Train_using_embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aaee65d4-fdcd-492e-8a12-24a9f6e6e9af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal : To classify the type of ovarian cancer from microscopy scans of biopsy samples.\n",
    "\n",
    "# Search SoTA model on Kaggle Approach, Papers with code - Medical Data, Graph Transformer, GNN, Global and Local Analysis\n",
    "\n",
    "# You should manage, including differences in image dimensions, quality, slide staining techniques, and more\n",
    "\n",
    "# There are 2 types of images - TMA and WSI (25 images are TMA and 513(rest) are WSI)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d1971b2-09c3-4a8d-b158-355c9772dfa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/projectnb/cs640grp/students/avarshn/.conda/envs/ocean'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.prefix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48222a33-507b-4f98-8df3-8538ed479f1c",
   "metadata": {},
   "source": [
    "# Check if GPU is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "143df7b3-2bc3-4651-b9a8-ed3f5a8108be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c193c329-3591-4572-9640-35b274bf754e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c04ae72-d26a-4c3c-8415-ba36cb557c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: nvidia: command not found\n"
     ]
    }
   ],
   "source": [
    "!nvidia smi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "150de13e-2b4e-499e-9092-e541baa686b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!lspci | grep -i nvidia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75827adc-9618-4d47-8fdc-44dbdc43cbe8",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7db4971-169f-47a7-9938-cc2d20e23b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from time import time\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "from PIL import Image\n",
    "import cv2\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.optim import AdamW\n",
    "\n",
    "from torchsummary import summary\n",
    "\n",
    "import torchvision\n",
    "from torchvision import transforms, utils\n",
    "from torchvision.models import efficientnet_b0, EfficientNet_B0_Weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c70d5b-6ba8-4faf-a3ff-5e37b6a0c467",
   "metadata": {},
   "source": [
    "# Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "4dcff8b2-936b-458c-a5f1-b7b711030b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG = {\n",
    "    \"seed\": 42,\n",
    "    # \"img_size\": 512,\n",
    "    # \"model_name\": \"tf_efficientnetv2_s_in21ft1k\",\n",
    "    \"num_classes\": 5,\n",
    "    \"device\": torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n",
    "    # \"valid_batch_size\": 32,\n",
    "    \"batch_size\" : 8,\n",
    "    \"epochs\" : 50,\n",
    "    \"learning_rate\" : 1e-5,\n",
    "    \"weight_decay\" : 1e-3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59ef42e6-00e5-40ff-a2b9-9e44b718dcee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed=42):\n",
    "    '''Sets the seed of the entire notebook so results are the same every time we run.\n",
    "    This is for REPRODUCIBILITY.'''\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    # When running on the CuDNN backend, two further options must be set\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # Set a fixed value for the hash seed\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    \n",
    "set_seed(CONFIG['seed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3350a3-92d5-4526-8c18-60568057476c",
   "metadata": {},
   "source": [
    "# Custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02dd6b7a-b315-49e5-ab7d-50141f6a92c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Image.MAX_IMAGE_PIXELS = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4e97aba-1582-4797-8187-9b2c1a82732c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c442f33a-4624-4992-a37f-2be8d5806e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_weights = EfficientNet_B0_Weights.DEFAULT\n",
    "# preprocess = model_weights.transforms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "467b1f55-b77b-414d-9fa2-65ceaed0ddd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess = transforms.Compose([\n",
    "#         # transforms.RandomSizedCrop(224),\n",
    "#         # transforms.RandomHorizontalFlip(),\n",
    "#         transforms.Resize((4000, 4000)),\n",
    "#         transforms.ToTensor(),   # Converts a PIL Image or a NumPy array with values in the range [0, 255] to a PyTorch tensor\n",
    "#                                  # with values in the range [0.0, 1.0].\n",
    "#         # transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "#         #                      std=[0.229, 0.224, 0.225])\n",
    "# ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e6883b4-e56f-40c6-adbd-df22f23f9632",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "002b334d-0f52-4bde-828c-eddcbb34a033",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ocean_embedding_dataset(Dataset):\n",
    "    \n",
    "    def __init__(self, numpy_file_name):\n",
    "        \n",
    "        self.ocean_np_arr = np.load(numpy_file_name)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.ocean_np_arr[\"labels\"].shape[0]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "\n",
    "        image = self.ocean_np_arr[\"embeddings\"][idx]\n",
    "        image = torch.tensor(image)\n",
    "        \n",
    "        label = self.ocean_np_arr[\"labels\"][idx]\n",
    "\n",
    "        return image, label\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33858d05-4c7f-4766-8cb5-84cc5b1dc770",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01054b83-dfda-4717-a6b5-64608e69e9d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"efficientnet_b0\"\n",
    "\n",
    "save_folder = \"./models/Baseline_model/embeddings/\"\n",
    "\n",
    "\n",
    "save_as = f\"{model_name}_224_224_train_embeddings_and_labels.npz\"\n",
    "train_np_arr_path = save_folder + save_as\n",
    "\n",
    "save_as = f\"{model_name}_224_224_test_embeddings_and_labels.npz\"\n",
    "test_np_arr_path = save_folder + save_as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27eba013-ad6c-4dc8-b686-e7e327206c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = ocean_embedding_dataset(train_np_arr_path)\n",
    "\n",
    "test_dataset = ocean_embedding_dataset(test_np_arr_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e15d1c-a392-41a3-a03f-5642fcdbfb96",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "50fbd20d-5ad0-44d1-8c8a-5bd32dcbc02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "296"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.__len__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86204af9-161a-488c-b890-b8f11b17911b",
   "metadata": {},
   "outputs": [],
   "source": [
    "a,b = train_dataset.__getitem__(torch.tensor(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52061b6a-000b-4ff2-8595-e37f9007f17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.2083, -0.0323, -0.1260,  ..., -0.0902, -0.1259, -0.1448])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd5247e2-7402-4bdd-b77d-f5d75b2ce125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b46cb26c-214f-4d06-a8c4-905d0ec08154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 4, 4, 0, 1, 1, 3, 0, 2, 0, 1, 3, 0, 0, 3, 0, 0, 2, 3, 3,\n",
       "       4, 3, 2, 1, 0, 0, 1, 3, 0, 0, 0, 1, 1, 2, 2, 3, 1, 1, 1, 0, 0, 0,\n",
       "       4, 0, 0, 0, 0, 1, 0, 1, 0, 3, 2, 0, 0, 3, 3, 0, 1, 0, 1, 3, 1, 1,\n",
       "       0, 2, 0, 0, 0, 3, 3, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 1, 3, 0, 3, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 3, 0, 4, 0, 0, 1, 0, 0, 0, 4, 4, 4, 0, 0, 0,\n",
       "       3, 0, 1, 0, 0, 2, 4, 0, 0, 4, 3, 1, 2, 0, 3, 3, 4, 4, 0, 1, 0, 1,\n",
       "       0, 1, 0, 4, 2, 0, 0, 3, 0, 0, 1, 2, 1, 0, 3, 0, 0, 3, 0, 3, 4, 0,\n",
       "       3, 3, 1, 1, 0, 1, 2, 0, 0, 4, 3, 1, 3, 3, 2, 3, 3, 0, 3, 1, 0, 1,\n",
       "       0, 2, 0, 0, 0, 0, 0, 4, 4, 1, 4, 0, 4, 0, 0, 1, 1, 3, 3, 1, 2, 2,\n",
       "       2, 3, 4, 0, 0, 2, 0, 1, 1, 1, 0, 0, 2, 4, 0, 1, 1, 0, 4, 0, 4, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 3, 0, 3, 3, 1,\n",
       "       1, 4, 1, 1, 3, 3, 1, 0, 0, 3, 0, 0, 1, 1, 0, 3, 3, 1, 1, 2, 1, 0,\n",
       "       0, 1, 0, 0, 4, 1, 1, 0, 0, 4, 2, 0, 3, 3, 0, 3, 0, 0, 2, 1, 0, 0,\n",
       "       2, 1, 0, 3, 0, 3, 1, 0, 1, 3])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.ocean_np_arr[\"labels\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20401933-47b3-404e-ba4e-be65e40f6ae6",
   "metadata": {},
   "source": [
    "# Class Weights - From Training Data since the dataset is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31f40cfe-c181-440a-8f3e-d440782b30c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 4, 4, 0, 1, 1, 3, 0, 2, 0, 1, 3, 0, 0, 3, 0, 0, 2, 3, 3,\n",
       "       4, 3, 2, 1, 0, 0, 1, 3, 0, 0, 0, 1, 1, 2, 2, 3, 1, 1, 1, 0, 0, 0,\n",
       "       4, 0, 0, 0, 0, 1, 0, 1, 0, 3, 2, 0, 0, 3, 3, 0, 1, 0, 1, 3, 1, 1,\n",
       "       0, 2, 0, 0, 0, 3, 3, 2, 3, 1, 3, 2, 0, 0, 0, 0, 0, 1, 3, 0, 3, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 3, 0, 4, 0, 0, 1, 0, 0, 0, 4, 4, 4, 0, 0, 0,\n",
       "       3, 0, 1, 0, 0, 2, 4, 0, 0, 4, 3, 1, 2, 0, 3, 3, 4, 4, 0, 1, 0, 1,\n",
       "       0, 1, 0, 4, 2, 0, 0, 3, 0, 0, 1, 2, 1, 0, 3, 0, 0, 3, 0, 3, 4, 0,\n",
       "       3, 3, 1, 1, 0, 1, 2, 0, 0, 4, 3, 1, 3, 3, 2, 3, 3, 0, 3, 1, 0, 1,\n",
       "       0, 2, 0, 0, 0, 0, 0, 4, 4, 1, 4, 0, 4, 0, 0, 1, 1, 3, 3, 1, 2, 2,\n",
       "       2, 3, 4, 0, 0, 2, 0, 1, 1, 1, 0, 0, 2, 4, 0, 1, 1, 0, 4, 0, 4, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 3, 0, 3, 3, 1,\n",
       "       1, 4, 1, 1, 3, 3, 1, 0, 0, 3, 0, 0, 1, 1, 0, 3, 3, 1, 1, 2, 1, 0,\n",
       "       0, 1, 0, 0, 4, 1, 1, 0, 0, 4, 2, 0, 3, 3, 0, 3, 0, 0, 2, 1, 0, 0,\n",
       "       2, 1, 0, 3, 0, 3, 1, 0, 1, 3])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.ocean_np_arr[\"labels\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e8ff4c7-62e4-4654-a53b-209323c92e3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75b28ad6-975a-4fc2-a19b-73bde1c09870",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2b246783-1d97-4989-ac02-f7e263919015",
   "metadata": {},
   "source": [
    "* Class weight Formula -\n",
    "  \n",
    "class_weight_of_that_class = $\\frac{\\text{Total samples}}{\\text{Total classes * samples of that class}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d3548690-c5de-462c-b869-e2ed6836beb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NumPy array of class labels - labels_arr\n",
    "\n",
    "labels_arr = train_dataset.ocean_np_arr[\"labels\"].copy()\n",
    "\n",
    "class_weights = compute_class_weight('balanced',classes = np.unique(labels_arr), y = labels_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3c848bee-deca-4c3d-ac8f-b6b0c58ae80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4852459 , 0.87058824, 2.27692308, 1.0962963 , 2.27692308])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42486cca-117d-4dcb-9a8b-a2a118939821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]), array([122,  68,  26,  54,  26]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label_mapper = {'HGSC' : 0, 'EC' : 1, 'MC' : 2, 'CC' : 3, 'LGSC' : 4}\n",
    "# Train Label Distribution\n",
    "np.unique(labels_arr, return_counts = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b19a847-2296-4df0-9897-8e5a295e6c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.4852, 0.8706, 2.2769, 1.0963, 2.2769], dtype=torch.float64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights_tensor = torch.tensor(class_weights)\n",
    "class_weights_tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d58fe0-b89b-44cb-847d-8085557d6e5b",
   "metadata": {},
   "source": [
    "# DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05e9ade-27f6-4b02-af47-feac75b859e6",
   "metadata": {},
   "source": [
    "* The EOFError you encountered in your DataLoader worker process could be related to using multiple workers (num_workers = 2)\n",
    "\n",
    "\n",
    "1) Concurrency Issues:\n",
    "\n",
    "    * When using multiple worker processes (num_workers > 0), there is potential for concurrency issues, especially when reading data from a file or other data sources. If your dataset is not designed to handle concurrent access properly, it can result in unexpected errors like EOFError.\n",
    "  \n",
    "2) Use torch.utils.data.SequentialSampler:\n",
    "    * Instead of relying on the default random sampler, you can use SequentialSampler to ensure that the data is loaded in a sequential order, which might help in scenarios where the order of loading matters.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "98abd35d-befe-40e6-ab4e-c678d48864ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader = DataLoader(train_dataset, batch_size = CONFIG['batch_size'], shuffle = True, drop_last = True)\n",
    "\n",
    "testloader = DataLoader(test_dataset, batch_size = CONFIG['batch_size'], shuffle = False, drop_last = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c34038c-0762-4061-85ee-cafd44eddddf",
   "metadata": {},
   "source": [
    "# Train - Evaluation, Training, Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "fd3de3db-b023-41f9-b2d9-cf9f8c245a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use `torch.no_grad()` here to disable gradient calculation. \n",
    "# It will reduce memory consumption as we don't need to compute gradients in inference.\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluate(model, testloader, criterion, device):\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    # Keep track of loss and other evaluation metrics\n",
    "    test_loss = 0.0\n",
    "\n",
    "    YPredict = []\n",
    "    YTrue = []\n",
    "    \n",
    "    # Loop through each batch on test set\n",
    "    for i, (images, labels) in enumerate(testloader, 1):\n",
    "        \n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        logits = model(images)\n",
    "\n",
    "        loss = criterion(logits, labels)\n",
    "        \n",
    "        test_loss += loss.detach().item()\n",
    "\n",
    "        predictions = torch.argmax(logits, dim = 1)\n",
    "        predictions = predictions.view(-1)\n",
    "        YTrue.extend(labels.tolist())\n",
    "        YPredict.extend(predictions.tolist())\n",
    "\n",
    "    # Compute Scores\n",
    "    accuracy = accuracy_score(YTrue, YPredict)\n",
    "    f1 = f1_score(YTrue, YPredict, average = \"macro\")\n",
    "\n",
    "    return (test_loss/i, accuracy, f1) ## avg loss and acc over all batches\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9af0610-8e73-45cd-987c-9556427a514a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "9857e5e7-7f67-45c6-90f9-9a6586b650c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, trainloader, testloader, CONFIG):\n",
    "    \n",
    "    optimizer = AdamW(model.parameters(), lr = CONFIG[\"learning_rate\"], weight_decay = CONFIG[\"weight_decay\"])\n",
    "\n",
    "    # criterion = nn.CrossEntropyLoss(weight = class_weights_tensor)\n",
    "\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "\n",
    "    ## Measure runtime\n",
    "    t_start = time()\n",
    "    \n",
    "    ## Store training log\n",
    "    history = defaultdict(list)\n",
    "    \n",
    "    # We will train the model `num_epochs` times\n",
    "    for i in range(1, CONFIG[\"epochs\"]+1):\n",
    "\n",
    "        # Training\n",
    "        train_loss, train_acc, train_f1 = train_one_epoch(model = model, trainloader = trainloader, optimizer = optimizer, criterion = criterion, device = CONFIG[\"device\"])\n",
    "\n",
    "        # Model Checkpointing\n",
    "        if i%10==0:\n",
    "            torch.save(model, f'models/Baseline_model/epoch{i}_batch_size{CONFIG[\"batch_size\"]}.pth')\n",
    "\n",
    "        # Evaluate on test set\n",
    "        test_loss, test_acc, test_f1 = evaluate(model = model, testloader = testloader, criterion = criterion, device = CONFIG[\"device\"])\n",
    "\n",
    "        ## Store train/test loss, accuracy, f1 scores\n",
    "        history[\"train_loss\"].append(train_loss)\n",
    "        history[\"train_acc\"].append(train_acc)\n",
    "        history[\"train_f1\"].append(train_f1)\n",
    "        \n",
    "        history[\"test_loss\"].append(test_loss)\n",
    "        history[\"test_acc\"].append(test_acc)\n",
    "        history[\"test_f1\"].append(test_f1)\n",
    "\n",
    "        \n",
    "        \n",
    "        ## print out train/test loss, accuracy\n",
    "        print(f'Epoch: {i} | Runtime: {((time()-t_start)/60):.2f}[m] | train_loss: {train_loss:.3f} | test_loss: {test_loss:.3f} | train_acc: {train_acc:.3f} |  test_acc: {test_acc:.3f} | train_f1: {train_f1:.3f} | test_f1: {test_f1:.3f}')    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "44a71bce-186f-4c0c-9b32-308f9819afc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_epoch(model, trainloader, device, optimizer, criterion):\n",
    "    \"\"\"\n",
    "      Train 1 epoch on trainloader.\"\n",
    "    \"\"\"\n",
    "\n",
    "    model = model.train()\n",
    "    \n",
    "    # Keep track of loss and other evaluation metrics\n",
    "    train_loss = 0.0\n",
    "\n",
    "    YPredict = []\n",
    "    YTrue = []\n",
    "\n",
    "    ## Loop over all the batches\n",
    "    for i, (images, labels) in tqdm(enumerate(trainloader, 1), total=len(trainloader), desc=f\"Training on whole batch...\"):\n",
    "            \n",
    "        # Move images and labels to `device` (CPU or GPU)\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        logits = model(images)\n",
    "\n",
    "        # print(logits)\n",
    "        # print(labels)\n",
    "        # print(logits.dtype, labels.dtype)\n",
    "        # print(type(labels[0]))\n",
    "        # print(type(labels))\n",
    "        loss = criterion(logits, labels)\n",
    "        # loss = criterion(logits, labels.to(torch.long))\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Compute loss and accuracy for this batch\n",
    "        train_loss += loss.detach().item()\n",
    "\n",
    "        predictions = torch.argmax(logits, dim = 1)\n",
    "        predictions = predictions.view(-1)\n",
    "        YTrue.extend(labels.tolist())\n",
    "        YPredict.extend(predictions.tolist())\n",
    "\n",
    "\n",
    "    # Compute Scores\n",
    "    accuracy = accuracy_score(YTrue, YPredict)\n",
    "    f1 = f1_score(YTrue, YPredict, average = \"macro\")\n",
    "\n",
    "    return (train_loss/i, accuracy, f1) ## avg loss and acc over all batches\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "05d4166d-2d7d-4c79-a87c-9366953343ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Baseline_Model(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        # self.base_model_headless = base_layers\n",
    "        # self.base_model_headless.requires_grad = False\n",
    "        \n",
    "        self.fc1 = nn.Linear(1280, 64)\n",
    "        self.fc2 = nn.Linear(64, 5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p = 0.5)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x = self.base_model_headless(x)\n",
    "        # # print(\"Output shape after Passing through Pretrained Model\")\n",
    "        # # print(x.shape)\n",
    "\n",
    "        # # Flatten\n",
    "        # x = x.view((x.shape[0], -1))\n",
    "\n",
    "        # FCNN\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        # x = self.relu(x)\n",
    "        # x = self.dropout(x)\n",
    "        # x = self.fc3(x)\n",
    "        # print(x.shape)\n",
    "\n",
    "        return x      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "1a15c053-9780-47d1-882f-15fbbb06ac7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Model\n",
    "model = Baseline_Model().to(CONFIG[\"device\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "6f956533-8f0b-4dc3-bbc6-03ac4975d84d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                   [-1, 64]          81,984\n",
      "              ReLU-2                   [-1, 64]               0\n",
      "           Dropout-3                   [-1, 64]               0\n",
      "            Linear-4                    [-1, 5]             325\n",
      "================================================================\n",
      "Total params: 82,309\n",
      "Trainable params: 82,309\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.31\n",
      "Estimated Total Size (MB): 0.32\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, input_size = (1280,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "3b3268f2-b071-4f84-a307-a41e365ce456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## compute accuracy score in a batch\n",
    "# def compute_accuracy(logits, labels, batch_size):\n",
    "#     ''' \n",
    "#       Obtain accuracy for a training batch \n",
    "#       logits: float Tensor, shape (batch_size, num_classes),  output from the model\n",
    "#       labels: Long Tensor, shape (batch_size, ), contains labels for the predictions\n",
    "#       batch_size: int, batch size\n",
    "\n",
    "#       Return accuracy for this batch, which should be a float number\n",
    "#     '''\n",
    "    \n",
    "#     # Write your code in this block ----------------\n",
    "    \n",
    "#     predictions = torch.argmax(logits, dim = 1)\n",
    "#     predictions = predictions.view(-1)\n",
    "    \n",
    "#     total_correct = (labels == predictions).sum().item()\n",
    "#     accuracy = total_correct/batch_size\n",
    "    \n",
    "#     return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb41d874-840c-46ba-a732-b538cff27f27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "84b0e68d-1d4a-4885-92ee-29a9abf92b05",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 400.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | Runtime: 0.01[m] | train_loss: 1.232 | test_loss: 1.347 | train_acc: 0.514 |  test_acc: 0.445 | train_f1: 0.322 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2 | Runtime: 0.03[m] | train_loss: 1.227 | test_loss: 1.345 | train_acc: 0.517 |  test_acc: 0.445 | train_f1: 0.307 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 403.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3 | Runtime: 0.04[m] | train_loss: 1.224 | test_loss: 1.342 | train_acc: 0.541 |  test_acc: 0.445 | train_f1: 0.377 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4 | Runtime: 0.06[m] | train_loss: 1.253 | test_loss: 1.340 | train_acc: 0.493 |  test_acc: 0.445 | train_f1: 0.286 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5 | Runtime: 0.07[m] | train_loss: 1.224 | test_loss: 1.338 | train_acc: 0.524 |  test_acc: 0.445 | train_f1: 0.353 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6 | Runtime: 0.09[m] | train_loss: 1.202 | test_loss: 1.336 | train_acc: 0.557 |  test_acc: 0.445 | train_f1: 0.410 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7 | Runtime: 0.10[m] | train_loss: 1.217 | test_loss: 1.333 | train_acc: 0.557 |  test_acc: 0.445 | train_f1: 0.417 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8 | Runtime: 0.11[m] | train_loss: 1.202 | test_loss: 1.331 | train_acc: 0.547 |  test_acc: 0.445 | train_f1: 0.387 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9 | Runtime: 0.13[m] | train_loss: 1.216 | test_loss: 1.330 | train_acc: 0.520 |  test_acc: 0.445 | train_f1: 0.345 | test_f1: 0.179\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10 | Runtime: 0.14[m] | train_loss: 1.182 | test_loss: 1.328 | train_acc: 0.605 |  test_acc: 0.453 | train_f1: 0.437 | test_f1: 0.189\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11 | Runtime: 0.16[m] | train_loss: 1.174 | test_loss: 1.326 | train_acc: 0.564 |  test_acc: 0.445 | train_f1: 0.437 | test_f1: 0.186\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 403.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12 | Runtime: 0.17[m] | train_loss: 1.165 | test_loss: 1.324 | train_acc: 0.547 |  test_acc: 0.445 | train_f1: 0.365 | test_f1: 0.186\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13 | Runtime: 0.19[m] | train_loss: 1.185 | test_loss: 1.322 | train_acc: 0.578 |  test_acc: 0.453 | train_f1: 0.416 | test_f1: 0.220\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14 | Runtime: 0.20[m] | train_loss: 1.179 | test_loss: 1.321 | train_acc: 0.557 |  test_acc: 0.461 | train_f1: 0.409 | test_f1: 0.236\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15 | Runtime: 0.21[m] | train_loss: 1.148 | test_loss: 1.319 | train_acc: 0.601 |  test_acc: 0.461 | train_f1: 0.458 | test_f1: 0.236\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 402.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16 | Runtime: 0.23[m] | train_loss: 1.149 | test_loss: 1.318 | train_acc: 0.571 |  test_acc: 0.461 | train_f1: 0.435 | test_f1: 0.242\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17 | Runtime: 0.24[m] | train_loss: 1.124 | test_loss: 1.316 | train_acc: 0.608 |  test_acc: 0.461 | train_f1: 0.488 | test_f1: 0.242\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18 | Runtime: 0.26[m] | train_loss: 1.144 | test_loss: 1.314 | train_acc: 0.551 |  test_acc: 0.453 | train_f1: 0.402 | test_f1: 0.248\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19 | Runtime: 0.27[m] | train_loss: 1.124 | test_loss: 1.312 | train_acc: 0.601 |  test_acc: 0.461 | train_f1: 0.490 | test_f1: 0.260\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 404.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 | Runtime: 0.29[m] | train_loss: 1.108 | test_loss: 1.311 | train_acc: 0.615 |  test_acc: 0.461 | train_f1: 0.487 | test_f1: 0.259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21 | Runtime: 0.30[m] | train_loss: 1.125 | test_loss: 1.308 | train_acc: 0.601 |  test_acc: 0.469 | train_f1: 0.475 | test_f1: 0.255\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22 | Runtime: 0.31[m] | train_loss: 1.124 | test_loss: 1.307 | train_acc: 0.588 |  test_acc: 0.461 | train_f1: 0.482 | test_f1: 0.259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23 | Runtime: 0.33[m] | train_loss: 1.095 | test_loss: 1.305 | train_acc: 0.615 |  test_acc: 0.461 | train_f1: 0.505 | test_f1: 0.259\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Epoch: 24 | Runtime: 0.34[m] | train_loss: 1.108 | test_loss: 1.303 | train_acc: 0.628 |  test_acc: 0.461 | train_f1: 0.520 | test_f1: 0.259\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 25 | Runtime: 0.36[m] | train_loss: 1.082 | test_loss: 1.301 | train_acc: 0.632 |  test_acc: 0.461 | train_f1: 0.516 | test_f1: 0.259\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26 | Runtime: 0.37[m] | train_loss: 1.072 | test_loss: 1.299 | train_acc: 0.625 |  test_acc: 0.461 | train_f1: 0.518 | test_f1: 0.259\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 27 | Runtime: 0.39[m] | train_loss: 1.119 | test_loss: 1.297 | train_acc: 0.605 |  test_acc: 0.461 | train_f1: 0.506 | test_f1: 0.258\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 28 | Runtime: 0.40[m] | train_loss: 1.076 | test_loss: 1.297 | train_acc: 0.611 |  test_acc: 0.461 | train_f1: 0.497 | test_f1: 0.258\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 29 | Runtime: 0.42[m] | train_loss: 1.052 | test_loss: 1.294 | train_acc: 0.666 |  test_acc: 0.461 | train_f1: 0.580 | test_f1: 0.258\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30 | Runtime: 0.43[m] | train_loss: 1.008 | test_loss: 1.291 | train_acc: 0.679 |  test_acc: 0.469 | train_f1: 0.607 | test_f1: 0.269\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31 | Runtime: 0.44[m] | train_loss: 1.081 | test_loss: 1.290 | train_acc: 0.601 |  test_acc: 0.461 | train_f1: 0.512 | test_f1: 0.266\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 32 | Runtime: 0.46[m] | train_loss: 1.058 | test_loss: 1.290 | train_acc: 0.628 |  test_acc: 0.461 | train_f1: 0.492 | test_f1: 0.266\n"
     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "Epoch: 33 | Runtime: 0.47[m] | train_loss: 1.042 | test_loss: 1.289 | train_acc: 0.625 |  test_acc: 0.461 | train_f1: 0.513 | test_f1: 0.265\n"
     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "Epoch: 34 | Runtime: 0.49[m] | train_loss: 1.039 | test_loss: 1.287 | train_acc: 0.649 |  test_acc: 0.461 | train_f1: 0.561 | test_f1: 0.266\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 35 | Runtime: 0.50[m] | train_loss: 1.036 | test_loss: 1.286 | train_acc: 0.635 |  test_acc: 0.461 | train_f1: 0.546 | test_f1: 0.267\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 36 | Runtime: 0.52[m] | train_loss: 1.029 | test_loss: 1.286 | train_acc: 0.618 |  test_acc: 0.461 | train_f1: 0.516 | test_f1: 0.267\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 37 | Runtime: 0.53[m] | train_loss: 1.026 | test_loss: 1.285 | train_acc: 0.652 |  test_acc: 0.461 | train_f1: 0.574 | test_f1: 0.267\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 38 | Runtime: 0.54[m] | train_loss: 1.004 | test_loss: 1.283 | train_acc: 0.689 |  test_acc: 0.461 | train_f1: 0.610 | test_f1: 0.266\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 39 | Runtime: 0.56[m] | train_loss: 1.033 | test_loss: 1.283 | train_acc: 0.666 |  test_acc: 0.469 | train_f1: 0.574 | test_f1: 0.296\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | Runtime: 0.57[m] | train_loss: 1.008 | test_loss: 1.282 | train_acc: 0.676 |  test_acc: 0.469 | train_f1: 0.573 | test_f1: 0.318\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41 | Runtime: 0.59[m] | train_loss: 1.028 | test_loss: 1.282 | train_acc: 0.655 |  test_acc: 0.461 | train_f1: 0.592 | test_f1: 0.315\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 42 | Runtime: 0.60[m] | train_loss: 1.020 | test_loss: 1.281 | train_acc: 0.642 |  test_acc: 0.469 | train_f1: 0.577 | test_f1: 0.318\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 43 | Runtime: 0.62[m] | train_loss: 0.980 | test_loss: 1.279 | train_acc: 0.649 |  test_acc: 0.461 | train_f1: 0.577 | test_f1: 0.315\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 44 | Runtime: 0.63[m] | train_loss: 0.989 | test_loss: 1.278 | train_acc: 0.693 |  test_acc: 0.461 | train_f1: 0.607 | test_f1: 0.315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 45 | Runtime: 0.64[m] | train_loss: 0.966 | test_loss: 1.278 | train_acc: 0.682 |  test_acc: 0.477 | train_f1: 0.622 | test_f1: 0.334\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46 | Runtime: 0.66[m] | train_loss: 0.997 | test_loss: 1.277 | train_acc: 0.682 |  test_acc: 0.461 | train_f1: 0.606 | test_f1: 0.311\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 47 | Runtime: 0.67[m] | train_loss: 1.004 | test_loss: 1.276 | train_acc: 0.632 |  test_acc: 0.477 | train_f1: 0.549 | test_f1: 0.329\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 48 | Runtime: 0.69[m] | train_loss: 0.995 | test_loss: 1.275 | train_acc: 0.686 |  test_acc: 0.469 | train_f1: 0.647 | test_f1: 0.326\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 49 | Runtime: 0.70[m] | train_loss: 0.956 | test_loss: 1.275 | train_acc: 0.706 |  test_acc: 0.477 | train_f1: 0.619 | test_f1: 0.330\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on whole batch...: 100%|██████████| 296/296 [00:00<00:00, 405.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50 | Runtime: 0.72[m] | train_loss: 0.991 | test_loss: 1.274 | train_acc: 0.666 |  test_acc: 0.477 | train_f1: 0.589 | test_f1: 0.330\n"
     ]
    }
   ],
   "source": [
    "# Train Model\n",
    "\n",
    "history_model = train_model(model, trainloader, testloader, CONFIG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad0b4e03-74a6-4f50-ab92-66ae5087fb36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fe441e3b-ca49-4169-9237-25c32dd4891b",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4d6bb5c3-1575-43e9-94b0-35e272d47f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1466353f8730>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hHiBpf1zczeGv8IuA+8xjhScha5+5/L7qZ9XwWV6aWQ6tPPc1PQPM3EV+XcxhtW7x0HUYuHk3xzcSkfoyDCgrdM69Xb1qNRfxpZdeYv/+/QwYMICnn34agODgYLKzs7nsssuYOXMmL7zwAkVFRTz22GPceOONfP/996SlpTFt2jT+/ve/M2XKFPLy8lizZg2GYfDII4+wZ88ecnNzHQFIQEBArZp96623cuDAAb744gt8fX157LHHmDhxIrt378bV1ZVZs2ZRWlrK6tWr8fb2Zvfu3fj4mFMM/vjHP7J7926++eYbgoKCSExMpKioqJ4PsGEUAImAOa/IkeH6DMU55nyizD2QnQL5GZCfCfnplT8zwF4ORSfNkrXH7EUCM/t15xgzGIoYYf70qt0vGBFpJmWF8EyYc+79/47V6n+S/Pz8cHNzw8vLi9DQUMfxV155hdjYWJ555hnHsbfeeovw8HD2799Pfn4+5eXlXHfddURERAAwcOBAR11PT09KSkqqXfNCqgKfdevWMWLECAD++9//Eh4ezqJFi7jhhhtISUlh6tSpjntFR0c7zk9JSSE2NpZhw4YBEBkZWet7NzYFQCLn4+Fn9uR0HVbz53a7meU6L90Mhk4kmvmLUjaYvUdHt5hlwytm/eA+Zs+Tfzfw7gTeweDTCbyDzPduXs321USkddu+fTsrVqxw9K6c6eDBg1x55ZVcfvnlDBw4kPHjx3PllVdy/fXX07Fjx3rfc8+ePbi4uBAXF+c4FhgYSO/evdmzZw8ADzzwAPfccw9Lly5l3LhxTJ06lUGDBgFwzz33MHXqVLZu3cqVV17Jtdde6wikmpsCIJGGsFort+8IgJB+0H0sXHSH2a2enWIGQikb4PAGc3l+1l6znIurt5nl2jvYTOwY2N3MZRTYHQK6a0K2SGNz9TJ7Ypx17wbIz89n0qRJ/O1vfzvrs86dO2Oz2Vi2bBnr169n6dKlvPzyy/z+979n06ZNREVFNeje5zNz5kzGjx/P119/zdKlS5k3bx7PPfcc999/PxMmTODw4cMsXryYZcuWcfnllzNr1iyeffbZJmvPuSgAEmkKFsvpVWkxvzSPFRw3e4eObYW8jMptPzLN4/mZZh6jsgI4VWBuF3Lkx7Ov6xV0OiAK7G6uUgvuCwFRyoItUh8WS6uYq+fm5kZFRUW1Y0OGDOHTTz8lMjISF5ea/5xbLBZGjhzJyJEjeeKJJ4iIiGDhwoXMnj27xmteSN++fSkvL2fTpk2OnpsTJ06wb98++vXr56gXHh7O3Xffzd13382cOXN44403uP/++wFz/tKMGTOYMWMGo0aN4tFHH1UAJNKmeQeZW4HUtB2IYUBJ3um90PIzzCCoKsnjiYPmvKPC42ZJ3Vj9fJs7BFcGQ1V7sQX3qdweRIs9RVq7yMhINm3aRHJyMj4+PgQEBDBr1izeeOMNpk2bxu9+9zsCAgJITEzkww8/5M0332Tz5s0sX76cK6+8kk6dOrFp0yaysrLo27ev45rffvst+/btIzAwED8/P1xdXc/bjp49ezJ58mTuuOMOXn/9dTp06MDjjz9Oly5dmDx5MgAPPfQQEyZMoFevXpw6dYoVK1Y47vnEE08wdOhQ+vfvT0lJCV999ZXjs+amAEikJbBYwMPXLIHda65TkmcGQicrA6ITiZUr1vZBeRGk7zDLmVy9zEAopL+5oWxIf7N41n8OgIg0v0ceeYQZM2bQr18/ioqKSEpKIjIyknXr1vHYY49x5ZVXUlJSQkREBFdddRVWqxVfX19Wr17Niy++SG5uLhERETz33HNMmGAmkb3jjjtYuXIlw4YNIz8/nxUrVjBmzJgLtuXtt9/mwQcf5JprrqG0tJRLL72UxYsXO4KniooKZs2axZEjR/D19eWqq67ihRdeAMyerDlz5pCcnIynpyejRo3iww8/bLLndj4WwzAMp9y5BcvNzcXPz4+cnBx8fX2d3RyR87NXmBvGZu4xS9ZeyNxrzjmqOEeOIt+u5h5pVQFRyABzHzUXt+Ztu0gzKy4uJikpiaioKDw8PJzdHKmH8/0zrMvfb/UAibR2Vpu5t1lANPS5+vTxinI4lQQZuypL5XYg2SmQe8Qs+5ecrm+xQcfIys1ke5glqKe5qaxPJ+2dJiJtigIgkbbK5mIGMEE9of+1p48X50DG7tMBUfpOs+eorMAcXjt58OxruftBUA8IHQRdhkDYEHNozaZfISLSOum3l0h74+F3dtJHwzCzXR8/YG4oezyx8ud+yE6FkpzTOY22VKatd/E0Ez2GxZ4OigKiNelaRFoFBUAiYg5v+YaZJXp09c/KiuDkIXNu0bEEOLbN/FmaZ65GO3NFmrufuSS/Q2foEFL5M/T0T59QczWcluyLiJMpABKR83P1PD1ZesBU85jdbvYQHdsGR7eauY3Sd5g9RWkJZjmXqrlGYbGnS+dB4N6hGb6MiEnrf1qvxvpnpwBIROrOaoXg3mapSvRYUWYuyc85UrmBbPrpn/nplT8zwag4Pddo5yeVF7SYc5XCYqHzYPNn6EBwPzvFv0hD2Gxm72NpaSmenp5Obo3UR2GhuXnthXIWXYgCIBFpHDZXc2l96IBz16koN5M8Zu05PZR2LMFckXZ8v1l++uh0ff9u5mTrqtKpDwT1VmAk9ebi4oKXlxdZWVm4urpi1Zy1VsMwDAoLC8nMzMTf398RzNaX8gDVQHmARJpZfpY5bHZs2+mSl3bu+n7dzN6nkH4QcQlEjFBQJLVWWlpKUlISdrvd2U2RevD39yc0NBRLDak56vL326kB0OrVq/nHP/7Bli1bSEtLY+HChVx77bXnrL927Voee+wx9u7dS2FhIREREdx11108/PDD1erNnz+ff/zjH6SnpxMTE8PLL7/MRRddVOt2KQASaQEKT1YmddxTmfG68md+xtl1rS7QdThEjTYncXcZpqSOcl52u53S0nMkCpUWy9XV9bw9P60mEWJBQQExMTHcfvvtXHfddRes7+3tzX333cegQYPw9vZm7dq13HXXXXh7e3PnnXcC8NFHHzF79mz+9a9/ERcXx4svvsj48ePZt28fnTp1auqvJCKNxSvA7NmJGFH9eOHJ0wHRsW1waJWZCTtlg1lW/RVcvc3zokdD1KXmsJmrsv7KaVarVZmg27kWMwRmsVgu2ANUk+uuuw5vb2/ee+89AOLi4hg+fDivvPIKYEb54eHh3H///Tz++OM1XqOkpISSkhLH+9zcXMLDw9UDJNJanEyCpFVmMJS02tww9uc6dDY3h+0YcfZP3y5ami/SBrSaHqCG2rZtG+vXr+fPf/4zYI7rbtmyhTlz5jjqWK1Wxo0bx4YNG855nXnz5vHUU081eXtFpIkERJll6K3mEv3MXZXB0Co4vMHMWZSXZpYz8xZVsbpAQPcz9karLH7h2gJEpI1qlQFQ165dycrKory8nLlz5zJz5kwAjh8/TkVFBSEhIdXqh4SEsHfv3nNeb86cOcyePdvxvqoHSERaIavVXEIfOhBG3GdmuS48AacOm0Nl2YdPvz51GHJSzU1jj+8zy67PTl/L3Rc69TsdEHWMBM+Op4uHnwIkkVaqVQZAa9asIT8/n40bN/L444/To0cPpk2bVu/rubu74+7u3ogtFJEWw2Ixs097B0HXoWd/brdD3jHI3GvujZa529w8NmsflOSene262rVt4OlfPSjy7QJRo8wJ2d5BTfrVRKT+WmUAFBUVBcDAgQPJyMhg7ty5TJs2jaCgIGw2GxkZ1VeJZGRkEBoa6oymikhLZ7WCX1ez9Bx3+nh5qZntOmPX6ZKXBkWnzFJWaCZ1LDxhljNV7ZcWMtCciB09BrrFa6m+SAvSKgOgM9ntdscEZjc3N4YOHcry5csdk6ntdjvLly/nvvvuc2IrRaTVcXE7PfRVk7JiKM4+HRAVnoSiyhVqh1aavUkZO8yy4RWwuppL9aPHmKXLULC1+l/BIq2WU//ry8/PJzEx0fE+KSmJhIQEAgIC6NatG3PmzOHo0aO8++67gJnfp1u3bvTp0wcw8wg9++yzPPDAA45rzJ49mxkzZjBs2DAuuugiXnzxRQoKCrjtttua98uJSNvm6gGuoeYmrzXJzzInYSetMgOi7BRIWW+Wlc+YG8d2HwM9xpnFN6w5Wy/S7jk1ANq8eTNjx451vK+aiDxjxgwWLFhAWloaKSkpjs/tdjtz5swhKSkJFxcXunfvzt/+9jfuuusuR52bbrqJrKwsnnjiCdLT0xk8eDBLliw5a2K0iEiT8gmGgdebBcyl+odWng6Iik7B7s/NAtCpP/S43AyGul0MLpqXKNKUWkweoJZEmaBFpEnZK8wkjonfmeXIZuCMX8Wu3hB5iblBbIdQM4fRmT/dvJ3WdJGWrNVshdFSKQASkWZVeBIOrYDE5WZAVNN2H2dy9zUDIZ8QcxPaijKwl5/+6XhdZm5Aa3Mxgyq3M4sPuHmdfu0VYC7579TPXNkm0gopAGogBUAi4jSGYU6gTl4LOUcgL72ypJk/ywqavg2+XSrzH/Uzh+ZC+kFQLw3LSYvXbjJBi4i0ORbL6USOP2cYUJJn9hBVBUSG3cxkbXUxe4OsrmaPj9X19Ht7OZTmQ2lBZck3l/Gf+T43zcyBlJMKuUfNkrjsjHbZICC6suepE3h3Muc5eXeqfB98+qfNTQkipcVTACQi0lpYLODha5agnk1zj+IcyNxj5j3K3A0Zu82tRYpzzLxIJw7UpqHVgzGb288CMzfw7QyBPc3vEdSrcr5TZwVO0mw0BFYDDYGJiJzBMCD3mBn85GdBQSbkZ0JBVuXPM97by+t/HzcfMxAKrAqKepivA7uDq2fjfR9pszQEJiIijcdiAb8uZjkfux1Kcsws2vYyc4+1ivLK11UTtUuhvMQcaju+H44nmj9PJZtDcce2meXn/MIhsEdlgNTj9GvfrmY2b5E6UgAkIiKNw2o190Orj/JSOJVUGRQdMH+eSDRfF2ebAVNOqrlarhoLuHqZPUSuXubKtqrXPz/u5l25Gu4cr22uZ8ylcvnZ68ohPHdfsNoa+qSkBVAAJCIizufiBsG9zXImwzD3WqsKhk4cgBMHzdcnD5m9S2UFzbM6DsxgyDcM/Lqd3kPOr6vZQ+Ufbq6g055vrYICIBERabksFvAOMku3i6t/VlFuBkdlhVBWVFkKKn8WQmlh5WdVr6tWvVW9rlwJV/W6Km+SvWq47ozXVXOb7OXmtibZKWe3tYpnAAREQcdI6Fj1M9I81iFMQ3YthAIgERFpnWwu0KGZtjkyDHMeU0GmmZ8p50jlsNwRyE49fawkx9wU9+hJOLqlhja7gX9E9aDIEShFKMt3M1IAJCIiciEWizlMVzXkdS7FOWbv0Klks5xMqnydZB6vKD1/OgHvTqeDIr+u4OJhzjmqmpNkdal8f8Y8JfcOZvZuDz/wqPzp5q2UAhegAEhERKSxePidO5GlvcJMMHkyyQyIqoKkqkCpONvsYSrIhNRNDWuH1aUyIKoMijqEnp6rdOacJe9O7XZITgGQiIhIc7DawL+bWRh99udFp6oHRHlpZ+zvVlF9PpK94nRagZI8KMo2e5+Ks0/XKTxhlvOxuZkTt/26mkGSh7+5ks/Tv4bX/mbPkmE3hwTtFZWvK39WvQcz8HL3NYcpW6iW2zIREZH2xLOjWcJi638NwzAnfRfnnA6Kik6ZwdTP5y3lHTMDqFOVPVJNwc3n9LCch98ZQ3V+0GUoDLqxae5bCwqARERE2gqLxeylcfM2l+ufT0W5GQRVBUQFWWawVJxtBk81va7q4Tl9Q7Nny2IDi9V8bdjNIAwq96DLh9wjZ99/wFQFQCIiItLMbC6nh+QialHfbjd7jKyVwY7Feu6J1hXlp4fkirOr90hVHQ8Z0GhfpT4UAImIiMiFWa1g9ahdXZsLeAeapYVqn1O/RUREpF1TACQiIiLtjgIgERERaXcUAImIiEi7owBIRERE2h0FQCIiItLuKAASERGRdkcBkIiIiLQ7CoBERESk3VEAJCIiIu2OAiARERFpdxQAiYiISLujAEhERETaHQVAIiIi0u4oAGrDCkrK2Zue6+xmiIiItDhODYBWr17NpEmTCAsLw2KxsGjRovPW/+yzz7jiiisIDg7G19eX+Ph4vv3222p15s6di8ViqVb69OnThN+i5frtx9u56sU1rNib6eymiIiItChODYAKCgqIiYlh/vz5taq/evVqrrjiChYvXsyWLVsYO3YskyZNYtu2bdXq9e/fn7S0NEdZu3ZtUzS/RcvMLWbp7nQA/rvpsJNbIyIi0rK4OPPmEyZMYMKECbWu/+KLL1Z7/8wzz/D555/z5ZdfEhsb6zju4uJCaGhora9bUlJCSUmJ431ubusfNvpi+zHshvl65b4sjueXEOTj7txGiYiItBCteg6Q3W4nLy+PgICAascPHDhAWFgY0dHR3HLLLaSkpJz3OvPmzcPPz89RwsPDm7LZzeKzrUcBsFktlNsNvkg45uQWiYiItBytOgB69tlnyc/P58Ybb3Qci4uLY8GCBSxZsoTXXnuNpKQkRo0aRV5e3jmvM2fOHHJychwlNTW1OZrfZPal57E7LRdXm4UHLusJwKdbjzi5VSIiIi2HU4fAGuL999/nqaee4vPPP6dTp06O42cOqQ0aNIi4uDgiIiL4+OOP+c1vflPjtdzd3XF3bzvDQ59tM4Odsb07MT0+gldWHGDXsVz2pufSJ9TXya0TERFxvlbZA/Thhx8yc+ZMPv74Y8aNG3feuv7+/vTq1YvExMRmap1zVdgNPt9mDnddN6QLHb3duKyPGSBWDYuJiIi0d60uAPrggw+47bbb+OCDD7j66qsvWD8/P5+DBw/SuXPnZmid8208dIL03GL8PF0ZWxn4TB3SFYCF245SXmF3ZvNERERaBKcGQPn5+SQkJJCQkABAUlISCQkJjknLc+bMYfr06Y7677//PtOnT+e5554jLi6O9PR00tPTycnJcdR55JFHWLVqFcnJyaxfv54pU6Zgs9mYNm1as343Z6nq5bl6UGfcXWwAjOndiY5ermTllbAm8bgzmyciItIiODUA2rx5M7GxsY4l7LNnzyY2NpYnnngCgLS0tGoruP79739TXl7OrFmz6Ny5s6M8+OCDjjpHjhxh2rRp9O7dmxtvvJHAwEA2btxIcHBw8345JygqrWDJzjQArovt4jju5mJl8mDzvYbBREREwGIYhuHsRrQ0ubm5+Pn5kZOTg69v65k0/HnCUR78MIFuAV6senQMFovF8dmOIzlMemUt7i5WfvzDOHw9XJ3YUhERkcZXl7/frW4OkJxbVe/OtbFdqgU/AAO6+NIrxIeScjtf/5TmjOaJiIi0GAqA2ojMvGLWHMgCYMoZw19VLBYL11VOhv50i3ICiYhI+6YAqI34IsHc+iK2mz9RQd411pkS2wWrBTYfPsXhEwXN3EIREZGWQwFQG7Fwmzn8dV0NvT9VQnw9uKSnORn8U02GFhGRdkwBUBuwPyOPXcfMrS+uGRR23rpTh1StBjuC3a757yIi0j4pAGoDqiY/j+ndiY7ebuete2W/UHzcXThyqogfk082R/NERERaHAVAzSi/pJxf/98mVuzLpLGyD9jtBp8nXHj4q4qnm42rB5pZsbVBqoiItFcKgJrRO+uTWXPgOLe9/SPX/2sD6xshK/PGQydIyynG18OFy/p2uvAJwNSh5mqwxTvSKSqtaHAbREREWhsFQM3opuHhzLwkCncXK1sOn+LmNzfxy39v4Iek+g9FfbatauuLMMfWFxcyPLIj4QGe5JeU8+2u9HrfW0REpLVSANSMgnzc+cM1/Vj9u7HMiI/AzWZl46GT3Pj6Bn79f5vYlnKqTtcrKq3gmx2VW18MufDwVxWLxcJ1sZU5gTQMJiIi7ZACICcI8fXgqckDWPnoGG6O64aL1cKaA8eZ8up6bl/wIzuP5lz4IsDS3ekUlFYQHuDJsIiOdWpD1Q7x6xKPk55TXOfvICIi0popAHKiMH9PnpkykBWPjOGGoV2xWS18vzeTa15ey6/e3MR/Nh4mI/fcwUlV7p8pg8/e+uJCugV6cVFkAHbj9HVERETaCwVALUB4gBf/uCGG72aP5trBYVgssDbxOH9YtJO4Z5Yz5dV1vLbyIAez8h3nZOWVsOaAOYl6SmVvTl1VDZt9uvVIo61KExERaQ20G3wNnL0b/OETBSzekc7S3elsS8mu9lmPTj5c2S+EorIK3l6XzOBwfxbNGlmv++QWlzH8z99RUm7ni/tGMqirf8MbLyIi4iR1+fvt0kxtkjqICPTmnjHduWdMdzJyi1m2O4OluzPYcPA4iZn5JGae7gmqy+Tnn/P1cGV8/1C+2H6M/2w8zN+v92+E1ouIiLR8GgJr4UJ8PfjVxRG8e/tFbPnjFbz0y8FcPagz3m42QnzdmXSBrS8uZMaISMDMJp16srARWiwiItLyqQeoFfH1cGXy4C5MHtyFsgo7FsDF1rAYdmhERy7pEcTaxOP8a9VB/jJlYOM0VkREpAVTD1Ar5WqzNjj4qXL/ZT0A+N/mI6TlFDXKNUVERFoyBUBCXHQgcVEBlFbYeX3VIWc3R0REpMkpABIAHri8JwAf/JBC5nlyD52LYZibsq490PD9zURERJqaAiABYET3QIZGdKSk3M6/V9e9F+izrUd58MMEbn37B3YcqV0m63NZtO0oM976gay8kgZdR0RE5FwUAAlg7g9WNRfov5tSOJ5f++Dj8IkCnvh8JwDldoMHP9pW713mt6Wc4pH/bWfV/ize35RSr2uIiIhciAIgcRjdK5iYrn4UlVXw5pqkWp1TVmHnwQ8TKCitYEg3fzp1cOdQVgHPLN5T5/vnFJZx3/vbKLebuTmX7tZO9SIi0jQUAImD2QtkzgV6b0MypwpKL3jOP5cfICE1mw4eLrx88xCevSHGPH/jYVbszaz1vQ3D4LFPf+JodhFd/D2xWmDXsVyOnFJuIhERaXwKgKSay/t2ol9nXwpKK3hr3fl7gX5IOsn8FYkAPDNlIF38Pbm0VzC3ViZXfPSTnzhRy6G09zYeZsmudFxtFl69ZQjDIgIA+G53Rv2/jIiIyDkoAJJqLBYLD1xuzgVasC6ZnKKyGuvlFJXx8EcJ2A2YOqQrk2JOZ6R+fEIfenby4Xh+CY9/tuOCG63uPJrDn7/aU3luX2LC/bmiXwgASxUAiYhIE1AAJGe5sl8ovUM6kFdSzoJ1yWd9bhgGv1+4g6PZRUQEevHU5P7VPvdwtfHiLwfjarOwbHcGH/2Yes575RWXcd/7WymtsDOubwi3j4wEcARAm5JOklNYcxAmIiJSXwqA5CxWq4X7KleEvbUuibzi6gHIp1uP8tVPadisFl68aTA+7mfvqNI/zI9HruwNwFNf7ibpeMFZdQzD4P8t3EnyiULC/Dx49oZBWCwWACKDvOkV4kOF3eD7feoFEhGRxqUASGo0cWBnugd7k1NUxrsbDjuOJx8v4MnKJe8Pj+tJbLeO57zGzFHRXBwdQFFZBQ99lEBZhb3a5x/9mMqX249hs1p4+eZY/L3cqn1+Zb9QAJZpGExERBqZAiCpkc16ekXY/61NoqCk3Fzy/pG55P2iqADuGdPjgtd47sbBdPBwYXtqNq98n+j4bG96Lk9+sQuAR67szdDKSc9nqhoGW7kvi+Ky+uUVEhERqYkCIDmnawZ1JjLQi5MFpfx302Fe+u4A21Oz8fVw4YWbBmOzWi54jS7+nvz52gEAvLIikS2HT1FYWs6s/26lpNzO6F7B3HVpdI3nDuziR6ivB4WlFaw/qC02RESk8SgAknNysVmZNdbs5Xn5+0Tmr6xc8n6dueS9tiYP7sLkwWFU2A1mf5zA45/u4GBWASG+7jx/YwzWcwRSVqvF0QukYTAREWlMTg2AVq9ezaRJkwgLC8NisbBo0aLz1v/ss8+44oorCA4OxtfXl/j4eL799tuz6s2fP5/IyEg8PDyIi4vjhx9+aKJv0PZdG9uFrh09ySsuxzDg+qFduWZQ2IVP/JmnJw8gzM+DwycK+WL7MawWeOmXsQT6uJ/3vNMBUCZ2+/mX04uIiNSWUwOggoICYmJimD9/fq3qr169miuuuILFixezZcsWxo4dy6RJk9i2bZujzkcffcTs2bN58skn2bp1KzExMYwfP57MzNpnJZbTXG1W7qvsBYoI9GLuL/pf4Iya+Xm68tyNg6lc5MWDl/fi4ujAC553cXQgHdxdOJ5fwrbU7HrdW0RE5OcsxoWy1DUTi8XCwoULufbaa+t0Xv/+/bnpppt44oknAIiLi2P48OG88sorANjtdsLDw7n//vt5/PHHa7xGSUkJJSWnMxbn5uYSHh5OTk4Ovr6+9ftCbYhhGHy7K4PYbv6E+Ho06FqLth0l9WQh947tUas5RAD3f7CNL7cf467R0cyZ0LdB9xcRkbYrNzcXPz+/Wv39btVzgOx2O3l5eQQEmCuISktL2bJlC+PGjXPUsVqtjBs3jg0bNpzzOvPmzcPPz89RwsPDm7ztrYnFYuGqAaENDn7AHFK7//KetQ5+AK7UPCAREWlkrToAevbZZ8nPz+fGG28E4Pjx41RUVBASElKtXkhICOnp595ZfM6cOeTk5DhKauq5MxdL8xvTOxhXm4VDWQUkZuY7uzkiItIGtNoA6P333+epp57i448/plOnTg26lru7O76+vtWKtBwdPFyJ7x4EwNLd5w5kRUREaqtVBkAffvghM2fO5OOPP6423BUUFITNZiMjo/pQSUZGBqGhoc3dTGlEGgYTEZHG1OoCoA8++IDbbruNDz74gKuvvrraZ25ubgwdOpTly5c7jtntdpYvX058fHxzN1UaUdVy+G0p2WTmFju5NSIi0to5NQDKz88nISGBhIQEAJKSkkhISCAlJQUw5+ZMnz7dUf/9999n+vTpPPfcc8TFxZGenk56ejo5OTmOOrNnz+aNN97gnXfeYc+ePdxzzz0UFBRw2223Net3k8YV4utBTLg/AN/tUUoDERFpGKcGQJs3byY2NpbY2FjADF5iY2MdS9rT0tIcwRDAv//9b8rLy5k1axadO3d2lAcffNBR56abbuLZZ5/liSeeYPDgwSQkJLBkyZKzJkZL61M1DKZ5QCIi0lAtJg9QS1KXPALSfA5k5HHFC6txs1nZ+sQV+Li7OLtJIiLSgrSbPEDSvvTo5ENUkDelFXZW7ctydnNERKQVUwAkrYbFcnpzVA2DiYhIQygAklalah7Q93szKauwO7k1IiLSWikAklYltltHgnzcyCsuZ9Ohk85ujoiItFIKgKRVsVktXN6nKili8w2DrdyXyb9XHyS7sLTZ7ikiIk1HAZC0Olf2r5oHlEFzLGJMPVnIne9u4ZnFexn19xXMX5FIYWl5k99XRESajgIgaXVG9gjC09VGWk4xO4/mNvn9nl26j9IKO242K3nF5fzj232M/sdK3tuQTGm55iGJiLRGCoCk1fFwtTG6VzAAT325i2PZRU12r5+OZPN5wjEAPr1nBC/eNJjwAE+y8kr44+e7GPf8Kj5POIrdrnRaIiKtiQIgaZXuuDQaLzcbmw+f4qoXV/Pl9mONfg/DMHhm8R4ApsR2YWBXP66N7cLy2WN4enJ/gnzcSTlZyIMfJnD1y2tZsTezWYbkRESk4ZQJugbKBN06JB8v4MGPEtiemg3AdUO68NQv+tPBw7VRrr9ibya3LfgRNxcr3/92NF07elX7vLC0nLfXJfOvlQfJKzHnBMVFBfDv6cPw82ycNoiISO0pE7S0C5FB3nxydzz3X9YDqwU+23qUif9cw5bDDV8eX15hZ943Zu/PbSMizwp+ALzcXJg1tgerfzeWuy6Nxt3Fyqakk7yzPrnB9xcRkaalAEhaNVebld9e2ZuP7oqni78nqSeLuOFfG3hh2X7KG5Ao8dOtR9ifkY+/lyv3ju1x3rodvd2YM7Evf752AABfbD+moTARkRZOAZC0CcMjA/jmoVFcOzgMuwEvLT/ADa9v4PCJgjpfq7C0nOeW7gfgvrE9aj2cNX5AKG42K4mZ+exNz6vzfUVEpPkoAJI2w9fDlRd/GctLvxxMB3cXtqVkM/GlNXyecLRO1/m/NUlk5pUQHuDJr+Mj6nT/Mb3N1WlNMSlbREQajwIgaXMmD+7CNw+N4qLIAApKK3jwwwReWLa/VsNSWXkl/GvVQQAeHd8Hdxdbne79i8FhAHz5k4bBRERaMgVA0iZ17ejFB3dezF2XRgPmkNjDHyVQUl5x3vP+ufwABaUVDOrqxzUDO9f5vpf16YSXm43Uk0UkVK5OExGRlkcBkLRZNquFORP7Mu+6gdisFhYlHONXb27iZEHN+3kdzMrn/R9SAJgzoS9Wq6XO9/Ryc2FcX3Orji80DCYi0mIpAJI2b9pF3Xjntovo4OHCj8mnmPLqOg5m5Z9V7+9L9lJhN7i8TyfiuwfW+36/iDGHwb7+KY0KZYgWEWmRFABJu3BJzyA+u2cEXTt6cvhEIde9up4NB084Pv8x+STf7srAaoHHJ/Rp0L1G9QrC18OFzLwSfkhqeE4iERFpfAqApN3oGdKBhfeOJLabPzlFZUx/axOfbDlSbcuLm4aH0zOkQ4Pu4+5iY8IAc/6QhsFERFomBUDSrgR3cOeDOy7m6kGdKasweOR/2/nNO5vZlpKNp6uNh8f1apT7TKocBvtmZxplDUjIKCIiTUMBkLQ7Hq42Xv5lLLPGdgfg+72ZgLnBaidfj0a5R3z3QIJ83MkuLGPtgeONck0REWk8CoCkXbJaLTw6vg//uH4QrjYLXfw9ubNyyXxjsFktXD0wFFBSRBGRlsjF2Q0QcaYbhoUztk8nXK1WfNwb9z+HXwwO450Nh/l2VzrFZRV4uNYtqaKIiDQd9QBJuxfk446fV+32+6qL2PCOdPH3pKC0ghWVw2wiItIy1CsAeuedd/j6668d73/3u9/h7+/PiBEjOHz4cKM1TqQ1s1otXDPIXA325U8aBhMRaUnqFQA988wzeHp6ArBhwwbmz5/P3//+d4KCgnj44YcbtYEirVnVarDlezLJKy5zcmtERKRKvQKg1NRUevToAcCiRYuYOnUqd955J/PmzWPNmjWN2kCR1qx/mC/RQd6UlNv5bk+Gs5sjIiKV6hUA+fj4cOKEmUV36dKlXHHFFQB4eHhQVFTUeK0TaeUsFoujF+iLBA2DiYi0FPUKgK644gpmzpzJzJkz2b9/PxMnTgRg165dREZGNmb7RFq9qgBozYHjnDrHRqwiItK86hUAzZ8/n/j4eLKysvj0008JDDQ3jtyyZQvTpk1r1AaKtHY9OvnQr7Mv5XaDJbvSnd0cEREBLIZhaLvqn8nNzcXPz4+cnBx8fX2d3RxpA15beZC/LdlLfHQgH9x5sbObIyLSJtXl73e9eoCWLFnC2rVrHe/nz5/P4MGDufnmmzl16lStr7N69WomTZpEWFgYFouFRYsWnbd+WloaN998M7169cJqtfLQQw+dVWfBggVYLJZqxcOjcbY3EKmvquXwG5NOkJlb7OTWiIhIvQKgRx99lNzcXAB27NjBb3/7WyZOnEhSUhKzZ8+u9XUKCgqIiYlh/vz5tapfUlJCcHAwf/jDH4iJiTlnPV9fX9LS0hxFuYnE2cIDvBjSzR/DgK9+SnN2c0RE2r165f5PSkqiX79+AHz66adcc801PPPMM2zdutUxIbo2JkyYwIQJE2pdPzIykpdeegmAt95665z1LBYLoaGhtb5uSUkJJSUljvdVwZ1IY5oUE8bWlGy+/OkYt18S5ezmiIi0a/UKgNzc3CgsLATgu+++Y/r06QAEBAS0iOAhPz+fiIgI7HY7Q4YM4ZlnnqF///7nrD9v3jyeeuqpZmyhtEdXD+rMn77azbaUbMcO8Vn5xWTllZCVV8Lx/FLH6+yiUm4dEcU9Y7o7udUiIm1TvQKgSy65hNmzZzNy5Eh++OEHPvroIwD2799P165dG7WBddW7d2/eeustBg0aRE5ODs8++ywjRoxg165d52zbnDlzqg3d5ebmEh4e3lxNlnaiUwcPLo4OZP3BE/zq/zZdsP5Ly/cz7aJw/L3cmqF1IiLtS70CoFdeeYV7772XTz75hNdee40uXboA8M0333DVVVc1agPrKj4+nvj4eMf7ESNG0LdvX15//XX+9Kc/1XiOu7s77u7uzdVEacfuGBXN9tRs3FysBHdwN4uPO0E+7qffd3DnL1/vYW96Hh/9mMpdo9ULJCLS2OoVAHXr1o2vvvrqrOMvvPBCgxvU2FxdXYmNjSUxMdHZTRFhbJ9O7HxqPBaL5bz1bhtZxGOf7uDdDYeZOSoam/X89UVEpG7qtQoMoKKigk8//ZQ///nP/PnPf2bhwoVUVFQ0ZtsaRUVFBTt27KBz587ObooIwAWDH4DJg7vg7+XK0eyieu8hZhgGD364jateXM1JZaAWEammXgFQYmIiffv2Zfr06Xz22Wd89tln/OpXv6J///4cPHiw1tfJz88nISGBhIQEwFxdlpCQQEpKCmDOzamaYF2lqn5+fj5ZWVkkJCSwe/dux+dPP/00S5cu5dChQ2zdupVf/epXHD58mJkzZ9bnq4o4hYerjV8O7wbAO+uT63WN1QeO83nCMfam5/H6qtr/dyki0h7UKwB64IEH6N69O6mpqWzdupWtW7eSkpJCVFQUDzzwQK2vs3nzZmJjY4mNjQVg9uzZxMbG8sQTTwBm4sOqYKhKVf0tW7bw/vvvExsbW23p/alTp7jjjjvo27cvEydOJDc3l/Xr1zuW7Yu0Fr+Oj8BqgfUHT7AvPa9O5xqGwT++3et4/86GZCVgFBE5Q722wvD29mbjxo0MHDiw2vHt27czcuRI8vPzG62BzqCtMKSluPu9LSzZlc7Ncd14ZsrAC59Q6Zsdadzz3614u9mIDPJm17FcZsRH8NTkAU3YWhER52ryrTDc3d3Jyzv7/0jz8/Nxc9OSXZHGcuvISAAWbj1KTmFZrc6psBs8u3QfADNHRfP7iX0BeP+HFI6cKmySdoqItDb1CoCuueYa7rzzTjZt2oRhGBiGwcaNG7n77rv5xS9+0dhtFGm34qIC6BPagaKyCj7enFqrcxZuO8rBrAL8vVyZOSqKET2CiI8OpKzC4JXvtRpSRATqGQD985//pHv37sTHx+Ph4YGHhwcjRoygR48evPjii43cRJH2y2KxcOuISADe3ZhMhf38I9Yl5RW8sGw/APeM7k4HD1cAHhnfC4D/bTlC8vGCpmuwiEgrUa8AyN/fn88//5z9+/fzySef8Mknn7B//34WLlyIv79/IzdRpH2bPLgLfp6upJ4s4vu9meet+9GPqRzNLqJTB3emx0c6jg+NCGBs72Aq7AYvLT/QxC0WEWn5ap0I8UK7vK9YscLx+vnnn69/i0SkGk83G7+8KJzXVx3infXJXNEvpMZ6haXl/HO5OcR1/+U98XSzVft89hW9WbEvi0UJR7l3THd6hnRo8raLiLRUtQ6Atm3bVqt6tUnyJiJ18+uLI3hj9SHWJh7nQEZejcHLO+sPczy/hPAAT24advZedgO7+nFV/1CW7Ernhe/28+otQ5uj6SIiLVKtA6Aze3hEpHl17ejFFf1C+HZXBu9sSObP11ZfEp9TVMa/KpMdPjyuF24uNY9uP3xFL77dnc7iHensPJrDgC5+Td52EZGWqN5bYYhI85pRORn60y1HySmqviT+zTWHyCkqo2cnHyYP7nLOa/QO7cAvYsIAHJOlRUTaIwVAIq1EfHQgvUPMJfH/O2NJ/PH8Ev5vbRIAv72y9wU3Tn3w8p7YrBaW781ka8qpJm2ziEhLpQBIpJWwWCyOXqB3Nxx2LIl/dcVBCksrGNTVj/H9a54gfaboYB+mDjF7iZ6rTJgoItLeKAASaUWujQ3D18OFlJOFrNyXydHsIv6z8TAAj47vXetFCPdf1hNXm4V1iSdYf/B4UzZZRKRFUgAk0op4ubnwy4vMXeIXrE/m5eUHKK2wc3F0AJf0CKr1dcIDvJhWeZ3nl+6nHlsCioi0arVeBSYiLcOvL47gjTWHWHPguGO+T116f6rMGtuDj35MZfPhU6zan8WY3p0a1K7isgr+ufwA5XaDrh09K4sXXfw98XbXrxoRaVn0W0mklQkP8GJc3xCW7c6gwm5weZ9ODI0IqPN1Qnw9mB4fwRtrknhu6X5G9wpuUB6vl5Yf4LWVB2v8LMDbjS7+no7A6LI+IcR3D6z3vUREGkpDYCKtUNX+YGCu/Kqvu0d3x8vNxo6jOXyx/Vi9r5OYmc+baw4BMCW2C1f2C6FfZ198Pcz/xzpZUMqOozl8szOdN9YkcevbP5BdWFrv+4mINJR6gERaoRHdA3l8Qh86ernSL8y33tcJ9HHn7tHdeX7Zfp76cjcjewQR5ONep2sYhsGTX+ykrMLgsj6deP7GmGo9SbnFZRw9VcSRU0UcOVXI2+uSSTlZyJc/pfHriyPq3XYRkYZQD5BIK2SxWLh7dHduGt6twde6e3R3+oR24GRBKX9ctLPOE6K/3pHGusQTuLlYmTup/1nDaL4ervTt7MsV/UK4bWQU0+PNoOfTLUca3HYRkfpSACTSzrm5WHn2hhhcrBa+2ZnOVz+l1frc/JJy/vTVbgDuHdOdboFeFzzn2tguuFgtJKRmk5iZV+92i4g0hAIgEWFAFz/uHdsDgCc+30lWXkmtzvvn8gNk5JbQLcCLu0d3r9U5QT7ujhVnn2w5Wr8GNzPDMHjqy108r+1DRNoMBUAiAsB9Y3vQt7MvpwrLajUUtj8jj7cqt+B46hf98XC11fpe1w81M1Ev3HbEkdG6Jdubnsfb65IrA75iZzdHRBqBAiARAaqGwgbhYrWwZNf5h8IMw+CPi3ZSbje4sl8IY/vULYfQZX1C6OjlSkZuCWsOZDW06U1uc/JJx+sNB084sSUi0lgUAImIQ/8wP2bVYijsi+3H2JR0Eg9XK3+8pl+d7+PmYnXsWv9JK5gM/WPy6U1jFQCJtA0KgESkmlkXGArLLS7jz1/vAcxhs/CAC098rsn1Q7sCsHR3BjmFZQ1rdBPbcviMAOiQAiCRtkABkIhU8/OhsC9/NhT24rIDZOWVEBXkzR2XRtf7Pv3DfOkT2oHScjtf/lT/JIxN7Vh2EUezi7BZLdisFlJOFnI0u8jZzRKRBlIAJCJnOXMo7MkzhsL2pOXyzoZkwJz47O5S+4nPP2exWBy9QJ9ubbnDYJsre3/6dfZlYBc/QMNgIm2BAiARqdGZQ2F/WLTDMfG5wm4wcWAol/YKbvA9Jg/ugs1qYVtKNomZ+Y3Q6sZXNQF6WGRHx/5l6w8ed2aTRKQRKAASkRqdORT27a4M7nt/G5sPn8LLzcYfrq77xOeaBHdwZ2xvM5Bqqb1AmysnQA+PDCA+2gyANh48UeeM2SLSsigAEpFz6h/mx32XmUNhX+8w5wI9cHlPwvw9G+0eU4eYw2Cfba1bTqDk4wW8vS6J0nJ7o7Xl53KLy9ibngvAsIiODIvsiKvNwrGcYlJOFjbZfUWk6SkAEpHzmjW2B/06mxuu9ujkw+0joxr1+pf17YR/ZU6gtYm1G1o6ml3Eja9v4Kkvd/NG5S70TWFbSjZ2A7oFeNHJ1wMvNxdiuvoDmgck0topABKR83K1WZl/yxCui+3CP38Zi5tL4/7acHexMTkmDKhdTqDc4jJuf/tHMisnZv9342HKK5qmF2hL1fyfiI6OYyMq5wFpObxI66YASEQuKCrIm+dvGky/MN8muf71Q8MB+HZXOjlF584JVFZh597/bGVfRh6dOrgT4O3GsZxivtuT0STtqkqAOCwywHHs4qoASPOARFo1BUAi4nQDuvjSO8TMCfT1ObbgMAyD3y/cwdrE43i52Xjr1uFMu8gMnBasT270NpVV2ElIzQbMFWBVhnTriJuLlcy8Eg4dL2j0+4pI83BqALR69WomTZpEWFgYFouFRYsWnbd+WloaN998M7169cJqtfLQQw/VWO9///sfffr0wcPDg4EDB7J48eLGb7yINJozcwJ9siW1xjrzVyTy8eYjWC0w/+YhDOjixy1xEdisFjYeOumYrNxYdh/LpaisAj9PV3oE+ziOe7jaGNLNH9A8IJHWzKkBUEFBATExMcyfP79W9UtKSggODuYPf/gDMTExNdZZv34906ZN4ze/+Q3btm3j2muv5dprr2Xnzp2N2XQRaWSTY8OwWS1sTcnmYFb1nECLth3l2aX7AXh68gDH5qth/p5c2S8EgHc3HG7U9lQlQBwW0RGr1VLts/joIEDzgERaM6cGQBMmTODPf/4zU6ZMqVX9yMhIXnrpJaZPn46fn1+NdV566SWuuuoqHn30Ufr27cuf/vQnhgwZwiuvvHLO65aUlJCbm1utiEjz6tTBg9GVyRU/PWMy9MZDJ3j0k+0A3HVpNL+6OKLaeTNGRAKwcOvRRt1TrCoB4tAzhr+qVCVEVD4gkdarzc0B2rBhA+PGjat2bPz48WzYsOGc58ybNw8/Pz9HCQ8Pb+pmikgNqobBPtt6lAq7QWJmHne+u5myCjP79GNX9TnrnLioAPqEdqCorIL/nWP4rK4Mw3BMgB5+xgToKjHhfni4WjlRUMr+jJaZwVpEzq/NBUDp6emEhIRUOxYSEkJ6evo5z5kzZw45OTmOkpraOL9ERaRuLu/bCT9PV9Jzi/k84Si3vv0jucXlDOnmz/M3Dj5rKArM+UPT4yMBcxjMXodkiueScrKQ4/kluNmsjv2/zuTuYmNYhBkYbdC2GCKtUpsLgOrD3d0dX1/fakVEmp+7i43Jg82cQL/933aOnCoiItCLN6YPw8P13BuvXhsbhq+HCyknC1m5P7PB7ajq/RnY1e+c941XPiCRVq3NBUChoaFkZFTPCZKRkUFoaKiTWiQidVE1DGYY4O/lytu3DifQx/2853i5uXDT8Kol8Q2fDL3l8NkJEH+uKgDalHSyUXqdRKR5tbkAKD4+nuXLl1c7tmzZMuLj453UIhGpi4Fd/Bga0RFPVxtvTB9G9BlL0M/n1xdHYrHA6v1ZHMpq2LycmhIg1tRObzcb2YVl7GnkJfgi0vScGgDl5+eTkJBAQkICAElJSSQkJJCSkgKYc3OmT59e7Zyq+vn5+WRlZZGQkMDu3bsdnz/44IMsWbKE5557jr179zJ37lw2b97Mfffd12zfS0Tqz2Kx8P4dcWz8f5fXOAH5XLoFenFZb3N5fEOWxJ8qKCUx0wyghp6nB8jVZmV4VNU8IA2DibQ2Tg2ANm/eTGxsLLGxsQDMnj2b2NhYnnjiCcBMfFgVDFWpqr9lyxbef/99YmNjmThxouPzESNG8P777/Pvf/+bmJgYPvnkExYtWsSAAQOa74uJSIO4u9jw83St83lVS+I/2XKE/JLyet17S2X+n+7B3gR4u523bnx05XJ4zQMSaXVcnHnzMWPGnDeHxoIFC846VpucGzfccAM33HBDQ5omIq3QJT2CiA725lBWAZ9tPeJYHVYXVQkQa9P75JgHdOgk5RV2XGxtblaBSJul/1pFpM2wWi3MqAx63lmfXK8khY4EiOcZ/qrSP8yPDh4u5JWUs+uY5gGJtCYKgESkTbluSBe83WwczCpgXWLdhqaKyyr46UgOULseIJvVQlzVPCANg4m0KgqARKRN6eDh6lhKX9dd4ncezaG0wk6QjxsRgV61OufiynlAmggt0rooABKRNmd65WTo5XszSD1ZWOvzHMvfIwKwWM7OOl2TEd2DKs89SVmFvW4NFRGnUQAkIm1O92AfRvUMwjDgPxtrvyTekQCxhg1Qz6VPaAc6erlSWHp6+ExEWj4FQCLSJlVNhv7wx1SKSisuWN9uNxwrwM6XAPHnrFYLcVFaDi/S2igAEpE2aWyfToQHeJJTVFarXqBDx/PJLizDw9VK/7C67Qfo2BdM84BEWg0FQCLSJtmsFn4zMgqAvyzew7sbks9bv2r+z+Bwf1zrmM+nKgD6MfkkJeUX7m1qLEeziygua777ibQlCoBEpM2aHh/JrZUTop/4fBcvLz9wztxAP1bm/6nL9htVenbyIcjHjZJyOwkp2fVtbp0kZuZx6d9X8Jt3fqxXviOR9k4BkIi0WVarhScn9eOhcT0BeG7Zfv789Z4ad2+v2gKjNgkQf85isRBXtRy+meYB/Zh8igq7wbrEE8pBJFIPCoBEpE2zWCw8NK4XT1zTD4D/W5vE7z79ifIzlqxn5hVz+EQhFgsMqUcABKf3BWuueUBJxwscr+evSGyWe4q0JQqARKRduP2SKJ67IQab1cInW44w6/2tjvkzWyrn//QO6YCvR903YQUYUTkPaFtKdrPMyzmUdToAWpd4gm0pp5r8niJtiQIgEWk3pg7tymu3DMHNZuXbXRn85p0fyS8pd0yArs/8nypRQd509vOgtMLOd3syGqvJ55R0PB+AyMqM1fNXHGzye4q0JQqARKRdubJ/KAtuG463m411iSe45c1NrE3MAuqWAPHnLBYLNwwLB+DtdcmN0dRzKq+wk1KZ4frpyQOwWOC7PRnsTdeGrCK1pQBIRNqdET2C+O8dF+Pv5cr21Gz2Z5i9KXVJgFiTX13cDVebhS2HT7E9NbsRWlqzo9lFlFUYuLlYuaRHEBMHdgbgVfUCidSaAiARaZcGh/vzv7viCfF1B6Cznwdd/D0bdM1OHTy4ZlAYAG+vS2pwG8+lagJ0VKA3VquFWWN6APDVT8dIPmNytIicmwIgEWm3eoZ04JO7R3BlvxAeHd+7Ua5528hIAL7ekUZmbnGjXPPnHAFQkDcA/cJ8uaxPJ+wGvLZSvUAitaEASETatfAAL/49fRjXDenaKNcb1NWfYREdKasw6rQRa104AqBgb8exWWPNXqDPth3hWHZRk9xXpC1RACQi0shuq9yC47+bUppkSfzPe4DATOB4cXQAZRUG/159qNHvKdLWKAASEWlk4/uHEObnwYmCUr7YfqzRr1+VAyj6jAAI4L6xZsbrD39M4Xh+SaPfV6QtUQAkItLIXGxWfh0fCZhL4htzr67isgqO5ZhDXFE/C4BG9ggkJtyf4jI7b61tuknYIm2BAiARkSYw7aJwPFyt7EnLZVPSyUa77uEThRgG+Hq4EODtVu0zi8XCrDHdAXhvw2Fyisoa7b4ibY0CIBGRJuDv5eaYWN2YS+KrMkBHBftgsVjO+nxc3xB6h3Qgr6Scd9cnN9p9RdoaBUAiIk3kthGRACzdnUFqZebmhjp0vOb5P1WsVgv3jjV7gd5al0RhaXmj3FekrVEAJCLSRHqGdGBUzyAMA95ppN6YpMoJ0JGBNQdAAFcP7ExEoBenCst4f1NKo9xXpK1RACQi0oRur1wS/9HmVApKGt4bU1MOoJ9zsVm5Z7TZC/TGmkOUlDf97vQirY0CIBGRJjS6VzDRQd7kFZfz6dYjDb5e0gWGwKpMGdKFUF8PMnJL+HTL0QbfV6StUQAkItKErFYLMyrnAi1Yl4zdXv8l8TlFZZwoKAUg8gIBkLuLjTsvjQbg1ZWJTZKQUaQ1UwAkItLEpg7tSgd3Fw4dL2DV/qx6X6dqo9NOHdzxcXe5YP1pF3UjxNedI6eK+OfyA/W+r0hbpABIRKSJ+bi7cNPwcMBcmVVfNW2BcT6ebjaenjwAgNdXH2LXsZx631ukrVEAJCLSDGaMiMRqgTUHjpOYmVevaziWwJ9nAvTPje8fysSBoVTYDR779CfKK+z1urdIW+PUAGj16tVMmjSJsLAwLBYLixYtuuA5K1euZMiQIbi7u9OjRw8WLFhQ7fO5c+disViqlT59+jTNFxARqaXwAC/G9Q0BzO0x6qOuPUBV5v6iP36eruw8msub2iJDBHByAFRQUEBMTAzz58+vVf2kpCSuvvpqxo4dS0JCAg899BAzZ87k22+/rVavf//+pKWlOcratWubovkiInVStUv8p1uPkF1YWufzHVmgg3zqdF6nDh784eq+ALywbL8jkBJpzy48i64JTZgwgQkTJtS6/r/+9S+ioqJ47rnnAOjbty9r167lhRdeYPz48Y56Li4uhIaGNnp7RUQa4uLoAPp29mVPWi5f/ZTGry6OqPW5hmE4kiDWtQcI4PqhXfli+zHWHDjO45/+xAd3XIzVevZWGiLtRauaA7RhwwbGjRtX7dj48ePZsGFDtWMHDhwgLCyM6OhobrnlFlJSzp8JtaSkhNzc3GpFRKSxWSwWJgww/+dsw6ETdTo3K6+EgtIKrBboFuBVr3s/M2Ugnq42NiWd5MMfU+t8DZG2pFUFQOnp6YSEhFQ7FhISQm5uLkVFRQDExcWxYMEClixZwmuvvUZSUhKjRo0iL+/ckw7nzZuHn5+fo4SHhzfp9xCR9isuKgCATYdOYhi1zwlUNQE6PMALN5f6/eoOD/Dit1f2AmDe4j2k5xTX6zoibUGrCoBqY8KECdxwww0MGjSI8ePHs3jxYrKzs/n444/Pec6cOXPIyclxlNRU/Z+RiDSNmHB/3FysHM8vcQQ1tVHfCdA/d9vIKGLC/ckrKecPi3bWKQgTaUtaVQAUGhpKRkZGtWMZGRn4+vri6elZ4zn+/v706tWLxMTEc17X3d0dX1/fakVEpCl4uNqIDfcHzF6g2qoKgM63CWpt2KwW/jZ1IC5WC9/tyeDrHWkNup5Ia9WqAqD4+HiWL19e7diyZcuIj48/5zn5+fkcPHiQzp07N3XzRERqJS46EIBNSbWfB3Qoq+45gM6lT6gv947tAcDcL3ZxqqDuK9JEWjunBkD5+fkkJCSQkJAAmMvcExISHJOW58yZw/Tp0x317777bg4dOsTvfvc79u7dy6uvvsrHH3/Mww8/7KjzyCOPsGrVKpKTk1m/fj1TpkzBZrMxbdq0Zv1uIiLncnE95gGdXgLf8AAIYNbY7vTo5MPx/FL+/PWeRrmmSGvi1ABo8+bNxMbGEhsbC8Ds2bOJjY3liSeeACAtLa3aCq6oqCi+/vprli1bRkxMDM899xxvvvlmtSXwR44cYdq0afTu3Zsbb7yRwMBANm7cSHBwcPN+ORGRc4jt1hFXm4X03GJSThZesH55hd1Rr7ECIHcXG3+bOgiLxcxLtLoBe5SJtEYWQzPgzpKbm4ufnx85OTmaDyQiTeL619az+fAp/j51EDcOP//K05QThVz6jxW4uVjZ+/RVjZq/Z+4Xu1iwPplOHdyZOrQrg7r4MbCrH138PbFYlCdIWpe6/P12aiJEEZH26uLoQDYfPsXGQycuGAAdqhr+CvRu9OSFj47vzfK9GaSeLOK1lQcdxwO93RjY1Y9BXf0Z1MWPQeF+dOrg0aj3FnEmBUAiIk4QFx3AKytgU9KFV4I11hL4mni7u/DFrEv4Zmc6O45m89ORHPal53GioJSV+7JYue/00FiYnwejegYztk8wI3sE0cHDtdHbI9JcFACJiDjB0IiOuFgtHM0uIvVkIeHnye7sCIAaYQVYTTp6u3FzXDegGwDFZRXsSctlx9EctqfmsONoNomZ+RzLKeajzal8tDkVV5uF4ZEBjO3dibF9guke7KMhM2lVFACJiDiBl5sLA7v6sS0lm01JJ2sXADVBD1BNPFxtxHbrSGy3jlCZZaSgpJwth0+xcl8WK/ZlknS8gPUHT7D+4An+sngP4QGelcFQJ0b1CMLF1qqyrEg7pABIRMRJ4qICzQDo0AmuH9r1nPUcOYCaKQCqibe7C5f2CubSXsE8MakfSccLWLkvk+/3ZrLp0ElSTxbx7obDvLvhMP3DfPn79YPoH+bntPaKXIgCIBERJ4mLDuBfqw6edx5QcVkFx3LMvQ6bqweoNqKCvIkKiuK2kVEUlpazPvEE3+/L5Kvtx9h1LJfJr6zj3jHdmXVZD9xdbM5urshZ1EcpIuIkwyI6YrVAyslC0iqDnJ87fKIQwwBfDxcCvN2auYW14+Xmwrh+ITwzZSDf/XY0EwaEUm43+Of3iVzzz7VsSznltLaVV9g5ml3zs5X2TQGQiIiTdPBwZUAXc5joXPuCnZkBujVMMu7UwYPXfjWUV28ZQpCPGwcy85n62nqeWbyHotKKZm/P35bsZeRfv2ex9jyTn1EAJCLiRHFV22KcY1+wQ808AbqxTBzYmWUPj2ZKbBfsBvx79SEmvLSaTYdqv/9ZQxWVVvDBD6kAvPJ9Yq23HZH2QQGQiIgTxUWZG6NuPFcPUFZVAOTTbG1qLB293XjhpsG8deswQn09SD5RyE3/3sgfF+0kp7Csye//7a508kvKAdidlsuPyc4bipOWRwGQiIgTDY8KwGIxl7pn5haf9XlT5wBqDpf1CWHp7EuZdpGZZ+i9jYcZ8udlTH1tPS9+t58th09SXmFv9Pt+uvUIAD7u5nqft9clNeh6BzLyyC4sbXC7pGVQACQi4kR+nq7062zuWbSxhtVgySecvwS+Mfh6uDLvuoG8PzOOPqEdqLAbbDl8ihe/O8DU1zYQ+/Qy7nx3M+9tSCb5eEGDh6vScopYm3gcgOdvjAHMHqH6Tohel3ic8S+uZuyzK1lzQBvHtgUKgEREnKxqGOzn82Nyiso4nm/2OES28gCoyogeQSx56FLWPjaWv143kKsHdcbfy5W8knKW7s7gj5/vYsyzKxn19xX839r699gs3HYUw4CLIgO4sn8o8dGB2A14d0Nyna9ltxv89Zu92A04VVjG9Ld+YP6KROx2zSlqzRQAiYg4WVx01UTo6j1AyZXDX506uDuGcdqKrh29+OVF3Zh/8xC2/OEKvrhvJI+O783F0QG42iwcOVXEn77aTUJqdp2vbRgGn24xh7+mDu0CwG0jIwH48IfUOq9GM/dJy8HbzcZ1Q7pgGPCPb/dx13+2kFvc9HOZpGkoABIRcbKLIs0AKDEzn+P5JY7jzb0FhrPYrBYGdfVn1tgefHhnPNufvJJrBnUG4NUViXW+3vYjORzMKsDD1crEgeZ1Lu8bQniAJzlFZSxKOFrra5VX2Hlu2T4AZo6K5vkbB/PX6wbiZrOybHcGk19Zx770vDq3UZxPAZCIiJN19HajT2gHAH44oxeoagl8dCueAF0fXm4uPDSuFxYLLN2dUecAo6r356r+oY4d621WCzPiIwFzMnRt5xh9uvUIh7IKCPB2Y+aoKAB+eVE3/nd3PGF+HiQdL+Da+ev4cvuxOrVRnE8BkIhIC+DIB3TGPKD20gNUkx6dfJgwIBSA11bWvheopLyCLyqDkak/21/thmHheLnZ2J+Rz4aDF85HVFxWwYvfHQDg3jHdHcEUQEy4P1/efwkjewRSVFbB/R9s409f7aasCVazSdNQACQi0gLERZ+dD+h0FujWlwOoMdw7pgcAX2w/RsqJwlqds3xPJjlFZYT6ejCie1C1z/w8XZk6xAyK3lqXfMFr/WfjYdJyignz8+BXF0ec9Xmgjzvv3h7HvWO6A/B/a5O45c1NZOadnc5AWh4FQCIiLcBFlT1A+zLyOFlQimEYZyRBbH89QAADuvgxpncwdgNeW3WwVudUDX9NGdIFm/XsrUNmjIgEYPnejPMGVXnFZcyvnH/04LieeLjWvKGrzWrhd1f14V+/GoqPuws/JJ3kl69vbJK8RtK4FACJiLQAQT7u9Ohk9vT8kHSSrLwSCkorsFqgW4CXk1vnPLPGmr1An245QnrO+XtWsvJKWLnfzNFT1dPzcz06+XBpr2AMA945z5L4N9ckcaqwjOhg73Ne60xXDQjl8/tG0sHDhUPHC9h+JOeC54hzKQASEWkhztwXrGoCdNeOXri5tN9f1cMjA7goKoDSCjtvrjl03rqfJxylwm4QE+7vCCZrcltlL9DHP6ZSULlVxplO5Jc47vXbK3rjYqvd8+8e7MOonuaw29oDx2t1jjhP+/2vSkSkhamaB7Tp0Ml2PQH656p6gf67KYWTBefeiuLTreby9uuHdDnv9Ub3CiYqyJu8knLHdhlnenXlQQpKKxjQxdcxEbu2LukRDKBs0a2AAiARkRbi4soeoD3puSSkZAMKgAAu7RnEwC5+FJVVsOAc+3ntPpbLnrRc3GxWJsWEnfd6VquFGfHmpOYF65OrZXQ+ml3EexsPA/Do+D5Ya5hHdD5VPUDbUrPJU5LEFk0BkIhIC9HJ14PoIG8MA776yVzK3d5yANXEYrEwa6y50mrB+uQaA4uqnpxx/Trh7+V2wWtePywcH3cXDmUVsPqM3pqXvttPabmduKgALu0ZdJ4r1Cw8wIvIQC8q7EatltqL8ygAEhFpQaq2xSio3K5BPUCmK/uF0qOTD7nF5fxnY0q1z8oq7Hxemd25NhOWwdwh/oZhZt0F65MBMxP3J5WryH53VR8slrr1/lQZ1dMcBqvajFVaJgVAIiItSNXGqFUUAJmsVssZ+XYOUVx2ej+vVfuyOJ5fSpCPG5f2Cq71NWfER2KxwMp9WRzMyuf5ZfuwGzCubwhDIzrWu62XVPYcrdFE6BZNAZCISAtS1QME4OZiJczP04mtaVkmxYTRtaMnx/NL+XhzquN41fDX5MFdcK3lii2AyCBvLuvdCYA/LNzJ4h3pWCzw6PjeDWpnfPdAbFYLSccLSD1ZuwSO0vwUAImItCCd/TwdeX+iAr3rPAm3LXO1WblrtNkL9PqqQ5RV2MkuLGX5nkyg9sNfZ7ptpLm/14bKLUiuHdyF3pX7stWXr4crg8P9AQ2DtWQKgEREWpiqfEAa/jrbDUO7EtzBnaPZRSzadpQvtx+jtMJO386+9AvzrfP1RvYIpGdlziAXq4WHx/VqlHYqH1DLpwBIRKSFmR4fSXSwNzcOr3uPRlvn4Wrjjspd2V9beZD/VU5annqB3D/nYrFYuO8yM8/Q7ZdE0S2wcbJuVwVA6w4ep8Jeu53npXm5OLsBIiJS3cCufnz/2zHObkaLdXNcBPNXHHRky7ZZLUweXL8ACMy5Q3FRgYT4ujdWE4np6k8HdxeyC8vYeTSHmMohMWk51AMkIiKtio+7C7eNjHS8H9MrmOAODQteQv086r3svSYuNivx3c0Vfa1hHlB+STn5NWwL0pY5NQBavXo1kyZNIiwsDIvFwqJFiy54zsqVKxkyZAju7u706NGDBQsWnFVn/vz5REZG4uHhQVxcHD/88EPjN15ERJzm1hGReLmZO7RPHdoyhwqrhsFW72+522LY7QbvbTzMxc8sZ+Rfv29XW3g4NQAqKCggJiaG+fPn16p+UlISV199NWPHjiUhIYGHHnqImTNn8u233zrqfPTRR8yePZsnn3ySrVu3EhMTw/jx48nMzGyqryEiIs3M38uNV28Zwu+u6s1V/eu2X1dzqUqIuDXlVI2brjpb0vECfvnGRv64aCf5JeXkFJVx69s/8va6JAyj7c9bshgt5FtaLBYWLlzItddee846jz32GF9//TU7d+50HPvlL39JdnY2S5YsASAuLo7hw4fzyiuvAGC32wkPD+f+++/n8ccfr1VbcnNz8fPzIycnB1/fuq8qEBERMQyDUX9fwZFTRbx16zAu6xPi7CYBUF5h5821SbywbD8l5Xa83Gw8cmVvdh7L4bPKDWWnXRTOU78YgJtL65opU5e/363qm23YsIFx48ZVOzZ+/Hg2bNgAQGlpKVu2bKlWx2q1Mm7cOEedmpSUlJCbm1utiIiINITFYnH0ArWUrNC7j+Uy5dX1/PWbvZSU2xnVM4hvH7qU2y+J4rkbYvh/E/tgscAHP6Tyq//bxIn8Emc3ucm0qgAoPT2dkJDqEXRISAi5ubkUFRVx/PhxKioqaqyTnp5+zuvOmzcPPz8/RwkPD2+S9ouISPsyqoVsi1FSXsFzS/fxi1fWsuNoDr4eLvzj+kG8e/tFhFcm3rRYLNx5aXf+b8YwfNxd+CHpJJPnr2NvetvsFGhVAVBTmTNnDjk5OY6Smpp64ZNEREQuYET3QKwWc6PVtJwip7Rha8oprv7nWl7+PpFyu8H4/iF8N3s0NwwLr3Hl22V9Qlh47wgiAr04cqqIqa+uZ+muc3citFatKgAKDQ0lIyOj2rGMjAx8fX3x9PQkKCgIm81WY53Q0HNPknN3d8fX17daERERaSh/LzcGdvUHnNMLlJiZx02vbyAxM58gH3Pi+Ou/HkYnX4/zntczpAOL7h1JfHQgBaUV3PWfLcxfkdimJke3qgAoPj6e5cuXVzu2bNky4uPjAXBzc2Po0KHV6tjtdpYvX+6oIyIi0pwudeK2GP/bfISyCoNhER1Z9vBoJg7sXOtzO3q78e5vLuLXF0dgGPCPb/fx8EcJ2NtIZmunBkD5+fkkJCSQkJAAmMvcExISSElJAcyhqenTpzvq33333Rw6dIjf/e537N27l1dffZWPP/6Yhx9+2FFn9uzZvPHGG7zzzjvs2bOHe+65h4KCAm677bZm/W4iIiIAl/So3BYj8XizBg92u8EX248BMHNUFB293ep8DVeblT9dO4A/XzsAF6uFRQnHWNWC8xrVhVO3wti8eTNjx451vJ89ezYAM2bMYMGCBaSlpTmCIYCoqCi+/vprHn74YV566SW6du3Km2++yfjx4x11brrpJrKysnjiiSdIT09n8ODBLFmy5KyJ0SIiIs0htltHvN1snCgoZXdaLgO6+DXLfX9MPklaTjEd3F0Y07tTg671q4sj2HUslw9+SGHV/izG9mnY9VoCpwZAY8aMOe94Yk1ZnseMGcO2bdvOe9377ruP++67r6HNExERaTA3FysXRweyfG8maxOPN1sA9Hll789VA0LxcLU1+HqjewXxwQ8prG5gtujScjsl5RV08HBtcJsaolXNARIREWmNTi+Hb57ho9JyO4t3pAE0aKPYM8V3D8JmtXAoq4AjpwrrfZ1V+7MY+qfveOyTnxqlXfWlAEhERKSJXVKZEPHH5FMUlVact67dbvDid/sZ/8Jqdh7Nqdf91hzIIruwjCAfd8emrA3l5+nK4Mpd7Ruyom3Z7nRKK+x4uDo3BFEAJCIi0sS6B3sT5udBabmdH5JPnrNeUWkFs97fyovfHWBfRh5/W7K3Xvf7PMEc/poU0xmbtfF2uW9oT1aF3WD5HnNvziv6OXcPNwVAIiIiTcxisXCJYzl8zcFDRm4xN76+gW92puNqs2CzWlhz4Hide4EKS8tZttvMh9dYw19VLu1l9mStPXCcinqsaNuWcooTBaV08HAhLjqgUdtWVwqAREREmsH59gXbeTSHya+sY8fRHAK83Xj/jou5ZpCZs+ffqw/V6T7LdmdQVFZBRKAXMV0bd8L1oC5++Hq4kFtczvYj2XU+vyowG9u7E642DYGJiIi0eSN7BGGxwN70PDJzix3Hv92Vzg3/2kB6bjE9Ovmw6N6RDI8M4M5LowH46qdjpJ6s/aTjLyqHvybHhNW41UVDuNisjp6sNfvrPg+oKgC6op/zU9MoABIREWkGAd5uDAgze2TWJh7HMAxeW3mQu/+zhaKyCkb1DOKze0fQLdDcnLR/mB+jegZhN+DNNbXrBTpVUOpIVPiLwWFN8j1O92TVbR5QYmY+h44X4GqzMKZ3cFM0rU4UAImIiDSTqt6T7/dm8rtPfuJvS/ZiGDA9PoK3bx2O789y49w9ujsAH21O5WRB6QWvv3hnGuV2g/5hvvTo1KHxvwCnJ0JvS80mt7is1udV9f5cHB3o9BxAoABIRESk2VQFD1/9lMb/thzBaoGnftGfpycPwKWGOTEjugcyoIsvxWV23t2QfMHrV63+mtxEvT8AXTt6ER3sTYXdYH3iiVqft2y3uaP8lS1g+AsUAImIiDSboREd8azMytzB3YW3bh3OjBGR56xvsVi461KzF+id9cnnzSF0LLuIH5JOYrHApJimC4AALq0cBqttVuisvBK2pWYDME4BkIiISPvi7mLj3jHdGRbRkU/vHVGrPbomDAglPMCTU4Vl/G9L6jnrfVm59cVFkQF09vNstDbX5NJeZk/W6v1Z593SqsryPRkYBgzs4tfkbastBUAiIiLN6P7Le/LJPSPoFVK7OTouNit3jDJXhL2x5hDlFfYa650e/mrc3D81iYsKxNVm4cipIg6fuPAKtZa0+quKAiAREZEW7oah4QR4u5F6sohvdqaf9fmBjDx2p+XiarMwYUDTZ1j2dndhWISZyPBCw2CFpeWsTTSXzCsAEhERkVrzdLMxIz4SgH+tOnjWsNMXlcNfl/YMpqO3W7O0aZRjGOz8+YDWHDhOSbmdrh096RPaNCvT6kMBkIiISCswPT4CT1cbu47lsu6M1VeGYTiGv5oq909NqiZCbzh4nNLymofloPrwV2MnZmwIBUAiIiKtQEdvN24aHg7A66sPOo4npGaTcrIQT1dbsw4x9evsS6C3GwWlFWxLOVVjnQq7wfd7qzY/bTnDX6AASEREpNX4zSVRZ22SWtX7c2X/ELzcXJqtLVbr6Q1ea9rfDGDL4VOcLCjFz9OViyKdu/npzykAEhERaSXCA7y4euDpTVLLK+x89VMa0LTJD8/lQvmAqpIfXtanU42JHp2pZbVGREREzqtqk9Svd6Tx8eYjHM8voaOXq2OPruZUldl6x9Gcs7bqMAyjRS5/r6IASEREpBUZ0MXcJLXCbjD3i10ATBzYGVcn9LB08vWgT2gHDAPWJVYfBkvMzCf5RCFuNiuX9nL+5qc/pwBIRESklanaHqO0MilicyQ/PJeq4Gb1/urDYEsre3/iuwfi4958c5NqSwGQiIhIKzOyh7lJKkCYnwfDIjo6rS2jzpgIfWZ+opY8/AUKgERERFodi8XCo+P74GazMnNUNFar8/LrDI8MwN3FSnpuMQcy8wHIzC0moXLzUwVAIiIi0mhG9wpm35+v4vZLopzaDg9XG3HRgcDpYbDv9pi5f2K6+hHi6+G0tp2PAiAREZFWqqVkVr70Z/mAqpa/t9TeH1AAJCIiIg1UNRF6U9IJThaUsu6guVXHFf2afmPW+lIAJCIiIg3Ss5MPob4eFJfZeW7pPkrL7XQL8KJXiI+zm3ZOCoBERESkQSwWi2M12Ac/pAAtb/PTn1MAJCIiIg02qnIYzF65Er4lz/8BBUAiIiLSCC7pEURVh4+/l6tTcxPVhgIgERERabAAbzcGdvEDWubmpz/XslsnIiIircYdo6KJDPTi9pHOzU1UGy0iAJo/fz6RkZF4eHgQFxfHDz/8cM66ZWVlPP3003Tv3h0PDw9iYmJYsmRJtTpz587FYrFUK3369GnqryEiItKuTYoJY+WjYxlQ2RPUkjk9AProo4+YPXs2Tz75JFu3biUmJobx48eTmZlZY/0//OEPvP7667z88svs3r2bu+++mylTprBt27Zq9fr3709aWpqjrF27tjm+joiIiLQCTg+Ann/+ee644w5uu+02+vXrx7/+9S+8vLx46623aqz/3nvv8f/+3/9j4sSJREdHc8899zBx4kSee+65avVcXFwIDQ11lKCgoOb4OiIiItIKODUAKi0tZcuWLYwbN85xzGq1Mm7cODZs2FDjOSUlJXh4VN9XxNPT86wengMHDhAWFkZ0dDS33HILKSkp52xHSUkJubm51YqIiIi0XU4NgI4fP05FRQUhIdVzBYSEhJCenl7jOePHj+f555/nwIED2O12li1bxmeffUZaWpqjTlxcHAsWLGDJkiW89tprJCUlMWrUKPLy8mq85rx58/Dz83OU8PDwxvuSIiIi0uI4fQisrl566SV69uxJnz59cHNz47777uO2227Daj39VSZMmMANN9zAoEGDGD9+PIsXLyY7O5uPP/64xmvOmTOHnJwcR0lNTW2uryMiIiJO4NQAKCgoCJvNRkZGRrXjGRkZhIbWvIFacHAwixYtoqCggMOHD7N37158fHyIjo4+5338/f3p1asXiYmJNX7u7u6Or69vtSIiIiJtl1MDIDc3N4YOHcry5csdx+x2O8uXLyc+Pv6853p4eNClSxfKy8v59NNPmTx58jnr5ufnc/DgQTp37txobRcREZHWy+lDYLNnz+aNN97gnXfeYc+ePdxzzz0UFBRw2223ATB9+nTmzJnjqL9p0yY+++wzDh06xJo1a7jqqquw2+387ne/c9R55JFHWLVqFcnJyaxfv54pU6Zgs9mYNm1as38/ERERaXlcnN2Am266iaysLJ544gnS09MZPHgwS5YscUyMTklJqTa/p7i4mD/84Q8cOnQIHx8fJk6cyHvvvYe/v7+jzpEjR5g2bRonTpwgODiYSy65hI0bNxIcHNzcX09ERERaIIthGIazG9HS5Obm4ufnR05OjuYDiYiItBJ1+fvt9CEwERERkeamAEhERETaHQVAIiIi0u4oABIREZF2x+mrwFqiqnnh2hNMRESk9aj6u12b9V0KgGpQtWeY9gQTERFpffLy8vDz8ztvHS2Dr4HdbufYsWN06NABi8XSqNfOzc0lPDyc1NRULbFvBnrezUvPu3npeTcvPe/mVZ/nbRgGeXl5hIWFVcshWBP1ANXAarXStWvXJr2H9hxrXnrezUvPu3npeTcvPe/mVdfnfaGenyqaBC0iIiLtjgIgERERaXcUADUzd3d3nnzySdzd3Z3dlHZBz7t56Xk3Lz3v5qXn3bya+nlrErSIiIi0O+oBEhERkXZHAZCIiIi0OwqAREREpN1RACQiIiLtjgKgZjR//nwiIyPx8PAgLi6OH374wdlNahNWr17NpEmTCAsLw2KxsGjRomqfG4bBE088QefOnfH09GTcuHEcOHDAOY1tA+bNm8fw4cPp0KEDnTp14tprr2Xfvn3V6hQXFzNr1iwCAwPx8fFh6tSpZGRkOKnFrdtrr73GoEGDHMng4uPj+eabbxyf61k3rb/+9a9YLBYeeughxzE988Yzd+5cLBZLtdKnTx/H5035rBUANZOPPvqI2bNn8+STT7J161ZiYmIYP348mZmZzm5aq1dQUEBMTAzz58+v8fO///3v/POf/+Rf//oXmzZtwtvbm/Hjx1NcXNzMLW0bVq1axaxZs9i4cSPLli2jrKyMK6+8koKCAkedhx9+mC+//JL//e9/rFq1imPHjnHdddc5sdWtV9euXfnrX//Kli1b2Lx5M5dddhmTJ09m165dgJ51U/rxxx95/fXXGTRoULXjeuaNq3///qSlpTnK2rVrHZ816bM2pFlcdNFFxqxZsxzvKyoqjLCwMGPevHlObFXbAxgLFy50vLfb7UZoaKjxj3/8w3EsOzvbcHd3Nz744AMntLDtyczMNABj1apVhmGYz9fV1dX43//+56izZ88eAzA2bNjgrGa2KR07djTefPNNPesmlJeXZ/Ts2dNYtmyZMXr0aOPBBx80DEP/fje2J5980oiJianxs6Z+1uoBagalpaVs2bKFcePGOY5ZrVbGjRvHhg0bnNiyti8pKYn09PRqz97Pz4+4uDg9+0aSk5MDQEBAAABbtmyhrKys2jPv06cP3bp10zNvoIqKCj788EMKCgqIj4/Xs25Cs2bN4uqrr672bEH/fjeFAwcOEBYWRnR0NLfccgspKSlA0z9rbYbaDI4fP05FRQUhISHVjoeEhLB3714ntap9SE9PB6jx2Vd9JvVnt9t56KGHGDlyJAMGDADMZ+7m5oa/v3+1unrm9bdjxw7i4+MpLi7Gx8eHhQsX0q9fPxISEvSsm8CHH37I1q1b+fHHH8/6TP9+N664uDgWLFhA7969SUtL46mnnmLUqFHs3LmzyZ+1AiARqbdZs2axc+fOamP20vh69+5NQkICOTk5fPLJJ8yYMYNVq1Y5u1ltUmpqKg8++CDLli3Dw8PD2c1p8yZMmOB4PWjQIOLi4oiIiODjjz/G09OzSe+tIbBmEBQUhM1mO2vmekZGBqGhoU5qVftQ9Xz17Bvffffdx1dffcWKFSvo2rWr43hoaCilpaVkZ2dXq69nXn9ubm706NGDoUOHMm/ePGJiYnjppZf0rJvAli1byMzMZMiQIbi4uODi4sKqVav45z//iYuLCyEhIXrmTcjf359evXqRmJjY5P9+KwBqBm5ubgwdOpTly5c7jtntdpYvX058fLwTW9b2RUVFERoaWu3Z5+bmsmnTJj37ejIMg/vuu4+FCxfy/fffExUVVe3zoUOH4urqWu2Z79u3j5SUFD3zRmK32ykpKdGzbgKXX345O3bsICEhwVGGDRvGLbfc4nitZ9508vPzOXjwIJ07d276f78bPI1aauXDDz803N3djQULFhi7d+827rzzTsPf399IT093dtNavby8PGPbtm3Gtm3bDMB4/vnnjW3bthmHDx82DMMw/vrXvxr+/v7G559/bvz000/G5MmTjaioKKOoqMjJLW+d7rnnHsPPz89YuXKlkZaW5iiFhYWOOnfffbfRrVs34/vvvzc2b95sxMfHG/Hx8U5sdev1+OOPG6tWrTKSkpKMn376yXj88ccNi8ViLF261DAMPevmcOYqMMPQM29Mv/3tb42VK1caSUlJxrp164xx48YZQUFBRmZmpmEYTfusFQA1o5dfftno1q2b4ebmZlx00UXGxo0bnd2kNmHFihUGcFaZMWOGYRjmUvg//vGPRkhIiOHu7m5cfvnlxr59+5zb6FaspmcNGG+//bajTlFRkXHvvfcaHTt2NLy8vIwpU6YYaWlpzmt0K3b77bcbERERhpubmxEcHGxcfvnljuDHMPSsm8PPAyA988Zz0003GZ07dzbc3NyMLl26GDfddJORmJjo+Lwpn7XFMAyj4f1IIiIiIq2H5gCJiIhIu6MASERERNodBUAiIiLS7igAEhERkXZHAZCIiIi0OwqAREREpN1RACQiIiLtjgIgERERaXcUAImI1MHcuXMZPHiws5shIg2kAEhEWoysrCzc3NwoKCigrKwMb29vUlJSnN0sEWmDFACJSIuxYcMGYmJi8Pb2ZuvWrQQEBNCtWzdnN0tE2iAFQCLSYqxfv56RI0cCsHbtWsfrKtnZ2cycOZPg4GB8fX257LLL2L59u+PzquGp119/nfDwcLy8vLjxxhvJyclx1LHb7Tz99NN07doVd3d3Bg8ezJIlS6rd58iRI0ybNo2AgAC8vb0ZNmwYmzZtqlbnvffeIzIyEj8/P375y1+Sl5fn+OyTTz5h4MCBeHp6EhgYyLhx4ygoKGi05yQiDefi7AaISPuWkpLCoEGDACgsLMRms7FgwQKKioqwWCz4+/tz88038+qrr3LDDTfg6enJN998g5+fH6+//jqXX345+/fvJyAgAIDExEQ+/vhjvvzyS3Jzc/nNb37Dvffey3//+18AXnrpJZ577jlef/11YmNjeeutt/jFL37Brl276NmzJ/n5+YwePZouXbrwxRdfEBoaytatW7Hb7Y42Hzx4kEWLFvHVV19x6tQpbrzxRv7617/yl7/8hbS0NKZNm8bf//53pkyZQl5eHmvWrEH7Tou0MI2yp7yISD2VlZUZSUlJxvbt2w1XV1dj+/btRmJiouHj42OsWrXKSEpKMrKysow1a9YYvr6+RnFxcbXzu3fvbrz++uuGYRjGk08+adhsNuPIkSOOz7/55hvDarUaaWlphmEYRlhYmPGXv/yl2jWGDx9u3HvvvYZhGMbrr79udOjQwThx4kSN7X3yyScNLy8vIzc313Hs0UcfNeLi4gzDMIwtW7YYgJGcnNzAJyMiTUlDYCLiVC4uLkRGRrJ3716GDx/OoEGDSE9PJyQkhEsvvZTIyEiCgoLYvn07+fn5BAYG4uPj4yhJSUkcPHjQcb1u3brRpUsXx/v4+Hjsdjv79u0jNzeXY8eOnTW0NnLkSPbs2QNAQkICsbGxjh6lmkRGRtKhQwfH+86dO5OZmQlATEwMl19+OQMHDuSGG27gjTfe4NSpU43yrESk8WgITEScqn///hw+fJiysjLsdjs+Pj6Ul5dTXl6Oj48PERER7Nq1i/z8fDp37szKlSvPuoa/v3+jtcfT0/OCdVxdXau9t1gsjiEym83GsmXLWL9+PUuXLuXll1/m97//PZs2bSIqKqrR2ikiDaMeIBFxqsWLF5OQkEBoaCj/+c9/SEhIYMCAAbz44oskJCSwePFiAIYMGUJ6ejouLi706NGjWgkKCnJcLyUlhWPHjjneb9y4EavVSu/evfH19SUsLIx169ZVa8O6devo168fAIMGDSIhIYGTJ0/W+ztZLBZGjhzJU089xbZt23Bzc2PhwoX1vp6IND4FQCLiVBEREfj4+JCRkcHkyZMJDw9n165dTJ06lR49ehAREQHAuHHjiI+P59prr2Xp0qUkJyezfv16fv/737N582bH9Tw8PJgxYwbbt29nzZo1PPDAA9x4442EhoYC8Oijj/K3v/2Njz76iH379vH444+TkJDAgw8+CMC0adMIDQ3l2muvZd26dRw6dIhPP/2UDRs21Or7bNq0iWeeeYbNmzeTkpLCZ599RlZWFn379m3kJyciDaEhMBFxupUrVzJ8+HA8PDxYs2YNXbt2pXPnztXqWCwWFi9ezO9//3tuu+02srKyCA0N5dJLLyUkJMRRr0ePHlx33XVMnDiRkydPcs011/Dqq686Pn/ggQfIycnht7/9LZmZmfTr148vvviCnj17AuDm5sbSpUv57W9/y8SJEykvL6dfv37Mnz+/Vt/F19eX1atX8+KLL5Kbm0tERATPPfccEyZMaIQnJSKNxWIYWpspIm3D3LlzWbRoEQkJCc5uioi0cBoCExERkXZHAZCIiIi0OxoCExERkXZHPUAiIiLS7igAEhERkXZHAZCIiIi0OwqAREREpN1RACQiIiLtjgIgERERaXcUAImIiEi7owBIRERE2p3/D3TYKB7O54qnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot train_loss, test_loss\n",
    "num_epochs = CONFIG['epochs']\n",
    "plt.plot(np.arange(num_epochs), history_model[\"train_loss\"], label='train loss')\n",
    "plt.plot(np.arange(num_epochs), history_model[\"test_loss\"], label='test loss')\n",
    "plt.xlabel('#epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "7060a727-2c6c-4528-ba8b-ca08f4c4123e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14663530b400>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jO/3cQoiz17HCSvLL1e3Vaw4WcMOkvm4ekfMcKVCXjAZFB5IY7s/jV53Dnz7dzT9+OMy0QZGM6hPqtrGd2fm9I14GPYOiA9mfU86hvIpOAyZXkxmgXmz37t389NNPBAYGWh9Dhw4F4OjRo4wePZrp06czcuRIrrvuOt566y1Onz5t9/scOXKEefPm0b9/f4KDg0lOTgbUKt8AaWlpjB071hr8nCktLY3p06c79iGFEGelfTll1q+1HUlni6OWGaCB0YEAXDMugctHxtFoVrj34zSq6xvdMq7MoirSskvR6+DyUXE2nePJLTFkBsgZvP3V2Rh3vG8XVFZWcuWVV/LMM8+0ei0uLg6DwcAPP/zAxo0b+f7773n11Vd5+OGH2bJlC/369bP5fa688kqSkpJ46623iI+Px2w2M2LECOrr6wF1Jqkjnb0uhOh99p9qarK5PqOQ2gYTvt4GN47IORRFsS6BDYxWgwedTsffrx7BjhOnOVZUxZNfH+Spq0d2dJluoSU/Tx0YSXSQbdX0PXknmMwAOYNOpy5FufrRQfb9mYxGIyaTqcVz48aNY//+/SQnJzNw4MAWj4CAAMtH0zF16lQef/xxdu3ahdFoZOXKle1e80zFxcWkp6fz17/+lenTpzNs2LBWs0ijRo0iLS2NkpKSNq8xatQoUlNTbf6sQoiz3/6cpgCotsFsTczt6XLKaqmuN+Gl15EU0fRLbqi/kReuV5f+P9ySxY8H8l0+Nuvur2ad3zszxLIT7KAHdoWXAKiXSE5OZsuWLWRmZlJUVITZbGbhwoWUlJQwb948tm3bxtGjR/nuu++YP38+JpOJLVu28NRTT7F9+3aysrL47LPPKCwsZNiwYdZr7tmzh/T0dIqKimhoaF2xNCwsjIiICN58800yMjJYs2YNixYtanHMvHnziI2NZc6cOWzYsIFjx47xv//9j02bNgGwePFiPvroIxYvXszBgwfZu3dvm7NWQojeQVEU9luWwMb1DQXOnmUwbQdYv8gAvA0tf0RPHRjJgmnq7Pv/+98eCitc12Kipt5kHdtFQ6NtPk+bAcosqqK2oeNfmF1NAqBe4v7778dgMDB8+HCioqLIysoiPj6eDRs2YDKZuOSSSxg5ciT33nsvoaGh6PV6goOD+fnnn7nssssYPHgwf/3rX3nhhRe49NJLAViwYAFDhgxhwoQJREVFsWHDhlbvq9fr+fjjj9mxYwcjRozgvvvu47nnnmtxjNFo5Pvvvyc6OprLLruMkSNH8vTTT2MwqNPZF154IZ9++imrVq1izJgxXHTRRWzdurX7b5oQwiOdPF1DeW0j3gYdd1yobphYcyjfI7da2+uIpWbOoJjANl+/f+YQhsYGUVxVzwMrdrusSrS2iyvY14vwAKPN50UH+RDq741ZweNKFkgOUC8xePBg64xKc4MGDeKzzz5r85xhw4axevXqdq8ZFRXF999/3+l7z5gxgwMHDrR47sz/aZOSklixYkW717jmmmu45pprOn0vIcTZT5v9GRQdxPmDIwkwGsgvr2NfTplbd0g5gxYkDIxqOwDy8TLw8g1jufK19fyUXsgHW7L47blJ3T4urRBjYrh9uac6nY6hsUFsPlbCobwKRiSEdMfwHCIBkBBCiB5Fy/8ZkRCMj5eBaYOiWL0/jx8PFpw9AZClm3pbhsQG8eCsoTzx1QH+/vUBth4voaOMUC+DjltSkhmdGOrwuLIsAVBfOwMgUCtCbz5WQrqH5QFJACSEEKJH0QKgc+LV2YTpw6JZvT+P1IP5LLp4sDuH1iXNd4ANim57Bkhz65Rkfkov4JcjRXy5u/NdyIUVdbz/u8kOj60rAZCnNkWVAEgIIUSPoi2BaY04LxoajU6nBkY5pTXEh/bM0hlFlfWU1TSg16lJ0B3R63W8duM4vtyd06Jv2Jnyymt58+djHCus6tLYHF0CA8+tBSQBkBBCiB6jsKKO/PI6dDoYFqcGQBGBPozrG8aOE6dJPVTgkpyY7qBVgO4b7m9TTaMQP29+08lnLa6s482fj5FTVtOlWkldmQEabFnOK6io43RVPWF2JFF3J9kF5iBXZd4L55E/M9FTmMwK+06VnRW7mpxNm/3pFxFAgE/T7/DTh6lbs1MPur4+jrNknFEA0RnCA4wE+XihKHDSwX5ciqKQXVIDODYDFOjjRWK4OivnSctgEgDZydvbG4Dqas9r7CY6pv2ZaX+GQniqxav2ccWr6/k87ZS7h+JxrPk/Z+wmunhYDAAbjxZTVeeeVhFdlXFGCwxn0Ol0JEWqQUtmkWM/t4oq66lpMKHTQYKDy4tDYtTZOk9KhJYlMDsZDAZCQ0MpKFCLbvn7+6OzoyKzcD1FUaiurqagoIDQ0FBrfSEhPFFGQSUfblH75O3PKeeacW4ekIc5YE2ADm7x/MDoQPqG+5NVUs0vR4qYNaLjJs2e6Ei+bQnQ9kqKCGDfqXIyix3LA9KWv+JD/DB6OTZvMjQ2iB8P5nvUDJAEQA7Qup9rQZDoGUJDQzvtXC+Eu73wfTrayldRpesq/fYUZyZAa3Q6HdOHRfOfDZmkHszvmQFQN8wAASRbWmqcKHZsBqgpAdrx5HJP3AkmAZADdDodcXFxREdHt9n+QXgeb29vmfkRHm/PyVK+3Zdn/d6VrQ56goraBjItP8S1LfDNXTwshv9syGTNoQJMZgWDvufMzpdW11sD3gHdMAMEdHkGyJEEaI3WEuNwfgVms4LeA/5sJADqAoPBID9UhRBO89x36QDWpRyZAWpJW/6KC/Ftsx3DxH7hBPl6UVxVT1p2KeOTwmy67pe7c3jh+3Se/fVoJvULd+qYbaXl/ySE+hHo49wfzcmWAMjRGSBnBED9IgMwGvRU15s4ebqGvhGOX8tZJAlaCCE8wMaMIn45UoS3QcejVwwHZAboTGcWQDyTt0HPBYOjANt3gx0vquKBFXvILK5m6bqjzhmoA7TlL2fP/kDTEtjJ09Ud1gxqT1YXagBpvAx669LeIQ9JhJYASAgh3ExRFJ6xzP7cOKkvYy0dzk9XN9Bgsv8H1tlqfzsJ0M3NsOwGS7WhO3yDycy9H++ixtKl/OfDhZRU1TthpPbLsLECtCOignzwNxowO7gV/qQTAiBoWgbzlIKIEgAJIYSbfX8gn93Zpfh5G7jrokGE+Rut+SvFle75geyJ2kuAbu7CIVEY9DrS8yusybvteTX1CLtPlhHs60X/yAAazQrf7M116phtZWsLDEfodDprHpC9y2B1jSZyy2uBri2BQbNE6HwJgIQQotczmRWet8z+3HZeMlFBPuj1OiIsOS6SB6SqbTBZZ0nOrAHUXKi/kQmW3J8fO1gG23GihNd+ygDg71ePZN6kvgB84abaSxmWoMDZO8A02jKYvYnQp07XoCjgbzRY/046ytNaYkgAJIQQbvT5rlMcKagkxM+b358/wPp8VJAPIHlAmsP5FTSaFUL9vYkP8e3w2M6WwSpqG7h3eRpmBa4Zm8CVo+O5YnQcOh1syzztcMVkR1XWNZJTps6ydFcA5OgMUPME6K7WvBsaq87cHS+qoq7R1KVrOYMEQEII4SZ1jSZe/OEwAHdcOIAQv6Yq5ZGBlgBIZoCApvyfEfEhnf4gnjFcDYC2HC+mvLZ1qZLHvzxAdkkNCaF+PDb7HADiQvyYbNkB9uVu1y6DHbXMbEUG+hDq3z19shydAdKWEfuEdX3XVkywDyF+3pjMinU2z50kABJCCDf5aEsWp0priA7y4ZaU5BavyQxQS7bk/2j6RQbQPyqABpPCz4cLW7z2zd5cVuw4iV4H/5g7hmDfpqBzzpgEwPXLYN2ZAK1xdAYo+7TaA6yr+T+g5iJZ84By3b8MJgGQEEK4QVVdozUH5Z4Zg/Aztqwpps0ASQ6QSpsBGm5DAARtL4PlldXy0Gd7AXXG7cyaP5eOiMPboONQXoVL81SsCdAx3RcAJVv6gWWXVNNox87CrGJtCczxKtDNWXeCeUAitARAQgjhBsvWH6eosp7kCH+un5DY6nWZAWpiMisczO24BtCZtADop/QCGk1mzGaF+z/dTVlNA6P6hHDvjMGtzgnx9+bCIWpXeVfOAmUUdG8CNEBMkC8+XnoazQo5pbU2n2fNAXJS4UJPaokhAZAQQrjY6ap63vz5GAD3XTwYb0Prf4ojA2UXmOZYYSW1DWb8vA30iwyw6ZxxfUMJ9femtLqBnVmlLNtwnPUZRfh66/nH3DFt3nOA2WPiAfgiLQdFUZz2GTrSHV3gz6TX60iyMw9IURRrDpAzlsCgKRHaE7rCSwAkhBAutnTdUSrqGhkWF8yVo+LbPEZmgJo0X/6ytb+Xl0HPryyzOUvXHeXZ1Wqpgb9ePpwBUe0HGtOHxhBgNHCqtIadWae7OPLO1TaYrLMsg6KDuvW9mvKAbAuASqsbqKhrBJyTBA1NM0D55XWUVru3xpUEQEII4UJ5ZbW8szETgAdmDmm3KWRUoARAGnsSoJvTlsHWHCqg3mRm+tBobprct8Nz/IwGZlo6yX+RluPAaO1zrLAKswIhft7WWb/u0rQTzLZEaC0wiw7ywdfbOX0vA3286BOm5hO5exlMAiAhhHCh1346Ql2jmYnJYVw4JKrd47QZoPLaRo+omeJOtrTAaMv5gyPxNqgBZmSgkWd+PcqmWjazLbvBvtqT2+2tSDIKm3aAdbXOTmesXeGLbJsByj7t3OUvjae0xJAASAghXOjnw0UA3PmrgR3+wAvx87b+8C7qxe0wFEXptAlqe4J8vbnknFi89Dqe+/Vo6866zkwdEEFEgJGSqnrWZxTZPWZ7aBWgu3MHmEbrCm9rDpAzusC3xVMSob3c+u5CCNGLNJrMnCpV66oMi+14NkOn0xEZ6ENuWS1FFXUkhDpnG3JPc/J0DWU1DXjpdQ4FCf+4fgxlVzZYZ9Rs4WXQc8WoON7ddIJVaTnWXKLuYO0C30FekrNoSdDZJTWYzEqn+VTZTmqCeqbrJyQyY1gMg2O6N+epMzIDJIQQLpJbVovJrGD00hNtww9kSYRuWv4aFBOEj5f9eShGL71dwY/mKssy2Hf786ip774lSGsRRBcEA/GhfhgNeupNZnLLajo9vrtmgJIiAhjbN4wAH/fOwUgAJIQQLmL9jTrMr93k5+akGCIcsCRAj7Az/6erxvUNJTHcj+p6U4dNVbuiwWTmuCUfpzu3wGsMeh2JloKGtlSEdnYNIE8jAZAQQrhIlp1LCrITzPEE6K7S6XTMHt29rTFOFFfRaFYIMBo6bfDqLLbmATWazNaCiYlO2gLvaSQAEkIIF7F3SSEySIohWgOgBPsSoJ1BK4q4Nr2Q01XOT0RvXgCxu3eAaWztCWbvcm1PJAGQEEK4iL0BUFQv7whfVFlHXnktOh0Mi3PtDBCoeTnD4oJpNCt8uy/P6dc/km9JgHbB8pdG6wnW2Vb4LDuXa3siCYCEEMJFtM7ati6BRVp+8y6q6J3b4LXZn+SIAALdlDA7x9oaw/nLYE01gFy3G8rWGaDuSoD2JBIACSGEi9jbV6m3zwA5WgHama4crQZAW46XkFPa+c4pe2gzQINcOQNkSWg+UVKF2dx+rzMJgIQQQjhFRW0DJZY8EvtngHprAORYAURnig/1Y1K/cAC+3O281hgms8LRwu5vgnqmhFA/vPQ6ahvMFHTw98rehP2eSAIgIYRwgewSdfYgPMBo83KOVr+moq6R2obe1w7jgJt2gJ2peYd4Zzl1uoa6RjNGL71Lgwwvg97ai+t4B3lAJyUAEkII4QyO/EYd5OOF0Uv9Z7q3bYWvqG2w/oB2dwB02Yg4vPQ6DuSWcyTfOe0bjhSo1+kfGWBzh3tnsaUrvCyBCSGEcAp7839ArUXTW/OADuaqAUJssC8RNvbw6i5hAUZr49rPnZQMfcSFFaDP1FlX+PLaBk5XNwAyAySEEKKLmjpr29fTq7fmAWkJ0CMS3Dv7o7l6bB8APticRVlNQ5evZ22B4cL8H01nM0BasB5hx3JtTyQBkBBCuEBTXRX7fqPurTNAWgL0cDcmQDc3a0Qsg2MCKatp4M2fj3b5ekcKXJ8ArbHWAmpnBqi7mqB6GgmAhBDCBRzNqYjSqkH3slpA7mqB0R6DXsefLhkCwLL1mRRU1Dp8LUVROOohM0CK0norfG/YAQYSAAkhRLczmxVOlthXBFHTNAPk+A/cnqau0WRNNvaUAAjgkuExjEkMpabBxOtrMhy+Tl55LZV1jXjpddZgxJUSw/zR66C63tTmzKK2Y9He5dqexu0B0Ouvv05ycjK+vr5MnjyZrVu3dnh8aWkpCxcuJC4uDh8fHwYPHsw333xjff2xxx5Dp9O1eAwdOrS7P4YQQrQrv6KWepMZL72OODubXvbGatDpeRU0mhVC/b1JCPWcH8I6nY4HZqmzQB9uzbIuFdlLK4CYFOFv3eXnSkYvPQlh7XeF7w07wMDNAdDy5ctZtGgRixcvZufOnYwePZqZM2dSUFDQ5vH19fVcfPHFZGZmsmLFCtLT03nrrbdISEhocdw555xDbm6u9bF+/XpXfBwhhGiT9ht1QpgfXgb7/tntjTlAu0+qCdCj+oS6rEmoraYMiGTaoEgaTAr/+OGwQ9doSoB2/Q4wjbUrfBu1gHpLDpBb07tffPFFFixYwPz58wFYunQpX3/9NcuWLePBBx9sdfyyZcsoKSlh48aNeHt7A5CcnNzqOC8vL2JjY20eR11dHXV1Tf+4lJeX2/lJhBCifV35jdo6A9SbAqDsUgDG9PGMBOgz/XnmEH45UsTKtFP84YIBDIm1L5BxZwK0JinCn1+OtJ4BMpkVTp7WlsDO7gDIbTNA9fX17NixgxkzZjQNRq9nxowZbNq0qc1zVq1aRUpKCgsXLiQmJoYRI0bw1FNPYTK1rJB65MgR4uPj6d+/PzfddBNZWVkdjmXJkiWEhIRYH4mJiV3/gEIIYaEFQH3s3AEGzWaA3LwNXlEUl1Wj1gKg0YmhLnk/e43qE8plI2NRFHj++3S7z8+wFEEcFOO+AEibATp+xlb4/PLmy7Wes/zYHdwWABUVFWEymYiJiWnxfExMDHl5eW2ec+zYMVasWIHJZOKbb77hkUce4YUXXuDJJ5+0HjN58mTeeecdVq9ezb/+9S+OHz/OtGnTqKhov3rnQw89RFlZmfWRnZ3tnA8phBA4VgRRo7XDqK43UVXX6NRx2eOBFXsY/7cf2HeqrFvfp6K2wdolfVSf0G59r65YdPEQ9Dr44UA+O06ctvm8E8VV1iKP7p0BarsWkBasJ4T5ubxCtau5PQnaHmazmejoaN58803Gjx/P3Llzefjhh1m6dKn1mEsvvZTrrruOUaNGMXPmTL755htKS0v55JNP2r2uj48PwcHBLR5CCOEsXVkCC/Dxws/bALhvGayyrpEv0nKoqjfx9LeHuvW99p4sQ1HUpp1a8OeJBkYH8uvxanHE57471OZ28jMdzq/guqWbqKxrZFB0IIPdUAVaY+0KX1TdYuxdCdZ7GrcFQJGRkRgMBvLz81s8n5+f327+TlxcHIMHD8ZgMFifGzZsGHl5edTXt71DIjQ0lMGDB5OR4fiWRSGE6Iqu7qrRAgF3LYOtP1JIvcmsfp1RxMaMom57r7STpQCM8dDlr+bumTEYo5eezcdK+OVIx/dk78ky5r6xiYKKOobGBvHfBZPxtjMh3pkSw/3R6dRGuyVVTT8/e0sCNLgxADIajYwfP57U1FTrc2azmdTUVFJSUto8Z+rUqWRkZGA2m63PHT58mLi4OIxGY5vnVFZWcvToUeLi4pz7AYQQwgY19SZr4OJoABQZaCmG6KYZoB8Pqjtz/Y3qL5/PfJdu04yHI5ryfzwzAbq5hFA/fntuEgDPfncIs7nte7L1eAk3vrWZ09UNjE4M5ePfn0t0kH3lEJzN19tAXLA6huYVoXvLFnhw8xLYokWLeOutt3j33Xc5ePAgd9xxB1VVVdZdYTfffDMPPfSQ9fg77riDkpIS7rnnHg4fPszXX3/NU089xcKFC63H3H///axbt47MzEw2btzI1VdfjcFgYN68eS7/fEIIcdLSAyzI14sQf2+HruHOGSCTWWHNITUAevraUfgbDezOLuX7A/mdnOmY3dlqjtFoD87/ae7OCwcQYDSw71Q53+5rnb+67nAhNy/bQkVdI5P7hfPf2ycT6t/2L+yu1lYekARALjJ37lyef/55Hn30UcaMGUNaWhqrV6+2JkZnZWWRm5trPT4xMZHvvvuObdu2MWrUKO6++27uueeeFlvmT548ybx58xgyZAjXX389ERERbN68maioKJd/PiGEcMYPlEhrLSDXF0NMyz5NSVU9Qb5eXDoiltum9gPg+e/SMbUz4+GovLJa8spr0etgRILnzwABRAT6cPu0/gC88EM6jaamFYrV+3K5/d1t1DaYuXBIFO/eNsmjmou21RMsS6tY7sCOxZ7G7X8Sd911F3fddVebr61du7bVcykpKWzevLnd63388cfOGpoQQnSZMwIgd84AactfFw6JxtugZ8H5/Xl/8wmOFFSyctcpayKwM+y25P8MjgkiwIMChc7cPq0f728+wbHCKv638yRzJ/bls50n+fOKPZjMCpeNjOWluWPdUvW5I2fOANXUm6zLrDIDJIQQokucOQPkjhygHy1LXTOGRQMQ4ufNnRcOAOAfPxymrtF5tYGsBRB7QAJ0c0G+TffkpR+P8O9fjrHok92YzAq/Ht+HV27wvOAHmnaCaTNA2Zbl2uAuLNf2JJ73JyKEEGeRbAeboDbnrhmgrOJqjhRUYtDruHBwtPX5W6YkExPsw6nSGj7c0nGhWXtoM0CeWgCxI785N4n4EF9yy2p58uuDANw6JZlnrx1ld/sTVzlzBijLEgj1jTj7Z39AAiAhhOhWzqir4q4ZoB8PqrM/E5PDWswI+HobuHv6IABeW5PhlAKNZrPCnh6WAN2cr7eBe2cMtn5/168GsvjK4eg9uJhgkiXQKa1uoLS6vlclQIMH5AAJIcTZSlEU6w+VrswARTebAVIUxWUNQlMPactfMa1eu35CIm/9fIzM4mqWrT/O/1kCIkcdK6qkoq4RX289g93YIqIrrh3fh6KqOuJD/JgzNqHzE9zM3+hFTLAP+eV1ZBZXO+Xvak8iM0BCCNFNiirrqWkwodOpNWMcpc0A1TWaqXRRO4zy2ga2HCsB2g6AvA16Fl0yBIA3fz7G6aqu7VBLs8z+jEwI8dglo84Y9DruvHBgjwh+NM2XwaxFEHvBDjCQAEgIIbqN9ht1fIhfl5Jg/YwG6/ZpV+UBrUsvpNGsMCAqgOTIgDaPuWJkHMPjgqmoa+Rf64526f2sBRB74PJXT2ZNhC6qtiZB95YlMAmAhBCimzS1Feh6V+2matCuqQWUerD95S+NXq/jz7PUWaB3N2aSV1br8Pv15ATonkybAcosrup1OUASAAkhRDdxZmNJV+4EazSZ+Sm9EIAZw9sPgAAuHBzFpORw6hrNvJx6xKH3q20wcTC3HOh5W+B7umRLALT9RAm1DWb0OojvwnJtTyIBkBBCdJMsJ+ZUuHIn2PYTpymraSDM35txfcM6PFan0/GAZRbok+3ZHC+q6vD4thzMLafBpBARYKRPWO/44esptJ1gWrmGuC4u1/YkveNTCiGEG1iXFJxQV8WVM0Da8tevhkRjsGEb94TkcC4aGo3JrPDC9+l2v19TA9RQl+1wE6qkM/5u9pblL5AASAghuk22E7cVu3IGSGt/0dnyV3N/njkEnQ6+2pPLvlNldr1fmiRAu02Qr7c1vwyck6/WU0gAJIQQ3aCu0URuuZoU3JNygI4WVnK8qApvg45pgyJtPm9YXDBXjY4H1G3x9th90lIAMbFnNEA922iJ0CAzQEIIIboop7QWRQF/o4GIAGPnJ3TCVTNA2vLXuf0jCPK1rx/UAktX9G/25lJQbtuOsNLqemvekMwAuUfzZbDeUgQRJAASQohu0XxLsTPyWlw1A2Rd/upg+3t7RiSEMD4pjEazwkdbs206Z49l9icpwp8wJwSKwn7JMgMkhBBnl9NV9aw7XEiDyezy99YCoD5OqqrbvA6QoihOueaZTlfVsz1Trf48fVh0J0e37ZYpyQD8d8sJm+67FEB0v+YzQL0pAJJeYEKIs05uWQ1v/Xycj7ZmUdNg4ldDovjXb8bj621w2RicWQMImpbA6k1mymsaWzQndZa1hwswKzA0NsjhwG3WObFEBflQUFHHd/vzuGJUfIfHSwFE9+tnqfQd6ONFeC+ahZMZICHEWeNYYSUPrNjN+c/+xLINx6lpMAHwU3ohtyzb6rI+WgBZxVoA5JxdNb7eBoJ9Le0wKh2vuNyRrix/aYxeem6c1BdQq0N3RFEUaw+wMZIA7TYjE0K4bWo/HrliWK8qQyABkBCix9t3qoyF/93J9BfX8cn2kzSYFCb3C+fd2ybxyR9SCPLxYsvxEm769xZKq13TSsKZNYA0kdY8IOd/hvpGM+ss1Z8dXf7S3Di5L156HdsyT7M/p/0t8TlltRRV1uGl13FOvARA7qLT6Xj0yuHMndjX3UNxKQmAhBA9kqIobD5WzM3LtnLFq+v5em8uigIzhkXzvzumsPwPKVwwOIpJ/cL5cMG5hPl7szu7lLlvbKagontmUJqPzdlLYABRlmWwwm7YCbb1eAmVdY1EBvp0OR8nJtiXWSNiAXh/04l2j0vLKgVgaFyQS5cnhQAJgIQQPdQjX+zjhjc38/PhQvQ6mD0mntX3TuPft0xkfFLL9g0j+4Sw/A8pRAf5kJ5fwdw3NnOqtKbbxlZW00CFZbnNWUnQ0DQDVGTjTrCymgYue/kX5ry+gW/25mIyt588/aNl+/v0odHobaj+3JlbLcnQn6edanfWzZr/IwnQwg0kABJC9Dh7TpbyweYsdDq4aXJf1t7/K16+YSxDY4PbPWdwTBCf/jGFPmF+HC+q4rp/beRYYWW3jE9b/ooO8nHqzIa9M0BrDuVzILectOxS7vzvTi5+cR2fbMumvrHl7ixFUUg9ZAmAurj8pRmfFMbwuGBqG8x8uv1km8ekNWuBIYSrSQAkhOhxnvtO7Td19ZgE/n71SJvzbJIiAvj0jyn0jwogp6yW69/YbO1C7kxZ3bD8BU21gGydAdqQUQyoSa7Bvl4cK6rigf/t4YLnfuLt9ceprldnqQ7nV5JdUoPRS895dlR/7ohOp+OWKUkAvLc5s9XsU6PJzN6TWgJ0qFPeUwh7SAAkhOhRNmYU8cuRIrwNOu67eLDd58eF+PHJH1IYHhdMUWUdN7y5mV1Zp506xm4LgOyYAVIUhQ0ZRQD8v1lD2fjQdP5y2VCig3zILavlb18dYOrTa3j5xyN8tkudoZk6IAJ/o/Oqo1w1OoEQP2+yS2pYm17Q4rWMwkpqGkwEGA0MiAp02nsKYSsJgIQQPYaiKDxjmf25cVJfh8v2Rwb68NHvz2Vc31DKahr4zb+3sONEidPGmV2i5hc5u61AZJBWDLHzAOhYURW5ZbUYvfRMSA4j0MeL358/gJ8f+BVPXT2SpAh/Tlc38I8fD/PGOrV3lz3NT23hZzQwd2IiAO+ekQytFUAc2SfEpo7zQjibBEBCiB7j+wP57M4uxc/bwF0XDerStUL8vHn/d5OZOjCCqnoTf//6oJNG6dwu8M1FBfoCtrXD2GiZ/ZmQFNYiD8nX28CNk/uSuugCXpk3lqGxQQAYDXqmD3VuAATw23OT0Ong58OFLXKutPo/kv8j3EUCICFEj2AyKzxvmf257bxkaz5MVwT4ePHi9WPQ6WBnVqk1cOmq7loC02aAiivrMXewowtgvSUAmjqw7ZweL4Oeq0bH8+090/hwwWSW/+FcYkN8nTpeUIPA6UPVxOr3ms0CaTNAY2QHmHATCYCEED3Cyl2nOFJQSYifN78/f4DTrhsT7EtK/wgAVu3O6fL1Gk1m6xZ7ZwdAEQFq0NdoViitaWj3OJNZYdNRNQG6vQBIo9PpmDIgkrF9wzo8rituTkkG4H87TlJZ10hNvYn0/AoAxvQN7bb3FaIjEgAJITxeXaOJf/xwGIA7LhxAiJ9z+2DNGZMAwOe7TnW50WhuWS0ms4LRS0+0E2apmjN66Qm19ADrKA9o36kyymsbCfL1YmSC+yssnzcwkv6RAVTUNbJy1yn25ZRhMitEB/kQG+z8WSchbCEBkBDC4320JYtTpTVEB/lwi2U2wZlmjojFaNBzpKCSQ3kVXbqWtvyVGObnlIKCZ7LuBOsgD0hb/krpH+ERCcZ6vY6bUyxb4jdmWitAj04M7VW9p4RnkQBICOHRquoaee2nDADunj4IP6PzWyaE+Hnzq6FRAHyR1rVlsO5ogdGc1hW+oxkgbfu7s2r6OMO14/sQYDRwpKCSdzdlAlL/R7iXBEBCCI+2bP1xiirrSYrwt26p7g6zLctgX+7O6TTBuCNZ3bQDTBMV1PEMUG2Die0n1LpGneX/uFKQrzfXjOsDwMnTao6UtMAQ7iQBkBDCY52uqufNn9UaNYsuHoy3ofv+ybpoaDSBPl6cKq1hRxcKI3bXDjBNZCfFELdnnqa+0UxssC/9IwO6ZQyO0pbBNCP7uD8/SfReEgAJITzW0nVHqahrZFhcMFeOiu/W9/L1Nlg7mH++65TD1+muGkCazmaAmm9/97T8mkExQUwZoO646x8V4PRkdiHsIQGQEMIj5ZXV8s7GTAAemDmkWxKKzzR7jBpkfb03t1XDUFt1/wyQVg267Q7rG49qAVBEt7x/V931q4F46XVcPjLO3UMRvZzzmr4IIYQTvZx6hLpGMxOTw7hwSJRL3jOlfwSRgT4UVdaxPqOQi+ysjFxR28DparU+jztmgEqr69l7Sq2w7En5P81NGRjJnscuwc/b+cnsQthDZoCEEB7neFEVn2zPBuCBWUNdtpTjZdBzxSh1ZsKR3WBaD7DwACOBPt3z+2VHu8A2HS1GUWBQdCAxHlxfx9/o5XHLc6L3kQBICNGprOJq/vHDYco6qD7sTC/+cBiTWeFXQ6KYmBzukvfUaMtg3+/Pp7q+0a5zu3sHGGAtrlhcWYfpjN1qG4523P5CCNFEAiAhRIcUReH/Pt7Fy6lHePKrA93+fiVV9Xy1R519uX/mkG5/vzONSQwlKcKfmgYTPxzIt+vc7q4BBOrskk4HZkW9V81tyLCt/YUQQgIgIUQnvtufb21c+b+dJ8ko6Fql5M5oyzhDYoI4J97126R1Oh2zR6uzQPYugzUlQPs5fVwaL4OecH8tEbppGexUaQ3Hi6ow6HVM7u/aWTMheiIJgIQQ7TKZFZ7/Xu3AHmA0YFbghe8Pd+t7dtbF3BWusiyD/Xy4sNUsS0e6eweYpq1EaK3686g+IQT7yvZyITojAZAQol2f7TxJRkElof7evPe7Seh08O2+POuMUHdoauPgvm3cA6ODOCc+mEazwjd7c20+L/t09+cAQduJ0Nb7JstfQthEAiAhRJvqGk289OMRAO68cADjk8K5eqzaLuK579K75T2zS6rJKqnGS69jUj/31rHRkqFX2bgMtuNECVnFWiNU184AKYpizf+ZMkACICFsIQGQEKJNH1o6sMcG+3KzpQP7fTMG423QsT6jiI2WGQdn0mYxxiSGdts2cltdOToenQ62ZpZw0jKz0571R4r4zb+30mhWOG9gJH3Cui8HCJoXQ1QDoMP5lRRV1uHrrWdcUmi3vrcQZwsJgIQQrVTWNfLamqYO7L6WonWJ4f7cNFnt5/TMd+koiuNNQ9viCfk/mrgQPyb3U5OJv9zd/jLYDwfyue2dbdQ0mJg2KJK3bp7Q7TVuzpwB0u7bpH4R+HhJgUEhbCEBkBCilWXrj1NcVU+/yACum9CnxWsLfzUQf6OB3dmlfLffvm3iHTGbFTYd9axt3FqH+C/S2u4N9kXaKf74wQ7qTWZmnRPLv2+ZgJ+x+wOQphwgNUFbmzmbOsAz218I4YkkABKiB6qqa2Tem5tZuu6o069dUlXPWx10YI8K8uG2qf0AeOH79FbF+Bx1KK+C4qp6/I0GxiSGOuWaXXXpiFi8DToO5VWQntdy+/+HW7K4d3kaJrPCNeMSeO3GsS6bfWk+A9RgMrPlmGcFjkL0BBIACdED/XKkkE3Hinl7/XGnX/tfazOoqGtkeFxwuw0rf39Bf0L9vTlSUMnKLnROb26DdRknHKOXZ/zTFOpv5ILB0QCs2t30Od/8+Sh/WbkXRYHfnpvE878ejZfBdWNuvgtsd3YpVfUmwvy9GR4X7LIxCNHTeca/MkIIu+zPKQfUGYDaBpPTrptbVsO7m04A8MCs9juwB/t6c8cFAwD4xw+HqWvs+hi0Ng6eto1b2w32RVoOZrPCiz8c5qlvDgFwx4UDeGL2OS7pVN+cNgNUUl3PusOFgLr7y9XjEKInkwBIiB5on6XjN0BOaY3TrvtK6hHqG81M6hfOBYM77sB+y5RkYoJ9OFVaw4dbsrr0vvWNZrYcKwE8bxlnxrAYAowGTp6u4fb3tvNKqloa4M8zh/D/XNiotbkwfyN6HSgKfLlb3abvafdNCE8nAZDokU6V1rBs/XG7m1WeLbQZIICTp50TAB0rrOST7ScB+H+zhnT6g93X28A90wcD8NqaDKrqHP+zSMsupabBRESAkSExQQ5fpzv4GQ3MPCcWgDWHCgB4/KpzWPirgW4bk0GvI8KyDJZpqT00daAkQAthDwmARI/00g+HeeKrA3yyLdvdQ3G5woo6Cpq1QHBWAPSCpQP79KHRjE+yrZfUdRP6kBzhT3FVPcu6kI+kbeOeMtAzl3FmWwpA6nXw/HWjuWVKsnsHRFMeEECfML9ub78hxNlGAiDRI52yLPvsPVXeyZFnn/05ZS2+76xIny32nSrj6z256HT2dWD3NuhZdIl6/Js/H+O0HX2zmmtq4+CZsxjnD4rkb3NG8MHtk/n1+D6dn+ACWh4QqHlT7liKE6InkwBI9EjFlvon6fm9MQBq+ZlPOSEH6FlLa4vZo+MZZudOoitGxjE8LpiKukb+5cC2/IraBtIsvcU8tY2DTqfjt+cmedT4tGrQoM6cCSHs4/YA6PXXXyc5ORlfX18mT57M1q1bOzy+tLSUhQsXEhcXh4+PD4MHD+abb77p0jVFz1NsmWk4kl/ptDo0rmA2K5TVNHTpGtoM0Og+IUDXl8A2Hyvm58OFeOl13HfxYLvP1+t1/HmWOgv07sZM8spq7Tp/6/ESTGaFpAj/bm8iejZpPgM0RQogCmE3twZAy5cvZ9GiRSxevJidO3cyevRoZs6cSUFBQZvH19fXc/HFF5OZmcmKFStIT0/nrbfeIiEhweFrip7HbFYoqVJzYOoazWQWV7l5RLZ785djjH78e+vWZUdoM0CXWBJzu7oE9vpPasuLeZP6khQR4NA1LhwcxaTkcOoazbxs2SVlK09qf9GTRFlygIbFBbfIBxJC2MatAdCLL77IggULmD9/PsOHD2fp0qX4+/uzbNmyNo9ftmwZJSUlfP7550ydOpXk5GQuuOACRo8e7fA1Rc9TWtNA80mfMyv0erI1B9VAXNu6bK/y2gZOWHb9aDuT8svrulSHZ89JdUZp3qS+Dl9Dp9PxgGUW6JPt2RwrrLT53I2WLuZTPWh5qSe4bGQcYxJD+b+L3LcbTYiezG0BUH19PTt27GDGjBlNg9HrmTFjBps2bWrznFWrVpGSksLChQuJiYlhxIgRPPXUU5hMJoevCVBXV0d5eXmLh/Bc2uyP5lAPCoCOFKhj3Z5Z4tD5By2zP/EhvgyICsDP0qQ0p9S+ZSdNaXW9dUmuX6Rjsz+aCcnhTB8ajclSLNAWBRW1pOdXoNNBiizj2CU+1I/PF07lsnaqdQshOua2AKioqAiTyURMTEyL52NiYsjLy2vznGPHjrFixQpMJhPffPMNjzzyCC+88AJPPvmkw9cEWLJkCSEhIdZHYmJiFz+d6E5aA0hNel7PCFiLK+s4Xa0GG5nF1RRU2B+0aMtfw+ND0Ol09AnzAxxfBtNqyMQG+zqlief9M4eg08FXe3JbFGtsjzb7c058MOEBxk6OFkII53F7ErQ9zGYz0dHRvPnmm4wfP565c+fy8MMPs3Tp0i5d96GHHqKsrMz6yM7ufbVlepISSwK0t0Hd9ttTlsCOFLRcFtqRedrua+yzJECfE6/u1NICoFMOJkKfsORPJUU4J/l4WFwwV41WW0c8Z9lZ1pGmLuay/CWEcC2HAqCffvqpy28cGRmJwWAgPz+/xfP5+fnExsa2eU5cXByDBw/GYGj6TXXYsGHk5eVRX1/v0DUBfHx8CA4ObvEQnqu4Ul0CG5sYBsCJkuoeURH6zABomwMB0AHLDFBTAKQGLo7uBMssUmeAkh1Mfm7LoosH46XXse5wIZstXcrboihKUwAkCdBCCBdzKACaNWsWAwYM4Mknn3R4tsRoNDJ+/HhSU1Otz5nNZlJTU0lJSWnznKlTp5KRkYHZbLY+d/jwYeLi4jAajQ5dU/Q82hLYwJhAIgN9UBQ4nG970q27HLUEQAmh6qzNNjvzgGobTNYgakSCugW+q0tg1hmgSOdtP0+KCOCGSeoy8rOrD6EobZcpyCyuJqesFqNBz8Rk2ypPCyGEszgUAJ06dYq77rqLFStW0L9/f2bOnMknn3xCfb19VWAXLVrEW2+9xbvvvsvBgwe54447qKqqYv78+QDcfPPNPPTQQ9bj77jjDkpKSrjnnns4fPgwX3/9NU899RQLFy60+Zqi59OWwCIDjAyNVftG9YQ8IC0Beu5ENTjYn1NGpR39sw7nV2AyK4T5exMX4gtAgjUAcnAGyBIAOXMGCODuiwbh661nZ1YpqQfbLkGhbX8flxTqlPwjIYSwh0MBUGRkJPfddx9paWls2bKFwYMHc+eddxIfH8/dd9/N7t27bbrO3Llzef7553n00UcZM2YMaWlprF692prEnJWVRW5urvX4xMREvvvuO7Zt28aoUaO4++67ueeee3jwwQdtvqbo+Yotu8DCA4wMsQRAPWEnWIZl9ua8QZEkhPphViAtq9Tm8/dbl79CrG0PuroEpm2pd1YOkCY62Jdbp/QD1FygtopVbjiitb+Q5S8hhOt5dfUC48aNIzY2loiICJ5++mmWLVvGP//5T1JSUli6dCnnnHNOh+ffdddd3HXXXW2+tnbt2lbPpaSksHnzZoevKXo+bQksItAHfx/1r7CnJ0KX1TSQX64GbgOjA5mYHMaptBq2ZZZw3iDbAgBtV5WW/wNNS2D5FbXUN5oxetn+O015bYO1orajBRA7cscFA/hwywnS8ytYtfsUV49t6qFlMitssuQHSf6PEMIdHN4F1tDQwIoVK7jssstISkriu+++47XXXiM/P5+MjAySkpK47rrrnDlWIYCmJbCIwOZLYJ4dAGmzP7HBvgT7ejPBkvOy/YTteUBNW+CbAqCIACO+3noUBXLL7JsFyrLM/kQG+hDo0+XfhVoJ8ffmDxcMAODFHw5T39iUu7c/p4yymgaCfLwYaclnEkIIV3IoAPq///s/4uLi+MMf/sDgwYPZtWsXmzZt4vbbbycgIIDk5GSef/55Dh065OzxCmHdBRYR4MOg6CB0OrU3WGFFXSdnuo+WAD0wOhDAmvS7K6uUBpO53fM0JrPCIUue04hmAYNaC8ixZbDjRVr+T/f135o/NZnIQB+yS2pYvi3L+ryW/3PugAi8DD2qGocQ4izh0L88Bw4c4NVXXyUnJ4eXXnqJESNGtDomMjLSKdvlhWiu0WS2FhOMCDTiZzRYE3g9eRZIS4DWAqBB0YGE+HlTXW/iYG7nCdzHCiupbTDjbzTQ74zlKkd3gjXVAHL+8pfG3+jFPdPVVg0vp2ZYyxVoBRAl/0cI4S4OBUCpqanMmzcPH5/2G/B5eXlxwQUXODwwIdqiBT86HYT5q5WDh8RoidCeuxNM274+KEYNgPR6HROS1DpGttQD0pa/hsUFo9frWrymbau3dwZIqwLdnTNAAHMn9iUx3I+iyjr+syGT2gYTWy0lAKYOlPYXQgj3cCgAWrJkSZvNRZctW8YzzzzT5UEJ0R5tB1iYvxGDJRAY0gPygLQcoIFRgdbntDygbcc7zwNqKwFa4+gSmDYDlNzFHmCdMXrpWXTxYADeWHeUNYcKqG80ExPsw4Bm90MIIVzJoQDojTfeYOjQoa2eP+ecc7rclkKIjhRrO8Ca9Y0aFufZW+Gr6xutwckgy2wVwMRkdQZo+4mSdosFarQZoBHxrROGHW2H0TQD1L0BEMBVoxMYEhNEeW0jf1m5F1DbX2jb+YUQwtUcCoDy8vKIi2vdgTgqKqpF3R4hnE3btt28ceaQWHVWRCsU6GmOFaozLeEBxhbjHtknBKOXnqLKemsw0hZFUdhv6QE2vM0ZIPtzgKrqGq1J4327eQkMwKDX8eeZQwAotSxjyvZ3IYQ7ORQAJSYmsmHDhlbPb9iwgfj4+C4PSoj2aDvAIgOb8s/6hvvj662nrtFsXdbxJGcmQGt8vAyM7qPO6HTUFuPk6RrKaxvxNugY3GwGSaMtgeWV17bYat4RrQBieICRED9vm87pqunDohnXN9T6vQRAQgh3cigAWrBgAffeey//+c9/OHHiBCdOnGDZsmXcd999LFiwwNljFMLKugQW2DSTYtA3BQaemAd0xNKnbFB063wXaz2gDgIgbflrUHRQm4UOIwON+HjpMSuQV1Zr05ic3QXeFjqdjgcvHYZeB6MTQ4m1tPMQQgh3cKj62Z///GeKi4u58847rf2/fH19+X//7/+16N0lhLO1tQQG6k6wPSfLOJRXwaUjWy/PulPGGTWAmpuYHMa/gO0d7ATTlr/aSoAGNbBICPPjWGEVJ09X27Sk5cr8n+Ym9Qtn9b3nt8jhEkIId3AoANLpdDzzzDM88sgjHDx4ED8/PwYNGtThtnghnMFaBDGw5d81T94JpgVAg6JbL1+N7xuOTgfHiqooqqxrsbSnsSZAd1AxuU+YvyUAsi0R2h0zQJq2lvGEEMLVulSCNTAwkIkTJzJixAgJfoRLNO8E39xQSyJ0er5nBUB1jSZrx3WtBlBzIf7e1jpG7S2DdTYDBM0SoUttC4C6qwu8EEL0FA43ANq+fTuffPIJWVlZ1mUwzWeffdblgQnRlnaXwCwzQJnFVdTUm/AzGlw+trZkFlVjViDIx4vooLZ/SZiQHMahvAq2ZZ5m1oiWy3dFlXXkl9eh06lFENtj706wzKLu6QIvhBA9hUMzQB9//DFTpkzh4MGDrFy5koaGBvbv38+aNWsICZHGhqL7FLWzBBYV5ENEgBFFadp15QmsO8BiAtuteTOxg0RobfmrX0QAAR00LLWnGGJNvYm8cjVZWmaAhBC9lUMB0FNPPcU//vEPvvzyS4xGIy+//DKHDh3i+uuvp2/fvs4eoxAA1DeaqahVe0lFBrZOotVmgQ7lek4A1JT/037FY20n2L6ccmuvLI1WAbqt+j/Nae0wbCmGmFWizv4E+3oR6u+aLfBCCOFpHAqAjh49yuWXXw6A0WikqqoKnU7Hfffdx5tvvunUAQqh0fJ/DHodwb6tf3BreUCeVBH6SAc7wDQJoX7Eh/hiMiukZZW2eO2ADQnQAImWJbDcsppOu8tnNmuBIZWYhRC9lUMBUFhYGBUV6g+ZhIQE9u3bB0BpaSnV1fZ1pBbCVtryV3iAsVVDUICh2k6wfM9pipqR3/4OsOYm9rP0BTtjO7wtCdCgFoY02lgLyBVd4IUQwtM5FACdf/75/PDDDwBcd9113HPPPSxYsIB58+Yxffp0pw5QCI02A9ReDRlP2wrfaDJzvEgNNjqaAYJmBRFPNOUBVdQ2WOv1nNNGD7Dm9HodfWzsCq9ds58kQAshejGHdoG99tpr1Naqv2U+/PDDeHt7s3HjRq699lr++te/OnWAQmi0TvARbeT/gFpfRqeDosr6dmvquFJWSTX1JjO+3nprjk57tMaoO0+cptFkxsug56AllykuxLfVrre2JIT5cayoyrITLKLd42QGSAghHAiAGhsb+eqrr5g5cyYAer2eBx980OkDE+JMTZ3g2w5s/IwGksL9ySyuJj2vgsiB7g2Amuf/tLVk19zg6CCCfL2oqG3kYG4FI/uEWBOgO1v+0ti6E0zbAp8cKTNAQojey+4lMC8vL/74xz9aZ4CEcJX2agA1Z90J5uRlsHc2HGfZ+uN2nWNtgRHV8fIXqEtYE5LUWSCtMaq2Bb6z5S9NUy2g9gOgukYTOWXq6zIDJITozRzKAZo0aRJpaWlOHooQHWvqBN9RAGSpCJ3nvETonNIaHvvyAE98dYAMO2oMWbfA29j64cw8IFsToDW2FEPMLqlBUSDQx0v6cQkhejWHcoDuvPNOFi1aRHZ2NuPHjycgoOVvkqNGjXLK4IRorqkTfPtLW0O7IRF6W7MChV+k5fCnS4bYdF5HTVDbohVE3JZ5mtoGk/X8czrZAq/RAqBTHbTDyCxq6gEmW+CFEL2ZQwHQDTfcAMDdd99tfU6n06EoCjqdDpPJ5JzRCdGMPUtgh/MrMZkVDJ3k3tiieaf2L9JyWHTx4E6DB7NZsTsAGtUnBKNBT2FFHT8ezKfRrBDq7018iK9N52s5QLlltdZE6jNJDzAhhFA5FAAdP25fLoQQzqDtAutoCSw5IgAfLz01DSaySqrpF9n1H/TNZ4CySqpJyy5lbN+wDs85VVpDTYMJb4OOpHDbko19vQ2M6hPC9hOneWdDJgAj4kNsnqmJCvTBaNBTbzKTV15rDYiaO1EsPcCEEAIcDICSkpKcPQ4hOtXZLjBQq0QPiglk36ly0vPKuxwAldU0WDvMTx0YwYaMYr5Iy+k0AMooVGd/+kUGtDkT054JyeFsP3Ga7SfUWSdb839ATaROCPPjeFEVJ0/XtBkAyQyQEEKoHAqA3nvvvQ5fv/nmmx0ajBDtqak3UV2vLq2GdzADBGpLjH2nyjmUV9Gqu7q9dmadRlEgOcKf28/rz4aMYr7ak8tfLx/WYWBjawXoM01MDmPpuqbvO+sBdqaE0KYAqC0yAySEECqHAqB77rmnxfcNDQ1UV1djNBrx9/eXAEg4nbb8ZTToCeqgKzo4NxFa69A+ITmc8wZFEh5gpKiyjo1Hizl/cFS751m7wNuY/6MZn9RyZsnWLfCajnaC1Tearc8nO2FpUAghejKHtsGfPn26xaOyspL09HTOO+88PvroI2ePUYimNhiBxk5zYpzZEkPrzTUxOQxvg57LR6ozSl+k5XR4nr0J0JpQfyODY9Rz/LwNdi/hWXeCtTEDdKq0BrMCvt56ooPcWyRSCCHczaEAqC2DBg3i6aefbjU7JIQzaPk/trSE0AKgzOIqahsc35FY12giLbsUaNqiPntMPADf7c9r99qKolirQA+KsS8AgqZ6QMPjg+3exdZRNejm+T+yBV4I0ds5LQACtUp0Tk7HvxkL4QitE3xHNYA0UYE+hAcYMStwxJKL44h9p8qobzQTEWC0zsSM6xtGQqgflXWNrDlU0OZ5hRV1VNQ2otfhUBL2teMS8PHSW4Mte1iXwEpbL4GdaFYDSAghejuHcoBWrVrV4ntFUcjNzeW1115j6tSpThmYEM1pS2CRNswA6XQ6hsQEselYMYfyyhnZx748Go22/DUhOcw6Y6LX67hqTDz/WnuUz3ed4rKRrZOstdmfpIgAfLwMdr/v+KRw0p+81KExJ1gCoNzS1rWAtC7wkv8jhBAOBkBz5sxp8b1OpyMqKoqLLrqIF154wRnjEqIFW4ogNjckVg2AupIHpCVAa8tfmtmWAGhteiFl1Q2E+Hu3eP1IvmMJ0M4QHeSLt0FHg0khv6KuRRf6E7IFXgghrBwKgMxms7PHIUSH7FkCg6adYI42RTWbFWstnglnBEBDY4MZGhvEobwKVu/PZe7Evi1e12oAuSMAMuh1xIf6caK4mpMl1S0CoEzZAi+EEFZOzQESortYd4HZMQMEjgdARwsrKa1uwNdb32Yxwqss+Tlt7QY7Yq0B5PoACNruCdZoMpNdYlkCkxkgIYRwLAC69tpreeaZZ1o9/+yzz3Ldddd1eVBCnKmpEaptAdBgSwf2oso6axd5e2j5P2MT1e3vZ7pylBoAbTpWTH55bYvXrF3g7SyC6Cx9QlvvBMspraXRrGD00hMbbFtvMSGEOJs5FAD9/PPPXHbZZa2ev/TSS/n555+7PCghzlRs5xJYgI8XfS09uBzJA2rK/2m75UViuD8TksJQFPhyd9MsUElVvTVfaUC0e2Za2iqGqG2BTwr3R++EBrFCCNHTORQAVVZWYjS2/k3c29ub8vLyLg9KiOYURbEGFbYugUHX8oC2nbAEQP3C2z1mdhvLYNrsT0KoH/5Gh1LsuizBGgA1zQBpCdBJsvwlhBCAgwHQyJEjWb58eavnP/74Y4YPH97lQQnRXFW9ibpGNfHe1iUwaAqAtGKGtsorqyW7pAa9jg6bnl4+Kh4vvY69p8o4akl8zuhCAURnaasYonULvCRACyEE4OAusEceeYRrrrmGo0ePctFFFwGQmprKRx99xKeffurUAQqhLX/5eRvsmlW5cGg0r6zJYPX+PEqq6m3eQr/Nsvw1PD6YwA76joUHGJk2KJKf0gtZlZbDfRcPbuoBFuXOAMhSC6isBpNZwaDXNc0ASQ0gIYQAHJwBuvLKK/n888/JyMjgzjvv5E9/+hMnT57kxx9/bFUjSIiusrcGkGZsYigjE0KobzSzfFu2zedZG6Amtb/8pZk9JgGAVbtzUBTFI2aAYoJ98dKrtYAKKtQEbZkBEkKIlhzeBn/55ZezYcMGqqqqKCoqYs2aNVxwwQXOHJsQQNMOsEg7lr9ALdB5c0oSAB9sPoHJrNh0XlMD1M4DoIuHx+Drred4URV7TpY53ATVmbRaQKAug5nMClnFsgVeCCGacygA2rZtG1u2bGn1/JYtW9i+fXuXByVEc/buAGvuytHxhPl7c6q0hh8P5nd6fHltA4fy1ET+Ce3sAGsuwMeLi4fHAvDfLSfILVNnXAZGuWcLvKb5TrC88lrqTWa8DTriQmQLvBBCgIMB0MKFC8nObr2kcOrUKRYuXNjlQQnRnKNLYAC+3gZumKRWan5vU2anx+/KKsWsQN9wf2JsrJcze7S6G+x/O08BEB3k06o9hqtpFaBPltSQaWmCmhjm36I3mBBC9GYO/Wt44MABxo0b1+r5sWPHcuDAgS4PSojm7C2CeKabJvdFr4MNGcVkFHS8Jd6a/2PD7I/m/MFRhPp7W5fY3Ln8pWm+E8xaA0jyf4QQwsqhAMjHx4f8/NbLCbm5uXh5uaf2iTh7FVepS2CRAfYvgYEaDMwYFgPAe5tOdHjstnYaoHbE6KVv0RXeXS0wmmveDuOEdIEXQohWHIpWLrnkEh566CG++OILQkJCACgtLeUvf/kLF198sVMHKNyvrLqBpT8fpaK2ocPjdOi4dEQsUwZGOvX9S7qwBKa5ZUoy3x/I5387TvLnmUMI8m29RFXfaLbWDLInAAJ1GezDLVkADIxxb/4PtMwB8jcaAEmAFkKI5hwKgJ5//nnOP/98kpKSGDt2LABpaWnExMTw/vvvO3WAwv3e35zJv9YetenY/+08yTd3T3PqbENRF5fAAKYMiGBgdCAZBZX8b8dJbp3ar9Ux+3PKqG0wE+bvzYAo+8Y/MTmcPmF+nDxdw4g2mqe6Wh9LG5BTpTXWXmayBCaEEE0cCoASEhLYs2cP//3vf9m9ezd+fn7Mnz+fefPm4e3t3uRP4Xw7s0oBdct3W53RNT8dKmD3yTLuXZ7Gij+mOC3htkRbAnNgF5hGp9NxS0oSj3yxn/c2neDmlORWPbG2WfN/wtHp7OuXpdfreGf+RI7kV3ZYPdpVYoJ8rLWAjli25ssMkBBCNHE4YScgIIDzzjuPvn37Ul+v/ob+7bffAnDVVVc5Z3TC7RRFYbdlWejOCwd0+MP9ugmJzHrpZ9KyS3l1TQb3XTzYKe+vJUF3ZQkM4OpxfXhmdTrHiqpYn1HE+YOjWrzeVP/HsQBmYHQQA93UAf5MXgY9sSG+1nYYBr3O2iNMCCGEgwHQsWPHuPrqq9m7dy86nQ5FUVr8xmwymZw2QOFeJ0/XUFxVj7dBx7C4jpd2EkL9+PvVI7n7o1289lMG5w+OYnxS12ZDymsaabTsrupqABTo48Wvx/fhnY2ZvLcps0UApChKsx1g9uX/eCptSU772lu2wAshhJVD/yLec8899OvXj4KCAvz9/dm3bx/r1q1jwoQJrF271slDFO60+2QpAMPigvH1NnR6/FWj45kzJh6TWeG+5WlU1jV26f21HWCBPl42vX9nfmupDJ16qIDskmrr80cLqzhd3YCPl54R8SFdfh9PoG2FB+kCL4QQZ3IoANq0aRNPPPEEkZGR6PV6DAYD5513HkuWLOHuu+929hiFG2nLX6P7hNp8zhNzRpAQ6kdWSTVPfLm/S++vFUHsSgJ0cwOiApk2KBJFUdtjaLTZnzGJoRi9zo6Zkj7NlrykB5gQQrTk0L/0JpOJoCA11yEyMpKcnBwAkpKSSE9Pt/t6r7/+OsnJyfj6+jJ58mS2bt3a7rHvvPMOOp2uxcPXt2XF3ltvvbXVMbNmzbJ7XAJ2Z5cBMDox1OZzgn29efH60eh08Mn2k6zel+vw+1vbYHRx+au5W1KSAfh4WzY19epyrT39v3oKmQESQoj2ORQAjRgxgt27dwMwefJknn32WTZs2MATTzxB//797brW8uXLWbRoEYsXL2bnzp2MHj2amTNnUlBQ0O45wcHB5ObmWh8nTrQubjdr1qwWx3z00Uf2fUhBo8nM3lNqADQm0b5locn9I7jjggEAPPjZXvLLax0aQ1MbDMd3gJ3pV0Oj6RPmR1lNA6t2q+0rtp+wvwK0p9PaYYDMAAkhxJkcCoD++te/YjabAXjiiSc4fvw406ZN45tvvuGVV16x61ovvvgiCxYsYP78+QwfPpylS5fi7+/PsmXL2j1Hp9MRGxtrfcTExLQ6xsfHp8UxYWFnzw82VzlSUElNg4kgHy/6R9pf3fjeGYMZkRBMaXUD93+6G7ON3dibc7QTfEcMeh2/PVfNBXp34wkKyms5UVyNXkeXk7Y9SfMlMJkBEkKIlhwKgGbOnMk111wDwMCBAzl06BBFRUUUFBRw0UUX2Xyd+vp6duzYwYwZM5oGpNczY8YMNm3a1O55lZWVJCUlkZiYyOzZs9m/v3Weydq1a4mOjmbIkCHccccdFBcXt3u9uro6ysvLWzwE1qrIoxJDWtXMsYXRS89Lc8fi663nlyNFvLMx0+5rNHWCd14ABDB3YiI+XnoO5JazdN0xAIbGBrdZIbqnigvxJT7El5hgH/qGywyQEEI057Rsz/Bw+4vHFRUVYTKZWs3gxMTEkJeX1+Y5Q4YMYdmyZXzxxRd88MEHmM1mpkyZwsmTJ63HzJo1i/fee4/U1FSeeeYZ1q1bx6WXXtru9vwlS5YQEhJifSQmJtr1Oc5WjiRAn2lgdCAPXz4cgKdXHyI9r+NmpGfqjiUwgFB/I3PGJADwn43HAcfr/3gqL4Oe7+47nx8WXXDWJHYLIYSz9Lh/FVNSUrj55psZM2YMF1xwAZ999hlRUVG88cYb1mNuuOEGrrrqKkaOHMmcOXP46quv2LZtW7tb9B966CHKysqsj+zsbBd9Gs+mzQDZkwDdlt9M7stFQ6OpbzRzz8e7qGu0vU5UdyyBabQt8YplZe5sqf/TXJCvN8Fn0ayWEEI4i1sDoMjISAwGQ6vO8vn5+cTGxtp0DW9vb8aOHUtGRka7x/Tv35/IyMh2j/Hx8SE4OLjFo7errm/kcL46WzOmiwGQTqfjmWtHERFg5FBeBc+ttn2noFYHKMLJM0AAIxJCmNAs5+dsSoAWQgjRMbcGQEajkfHjx5Oammp9zmw2k5qaSkpKik3XMJlM7N27l7i4uHaPOXnyJMXFxR0eI1rad6ocswKxwb7EBPt2fkInooJ8ePbXowD4z8ZMTluWtjrjjE7wHbllSjKgNgqNC5FWEUII0Vs43AvMWRYtWsQtt9zChAkTmDRpEi+99BJVVVXMnz8fgJtvvpmEhASWLFkCqLvOzj33XAYOHEhpaSnPPfccJ06c4PbbbwfUBOnHH3+ca6+9ltjYWI4ePcoDDzzAwIEDmTlzpts+Z09jzf+xc/t7R6YPi2FwTCCH8yvZfKyYS0d2HJCazIo1AOqOJTCAK0bFUVXXyHAP6OAuhBDCddweAM2dO5fCwkIeffRR8vLyGDNmDKtXr7YmRmdlZaHXN01UnT59mgULFpCXl0dYWBjjx49n48aNDB+uJtoaDAb27NnDu+++S2lpKfHx8VxyySX87W9/w8fH+csoZ6s0SwuMrub/nGnKgEgO51eyPqOo0wCotLoebed8WDfNAOl0Om6Y1Ldbri2EEMJz6RRFsb84y1muvLyckJAQysrKem0+0HnPrOHk6Ro+vH0yUwZGOu26Px7I5/b3tpMc4c/aP/+qw2OP5Fdw8T9+JsTPm92LL3HaGIQQQpyd7Pn53eN2gYnuV1RZx8nTNeh0MLKPcxuDTu4fjkGvI7O4mpOnqzs8tqjSuX3AhBBCCI0EQKIVLf9nYFSg0wsDBvl6M9oSVG3MaL84JTQlQEd2ww4wIYQQvZsEQKKV3U6q/9Oe8yxLahuOFnV4nLYFvrt2gAkhhOi9JAASraSdtL8DvD2magFQRhEdpaDJEpgQQojuIgFQD6QoCk9+dYAXv7e9oKA919ZmgMZ0oQVGR8b2DcPP20BRZT3p+e23xiixFkGUAEgIIYRzSQDUA+07Vc6/1x/nlTUZNhcUtNWJ4mrKahoweukZEhvk1GtrjF56JvVT205s6CAPqNg6AyQ5QEIIIZxLAqAe6MeDTa1DjhVVOfXauy31f86JD+7WBppTB0YA6jJYe4plCUwIIUQ3kQCoB0o91CwAKqx06rXTnNAB3hZaHtCWY8U0mMxtHiNJ0EIIIbqLBEA9TG5ZDftOlVu/d/oMkCUAGts31KnXPdOw2GDCA4xU1Zus73mmYmsbDFkCE0II4VwSAPUwqQcLWnx/vNB5AVB9o5l9OWpw1d0zQHq9jpQB6jLY+jaWwRpMZkqrGwBJghZCCOF8EgD1MKmW/B8tifhYkfOWwNLzKqhvNBPi501ShL/Trtue85pthz/T6Wp19keng1B/CYCEEEI4lwRAPUh1fSMbjqq7pv5wfn8AMourMZmd086teQNUnU7nlGt2RAuAdmWVUlXX2OI1LQE63N+IQd/9YxFCCNG7SADUg/xypIj6RjOJ4X5cOCQao0FPfaOZnNIap1y/qf6Pc/t/tScx3J/EcD8azQpbj5e0eE12gAkhhOhOEgD1INry14xhMRj0Ousy1VEn7QTr7hYYbdFmgc7MA5IdYEIIIbqTBEA9hNmssOaQmgA9Y1gMAP2jAgA47oSdYBW1DWRYAqlR3ZwA3dyUAW3nAUkRRCGEEN1JAqAeIu1kKUWV9QT5eDExWU2A7h8VCMAxJ+wE23uqDEWBhFA/ooJcF3RMsewEO5RXQWFFnfV5bQYoUmaAhBBCdAMJgHoIbfnr/CFR1grN/SLVGSBn7ATbna02QB3TzfV/zhQR6MPwuGAANjbrDl9iqQEUHiAzQEIIIZxPAqAe4scD6vLXxZblL4AB2hKYE2aA0rJPA93XALUjWluMjc36gkkneCGEEN1JAqAeILukmvT8Cgx6HRcOibI+3z9SXQLLKaulur6xvdNtos0AuTIBWjO1WSK0oqhb+kusVaAlABJCCOF8EgD1ANry1/iksBZFAcMCjIT6ewNdS4TOK6slr7wWvQ5GJAR3bbAOmNQvHG+DjlOlNWSVVANQXKntApMlMCGEEM4nAVAP8OPB1stfmv6RXd8JpnWAHxwThL/Ry+HrOMrf6MXYvmFA03Z4qQMkhBCiO0kA5OEqahvYclzNjZk+LLrV687YCWYtgOiG5S9N87YYdY0mKiyVoaUPmBBCiO4gAZCH+/lwEQ0mhf6RAdZgpznrTrAuFEPc3awFhrtoeUAbjxZbE6C99DqCfb3dNiYhhBBnLwmAPNyPWvXn4a2Xv6DZTjAHl8DMZoU9WgK0G3aAaUb3CSHQx4vS6gZ+OVwIqFWg9dIHTAghRDeQAMiDNZrM/JSu5v9MH9p6+QtaLoFpO6jscayoioq6Rvy8DQyOaT3D5CpeBj3n9lcLPK7anQNIGwwhhBDdRwIgD7Yzq5TS6gZC/LwZnxTW5jF9w/3R6aCirpHCyro2j+mIlv8zMiEEL4N7/zpobTE2HVNzniKlDYYQQohuIgGQB9OWv341JKrd4MTX20CfMD/AsYKIadYGqK7pAN+R8wapAZA2kSU7wIQQQnQXCYA8WGf5PxqtIOIxB/KAdpywVIBObHuGyZUGRQe26EMmS2BCCCG6iwRAHupYYSXHCqvw0us4f3BUh8c6uhOsvLaBQ3nlAExMdn8ApNPpmGppjgqyBCaEEKL7SADkoVItxQ8n9w/vdCu4ozvBdmWVYlYgKcKf6GBfxwbqZNp2eJAaQEIIIbqPBEAeyrr81Ub15zM5Wgxx2/ESACYkhds5uu7TPACSJTAhhBDdRQIgD1RW3cB2S26OLQGQtgSWVVJNg8ls8/tsy1QDIE9Y/tLEh/oxMiEEvQ4GRrtvW74QQoizm+sbP4lOrT1cgMmsMDgmkMRw/06Pjw32xc/bQE2DieyS6jYrRp+pvtFs3QE2IdlzZoAA/n3LBPLKam36HEIIIYQjZAbIA/1wwPblLwC9XtcsEdq2ZbB9OWXUNZoJ8/e25hB5iphgX7e25RBCCHH2kwDIw9Q3mllnaQUx3cYACKCfJYg5VmTbTrDtluWvCcnh6HTSbkIIIUTvIktgHqK+0czKXSdZuu4YFbWNRAQY7erOPiDSvp1g2zLVHKNJHrb8JYQQQriCBEBuVlXXyEdbs/j3L8fJK68FIMTPmyfnjMBgRyNQbQboqA1LYIqiNJsB8pwEaCGEEMJVJAByk9Lqet7ZmMk7GzMprW4AICbYhwXT+jNvUl8CfOz7o7FWg7YhADpaWMnp6gZ8vfWcE+/+FhhCCCGEq0kA5GJ5ZbX8+5djfLg1i+p6EwDJEf784YIBXDMuAR8vg0PX1WaAiirrKK9t6LB4orb8NSYxFKOXpIEJIYTofSQAcqHXf8rg5R+PUG+p1TMsLpg7LxzAZSPj7FruakuwrzeRgT4UVdZxvLCqw11UTfV/JP9HCCFE7yQBkAvFBvtSbzIzKTmcO341gAsHRzl1B1b/qACKKus4VlTZYQC03TID5Gn1f4QQQghXkQDIha4aE09ypD/ju6n1xICoALYeL+F4B3lA+eW1ZJVUo9fBuL6h3TIOIYQQwtNJAogLeRv03Rb8QFNLjKMdbIXXZn+GxgYT1EmTVSGEEOJsJQHQWUTbCdbRDJCW/zOpnyx/CSGE6L0kADqL9I9qKoZoNittHrNN6v8IIYQQEgCdTRLD/fHS66hpMFmLKjZXUdvAwdxyACZ041KcEEII4ekkADqLeBv09LV0j2+rJcaurFLMCiSG+xEb4uvq4QkhhBAeQwKgs4y2DHassHVTVK39xUSZ/RFCCNHLSQB0lrHuBGsjEXqb1P8RQgghAAmAzjr9oyw7wc5YAmswmdmVrQZAEyUBWgghRC8nAdBZpr9lBuhYUcslsH2nyqhtMBPq780AS5AkhBBC9FYSAJ1ltKaoJ0/XUNtgsj5vbX+RFIa+i33HhBBCiJ5OAqCzTFSgD0E+XigKZJVUW5+XBqhCCCFEEwmAzjI6na7VTjBFUdh+QhKghRBCCI1HBECvv/46ycnJ+Pr6MnnyZLZu3druse+88w46na7Fw9e3ZU0bRVF49NFHiYuLw8/PjxkzZnDkyJHu/hge48ydYMeKqiipqsfHS8+IhGB3Dk0IIYTwCG4PgJYvX86iRYtYvHgxO3fuZPTo0cycOZOCgoJ2zwkODiY3N9f6OHHiRIvXn332WV555RWWLl3Kli1bCAgIYObMmdTWtq6OfDY6cyeYVv9ndGIoPl4Gt41LCCGE8BRuD4BefPFFFixYwPz58xk+fDhLly7F39+fZcuWtXuOTqcjNjbW+oiJibG+pigKL730En/961+ZPXs2o0aN4r333iMnJ4fPP/+8zevV1dVRXl7e4tGTnbkEptX/ke3vQgghhMqtAVB9fT07duxgxowZ1uf0ej0zZsxg06ZN7Z5XWVlJUlISiYmJzJ49m/3791tfO378OHl5eS2uGRISwuTJk9u95pIlSwgJCbE+EhMTnfDp3KefdSu8OgPU1ABV8n+EEEIIcHMAVFRUhMlkajGDAxATE0NeXl6b5wwZMoRly5bxxRdf8MEHH2A2m5kyZQonT54EsJ5nzzUfeughysrKrI/s7OyufjS30gKg0uoG0vMqOFFcjU4H4/rKDJAQQggB4OXuAdgrJSWFlJQU6/dTpkxh2LBhvPHGG/ztb39z6Jo+Pj74+Pg4a4hu52/0Ij7El5yyWj7ZrgZzQ2KCCPHzdvPIhBBCCM/g1hmgyMhIDAYD+fn5LZ7Pz88nNjbWpmt4e3szduxYMjIyAKzndeWaZwOtIOLKXacAmNRPlr+EEEIIjVsDIKPRyPjx40lNTbU+ZzabSU1NbTHL0xGTycTevXuJi4sDoF+/fsTGxra4Znl5OVu2bLH5mmeD/pHqTrCSqnpA8n+EEEKI5ty+BLZo0SJuueUWJkyYwKRJk3jppZeoqqpi/vz5ANx8880kJCSwZMkSAJ544gnOPfdcBg4cSGlpKc899xwnTpzg9ttvB9QdYvfeey9PPvkkgwYNol+/fjzyyCPEx8czZ84cd31Ml9N2gmlkB5gQQgjRxO0B0Ny5cyksLOTRRx8lLy+PMWPGsHr1amsSc1ZWFnp900TV6dOnWbBgAXl5eYSFhTF+/Hg2btzI8OHDrcc88MADVFVV8fvf/57S0lLOO+88Vq9e3apg4tlMS4QGSAj1Iy7Ez42jEUIIITyLTlEUxd2D8DTl5eWEhIRQVlZGcHDPrJycXVLNtGd/AmDOmHheumGsm0ckhBBCdC97fn67vRCi6B7xoX4YvdQ/Xsn/EUIIIVqSAOgsZdDrmDYwkgCjgQuHRLl7OEIIIYRHcXsOkOg+r980jtoGE6H+RncPRQghhPAoEgCdxXy9Dfh6S/NTIYQQ4kyyBCaEEEKIXkcCICGEEEL0OhIACSGEEKLXkQBICCGEEL2OBEBCCCGE6HUkABJCCCFEryMBkBBCCCF6HQmAhBBCCNHrSAAkhBBCiF5HAiAhhBBC9DoSAAkhhBCi15EASAghhBC9jgRAQgghhOh1JAASQgghRK8jAZAQQggheh0JgIQQQgjR60gAJIQQQohex8vdAxBCCCGEAxpq4dhaGDgdDN5du1bWFgiIhIgBXbtOVTEc+Q7MjZ0fGzMCEsZ17f26QAIgIYQQoif67iHYvgym3gsXP+74dXJ2wbKZYAyAW7+C+LGOXafsFLx9CZSftO348xZJACSEEEIIO1Tkw64P1K+3vQ3TFoFviGPX2vAKoEB9JXzwa/jd9/bPBFWXwPtXq8FPcB+IHdH5OZGDHRqus0gAJIQQQvQ0W98AU736dX0F7HgHpt5j/3VKjsOBz9WvIwZCcQa8Pwdu+x6C42y7Rn0VfHg9FKVDUDzcthpCE+0fi4tJErQQQgjRk9RVqrM+AEOvUP+7eSk01tt/rc3/BMUMA6bD/NUQ3h9Ks+CDa6DmdOfnmxrgk5vh5DbwDYXfruwRwQ9IACSEEEL0LLveh9pSNVi59t8QGAsVObBvhX3XqS5pWkabejcERqkBTGAsFByAD2+A+ur2zzeb4fM7IeNH8PaHmz6F6KEOfyxXkwBICCGE6ClMjbDpn+rXKXeBtx+c+0f1+42vgqLYfq1t/4aGaogdBf0uUJ8LS4bffqbmE2VvhhXz1VmeMykKfPcX2PsJ6L3g+vcgcVKXPpqrSQAkhBBC9BQHPoeyLPCPhDE3qs+Nnw/GQHXWJuNH267TUANb3lC/nnoP6HRNr8WcA/OWg5cvHF4Nq+5uHVitfxG2/Ev9es6/YNDFXfpY7iABkBBCCNETKApseFn9evIf1NkfAL9QGH+r+rX2emd2fwTVRRDSF4bPaf16Ugpc9w7oDLD7Q/jh0abXdrwLqU+oX896GkZdb/9n8QASAAkhhBA9wfF1kLdHzbeZeHvL1869Q12KyvxFrevTEbMJNr6mfp1yJxja2RA+5FKYbTlu4ytqcHXwS/jqXvW5aX9S37eHkm3wQgjnUBTI2alWd/XycfdoejZTA+TuUQvS6eX31C6pq4DjP6s/9DviGwLJ07p+vwsPg38EBER07Tpt2fCK+t+xvwH/8JavhfSBEdfCnuXqcdf9p/3rpH8DJUfVXVtjf9vxe465EaqK4IdH1Fkgvbe6a2zczXDRI136OO4mAZAQousUBb66D3b8BxLGw82rwCfQ3aPqmRpq4b+/Vn+TH3k9XP2GBEGOqsiHZZfA6Uzbjh8+B369DPQGx95vzyfw2QLwC4fbvoMoJxb6y9sHR1NBp4eUhW0fM+X/1ADowOdwerGa0NwWLZCa+Dvb/j+dejdUFaqzQOYGdev95f9omTfUA0kAJIToup+eUoMfgFM71Log8z4GL6N7x9XTmE3wv9+pwQ+oO2z8I2DWkh7/w8blasvgg2vV4CcgCsI7qWx8aocaOHwbCZc9b//9PvIDfG5ZDqopUevo3PYdhCQ4MvrWNr6q/nf47PYDm9iRMOAiOLpG3Sl22bOtj8naDCe3gsEIk/5g+/tf/AT4BkNlofp1e8tmPUjP/wRCCPfa8gb8bPmH9tyFaiB0NFX9YXDNWzJ7YSttFu3QV5YfTr+HTa+pO20Co9R8C2Gbhlr4aB7k71WDn9u+67y1w77/wYrfqVvDA6Lgwgdtf7/srbD8t2oD0OFzIH+fWlH5g2tg/retl6vsVXayqcbPlLs7PnbK3WoAtOt99TOc+d7a7M/oGyAoxvYx6HRw/p9tP74HkH+ZhBCO27sCvn1A/fpXD8Osp+D699VkzH0rYPWD9tUl6c3WPAk731WXOK59G2b+HWYuUV9LfULdeSM6Z2pUZ9FObACfYPjN/2zrazXiWrjsOfXrtUtg61u2vV/BIfjvddBYAwNnqEH/b1dCUBwUHoIP56qtIrpi87/U4Cp5WufNQ/tfqM4ENVQ3VYvWFB1R838AUv6va2M6C0gAJIRwTMaPsNIyhT7p902/HQ6aAXOWql9vfQN+ed494+tJNv+r6T5d/iIMv0r9OuVOtWM2qDtvDn7pluH1GIqi3qdDX4HBB274EOJG237+pAVwgWXm55s/w77POj6+NFud5akthYQJajFALyOE9lWDIN9Qdbnpk1vaLiZoi9qypuC3s9kfUGdqplh6gm19Q50N02x8FVBgyGXOzU/qoSQAEkLY7+R2WH6z+lvpiGth1jMtcyZGXac+B+rMxvYOdqT0dns+UWfKAC76K0yY3/L16Y+qO3UUs7pEc/wX14+xp0h9Ql360enh129Dv2n2X+PCB2HC7wAFPvs9HP2p7eOqitXgp/wURA5R20AYA5pejx4GN34CXn6Q8YPaMsJstn882/+jNjuNGmZ7scFz5kBIopq4vPsj9bnKAtj9sfq1LYFULyABkBDCPoXp6pR/Q5WacDlnadt5Puf+Eabdr3799SI48IVrx9kTHPmxKXF2crP71ZxOB1e8pO68MdWpuS25u106zB5h0+tqdWJQ79ewKx27jk6nLoUNn6PuePr4JjVBurm6SvjwOig6DMF91NYRbeX59J2szgrpDGpC+3d/sW9JuLEetlhmU6f8n+2J2QZvOPdO9etNr6mB15Y31L8/fSZC33NtH8NZTAIgIYTtyk7C+9eou1wSxqv5Ph3t9LrorzDuFnX24n+3q/VYhCp7G3xiSZwd8Ws136e9H3AGLzUvKOk8dTbgg2uh+Khrx+vJdi9XgwtQZ8zG39K16+kNcM2ban+shio14C86or7WWK/+uZ3aoW53/+1KtQZPewZfAnMsvbu2/KspSLPF3k+hIlfNJxp5nX2fYdzNam2j4gw1H2/bv9Xnp9wtOwotJAASQtimukQNfspPQsQguPHTzmuI6HRwxT/U38ZN9fDRjZCT5pLherSCQ+oMQkM1DJiu9lLqbLecty/M+xBiRqpLGx9cAxV5rhmvJzv8PXxhme04t1nOVFd5+cAN/4W4MVBdDO9frf4C8Pkf1V1W3gFw0wrbcmlG3wAzn1K/tjWhXVGatr5P/qP9JSV8Ai1LecCq/2vqHj/0cvuucxbTKYps0ThTeXk5ISEhlJWVERwc7O7hiJ6ioUb9rVBxYJ3fETq9Oguj9QNyVEMtnNre8bgVRf2H+9R2CIqH330PoYn2vYdW3M8/Ema/Dkb/ro27p2qsgy/vUXNHEibALata5o50pnlxv5gR6m4xXS/9XbayAL64S92B1V1FI6uKYNlMdSbFGKTOwOm94cblMHC6fdf68TFY/w/1z+uy5yCyg+Cp4KC6w9IYBIv2q7M59qrIg5dGqr98AFz+QusWGmcZe35+SwDUBgmAhN0qC9R/JEuOufZ9w/qpNU7sqefRXGUh/GeW+o+7LXxD1feLHmr/e9WWwzuXq72MhJo4e9tqx2rElByDt2dCVYHzx9UTDbwY5n2k5r50h9Mn1P+/K3IBHVz7bxj5a/uvoyiw6i7Y9YHt56TcpQa5jvriLjUx3D8C7tvf9V+YPJwEQF0kAZCwS22Z5Qf7XvW3tKA417xvRZ46rR0zEuZ/bf9viLXl8O4VakKtLeP2C4dLnoQ+4x0eMpUFarE/WwOus1VoElzxYse5I53J26fuHqsqdN64eqL4cXD58/bNojmi4CB8/4ga+Iy+wfHrmBrVnlpHUzs/NiAKfv0ftRCmo8pOqUHXuJvhnKsdv04PIQFQF0kAJGzWUKsmpJ5Yb3vFWWdpPguQNBV+85maJ2KLxjp1Ser4z+qS1G3fQeTA7h2vEEJ0M3t+fvfShWMhnMBacXa9uk5va8VZZwnvr76nT7Ba9fZ/v1PH1BmzSW3YePxnMAbCb1ZI8COE6HUkABLCEYoCXzfr2zTvI/sqzjpL3ChL7oOPOpav7u24zoiiwNd/UmvyGIxqpdz4sS4brhBCeAoJgIRwxJq/wc73mvo2OVJx1lmSz4NfL1PHsut9dbdWe9YusXRt16k9i/pf4LJhCiGEJ5EASAh7bfon/PKC+vUV/2jq2+ROw66AK19Wv17/oloV90xb3oR1lvYUl7+glssXQoheSgIgIeyx5xP47iH164segfG3unU4LYy7GaYvVr/+7i9qdVzNmV3bJ/7O9eMTQggP4uXuAQjRYxz5oVnfpjtg2p/cO562nHefWrht8+tqdVy/MLWs/8o/AkrLru1CCNGLSQAkeiZFUcvTB0S65v2yt8JyS9+mkdepZe09sZ+OTqfW6qkugj3L4ZOb1dwgc0PbXduFEKKXkiUw0fOYGuHTW+C5AU25ON2p4BB8eL1abn/gDJj9T+eX23cmvV5tNTHwYnXMnXVtF0KIXsgj/jV8/fXXSU5OxtfXl8mTJ7N161abzvv444/R6XTMmTOnxfO33norOp2uxWPWrFndMHLhcoqibvU+8IX6feoTsOOd7nu/0my1CWLNabVv0/Xv2d+U0B0M3nD9u+qsz/DZnXdtF0KIXsbtAdDy5ctZtGgRixcvZufOnYwePZqZM2dSUNBxj5vMzEzuv/9+pk1re/vxrFmzyM3NtT4++uij7hi+cLXUJ9St3jo9DL1Cfe6r++DAKue/V5WlA3RFjtq36aZPu7/cvjMZA9Tt8de/13nXdiGE6GXcHgC9+OKLLFiwgPnz5zN8+HCWLl2Kv78/y5Yta/cck8nETTfdxOOPP07//v3bPMbHx4fY2FjrIywsrLs+gnCVTa+rW7wBrngJ5n4A425Ru5j/73dqZWNnqatUW0UUH4HgPvDbzxxrWimEEMIjuTUAqq+vZ8eOHcyYMcP6nF6vZ8aMGWzatKnd85544gmio6P53e/a38q7du1aoqOjGTJkCHfccQfFxcXtHltXV0d5eXmLh/Awu5erW7sBpj8K429Rk3mv+AcMuxJM9fDRjWpjz65qrIflv4GcnWoD0N+u7FrTSiGEEB7HrQFQUVERJpOJmJiYFs/HxMSQl5fX5jnr16/n7bff5q233mr3urNmzeK9994jNTWVZ555hnXr1nHppZdiMpnaPH7JkiWEhIRYH4mJiY5/KOF8h79Xt3QDnHsnnLeo6TW9Aa75NyRPg/oKtTFp8VHH38tshpV/gGM/gXcA3LQCogZ3bfxCCCE8jtuXwOxRUVHBb3/7W9566y0iI9vf/nzDDTdw1VVXMXLkSObMmcNXX33Ftm3bWLt2bZvHP/TQQ5SVlVkf2dnZ3fQJhN2ytqhbuc2NMPJ6uOTvrbdxe/uqPa1iR0FVoSVvp+0AukOKAqv/H+z/DPTeMPd96DPeOZ9DCCGER3FrHaDIyEgMBgP5+fktns/Pzyc2NrbV8UePHiUzM5Mrr7zS+pzZbAbAy8uL9PR0Bgxo3Y27f//+REZGkpGRwfTp01u97uPjg4+PT1c/jnC2/APw4XWW7ecXw5wOtp/7Bqud0d++BE4fh/evgfnfgF+o7e/383Ow9U1AB1cvhYGt/64IIYQ4O7h1BshoNDJ+/HhSU1Otz5nNZlJTU0lJSWl1/NChQ9m7dy9paWnWx1VXXcWvfvUr0tLS2l26OnnyJMXFxcTFxXXbZxFOVpoFH1wDtWXQZ6K6pdvg3fE5gdFqvk5gDBTsh49ugIYa295v29vw09/Vry99Bkb+umvjF0II4dHcXgl60aJF3HLLLUyYMIFJkybx0ksvUVVVxfz58wG4+eabSUhIYMmSJfj6+jJixIgW54eGhgJYn6+srOTxxx/n2muvJTY2lqNHj/LAAw8wcOBAZs6c6dLP1kptmfoQHauvhuU3QUUuRA2FGz+xfft5eD/4zWfwn8sgaxN8Ol8NaDqqfnxiI3xtaWtx/gMw+Q9d/wxCCCE8mtsDoLlz51JYWMijjz5KXl4eY8aMYfXq1dbE6KysLPR2VK81GAzs2bOHd999l9LSUuLj47nkkkv429/+5v5lrm1vQ+rj7h1DTxLcRw1m7N1+HjsCbvxYzQU6/K36sMX4+fCrv9g/TiGEED2OTlEUxd2D8DTl5eWEhIRQVlZGcHCw8y684ZWmZRbRsfABcN07XduBlb4avl6k9gzriE4Po+bC5S+ou8qEEEL0SPb8/JYAqA3dFgAJIYQQotvY8/O7R22DF0IIIYRwBgmAhBBCCNHrSAAkhBBCiF5HAiAhhBBC9DoSAAkhhBCi15EASAghhBC9jgRAQgghhOh1JAASQgghRK8jAZAQQggheh0JgIQQQgjR60gAJIQQQoheRwIgIYQQQvQ6EgAJIYQQoteRAEgIIYQQvY6XuwfgiRRFAaC8vNzNIxFCCCGErbSf29rP8Y5IANSGiooKABITE908EiGEEELYq6KigpCQkA6P0Sm2hEm9jNlsJicnh6CgIHQ6nVOvXV5eTmJiItnZ2QQHBzv12qI1ud+uJffbteR+u5bcb9dy5H4rikJFRQXx8fHo9R1n+cgMUBv0ej19+vTp1vcIDg6W/4FcSO63a8n9di25364l99u17L3fnc38aCQJWgghhBC9jgRAQgghhOh1JAByMR8fHxYvXoyPj4+7h9IryP12LbnfriX327XkfrtWd99vSYIWQgghRK8jM0BCCCGE6HUkABJCCCFEryMBkBBCCCF6HQmAhBBCCNHrSADkQq+//jrJycn4+voyefJktm7d6u4hnRV+/vlnrrzySuLj49HpdHz++ectXlcUhUcffZS4uDj8/PyYMWMGR44ccc9gzwJLlixh4sSJBAUFER0dzZw5c0hPT29xTG1tLQsXLiQiIoLAwECuvfZa8vPz3TTinu1f//oXo0aNshaDS0lJ4dtvv7W+Lve6ez399NPodDruvfde63Nyz53nscceQ6fTtXgMHTrU+np33msJgFxk+fLlLFq0iMWLF7Nz505Gjx7NzJkzKSgocPfQeryqqipGjx7N66+/3ubrzz77LK+88gpLly5ly5YtBAQEMHPmTGpra1080rPDunXrWLhwIZs3b+aHH36goaGBSy65hKqqKusx9913H19++SWffvop69atIycnh2uuucaNo+65+vTpw9NPP82OHTvYvn07F110EbNnz2b//v2A3OvutG3bNt544w1GjRrV4nm55851zjnnkJuba32sX7/e+lq33mtFuMSkSZOUhQsXWr83mUxKfHy8smTJEjeO6uwDKCtXrrR+bzabldjYWOW5556zPldaWqr4+PgoH330kRtGePYpKChQAGXdunWKoqj319vbW/n000+txxw8eFABlE2bNrlrmGeVsLAw5d///rfc625UUVGhDBo0SPnhhx+UCy64QLnnnnsURZG/3862ePFiZfTo0W2+1t33WmaAXKC+vp4dO3YwY8YM63N6vZ4ZM2awadMmN47s7Hf8+HHy8vJa3PuQkBAmT54s995JysrKAAgPDwdgx44dNDQ0tLjnQ4cOpW/fvnLPu8hkMvHxxx9TVVVFSkqK3OtutHDhQi6//PIW9xbk73d3OHLkCPHx8fTv35+bbrqJrKwsoPvvtTRDdYGioiJMJhMxMTEtno+JieHQoUNuGlXvkJeXB9DmvddeE44zm83ce++9TJ06lREjRgDqPTcajYSGhrY4Vu654/bu3UtKSgq1tbUEBgaycuVKhg8fTlpamtzrbvDxxx+zc+dOtm3b1uo1+fvtXJMnT+add95hyJAh5Obm8vjjjzNt2jT27dvX7fdaAiAhhMMWLlzIvn37WqzZC+cbMmQIaWlplJWVsWLFCm655RbWrVvn7mGdlbKzs7nnnnv44Ycf8PX1dfdwznqXXnqp9etRo0YxefJkkpKS+OSTT/Dz8+vW95YlMBeIjIzEYDC0ylzPz88nNjbWTaPqHbT7K/fe+e666y6++uorfvrpJ/r06WN9PjY2lvr6ekpLS1scL/fccUajkYEDBzJ+/HiWLFnC6NGjefnll+Ved4MdO3ZQUFDAuHHj8PLywsvLi3Xr1vHKK6/g5eVFTEyM3PNuFBoayuDBg8nIyOj2v98SALmA0Whk/PjxpKamWp8zm82kpqaSkpLixpGd/fr160dsbGyLe19eXs6WLVvk3jtIURTuuusuVq5cyZo1a+jXr1+L18ePH4+3t3eLe56enk5WVpbccycxm83U1dXJve4G06dPZ+/evaSlpVkfEyZM4KabbrJ+Lfe8+1RWVnL06FHi4uK6/+93l9OohU0+/vhjxcfHR3nnnXeUAwcOKL///e+V0NBQJS8vz91D6/EqKiqUXbt2Kbt27VIA5cUXX1R27dqlnDhxQlEURXn66aeV0NBQ5YsvvlD27NmjzJ49W+nXr59SU1Pj5pH3THfccYcSEhKirF27VsnNzbU+qqurrcf88Y9/VPr27ausWbNG2b59u5KSkqKkpKS4cdQ914MPPqisW7dOOX78uLJnzx7lwQcfVHQ6nfL9998riiL32hWa7wJTFLnnzvSnP/1JWbt2rXL8+HFlw4YNyowZM5TIyEiloKBAUZTuvdcSALnQq6++qvTt21cxGo3KpEmTlM2bN7t7SGeFn376SQFaPW655RZFUdSt8I888ogSExOj+Pj4KNOnT1fS09PdO+gerK17DSj/+c9/rMfU1NQod955pxIWFqb4+/srV199tZKbm+u+Qfdgt912m5KUlKQYjUYlKipKmT59ujX4URS5165wZgAk99x55s6dq8TFxSlGo1FJSEhQ5s6dq2RkZFhf7857rVMURen6PJIQQgghRM8hOUBCCCGE6HUkABJCCCFEryMBkBBCCCF6HQmAhBBCCNHrSAAkhBBCiF5HAiAhhBBC9DoSAAkhhBCi15EASAghhBC9jgRAQghhh8cee4wxY8a4exhCiC6SAEgI4TEKCwsxGo1UVVXR0NBAQEAAWVlZ7h6WEOIsJAGQEMJjbNq0idGjRxMQEMDOnTsJDw+nb9++7h6WEOIsJAGQEMJjbNy4kalTpwKwfv1669ea0tJSbr/9dqKioggODuaiiy5i9+7d1te15ak33niDxMRE/P39uf766ykrK7MeYzabeeKJJ+jTpw8+Pj6MGTOG1atXt3ifkydPMm/ePMLDwwkICGDChAls2bKlxTHvv/8+ycnJhISEcMMNN1BRUWF9bcWKFYwcORI/Pz8iIiKYMWMGVVVVTrtPQoiu83L3AIQQvVtWVhajRo0CoLq6GoPBwDvvvENNTQ06nY7Q0FBuvPFG/vnPf3Ldddfh5+fHt99+S0hICG+88QbTp0/n8OHDhIeHA5CRkcEnn3zCl19+SXl5Ob/73e+48847+e9//wvAyy+/zAsvvMAbb7zB2LFjWbZsGVdddRX79+9n0KBBVFZWcsEFF5CQkMCqVauIjY1l586dmM1m65iPHj3K559/zldffcXp06e5/vrrefrpp/n73/9Obm4u8+bN49lnn+Xqq6+moqKCX375Bek7LYSHcUpPeSGEcFBDQ4Ny/PhxZffu3Yq3t7eye/duJSMjQwkMDFTWrVunHD9+XCksLFR++eUXJTg4WKmtrW1x/oABA5Q33nhDURRFWbx4sWIwGJSTJ09aX//2228VvV6v5ObmKoqiKPHx8crf//73FteYOHGicueddyqKoihvvPGGEhQUpBQXF7c53sWLFyv+/v5KeXm59bk///nPyuTJkxVFUZQdO3YogJKZmdnFOyOE6E6yBCaEcCsvLy+Sk5M5dOgQEydOZNSoUeTl5RETE8P5559PcnIykZGR7N69m8rKSiIiIggMDLQ+jh8/ztGjR63X69u3LwkJCdbvU1JSMJvNpKenU15eTk5OTqultalTp3Lw4EEA0tLSGDt2rHVGqS3JyckEBQVZv4+Li6OgoACA0aNHM336dEaOHMl1113HW2+9xenTp51yr4QQziNLYEIItzrnnHM4ceIEDQ0NmM1mAgMDaWxspLGxkcDAQJKSkti/fz+VlZXExcWxdu3aVtcIDQ112nj8/Pw6Pcbb27vF9zqdzrpEZjAY+OGHH9i4cSPff/89r776Kg8//DBbtmyhX79+ThunEKJrZAZICOFW33zzDWlpacTGxvLBBx+QlpbGiBEjeOmll0hLS+Obb74BYNy4ceTl5eHl5cXAgQNbPCIjI63Xy8rKIicnx/r95s2b0ev1DBkyhODgYOLj49mwYUOLMWzYsIHhw4cDMGrUKNLS0igpKXH4M+l0OqZOncrjjz/Orl27MBqNrFy50uHrCSGcTwIgIYRbJSUlERgYSH5+PrNnzyYxMZH9+/dz7bXXMnDgQJKSkgCYMWMGKSkpzJkzh++//57MzEw2btzIww8/zPbt263X8/X15ZZbbmH37t388ssv3H333Vx//fXExsYC8Oc//5lnnnmG5cuXk56ezoMPPkhaWhr33HMPAPPmzSM2NpY5c+awYcMGjh07xv/+9z82bdpk0+fZsmULTz31FNu3bycrK4vPPvuMwsJChg0b5uQ7J4ToClkCE0K43dq1a5k4cSK+vr788ssv9OnTh7i4uBbH6HQ6vvnmGx5++GHmz59PYWEhsbGxnH/++cTExFiPGzhwINdccw2XXXYZJSUlXHHFFfzzn/+0vn733XdTVlbGn/70JwoKChg+fDirVq1i0KBBABiNRr7//nv+9Kc/cdlll9HY2Mjw4cN5/fXXbfoswcHB/Pzzz7z00kuUl5eTlJTECy+8wKWXXuqEOyWEcBadosjeTCHE2eGxxx7j888/Jy0tzd1DEUJ4OFkCE0IIIUSvIwGQEEIIIXodWQITQgghRK8jM0BCCCGE6HUkABJCCCFEryMBkBBCCCF6HQmAhBBCCNHrSAAkhBBCiF5HAiAhhBBC9DoSAAkhhBCi15EASAghhBC9zv8HeZIVZu3GCRoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot accuracy\n",
    "\n",
    "plt.plot(np.arange(num_epochs), history_model[\"train_acc\"], label='train acc')\n",
    "plt.plot(np.arange(num_epochs), history_model[\"test_acc\"], label='test acc')\n",
    "plt.xlabel('#epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "82512b88-1775-4423-832e-7f1d865de437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1466352828b0>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot accuracy\n",
    "\n",
    "plt.plot(np.arange(num_epochs), history_model[\"train_f1\"], label='train f1')\n",
    "plt.plot(np.arange(num_epochs), history_model[\"test_f1\"], label='test f1')\n",
    "plt.xlabel('#epochs')\n",
    "plt.ylabel('f1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b3f44965-b6e6-4932-a517-08239c27afb5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Baseline_Model(\n",
       "  (fc1): Linear(in_features=1280, out_features=64, bias=True)\n",
       "  (fc2): Linear(in_features=64, out_features=5, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5ddf34-6152-4ffd-9fdf-e0ff7d64c1c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "71b5f7d9-95af-4002-9b4e-f0a520d09dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(model.state_dict(), f'models/Baseline_model/state_dict_epoch{i}_batch_size{CONFIG[\"batch_size\"]}.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66a740e-4beb-46e0-8208-cf3d4da05782",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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