0,3,0,0,0,0,0,4,3,0,1,0,0,13,2,0,8,3,0,1,0,1,2,4,1,5,8,0,1,2,0,0,0,1,0,7,0,5,3,0,3,0,2,0,0,0,0,14,1,2,1,0,1,Class_8
+49594,0,3,1,1,0,0,3,0,1,0,1,0,1,1,0,4,0,0,51,0,0,0,1,8,0,0,0,0,0,1,0,2,1,0,7,0,0,12,0,0,0,0,0,2,0,1,0,1,1,4,0,0,0,5,0,0,0,4,0,0,0,3,0,0,0,2,0,0,0,6,0,1,3,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,Class_8
+49595,0,0,0,0,0,0,1,1,0,14,0,1,2,0,0,1,0,0,0,3,0,2,0,15,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,7,6,0,3,0,0,2,1,3,0,0,2,0,0,0,1,1,0,2,3,13,11,0,4,6,0,0,2,0,0,0,2,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,Class_8
+49596,0,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49597,26,2,1,0,1,0,8,1,0,0,1,1,2,2,0,1,0,0,0,2,0,1,0,3,3,0,0,0,1,0,0,4,0,0,0,4,1,1,0,2,0,1,0,6,0,0,1,4,2,5,0,0,6,6,5,0,0,0,1,1,0,2,0,0,2,1,12,0,0,6,0,0,1,6,1,19,0,39,0,2,1,0,0,1,0,1,0,0,1,0,2,0,0,Class_8
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+49599,0,1,2,1,0,0,2,1,4,0,0,3,10,0,0,3,6,0,2,1,0,3,0,29,0,1,0,0,0,0,0,12,0,0,2,7,5,14,2,0,2,0,0,0,0,0,0,4,3,0,0,0,0,3,0,0,0,10,0,4,0,1,0,2,0,0,37,0,3,2,0,1,0,0,0,55,0,0,2,1,0,1,1,0,0,0,0,0,1,3,0,0,0,Class_8
+49600,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,35,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,Class_8
+49601,0,0,0,0,2,0,2,0,0,2,0,0,0,1,0,2,0,1,0,2,0,2,1,5,0,0,0,0,1,0,0,3,1,0,0,0,1,1,0,0,0,0,0,5,0,0,7,1,1,2,1,0,0,1,4,0,0,0,0,0,0,1,0,0,2,1,7,0,1,2,1,0,0,0,0,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49602,0,14,2,10,0,0,0,0,0,1,4,0,2,2,0,3,1,25,4,4,0,13,0,9,4,7,0,3,0,0,12,0,2,0,36,2,0,9,2,0,0,0,1,3,0,0,0,1,12,0,0,5,0,15,0,1,0,12,0,0,2,0,2,0,0,4,2,0,11,3,0,3,0,0,0,5,0,2,0,10,2,0,2,0,0,0,0,0,8,2,0,4,0,Class_8
+49603,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,14,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,3,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,0,0,Class_8
+49604,1,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,9,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,1,0,0,1,1,1,10,0,2,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,Class_8
+49605,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,4,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+49631,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,2,3,5,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_8
+49632,0,0,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,4,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,1,1,5,0,0,3,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+49633,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,26,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+49634,0,1,0,0,0,0,0,1,0,0,0,0,11,1,0,1,0,1,1,4,0,0,0,5,0,0,0,0,0,1,0,5,0,1,0,1,1,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,2,1,0,10,0,1,0,1,0,7,1,0,7,0,12,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,Class_8
+49635,0,0,0,1,0,0,0,2,0,0,0,2,1,0,0,0,0,1,90,2,0,2,8,6,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,3,0,1,0,0,0,0,0,0,0,3,0,1,0,0,1,0,0,0,4,0,25,0,0,0,10,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,7,0,0,2,Class_8
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+49639,0,3,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,1,0,6,0,0,0,0,0,0,0,0,1,0,1,0,0,4,0,0,0,0,0,2,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,19,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,4,0,0,4,30,0,0,0,Class_8
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+49644,1,2,2,0,0,0,1,0,0,0,1,2,2,0,0,1,0,2,8,2,0,3,0,48,0,0,1,0,0,1,1,11,0,0,1,3,0,14,2,1,0,0,0,0,2,0,0,10,0,2,1,0,0,7,1,0,0,20,0,0,0,1,1,0,1,0,27,0,29,0,0,1,5,4,3,102,0,1,1,2,1,0,8,0,0,3,0,3,0,31,0,0,2,Class_8
+49645,0,21,4,1,0,0,0,1,1,0,1,0,0,0,0,4,3,0,0,7,0,5,2,10,0,1,1,1,0,0,0,2,0,1,4,0,1,2,1,0,0,1,0,1,1,1,0,1,2,0,11,2,0,2,1,4,0,15,0,0,0,0,0,0,0,2,8,0,5,1,0,0,0,2,0,1,0,1,3,32,0,1,14,0,0,0,2,0,2,9,0,0,0,Class_8
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+49750,0,1,0,1,0,0,1,2,0,0,0,0,1,2,0,2,3,0,9,1,0,2,0,14,2,0,0,0,0,2,0,1,0,0,0,0,1,5,5,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,2,5,0,37,2,1,0,0,1,2,1,0,0,0,1,0,0,1,0,0,0,1,3,2,2,1,0,0,Class_8
+49751,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
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+49761,1,1,0,1,0,1,14,0,0,5,2,0,0,0,0,0,4,2,1,12,0,3,0,10,0,0,0,0,4,0,2,1,0,0,0,6,2,3,3,0,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,1,1,0,0,0,1,0,3,2,1,2,0,13,8,1,0,1,2,1,16,0,4,0,0,0,0,1,0,3,0,2,3,0,3,0,0,0,Class_8
+49762,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,2,4,0,2,0,3,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,1,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,1,7,0,1,0,Class_8
+49763,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,3,0,0,2,2,0,2,0,2,0,0,0,0,0,0,0,0,1,0,0,1,1,3,2,1,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,16,0,0,0,2,0,2,0,1,4,0,8,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,0,Class_8
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+49828,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
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+49830,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49831,0,4,0,0,0,0,3,0,0,2,2,0,1,0,1,1,1,0,0,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,0,1,2,1,0,6,3,0,0,0,1,0,4,0,0,0,1,0,0,1,0,1,0,0,0,0,10,0,0,2,Class_8
+49832,0,0,0,0,0,0,0,2,3,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,1,0,0,2,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,2,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
+49833,2,0,0,0,0,0,2,1,1,1,1,0,10,3,0,2,0,2,3,1,0,1,2,12,1,0,0,0,1,0,0,2,0,0,1,1,0,8,3,1,0,0,0,1,0,0,0,2,2,1,0,1,0,2,1,0,1,14,0,0,0,1,0,1,0,1,6,0,2,3,0,0,1,0,1,2,1,1,0,5,0,0,0,0,1,0,1,0,6,2,0,1,0,Class_8
+49834,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,1,1,0,3,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,2,0,0,0,16,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49835,1,1,0,0,0,1,1,0,0,0,2,0,0,0,0,2,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,4,1,0,0,25,0,0,0,3,0,0,0,1,3,0,4,0,0,0,0,1,0,1,0,2,0,1,0,0,0,0,0,0,0,0,1,9,2,0,0,Class_8
+49836,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,2,0,0,0,0,0,0,1,1,1,0,0,0,0,3,1,0,0,0,0,0,0,5,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,1,0,2,0,1,3,0,2,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,Class_8
+49837,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49838,0,3,0,0,1,0,0,2,0,0,0,0,1,0,0,0,3,3,0,1,0,0,0,3,0,2,0,0,2,0,0,2,0,1,5,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,15,0,0,0,2,0,1,0,1,11,0,8,0,0,1,0,0,1,6,0,0,0,0,0,0,5,0,0,0,0,0,0,3,1,3,0,Class_8
+49839,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,2,0,0,2,0,0,0,10,1,0,0,0,0,0,0,2,0,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,14,0,2,0,1,0,0,0,2,0,0,1,0,0,0,0,1,0,0,1,0,0,0,11,0,0,0,Class_8
+49840,1,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,13,0,5,0,0,0,0,0,2,3,0,0,1,0,0,0,54,0,0,1,0,0,0,1,0,0,0,Class_8
+49841,0,0,0,0,0,0,0,2,0,0,1,4,0,0,0,2,1,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,1,3,0,0,0,1,0,0,11,0,12,7,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+49842,2,2,2,4,0,0,2,0,0,0,2,0,0,0,0,12,0,2,4,4,0,4,0,4,2,8,0,2,0,0,2,0,0,0,18,2,0,0,0,0,0,10,2,10,0,0,0,0,34,0,0,0,0,30,0,0,2,0,0,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_8
+49843,0,0,1,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,1,0,11,0,0,0,1,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,4,0,0,0,0,Class_8
+49844,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,4,3,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,18,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,Class_8
+49845,3,0,0,0,0,0,0,3,0,0,0,0,0,1,1,1,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,1,0,0,0,0,1,0,7,0,0,0,1,4,4,1,0,4,0,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_8
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+49868,1,7,1,2,0,0,1,1,0,2,0,1,0,1,0,0,1,1,3,11,0,1,2,22,1,0,0,0,1,0,0,0,1,0,3,1,3,1,4,0,0,1,1,0,3,0,0,2,1,1,0,3,0,2,3,0,1,65,0,1,0,3,0,4,0,1,4,0,19,1,1,0,7,3,2,8,0,1,0,1,0,0,13,0,0,4,1,0,1,9,2,0,1,Class_8
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+49870,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,1,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,2,0,0,1,0,2,0,1,6,0,6,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,Class_8
+49871,0,0,2,1,2,0,0,0,0,1,0,1,1,0,0,4,0,1,0,1,0,2,0,3,2,0,0,0,0,2,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,1,0,0,0,5,0,0,0,1,2,19,0,1,0,1,0,3,0,0,0,1,0,9,0,0,0,0,0,0,1,0,1,0,0,3,0,Class_8
+49872,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,2,1,0,0,0,1,1,0,3,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,16,0,1,2,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+49873,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,9,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+49874,0,4,0,0,0,0,0,4,0,1,0,0,0,1,0,0,0,0,15,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,1,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,3,0,0,0,0,0,0,17,0,0,0,Class_8
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+49876,8,0,0,0,0,0,0,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,2,23,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,31,22,9,1,0,0,0,0,0,0,10,0,0,0,0,0,0,17,2,0,5,Class_8
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+49879,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,0,0,0,Class_8
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+49881,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,1,0,7,1,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,2,1,0,1,0,0,0,0,2,0,0,1,0,0,0,2,0,0,0,Class_8
+49882,1,10,1,0,0,1,0,0,0,0,4,0,0,0,0,2,1,1,4,2,0,2,0,12,0,0,1,0,0,0,0,4,0,3,2,2,1,10,0,3,0,1,0,2,0,0,0,3,2,0,0,0,0,3,1,1,0,1,0,1,0,2,0,0,0,7,6,0,4,0,0,0,0,0,0,5,0,5,1,0,0,0,0,0,0,0,1,0,2,20,0,0,1,Class_8
+49883,0,0,0,0,0,0,0,2,0,2,0,0,2,0,1,0,1,0,0,0,0,1,0,1,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,2,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+49884,0,5,0,0,0,0,0,2,0,5,2,0,10,3,3,2,0,0,0,3,0,1,2,14,0,0,0,0,0,0,3,0,1,1,0,0,3,4,0,3,0,0,0,2,4,0,1,2,0,0,0,0,1,3,1,1,0,22,0,0,0,3,0,2,1,1,4,0,15,1,1,0,0,2,5,10,0,0,1,0,0,0,7,0,0,1,1,0,0,5,0,1,3,Class_8
+49885,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0,1,0,0,3,2,0,0,0,0,0,0,1,0,1,1,0,0,0,0,3,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,7,0,0,0,0,4,6,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
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+50159,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,23,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+50161,0,0,1,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,8,0,4,0,10,0,0,0,0,0,0,0,5,2,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,2,0,2,1,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,2,0,0,4,0,0,0,Class_8
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+50188,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,2,0,1,0,1,0,1,0,0,0,0,0,0,1,1,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,3,6,2,1,1,1,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,Class_8
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+50190,0,0,2,5,0,0,0,0,0,0,1,1,0,0,1,4,0,3,0,0,0,3,0,4,1,1,0,1,0,0,0,1,1,0,1,1,0,3,0,1,0,1,0,1,0,1,0,7,2,0,0,0,1,2,1,0,0,0,0,1,0,10,6,4,0,28,6,0,1,2,0,1,0,1,1,0,0,0,0,4,0,0,2,0,1,0,5,3,8,1,0,1,3,Class_8
+50191,1,3,0,0,0,0,0,0,0,2,3,0,0,0,1,0,0,0,1,3,0,1,2,18,0,0,1,0,0,0,0,7,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,47,0,0,0,0,0,0,0,0,10,0,18,1,0,1,1,3,3,2,0,0,0,0,0,0,3,0,0,1,0,0,0,20,1,0,1,Class_8
+50192,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,2,0,0,0,0,1,0,1,1,0,0,1,1,0,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,7,0,0,0,0,0,0,0,12,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50193,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,2,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+50194,0,2,0,2,0,0,1,1,0,2,0,0,2,1,0,5,1,1,2,8,0,2,0,2,1,0,0,0,1,0,0,0,1,1,0,1,1,1,0,0,0,3,0,11,0,0,0,1,1,1,0,0,0,1,2,0,0,0,0,0,0,0,2,3,5,0,2,0,2,3,0,0,0,0,1,1,0,3,0,0,0,0,6,0,2,1,1,1,0,14,0,0,0,Class_8
+50195,1,11,0,2,0,0,0,0,3,0,2,0,10,0,0,2,0,0,3,4,0,1,1,8,1,1,3,1,0,0,1,0,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,5,0,1,5,1,0,2,0,2,0,12,0,0,2,0,0,1,0,0,4,1,10,0,0,0,1,1,0,2,0,0,1,15,0,2,11,0,0,0,1,0,0,10,0,0,0,Class_8
+50196,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+50198,0,1,0,0,0,0,0,1,1,7,1,0,0,1,1,0,0,0,0,5,0,1,0,3,1,0,0,0,0,1,0,5,0,0,0,2,3,0,0,3,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,2,2,0,0,2,0,0,0,2,8,0,2,1,0,0,0,0,0,1,0,2,0,0,0,0,1,0,1,0,0,1,0,6,0,0,0,Class_8
+50199,3,5,0,0,0,0,2,6,0,0,1,2,0,2,0,0,4,1,2,9,0,0,0,36,0,0,0,0,0,3,0,3,5,0,0,2,1,7,1,1,8,0,0,0,2,0,0,6,2,2,0,0,0,1,1,1,0,55,0,1,0,2,0,1,0,2,31,0,32,5,1,1,2,2,1,45,0,1,0,0,0,3,4,0,0,2,4,1,0,11,4,0,0,Class_8
+50200,1,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,10,7,0,0,0,9,0,0,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,20,0,0,0,0,0,2,0,3,2,0,22,1,0,0,2,1,1,0,0,1,2,1,0,0,16,0,0,0,0,0,0,19,3,0,0,Class_8
+50201,0,14,13,6,0,0,1,0,0,1,2,0,1,1,0,7,2,12,0,14,1,6,0,3,0,2,0,0,3,0,7,3,0,2,43,2,0,6,0,0,2,1,3,5,0,1,1,0,15,2,0,1,2,35,1,5,0,1,1,0,0,4,1,1,1,4,1,0,0,9,0,2,1,0,2,2,0,0,1,10,0,2,0,0,1,2,4,0,7,3,0,0,0,Class_8
+50202,0,0,1,0,0,0,4,1,0,1,0,0,1,5,0,1,0,0,0,4,0,3,0,2,1,0,0,0,0,0,0,0,0,0,0,2,1,2,0,1,0,0,0,1,0,2,3,0,0,0,0,0,0,6,2,0,2,1,0,1,0,0,0,2,8,0,3,0,2,3,2,1,2,1,0,2,0,4,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,Class_8
+50203,0,0,0,0,0,0,1,1,0,0,1,0,11,0,0,3,2,0,1,2,0,2,0,9,2,1,0,0,0,0,0,5,0,0,0,2,1,3,0,1,0,0,0,1,0,0,8,3,1,0,0,0,0,9,4,0,2,0,0,0,0,0,0,2,0,3,5,1,0,2,2,0,0,0,0,5,0,7,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,Class_8
+50204,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,3,0,2,0,0,0,1,1,0,0,1,0,0,2,1,0,0,0,1,1,0,0,1,0,1,0,2,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,2,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+50205,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,2,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,0,13,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+50418,2,5,0,0,0,0,2,0,0,0,5,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,1,1,21,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,0,0,0,0,0,2,0,0,0,Class_8
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+50427,1,10,1,3,0,0,0,4,0,0,6,1,2,0,0,6,3,3,86,2,0,3,2,36,0,0,0,0,0,0,0,11,0,0,7,0,4,7,0,2,1,0,0,3,0,0,0,6,1,4,0,1,0,3,2,2,0,23,0,0,0,5,0,1,0,1,3,0,25,4,0,1,9,1,2,9,0,0,0,0,0,0,0,0,0,2,2,1,0,53,0,0,1,Class_8
+50428,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,5,2,0,0,1,0,0,0,0,0,5,0,1,0,0,0,5,0,1,0,17,0,0,0,1,0,0,0,3,2,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,Class_8
+50429,0,4,1,2,0,0,4,1,1,4,5,0,1,0,0,4,0,2,0,1,0,1,0,37,0,0,0,0,0,1,0,5,0,1,3,4,5,1,3,4,0,0,0,3,0,0,2,7,5,0,0,0,0,6,3,0,0,15,0,0,0,0,0,0,6,2,15,0,3,3,2,1,4,0,27,23,0,6,1,3,0,1,12,0,1,0,5,0,0,2,1,0,0,Class_8
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+50433,0,6,0,2,1,0,0,0,0,0,2,0,0,0,1,1,0,1,2,5,0,0,0,5,2,0,0,0,0,0,1,0,0,1,6,1,4,8,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,2,0,0,1,2,0,1,2,1,3,0,9,0,0,0,0,2,0,11,0,4,0,0,0,1,3,0,1,0,0,0,1,9,2,0,0,Class_8
+50434,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50435,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,7,0,0,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+50436,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21,0,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_8
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+50440,1,0,0,0,0,0,0,1,0,6,0,0,0,0,0,2,1,1,0,1,0,2,0,4,2,0,0,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,0,1,0,0,12,0,0,0,0,0,0,2,2,0,0,1,0,1,0,2,0,2,2,0,3,2,1,2,23,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,1,1,0,0,1,0,Class_8
+50441,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50442,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,0,0,0,1,0,0,1,0,1,2,0,0,0,1,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,10,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,3,3,5,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+50443,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,4,0,0,2,1,0,8,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,10,2,0,2,4,0,1,0,0,1,0,0,0,0,1,0,0,0,0,3,0,1,1,0,0,0,0,Class_8
+50444,0,3,1,0,0,2,5,0,0,11,0,0,0,0,0,0,0,1,1,1,0,2,0,9,0,0,0,0,0,0,0,1,0,1,1,1,0,2,0,0,1,1,0,0,1,1,0,0,2,0,0,0,0,0,0,0,2,5,0,0,0,0,1,0,0,5,0,0,23,3,0,2,5,0,0,8,0,0,0,0,0,0,18,0,0,0,0,0,0,9,0,0,0,Class_8
+50445,0,0,0,0,0,0,0,0,0,13,1,1,0,0,0,0,2,0,0,0,0,1,0,4,1,0,0,0,10,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,3,0,0,1,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,Class_8
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+50747,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,2,1,0,0,9,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,Class_8
+50748,0,0,1,0,0,0,0,0,0,0,5,0,0,3,0,1,0,0,0,3,1,1,0,2,1,1,4,0,0,1,1,0,4,2,0,1,0,1,0,0,1,0,0,1,0,0,1,1,1,0,0,0,0,3,0,0,0,1,0,0,0,2,1,0,0,0,1,14,0,0,0,3,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,5,0,0,0,0,Class_8
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+50750,0,0,1,0,0,0,0,1,0,0,1,0,1,3,0,4,0,10,0,1,0,3,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,6,0,0,0,3,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_8
+50751,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+50752,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,8,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,9,0,0,0,0,0,1,0,1,5,0,8,0,0,0,1,0,4,3,0,0,0,0,0,0,12,0,0,0,0,0,0,2,0,0,0,Class_8
+50753,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,1,0,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,2,0,0,0,23,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_8
+50754,4,0,0,0,0,0,0,0,0,0,0,0,2,1,2,1,0,1,0,0,0,0,0,51,1,0,0,0,0,0,0,18,0,0,0,0,2,0,0,0,0,0,0,0,0,0,30,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,36,3,1,0,4,0,0,0,4,0,0,1,0,0,0,0,1,0,0,1,0,0,4,1,0,0,0,Class_8
+50755,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,1,0,0,2,0,0,1,0,0,1,0,5,0,1,0,1,10,0,0,0,2,0,0,0,0,1,0,1,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+50756,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,4,1,0,0,0,0,0,0,0,2,0,0,0,2,0,1,1,0,0,1,2,0,0,0,0,0,0,0,4,0,0,1,0,0,0,3,0,0,0,3,0,0,0,1,0,0,0,1,4,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,17,0,0,0,Class_8
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+50758,0,9,2,1,0,0,2,4,0,1,2,1,0,0,0,4,0,4,15,4,0,4,0,20,0,0,0,0,0,1,0,2,0,0,3,0,0,4,2,1,0,1,0,1,1,1,0,4,2,3,0,3,0,8,2,0,1,5,0,1,0,0,1,1,0,1,10,0,25,2,0,0,3,9,1,6,0,3,0,1,0,0,2,0,0,2,1,0,1,73,0,0,0,Class_8
+50759,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,3,0,7,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,30,0,0,0,2,0,1,0,3,2,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+50760,2,2,0,1,0,0,0,0,0,1,0,0,0,1,0,2,0,4,0,0,0,0,0,11,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,9,0,2,1,1,0,1,0,1,3,0,11,1,0,0,0,0,0,6,0,1,0,1,0,1,7,0,0,0,0,1,0,34,0,0,1,Class_8
+50761,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,59,12,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+50762,0,3,20,51,0,0,0,0,2,0,1,2,0,1,0,9,0,3,4,3,2,4,0,0,1,3,0,0,0,0,17,0,1,0,53,1,0,6,0,0,2,1,0,6,0,4,0,5,13,3,0,6,0,19,2,0,0,0,1,0,2,2,2,1,2,1,0,0,2,1,0,0,2,0,2,0,0,0,0,9,0,4,0,0,0,0,2,0,10,1,0,0,0,Class_8
+50763,0,2,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,8,0,0,0,0,0,0,0,0,0,0,2,1,1,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,2,0,15,1,0,0,0,0,1,1,0,0,1,0,0,0,2,0,0,3,1,0,1,15,0,0,0,Class_8
+50764,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,9,0,0,0,0,4,22,0,2,1,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+50765,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,3,5,0,0,0,0,0,2,0,2,6,0,0,0,0,0,1,0,1,3,0,21,0,0,0,0,1,0,4,0,0,0,0,0,0,2,0,0,4,0,0,2,20,0,0,0,Class_8
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+50828,2,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,4,1,0,0,0,12,0,0,0,0,0,0,0,3,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,2,25,0,0,0,0,0,3,1,0,10,0,1,2,0,1,1,0,2,3,0,1,0,1,0,0,9,0,0,1,0,0,0,7,0,0,1,Class_8
+50829,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,1,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50830,0,0,3,16,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,4,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,1,0,1,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+50831,2,0,0,0,0,0,0,1,3,0,0,0,0,0,0,2,0,5,0,1,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,0,4,3,1,0,0,0,0,3,0,0,0,1,0,1,0,3,0,0,0,3,3,9,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_8
+50832,1,0,1,0,0,0,0,1,4,0,0,0,0,1,0,1,0,0,0,1,0,1,0,5,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,2,3,0,0,10,1,0,0,0,0,0,1,1,0,4,1,0,0,1,2,0,0,1,0,0,0,0,Class_8
+50833,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,3,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,7,0,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,4,2,2,7,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50834,0,5,0,1,0,0,0,2,0,1,1,0,1,0,0,1,0,1,0,5,0,6,0,2,0,0,0,2,0,0,0,1,1,0,0,0,1,2,0,0,0,1,0,1,0,1,0,2,2,3,0,0,0,5,2,0,0,6,0,0,0,2,0,1,0,0,0,1,1,1,0,1,0,0,3,0,0,0,0,0,0,0,2,0,0,0,1,0,0,17,0,0,0,Class_8
+50835,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,2,0,8,0,0,0,2,0,0,0,27,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50836,0,1,0,0,0,1,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,3,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,0,0,0,2,0,2,6,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,Class_8
+50837,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,1,1,1,4,1,0,0,8,19,0,0,0,0,1,0,0,7,0,1,0,0,1,0,0,1,0,0,0,0,4,0,0,3,0,0,0,0,0,1,0,0,0,7,0,1,0,0,0,2,0,0,7,0,22,0,0,0,0,2,5,0,0,2,2,1,0,0,3,0,0,1,0,0,0,54,1,0,0,Class_8
+50838,0,0,0,0,0,0,0,1,0,1,1,0,9,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,4,0,1,0,0,0,2,0,0,1,0,0,1,0,0,1,3,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,0,0,Class_8
+50839,2,4,0,1,1,0,0,0,0,0,1,0,1,2,0,5,2,4,3,6,0,2,0,11,0,0,0,1,0,0,3,0,0,1,13,0,0,2,0,3,0,0,0,1,0,0,0,1,1,0,1,0,0,0,4,0,0,5,2,1,0,2,0,1,0,1,1,0,4,1,0,0,0,0,1,1,0,0,1,0,0,0,9,0,0,1,1,0,1,22,0,0,1,Class_8
+50840,2,5,1,2,0,0,1,0,1,0,1,0,1,2,1,6,2,8,1,3,1,5,1,1,1,0,1,0,1,2,0,2,3,0,3,1,0,6,0,4,0,1,6,6,0,4,1,0,3,1,0,6,0,11,1,1,0,1,0,0,0,14,3,1,1,5,3,2,1,2,0,3,2,5,0,1,0,2,1,14,3,4,13,0,1,5,5,1,52,0,1,0,0,Class_8
+50841,0,0,0,0,0,0,0,0,2,2,0,0,0,0,1,1,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,3,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,1,3,1,0,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+50842,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50843,0,2,24,54,0,0,0,0,0,2,0,0,4,0,0,0,0,4,0,0,0,10,0,4,0,2,2,0,2,0,2,0,0,2,50,0,0,0,0,0,0,2,0,4,0,2,0,0,2,0,0,0,0,30,0,0,0,0,0,0,0,4,0,4,0,0,2,0,0,2,0,0,0,0,0,0,2,0,0,6,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_8
+50844,1,2,0,0,0,1,0,0,0,0,0,0,0,0,1,3,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,3,0,0,1,1,0,2,2,0,0,0,1,0,0,0,0,4,1,0,0,0,2,1,0,0,0,21,0,0,0,4,0,2,0,0,2,0,6,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,1,3,0,0,Class_8
+50845,0,0,0,0,1,0,2,4,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,3,0,0,0,0,0,2,0,4,0,3,0,5,3,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,17,3,0,1,0,1,4,8,2,9,0,36,0,1,0,0,2,0,0,0,1,0,0,0,4,0,0,Class_8
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+50872,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,4,0,1,0,10,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,10,0,1,0,0,1,1,2,1,6,0,20,0,0,0,0,2,0,0,0,0,1,0,3,0,0,0,Class_8
+50873,0,5,1,1,0,0,1,1,0,0,1,0,0,2,0,0,0,1,22,8,0,0,3,14,0,1,0,0,0,0,0,9,1,1,3,1,1,6,0,0,0,1,0,1,0,0,0,4,1,0,0,0,0,1,1,0,2,32,0,0,0,1,0,1,3,2,6,0,10,0,0,0,1,1,0,3,0,0,0,2,0,1,7,0,1,0,0,0,0,41,0,1,0,Class_8
+50874,0,7,0,0,0,0,0,1,1,0,3,0,10,1,1,2,0,2,10,4,0,0,0,25,1,1,0,0,0,0,0,3,0,0,1,2,2,14,1,1,0,1,0,0,0,0,0,2,0,1,0,2,0,5,0,1,1,7,0,0,0,8,0,3,0,0,10,0,16,2,1,0,5,0,0,14,1,2,0,2,0,0,9,0,0,1,0,0,2,72,0,1,0,Class_8
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+50876,1,18,0,2,0,0,2,1,0,0,3,0,11,1,0,4,0,1,3,7,0,0,0,14,0,1,0,0,0,0,0,0,0,0,3,2,0,10,0,0,0,4,1,0,0,0,0,2,4,1,0,0,0,3,0,1,1,5,0,0,0,6,0,3,0,1,12,0,13,5,0,2,0,0,1,12,0,2,0,0,0,0,16,0,0,1,2,0,0,36,0,0,1,Class_8
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+50881,0,0,0,0,0,0,0,4,2,0,0,0,1,0,2,1,0,2,0,0,0,0,0,4,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,6,0,2,0,1,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,Class_8
+50882,0,1,0,0,0,0,0,3,0,1,1,0,1,1,0,2,0,4,0,4,0,0,0,2,1,0,0,0,0,1,0,3,0,1,1,2,0,2,0,0,1,1,0,1,0,0,0,0,1,2,0,0,1,1,1,0,0,0,1,0,0,11,1,3,0,0,9,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,2,0,0,0,Class_8
+50883,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+50884,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,2,0,1,0,12,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,33,1,0,0,2,1,2,5,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_8
+50885,1,6,15,30,0,0,1,0,0,1,2,1,1,0,0,10,1,5,3,0,1,16,0,0,0,14,0,9,0,0,8,0,4,1,40,1,8,7,2,7,0,2,1,8,0,5,1,0,2,3,0,1,0,32,0,1,0,1,0,0,0,1,1,0,0,4,4,1,1,4,0,2,0,4,0,0,0,0,0,18,0,3,1,0,0,3,0,1,7,1,0,0,0,Class_8
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+50907,0,2,0,0,0,0,0,1,0,0,1,0,2,1,0,0,1,0,10,4,0,2,0,18,2,0,0,0,0,0,0,4,0,1,0,0,1,0,2,0,2,0,0,1,0,0,0,3,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,1,0,42,1,0,0,0,0,1,2,0,2,0,0,0,0,4,0,0,0,1,1,0,6,0,1,0,Class_8
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+50916,2,2,0,1,0,0,12,1,0,2,0,0,2,0,3,1,0,1,0,8,0,9,0,7,1,0,0,0,0,1,0,4,0,1,0,8,2,8,0,1,0,0,0,8,1,0,1,4,1,1,0,0,2,6,0,2,0,2,0,1,0,3,0,3,1,1,9,1,3,3,0,2,3,9,1,10,0,31,0,2,0,0,8,0,0,1,1,0,0,3,4,0,0,Class_8
+50917,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,1,1,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,0,0,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+50947,0,4,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,9,3,0,0,2,7,0,0,0,1,1,0,0,1,0,0,2,0,0,3,0,0,0,0,0,1,0,0,0,6,0,0,0,1,0,0,1,0,2,1,0,0,0,0,0,4,0,1,4,0,22,1,0,0,0,1,0,3,0,2,1,0,0,0,1,0,0,1,1,1,0,51,0,0,0,Class_8
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+50959,1,0,0,1,0,0,6,3,0,3,1,0,2,1,0,5,1,8,0,1,0,4,0,24,1,0,0,1,4,1,0,5,0,2,0,8,1,3,1,3,1,1,0,4,0,1,15,4,0,0,1,0,2,6,0,0,3,0,0,0,1,2,0,1,1,7,27,1,5,4,2,1,4,4,0,9,0,61,0,1,1,0,1,0,1,0,0,0,2,0,4,0,0,Class_8
+50960,0,0,2,2,0,0,0,0,0,0,0,0,0,1,2,5,0,1,0,0,1,1,0,2,2,5,0,0,0,0,0,1,0,0,1,2,0,0,1,3,1,2,2,0,0,2,3,3,0,0,0,3,0,3,0,2,1,0,0,0,1,2,0,0,0,1,3,4,0,1,0,0,1,1,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50961,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+50962,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,3,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,2,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+50963,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,4,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,Class_8
+50964,0,4,8,22,0,0,0,0,0,2,0,0,0,0,0,2,0,8,0,2,0,6,0,0,0,22,8,8,4,0,2,0,0,0,38,0,0,0,0,0,0,4,0,2,0,6,2,4,8,0,0,0,0,44,8,0,2,0,0,0,0,0,2,0,0,0,6,0,4,6,0,0,0,0,0,0,2,0,0,4,2,4,0,2,0,0,0,2,2,0,0,0,0,Class_8
+50965,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,2,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
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+51040,0,7,0,0,0,0,1,0,0,0,4,1,0,0,0,2,0,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,1,0,19,0,0,0,1,0,0,0,3,2,0,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_8
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+51111,0,3,0,1,0,0,0,1,0,3,0,0,0,0,0,1,0,2,1,2,0,4,0,0,3,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,2,1,0,1,0,0,0,1,0,5,4,0,32,1,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,4,0,3,0,9,0,0,0,Class_8
+51112,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,2,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,2,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,Class_8
+51113,1,0,0,1,0,0,5,0,0,1,2,0,10,0,0,4,1,1,9,5,0,0,1,27,0,0,0,0,0,0,0,7,1,2,1,0,1,8,5,0,1,0,0,1,2,0,0,4,0,0,0,0,0,4,0,0,0,30,0,0,0,1,1,1,1,2,23,0,7,9,0,2,2,3,0,25,0,0,1,0,0,0,3,0,0,1,0,0,1,14,3,0,0,Class_8
+51114,1,2,1,1,1,0,0,0,0,1,4,0,0,1,0,0,0,0,1,8,0,0,0,6,0,0,0,0,0,0,1,0,0,0,7,0,0,5,0,0,1,2,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,2,0,1,0,1,0,0,0,0,2,0,3,4,2,0,0,0,1,0,0,0,0,1,0,0,0,0,3,2,0,0,1,15,0,0,0,Class_8
+51115,2,0,3,0,0,0,2,0,5,2,4,1,1,1,0,5,1,5,0,0,0,3,0,3,1,0,1,1,0,0,0,0,2,2,4,0,0,5,1,1,0,0,0,2,0,2,9,0,4,1,0,1,2,9,3,0,0,0,0,0,3,13,2,0,0,1,6,0,1,4,14,0,0,2,1,1,0,0,1,17,1,10,2,0,0,1,1,0,32,0,0,1,1,Class_8
+51116,1,0,0,0,0,1,0,0,0,0,0,1,1,1,0,2,1,1,0,6,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,6,2,0,1,1,0,0,3,0,1,0,2,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,2,0,13,3,1,0,0,0,0,8,0,0,0,0,1,0,1,0,0,0,0,0,1,2,0,0,0,Class_8
+51117,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,0,0,1,0,0,3,3,0,0,0,0,0,0,0,2,0,1,0,0,1,2,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,1,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,Class_8
+51118,0,10,0,4,1,0,3,1,1,0,0,1,0,0,0,1,0,1,0,9,0,3,1,17,1,1,0,2,0,0,0,0,1,0,1,0,1,10,0,0,0,2,0,1,3,0,0,0,0,1,0,2,1,6,0,0,1,49,0,0,0,2,0,3,0,3,4,2,12,2,0,1,5,2,0,6,0,0,1,0,1,0,2,0,2,0,0,0,1,11,1,0,0,Class_8
+51119,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51120,1,0,0,1,0,0,0,1,0,6,0,0,0,0,0,1,1,4,0,0,0,2,0,6,1,0,0,0,0,4,1,3,0,1,0,0,0,4,2,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,2,0,2,7,3,1,1,21,0,0,0,0,0,0,2,2,0,1,0,0,0,0,1,0,0,1,0,0,0,0,Class_8
+51121,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,2,0,1,1,0,1,0,0,1,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51122,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,1,0,3,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,1,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,0,1,1,6,6,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+51123,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+51125,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_8
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+51133,0,3,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,2,5,0,5,0,22,1,0,0,0,0,0,0,1,0,1,0,0,1,5,0,0,1,0,0,2,0,0,0,1,1,3,0,0,0,2,1,0,1,5,0,0,0,1,0,1,2,1,5,0,15,2,0,0,0,0,1,23,0,0,0,0,1,0,10,0,1,2,0,1,2,15,0,0,0,Class_8
+51134,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51135,0,0,0,0,4,0,1,2,1,0,1,0,2,0,0,4,0,0,0,0,0,0,0,3,1,1,5,3,0,0,0,2,2,0,0,0,0,1,0,0,0,2,0,2,0,0,2,2,1,0,0,0,0,3,0,0,0,0,0,2,0,2,0,0,1,2,2,7,0,0,0,0,2,0,1,0,0,0,0,2,3,0,0,7,1,0,0,0,1,0,0,0,0,Class_8
+51136,0,1,1,1,1,0,5,0,0,0,0,0,1,1,1,0,0,1,0,1,0,0,2,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,2,0,1,1,2,2,1,0,0,0,2,0,2,0,0,2,0,0,0,0,1,0,1,0,9,1,1,0,1,2,2,1,0,0,1,0,0,0,17,0,0,1,0,0,0,6,0,0,0,Class_8
+51137,0,0,0,0,0,0,0,1,1,5,2,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,4,0,0,0,Class_8
+51138,0,2,0,0,0,0,1,0,0,1,2,0,0,1,0,1,0,1,10,0,0,3,0,7,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,1,1,2,0,0,0,0,1,0,0,0,1,0,1,1,0,10,1,0,0,1,1,2,1,0,0,0,2,0,0,0,0,0,0,1,0,0,16,0,0,0,Class_8
+51139,2,2,1,0,0,0,16,1,0,4,0,0,11,0,0,0,0,0,0,2,0,6,0,8,0,0,0,3,2,0,0,4,1,0,0,2,1,5,1,3,0,0,0,5,1,0,1,2,2,1,0,0,2,4,9,0,1,0,0,1,0,2,0,3,0,9,5,0,5,4,0,1,0,1,1,10,0,35,0,0,1,0,12,0,3,0,4,0,2,1,0,0,2,Class_8
+51140,0,9,1,1,0,0,0,0,0,0,2,0,2,0,0,2,2,6,0,8,0,2,0,8,0,0,0,0,2,0,2,3,0,0,3,2,0,4,0,0,1,0,0,1,0,1,0,0,4,0,0,0,0,4,0,0,0,9,2,1,0,0,0,1,0,3,5,0,27,2,0,1,6,1,0,1,0,1,0,0,0,1,19,0,2,0,2,3,0,23,2,0,1,Class_8
+51141,0,0,0,1,0,0,0,6,0,0,0,0,1,3,0,1,6,4,1,2,0,0,4,22,1,0,0,0,0,0,4,1,1,0,2,1,1,1,4,0,1,0,0,0,0,0,0,1,1,1,0,0,0,2,3,0,0,1,0,0,0,5,0,0,0,0,8,0,50,0,0,0,1,0,1,1,0,0,0,2,0,0,0,0,2,0,1,1,5,2,3,0,0,Class_8
+51142,0,1,0,0,1,0,0,1,0,0,6,1,0,2,0,1,0,1,0,4,0,1,0,14,1,0,0,0,0,0,2,1,2,1,2,4,0,3,0,3,0,1,0,2,0,0,0,3,1,2,0,0,0,0,0,0,1,10,0,0,0,1,0,0,0,0,14,0,11,2,0,0,0,0,1,25,0,3,2,1,0,0,1,0,0,0,0,0,1,18,1,0,0,Class_8
+51143,0,0,0,0,0,0,0,1,3,5,0,0,0,1,2,1,0,0,1,3,0,2,0,8,1,0,0,0,0,0,0,0,0,0,0,2,2,1,0,2,3,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,7,0,0,7,0,1,2,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,2,0,0,3,Class_8
+51144,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,2,1,2,1,0,0,39,4,11,6,0,0,1,0,0,0,0,0,0,0,0,0,0,4,1,0,0,Class_8
+51145,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,2,0,1,1,0,0,0,10,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,2,9,0,0,0,0,1,1,0,0,9,0,11,0,0,0,0,0,5,14,0,2,0,0,0,0,11,0,0,0,0,0,0,8,0,0,0,Class_8
+51146,0,2,1,1,0,0,1,0,0,0,3,0,0,0,0,1,1,1,0,0,0,1,0,28,0,1,0,0,0,0,0,4,0,1,2,2,0,2,2,0,0,1,0,1,0,1,0,2,0,0,0,0,0,5,0,0,1,2,0,1,0,2,0,1,0,0,22,0,9,2,0,0,1,3,1,52,0,2,0,0,0,0,0,0,2,0,1,0,0,5,0,0,1,Class_8
+51147,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,2,0,1,0,0,0,0,0,0,0,0,1,0,0,1,3,0,1,0,26,0,0,0,3,0,1,0,2,8,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,0,0,Class_8
+51148,1,5,0,1,0,0,4,1,6,0,4,3,0,7,0,0,0,0,0,7,0,1,4,34,2,0,0,0,0,0,1,5,0,0,1,1,10,3,1,2,1,0,1,2,1,0,0,4,0,2,0,1,0,1,0,0,0,7,0,0,0,1,1,1,0,3,21,0,2,0,0,0,15,2,47,27,0,5,1,0,0,1,2,0,0,2,0,0,0,10,0,0,0,Class_8
+51149,0,3,0,1,0,0,1,0,1,3,1,1,1,1,0,3,0,1,21,7,0,0,5,15,0,0,0,0,2,0,2,5,0,1,2,2,1,3,4,1,0,0,0,1,0,0,0,1,0,2,0,0,0,3,1,1,1,0,0,0,0,8,0,1,1,0,6,0,19,3,0,2,5,1,0,3,0,2,0,3,0,0,5,0,1,0,0,0,0,26,2,0,0,Class_8
+51150,0,6,3,1,0,0,1,2,0,4,3,0,9,2,0,5,1,6,0,10,0,10,0,9,1,2,0,1,0,0,0,3,2,0,2,2,1,16,0,3,0,0,0,2,0,1,0,3,1,2,0,0,0,10,5,0,0,1,0,1,0,11,0,0,2,3,8,0,8,9,1,0,0,0,1,7,0,0,1,0,1,0,6,0,3,0,3,2,1,25,0,0,2,Class_8
+51151,0,0,3,3,1,0,0,0,0,2,2,0,1,2,0,12,0,1,0,0,0,3,0,3,1,1,0,5,0,2,0,1,3,3,0,0,0,1,0,1,1,0,0,3,0,1,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,7,1,0,1,0,5,4,0,0,0,1,0,0,2,0,0,1,0,2,0,1,0,0,0,0,0,0,3,0,0,3,0,Class_8
+51152,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,2,0,2,1,0,0,1,0,1,0,0,0,1,2,1,0,1,0,0,0,0,0,1,0,0,2,0,0,2,0,0,1,2,0,0,0,0,0,0,0,2,0,0,0,0,1,19,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,2,0,0,1,Class_8
+51153,0,2,0,0,0,0,0,1,0,0,1,0,0,2,0,4,1,0,10,6,0,1,0,21,1,0,0,0,0,0,1,1,0,2,0,2,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,2,0,0,0,0,0,2,0,2,6,0,25,2,1,1,0,0,1,0,0,1,0,0,0,0,1,0,2,0,0,2,1,7,1,2,0,Class_8
+51154,0,1,0,0,0,0,0,2,1,0,0,1,0,0,0,1,0,0,24,4,0,0,0,9,0,0,0,0,0,0,0,4,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,5,0,13,1,0,1,3,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,0,4,0,0,0,Class_8
+51155,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,6,2,0,0,0,0,1,0,1,0,3,0,0,0,0,2,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,1,1,19,0,0,0,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+51156,1,0,4,11,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,2,0,0,1,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,4,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_8
+51157,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,8,9,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51158,0,0,0,0,0,0,0,1,0,4,1,0,0,0,0,2,0,1,0,0,0,2,0,2,0,1,0,0,0,0,0,1,0,1,1,1,1,1,0,0,8,0,0,0,0,0,0,2,0,0,2,0,0,6,2,0,1,3,0,1,0,4,0,1,1,6,7,0,1,1,0,0,0,2,6,1,0,1,0,2,0,0,1,0,0,0,0,0,2,2,0,3,0,Class_8
+51159,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,111,0,15,14,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51160,0,2,0,0,0,0,0,4,0,0,1,1,0,2,0,4,0,0,0,4,0,1,0,3,1,0,0,0,1,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,14,1,1,0,0,0,1,4,2,0,0,0,0,0,0,15,0,0,0,2,5,0,2,4,14,1,0,0,0,4,0,12,0,0,0,0,1,2,1,0,2,1,4,0,1,1,0,Class_8
+51161,0,4,1,0,0,1,0,0,0,2,3,0,0,1,0,2,0,0,7,1,0,5,1,3,0,0,0,0,0,0,0,1,1,1,2,0,1,5,0,0,1,2,0,1,0,0,0,1,0,3,0,0,1,3,2,0,1,2,0,1,0,1,1,2,1,1,0,0,4,3,0,1,0,3,0,1,0,2,1,0,3,0,0,0,1,1,0,0,0,23,2,1,0,Class_8
+51162,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,6,0,1,0,0,0,0,25,0,0,0,0,0,0,2,3,0,0,Class_8
+51163,1,12,0,0,0,0,0,0,1,4,3,2,0,0,0,0,0,1,11,2,0,0,3,14,0,0,1,0,0,1,1,5,1,0,2,0,1,2,1,0,1,0,2,1,0,0,0,6,0,1,0,2,0,0,1,0,1,43,0,0,1,0,1,3,0,0,13,0,13,0,0,2,4,8,2,7,0,1,1,2,0,0,15,0,1,0,0,1,0,85,1,1,1,Class_8
+51164,0,1,0,0,0,1,1,0,2,0,4,0,0,0,0,0,4,2,1,0,0,1,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,4,2,0,0,0,0,1,0,0,1,2,0,0,0,0,0,1,0,0,7,0,12,0,0,0,2,4,1,18,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,Class_8
+51165,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,2,1,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,Class_8
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+51175,0,0,0,3,0,0,0,1,9,0,1,0,0,0,0,1,0,2,1,0,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,2,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,2,7,0,1,13,2,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,1,0,Class_8
+51176,0,0,0,0,0,0,0,0,1,0,0,0,10,1,1,0,1,0,2,3,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,1,0,0,14,0,3,1,0,0,1,0,1,30,0,0,0,0,0,0,0,0,0,1,0,1,1,3,2,0,1,Class_8
+51177,1,6,2,1,0,0,3,1,0,0,3,0,0,0,0,2,0,2,7,7,0,2,0,13,0,1,0,0,0,0,3,0,1,1,4,1,0,21,0,0,6,0,0,1,0,1,0,0,2,4,0,1,1,2,0,1,0,36,0,0,0,5,1,0,0,2,0,0,10,9,1,2,0,1,0,7,0,1,0,3,1,0,3,0,1,0,0,1,0,10,4,2,0,Class_8
+51178,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,2,3,0,2,0,0,0,11,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,1,0,2,0,0,1,3,0,3,0,0,0,0,0,0,0,0,1,5,4,7,0,2,0,0,4,2,1,0,4,3,1,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_8
+51179,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,1,0,0,0,1,1,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,5,0,0,8,0,0,0,0,0,0,1,27,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51180,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,6,1,0,6,0,0,0,13,1,0,0,0,0,0,0,5,0,0,0,2,1,3,1,0,0,0,0,1,2,0,0,1,1,1,0,1,0,1,1,0,0,4,0,0,0,0,0,1,0,1,12,1,2,3,0,0,0,1,0,5,0,1,0,0,0,0,1,0,2,0,2,5,0,3,0,0,0,Class_8
+51181,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,1,2,0,1,0,4,0,0,0,0,0,0,0,0,0,0,1,1,0,2,1,0,0,0,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,34,0,0,0,2,0,1,0,0,5,0,5,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,Class_8
+51182,0,1,1,0,0,0,3,1,0,0,2,0,0,0,2,3,4,1,0,2,0,0,1,18,0,0,0,0,0,0,0,3,4,0,1,0,1,5,2,0,1,0,0,0,1,1,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,1,7,0,7,2,2,1,1,0,0,2,0,2,0,0,0,2,1,0,2,0,0,0,1,5,2,0,0,Class_8
+51183,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51184,0,0,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,2,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,4,0,2,0,0,1,6,0,0,1,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,2,0,Class_8
+51185,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,6,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,3,0,2,0,1,0,0,0,0,0,Class_8
+51186,6,1,1,1,0,0,10,4,0,1,0,0,3,2,2,2,2,1,0,4,0,5,0,5,2,0,0,0,1,1,0,8,1,1,0,9,2,1,0,1,0,0,0,2,1,0,2,2,0,2,0,0,2,2,1,0,0,1,0,1,0,4,0,3,0,2,12,2,0,4,1,3,1,7,1,12,0,43,0,1,0,0,8,0,1,1,2,0,0,2,1,0,0,Class_8
+51187,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,0,1,0,2,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,8,0,0,0,0,1,0,0,0,0,2,0,2,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,3,0,0,0,0,0,0,Class_8
+51188,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51189,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,3,0,0,4,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,3,0,0,0,3,1,17,0,0,0,0,1,0,1,1,3,0,0,0,0,0,0,Class_8
+51190,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,26,0,0,2,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,4,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,Class_8
+51191,1,1,0,0,0,0,0,0,0,1,2,0,2,1,0,1,1,0,68,2,0,2,0,19,0,0,0,0,0,0,0,5,0,0,0,0,1,5,0,1,1,0,0,1,0,0,0,3,0,4,0,0,0,6,1,1,1,11,0,3,0,0,0,0,0,0,2,0,31,1,0,0,2,4,1,8,0,0,0,0,0,0,0,0,1,1,1,0,2,42,0,0,0,Class_8
+51192,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,5,0,0,0,0,17,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,24,2,0,0,5,0,0,13,0,1,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,Class_8
+51193,0,11,1,1,0,0,0,2,2,1,3,0,2,0,1,1,3,2,0,6,0,2,0,22,0,0,0,0,2,0,3,8,0,1,1,6,2,9,0,0,2,1,0,1,1,0,1,1,1,2,0,0,3,5,2,0,1,33,0,0,0,5,2,3,0,1,27,0,28,3,0,1,1,0,0,27,0,3,0,1,0,0,12,0,0,2,2,2,3,37,0,0,0,Class_8
+51194,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,2,0,0,1,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,12,1,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,3,3,4,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51195,0,1,0,0,0,0,0,1,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,5,0,0,0,0,0,0,0,44,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+51196,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,2,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,0,1,2,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,3,0,0,3,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,2,0,0,0,0,Class_8
+51197,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,6,2,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,1,2,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51198,0,1,1,0,0,0,0,2,0,1,0,1,2,3,0,2,5,3,2,4,0,2,0,0,2,0,0,0,0,0,0,2,1,0,0,14,0,2,2,0,3,0,0,2,0,0,0,5,0,1,0,0,0,0,1,0,0,2,0,0,0,9,0,1,1,2,31,0,3,2,3,0,0,0,1,1,0,0,0,1,0,0,3,0,0,0,2,0,3,2,2,0,1,Class_8
+51199,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,30,1,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,9,1,0,1,5,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,6,0,0,0,Class_8
+51200,0,2,4,0,0,0,2,0,0,1,0,0,0,0,0,1,2,3,1,6,0,6,0,4,0,1,2,0,1,0,0,2,2,3,1,0,0,3,0,0,1,0,0,4,0,0,0,1,1,2,0,0,0,11,3,0,2,33,1,1,0,3,0,1,2,3,2,0,9,1,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,0,15,0,0,0,Class_8
+51201,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,1,0,2,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,Class_8
+51202,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,6,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,Class_8
+51203,0,1,0,0,0,0,3,0,3,0,0,0,1,0,0,0,10,0,1,11,0,2,0,14,0,0,0,0,2,0,0,6,9,1,0,6,1,10,0,0,10,0,0,2,0,0,0,0,0,1,0,0,3,6,1,0,1,5,0,1,0,1,0,0,2,6,22,0,0,6,0,0,4,0,1,0,0,0,0,1,0,0,0,0,1,0,4,0,1,0,2,0,0,Class_8
+51204,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51205,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,2,3,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_8
+51206,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,3,0,0,0,0,0,89,0,9,10,0,0,0,0,0,0,0,0,0,1,0,0,0,6,0,0,0,Class_8
+51207,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,0,0,0,1,7,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,3,0,0,0,0,2,0,0,0,2,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,9,0,0,0,Class_8
+51208,0,2,18,50,0,0,0,0,2,2,0,0,0,0,0,4,2,4,0,0,6,0,0,0,0,2,2,4,0,0,6,0,0,0,60,0,0,2,0,0,0,2,0,4,0,4,0,0,4,0,0,0,2,22,2,0,0,0,0,0,2,0,0,0,0,4,2,0,4,2,0,0,0,2,0,2,0,0,2,10,0,0,0,0,2,0,0,0,2,0,0,0,0,Class_8
+51209,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,1,0,1,4,0,0,0,0,0,0,2,0,0,1,2,0,0,0,0,1,0,Class_8
+51210,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51211,0,11,2,0,0,0,1,1,1,0,3,0,13,1,0,5,0,1,4,4,0,0,0,15,0,1,0,1,1,0,0,2,0,1,4,0,0,12,1,2,1,1,1,0,0,0,0,0,0,3,0,5,1,3,0,1,2,12,1,0,0,5,0,1,0,0,9,0,13,3,1,2,8,0,0,15,0,2,1,4,0,0,12,0,0,1,0,0,4,52,0,0,0,Class_8
+51212,1,7,1,2,0,0,2,0,1,0,3,0,11,1,0,1,1,0,46,8,0,0,3,40,0,0,1,0,0,0,0,6,1,2,3,0,1,5,1,2,1,0,0,1,0,0,0,1,0,6,0,1,0,1,0,0,0,19,0,2,0,2,0,1,1,0,13,0,16,3,1,0,5,4,0,5,0,0,2,6,0,0,13,0,0,2,0,1,0,38,2,0,0,Class_8
+51213,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,3,0,15,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,2,0,0,0,16,0,0,0,Class_8
+51214,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,55,0,11,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51215,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,8,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51216,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,2,2,0,4,1,0,0,3,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,6,0,0,0,Class_8
+51217,0,0,5,9,1,0,1,0,0,1,0,1,6,0,0,10,1,3,0,1,0,12,0,3,2,4,0,3,0,2,1,0,0,1,28,0,0,1,0,0,0,0,0,7,0,1,0,0,9,0,0,0,0,34,3,0,2,0,3,1,0,2,0,0,3,7,0,1,0,3,0,0,0,3,1,0,0,1,2,0,0,1,1,0,1,0,0,0,9,3,0,0,3,Class_8
+51218,0,1,1,0,0,0,1,1,0,1,0,1,1,1,0,4,0,1,1,2,0,1,0,5,1,1,0,0,1,0,0,2,0,0,0,2,3,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,1,2,1,0,1,0,0,0,0,9,0,0,0,0,8,0,5,0,0,0,1,1,1,2,0,0,0,0,0,0,3,0,0,0,1,0,0,0,3,0,0,Class_8
+51219,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,5,0,0,0,0,0,0,0,2,1,0,14,1,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
+51220,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,Class_8
+51221,0,2,2,1,0,0,6,1,4,5,0,0,11,0,0,0,0,4,0,3,0,4,1,19,0,1,0,0,1,0,0,1,0,1,2,2,1,6,1,1,0,0,0,0,1,0,1,1,3,1,3,0,1,11,11,0,1,0,0,4,0,1,1,2,4,1,10,1,2,4,0,0,3,0,2,5,1,30,1,0,0,0,10,0,0,0,1,1,1,1,0,0,0,Class_8
+51222,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51223,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,Class_8
+51224,1,0,1,1,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,1,0,1,1,0,0,0,0,3,1,0,0,0,1,0,0,8,2,0,0,2,4,1,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,Class_8
+51225,0,3,0,0,0,1,0,0,0,0,4,0,12,0,0,0,1,2,4,4,0,0,2,9,0,0,0,0,0,0,0,5,0,0,0,0,1,4,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,1,0,28,1,0,0,0,0,1,0,0,8,0,30,0,0,0,3,0,0,2,0,2,0,3,0,0,3,0,0,1,0,0,0,16,0,0,0,Class_8
+51226,0,5,3,1,0,2,1,0,2,0,1,2,1,0,0,3,1,0,2,3,0,1,3,19,1,0,5,0,0,0,0,2,0,2,0,4,0,3,4,1,1,3,0,0,0,0,2,3,2,0,0,3,2,5,0,6,1,8,0,0,0,0,0,3,1,4,18,0,7,0,0,0,2,3,0,11,0,1,0,3,0,2,22,0,1,0,3,0,0,4,0,0,1,Class_8
+51227,2,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,3,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,16,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+51228,0,5,0,0,1,0,0,0,0,1,2,0,1,0,0,1,0,0,0,11,0,3,0,5,0,1,0,0,0,0,0,1,1,0,19,0,1,11,0,1,0,0,0,2,0,0,0,1,2,0,0,2,0,8,0,0,0,4,0,0,0,2,0,1,0,0,3,1,7,3,0,1,3,0,0,9,0,2,0,0,0,1,1,0,0,0,1,5,0,28,0,0,0,Class_8
+51229,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,7,5,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51230,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,0,0,0,0,0,0,27,1,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,Class_8
+51231,0,0,0,0,1,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,4,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,4,0,23,0,16,4,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,Class_8
+51232,1,1,1,0,0,1,1,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,4,2,0,0,1,1,0,0,0,0,4,0,2,0,0,0,1,0,0,0,25,0,0,0,3,0,1,0,3,4,0,0,1,0,2,2,0,0,2,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,Class_8
+51233,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,1,0,2,4,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51234,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,2,0,0,5,0,0,0,5,0,0,0,0,1,0,0,1,0,0,1,0,0,3,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,7,1,0,1,2,1,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_8
+51235,1,1,0,1,0,0,0,4,1,0,0,1,0,2,0,0,0,0,3,4,0,0,0,15,0,0,0,0,0,0,0,2,0,0,1,4,1,8,0,0,0,0,0,2,0,0,0,3,0,3,0,0,0,2,0,0,0,5,0,1,0,3,0,0,0,1,13,0,11,4,0,0,0,0,0,29,0,0,0,0,0,0,1,0,1,2,0,0,0,3,1,0,0,Class_8
+51236,0,0,0,1,0,0,0,3,0,0,0,0,1,3,0,1,7,3,7,10,0,1,1,23,1,0,0,0,0,0,3,1,0,0,0,0,0,3,2,0,2,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,6,1,1,14,0,45,4,0,1,0,0,3,0,0,0,0,0,0,0,1,0,1,0,0,4,0,1,3,1,0,Class_8
+51237,0,0,1,0,0,0,0,1,0,3,0,1,0,0,0,0,3,0,0,3,0,0,0,6,2,0,0,0,0,0,0,0,0,1,0,2,1,4,0,1,2,0,0,0,0,0,0,2,1,2,0,0,1,1,0,0,1,3,0,0,0,0,2,2,0,1,3,1,40,7,1,0,2,0,1,5,0,1,0,0,0,0,1,0,5,2,1,2,0,1,0,1,0,Class_8
+51238,0,3,0,1,0,0,0,2,1,6,0,0,0,2,2,1,0,0,0,10,0,4,0,8,1,0,0,0,0,1,0,4,0,0,0,2,3,1,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,2,1,0,0,2,1,1,0,1,0,2,1,2,10,0,2,1,1,2,0,0,0,0,0,1,0,0,0,0,1,0,3,1,1,0,0,6,0,0,0,Class_8
+51239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,1,0,0,0,3,0,1,1,0,0,1,0,0,0,17,0,0,0,0,0,0,1,0,0,0,Class_8
+51240,11,1,2,0,0,0,19,1,0,0,1,2,3,1,0,2,2,1,1,1,0,4,2,20,0,0,0,0,0,0,0,2,0,0,1,7,1,1,0,1,0,0,0,3,0,2,3,9,0,0,0,0,4,4,5,0,0,4,0,0,0,0,0,0,2,3,19,2,1,6,1,0,2,8,1,14,3,54,0,2,3,0,2,1,3,0,3,0,0,1,1,0,2,Class_8
+51241,0,0,0,0,0,0,0,0,1,15,0,0,0,0,0,0,0,1,0,2,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,2,0,1,2,0,0,0,Class_8
+51242,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,10,1,0,0,0,0,0,0,0,0,0,0,5,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,15,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,2,0,0,1,Class_8
+51243,0,0,6,16,0,0,0,0,0,2,0,0,6,2,0,6,0,2,0,2,0,16,0,0,0,0,0,2,0,0,0,0,0,2,36,4,0,2,0,0,0,2,0,4,0,0,0,0,8,0,0,0,2,30,0,0,0,0,0,0,0,2,2,0,0,8,0,0,0,4,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,6,0,Class_8
+51244,1,0,2,1,0,2,0,0,0,0,0,0,1,0,0,1,0,1,1,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,1,0,5,0,0,0,0,1,2,0,0,0,21,0,0,0,2,1,1,0,0,2,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,2,0,0,Class_8
+51245,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,13,1,0,0,0,0,0,0,6,0,1,0,3,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,5,0,1,10,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,2,0,2,0,Class_8
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+51262,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,3,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51263,14,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,58,0,7,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+51264,0,0,5,3,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,2,0,0,1,0,0,4,0,0,0,0,2,0,0,9,0,0,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,2,0,0,0,0,0,0,1,2,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,4,0,0,2,0,0,0,0,0,0,1,0,0,2,Class_8
+51265,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,1,1,0,0,0,0,0,0,22,0,0,0,0,0,0,2,1,0,0,Class_8
+51266,0,0,0,0,0,0,0,0,26,0,0,0,0,0,7,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,6,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,17,0,0,7,0,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,Class_8
+51267,2,1,0,0,0,0,0,0,0,0,1,0,1,0,0,3,11,0,2,10,0,5,6,4,0,0,1,0,1,0,0,3,0,1,1,1,1,4,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,3,0,1,0,4,0,4,0,3,3,0,0,0,0,1,23,0,0,1,0,1,2,3,0,0,4,0,1,0,5,0,0,3,1,0,0,Class_8
+51268,0,0,0,1,0,0,1,0,1,0,2,0,0,4,0,0,0,1,0,7,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,5,0,0,12,1,1,0,0,0,0,3,2,0,4,0,0,0,0,0,1,3,1,1,5,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,Class_8
+51269,0,0,3,0,0,0,0,1,0,0,1,0,0,0,0,3,1,0,0,1,0,3,1,2,1,0,2,0,1,0,0,2,1,1,0,0,0,4,0,1,0,1,1,2,0,0,21,0,1,0,0,0,0,5,2,0,0,0,0,2,0,9,1,4,0,2,4,1,0,2,4,0,1,0,1,1,0,0,0,3,0,0,1,0,10,3,0,0,17,0,0,0,0,Class_8
+51270,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,8,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_8
+51271,0,0,0,0,0,0,1,0,0,0,0,0,2,1,0,3,4,3,1,2,0,0,0,8,1,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,3,0,9,0,0,0,0,1,0,2,1,19,1,0,0,1,0,1,5,0,0,1,4,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_8
+51272,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,1,0,3,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,5,0,0,0,1,0,0,0,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51273,0,2,0,0,0,0,0,4,1,0,6,0,12,1,0,1,2,0,43,1,0,0,0,20,0,0,1,0,0,0,2,2,0,1,0,0,1,6,1,0,1,0,0,0,0,0,0,3,0,1,0,1,0,1,0,1,2,8,0,3,0,0,0,2,0,1,2,0,30,0,1,0,3,6,2,2,0,0,0,0,0,0,0,0,1,3,0,0,0,31,0,0,2,Class_8
+51274,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+51275,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,10,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51276,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,5,0,0,0,3,1,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,7,2,1,0,4,0,0,0,0,0,0,6,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,Class_8
+51277,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,9,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51278,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,4,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_8
+51279,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51280,0,0,0,0,0,0,0,1,1,3,1,0,0,0,0,0,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,1,2,2,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,4,0,0,14,0,1,1,0,1,2,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,4,0,0,0,Class_8
+51281,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,4,0,0,0,12,2,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,2,0,2,0,1,1,2,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
+51282,2,2,2,30,0,0,0,0,0,0,2,0,0,0,0,4,0,6,0,0,0,0,0,2,0,8,2,2,0,0,0,0,0,0,50,0,0,2,0,2,0,0,0,2,0,0,0,0,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,4,0,10,0,0,0,0,0,16,0,0,0,0,0,0,0,0,2,0,0,0,0,Class_8
+51283,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
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+51299,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,11,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+51300,1,0,0,0,0,1,0,0,0,0,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,2,0,1,0,0,0,0,0,0,1,0,1,0,0,1,3,0,1,0,24,0,0,0,3,0,0,0,2,6,0,0,2,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,8,3,0,0,Class_8
+51301,0,11,0,0,0,0,0,1,3,0,2,0,1,0,0,0,2,1,17,3,0,0,0,20,0,0,0,0,4,0,0,3,0,0,1,1,1,2,1,0,0,1,0,0,0,1,0,8,0,1,0,0,0,1,0,0,2,2,0,0,0,0,0,1,0,4,5,0,31,5,0,0,5,5,0,5,0,0,1,2,0,0,1,0,0,2,0,0,1,89,0,0,1,Class_8
+51302,0,0,0,2,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,22,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_8
+51303,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,4,0,0,0,0,0,2,0,2,1,0,0,1,0,0,0,0,0,0,0,2,1,0,0,2,0,1,0,1,0,0,1,1,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,1,0,1,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+51304,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,1,0,4,4,0,0,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_8
+51305,0,10,0,1,0,0,0,0,0,0,2,0,0,1,1,3,1,2,39,1,0,1,0,31,2,1,0,0,0,0,0,7,0,2,4,0,0,1,0,0,0,1,1,0,1,0,0,1,7,2,0,0,0,0,1,2,0,34,0,2,0,0,0,4,1,3,14,0,19,1,0,1,5,3,2,3,0,2,0,0,0,0,7,0,1,2,0,0,1,39,0,2,2,Class_8
+51306,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,4,0,0,0,2,0,2,0,1,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,0,0,0,0,2,2,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,4,0,Class_8
+51307,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,0,0,0,5,1,0,0,0,1,1,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,9,0,0,0,0,0,3,0,1,0,1,0,0,1,0,0,1,0,0,Class_8
+51308,3,0,0,0,0,0,0,7,0,0,0,0,3,0,0,0,1,3,0,0,0,1,0,4,0,0,1,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,0,0,0,6,0,0,0,0,0,2,0,0,1,0,5,0,0,2,0,1,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,0,0,Class_8
+51309,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,4,2,0,0,0,1,0,1,1,0,1,0,0,0,0,1,0,0,0,0,2,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,1,0,0,0,0,0,Class_8
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+51311,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,37,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+51312,0,0,0,0,0,0,2,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_8
+51313,0,3,0,1,0,0,1,0,0,0,2,0,1,0,2,0,0,1,0,2,1,4,0,4,0,0,2,0,0,0,0,0,0,1,0,0,0,5,1,0,0,0,0,2,0,0,3,0,1,1,0,0,0,3,0,0,3,4,0,2,2,1,0,0,0,0,0,0,15,2,0,0,0,1,1,3,0,0,0,0,0,0,2,0,0,0,1,0,0,13,0,0,0,Class_8
+51314,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51315,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,1,2,0,0,0,9,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,7,0,0,0,0,1,1,0,0,8,0,18,0,0,0,0,0,2,7,0,1,0,0,0,0,9,0,0,2,0,0,0,14,0,0,0,Class_8
+51316,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,5,0,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+51317,0,3,2,7,0,0,0,4,0,0,1,0,0,0,0,3,0,2,1,0,0,2,0,0,0,0,0,0,0,0,4,0,1,0,26,0,0,0,0,0,2,0,2,0,0,2,1,1,0,1,0,5,0,7,1,0,1,2,0,0,0,0,0,0,0,2,2,0,0,0,0,1,2,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51318,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+51319,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,6,0,0,0,1,0,0,3,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,Class_8
+51320,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,8,0,0,0,13,1,0,0,0,0,0,0,0,0,1,1,1,1,0,2,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,1,1,11,0,3,0,0,2,1,10,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,Class_8
+51321,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,4,0,0,0,0,0,3,1,1,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,4,12,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,Class_8
+51322,0,15,1,1,0,0,2,0,0,0,10,0,10,1,1,4,0,3,3,8,0,7,1,14,1,1,0,0,0,0,0,6,0,0,5,3,0,10,2,1,0,3,0,0,1,0,0,1,3,0,0,0,0,7,2,0,2,49,0,1,0,6,1,2,0,4,9,0,16,4,0,2,5,6,1,32,0,0,0,2,0,0,4,0,0,1,1,2,3,34,0,0,0,Class_8
+51323,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,2,0,4,0,0,0,10,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,11,0,0,0,1,1,0,0,1,1,1,3,1,0,0,1,0,0,6,0,0,0,1,0,0,3,0,0,0,0,1,0,3,1,0,0,Class_8
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+51399,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,1,0,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,19,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+51403,1,5,1,0,0,0,8,0,0,0,0,0,2,1,0,3,3,1,0,2,0,1,3,4,0,0,0,0,0,0,0,2,0,0,1,1,0,1,0,1,0,0,6,3,0,0,0,1,2,6,0,0,0,2,0,0,3,4,0,5,0,2,0,3,0,4,3,1,6,1,0,1,0,2,4,3,0,2,0,0,0,0,26,0,1,4,0,0,2,4,0,0,1,Class_8
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+51438,0,0,0,0,0,0,1,1,0,1,2,0,0,0,0,0,0,0,3,3,0,0,0,25,0,0,0,0,0,0,0,5,0,0,11,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,29,0,0,0,0,1,1,0,0,11,1,5,0,1,0,1,0,3,6,0,0,0,0,0,0,10,0,0,0,0,0,0,18,0,0,0,Class_8
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+51440,0,2,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,0,5,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,17,0,0,0,1,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,13,0,0,0,Class_8
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+51973,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+51975,0,3,0,1,0,0,0,2,0,0,0,0,1,0,0,1,0,1,87,2,0,0,0,12,0,0,0,0,0,0,0,4,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,3,0,1,0,0,1,1,0,0,1,0,20,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,3,0,9,0,0,0,Class_8
+51976,2,1,1,0,0,0,2,0,0,1,0,0,0,0,0,3,0,0,0,0,0,2,0,2,1,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,1,0,0,0,3,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,3,1,2,4,0,10,0,0,0,0,0,1,3,0,2,0,0,0,0,17,0,0,0,0,0,0,9,2,0,0,Class_8
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+51980,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,2,1,0,0,0,0,0,Class_8
+51981,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+51982,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,5,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,9,0,0,0,0,0,0,23,0,0,0,0,0,0,0,2,0,0,Class_8
+51983,0,6,1,7,1,0,1,1,1,1,5,1,0,0,0,4,5,3,2,11,0,3,6,3,0,0,0,1,0,1,10,2,1,2,16,4,0,11,10,1,0,2,0,4,4,0,0,2,6,3,1,5,1,9,4,2,1,7,1,1,0,2,1,2,1,1,6,0,0,2,1,0,0,1,1,5,0,1,0,3,0,1,1,0,0,0,3,1,7,5,0,1,3,Class_8
+51984,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,9,4,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,Class_8
+51985,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,3,0,0,0,10,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,2,0,1,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
+51986,1,4,26,56,0,2,0,0,0,3,1,0,2,0,0,5,3,19,2,1,0,7,0,8,0,2,2,8,0,0,6,1,0,0,33,2,0,3,0,3,2,0,0,5,0,10,0,0,3,0,0,0,0,10,0,1,1,4,2,2,0,2,4,0,2,4,2,0,0,2,0,0,0,0,4,1,0,0,0,3,0,13,0,0,0,1,0,0,5,0,0,0,0,Class_8
+51987,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51988,1,0,1,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,3,0,1,3,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_8
+51989,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,1,0,7,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,17,0,0,0,0,1,0,0,0,0,0,17,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,Class_8
+51990,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,0,0,Class_8
+51991,0,0,0,1,0,0,0,1,0,1,0,0,2,0,0,5,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,3,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_8
+51992,1,2,0,0,0,0,2,0,0,0,0,0,10,2,0,1,1,2,1,8,0,0,0,7,1,0,0,0,0,0,0,0,0,1,0,0,0,16,0,0,0,1,0,1,0,0,0,1,0,3,0,0,0,2,2,0,0,0,0,1,0,1,0,1,1,2,2,0,4,16,0,2,0,0,0,13,0,0,0,0,0,0,0,0,6,0,4,0,1,4,0,0,0,Class_8
+51993,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,1,2,1,0,0,1,6,0,0,0,0,0,0,4,0,0,0,0,0,0,9,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,1,1,14,0,0,0,3,0,1,1,0,0,2,19,1,0,1,2,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,10,0,0,0,Class_8
+51994,1,0,0,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+51995,0,0,2,2,0,0,0,0,0,4,0,0,2,0,0,12,2,16,2,0,0,0,0,0,4,2,4,0,0,0,4,0,0,0,20,0,0,0,0,0,0,6,0,0,0,4,0,2,0,0,0,38,0,4,0,4,0,0,0,0,0,2,0,2,0,0,4,0,0,0,0,0,4,12,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_8
+51996,2,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,3,0,0,0,3,0,1,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,Class_8
+51997,0,4,0,1,1,0,4,2,0,1,4,0,0,0,0,1,0,1,0,2,0,3,0,6,1,0,0,0,0,1,0,1,1,2,0,1,1,6,0,0,4,1,0,4,0,0,0,7,1,1,0,0,1,2,1,0,3,9,0,0,0,3,0,0,0,2,4,0,7,3,0,2,1,0,37,3,0,0,1,0,1,0,5,0,0,0,0,1,3,34,1,0,0,Class_8
+51998,0,2,0,0,0,1,4,0,0,0,2,0,1,0,0,1,7,1,2,0,0,3,0,7,0,0,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,0,2,1,1,0,0,0,4,0,0,1,0,0,0,0,0,1,1,0,1,7,0,14,0,0,0,1,1,1,13,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_8
+51999,0,1,0,2,0,0,0,1,0,0,0,0,1,1,0,2,4,3,0,2,0,2,0,4,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,2,2,0,1,0,0,3,1,0,5,2,0,1,0,1,0,0,0,0,5,0,4,1,0,0,1,1,1,3,0,4,0,5,0,0,8,0,0,0,0,0,0,1,0,0,0,Class_8
+52000,3,1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,1,5,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,1,1,0,0,6,0,1,0,0,1,0,0,0,2,0,8,1,0,0,0,2,1,2,0,1,0,0,0,0,1,0,0,1,0,0,0,13,0,0,0,Class_8
+52001,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,5,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,5,0,0,0,0,0,0,0,37,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_8
+52002,0,6,2,4,0,0,0,0,0,0,0,1,1,1,0,1,1,2,0,0,0,3,0,1,0,2,0,1,0,0,0,0,0,2,15,2,0,0,0,1,0,0,0,4,0,0,0,0,3,0,0,1,0,5,0,0,0,0,0,0,0,3,0,1,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,1,1,Class_8
+52003,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52004,0,0,0,0,0,0,0,7,0,0,0,1,1,3,0,1,1,0,1,4,0,0,0,35,0,0,0,0,0,5,0,7,2,0,1,0,1,7,0,1,7,0,0,0,2,0,0,2,0,0,0,0,1,1,2,0,0,46,0,0,0,2,0,1,0,2,22,0,19,3,0,2,3,1,1,28,0,1,0,0,0,0,2,0,0,2,0,0,0,4,4,0,0,Class_8
+52005,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,4,0,0,0,0,0,9,0,0,0,0,4,22,0,2,1,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+52006,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,2,0,0,0,0,0,0,0,39,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_8
+52007,0,4,0,3,0,0,0,0,0,0,2,0,1,0,1,1,1,0,0,1,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,5,5,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,4,0,0,0,20,0,0,0,2,0,0,0,0,3,0,5,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,0,0,0,0,9,0,0,0,Class_8
+52008,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,0,12,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52009,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,0,0,4,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,9,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,5,0,1,8,0,0,0,1,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,Class_8
+52010,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+52011,0,0,0,0,0,0,1,1,0,1,0,0,10,0,0,0,0,0,0,3,0,1,0,15,0,1,0,0,0,0,0,3,0,0,0,0,0,8,3,1,0,0,0,1,0,0,0,1,0,1,0,0,0,4,0,0,0,1,0,0,0,1,0,1,0,0,17,0,4,1,0,0,1,0,0,16,0,0,0,1,0,0,2,0,0,0,0,1,0,5,0,0,0,Class_8
+52012,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1,0,1,0,0,1,0,14,0,0,0,0,8,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,Class_8
+52013,0,0,14,18,0,0,0,0,4,0,0,0,0,0,0,4,0,2,2,0,0,0,0,8,0,2,0,4,0,0,0,0,0,0,26,0,2,4,0,0,2,0,0,2,0,2,0,0,6,2,0,0,0,8,0,2,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,20,2,0,0,0,0,0,2,0,6,0,0,0,0,0,0,2,0,0,0,0,Class_8
+52014,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,9,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_8
+52015,0,0,0,0,0,0,3,0,0,5,3,1,0,0,0,0,0,0,3,0,0,1,7,25,0,0,0,0,0,1,0,2,0,0,1,0,0,3,2,0,0,0,0,0,1,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,32,2,0,0,3,1,0,29,0,0,5,0,0,0,1,0,0,1,2,1,0,6,0,0,0,Class_8
+52016,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,5,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52017,0,3,0,1,0,0,3,0,0,0,0,0,3,0,0,3,0,0,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,9,0,1,0,0,0,0,4,5,0,0,0,0,0,0,1,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,Class_8
+52018,0,2,0,1,0,0,0,3,12,0,0,0,1,0,0,1,0,2,96,2,0,0,0,14,0,0,0,0,0,0,0,2,0,0,1,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,1,1,0,0,0,0,19,0,0,0,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,12,0,0,0,Class_8
+52019,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,1,0,2,2,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,6,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52020,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,1,0,2,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,12,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,Class_8
+52021,0,0,1,1,3,0,3,0,0,1,0,2,0,1,0,1,0,0,0,5,0,0,0,2,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,3,0,0,0,0,1,5,0,0,1,2,1,0,0,0,0,0,0,1,0,2,24,1,0,0,0,6,2,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_8
+52022,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,4,1,0,0,23,0,0,0,0,0,0,0,2,3,0,6,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,Class_8
+52023,0,0,0,0,0,0,0,0,18,0,0,0,0,0,1,4,0,0,0,0,2,0,0,1,3,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,14,0,0,6,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,Class_8
+52024,0,0,0,0,0,0,0,1,1,5,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,3,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,3,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,0,1,Class_8
+52025,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,Class_8
+52026,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,5,1,1,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,1,4,1,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,6,2,2,0,3,0,1,1,0,3,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,Class_8
+52027,0,6,1,0,0,0,5,1,0,3,0,0,2,0,0,1,1,1,4,11,0,5,1,9,0,0,0,1,1,0,0,0,0,0,3,2,3,4,0,0,0,1,1,7,0,1,0,3,1,1,0,0,0,0,2,1,3,1,0,1,0,0,0,0,1,8,3,0,34,5,0,0,4,3,1,10,0,2,0,1,0,0,5,0,0,1,4,0,1,34,0,0,1,Class_8
+52028,0,0,6,36,0,0,0,0,2,0,2,0,2,0,0,0,0,6,0,0,0,6,0,2,0,4,0,4,0,0,12,0,2,0,30,0,0,4,0,0,0,2,2,0,0,6,0,0,4,0,0,0,0,12,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,4,0,0,2,0,0,0,0,2,0,4,0,0,0,2,2,0,6,0,0,0,4,Class_8
+52029,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52030,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,2,1,1,0,0,1,6,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,10,1,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_8
+52031,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,3,0,1,0,2,0,3,1,0,1,1,0,0,0,0,1,0,2,0,0,2,0,1,1,0,0,1,0,2,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,3,1,3,0,1,0,3,1,0,8,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,Class_8
+52032,0,6,0,0,0,0,1,1,0,0,0,0,2,0,0,0,2,1,3,9,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,3,0,0,0,0,2,1,0,0,15,0,2,0,0,0,1,0,1,0,0,15,1,0,0,0,3,0,0,0,0,2,1,0,0,4,0,0,0,0,0,1,17,1,0,0,Class_8
+52033,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,2,0,1,0,0,0,3,0,0,0,0,1,1,0,3,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,0,0,17,0,0,0,0,0,1,0,0,0,1,8,0,2,0,0,0,0,0,Class_8
+52034,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,1,3,0,1,0,12,1,0,0,0,0,0,0,0,0,1,0,2,0,10,0,1,1,0,1,0,0,0,0,1,2,0,0,0,2,2,0,0,0,0,1,1,0,2,0,3,3,3,1,0,20,0,0,1,0,0,1,15,0,0,0,0,0,0,0,0,1,2,1,0,0,7,0,0,0,Class_8
+52035,0,1,0,1,2,0,0,0,6,0,2,1,10,0,0,1,3,0,2,3,0,1,9,12,0,0,0,0,0,0,0,4,0,0,0,2,0,8,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,4,0,3,2,1,0,0,0,4,0,2,0,1,13,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,5,0,0,0,Class_8
+52036,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,18,1,0,1,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52037,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,4,0,2,0,4,0,0,0,1,1,0,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,2,6,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52038,2,0,0,0,1,0,0,0,2,0,0,0,0,0,0,2,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,1,0,2,1,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,5,10,0,1,2,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,2,0,0,0,0,Class_8
+52039,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,1,3,0,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,1,0,0,0,1,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,Class_8
+52040,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,15,0,0,0,5,0,3,1,3,6,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,3,0,0,Class_8
+52041,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,Class_8
+52042,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,2,0,0,3,3,0,2,1,4,2,0,0,0,0,1,0,1,3,0,0,0,0,1,3,0,0,0,0,2,0,0,1,2,0,0,3,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,5,11,0,0,1,0,0,3,1,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,Class_8
+52043,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+52318,2,0,0,2,0,0,0,1,0,1,0,0,2,0,0,0,1,0,2,3,0,0,0,9,0,0,0,0,0,1,0,9,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,9,0,3,3,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,Class_8
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+52399,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,1,7,0,0,0,0,0,0,0,0,0,1,0,4,4,0,0,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,Class_8
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+52425,0,3,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,2,0,0,0,2,0,2,1,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,2,0,0,2,0,0,0,0,0,1,1,0,2,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,Class_8
+52426,0,3,1,0,0,0,2,0,1,0,4,1,12,0,2,2,3,1,0,8,0,1,0,11,0,0,0,0,0,0,0,7,0,1,3,5,1,19,1,1,0,0,0,0,0,1,0,0,5,3,0,1,1,6,1,0,0,11,0,0,0,2,0,1,0,4,10,0,32,1,0,4,2,0,0,35,0,2,2,0,0,2,0,0,0,2,0,1,2,9,1,0,0,Class_8
+52427,1,0,0,1,0,1,0,0,0,0,0,1,1,0,0,1,0,1,0,2,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,2,4,1,1,0,0,0,0,0,0,2,0,0,0,0,0,3,0,0,0,17,0,0,0,1,0,2,0,0,2,0,4,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,1,0,Class_8
+52428,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,Class_8
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+52431,0,0,0,0,0,0,0,2,0,2,0,1,0,0,2,2,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,1,0,3,0,0,0,Class_8
+52432,0,3,0,0,0,0,2,0,1,0,1,0,0,0,0,4,0,0,0,3,0,3,1,14,1,0,0,0,0,0,0,3,0,0,0,2,0,1,1,0,0,1,0,0,0,0,0,4,0,1,0,0,0,5,2,1,0,0,0,0,0,0,0,1,0,0,11,0,8,0,0,0,0,0,0,6,0,0,1,0,0,0,8,0,0,0,1,0,0,5,0,0,0,Class_8
+52433,1,19,0,1,0,0,6,0,1,0,1,1,3,0,0,2,0,3,69,1,0,2,5,12,0,0,0,0,0,0,0,10,0,0,1,2,0,4,1,1,1,0,0,1,1,0,0,11,2,2,3,0,0,3,0,1,2,4,0,1,0,0,2,0,0,4,7,0,11,7,0,1,7,0,4,23,0,0,0,1,0,0,0,0,0,2,0,0,0,10,0,0,0,Class_8
+52434,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,1,0,0,1,10,2,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,16,3,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,Class_8
+52435,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,18,0,0,0,0,14,1,0,0,0,0,0,0,2,0,0,0,0,0,3,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,2,0,1,0,1,4,0,0,2,0,0,2,0,0,1,0,1,0,1,0,0,2,0,0,0,0,0,0,8,0,0,0,Class_8
+52436,0,1,0,0,0,0,0,1,1,8,1,0,0,1,2,0,1,0,0,11,0,2,0,6,1,0,0,0,1,0,0,5,1,0,0,2,2,0,2,0,3,0,0,1,2,0,2,0,0,0,0,0,0,0,4,0,0,1,0,1,0,2,0,2,0,1,9,0,2,2,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,2,0,1,0,7,0,0,0,Class_8
+52437,0,0,12,38,0,0,0,0,0,2,0,0,4,0,0,2,0,12,0,0,2,6,0,8,0,10,0,6,0,0,2,0,0,0,34,0,0,2,0,0,0,6,0,4,0,4,0,0,0,0,0,4,0,10,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,0,4,0,0,4,0,Class_8
+52438,4,0,0,1,1,0,8,0,0,1,0,0,13,0,2,1,0,1,0,1,0,4,0,11,0,0,0,0,0,2,0,7,0,0,0,4,0,2,0,1,0,0,0,2,1,0,2,3,0,0,0,0,2,3,0,2,0,0,0,1,0,1,0,3,0,1,19,3,0,3,0,0,3,7,2,12,0,43,0,1,0,0,5,0,0,1,1,0,0,2,2,0,0,Class_8
+52439,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,Class_8
+52440,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,1,0,0,2,2,0,0,0,0,0,0,0,0,1,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,Class_8
+52441,0,3,16,63,0,0,0,0,0,2,0,2,0,0,0,4,2,8,0,0,0,2,0,6,0,2,0,8,0,0,14,0,0,0,59,2,2,0,0,0,0,4,0,2,0,0,0,0,6,0,0,0,0,12,0,0,0,2,2,0,0,2,2,2,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,22,0,4,2,0,0,0,0,2,8,4,0,0,0,Class_8
+52442,2,14,0,0,0,0,1,5,0,0,0,2,0,2,0,1,2,4,1,12,0,2,0,28,0,0,1,0,0,1,0,3,4,2,1,3,1,3,0,1,9,0,0,1,1,0,0,5,0,0,0,0,2,2,1,0,0,60,0,0,0,2,1,0,1,2,29,2,30,5,1,0,9,3,1,41,0,1,1,0,0,0,9,0,0,2,2,0,0,6,4,0,0,Class_8
+52443,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
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+52479,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,Class_8
+52480,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,4,0,0,0,0,0,0,0,40,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52481,4,4,0,1,1,0,9,3,1,2,0,0,2,2,3,3,0,1,0,1,0,5,0,8,0,1,0,1,0,1,0,8,2,0,0,10,2,3,0,1,0,0,0,6,1,1,1,3,0,1,0,1,2,9,1,2,1,0,1,1,0,1,0,2,0,2,13,2,0,4,0,2,3,10,1,11,0,33,1,1,0,0,7,0,0,1,0,1,1,2,3,1,1,Class_8
+52482,0,19,4,20,0,0,0,0,0,6,0,0,0,2,0,6,2,4,1,22,0,5,0,2,0,3,0,0,3,0,12,0,0,0,61,0,0,9,2,3,2,1,9,8,0,0,0,0,8,0,0,2,0,8,1,0,0,11,0,0,2,0,0,2,0,0,0,0,8,0,0,0,4,0,2,0,2,0,0,10,0,1,0,0,0,0,2,0,0,6,0,0,0,Class_8
+52483,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,3,0,0,2,0,24,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,3,0,0,0,0,0,0,0,0,5,0,0,0,22,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
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+52813,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52814,0,2,0,0,0,0,0,1,3,9,0,0,0,0,1,0,0,0,0,7,0,2,0,9,2,0,0,0,1,0,0,6,0,0,0,2,3,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,1,0,2,2,4,0,0,9,0,2,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,3,0,0,0,2,0,0,0,Class_8
+52815,0,0,0,0,1,0,0,4,0,0,1,0,11,0,0,0,0,0,0,0,1,0,0,3,1,0,1,0,0,0,0,0,2,1,0,2,0,2,0,0,1,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,1,0,3,0,1,0,0,4,1,0,1,2,1,0,0,0,2,0,6,0,0,0,0,8,0,1,0,0,0,0,0,0,2,0,Class_8
+52816,0,0,0,0,0,0,0,0,1,0,1,0,10,0,0,0,0,0,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,8,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+52817,0,3,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,1,0,2,0,0,0,2,0,4,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,2,0,1,0,0,1,0,0,0,0,2,0,3,1,1,0,6,0,0,0,0,0,0,13,0,0,0,1,0,0,0,0,0,0,Class_8
+52818,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,2,0,0,1,6,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,2,0,0,19,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,Class_8
+52819,0,2,2,0,0,0,0,2,0,0,0,0,10,0,0,0,0,0,3,4,0,0,0,23,0,0,0,0,0,0,0,7,1,0,1,2,1,10,6,1,0,0,1,0,0,0,0,0,0,4,0,0,0,1,0,1,1,5,0,0,0,0,0,1,0,0,10,0,5,1,0,0,3,0,0,21,0,2,0,0,0,0,1,0,2,0,0,0,0,27,0,0,0,Class_8
+52820,0,4,1,0,0,0,0,2,0,0,3,0,0,0,0,0,0,2,0,10,0,3,0,8,1,0,0,0,0,0,0,2,0,0,2,1,1,1,0,0,1,0,0,0,0,0,0,6,0,3,0,1,0,0,0,0,1,2,0,0,0,1,0,0,0,4,0,0,16,1,0,0,2,0,1,5,0,3,0,0,0,0,2,0,1,0,1,0,0,11,1,0,0,Class_8
+52821,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,2,0,0,0,0,0,44,0,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52822,0,1,0,0,0,0,0,0,0,0,2,0,2,1,0,0,1,2,65,0,0,0,0,16,0,1,0,0,0,0,3,5,1,0,4,0,0,2,0,1,1,1,0,0,0,0,0,4,0,1,0,1,0,0,1,0,1,6,0,3,0,0,0,0,0,1,4,0,41,0,0,0,1,3,2,7,0,0,0,0,0,0,0,0,0,1,0,0,0,37,0,0,0,Class_8
+52823,0,1,0,0,1,0,1,0,0,1,2,0,0,2,0,0,0,0,1,6,0,1,0,6,0,0,0,0,0,0,0,1,2,0,7,0,0,3,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,1,1,0,0,1,0,0,2,0,0,0,0,2,1,7,0,0,0,Class_8
+52824,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,6,0,1,1,0,0,7,8,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,5,0,0,0,Class_8
+52825,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,24,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52826,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,6,0,0,3,0,0,0,16,0,0,0,0,0,0,0,1,3,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,4,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0,0,7,0,2,2,1,0,1,0,3,17,0,2,0,0,1,0,0,0,2,1,1,1,0,0,0,0,0,Class_8
+52827,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,30,0,0,0,0,7,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,2,3,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_8
+52828,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,5,0,41,0,0,0,0,0,1,4,2,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,Class_8
+52829,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,2,1,0,0,36,4,9,6,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_8
+52830,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,10,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,4,0,0,5,0,0,0,0,0,1,10,0,2,0,0,0,0,0,3,0,1,0,0,1,6,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,5,0,0,14,0,Class_8
+52831,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,4,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,Class_8
+52832,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,2,1,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,2,3,5,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+52833,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,6,0,1,2,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,0,0,7,1,0,0,1,0,0,5,1,1,0,1,0,0,3,0,0,3,0,0,0,7,0,0,0,Class_8
+52834,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,7,0,0,0,0,0,0,0,47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52835,0,0,0,0,0,0,2,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,13,1,0,0,0,1,0,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,1,0,7,2,3,1,0,0,1,13,0,1,0,0,0,0,2,0,0,1,1,1,0,0,0,0,0,Class_8
+52836,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,2,0,1,1,0,0,2,0,11,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2,0,0,0,0,1,0,3,0,0,2,0,0,1,0,0,0,0,0,0,2,0,3,0,4,76,1,0,0,0,13,1,2,0,0,0,0,0,0,1,0,0,1,1,0,0,5,0,0,1,Class_8
+52837,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_8
+52838,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,0,0,1,46,1,0,1,0,5,0,0,0,1,0,0,0,3,0,1,1,1,0,1,0,0,0,3,0,2,2,0,0,3,2,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,0,3,9,0,15,2,0,1,2,1,1,12,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,Class_8
+52839,1,0,0,0,0,0,2,1,0,0,2,0,6,0,0,1,0,1,0,0,0,0,0,14,1,0,0,0,0,0,0,1,0,0,1,2,0,1,2,0,1,0,0,0,1,0,0,2,0,0,0,0,2,1,0,0,2,3,0,1,0,1,0,0,0,0,27,1,0,1,2,0,0,0,0,9,0,14,0,0,0,0,7,0,0,1,0,0,0,0,6,0,0,Class_8
+52840,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,19,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_8
+52841,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_8
+52842,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,3,3,0,0,0,0,1,0,2,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,7,0,0,0,0,0,4,0,0,0,1,0,0,1,1,0,0,0,0,Class_8
+52843,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,4,0,4,0,0,0,1,0,3,1,0,0,1,0,0,0,0,0,3,1,3,0,0,0,0,1,0,0,1,0,0,3,2,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,1,0,3,5,1,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,Class_8
+52844,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,8,2,0,0,0,1,1,0,2,1,0,0,2,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,6,0,0,0,0,0,0,0,23,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,Class_8
+52845,1,0,0,0,0,0,10,0,0,2,0,0,0,6,2,1,1,2,0,2,0,1,0,4,0,0,0,0,1,1,0,0,1,0,0,1,2,4,0,1,1,0,0,4,0,0,0,0,0,5,0,0,0,0,1,0,0,1,1,0,0,3,0,3,3,1,4,1,0,6,0,0,0,0,1,0,0,3,0,0,8,0,8,0,3,1,4,2,1,0,2,0,0,Class_8
+52846,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52847,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,3,0,0,0,2,0,0,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52848,2,2,2,0,0,1,0,0,1,0,2,0,10,0,0,0,0,1,6,4,0,0,0,19,0,0,0,0,0,0,0,6,0,0,1,4,0,4,2,1,0,1,0,0,2,0,0,0,0,1,0,0,0,1,0,0,1,31,0,0,0,0,0,2,1,0,22,0,21,0,0,0,2,0,1,21,0,4,0,0,0,0,16,0,0,4,0,0,0,36,0,0,0,Class_8
+52849,1,3,1,0,0,1,1,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,32,0,0,0,1,0,0,0,1,6,0,4,0,0,0,3,1,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,9,2,0,0,Class_8
+52850,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,Class_8
+52851,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,115,0,7,7,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52852,1,0,0,0,0,0,3,1,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,7,0,0,0,0,0,2,0,6,0,0,0,4,2,0,0,4,0,0,1,0,1,0,0,4,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,2,0,0,14,3,0,1,2,0,2,6,2,10,0,35,0,1,0,0,4,0,0,2,0,1,0,0,4,0,0,Class_8
+52853,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,2,0,0,1,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52854,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,7,0,0,0,0,1,1,0,2,1,1,0,3,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,9,0,0,0,1,0,0,1,20,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_8
+52855,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,8,3,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52856,0,0,0,0,0,1,0,0,0,0,8,0,0,0,0,0,2,0,3,4,0,0,0,7,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2,32,0,0,0,0,0,0,0,0,10,0,24,0,0,1,1,0,7,0,0,0,0,0,0,0,4,0,0,0,2,0,0,21,0,0,0,Class_8
+52857,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,2,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,2,7,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,0,4,0,0,0,Class_8
+52858,0,20,3,6,0,1,2,0,1,3,0,1,0,0,2,13,0,3,3,0,6,11,3,4,0,7,12,0,0,1,7,0,0,0,54,1,0,6,1,2,0,1,2,4,0,4,1,1,5,3,1,17,2,17,0,0,0,13,0,0,5,0,0,2,0,4,1,0,1,0,0,2,0,50,0,1,0,0,0,7,0,1,0,0,0,0,0,0,4,9,0,2,2,Class_8
+52859,0,0,0,0,2,0,0,2,1,1,2,1,0,2,0,2,0,0,0,0,0,3,0,5,1,0,2,1,0,1,0,0,0,1,0,1,0,3,0,0,0,0,0,7,0,0,3,1,0,0,0,0,1,1,0,0,0,0,0,0,0,4,0,0,1,0,1,33,1,4,0,0,1,0,1,0,0,0,2,1,3,0,0,0,0,0,0,0,8,0,0,0,0,Class_8
+52860,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,4,1,7,0,1,0,1,0,0,0,0,0,0,0,4,0,0,0,4,0,3,0,0,4,1,1,0,0,0,0,0,1,0,3,0,0,6,0,0,2,3,0,2,0,7,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,Class_8
+52861,0,0,0,0,1,0,0,1,0,2,6,0,0,0,0,2,1,3,0,0,0,1,0,2,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,3,4,0,0,0,1,0,3,0,1,0,5,6,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,Class_8
+52862,0,0,0,0,0,0,1,6,0,1,1,0,1,2,0,0,7,2,0,1,0,1,0,9,0,0,0,0,0,0,0,0,2,2,0,0,0,3,0,0,1,1,0,1,0,0,0,6,0,0,1,0,0,0,2,0,1,0,0,7,0,3,0,6,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,1,1,0,0,0,1,Class_8
+52863,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,46,1,7,13,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+52864,0,0,0,0,0,0,0,1,1,1,0,0,0,1,3,0,0,0,0,1,0,0,0,9,5,0,0,0,0,0,0,2,0,0,0,2,2,0,0,3,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,10,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,7,0,0,0,Class_8
+52865,2,2,1,1,1,0,6,1,0,0,0,0,14,0,1,0,0,2,0,8,0,2,0,10,1,0,0,0,0,0,0,8,1,0,1,3,1,7,3,1,0,0,0,2,0,0,12,0,3,3,0,0,1,12,3,1,0,1,0,0,0,1,0,1,0,3,3,6,1,5,2,0,1,2,0,13,0,37,2,1,0,0,9,1,2,0,5,2,6,1,0,0,0,Class_8
+52866,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,3,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,0,22,0,0,0,2,1,1,0,0,3,0,4,0,0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,6,2,0,0,Class_8
+52867,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,11,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,10,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,Class_8
+52868,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,0,0,1,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52869,0,0,0,1,1,0,1,0,0,2,1,0,11,2,0,0,1,0,0,8,0,1,0,8,2,0,0,0,0,0,0,0,0,2,0,1,1,11,0,0,2,2,0,2,0,3,1,2,0,1,0,0,1,1,1,0,2,8,0,1,0,3,0,2,0,0,1,0,12,10,0,2,0,0,0,17,0,0,0,0,0,0,0,0,3,0,4,1,1,2,0,0,0,Class_8
+52870,0,1,0,0,0,0,0,1,1,5,0,0,0,1,1,0,0,0,1,4,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,10,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,Class_8
+52871,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,1,0,4,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,1,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+52872,3,8,1,1,0,0,0,3,1,0,1,0,10,0,0,0,3,2,3,15,1,3,0,37,0,0,0,0,0,1,0,8,0,2,2,2,2,17,0,0,0,2,0,3,0,0,0,1,3,4,0,3,0,6,2,1,0,11,0,0,0,3,0,1,0,1,6,0,24,6,0,2,10,0,1,20,0,2,0,5,0,0,10,0,2,0,0,2,0,109,0,1,0,Class_8
+52873,0,14,6,10,0,0,0,0,12,0,6,2,0,0,0,8,2,0,2,0,2,2,0,12,0,2,0,4,0,0,16,0,0,4,30,0,0,4,0,0,4,0,0,2,0,0,0,0,4,0,2,0,0,16,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,2,0,2,0,0,0,0,0,0,2,12,0,2,0,0,0,2,0,0,0,0,0,0,0,Class_8
+52874,0,1,0,0,0,0,7,1,0,1,0,0,12,0,0,2,2,1,1,2,0,1,3,10,0,1,0,0,3,0,0,0,1,0,0,2,4,5,1,2,0,0,0,2,1,1,1,3,0,4,0,0,2,2,8,0,1,0,0,0,0,0,1,5,2,0,4,0,1,4,1,0,0,0,0,10,0,57,0,0,0,0,3,0,0,0,3,0,2,2,3,0,3,Class_8
+52875,0,0,10,26,0,0,0,0,0,0,0,2,0,2,0,2,0,2,0,0,0,0,0,0,0,2,0,2,0,0,4,2,0,0,24,0,0,6,0,0,0,4,0,0,0,0,0,0,8,0,0,12,0,4,0,0,0,2,0,2,0,2,0,0,0,0,0,0,0,2,0,0,0,4,0,2,0,0,0,4,0,0,0,0,0,0,2,0,0,4,0,0,0,Class_8
+52876,0,2,0,0,0,0,0,2,1,0,2,1,12,0,0,1,9,1,1,1,0,0,0,7,1,0,0,0,7,0,0,0,0,1,0,3,0,1,0,0,1,0,0,10,2,0,0,8,1,0,0,0,0,6,5,0,1,1,1,1,0,4,0,1,0,3,16,1,1,3,3,0,1,0,0,4,5,16,0,1,0,0,2,0,0,0,0,0,0,0,5,0,0,Class_8
+52877,0,1,0,0,0,0,0,0,0,0,2,0,1,0,0,2,0,1,0,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,1,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,Class_8
+52878,0,6,0,0,2,0,2,3,0,2,3,0,1,4,1,4,0,5,0,12,0,3,0,11,1,0,0,0,0,0,0,2,3,2,1,0,1,38,1,5,3,4,0,6,1,0,0,5,2,4,0,1,1,8,0,0,4,1,0,3,0,9,3,0,0,1,0,0,29,14,1,4,0,0,1,14,0,0,0,0,0,0,15,0,4,1,0,0,4,11,0,1,0,Class_8
+52879,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,8,0,1,1,1,0,0,0,2,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,Class_8
+52880,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,2,0,2,1,2,0,0,0,0,0,2,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,7,11,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,Class_8
+52881,0,1,0,0,0,0,0,1,0,0,0,0,10,0,0,0,0,0,0,1,0,0,0,7,0,0,0,0,0,0,0,2,0,1,0,2,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,10,0,0,0,0,0,1,0,0,8,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,Class_8
+52882,2,5,0,1,1,0,2,3,1,0,4,1,0,0,0,0,0,0,2,5,0,2,0,15,0,0,0,0,0,1,0,5,1,1,0,0,1,4,0,0,3,0,0,0,0,0,0,9,3,0,0,0,1,5,1,0,5,11,0,0,0,1,2,1,0,1,11,0,5,3,0,0,11,1,75,8,0,0,0,0,0,2,10,0,0,0,2,1,3,52,1,0,0,Class_8
+52883,1,0,1,0,0,0,0,0,2,2,0,0,1,0,0,5,0,0,0,0,0,2,0,5,2,2,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,1,4,4,0,0,0,0,0,11,4,0,0,0,0,0,1,0,0,2,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_8
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+52885,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,2,1,5,0,0,0,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,6,0,0,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_8
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+52920,2,0,4,0,1,0,2,1,0,4,1,0,0,0,0,4,0,0,0,2,0,2,1,2,1,0,2,0,1,0,0,0,0,1,1,1,0,7,0,1,1,0,0,2,0,0,11,0,1,1,0,0,0,3,1,0,0,0,4,0,0,11,1,0,0,2,5,2,0,4,0,1,2,0,1,0,0,0,0,3,7,1,1,0,8,2,0,0,19,0,0,0,0,Class_8
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+52922,0,0,0,0,1,0,9,1,0,2,0,1,1,0,0,1,1,0,0,2,0,0,0,6,0,0,0,0,2,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,4,2,1,0,2,1,0,0,1,0,0,1,0,3,0,0,1,0,10,0,0,0,1,0,0,1,0,0,0,Class_8
+52923,2,0,5,1,6,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,5,0,0,1,1,1,4,0,1,0,1,1,0,8,1,0,4,0,0,0,2,0,2,0,0,1,1,2,0,1,0,1,8,2,0,5,0,0,0,0,3,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,1,8,1,5,0,26,0,1,0,0,19,0,0,0,0,Class_8
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+53221,0,11,0,1,0,0,0,1,0,0,4,0,2,1,0,2,1,3,2,6,0,0,2,12,1,0,0,0,1,0,1,1,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,42,1,0,0,1,0,0,0,1,3,0,21,2,0,0,1,5,2,7,0,2,0,1,0,0,16,0,0,0,1,0,0,37,1,0,0,Class_8
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+53224,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,3,3,0,1,5,0,2,1,3,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,2,0,2,0,0,0,5,2,1,0,0,0,2,0,0,0,1,0,1,0,0,0,0,1,4,2,0,4,3,0,0,0,0,0,2,0,1,0,0,0,0,16,0,0,0,0,0,0,1,1,0,0,Class_8
+53225,0,5,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,2,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,0,7,0,1,0,1,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_8
+53226,0,1,0,3,1,4,0,0,0,0,1,2,0,3,0,5,0,2,0,2,0,1,0,2,1,0,0,1,1,0,0,2,2,4,3,1,1,8,0,0,3,0,0,2,0,0,1,4,6,1,0,0,0,7,0,0,0,0,0,1,0,5,1,0,0,0,2,16,0,4,1,2,0,0,0,0,0,3,0,2,2,3,0,0,1,0,0,0,17,0,2,1,0,Class_8
+53227,0,3,1,0,0,0,11,1,1,1,0,0,14,2,0,1,1,1,3,7,0,1,0,29,0,0,0,0,0,2,1,4,3,1,0,0,3,6,0,2,3,0,0,0,2,0,0,10,1,3,0,0,0,1,3,0,0,35,0,1,1,1,0,1,0,5,11,0,1,7,0,0,0,1,1,0,0,2,0,1,0,0,4,0,2,0,0,0,0,6,2,1,0,Class_8
+53228,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,9,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,Class_8
+53229,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53230,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,7,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+53231,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,7,0,0,0,0,0,0,0,49,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,Class_8
+53232,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,2,0,2,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,1,3,10,0,1,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+53233,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,2,0,2,0,2,0,3,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,7,0,0,4,0,0,1,0,0,0,3,1,0,0,0,0,0,0,0,0,1,1,1,0,3,0,2,0,0,0,0,0,0,1,1,0,2,1,0,0,0,1,0,0,0,5,0,0,1,0,Class_8
+53234,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,6,0,0,0,Class_8
+53235,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,3,0,0,1,0,2,3,0,3,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,2,13,5,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,5,4,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,Class_8
+53236,3,10,1,1,0,0,1,5,0,1,1,0,10,0,0,1,0,2,35,6,0,2,0,24,0,0,0,0,0,0,0,6,0,0,1,2,0,4,1,0,0,0,0,3,0,0,0,6,0,1,0,0,0,1,1,0,0,33,0,0,0,2,0,3,0,1,19,0,55,0,0,1,5,1,1,13,0,1,0,2,0,0,10,0,0,1,1,3,0,46,2,0,5,Class_8
+53237,0,0,0,0,0,0,1,2,1,0,0,1,0,0,0,1,0,0,25,1,0,1,0,12,0,0,0,0,0,4,0,6,0,0,0,0,1,0,1,2,0,0,0,0,1,0,0,1,0,0,0,0,0,1,3,1,3,0,0,0,0,1,0,1,0,0,6,0,19,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_8
+53238,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,3,0,8,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,8,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,Class_8
+53239,3,6,0,0,0,1,0,1,0,1,17,3,10,0,0,0,0,0,11,5,0,0,2,15,0,0,0,0,0,0,0,4,0,1,1,2,2,4,0,2,0,0,0,1,0,0,0,3,0,1,0,4,0,2,0,1,1,116,0,0,0,0,1,1,0,0,12,1,35,0,0,0,3,12,3,25,1,0,0,1,0,0,4,0,1,0,0,0,0,39,0,0,0,Class_8
+53240,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,4,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,1,3,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,1,0,1,0,0,6,0,13,0,0,0,0,5,0,1,0,2,0,0,0,0,0,0,Class_8
+53241,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+53242,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,7,0,0,1,0,0,1,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+53277,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,10,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53278,0,0,0,0,0,0,0,0,18,3,0,0,0,0,0,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,0,10,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,2,7,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,2,0,0,0,Class_8
+53279,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,0,0,0,3,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,1,0,1,0,11,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+53280,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,4,2,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,3,1,0,0,18,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Class_8
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+53282,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,2,2,1,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,2,0,0,2,0,1,0,0,0,3,0,0,1,0,6,0,0,0,0,0,2,3,0,2,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,Class_8
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+53304,0,0,4,3,0,0,1,2,0,1,1,0,1,2,0,6,1,1,0,1,0,5,1,1,2,3,3,1,0,0,0,0,2,1,0,0,0,8,0,1,3,0,0,4,0,1,29,1,1,1,0,0,2,13,4,0,2,0,0,0,0,22,2,5,3,4,6,1,1,2,2,3,2,0,1,0,0,1,0,3,16,0,2,0,17,1,1,1,24,0,0,1,0,Class_8
+53305,5,1,0,0,0,0,6,2,0,0,1,0,11,2,0,1,0,0,0,7,1,1,0,2,2,0,0,0,0,0,0,7,0,1,0,2,1,3,0,4,0,0,0,0,0,0,5,4,0,1,0,0,0,1,0,1,0,0,0,1,0,3,1,2,2,1,6,0,1,3,1,0,3,0,0,14,0,27,2,0,1,0,0,0,1,0,1,0,1,0,1,0,0,Class_8
+53306,0,14,0,3,0,0,1,0,0,1,0,1,1,1,0,7,0,1,1,4,0,5,0,16,0,1,0,0,0,0,1,0,1,0,1,0,1,11,0,1,2,1,0,2,2,3,0,0,4,3,0,0,0,8,2,1,0,43,0,0,0,5,0,1,1,2,3,0,28,1,1,2,0,1,3,3,0,5,0,1,0,0,0,0,0,0,1,1,0,39,0,0,0,Class_8
+53307,0,1,0,1,0,0,1,0,0,0,2,0,1,0,0,1,8,4,4,2,0,0,1,7,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,0,1,0,0,2,1,0,0,2,0,0,0,0,0,2,0,0,1,0,0,1,0,1,0,1,0,1,6,0,17,2,1,1,3,1,1,15,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,Class_8
+53308,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,2,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,Class_8
+53309,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+53310,2,4,0,0,0,0,3,1,0,1,0,0,3,0,0,2,2,3,4,4,0,0,0,5,0,0,0,0,0,0,2,1,0,0,1,1,1,3,0,0,0,0,0,1,1,0,0,2,2,1,0,0,0,1,1,1,3,2,0,0,0,0,0,1,0,1,7,0,33,2,0,0,3,2,1,2,0,1,0,0,0,0,2,0,0,3,0,1,0,39,1,0,0,Class_8
+53311,0,1,0,0,0,2,0,0,0,0,3,0,2,1,0,0,1,1,60,0,0,0,0,18,0,0,0,0,0,0,1,3,0,1,1,0,2,2,0,0,1,0,0,0,0,0,0,5,0,3,0,0,0,0,0,1,1,11,0,3,0,0,0,0,0,0,3,0,34,0,0,0,2,3,2,8,0,0,0,0,0,0,0,0,0,1,0,0,1,43,0,0,0,Class_8
+53312,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,2,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,2,2,0,20,1,0,0,0,0,1,4,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,Class_8
+53313,0,0,0,0,0,0,0,0,0,0,1,0,1,3,0,0,0,0,32,0,0,1,0,25,0,0,0,0,0,0,0,7,1,0,0,1,1,1,0,3,1,0,0,0,0,0,0,5,0,3,0,0,0,0,0,0,1,16,0,2,0,0,0,1,0,1,8,0,22,0,0,0,4,1,1,6,0,0,0,0,0,0,0,0,1,2,0,0,0,26,1,0,0,Class_8
+53314,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,17,0,0,0,0,0,0,6,0,0,0,Class_8
+53315,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5,1,1,0,0,0,0,10,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,7,9,0,0,8,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,2,0,Class_8
+53316,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,8,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,0,0,1,1,0,8,0,18,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,Class_8
+53317,2,0,0,1,0,0,9,3,0,1,0,0,2,0,3,1,0,1,0,0,0,4,0,15,1,0,0,0,0,2,0,10,0,0,0,6,3,0,0,1,0,0,0,2,1,0,1,5,0,0,0,0,2,3,0,1,0,1,0,1,0,1,0,3,0,1,15,3,0,4,0,0,2,8,2,13,0,35,0,1,0,0,7,0,0,2,1,0,0,2,1,0,0,Class_8
+53318,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,0,0,0,Class_8
+53319,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,9,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,53,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,7,0,0,0,Class_8
+53320,0,6,0,16,0,0,0,0,4,0,0,0,0,0,2,6,2,18,0,0,2,4,0,0,0,0,0,0,0,0,14,0,0,2,60,0,0,6,0,4,2,0,16,12,0,2,0,0,10,0,0,4,0,14,0,8,2,12,0,0,0,4,0,2,0,0,0,0,2,2,0,2,0,0,4,2,0,0,0,12,2,2,0,0,0,0,0,0,0,12,0,0,0,Class_8
+53321,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0,0,8,0,8,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
+53322,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,1,1,3,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,12,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+53391,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2,0,0,0,0,2,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53392,9,0,0,0,0,0,0,2,0,0,4,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,68,0,11,8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+53393,1,2,0,0,0,0,6,0,0,2,0,0,11,0,0,0,1,0,0,2,0,5,0,8,0,0,0,0,0,0,0,1,0,0,0,2,0,5,2,1,0,0,0,0,1,1,1,2,1,0,0,0,0,3,8,0,3,0,0,0,0,0,0,4,2,1,2,0,1,0,0,0,1,0,1,7,0,25,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Class_8
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+53395,0,3,0,4,0,0,0,0,0,0,1,1,10,0,0,3,0,0,1,4,0,1,0,9,0,0,0,0,0,0,0,2,0,0,9,2,0,12,0,1,1,1,0,1,0,0,1,3,1,3,0,0,0,4,1,1,0,5,0,0,0,2,0,1,0,0,8,0,6,1,0,0,1,0,2,3,0,0,0,0,0,0,5,0,0,0,0,0,1,17,0,0,0,Class_8
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+53398,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,3,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,20,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53399,0,0,0,0,0,0,4,2,0,1,0,0,1,1,0,0,0,0,0,1,0,1,1,12,0,0,0,0,1,0,0,3,1,1,0,1,1,0,1,0,0,0,0,1,0,0,10,4,1,0,0,0,2,2,1,0,1,2,0,2,0,0,0,1,0,0,4,1,0,1,11,0,0,0,0,1,0,3,0,0,0,0,7,0,0,0,3,0,1,0,0,0,0,Class_8
+53400,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+53401,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,0,16,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,2,2,0,24,0,0,0,0,3,1,3,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,Class_8
+53402,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,7,0,1,0,0,0,1,0,0,4,0,0,0,1,0,3,0,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+53433,2,0,5,1,7,0,0,0,0,0,0,0,0,0,0,2,2,1,0,0,0,2,0,5,1,0,1,1,0,0,0,3,1,1,1,3,0,2,0,0,0,0,0,1,0,0,5,3,0,0,1,0,1,2,2,0,3,0,1,0,0,3,0,0,3,2,4,0,0,0,0,0,0,0,3,3,0,0,0,8,0,5,0,38,0,0,0,0,18,0,0,7,0,Class_8
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+53435,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,0,7,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+53436,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53437,0,1,0,0,0,0,1,0,1,1,1,0,10,0,0,0,0,1,2,3,0,1,1,9,0,0,0,0,0,0,0,0,1,0,0,0,1,6,1,0,0,0,0,0,1,0,0,1,0,2,0,0,0,5,4,0,0,2,1,1,0,0,0,2,0,4,4,0,1,1,0,0,1,0,1,20,0,0,0,2,2,0,0,0,0,0,1,1,0,0,0,0,0,Class_8
+53438,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,37,4,13,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_8
+53439,0,1,0,0,0,0,4,1,0,0,0,0,0,4,0,4,4,2,0,6,0,2,0,3,1,0,0,0,5,0,0,0,1,0,0,4,2,1,0,2,0,2,0,2,0,0,0,1,0,2,0,0,0,2,3,0,0,1,0,3,0,1,1,1,13,0,4,1,0,0,0,0,0,3,1,9,0,2,2,0,1,0,6,0,0,0,0,2,2,1,1,0,0,Class_8
+53440,0,18,0,0,0,0,1,0,0,0,5,0,1,1,0,1,1,3,0,16,0,2,2,7,0,0,0,0,0,0,0,0,0,0,2,5,1,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,3,1,2,0,15,0,0,0,0,0,3,0,9,1,0,12,0,0,0,0,2,0,1,0,2,0,2,0,0,23,0,2,0,1,1,0,9,0,0,0,Class_8
+53441,0,9,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,20,5,0,0,1,35,0,0,0,0,0,1,0,6,0,0,3,3,0,4,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,32,0,0,0,1,0,3,0,0,14,0,8,0,1,0,5,1,2,3,0,1,1,1,0,0,18,0,0,0,0,1,0,52,0,0,0,Class_8
+53442,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,25,0,0,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,Class_8
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+53479,5,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,7,0,0,1,0,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,4,0,0,9,0,1,0,0,0,1,0,1,0,0,3,0,0,0,0,30,0,1,0,0,0,0,1,0,0,0,Class_8
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+53512,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,0,3,1,0,0,2,0,0,0,21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,Class_8
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+53514,0,0,1,0,0,0,0,0,0,0,5,0,0,3,0,1,0,0,0,3,1,1,0,5,1,1,4,0,0,1,1,1,4,1,0,1,0,1,0,0,1,0,0,1,0,0,1,1,1,0,0,0,0,3,0,0,0,1,0,0,0,2,1,0,0,0,6,18,0,0,0,3,0,0,1,1,0,1,2,0,0,0,0,0,0,0,0,0,5,0,0,0,0,Class_8
+53515,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,0,2,0,0,1,0,0,0,6,2,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,22,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,Class_8
+53516,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,0,0,23,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+53517,0,2,0,0,0,0,0,4,1,0,1,1,10,0,1,1,0,0,0,2,0,0,2,18,1,0,0,0,0,0,0,6,0,0,1,1,1,4,4,0,0,0,0,0,0,0,0,0,0,2,0,2,0,1,0,1,0,27,0,0,0,0,0,2,0,0,14,0,39,0,0,0,2,1,1,15,0,0,0,0,0,0,3,0,0,0,0,1,0,36,0,0,1,Class_8
+53518,0,3,0,0,0,0,1,0,1,0,4,1,10,1,0,0,0,0,19,6,0,4,0,21,0,0,0,0,0,0,1,7,0,1,2,1,0,3,3,1,2,0,0,1,0,0,0,4,0,1,0,0,0,2,0,0,0,22,0,1,0,1,0,1,1,2,11,0,11,0,0,0,1,2,1,4,0,0,1,0,0,0,8,0,1,0,0,0,0,26,1,0,0,Class_8
+53519,0,2,12,20,0,0,0,0,0,0,0,0,0,0,0,4,0,0,2,0,0,0,0,2,0,0,0,2,0,0,0,0,0,2,30,0,2,0,0,0,2,0,2,0,0,0,0,0,2,0,0,4,2,12,0,4,0,0,0,0,2,4,2,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,4,0,Class_8
+53520,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,10,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+53521,0,1,0,0,0,0,0,1,1,5,0,0,0,1,1,0,0,0,0,4,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,10,0,0,0,0,1,1,0,1,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,Class_8
+53522,0,0,0,0,0,0,2,1,0,20,0,1,0,0,0,1,0,1,1,1,0,0,0,12,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,8,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,2,0,0,0,0,0,0,2,1,0,0,3,0,1,0,0,3,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,Class_8
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+53837,4,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,1,1,0,0,4,0,0,0,0,0,0,9,0,0,1,0,0,0,0,0,0,0,Class_8
+53838,1,0,0,0,0,0,0,0,0,0,0,0,10,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+53839,0,0,0,0,0,0,1,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,0,6,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,21,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,2,0,Class_8
+53840,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,4,2,0,0,0,0,0,0,3,0,2,0,4,1,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,14,1,0,0,4,2,0,0,1,0,0,8,1,0,0,0,3,0,0,1,2,0,0,0,1,0,0,Class_8
+53841,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,1,0,1,0,0,0,2,0,1,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,3,3,0,0,3,0,1,0,1,1,0,0,0,0,0,0,1,0,3,2,0,0,1,0,0,0,0,Class_8
+53842,0,1,0,0,0,0,0,2,1,0,0,0,0,0,2,0,0,1,0,0,0,2,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,2,0,1,0,0,0,2,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,Class_8
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+53864,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,2,0,1,0,0,0,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,3,0,1,1,3,0,0,0,0,0,4,1,0,0,0,0,0,0,2,0,0,0,1,3,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,0,Class_8
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+53872,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_8
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+53875,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,Class_8
+53876,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,2,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,Class_8
+53877,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53878,0,4,8,11,0,0,0,0,0,0,3,0,1,2,1,10,0,4,2,4,1,4,0,2,1,1,1,4,1,0,4,0,0,0,32,0,0,4,0,0,0,2,2,2,0,2,0,0,10,0,0,0,0,18,1,0,2,6,0,0,0,3,1,0,0,3,2,0,0,2,0,3,2,0,0,0,1,0,1,11,0,2,1,0,0,0,2,0,4,1,0,2,0,Class_8
+53879,0,12,1,0,1,0,2,0,1,0,5,1,11,0,0,5,2,5,2,7,2,5,0,29,1,0,0,0,1,0,0,0,2,1,4,8,1,22,3,1,3,0,0,3,0,2,0,0,2,5,0,0,2,5,3,0,1,12,0,0,0,12,1,5,0,2,2,0,12,3,0,2,5,4,0,14,0,0,0,5,2,1,10,0,0,0,2,5,5,21,0,2,0,Class_8
+53880,0,6,0,1,0,0,1,1,0,0,2,0,1,3,5,6,3,2,2,13,0,6,2,17,1,0,1,0,1,0,2,0,0,1,4,1,0,15,3,4,1,2,0,1,2,0,5,6,0,1,0,4,1,2,0,1,3,10,0,0,0,3,2,3,1,2,2,1,34,0,0,1,2,10,2,3,0,2,1,1,1,0,4,0,2,0,0,0,2,54,1,0,0,Class_8
+53881,0,1,0,0,0,0,0,4,0,0,0,1,0,1,0,1,0,0,33,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,1,6,0,0,0,Class_8
+53882,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,9,0,0,0,1,0,4,0,0,1,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+53914,1,10,0,2,0,0,2,0,0,0,1,0,11,5,0,2,1,0,2,15,0,1,1,23,0,1,0,0,0,0,0,5,1,0,0,3,2,4,1,0,2,0,0,0,1,1,0,12,0,1,0,1,0,2,0,1,1,9,0,0,0,1,0,2,0,0,15,1,12,2,0,0,6,4,10,16,0,0,0,0,1,0,8,0,0,1,0,0,0,80,0,0,1,Class_8
+53915,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+53917,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,1,0,5,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,4,2,0,2,0,0,2,0,0,0,0,0,0,0,0,0,2,2,0,1,3,0,0,0,1,0,0,1,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,10,1,0,0,Class_8
+53918,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+53919,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,1,1,0,5,0,1,0,20,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,5,1,0,0,0,0,0,0,0,1,5,0,0,0,1,0,1,1,3,6,0,3,2,0,0,2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,Class_8
+53920,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+53921,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5,0,1,1,0,0,0,4,2,0,0,0,0,0,0,0,1,3,0,2,2,1,3,0,1,0,0,1,0,0,0,2,0,1,0,0,1,1,0,0,0,1,0,1,0,2,0,1,0,0,5,0,5,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+53922,2,4,1,0,3,0,4,0,2,1,1,0,3,2,0,0,3,1,0,8,0,1,2,16,0,0,0,0,0,2,0,4,1,0,1,3,0,11,1,1,2,0,0,4,2,0,5,0,0,2,0,0,0,3,4,0,0,3,0,0,0,3,0,4,0,2,13,0,0,1,0,0,0,0,0,4,1,5,0,0,0,0,12,2,0,1,0,0,0,0,0,0,0,Class_8
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+54002,1,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,3,2,0,0,0,13,0,0,0,0,0,0,0,1,0,0,0,2,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,0,12,0,21,0,0,1,3,0,0,29,0,0,1,0,0,0,1,0,0,1,1,0,0,2,0,0,0,Class_8
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+54041,0,6,0,1,0,0,0,1,0,0,0,0,1,1,0,2,0,3,0,5,0,3,0,4,1,0,0,0,0,0,0,0,0,0,1,2,1,3,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,2,0,0,1,0,0,0,0,0,0,0,2,4,2,15,1,0,0,0,6,0,0,1,0,0,0,0,0,3,0,0,2,0,1,0,48,1,0,1,Class_8
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+54061,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,0,0,22,0,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+54062,0,4,0,1,0,0,0,0,0,0,6,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,1,2,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,16,0,0,0,Class_8
+54063,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,0,0,1,0,0,2,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54064,1,1,0,1,0,0,0,1,0,1,2,0,0,0,0,0,5,2,0,4,0,0,0,18,1,0,0,0,0,0,0,14,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,29,0,3,2,1,0,0,0,3,6,0,1,1,0,0,0,1,0,0,2,0,0,0,3,3,0,2,Class_8
+54065,0,0,0,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,2,0,0,4,0,0,0,3,0,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,2,0,2,0,0,0,0,2,0,2,0,0,1,1,0,0,1,0,0,0,0,1,0,1,1,0,0,0,6,0,0,0,Class_8
+54066,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,2,3,0,0,0,3,1,0,0,0,0,0,0,3,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,12,1,2,0,0,0,0,2,0,0,0,0,1,0,2,0,1,0,0,0,0,5,2,2,0,Class_8
+54067,0,2,0,1,0,0,7,2,5,1,2,0,8,2,0,3,19,5,0,12,0,4,2,18,0,0,0,0,3,0,0,6,10,3,3,7,1,9,0,1,13,0,0,2,0,1,0,1,0,2,0,0,3,4,3,0,1,2,0,3,0,8,0,1,1,4,28,0,6,7,2,0,2,0,2,9,3,0,0,0,3,0,14,0,1,0,5,0,4,1,1,0,2,Class_8
+54068,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,Class_8
+54069,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,2,0,0,0,0,1,0,1,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54070,0,0,2,26,0,0,0,0,0,2,2,0,0,0,0,2,0,2,2,0,2,2,0,0,0,0,0,2,0,0,0,0,0,2,26,0,0,0,0,0,0,2,0,2,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_8
+54071,0,1,0,0,0,0,4,0,0,9,5,2,0,0,0,0,0,0,10,1,0,0,6,26,0,0,0,0,0,0,0,3,0,0,1,0,0,6,5,0,1,0,0,1,2,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,3,0,1,1,12,0,32,5,0,0,1,1,0,33,0,1,3,0,0,0,2,0,1,1,2,1,0,10,0,0,0,Class_8
+54072,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,28,0,0,2,2,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,Class_8
+54073,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54074,0,20,1,2,0,1,11,0,2,2,1,0,3,1,0,2,7,3,27,1,0,5,1,20,0,0,1,0,1,0,0,2,0,4,3,2,1,11,1,1,0,1,0,13,0,0,0,5,4,3,0,0,0,9,4,1,2,1,0,2,0,3,0,2,1,7,9,2,7,8,0,2,1,0,4,10,1,2,2,0,0,0,7,0,1,0,3,1,2,57,1,0,3,Class_8
+54075,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,1,0,0,2,0,1,3,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,1,6,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,1,1,0,Class_8
+54076,0,0,0,0,0,0,3,0,0,5,3,1,0,0,0,0,0,1,3,0,0,1,5,25,0,0,0,0,0,1,0,2,0,0,1,0,0,3,2,0,0,0,0,0,1,0,0,2,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,34,2,0,0,3,1,0,29,0,0,5,0,0,0,1,0,0,1,2,0,0,6,0,0,0,Class_8
+54077,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,2,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54078,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,4,2,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,1,1,19,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+54079,3,1,0,0,0,1,1,1,0,0,1,0,0,0,0,3,0,0,1,2,0,0,0,9,1,0,0,0,0,0,0,2,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,3,2,0,0,2,0,0,0,1,1,0,0,1,3,0,3,0,1,0,1,0,4,1,0,5,1,0,0,0,34,0,0,0,0,0,1,2,1,0,0,Class_8
+54080,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54081,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+54082,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,37,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+54103,2,0,6,1,7,0,0,0,0,0,1,1,0,0,0,5,0,1,0,0,0,5,0,3,1,0,1,0,0,0,0,3,5,0,2,3,2,2,0,0,4,0,0,1,0,0,3,3,0,0,1,2,2,4,2,0,5,2,3,0,0,6,0,0,2,2,5,1,0,1,0,1,0,0,2,0,0,0,0,8,0,5,0,30,0,0,0,0,17,0,0,9,0,Class_8
+54104,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,3,0,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,0,1,2,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,Class_8
+54105,0,4,16,24,0,0,2,0,0,0,0,0,2,0,0,2,2,16,4,6,0,2,0,4,2,0,0,2,0,0,8,0,2,0,48,0,0,10,0,0,0,2,0,4,0,2,0,2,6,0,0,4,0,8,0,0,0,6,0,2,0,8,0,2,2,2,2,2,0,4,0,0,0,0,0,2,0,0,2,4,0,4,0,0,2,0,2,0,2,0,0,0,0,Class_8
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+54108,0,0,1,0,0,0,2,2,0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,5,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,11,0,0,0,0,0,1,2,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,Class_8
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+54111,2,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,8,0,0,2,0,0,0,0,4,0,0,0,2,1,2,1,1,3,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,2,0,0,0,0,5,0,2,0,3,4,4,3,2,2,0,1,0,0,11,0,20,0,0,0,0,5,0,0,0,0,1,0,0,0,0,0,Class_8
+54112,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
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+54117,0,6,0,6,0,0,2,1,1,0,2,0,2,0,0,5,0,1,3,4,0,3,0,7,0,0,2,0,0,0,0,1,0,1,5,0,1,6,0,1,0,0,0,4,0,0,0,0,5,0,5,2,0,4,2,4,9,8,0,0,2,1,0,3,0,1,3,0,5,4,0,0,4,0,3,1,0,0,0,26,0,3,8,0,0,0,0,0,1,8,0,0,0,Class_8
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+54121,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,19,0,0,0,0,0,1,0,2,0,1,0,5,1,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,2,0,1,1,0,0,4,0,0,9,0,1,0,0,0,1,0,9,1,0,2,0,0,0,0,11,0,0,1,0,0,1,2,3,0,0,Class_8
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+54917,0,3,1,0,0,0,1,0,1,1,17,0,0,2,0,2,0,1,8,3,0,0,2,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,1,1,1,1,0,1,0,1,0,2,0,1,0,0,0,53,0,2,0,1,0,4,0,0,3,0,6,1,0,0,1,9,3,1,0,0,0,3,0,0,2,0,0,1,1,0,0,6,1,0,0,Class_8
+54918,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,1,0,0,0,1,0,4,2,0,0,1,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,32,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,Class_8
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+54920,0,5,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,1,11,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,1,0,0,3,0,1,0,1,0,0,0,0,0,6,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,2,1,0,0,0,4,1,2,0,0,1,1,0,1,3,0,0,0,0,0,0,20,0,0,0,Class_8
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+55560,0,2,38,12,0,0,0,0,0,0,0,0,0,0,0,2,0,6,0,0,4,4,0,2,0,2,6,0,0,0,2,0,0,2,24,0,0,14,0,0,0,0,0,4,2,14,0,0,4,0,0,4,0,36,0,0,2,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,16,0,8,2,0,0,0,0,0,6,0,0,0,0,Class_8
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+56437,0,0,0,0,4,0,0,0,0,0,0,4,2,2,0,4,0,0,0,1,0,2,0,0,1,0,0,0,1,0,0,0,2,0,0,0,1,2,0,2,1,1,0,1,0,0,0,0,0,1,0,0,0,5,1,0,0,0,0,0,0,1,0,0,28,1,0,1,1,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_8
+56438,0,6,3,2,0,0,6,0,0,3,2,1,1,1,1,7,0,3,6,3,1,7,3,13,1,0,0,2,0,1,0,6,0,1,0,2,6,11,0,1,1,5,1,2,0,1,0,3,3,3,0,0,0,8,1,1,0,4,0,2,1,5,2,0,2,5,8,0,7,4,1,1,1,5,4,10,0,3,0,5,0,3,25,0,3,0,3,3,11,23,1,0,1,Class_8
+56439,0,0,0,0,0,0,0,0,0,0,0,0,2,8,0,0,0,1,0,0,0,1,0,2,1,0,1,0,2,0,0,3,1,0,0,1,0,1,0,0,0,0,0,0,0,0,3,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,3,5,0,1,3,1,0,5,0,1,0,1,2,0,0,0,0,1,0,0,0,1,0,0,0,0,0,Class_8
+56440,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56441,0,0,1,0,0,0,0,1,1,6,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,Class_8
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+56461,1,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56462,19,1,2,0,0,2,8,1,0,0,3,0,0,0,0,2,0,1,0,1,0,0,0,12,2,0,0,0,0,0,0,9,0,0,0,4,1,1,0,3,0,0,0,1,0,0,1,4,0,0,0,0,4,4,1,0,0,2,1,0,0,1,0,2,1,1,12,0,1,2,0,1,1,7,0,15,0,41,0,2,0,0,2,1,0,0,0,0,0,4,2,0,0,Class_8
+56463,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,17,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+56464,1,4,0,1,0,0,0,0,3,0,0,0,1,0,0,1,0,0,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,1,0,0,0,1,0,0,0,2,1,3,0,1,0,0,0,26,0,1,0,1,0,0,0,0,1,0,9,0,0,1,2,2,2,2,0,0,2,0,0,0,0,0,0,0,0,0,0,10,0,1,0,Class_8
+56465,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,5,0,0,4,0,1,0,0,0,1,0,0,0,0,0,0,0,1,3,1,0,0,0,1,1,0,0,0,1,0,6,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,1,0,0,0,0,1,Class_8
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+56467,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,2,0,0,0,0,0,0,1,0,1,0,0,1,2,1,1,0,26,0,0,0,2,0,2,0,4,6,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,Class_8
+56468,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,9,0,0,1,0,0,1,0,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,2,7,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,Class_8
+56469,0,1,0,1,0,0,4,0,1,2,0,0,10,0,0,1,2,2,1,2,0,0,3,13,2,0,0,0,0,0,0,4,2,0,0,2,1,3,0,1,0,0,0,2,0,1,21,2,2,0,0,0,1,6,1,0,3,0,0,1,1,4,0,3,0,0,3,3,3,6,11,0,1,0,0,10,0,16,0,1,3,0,0,4,1,0,0,1,1,1,0,1,0,Class_8
+56470,48,4,0,0,1,0,38,5,1,12,2,0,1,0,0,1,2,1,0,2,0,1,4,6,4,0,0,0,1,0,0,5,0,0,1,3,2,6,0,1,0,0,2,0,1,0,1,4,0,5,1,1,8,0,10,0,0,1,1,0,0,2,0,0,2,0,10,0,2,9,2,0,1,11,0,16,0,47,4,3,0,0,2,7,2,1,0,1,1,1,4,0,0,Class_8
+56471,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,8,0,0,3,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,Class_8
+56472,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,33,1,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,Class_8
+56473,7,0,1,1,0,0,1,0,0,0,0,0,0,1,0,3,3,1,0,0,0,9,0,8,1,0,0,0,4,0,0,0,2,0,0,1,0,2,0,0,0,2,0,1,0,1,0,1,2,0,0,0,0,3,2,0,0,0,0,1,0,1,0,0,1,7,18,0,1,0,0,0,0,0,0,1,2,0,1,0,0,0,0,0,2,2,3,1,0,0,0,0,0,Class_8
+56474,0,0,0,0,0,0,0,1,0,0,1,0,11,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,1,0,1,0,0,0,4,0,2,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,16,0,2,0,0,0,0,1,0,7,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56475,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,6,0,1,5,2,0,0,0,0,0,0,0,0,0,1,1,0,0,3,4,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,1,1,0,1,0,0,2,1,1,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,0,0,1,Class_8
+56476,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,2,0,0,0,0,1,0,4,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,1,0,27,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+56477,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,2,0,1,0,2,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,2,2,6,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Class_8
+56478,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,1,0,0,0,22,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,Class_8
+56479,0,1,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,3,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,2,1,0,0,2,0,0,0,2,0,0,0,3,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,1,0,Class_8
+56480,0,6,0,0,0,0,1,0,0,0,1,0,1,3,0,2,0,1,30,6,0,11,0,27,0,0,0,0,0,0,0,15,0,0,3,3,0,1,1,0,0,0,0,5,0,0,0,0,1,0,0,0,0,6,3,0,0,27,0,0,1,1,0,1,2,3,14,0,11,1,0,2,6,2,1,3,0,2,0,1,0,0,5,0,2,1,2,0,1,39,0,0,4,Class_8
+56481,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,1,0,3,1,0,0,0,0,0,0,2,0,1,1,0,0,1,0,1,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,Class_8
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+56512,6,4,0,0,0,1,1,1,0,0,2,1,1,0,0,1,0,0,1,7,0,0,0,32,0,0,0,0,1,0,0,7,3,0,0,3,1,0,3,0,2,0,0,0,1,0,0,4,0,2,0,1,0,0,1,0,1,31,0,1,0,0,2,1,2,0,33,0,10,1,1,1,5,1,0,18,0,2,2,0,0,0,4,0,0,3,1,0,0,2,2,0,1,Class_8
+56513,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,1,1,5,0,0,1,1,0,0,0,0,0,0,0,1,0,0,3,0,1,0,0,0,0,3,0,1,0,1,0,0,9,0,1,4,0,0,0,1,5,1,0,1,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,Class_8
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+56515,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+56516,0,2,18,32,0,0,0,0,0,2,2,0,0,0,0,0,0,2,2,0,0,0,0,2,0,2,0,4,0,0,2,0,0,0,38,0,0,0,0,2,0,0,2,4,0,2,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,2,0,0,2,0,Class_8
+56517,0,0,0,1,3,0,0,2,0,1,1,0,2,3,0,0,0,0,0,0,0,1,0,9,1,0,0,0,0,0,0,2,0,2,0,2,0,0,0,0,2,0,0,6,0,0,11,4,0,1,0,0,0,3,0,0,0,0,0,0,0,1,0,0,1,0,3,8,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,Class_8
+56518,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,1,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,8,0,0,0,0,0,1,0,19,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,Class_8
+56519,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,7,0,0,3,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,Class_8
+56520,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,4,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56521,0,0,0,0,0,0,0,1,1,0,2,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
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+56736,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,3,3,0,2,1,4,2,0,0,0,0,1,0,0,2,0,0,0,0,1,3,0,0,0,0,2,0,0,1,2,0,0,3,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,2,10,0,0,0,0,0,3,1,0,0,3,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,Class_8
+56737,0,0,0,0,0,0,1,0,0,2,0,0,0,0,2,2,0,0,0,0,0,0,0,2,2,0,0,0,0,1,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,16,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,Class_8
+56738,3,9,0,0,0,0,1,0,2,0,1,1,0,2,0,2,0,1,3,6,0,0,2,11,0,0,0,0,0,0,1,3,1,0,1,0,2,5,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,3,1,1,0,20,0,0,0,0,0,0,0,0,4,0,10,5,0,0,5,1,0,9,0,1,0,0,0,0,10,0,1,0,1,1,0,6,2,0,0,Class_8
+56739,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,27,1,0,0,0,4,0,0,0,0,0,0,0,2,0,0,2,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,12,1,0,0,2,1,0,2,0,0,0,0,0,0,0,1,1,0,0,0,0,5,0,0,1,Class_8
+56740,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,25,0,0,0,0,8,0,0,0,0,0,0,0,2,0,0,1,0,0,1,2,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,3,4,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,8,0,0,0,Class_8
+56741,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,7,0,3,0,5,0,0,0,0,0,1,0,2,0,0,0,0,2,0,0,0,3,0,3,0,0,0,0,0,0,3,0,0,0,0,1,0,1,3,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,1,0,0,0,0,1,0,1,0,1,0,1,1,0,0,0,Class_8
+56742,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,7,0,0,0,0,0,1,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,5,0,1,0,0,0,2,0,6,2,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,Class_8
+56743,1,0,2,0,0,0,0,3,1,0,5,1,0,1,1,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,0,5,0,0,0,0,0,23,0,0,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,1,0,Class_8
+56744,0,4,0,1,0,0,0,1,2,0,7,0,10,0,0,0,0,0,0,1,0,0,1,8,0,0,2,0,0,0,0,3,0,0,0,2,1,2,0,0,1,0,0,0,1,0,0,1,2,1,9,1,0,2,0,4,0,22,0,0,2,0,0,2,0,0,5,0,10,0,0,0,2,3,1,1,1,0,2,31,0,2,4,1,0,1,1,0,0,9,0,0,0,Class_8
+56745,1,6,0,0,0,0,0,2,1,0,4,1,2,2,0,2,1,2,59,0,0,2,0,23,1,0,0,0,0,0,0,11,0,0,4,3,3,7,2,1,0,0,1,0,1,0,0,3,1,3,0,0,0,0,0,2,1,17,1,6,0,0,0,1,1,3,13,0,24,0,0,0,3,2,1,5,0,0,3,5,0,0,11,0,0,0,0,0,0,41,1,0,0,Class_8
+56746,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,Class_8
+56747,0,1,0,0,0,0,0,1,0,10,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,Class_8
+56748,0,0,0,1,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,11,0,0,0,0,0,0,0,0,4,0,5,0,0,0,3,0,0,6,0,0,0,0,0,0,7,0,0,0,0,0,0,5,0,0,0,Class_8
+56749,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,10,1,0,2,0,9,0,0,0,0,0,0,0,2,0,0,2,0,0,1,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,5,0,0,0,2,0,2,0,1,9,0,1,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,25,0,0,0,Class_8
+56750,0,0,0,0,0,0,1,0,0,0,0,0,10,0,0,0,0,0,0,2,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,6,0,2,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,Class_8
+56751,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,1,0,15,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,Class_8
+56752,0,0,1,0,0,0,0,1,0,0,3,0,0,0,0,1,1,0,0,0,0,0,4,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,1,9,0,0,0,1,0,0,0,0,0,0,15,1,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,2,1,0,0,17,0,1,0,Class_8
+56753,0,0,1,0,0,0,1,3,0,0,1,0,10,0,1,2,0,1,6,2,0,2,0,18,0,0,0,0,0,0,0,11,0,0,3,2,1,3,2,4,1,0,0,2,1,0,0,2,0,2,0,0,0,2,0,0,0,1,0,1,0,1,0,2,1,2,4,0,20,5,0,0,0,0,0,1,0,0,3,0,0,0,3,0,0,2,0,0,0,7,0,0,0,Class_8
+56754,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,5,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,2,1,0,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,22,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,1,5,0,0,4,0,Class_8
+56755,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,1,1,0,2,0,0,0,8,0,0,0,0,0,0,0,2,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,2,1,2,0,0,0,1,0,0,0,9,0,0,0,1,0,0,0,2,8,0,15,1,0,0,0,0,0,5,0,0,0,0,0,0,5,0,0,0,0,0,0,19,0,0,1,Class_8
+56756,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,43,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,Class_8
+56757,1,3,0,0,0,0,0,1,1,0,0,0,0,0,0,0,3,0,6,4,0,1,1,26,0,0,0,0,0,0,0,4,0,0,0,0,1,1,0,1,0,0,0,2,0,0,0,5,0,0,0,0,0,0,0,0,0,30,1,0,0,0,0,0,0,0,11,0,22,1,0,0,4,0,1,4,0,2,0,0,0,1,4,0,0,1,1,0,0,18,0,0,0,Class_8
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+56759,0,1,0,0,0,0,1,0,0,1,2,0,0,0,0,1,4,5,8,5,0,6,0,13,1,0,0,0,1,0,1,0,1,0,2,0,0,7,0,2,0,0,0,1,0,0,0,0,1,2,0,4,0,6,4,0,1,0,0,1,0,1,1,1,0,1,1,0,26,4,0,1,5,1,1,12,0,0,2,1,0,0,0,0,0,0,6,0,5,1,0,0,0,Class_8
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+56775,2,0,0,0,0,0,0,2,0,0,0,1,1,0,1,1,0,0,0,0,1,0,0,1,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,2,2,0,0,0,2,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,7,0,0,0,0,4,7,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Class_8
+56776,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,2,0,0,0,0,0,7,2,0,0,0,0,0,0,3,0,0,0,1,3,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56777,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,19,0,0,0,0,5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,Class_8
+56778,1,0,1,0,2,0,6,2,0,0,1,0,0,0,0,3,1,2,0,0,0,4,0,6,2,0,0,0,1,1,0,0,0,1,0,0,0,1,2,0,0,0,0,9,0,0,18,0,1,5,0,0,0,6,3,0,0,0,0,1,0,2,0,2,1,1,5,3,0,2,17,1,0,0,1,1,0,2,2,1,1,0,1,0,1,2,0,1,1,0,0,0,0,Class_8
+56779,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,0,0,1,0,3,0,0,1,0,0,0,3,0,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,0,0,0,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,1,0,0,0,0,0,1,0,3,0,0,0,0,1,1,3,0,0,0,Class_8
+56780,0,2,0,1,0,0,1,1,0,0,1,0,1,1,0,0,2,5,1,10,0,2,0,13,0,0,0,0,0,0,0,0,0,1,0,2,2,0,0,0,0,0,0,0,0,0,0,4,1,3,0,1,0,4,3,0,1,9,0,0,1,1,0,1,0,3,4,1,19,3,1,0,3,0,1,0,0,1,1,0,0,1,17,0,1,0,3,0,0,6,0,0,4,Class_8
+56781,0,0,1,0,0,0,0,2,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,5,2,0,0,0,0,0,0,0,0,1,0,0,1,2,0,0,1,0,1,0,0,0,0,2,0,1,0,0,1,0,1,0,0,4,0,1,0,0,0,1,0,1,2,0,38,4,0,0,0,0,1,6,0,2,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,Class_8
+56782,0,7,0,0,0,0,0,1,0,0,1,0,1,2,0,1,0,1,0,8,0,2,0,4,1,0,0,0,0,0,0,0,0,1,0,0,1,2,0,1,0,0,0,2,0,0,0,0,3,0,0,0,0,1,0,0,2,5,0,1,0,1,0,1,0,2,0,2,34,2,0,2,2,0,1,0,0,0,0,2,0,0,7,0,0,0,2,1,0,15,1,0,1,Class_8
+56783,4,1,0,0,0,0,2,0,0,0,1,0,0,0,0,1,2,1,0,0,0,0,0,13,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,6,0,1,0,0,1,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,Class_8
+56784,0,0,0,0,1,0,0,0,2,0,0,0,2,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,0,0,3,0,3,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,Class_8
+56785,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56786,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,24,4,0,0,0,12,1,0,0,0,0,0,0,3,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,2,7,0,16,1,0,1,1,0,2,7,0,0,0,0,0,0,1,0,1,0,0,0,0,5,0,0,0,Class_8
+56787,0,0,0,0,0,0,7,1,0,2,0,0,0,0,0,0,0,0,0,1,0,4,0,10,0,0,0,0,0,0,0,12,0,0,0,2,0,2,0,1,1,0,0,2,0,0,3,4,0,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,1,0,11,0,18,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,Class_8
+56788,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,Class_8
+56789,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,30,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,Class_8
+56790,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,7,0,0,2,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,4,0,0,Class_8
+56791,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,7,2,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,3,24,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_8
+56792,1,10,1,0,2,0,3,0,0,1,0,0,0,2,0,0,1,0,3,10,0,0,1,5,0,0,0,0,1,0,3,1,1,0,1,0,1,8,0,2,0,1,1,1,1,2,0,1,0,2,1,0,0,1,3,0,0,16,0,2,0,1,0,0,1,2,10,0,3,2,0,1,2,0,0,10,0,2,1,1,0,0,8,0,0,1,0,0,0,21,1,0,1,Class_8
+56793,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,1,0,5,1,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,7,0,0,0,0,0,0,0,33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_8
+56794,0,0,3,2,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,0,0,1,0,0,2,1,0,2,0,2,0,2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,3,0,0,0,0,3,8,0,0,0,1,0,2,0,0,0,1,0,9,0,0,0,0,0,0,0,0,2,0,0,1,0,Class_8
+56795,5,3,0,0,0,0,1,1,0,0,0,0,0,0,0,3,1,3,1,1,0,1,0,12,0,0,0,0,1,0,0,2,0,1,1,0,1,1,1,0,0,0,0,0,0,0,0,2,1,3,0,0,0,1,0,0,1,2,0,0,0,0,0,2,0,0,14,0,0,0,0,1,0,1,0,3,0,0,0,2,0,1,20,0,0,1,0,0,0,2,0,0,0,Class_8
+56796,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,8,0,2,0,0,4,2,0,2,6,0,0,0,6,2,0,4,16,0,2,2,2,2,0,0,0,2,0,0,0,0,2,0,28,2,0,4,0,2,2,0,0,0,0,0,2,0,0,0,0,2,2,0,0,0,6,6,0,4,0,2,0,2,0,8,0,0,0,0,0,0,4,4,0,0,0,Class_8
+56797,0,3,0,0,0,0,0,2,0,0,2,0,0,0,3,0,2,3,2,5,0,1,0,15,0,0,0,0,0,0,0,2,0,0,1,1,1,3,0,0,0,0,0,1,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,6,0,8,1,0,0,0,0,0,4,0,0,0,2,0,0,9,0,0,1,1,0,1,17,0,0,0,Class_8
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+57742,0,0,1,0,0,0,0,20,0,0,0,0,3,1,0,0,4,0,0,1,0,0,0,1,2,1,0,0,0,0,0,2,2,0,0,3,0,1,2,0,0,1,0,0,0,0,0,1,1,0,0,0,0,6,0,0,0,0,1,0,1,8,0,0,0,0,9,1,1,1,2,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,Class_9
+57743,0,0,0,0,0,0,0,1,0,1,2,0,2,0,0,5,0,2,0,0,0,4,0,2,1,0,0,0,0,1,0,0,0,0,0,2,1,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,0,4,0,0,0,3,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,0,Class_9
+57744,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,1,0,3,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,5,0,0,0,0,9,0,1,0,1,0,0,0,0,4,0,1,0,0,0,0,0,0,0,0,0,0,1,0,21,1,0,Class_9
+57745,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_9
+57746,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,Class_9
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+57749,0,0,3,2,0,0,0,5,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,4,1,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,1,0,0,0,0,3,1,2,3,0,0,0,0,9,0,0,1,0,2,0,0,4,0,3,0,4,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,Class_9
+57750,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,6,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Class_9
+57751,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,0,0,1,0,1,0,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,Class_9
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+57753,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,Class_9
+57754,4,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,1,9,0,0,0,0,0,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,2,0,1,0,0,0,2,0,Class_9
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+57794,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,17,0,0,0,17,0,1,0,5,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,4,1,0,0,0,0,0,0,Class_9
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+60137,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,3,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,Class_9
+60138,1,0,0,3,1,0,0,5,0,0,0,0,0,1,4,11,0,2,0,0,0,5,0,3,4,2,0,0,57,1,0,10,2,3,1,1,1,1,0,1,2,6,0,4,0,0,0,10,0,0,0,0,7,9,3,2,0,0,0,3,0,21,0,1,4,9,4,0,0,3,0,2,1,2,2,0,25,0,0,0,0,0,0,0,2,0,0,0,1,0,0,4,0,Class_9
+60139,2,0,0,0,3,0,0,13,2,0,0,0,0,1,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,18,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,3,2,0,0,0,0,0,0,0,4,11,0,0,0,15,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,3,1,0,0,0,0,Class_9
+60140,2,0,0,3,0,0,0,0,0,2,1,0,9,1,0,3,0,2,0,2,0,4,0,5,4,1,0,1,11,0,0,2,3,0,1,0,0,1,1,3,0,2,0,3,0,0,0,7,1,0,0,0,36,4,12,0,1,0,0,1,0,16,3,0,1,0,1,0,0,3,0,1,0,0,0,1,1,1,1,1,1,0,0,0,1,0,0,0,18,0,0,0,0,Class_9
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+61176,6,0,2,0,0,0,0,1,1,2,0,1,23,3,0,3,1,2,0,1,0,6,0,2,2,0,0,2,1,0,0,0,7,1,0,2,0,1,0,0,3,2,0,1,0,0,0,0,1,0,0,0,6,6,1,0,0,0,0,1,0,12,0,1,1,1,4,0,0,2,0,0,0,0,1,0,9,0,8,0,0,0,0,0,0,3,0,0,2,1,0,5,0,Class_9
+61177,0,0,1,2,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,2,0,4,2,1,0,0,1,0,0,2,1,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4,0,0,0,0,28,2,7,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,6,0,0,0,0,0,0,2,0,0,1,0,0,2,0,Class_9
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diff --git a/data/train.csv.zip b/data/train.csv.zip
new file mode 100644
index 0000000..2adadcc
Binary files /dev/null and b/data/train.csv.zip differ
diff --git a/deep-learning-osx.yml b/deep-learning-osx.yml
index 86c3017..2ce54fa 100644
--- a/deep-learning-osx.yml
+++ b/deep-learning-osx.yml
@@ -1,12 +1,11 @@
-name: deep-learning
-channels:
-- conda-forge
-- defaults
+name: deep-learning-pydatait-tutorial
 dependencies:
 - accelerate=2.3.0=np111py35_3
 - accelerate_cudalib=2.0=0
 - appnope=0.1.0=py35_0
 - bokeh=0.12.1=py35_0
+- boto=2.42.0=py35_0
+- bz2file=0.98=py35_0
 - cffi=1.6.0=py35_0
 - backports.shutil_get_terminal_size=1.0.0=py35_0
 - blas=1.1=openblas
@@ -23,6 +22,7 @@ dependencies:
 - ipython=5.1.0=py35_0
 - ipywidgets=5.2.2=py35_0
 - jinja2=2.8=py35_1
+- jpeg=9b=0
 - jsonschema=2.5.1=py35_0
 - jupyter_client=4.3.0=py35_0
 - jupyter_console=5.0.0=py35_0
@@ -30,6 +30,7 @@ dependencies:
 - libgfortran=3.0.0=0
 - libpng=1.6.24=0
 - libsodium=1.0.10=0
+- libtiff=4.0.6=6
 - markupsafe=0.23=py35_0
 - matplotlib=1.5.2=np111py35_5
 - mistune=0.7.3=py35_0
@@ -42,8 +43,10 @@ dependencies:
 - openblas=0.2.18=4
 - openssl=1.0.2h=2
 - pandas=0.18.1=np111py35_1
+- patsy=0.4.1=py35_0
 - pexpect=4.2.0=py35_1
 - pickleshare=0.7.3=py35_0
+- pillow=3.3.1=py35_0
 - pip=8.1.2=py35_0
 - prompt_toolkit=1.0.6=py35_0
 - ptyprocess=0.5.1=py35_0
@@ -57,13 +60,15 @@ dependencies:
 - qtconsole=4.2.1=py35_0
 - readline=6.2=0
 - requests=2.11.0=py35_0
-- scikit-learn=0.17.1=np111py35_blas_openblas_201
+- scikit-learn=0.18=np111py35_blas_openblas_202
 - scipy=0.18.0=np111py35_blas_openblas_201
+- seaborn=0.7.1=py35_0
 - setuptools=25.1.6=py35_0
 - simplegeneric=0.8.1=py35_0
 - sip=4.18=py35_0
 - six=1.10.0=py35_0
 - sqlite=3.13.0=1
+- statsmodels=0.6.1=np111py35_0
 - terminado=0.6=py35_0
 - tk=8.5.19=0
 - tornado=4.4.1=py35_1
@@ -76,6 +81,7 @@ dependencies:
 - zeromq=4.1.5=0
 - zlib=1.2.8=3
 - cudatoolkit=7.5=0
+- gensim=0.12.4=np111py35_0
 - ipython_genutils=0.1.0=py35_0
 - jupyter=1.0.0=py35_3
 - llvmlite=0.11.0=py35_0
@@ -86,6 +92,7 @@ dependencies:
 - pyqt=4.11.4=py35_4
 - python.app=1.2=py35_4
 - qt=4.8.7=4
+- smart_open=1.3.4=py35_0
 - snakeviz=0.4.1=py35_0
 - pip:
   - backports.shutil-get-terminal-size==1.0.0
@@ -103,7 +110,7 @@ dependencies:
   - jupyter-client==4.3.0
   - jupyter-console==5.0.0
   - jupyter-core==4.1.1
-  - keras==1.0.7
+  - keras==2.0.2
   - markupsafe==0.23
   - matplotlib==1.5.2
   - mistune==0.7.3
@@ -113,10 +120,13 @@ dependencies:
   - notebook==4.2.2
   - numpy==1.11.1
   - pandas==0.18.1
+  - patsy==0.4.1
   - pexpect==4.2.0
   - pickleshare==0.7.3
+  - pillow==3.3.1
   - pip==8.1.2
   - prompt-toolkit==1.0.6
+  - protobuf==3.0.0b2
   - ptyprocess==0.5.1
   - pygments==2.1.3
   - pyparsing==2.1.7
@@ -126,17 +136,18 @@ dependencies:
   - pyzmq==15.4.0
   - qtconsole==4.2.1
   - requests==2.11.0
-  - scikit-learn==0.17.1
+  - scikit-learn==0.18
   - scipy==0.18.0
+  - seaborn==0.7.1
   - setuptools==25.1.6
   - simplegeneric==0.8.1
   - six==1.10.0
+  - smart-open==1.3.4
+  - statsmodels==0.6.1
   - terminado==0.6
-  - theano==0.8.2
+  - theano==0.9.0
   - tornado==4.4.1
   - traitlets==4.2.2
   - wcwidth==0.1.7
   - wheel==0.29.0
   - widgetsnbextension==1.2.6
-prefix: /Users/valerio/anaconda/envs/deep-learning
-
diff --git a/deep-learning.yml b/deep-learning.yml
index 51157c3..cd4acc7 100644
--- a/deep-learning.yml
+++ b/deep-learning.yml
@@ -1,11 +1,10 @@
-name: deep-learning
-channels:
-- conda-forge
-- defaults
+name: deep-learning-pydatait-tutorial
 dependencies:
 - accelerate=2.3.0=np111py35_3
 - accelerate_cudalib=2.0=0
 - bokeh=0.12.1=py35_0
+- boto=2.40.0=py35_0
+- bz2file=0.98=py35_0
 - cffi=1.6.0=py35_0
 - backports.shutil_get_terminal_size=1.0.0=py35_0
 - blas=1.1=openblas
@@ -56,6 +55,7 @@ dependencies:
 - pcre=8.38=1
 - pexpect=4.2.0=py35_1
 - pickleshare=0.7.3=py35_0
+- pillow=3.3.1=py35_0
 - pip=8.1.2=py35_0
 - pixman=0.32.6=0
 - prompt_toolkit=1.0.6=py35_0
@@ -91,6 +91,8 @@ dependencies:
 - zeromq=4.1.5=0
 - zlib=1.2.8=3
 - cudatoolkit=7.5=0
+- damianavila82::rise=4.0.0b1=py35_0
+- gensim=0.12.4=np111py35_0
 - ipython_genutils=0.1.0=py35_0
 - jupyter=1.0.0=py35_3
 - libgfortran=3.0.0=1
@@ -100,6 +102,7 @@ dependencies:
 - numba=0.26.0=np111py35_0
 - pycparser=2.14=py35_1
 - pyqt=4.11.4=py35_4
+- smart_open=1.3.3=py35_0
 - snakeviz=0.4.1=py35_0
 - pip:
   - backports.shutil-get-terminal-size==1.0.0
@@ -117,7 +120,7 @@ dependencies:
   - jupyter-client==4.3.0
   - jupyter-console==5.0.0
   - jupyter-core==4.1.1
-  - keras==1.0.7
+  - keras==2.0.2
   - mako==1.0.4
   - markupsafe==0.23
   - matplotlib==1.5.2
@@ -131,11 +134,13 @@ dependencies:
   - path.py==8.2.1
   - pexpect==4.2.0
   - pickleshare==0.7.3
+  - pillow==3.3.1
   - pip==8.1.2
   - prompt-toolkit==1.0.6
   - protobuf==3.0.0b2
   - ptyprocess==0.5.1
   - pygments==2.1.3
+  - pygpu==0.2.1
   - pyparsing==2.1.7
   - python-dateutil==2.5.3
   - pytz==2016.6.1
@@ -143,17 +148,18 @@ dependencies:
   - pyzmq==15.4.0
   - qtconsole==4.2.1
   - requests==2.11.0
+  - rise==4.0.0b1
   - scikit-learn==0.17.1
   - scipy==0.18.0
   - setuptools==25.1.4
   - simplegeneric==0.8.1
   - six==1.10.0
+  - smart-open==1.3.3
   - terminado==0.6
-  - theano==0.8.2
+  - theano==0.9.0
   - tornado==4.4.1
   - traitlets==4.2.2
   - wcwidth==0.1.7
   - wheel==0.29.0
   - widgetsnbextension==1.2.6
-prefix: /home/valerio/anaconda3/envs/deep-learning
 
diff --git a/envs/deep-learning-linux.yml b/envs/deep-learning-linux.yml
new file mode 100644
index 0000000..98b1877
--- /dev/null
+++ b/envs/deep-learning-linux.yml
@@ -0,0 +1,157 @@
+name: deep-learning
+channels:
+- conda-forge
+- defaults
+dependencies:
+- accelerate=2.3.0=np111py35_3
+- accelerate_cudalib=2.0=0
+- bokeh=0.12.1=py35_0
+- cffi=1.6.0=py35_0
+- backports.shutil_get_terminal_size=1.0.0=py35_0
+- blas=1.1=openblas
+- ca-certificates=2016.8.2=3
+- cairo=1.12.18=8
+- certifi=2016.8.2=py35_0
+- cycler=0.10.0=py35_0
+- cython=0.24.1=py35_0
+- decorator=4.0.10=py35_0
+- entrypoints=0.2.2=py35_0
+- fontconfig=2.11.1=3
+- freetype=2.6.3=1
+- gettext=0.19.7=1
+- glib=2.48.0=4
+- h5py=2.6.0=np111py35_6
+- harfbuzz=1.0.6=0
+- hdf5=1.8.17=2
+- icu=56.1=4
+- ipykernel=4.3.1=py35_1
+- ipython=5.1.0=py35_0
+- ipywidgets=5.2.2=py35_0
+- jinja2=2.8=py35_1
+- jpeg=9b=0
+- jsonschema=2.5.1=py35_0
+- jupyter_client=4.3.0=py35_0
+- jupyter_console=5.0.0=py35_0
+- jupyter_core=4.1.1=py35_1
+- libffi=3.2.1=2
+- libiconv=1.14=3
+- libpng=1.6.24=0
+- libsodium=1.0.10=0
+- libtiff=4.0.6=6
+- libxml2=2.9.4=0
+- markupsafe=0.23=py35_0
+- matplotlib=1.5.2=np111py35_6
+- mistune=0.7.3=py35_0
+- nbconvert=4.2.0=py35_0
+- nbformat=4.0.1=py35_0
+- ncurses=5.9=8
+- nose=1.3.7=py35_1
+- notebook=4.2.2=py35_0
+- numpy=1.11.1=py35_blas_openblas_201
+- openblas=0.2.18=4
+- openssl=1.0.2h=2
+- pandas=0.18.1=np111py35_1
+- pango=1.40.1=0
+- path.py=8.2.1=py35_0
+- pcre=8.38=1
+- pexpect=4.2.0=py35_1
+- pickleshare=0.7.3=py35_0
+- pip=8.1.2=py35_0
+- pixman=0.32.6=0
+- prompt_toolkit=1.0.6=py35_0
+- protobuf=3.0.0b3=py35_1
+- ptyprocess=0.5.1=py35_0
+- pygments=2.1.3=py35_1
+- pyparsing=2.1.7=py35_0
+- python=3.5.2=2
+- python-dateutil=2.5.3=py35_0
+- pytz=2016.6.1=py35_0
+- pyyaml=3.11=py35_0
+- pyzmq=15.4.0=py35_0
+- qt=4.8.7=0
+- qtconsole=4.2.1=py35_0
+- readline=6.2=0
+- requests=2.11.0=py35_0
+- scikit-learn=0.17.1=np111py35_blas_openblas_201
+- scipy=0.18.0=np111py35_blas_openblas_201
+- setuptools=25.1.6=py35_0
+- simplegeneric=0.8.1=py35_0
+- sip=4.18=py35_0
+- six=1.10.0=py35_0
+- sqlite=3.13.0=1
+- terminado=0.6=py35_0
+- tk=8.5.19=0
+- tornado=4.4.1=py35_1
+- traitlets=4.2.2=py35_0
+- wcwidth=0.1.7=py35_0
+- wheel=0.29.0=py35_0
+- widgetsnbextension=1.2.6=py35_3
+- xz=5.2.2=0
+- yaml=0.1.6=0
+- zeromq=4.1.5=0
+- zlib=1.2.8=3
+- cudatoolkit=7.5=0
+- ipython_genutils=0.1.0=py35_0
+- jupyter=1.0.0=py35_3
+- libgfortran=3.0.0=1
+- llvmlite=0.11.0=py35_0
+- mkl=11.3.3=0
+- mkl-service=1.1.2=py35_2
+- numba=0.26.0=np111py35_0
+- pycparser=2.14=py35_1
+- pyqt=4.11.4=py35_4
+- snakeviz=0.4.1=py35_0
+- pip:
+  - backports.shutil-get-terminal-size==1.0.0
+  - certifi==2016.8.2
+  - cycler==0.10.0
+  - cython==0.24.1
+  - decorator==4.0.10
+  - h5py==2.6.0
+  - ipykernel==4.3.1
+  - ipython==5.1.0
+  - ipython-genutils==0.1.0
+  - ipywidgets==5.2.2
+  - jinja2==2.8
+  - jsonschema==2.5.1
+  - jupyter-client==4.3.0
+  - jupyter-console==5.0.0
+  - jupyter-core==4.1.1
+  - keras==1.0.7
+  - mako==1.0.4
+  - markupsafe==0.23
+  - matplotlib==1.5.2
+  - mistune==0.7.3
+  - nbconvert==4.2.0
+  - nbformat==4.0.1
+  - nose==1.3.7
+  - notebook==4.2.2
+  - numpy==1.11.1
+  - pandas==0.18.1
+  - path.py==8.2.1
+  - pexpect==4.2.0
+  - pickleshare==0.7.3
+  - pip==8.1.2
+  - prompt-toolkit==1.0.6
+  - protobuf==3.0.0b2
+  - ptyprocess==0.5.1
+  - pygments==2.1.3
+  - pyparsing==2.1.7
+  - python-dateutil==2.5.3
+  - pytz==2016.6.1
+  - pyyaml==3.11
+  - pyzmq==15.4.0
+  - qtconsole==4.2.1
+  - requests==2.11.0
+  - scikit-learn==0.17.1
+  - scipy==0.18.0
+  - setuptools==25.1.4
+  - simplegeneric==0.8.1
+  - six==1.10.0
+  - terminado==0.6
+  - theano==0.8.2
+  - tornado==4.4.1
+  - traitlets==4.2.2
+  - wcwidth==0.1.7
+  - wheel==0.29.0
+  - widgetsnbextension==1.2.6
diff --git a/envs/deep-learning-osx.yml b/envs/deep-learning-osx.yml
new file mode 100644
index 0000000..e235d31
--- /dev/null
+++ b/envs/deep-learning-osx.yml
@@ -0,0 +1,140 @@
+name: deep-learning
+channels:
+- conda-forge
+- defaults
+dependencies:
+- accelerate=2.3.0=np111py35_3
+- accelerate_cudalib=2.0=0
+- appnope=0.1.0=py35_0
+- bokeh=0.12.1=py35_0
+- cffi=1.6.0=py35_0
+- backports.shutil_get_terminal_size=1.0.0=py35_0
+- blas=1.1=openblas
+- ca-certificates=2016.8.2=3
+- certifi=2016.8.2=py35_0
+- cycler=0.10.0=py35_0
+- cython=0.24.1=py35_0
+- decorator=4.0.10=py35_0
+- entrypoints=0.2.2=py35_0
+- freetype=2.6.3=1
+- h5py=2.6.0=np111py35_6
+- hdf5=1.8.17=2
+- ipykernel=4.3.1=py35_1
+- ipython=5.1.0=py35_0
+- ipywidgets=5.2.2=py35_0
+- jinja2=2.8=py35_1
+- jsonschema=2.5.1=py35_0
+- jupyter_client=4.3.0=py35_0
+- jupyter_console=5.0.0=py35_0
+- jupyter_core=4.1.1=py35_1
+- libgfortran=3.0.0=0
+- libpng=1.6.24=0
+- libsodium=1.0.10=0
+- markupsafe=0.23=py35_0
+- matplotlib=1.5.2=np111py35_5
+- mistune=0.7.3=py35_0
+- nbconvert=4.2.0=py35_0
+- nbformat=4.0.1=py35_0
+- ncurses=5.9=8
+- nose=1.3.7=py35_1
+- notebook=4.2.2=py35_0
+- numpy=1.11.1=py35_blas_openblas_201
+- openblas=0.2.18=4
+- openssl=1.0.2h=2
+- pandas=0.18.1=np111py35_1
+- pexpect=4.2.0=py35_1
+- pickleshare=0.7.3=py35_0
+- pip=8.1.2=py35_0
+- prompt_toolkit=1.0.6=py35_0
+- ptyprocess=0.5.1=py35_0
+- pygments=2.1.3=py35_1
+- pyparsing=2.1.7=py35_0
+- python=3.5.2=2
+- python-dateutil=2.5.3=py35_0
+- pytz=2016.6.1=py35_0
+- pyyaml=3.11=py35_0
+- pyzmq=15.4.0=py35_0
+- qtconsole=4.2.1=py35_0
+- readline=6.2=0
+- requests=2.11.0=py35_0
+- scikit-learn=0.17.1=np111py35_blas_openblas_201
+- scipy=0.18.0=np111py35_blas_openblas_201
+- setuptools=25.1.6=py35_0
+- simplegeneric=0.8.1=py35_0
+- sip=4.18=py35_0
+- six=1.10.0=py35_0
+- sqlite=3.13.0=1
+- terminado=0.6=py35_0
+- tk=8.5.19=0
+- tornado=4.4.1=py35_1
+- traitlets=4.2.2=py35_0
+- wcwidth=0.1.7=py35_0
+- wheel=0.29.0=py35_0
+- widgetsnbextension=1.2.6=py35_3
+- xz=5.2.2=0
+- yaml=0.1.6=0
+- zeromq=4.1.5=0
+- zlib=1.2.8=3
+- cudatoolkit=7.5=0
+- ipython_genutils=0.1.0=py35_0
+- jupyter=1.0.0=py35_3
+- llvmlite=0.11.0=py35_0
+- mkl=11.3.3=0
+- mkl-service=1.1.2=py35_2
+- numba=0.26.0=np111py35_0
+- pycparser=2.14=py35_1
+- pyqt=4.11.4=py35_4
+- python.app=1.2=py35_4
+- qt=4.8.7=4
+- snakeviz=0.4.1=py35_0
+- pip:
+  - backports.shutil-get-terminal-size==1.0.0
+  - certifi==2016.8.2
+  - cycler==0.10.0
+  - cython==0.24.1
+  - decorator==4.0.10
+  - h5py==2.6.0
+  - ipykernel==4.3.1
+  - ipython==5.1.0
+  - ipython-genutils==0.1.0
+  - ipywidgets==5.2.2
+  - jinja2==2.8
+  - jsonschema==2.5.1
+  - jupyter-client==4.3.0
+  - jupyter-console==5.0.0
+  - jupyter-core==4.1.1
+  - keras==1.0.7
+  - markupsafe==0.23
+  - matplotlib==1.5.2
+  - mistune==0.7.3
+  - nbconvert==4.2.0
+  - nbformat==4.0.1
+  - nose==1.3.7
+  - notebook==4.2.2
+  - numpy==1.11.1
+  - pandas==0.18.1
+  - pexpect==4.2.0
+  - pickleshare==0.7.3
+  - pip==8.1.2
+  - prompt-toolkit==1.0.6
+  - ptyprocess==0.5.1
+  - pygments==2.1.3
+  - pyparsing==2.1.7
+  - python-dateutil==2.5.3
+  - pytz==2016.6.1
+  - pyyaml==3.11
+  - pyzmq==15.4.0
+  - qtconsole==4.2.1
+  - requests==2.11.0
+  - scikit-learn==0.17.1
+  - scipy==0.18.0
+  - setuptools==25.1.6
+  - simplegeneric==0.8.1
+  - six==1.10.0
+  - terminado==0.6
+  - theano==0.8.2
+  - tornado==4.4.1
+  - traitlets==4.2.2
+  - wcwidth==0.1.7
+  - wheel==0.29.0
+  - widgetsnbextension==1.2.6
diff --git a/envs/pip_requirements.txt b/envs/pip_requirements.txt
new file mode 100644
index 0000000..474c248
--- /dev/null
+++ b/envs/pip_requirements.txt
@@ -0,0 +1,149 @@
+accelerate==2.3.0
+accelerate_cudalib==2.0
+bokeh==0.12.1
+cffi==1.6.0
+backports.shutil_get_terminal_size==1.0.0
+blas==1.1
+ca-certificates==2016.8.2
+cairo==1.12.18
+certifi==2016.8.2
+cycler==0.10.0
+cython==0.24.1
+decorator==4.0.10
+entrypoints==0.2.2
+fontconfig==2.11.1
+freetype==2.6.3
+gettext==0.19.7
+glib==2.48.0
+h5py==2.6.0
+harfbuzz==1.0.6
+hdf5==1.8.17
+icu==56.1
+ipykernel==4.3.1
+ipython==5.1.0
+ipywidgets==5.2.2
+jinja2==2.8
+jpeg==9b
+jsonschema==2.5.1
+jupyter_client==4.3.0
+jupyter_console==5.0.0
+jupyter_core==4.1.1
+libffi==3.2.1
+libiconv==1.14
+libpng==1.6.24
+libsodium==1.0.10
+libtiff==4.0.6
+libxml2==2.9.4
+markupsafe==0.23
+matplotlib==1.5.2
+mistune==0.7.3
+nbconvert==4.2.0
+nbformat==4.0.1
+ncurses==5.9
+nose==1.3.7
+notebook==4.2.2
+numpy==1.11.1
+openblas==0.2.18
+openssl==1.0.2h
+pandas==0.18.1
+pango==1.40.1
+path.py==8.2.1
+pcre==8.38
+pexpect==4.2.0
+pickleshare==0.7.3
+pip==8.1.2
+pixman==0.32.6
+prompt_toolkit==1.0.6
+protobuf==3.0.0b3
+ptyprocess==0.5.1
+pygments==2.1.3
+pyparsing==2.1.7
+python==3.5.2
+python-dateutil==2.5.3
+pytz==2016.6.1
+pyyaml==3.11
+pyzmq==15.4.0
+qt==4.8.7
+qtconsole==4.2.1
+readline==6.2
+requests==2.11.0
+scikit-learn==0.17.1
+scipy==0.18.0
+setuptools==25.1.6
+simplegeneric==0.8.1
+sip==4.18
+six==1.10.0
+sqlite==3.13.0
+terminado==0.6
+tk==8.5.19
+tornado==4.4.1
+traitlets==4.2.2
+wcwidth==0.1.7
+wheel==0.29.0
+widgetsnbextension==1.2.6
+xz==5.2.2
+yaml==0.1.6
+zeromq==4.1.5
+zlib==1.2.8
+cudatoolkit==7.5
+ipython_genutils==0.1.0
+jupyter==1.0.0
+libgfortran==3.0.0
+llvmlite==0.11.0
+mkl==11.3.3
+mkl-service==1.1.2
+numba==0.26.0
+pycparser==2.14
+pyqt==4.11.4
+snakeviz==0.4.11
+backports.shutil-get-terminal-size==1.0.0
+certifi==2016.8.2
+cycler==0.10.0
+cython==0.24.1
+decorator==4.0.10
+h5py==2.6.0
+ipykernel==4.3.1
+ipython==5.1.0
+ipython-genutils==0.1.0
+ipywidgets==5.2.2
+jinja2==2.8
+jsonschema==2.5.1
+jupyter-client==4.3.0
+jupyter-console==5.0.0
+jupyter-core==4.1.1
+keras==1.0.7
+mako==1.0.4
+markupsafe==0.23
+matplotlib==1.5.2
+mistune==0.7.3
+nbconvert==4.2.0
+nbformat==4.0.1
+nose==1.3.7
+notebook==4.2.2
+numpy==1.11.1
+pandas==0.18.1
+path.py==8.2.1
+pexpect==4.2.0
+pickleshare==0.7.3
+pip==8.1.2
+prompt-toolkit==1.0.6
+ptyprocess==0.5.1
+pygments==2.1.3
+pyparsing==2.1.7
+python-dateutil==2.5.3
+pytz==2016.6.1
+pyyaml==3.11
+pyzmq==15.4.0
+qtconsole==4.2.1
+requests==2.11.0
+scikit-learn==0.17.1
+scipy==0.18.0
+simplegeneric==0.8.1
+six==1.10.0
+terminado==0.6
+theano==0.8.2
+tornado==4.4.1
+traitlets==4.2.2
+wcwidth==0.1.7
+wheel==0.29.0
+widgetsnbextension==1.2.6
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diff --git a/mnist_data.py b/mnist_data.py
new file mode 100644
index 0000000..0f4233d
--- /dev/null
+++ b/mnist_data.py
@@ -0,0 +1,216 @@
+# Copyright 2015 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Functions for downloading and reading MNIST data."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gzip
+import os
+
+import numpy
+from six.moves import urllib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
+
+
+def maybe_download(filename, work_directory):
+  """Download the data from Yann's website, unless it's already here."""
+  if not tf.gfile.Exists(work_directory):
+    tf.gfile.MakeDirs(work_directory)
+  filepath = os.path.join(work_directory, filename)
+  if not tf.gfile.Exists(filepath):
+    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
+    with tf.gfile.GFile(filepath) as f:
+      size = f.size()
+    print('Successfully downloaded', filename, size, 'bytes.')
+  return filepath
+
+
+def _read32(bytestream):
+  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
+  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
+
+
+def extract_images(filename):
+  """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
+  print('Extracting', filename)
+  with tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
+    magic = _read32(bytestream)
+    if magic != 2051:
+      raise ValueError(
+          'Invalid magic number %d in MNIST image file: %s' %
+          (magic, filename))
+    num_images = _read32(bytestream)
+    rows = _read32(bytestream)
+    cols = _read32(bytestream)
+    buf = bytestream.read(rows * cols * num_images)
+    data = numpy.frombuffer(buf, dtype=numpy.uint8)
+    data = data.reshape(num_images, rows, cols, 1)
+    return data
+
+
+def dense_to_one_hot(labels_dense, num_classes=10):
+  """Convert class labels from scalars to one-hot vectors."""
+  num_labels = labels_dense.shape[0]
+  index_offset = numpy.arange(num_labels) * num_classes
+  labels_one_hot = numpy.zeros((num_labels, num_classes))
+  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
+  return labels_one_hot
+
+
+def extract_labels(filename, one_hot=False):
+  """Extract the labels into a 1D uint8 numpy array [index]."""
+  print('Extracting', filename)
+  with tf.gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
+    magic = _read32(bytestream)
+    if magic != 2049:
+      raise ValueError(
+          'Invalid magic number %d in MNIST label file: %s' %
+          (magic, filename))
+    num_items = _read32(bytestream)
+    buf = bytestream.read(num_items)
+    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
+    if one_hot:
+      return dense_to_one_hot(labels)
+    return labels
+
+
+class DataSet(object):
+
+  def __init__(self, images, labels, fake_data=False, one_hot=False,
+               dtype=tf.float32):
+    """Construct a DataSet.
+
+    one_hot arg is used only if fake_data is true.  `dtype` can be either
+    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+    `[0, 1]`.
+    """
+    dtype = tf.as_dtype(dtype).base_dtype
+    if dtype not in (tf.uint8, tf.float32):
+      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
+                      dtype)
+    if fake_data:
+      self._num_examples = 10000
+      self.one_hot = one_hot
+    else:
+      assert images.shape[0] == labels.shape[0], (
+          'images.shape: %s labels.shape: %s' % (images.shape,
+                                                 labels.shape))
+      self._num_examples = images.shape[0]
+
+      # Convert shape from [num examples, rows, columns, depth]
+      # to [num examples, rows*columns] (assuming depth == 1)
+      assert images.shape[3] == 1
+      images = images.reshape(images.shape[0],
+                              images.shape[1] * images.shape[2])
+      if dtype == tf.float32:
+        # Convert from [0, 255] -> [0.0, 1.0].
+        images = images.astype(numpy.float32)
+        images = numpy.multiply(images, 1.0 / 255.0)
+    self._images = images
+    self._labels = labels
+    self._epochs_completed = 0
+    self._index_in_epoch = 0
+
+  @property
+  def images(self):
+    return self._images
+
+  @property
+  def labels(self):
+    return self._labels
+
+  @property
+  def num_examples(self):
+    return self._num_examples
+
+  @property
+  def epochs_completed(self):
+    return self._epochs_completed
+
+  def next_batch(self, batch_size, fake_data=False):
+    """Return the next `batch_size` examples from this data set."""
+    if fake_data:
+      fake_image = [1] * 784
+      if self.one_hot:
+        fake_label = [1] + [0] * 9
+      else:
+        fake_label = 0
+      return [fake_image for _ in xrange(batch_size)], [
+          fake_label for _ in xrange(batch_size)]
+    start = self._index_in_epoch
+    self._index_in_epoch += batch_size
+    if self._index_in_epoch > self._num_examples:
+      # Finished epoch
+      self._epochs_completed += 1
+      # Shuffle the data
+      perm = numpy.arange(self._num_examples)
+      numpy.random.shuffle(perm)
+      self._images = self._images[perm]
+      self._labels = self._labels[perm]
+      # Start next epoch
+      start = 0
+      self._index_in_epoch = batch_size
+      assert batch_size <= self._num_examples
+    end = self._index_in_epoch
+    return self._images[start:end], self._labels[start:end]
+
+
+def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
+  class DataSets(object):
+    pass
+  data_sets = DataSets()
+
+  if fake_data:
+    def fake():
+      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
+    data_sets.train = fake()
+    data_sets.validation = fake()
+    data_sets.test = fake()
+    return data_sets
+
+  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
+  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
+  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
+  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
+  VALIDATION_SIZE = 5000
+
+  local_file = maybe_download(TRAIN_IMAGES, train_dir)
+  train_images = extract_images(local_file)
+
+  local_file = maybe_download(TRAIN_LABELS, train_dir)
+  train_labels = extract_labels(local_file, one_hot=one_hot)
+
+  local_file = maybe_download(TEST_IMAGES, train_dir)
+  test_images = extract_images(local_file)
+
+  local_file = maybe_download(TEST_LABELS, train_dir)
+  test_labels = extract_labels(local_file, one_hot=one_hot)
+
+  validation_images = train_images[:VALIDATION_SIZE]
+  validation_labels = train_labels[:VALIDATION_SIZE]
+  train_images = train_images[VALIDATION_SIZE:]
+  train_labels = train_labels[VALIDATION_SIZE:]
+
+  data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
+  data_sets.validation = DataSet(validation_images, validation_labels,
+                                 dtype=dtype)
+  data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
+
+  return data_sets
\ No newline at end of file