Loan_Eligibility_Prediction_Machine_Learning.ipynb

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        "<a href=\"https://colab.research.google.com/github/vikas-ukani/Loan-Eligiblity-Prediction---Machine-Learning/blob/master/Loan_Eligibility_Prediction_Machine_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "<div class='text-center'>\n",
        "    <h1>🏧 Loan Eligibility Prediction 💰 using Machine Learning Models 🤖   </h1>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Introduction"
      ]
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    {
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      "source": [
        "### In this notebook kernal, I'm going to predictions customers are eligible for the loan and check whether what are the missing criteria to know why customer not getting loan to make there own house.\n",
        "\n",
        "\n",
        "<div class=\"text-success \"><h4> We will learning about, Data Analysis Preprocess such as, </h4></div>\n",
        "\n",
        "--- \n",
        "\n",
        "> ### Steps are:\n",
        "\n",
        "\n",
        "1. [Gathering Data](#1)\n",
        "- [Exploratory Data Analysis](#2)\n",
        "- [Data Visualizations](#3)\n",
        "- [Machine Learning Model Decision.](#4)\n",
        "- [Traing the ML Model](#5)\n",
        "- [Predict Model](#6)\n",
        "- [Deploy Model](#7)\n",
        "\n",
        "\n",
        "\n",
        " \n",
        "**Hope** you guys ****Love It**** and get a better **learning experience**.  🙏"
      ]
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      "source": [
        "\n",
        "<img align=\"center\" src=\"https://www.rdccbank.com/uploads/loan-sub-types-template/housing-b.jpg\" alt=\"House Loan\" />\n",
        "\n",
        "---\n",
        "\n",
        "<div class=\"text-danger\" >\n",
        "    <h4>Let's Say, You are the owner of the <b>Housing Finance Company</b> and you want to build your own model to predict the customers are applying for the home loan and company want to check and validate the customer are eligible for the home loan.\n",
        "    </h4>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "\n",
        "# <div class=\"text-primary\"> The Problem is,  </div>\n",
        "\n",
        "### In a Simple Term, Company wants to make automate the Loan Eligibility Process in a real time scenario related to customer's detail provided while applying application for home loan forms.\n",
        "\n",
        "\n",
        "You will use the training set to build your model, and the test set to validate it. Both the files are stored on the web as CSV files; their URLs are already available as character strings in the sample code.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3u8AJ6GCVRW6",
        "colab_type": "text"
      },
      "source": [
        "First of all, we need to importing the necessary packages to work with the data to solve our problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6M6VT8LVRW8",
        "colab_type": "text"
      },
      "source": [
        "# Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "SMmYjqU5VRW-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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          "height": 151
        },
        "outputId": "5be9054f-cf7b-4ae7-8cb1-e65d9b5d87fb"
      },
      "source": [
        "# Show the Dataset Path to get detaset\n",
        "\n",
        "import os\n",
        "\n",
        "for dirname, _, filenames in os.walk('./sample_data/'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./sample_data/anscombe.json\n",
            "./sample_data/README.md\n",
            "./sample_data/loan-train.csv\n",
            "./sample_data/loan-test.csv\n",
            "./sample_data/mnist_train_small.csv\n",
            "./sample_data/california_housing_test.csv\n",
            "./sample_data/california_housing_train.csv\n",
            "./sample_data/mnist_test.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52YAx84YVRXK",
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      "source": [
        "\n",
        " You can load this data with the `read_csv()` method from `pandas` package. It converts the data set to a python dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBDce27kVRXL",
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      "source": [
        "\n",
        "## Dataset Key Information.\n",
        "  \n",
        "\n",
        "- `Loan_ID` Unique Loan ID.\n",
        "\n",
        "- `Gender`  Male/ Female \n",
        "- `Married`  Applicant married (Y/N) \n",
        "- `Dependents`  Number of dependents \n",
        "- `Education`  Applicant Education (Graduate/ Under Graduate) \n",
        "- `Self_Employed`  Self-employed (Y/N) \n",
        "- `ApplicantIncome`  Applicant income \n",
        "- `CoapplicantIncome`  Coapplicant income \n",
        "- `LoanAmount`  Loan amount in thousands \n",
        "- `Loan_Amount_Term`  Term of a loan in months \n",
        "- `Credit_History`  credit history meets guidelines \n",
        "- `Property_Area`  Urban/ Semi-Urban/ Rural \n",
        "- `Loan_Status`  Loan approved (Y/N) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dm6T0iHTVRXM",
        "colab_type": "text"
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      "source": [
        "<a id=\"1\"></a><br>\n",
        "# 1. Gathering Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "trusted": true,
        "id": "sedfqszUVRXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  Create New Variable and stores the dataset values as Data Frame\n",
        "\n",
        "# From Google Colab\n",
        "# \"./sample_data/loan-train.csv\"\n",
        "# \"./sample_data/loan-test.csv\"\n",
        "\n",
        "loan_train = pd.read_csv(\"./sample_data/loan-train.csv\")\n",
        "loan_test = pd.read_csv(\"./sample_data/loan-test.csv\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUQ5xTHEVRXX",
        "colab_type": "text"
      },
      "source": [
        "- Lets display the some few information from our large datasets\n",
        "\n",
        "Here, We shows the first five rows from datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "24f9e1d9-ba14-416e-d851-ebe3600188a7"
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      "source": [
        "loan_train.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
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              "      <th>CoapplicantIncome</th>\n",
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              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
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              "      <td>LP001003</td>\n",
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              "      <td>LP001006</td>\n",
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              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
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              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Loan_ID Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0  LP001002   Male      No  ...            1.0         Urban           Y\n",
              "1  LP001003   Male     Yes  ...            1.0         Rural           N\n",
              "2  LP001005   Male     Yes  ...            1.0         Urban           Y\n",
              "3  LP001006   Male     Yes  ...            1.0         Urban           Y\n",
              "4  LP001008   Male      No  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BTsPpsbVRXd",
        "colab_type": "text"
      },
      "source": [
        "- As we can see in the above output, there are too many columns, ( columns known as features as well. )\n",
        "\n",
        "We can also use `loan_train` to show few rows from the first five and last five record from the dataset"
      ]
    },
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      "cell_type": "code",
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          "height": 438
        },
        "outputId": "9a5a8f88-97a8-4635-bbdc-7e4e60123e7a"
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      "source": [
        "loan_train"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
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              "      <th>Loan_Status</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
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              "      <td>LP001003</td>\n",
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              "      <td>LP001005</td>\n",
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              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
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              "      <td>360.0</td>\n",
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              "      <th>609</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>LP002983</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>8072</td>\n",
              "      <td>240.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>LP002984</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>7583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>LP002990</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>614 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Loan_ID  Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0    LP001002    Male      No  ...            1.0         Urban           Y\n",
              "1    LP001003    Male     Yes  ...            1.0         Rural           N\n",
              "2    LP001005    Male     Yes  ...            1.0         Urban           Y\n",
              "3    LP001006    Male     Yes  ...            1.0         Urban           Y\n",
              "4    LP001008    Male      No  ...            1.0         Urban           Y\n",
              "..        ...     ...     ...  ...            ...           ...         ...\n",
              "609  LP002978  Female      No  ...            1.0         Rural           Y\n",
              "610  LP002979    Male     Yes  ...            1.0         Rural           Y\n",
              "611  LP002983    Male     Yes  ...            1.0         Urban           Y\n",
              "612  LP002984    Male     Yes  ...            1.0         Urban           Y\n",
              "613  LP002990  Female      No  ...            0.0     Semiurban           N\n",
              "\n",
              "[614 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YopeEl55VRXk",
        "colab_type": "text"
      },
      "source": [
        "> ### Here, we can see there are many rows and many columns, To know how many records and columns are available in our dataset, we can use the `shape` attribute or we can use `len()` to know how many records and how many features available in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-n05Fpv0VRXl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f799c34c-4898-45d0-c9e5-cb1e3b85c09b"
      },
      "source": [
        "print(\"Rows: \", len(loan_train))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows:  614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtDVgbkwVRXr",
        "colab_type": "text"
      },
      "source": [
        "Pandas has inbuild attribute to get all column from the dataset, With the help of this feature we can get the how many column available we have."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "7K_gv7JCVRXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd6b2636-04e4-4cca-bc6b-6752b27fdd3a"
      },
      "source": [
        "print(\"Columns: \", len(loan_train.columns))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns:  13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi4huiWEVRXx",
        "colab_type": "text"
      },
      "source": [
        "Also we can get the shape of the dataset using `shape` attribute"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fa_d7hVqVRXx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e394e964-970b-41a8-feef-aba4f325ff92"
      },
      "source": [
        "print(\"Shape : \", loan_train.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape :  (614, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hxs6GmyrVRX2",
        "colab_type": "text"
      },
      "source": [
        "> ### *After we collecting the data, Next step we need to understand what kind of data we have.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIMdemvqVRX3",
        "colab_type": "text"
      },
      "source": [
        "### Also we can get the column as an list(array) from dataset\n",
        "\n",
        "> **Note: DataFrame.columns returns the total columns of the dataset,\n",
        "> Store the number of columns in variable `loan_train_columns`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "7G3d2jdLVRX5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "3d3a5143-683d-4599-aedb-68bd34a7fb01"
      },
      "source": [
        "loan_train_columns = loan_train.columns # assign to a variable\n",
        "loan_train_columns # print the list of columns"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tteH6_QMVRX-",
        "colab_type": "text"
      },
      "source": [
        "### Now, Understanding the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JjfVj_wVRX_",
        "colab_type": "text"
      },
      "source": [
        "- First of all we use the `loan_train.describe()` method to shows the important information from the dataset\n",
        "- It provides the `count`, `mean`, `standard deviation (std)`, `min`, `quartiles` and `max` in its output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "6SoWUVJnVRYA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "9a352de8-a68e-4a23-97d2-45c667b32730"
      },
      "source": [
        "loan_train.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
              "      <td>0.842199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  ...  Loan_Amount_Term  Credit_History\n",
              "count       614.000000         614.000000  ...         600.00000      564.000000\n",
              "mean       5403.459283        1621.245798  ...         342.00000        0.842199\n",
              "std        6109.041673        2926.248369  ...          65.12041        0.364878\n",
              "min         150.000000           0.000000  ...          12.00000        0.000000\n",
              "25%        2877.500000           0.000000  ...         360.00000        1.000000\n",
              "50%        3812.500000        1188.500000  ...         360.00000        1.000000\n",
              "75%        5795.000000        2297.250000  ...         360.00000        1.000000\n",
              "max       81000.000000       41667.000000  ...         480.00000        1.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOsHhfZtVRYG",
        "colab_type": "text"
      },
      "source": [
        "#### As I said the above cell, this the information of all the methamatical details from dataset. Like `count`, `mean`, `standard deviation (std)`, `min`, `quartiles(25%, 50%, 75%)` and `max`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2aZDaD-VRYH",
        "colab_type": "text"
      },
      "source": [
        "> ### Another method is `info()`, This method show us the information about the dataset, Like\n",
        "\n",
        "1. What's the type of culumn have?\n",
        "- How many rows available in the dataset?\n",
        "- What are the features are there?\n",
        "- How many null values available in the dataset?\n",
        "- Ans so on..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "eW5bnpN-VRYI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "98ed7745-fb48-4b68-c322-714f1ec2e61e"
      },
      "source": [
        "loan_train.info()\n",
        " "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bH5a8hN1VRYR",
        "colab_type": "text"
      },
      "source": [
        "As we can see in the output.\n",
        "\n",
        "1. There are `614` entries\n",
        "- There are total 13 features (0 to 12)\n",
        "- There are three types of datatype `dtypes: float64(4), int64(1), object(8)`\n",
        "- It's Memory usage that is, `memory usage: 62.5+ KB`\n",
        "- Also, We can check how many missing values available in the `Non-Null Count` column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elMMDDT3VRYS",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"2\"></a><br>\n",
        "# 2. Exploratory Data Analysis\n",
        "\n",
        "In this section, We learn about extra information about data and it's characteristics.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSnsUtjfVRYU",
        "colab_type": "text"
      },
      "source": [
        "- First of all, We explore object type of data\n",
        "So let's make a function to know how many types of values available in the column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aWC9U7QTVRYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def explore_object_type(df ,feature_name):\n",
        "    \"\"\"\n",
        "    To know, How many values available in object('categorical') type of features\n",
        "    And Return Categorical values with Count.\n",
        "    \"\"\"    \n",
        "    if df[feature_name].dtype ==  'object':\n",
        "        print(df[feature_name].value_counts())"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcMcC3LRVRYZ",
        "colab_type": "text"
      },
      "source": [
        "- After defined a function, Let's call it. and check what's the output of our created function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ofNsN0wnVRYa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "363736f5-4af3-4954-8df0-4dfe0d9ece96"
      },
      "source": [
        "# Now, Test and Call a function for gender only\n",
        "explore_object_type(loan_train, 'Gender')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Male      489\n",
            "Female    112\n",
            "Name: Gender, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7MvoOThYK-O",
        "colab_type": "text"
      },
      "source": [
        "> To Get all in `object` type of feature from the dataset, You can write like below code.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQTjwbBFXFbt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "objects_features_list = list(loan_train.describe(include=['O']).columns)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_O7vKjt8VRYf",
        "colab_type": "text"
      },
      "source": [
        "- Here's one little issue occurred, Suppose in your datasets there are lots of feature to defined like this above code. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "PBaqkAUVVRYg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "bde7f886-9d61-41e8-c585-f743026e207a"
      },
      "source": [
        "# Solution is, Do you remember we have variable with name of `loan_train_columns`, Right,  let's use it\n",
        "# 'Loan_ID', 'Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status'\n",
        "\n",
        "for featureName in objects_features_list:\n",
        "      print('\\n\"' + str(featureName) + '\\'s\" Values with count are :')\n",
        "      explore_object_type(loan_train, str(featureName))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\"Loan_ID's\" Values with count are :\n",
            "LP001891    1\n",
            "LP002868    1\n",
            "LP002190    1\n",
            "LP002484    1\n",
            "LP001910    1\n",
            "           ..\n",
            "LP002692    1\n",
            "LP001643    1\n",
            "LP002434    1\n",
            "LP001914    1\n",
            "LP001316    1\n",
            "Name: Loan_ID, Length: 614, dtype: int64\n",
            "\n",
            "\"Gender's\" Values with count are :\n",
            "Male      489\n",
            "Female    112\n",
            "Name: Gender, dtype: int64\n",
            "\n",
            "\"Married's\" Values with count are :\n",
            "Yes    398\n",
            "No     213\n",
            "Name: Married, dtype: int64\n",
            "\n",
            "\"Dependents's\" Values with count are :\n",
            "0     345\n",
            "1     102\n",
            "2     101\n",
            "3+     51\n",
            "Name: Dependents, dtype: int64\n",
            "\n",
            "\"Education's\" Values with count are :\n",
            "Graduate        480\n",
            "Not Graduate    134\n",
            "Name: Education, dtype: int64\n",
            "\n",
            "\"Self_Employed's\" Values with count are :\n",
            "No     500\n",
            "Yes     82\n",
            "Name: Self_Employed, dtype: int64\n",
            "\n",
            "\"Property_Area's\" Values with count are :\n",
            "Semiurban    233\n",
            "Urban        202\n",
            "Rural        179\n",
            "Name: Property_Area, dtype: int64\n",
            "\n",
            "\"Loan_Status's\" Values with count are :\n",
            "Y    422\n",
            "N    192\n",
            "Name: Loan_Status, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iULXE2kEVRYk",
        "colab_type": "text"
      },
      "source": [
        "> ## *Note: Your output maybe shorter or longer, It's totally depend upon your dataset's columns* "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1xAkZnVVRYl",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BSIFLRfVRYm",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "- We need to fill null values with `mean` and `median` using `missingno` package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8q8qcooQVRYn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "0c1e1145-9ef7-4713-9e7c-11d8e2cfd75d"
      },
      "source": [
        "import missingno as msno"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D2iNfwDBVRYq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "0927ca90-8a73-4a9d-c289-55a6c80e7f5b"
      },
      "source": [
        "# list of how many percentage values are missing\n",
        "loan_train\n",
        "\n",
        "loan_train.isna().sum()\n",
        "# round((loan_train.isna().sum() / len(loan_train)) * 100, 2)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aCkvXszBVRYu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "35c30b10-660b-4da8-f269-1e19c14bc02c"
      },
      "source": [
        "msno.bar(loan_train)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f2887de10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AdlsnC0YVRY0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "outputId": "cbfb9413-89f3-4fbb-c1c1-00ca517f74a3"
      },
      "source": [
        "msno.matrix(loan_train )\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f24a50048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qe0pWB_GVRY4",
        "colab_type": "text"
      },
      "source": [
        "- As we can see here, there are too many columns missing with small amount of null values so we use `mean` amd `mode` to replace with `NaN` values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zoQeMjhVVRY4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_train['Credit_History'].fillna(loan_train['Credit_History'].mode(), inplace=True) # Mode\n",
        "loan_test['Credit_History'].fillna(loan_test['Credit_History'].mode(), inplace=True) # Mode\n",
        "\n",
        "\n",
        "loan_train['LoanAmount'].fillna(loan_train['LoanAmount'].mean(), inplace=True) # Mean\n",
        "loan_test['LoanAmount'].fillna(loan_test['LoanAmount'].mean(), inplace=True) # Mean"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpIJ_-PkVRZA",
        "colab_type": "text"
      },
      "source": [
        "### # convert Categorical variable with Numerical values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sLrkcuRVRZB",
        "colab_type": "text"
      },
      "source": [
        "`Loan_Status` feature boolean values, So we replace `Y` values with `1` and `N` values with `0`\n",
        "and same for other `Boolean` types of columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "dhvx_qffVRZD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loan_train.Loan_Status = loan_train.Loan_Status.replace({\"Y\": 1, \"N\" : 0})\n",
        "# loan_test.Loan_Status = loan_test.Loan_Status.replace({\"Y\": 1, \"N\" : 0}) \n",
        "\n",
        "loan_train.Gender = loan_train.Gender.replace({\"Male\": 1, \"Female\" : 0})\n",
        "loan_test.Gender = loan_test.Gender.replace({\"Male\": 1, \"Female\" : 0})\n",
        "\n",
        "loan_train.Married = loan_train.Married.replace({\"Yes\": 1, \"No\" : 0})\n",
        "loan_test.Married = loan_test.Married.replace({\"Yes\": 1, \"No\" : 0})\n",
        "\n",
        "loan_train.Self_Employed = loan_train.Self_Employed.replace({\"Yes\": 1, \"No\" : 0})\n",
        "loan_test.Self_Employed = loan_test.Self_Employed.replace({\"Yes\": 1, \"No\" : 0})\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "vTjiX747VRZJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "loan_train['Gender'].fillna(loan_train['Gender'].mode()[0], inplace=True)\n",
        "loan_test['Gender'].fillna(loan_test['Gender'].mode()[0], inplace=True)\n",
        "\n",
        "loan_train['Dependents'].fillna(loan_train['Dependents'].mode()[0], inplace=True)\n",
        "loan_test['Dependents'].fillna(loan_test['Dependents'].mode()[0], inplace=True)\n",
        "\n",
        "loan_train['Married'].fillna(loan_train['Married'].mode()[0], inplace=True)\n",
        "loan_test['Married'].fillna(loan_test['Married'].mode()[0], inplace=True)\n",
        "\n",
        "loan_train['Credit_History'].fillna(loan_train['Credit_History'].mean(), inplace=True)\n",
        "loan_test['Credit_History'].fillna(loan_test['Credit_History'].mean(), inplace=True)\n"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOxxFmoRVRZN",
        "colab_type": "text"
      },
      "source": [
        "* Here, `Property_Area`, `Dependents` and `Education` has multiple values so now we can use `LabelEncoder` from `sklearn` package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "uZbGSJ3yVRZO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder # You can also use OneHotEncoder Here\n",
        "feature_col = ['Property_Area','Education', 'Dependents']\n",
        "le = LabelEncoder()\n",
        "for col in feature_col:\n",
        "    loan_train[col] = le.fit_transform(loan_train[col])\n",
        "    loan_test[col] = le.fit_transform(loan_test[col])"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTWrWQGFVRZT",
        "colab_type": "text"
      },
      "source": [
        "> ### Finally, We have all the features with numerical values,"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuyVsA0XVRZU",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"3\"></a><br>\n",
        "# 3. Data Visualizations\n",
        "\n",
        "\n",
        "In this section, We are showing the visual information from the dataset, For that we need some pakages that are `matplotlib` and `seaborn`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XdwfpbVkVRZY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_style('dark')\n"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "F5GfiIKTVRZc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "60bdef19-f6ce-49f8-a0e8-cc66e3afcbc1"
      },
      "source": [
        "loan_train"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>LP002978</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>LP002979</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4106</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>LP002983</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8072</td>\n",
              "      <td>240.0</td>\n",
              "      <td>253.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>LP002984</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>LP002990</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>614 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Loan_ID  Gender  Married  ...  Credit_History  Property_Area  Loan_Status\n",
              "0    LP001002     1.0      0.0  ...             1.0              2            1\n",
              "1    LP001003     1.0      1.0  ...             1.0              0            0\n",
              "2    LP001005     1.0      1.0  ...             1.0              2            1\n",
              "3    LP001006     1.0      1.0  ...             1.0              2            1\n",
              "4    LP001008     1.0      0.0  ...             1.0              2            1\n",
              "..        ...     ...      ...  ...             ...            ...          ...\n",
              "609  LP002978     0.0      0.0  ...             1.0              0            1\n",
              "610  LP002979     1.0      1.0  ...             1.0              0            1\n",
              "611  LP002983     1.0      1.0  ...             1.0              2            1\n",
              "612  LP002984     1.0      1.0  ...             1.0              2            1\n",
              "613  LP002990     0.0      0.0  ...             0.0              1            0\n",
              "\n",
              "[614 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IjNTmuJCVRZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "b355f4a5-792e-470d-fa15-c4f142e4caca"
      },
      "source": [
        "loan_train.plot(figsize=(18, 8))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7f75v/WXKlGXo0AHUrl2H2rXrZNnnkpMohBARERERm3W3J4SGY4hkvYZFGz9yr4Wn4dKlSOzs7PHwyI/ZbKZXrz7UqFEz3TEREfswGAzpthkMd/6NqFq1BkOGjLxv246OTgDY29thMhkfWIfZbKZx4zf5v//rds8+BwcHy/Xt7OwwmUz3bWPMmAkcOnSAH3/czvz5s5k37zscHGzrsV1LdIqIiIiI7dNwDBGbFBMTw9ixo2jVqg0Gg4Hq1WuybFkYRuOdwODChfMkJiYC8OuvR7l8+RJpaWls3ryRihUrU6HCK/zyyyEiIy8CkJiYyIUL5x94zcqVq7Bx4zoAdu36kVu3bgLw6qvV2bp1EzExNwC4eTOOqKgrD2zL1dWVhIQEANLS0rh6NRo/v6qEhPQgPj7eUrstsa1IRURERETkL/7sCSEitiI5OZlOndpZluhs1KgJbdu+C0CzZi2IirpC587vYjabcXf3YNSocQD4+pZn/PjRlokpAwLqYmdnR//+oYSG9ic1NQWAjz4KoXjxEhle//33PyI0tD/t27fhlVcq4u3tA0CpUqX56KMQevXqhtmchr29A5988hk+PoUybKt+/YaMHj2CsLDvGDJkJKNGDeP27XjMZjPBwW3JkydPVn1sOYbBbLbOWDg11URsbEJ2lyEiIiIiOdjxbRvYs3gOdT76hOKVqmV3OSJWLyrqPD4+GT+g51QREfv47ruFjB49IbtLsTn3+53w8so4PNFwDBERERGxYVb59zYREZul4RgiIiIiYrMswzGss/OviGQRP7+q+PlVze4yBPWEEBEREZHngUIIEZEcQSGEiIiIiNguhQ8iIjmKQggRERERsVlmNBxDRCQnUQghIiIiIrbLfM8LERHJRpqYUkRERERsl2ViymyuQ0SyTEBAdUqXfsHyvn79hnTo0CndMU9jSc6IiH04OjryyiuVAFi2LAxnZxcaN34zy67xPFAIISIiIiI26+5wDKUQIrbD2dmZuXO/eebXPXBgP7lyuVpCiBYtgp95DbZAIYSIiIiI2C4NxxB5buze/ROTJo3DxcWFihUrW7bPmjWNXLlcadeuAwAdOrRh9OgJFCpUmLVrV/HddwsBAy+88AIDBw5j587tzJs3C6Mxlbx53Rk8eBjJycksX74EOzs7NmxYS69efdi3b4+l3VOnTjBmzCiSk5MoXLgoffsOIm/evHTr1oXy5V/mwIF93LoVT9++A6lUqUo2fUI5g0IIEREREbFZZnPaHz+zuRARG5S0bjVJq1dmaZsuTZvh8kbTBx6TnJxMp07tLO87dOiEv38go0ePYOLEryhatBiDBvV96LXOnj3DvHmzmTp1Nu7u7ty8GQdAxYqVmT59LgaDgZUrl/H11/Pp3r0Xb731j3Rhxr59eyxtDR8+mJ49+1ClyqvMnDmVOXNm8PHHvQEwmUzMmDGfXbt2Mnv2DCZOnPLIn4stUQghIiIiIs8BpRAituJ+wzFOnTpBoUKFKVasOACNGjVmxYqlD2wnImIvdevWx93dHYC8efMB8PvvVxk8uC/Xr18jNTWVQoWKPLCd+Ph4bt26RZUqrwLQuPGbDBz4mWV/YGBdAF56yZeoqMuPcKe2SSGEiIiIiNgsy9Kc6gohkuVc3mj60F4LOYG9vb2lVxRASkrKA48fP340bdu+i79/IBER+5g9e/oTXd/JyQkAOzt7TCbTE7VlC7REp4iIiIjYrrurY2RzGSLydBUvXpIrVy5z6VIkABs3rrfsK1SoMCdPHgfgxInjXLlypzeCn181tmzZRFxcLIBlOMbt2/F4ehYEYN261ZZ2XF3dSExMuOfauXPnJk+evBw6dMByTuXKfll9izZDPSFERERExPapJ4SIzfj7nBA1atQkJKQ7n37anz59Pv5jYsoqlsCgTp16rFu3mvbt21C+fAXLkI3SpcvQsWNnunXrgp2dPS+++BL9+4fSuXMXBg78nDx58vDqq9W4fPkSALVq1WbgwM/YsWMbvXr1SVfTgAGhf5mYsgh9+w5+Rp+G9TGYzdb5L3JqqonY2HtTKBERERGRuw6vW8rBVYuo1f7/KPNaYHaXI2L1oqLO4+NTIrvLkBzkfr8TXl55MjxewzFERERExIZpOIaISE6iEEJEREREbNfd9ME6O/+KiNgchRAiIiIiYrPMlp4QCiFERHIChRAiIiIiYrssS3RmbxkiInKHQggRERERsVl/jsJQCiEikhMohBARERERG/bHcAzNCSEikiMohBARERER22X+208REclWDw0hzp49y1tvvWX5z8/Pj7lz5xIbG8v7779Pw4YNef/994mLiwPupMzDhw8nKCiIZs2acfToUUtbS5cupWHDhjRs2JClS5dath85coRmzZoRFBTE8OHDlVSLiIiISJYwm9PS/RQR2zBv3izat29Dx45t6dSpHUePHsnw2BEjQtmyJRyAQ4cO0L59Gzp1akdyctI9x165cpl69WrRqVM7y39r167KkpqDgmpnSTsZ+et95mQODzugdOnSLF++HACTyURAQABBQUFMnz6dmjVr0qVLF6ZPn8706dPp06cP27dv59y5c2zYsIFDhw4RGhrK4sWLiY2NZfLkyfzwww8YDAb+8Y9/UK9ePfLly0doaCjDhg2jUqVKfPTRR2zfvp3AwMCnfvMiIiIiYuP0ty0Rm3PkyGF++mkns2cvxMnJidjYWIzG1Eydu2HDWjp06ESjRk0yPKZIkSLMnftNVpUrf/PQEOKvdu3aRbFixShSpAibNm1iwYIFALRo0YIOHTrQp08fNm3aRIsWLTAYDFSuXJmbN29y9epV9uzZQ61atXB3dwegVq1a7Nixg+rVqxMfH0/lypUtbW3atEkhhIiIiIg8MbPmhBB5as4duMZvEdeytM1Sfp6UrOL5wGOuX79GvnzuODk5AVieMY8fP8bkyeNJSEjA3d2dfv1C8fT8s62VK5exeXM4e/bsZvfunxg8ePgj1RYUVJsWLYLZtetHChTw5J//7MqUKZOIjo7m448/wd8/kDVrVrJ9+xbi4+O5du13GjZsTOfOXdK1YzabmTJlErt3/4jBYKBjxw+oX78hw4YNIjCwHgEBdQAYMmQA9eo14PXXazN16mQOHNhPamoKLVu2pkWLVpjNZsaPH83evT9TsKAPjo6P9HifbR5pTojVq1fz5ptvAnD9+nUKFiwIgJeXF9evXwcgOjoaHx8fyzk+Pj5ER0ffs93b2/u+2+8eLyIiIiLyxLREp4jNqVbtNa5ejaZt238wduwXHDiwH6PRyIQJYxg27D/Mnr2Qpk2bM336l+nOa9asBf7+AXTt2uOBAcSlS5fSDcc4dOgAAImJifj5VWXhwkW4uroyY8ZXTJgwhZEjxzBz5jTL+ceOHWXEiNHMm/ctW7aEc/z4r+na37ZtM6dOnWDu3G+ZMGEKX345kWvXrvHmm2+xdu1KAOLj4zly5DA1a/qzatVy3NzcmDlzPjNmzGflymVcvnyJ7du3cOHCeRYuXMzAgUM4cuRwVn3ET1Wmo5KUlBQ2b95M796979lnMBgwGAxZWpiIiIiIyJMy3+eViGSNklUe3mvhaXB1dWXWrAUcOnSAAwf2M3hwPzp27MzZs2fo1etfAKSlmShQ4PFqy2g4hqOjI6+99joAZcq8gKOjIw4ODpQp8wJRUZctx1WtWoN8+e70zggMrMfhwwcpV668Zf/hwwdp0KAR9vb25M9fgCpV/Dh+/Cj+/oGMG/cfYmJi2LZtE4GB9XBwcGDv3t2cPn2arVs3A3D7djyRkRc5ePCApR1PTy/8/Ko91v0+a5kOIbZv306FChUs3VkKFCjA1atXKViwIFevXiV//vzAnR4OUVFRlvOioqLw9vbG29ubPXv2WLZHR0dTvXr1DI8XEREREXliZg3HELFF9vb2+PlVxc+vKqVLl2HJksWUKlWaadPmPLVrOjg4WP74bjAYcHS8MxzEzs4Ok8lkOe7eP9Bn/g/2b7zRhA0b1hAevoF+/QYDd/796tWrDzVq1Ex37K5dPz7GXWS/TA/HWL16NU2bNrW8r1evHsuWLQNg2bJl1K9fP912s9nMwYMHyZMnDwULFsTf35+dO3cSFxdHXFwcO3fuxN/fn4IFC5I7d24OHjyI2WxO15aIiIiIyJNR+CBiay5cOMfFixcs70+dOkmJEqWIjY2xDEkwGo2cPXsmW+rbu/dnbt6MIzk5iR07tlKxYqV0+ytVqsLmzRsxmUzExMRw8OABfH0rANCkSTMWLfoWgFKlSgNQvXpNli0Lw2g0AnDhwnkSExOpXPnPdq5du0ZExL5neJePL1M9IRISEvjpp58YOnSoZVuXLl3o2bMnYWFhFC5cmAkTJgAQGBjItm3bCAoKIleuXIwcORK4M1lI165dCQ4OBuBf//qXZQKRwYMH07dvX5KSkggICCAgICBLb1JEREREnk+WDhDqCSFiMxISEpkwYQzx8bewt7enSJFifPppf956qyUTJowlPj4ek8lEmzbvULp0mUdu/+6cEHc1bdqc1q3bZvr88uUr0L//p/z++1UaNmycbigGQEBAXY4c+YVOnd7BYDDQtWsPy9CR/PkLUKJEKQIC/lyooVmzFkRFXaFz53cxm824u3swatQ4AgLqsn//Xtq3b423tw8vv/zKI99rdjCYrbRvWmqqidjYhOwuQ0RERERysH1LFvLr5tVUbdWB8nUzXpJPRDInKuo8Pj4lsruMHGvNmpUcP/4rn3zy2WOdn5SUxHvvvc3s2V+TO3fuLK7u6bjf74SXV54Mj3+k1TFERERERKyLVscQEeuwd+/PvPtuMMHBb1tNAPE4rGMhURERERGRx6DhGCJyP2fOnGbYsEHptjk6OjJjxrwnardJk2Y0adLssc6tVq0GP/yw6omubw0UQoiIiIiIDftjdQx1hRCRvyhT5oX7LsMpT5+GY4iIiIiI7TJrOIaISE6iEEJEREREbJZlNIZSCBGRHEEhhIiIiIjYLHNa2h8vFEKIiOQECiFERERERETEqmzfvhV//6qcP3/usdsYMSKULVvCAfjii2H89tvZLKrujvnzZ6d7HxRUO0vbt1YKIURERETEdv3RA8KsnhAiNiU8fD0VK1YmPHx9lrT3+ecDKVWqdJa0ddeCBXOytD1bodUxRERERMRmWeaCUAghYjMSEhI4fPggkyZN5bPPevHBB/8kImIfs2ZNw9XVlcjIi/j5VaV378+xs7MjKKg2zZq1YM+enylQoAChoSPx8PBI12a3bl3o1q0n5cqVZ/fun5g+/UtMpjTc3d2ZOPErfv31CBMnjiMlJRlnZxf69RtE8eIlWbNmJTt3bicpKYnLlyMJCKhD164f89VX/yM5OZlOndpRqlRpBg8ebrlWRMQ+Zs+ejru7O2fPnuGll3wZNGgYBoOBY8eOMnHiOBITE3FycmTixK+wt3dg3LgvOH78V+zt7ene/RP8/KqyZs1KduzYSmJiIpGRF3nnnfakpqayfv0aHB2dGDt2Innz5uPSpUjGjfsPsbExuLi48NlnAyhRouQz/tb+pBBCRERERGyXsgeRp+bMz9s5vWtrlrb5Qs06lKkR8MBjdu7cRo0aNSlevAT58rlz/PgxAI4dO8qCBYvw8SlE797d2bZtM3XrNiAxMZFy5crTo0dv5syZwZw50/nkk8/u23ZMTAyjR49g8uTpFC5chJs34wAoUaIkX345AwcHB/bu/Zlp075kxIgxAJw6dZI5c77G0dGRdu1a0arV24SEdGfJkkUZLgN66tQJFixYhKenFyEhH3D48CHKl6/AoEH9GDp0JL6+Fbh9Ox4nJ2cWL/4OgPnzv+f8+XP06vUvvv12CQBnz55hzpyvSU5OoW3bFoSEdGfOnG+YNGkc69atpk2bdowePYJ//7svxYoV5+jRI4wb9wWTJk199C8niyiEEBERERHbpeEYIjYnPHw9rVu3BaB+/YaEh6/n9df98fWtQJEiRQFo0KARhw8fom7dBtjZ2VGvXhAADRs2pn//TzNs++jRX6hUqQqFCxcBIG/efADEx8czfHgokZEXMBgMGI1GyzlVq1Yjd+7cAJQsWZqoqCi8vX0eeA++vhUoWNAbgLJlXyQq6jK5c+fG07MAvnBiEJsAACAASURBVL4VAHBzu9Pm4cMHCQ5+G7gThvj4FOLixQsA+PlVxdXVDVdXN9zcclOr1p0Ap3TpFzhz5jQJCQn88sthBg783HLt1NSUB9b2tCmEEBERERGbZb7PKxHJGmVqBDy010JWu3kzjv3793LmzGkMBgNpf6yA8/rr/hgMhnTH/u3tQ7c/yMyZU/Hzq8qoUWO5cuUy3bv/07LP0dHR8tre3g6TyXi/JtJxcnKyvLazs8NkMj16UX+7tp2dHY6OTn9p04jZnEaePLkz7JGRHTQxpYiIiIjYrrs9IJRBiNiELVs20ahRE374YRVhYStZsmQ1hQsX4dChA/z661EuX75EWloamzdvpGLFygCkpaWxdesmADZuXGfZfj8VKrzCoUMHuHz5EoBlOEZ8fDxeXl4ArFmzMlO12ts7pOsx8TDFi5fg2rXrHDt2FICEhNsYjUYqVarMhg1rAbhw4TzR0VEUL14iU226ueWmUKEibN58ZxUQs9nMqVMnM13T06CeECIiIiJis+5OTGlWCiFiE8LD1/Puux3TbQsMrMeyZWH4+pZn/PjRlokpAwLqApArVy6OHTvKvHmz8PDIz5AhozJs38PDgz59+tG/fx/S0sx4eHgwYcIU3n33PYYPD2XevFnUrOmfqVqbN29Jx45tefHFcukmpsyIo6MjQ4eOZPz4MSQnJ+Ps7MyECVNo2bI148Z9wXvvvY29vT39+4em60nxMIMGDWPs2C+YN28WJpOR+vUbUrbsi5k+P6sZzFY6QC411URsbEJ2lyEiIiIiOdhPX0/n9K4tVGrSikpNgrO7HBGrFxV1Hh+fzP0V/lmKiNjHd98tZPToCffsCwqqzcaNO7KhqufD/X4nvLzyZHi8hmOIiIiIiA27OzFlNpchIiKAhmOIiIiIiA37s9OvUggRW+bnVxU/v6r33adeEDmLekKIiIiIiO1SBiEikqMohBARERERm2U231m+TxNTiojkDAohRERERMT2aVIIEZEcQSGEiIiIiNiuu+GDMggRkRxBIYSIiIiI2Ky7wzA0HEPEdly/fo3Bg/vSps1bdO7cnn//uwcXLpx/6tedNWsa33yzAICZM6eyd+/PWdr+okXfkJSUZHkfHNyM2NjYLL1GTqDVMURERETEdlkmplQIIWILzGYz/fr1oXHjpgwZMgqAU6dOEhNzg+LFSzyzOj788P+yvM1Fi76lYcMmuLi4ZHnbOYlCCBERERGxWXeX6FQEIWIbIiL24eDgQIsWwZZtZcu+iNls5ssvJ7J7948YDAY6dvyA+vUbkpCQQN++vbl16yZGo5GPPgqhdu06XLlymd69u/PSS76cPHmcUqVKM2DAUFxcXAgObka9eg3YvfsnnJ2dGTx4BEWLFktXx4gRobz+uj916zbg2LGjTJw4jsTERJycHJk48Svi4uIYNmwQSUmJAPTq9SmvvFKJiIh9zJ49HXd3d86ePcNLL/kyaNAwwsK+59q13+nR45/ky+fO//43zXKtK1cu8+9/96Bixcr88sthvLy8+OKLcTg7uxAZeZExY0YRGxuDvb0dw4b9h8KFizBlyqR7Pou7186dOzdnzpyhXr0GlCnzAosXf0tycjKjRo2jSJGixMTEMHbsSKKjowHo0eMTKlasnGXfoUIIEREREbFhd+eEUAwhktVMR29gOnI9S9u0f7kA9hXyZ7j/zoN7uXu2b9u2mVOnTjB37rfExcXy4YfvUamSH+7u7owcOQY3t9zExsbyz392wt8/EIALF87z+ecDqVixMiNHDmHJksW0a9cBADe33Myf/z1r165i0qRxjB494b71pKamMmhQP4YOHYmvbwVu347HyckZD4/8jB//Jc7Ozly8eIHQ0P7MmnVnKMepUydYsGARnp5ehIR8wOHDh2jdui3ff/81kyZNw93d/Z7rREZeJDR0BJ99NoCBAz9n69bNNGrUhCFDBtC+fScCA+uSnJyM2WzO8LMAOH36JAsXhpE3b17atHmLZs1aMGPGfBYt+pawsO/5+OPeTJw4ljZt3qVSpcpERUXRu3c3vv467NG+yAdQCCEiIiIitst8zwsRsUGHDx+kQYNG2Nvbkz9/AapU8eP48aO89lotpk37kkOHDmAw2PH7779z48ad4KRgQW/LX/gbNWpCWNh3wJ0QokGDRgAEBb3B//43PsPrXrhwHk/PAvj6VgDuhBcASUlJjB//H06dOomdnT0XL/45Z4WvbwUKFvQG7vTiiIq6TKVKD+5pUKhQYcqWfQmAl14qx5Url0lIuM21a78TGFgXAGdn5wd+Fq6ubpQrVx5PT08AihQpSrVqNQAoU+YFDhzYB8C+fXs4d+43y7Vv375NQkICrq6uD6wxsxRCiIiIiGTgyokjnPl5O/7vdc3uUuQxWSamVAYhkuXsK+R/YK+Fp6FUqdJs3bop08dv2LCW2NhYZs1aiIODA8HBzUhJSQHAYDD87eg/3/913z2HZcL333+Nh0cB5s79lrS0NOrXr2XZ5+TkZHltZ2eHyWR6aHuOjo5/Occekyn50Yv627UNBoPlvcFgsNRhNqcxbdocS6iR1bQ6hoiIiEgGok79ytk9O7K7DHkSZg3HELElr75ajZSUFJYvX2LZdvr0KXLnzsPmzRsxmUzExMRw8OABfH0rEB8fj4eHBw4ODkRE7CMq6orlvOjoKI4cOQzAxo3r0s17sGnTxj9+bqBChYoZ1lO8eAmuXbvOsWNHAUhIuI3RaOT27XgKFPDEzs6O9evXZCpocHV1JSHhdqY/C1dXN7y8CrJ9+1YAUlJSSEpKolKlKvf9LDKrWrXX+OGH7y3vT506kelzM0M9IUREREQycndSQ7P5Pn8xE+uiEELEFhgMBkaNGsvEieP4+ut5ODk5U6hQIXr06E1iYiKdOr2DwWCga9ceFCjgScOGjfnss168997blCtXnhIlSlraKl68BEuWLGbUqKGULFmKli3/nOzy1q2bdOzYFkdHJ0JDR2RYj6OjI0OHjmT8+DEkJyfj7OzMhAlTaNmyNQMGfMq6daupUaMmuXLleui9NW/ekt69u+Pp6ZVuYsoHGThwKGPGjGTWrKnY2zswbNgXBATU5ciRX+75LM6fP5epNnv27MN///sfOnZsi8lkolKlKvTp0y9T52aGwWy2zlg4NdVEbGxCdpchIiIiNuzAiu/5ZcMy2k/6Gjs7dSC1RltnjufCwT341m1CtVYdsrscEasXFXUeH59ntxTm03LlymU+/bQnCxYsumdfcHAzZs5ccN8JIuVe9/ud8PLKk+Hx+r+piIiISAbM5rS7L7K3EHl8mphSRCRH0XAMERERkQzcfWy10o6jAmiJThG5n0KFCt+3FwRAWNjKZ1zN80U9IUREREQyokkNrd7dAEnfoIhIzqAQQkRERCRD5r/9FGtjVpAkIpKjKIQQERERyYA57c/VMcRKmfUdiojkJAohRERERDKkB1gREZGspBBCREREJANm899fiLXRcAwR2xMUVPupX2PRom+oV+914uPjn/q1HmT+/NnZev2nQSGEiIiISEb+WKJTPSGs2d2JKfUdikjmbdy4nnLlyrNt2+ZsrWPBgjnZev2nQUt0ioiIiGTA8tiqEMJ6aW5RkefCqVMnGDNmFMnJSRQuXJS+fQeRN29eVqxYyooVS0lNTaVo0aIMHDgMFxcXRowIxc3NjePHj3H9+nW6du1O3boNALh0KZLExES6du3B/Pmzadq0OQBr1qxkx46tJCYmEhl5kXfeaU9qairr16/B0dGJsWMnkjdvvgxr6datC9269aRcufLExsby4YcdCAtbyZo1K9m5cztJSUlcvhxJQEAdunb9mK+++h/Jycl06tSOUqVKM3jw8Oz8iLNMpkKImzdvMmDAAE6ePInBYGDkyJGUKlWKXr16cenSJYoUKcKECRPIly8fZrOZESNGsG3bNlxcXPjiiy+oUKECAEuXLuWrr74CICQkhJYtWwJw5MgR+vbtS1JSEoGBgfTv3x+DwfCUbllEREQkk8z6K7q1s3x3CpJEstzx40c5duxIlrbp6/sy5cpVeOTzhg8fTM+efahS5VVmzpzKnDkz+Pjj3gQG1qV58zvPndOnT2HVqmUEB7cF4Nq1a0yZMpPz58/x+eefWEKI8PD1NGjQkEqVqnDhwnlu3LhO/vwFADh79gxz5nxNcnIKbdu2ICSkO3PmfMOkSeNYt241bdq0y7CWBzl16iRz5nyNo6Mj7dq1olWrtwkJ6c6SJYuYO/ebR/48crJMDccYMWIEtWvXZt26dSxfvpwyZcowffp0atasyYYNG6hZsybTp08HYPv27Zw7d44NGzYwbNgwQkNDAYiNjWXy5MksWrSIxYsXM3nyZOLi4gAIDQ1l2LBhbNiwgXPnzrF9+/anc7ciIiIij+DuMIy7q2SIFbIESSJiq+Lj47l16xZVqrwKQOPGb3LoUARwJzTo2vVD3nvvbTZuXMdvv521nBcQUAc7OztKlSrNjRs3LNvDw9dTv35D7OzsqFOnHlu2hFv2+flVxdXVDQ8PD9zcclOrVgAApUu/wJUrVx5Yy4NUrVqN3Llz4+zsTMmSpYmKinryDyaHemhPiFu3brF3716++OILAJycnHBycmLTpk0sWLAAgBYtWtChQwf69OnDpk2baNGiBQaDgcqVK3Pz5k2uXr3Knj17qFWrFu7u7gDUqlWLHTt2UL16deLj46lcubKlrU2bNhEYGPi07llEREQkc8zqy2/tNLmoyNNTrlyFx+q18CyNHDmEkSPHUrbsi6xZs5IDB/Zb9jk6Ov7lyDv/Rpw5c5rIyIv06vUvAFJTUylcuDCtWr19zzl2dnY4OjpZXptMxgfWYm/vQFranbmGUlKS0+37a7v29g9vy5o9tCdEZGQk+fPnp2/fvrRo0YL+/fuTkJDA9evXKViwIABeXl5cv34dgOjoaHx8fCzn+/j4EB0dfc92b2/v+26/e7yIiIhIdjNriU4boCE1IrYud+7c5MmTl0OHDgCwbt1qKlf2AyAh4Taenp4YjUY2bFj70LbCw9fTuXMXwsJWEha2kuXL13Ht2jWioq48cS2FChXixInjAGzduilT7dnbO2A02lYg8dCeEEajkV9//ZWBAwdSqVIlhg8fbhl6cZfBYNAcDiIiImJ79Fd062dZojN7yxCRrJOUlETLlk0s799+ux0DBoT+ZTLIIvTtOxiADz8MoUuXTri7u1O+/MskJCQ8sO3w8A2MHTsx3baAgDqEh6+3zAvxMBnV8s47HRg06HNWrFhCzZr+mWqrefOWdOzYlhdfLPf8TEzp4+ODj48PlSpVAuCNN95g+vTpFChQgKtXr1KwYEGuXr1K/vz5gTs9HP46fiUqKgpvb2+8vb3Zs2ePZXt0dDTVq1fP8HgRERGR7GaZE0IhhNUy3+eViFi3HTv23nf79Olz79nWsmUwLVsG37O9f//QdO83btwBwOLFy+85tnv3TyyvmzRpZnkdFrYy3fa7+8qWfem+tZQoUZJ5876zvO/Spes95wKMHj3B8rpr1x507drjnras2UOHY3h5eeHj48PZs3cm8Ni1axdlypShXr16LFu2DIBly5ZRv359AMt2s9nMwYMHyZMnDwULFsTf35+dO3cSFxdHXFwcO3fuxN/fn4IFC5I7d24OHjyI2WxO15aIiIhItjJrZQWrZ74z/lpBkohIzpCpJToHDhzIv//9b1JTUylWrBijRo0iLS2Nnj17EhYWRuHChZkw4U5aExgYyLZt2wgKCiJXrlyMHDkSAHd3d7p27Upw8J0U6l//+pdlksrBgwdblugMCAggICDgadyriIiIyCPRPALWT3OLiojkLJkKIXx9fVmyZMk92+fNm3fPNoPBwODBg+/bTnBwsCWE+KtXXnmFVatWZaYUERERkWfH0hFCT7BWSymEiEiO8tDhGCIiIiLPK7O68tsAzeshIpKTKIQQERERyYhWx7B6+upERHIWhRAiIiIiGdJf0a2fJhcVEclJFEKIiIiIZMCs1TGsnuUr1JwQIiI5QqYmphQRERF5Lv3xBKsHWGtmTvdDRKxfUFBtNm7c8cyvu2jRN0ydOpkVKzaQO3fuZ379u+bPn81773W+7764uFg+/rgrADduXMfOzg53dw8AZsyYh6Oj4zOrMyMKIUREREQyYAkf0vQEa7XMGlIjIllj48b1lCtXnm3bNtO0afNsq2PBgjkZhhD58rkzd+43AMyaNY1cuVxp165Dpto1Go04ODz9iEAhhIiIiEgGzGnqCWHt/swe9B2KZLWYmJXciFmWpW3m92iBh0ezRz7v1KkTjBkziuTkJAoXLkrfvoPImzcvK1YsZcWKpaSmplK0aFEGDhyGi4sLI0aE4ubmxvHjx7h+/Tpdu3anbt0GGbZ/6VIkiYmJdO3ag/nzZ1tCiDVrVrJjx1YSExOJjLzIO++0JzU1lfXr1+Do6MTYsRPJmzdfhvV169aFbt16Uq5ceWJjY/nwww6Eha1kzZqV7Ny5naSkJC5fjiQgoA5du37MV1/9j+TkZDp1akepUqUZPHh4pj6f48ePMXnyeBISEnB3d6dfv1A8PT3p1q0LZcu+xOHDB2nQoBE//ridF198iUOHDpKUlMiAAUNYsGAuZ8+epl69ILp06frI383faU4IERERkQzpr+jWT8MxRJ4Hw4cPJiSkO/PmfUeZMi8wZ84MAAID6zJz5nzmzfuWEiVKsWrVn6HJtWvXmDJlJqNHj2fq1MkPbD88fD0NGjSkUqUqXLhwnhs3rlv2nT17hpEjxzBjxnymT5+Ci4sLc+Z8w8svv8K6dasfWN+DnDp1kqFDRzFv3nds2rSR6OgoQkK64+zszNy532Q6gDAajUyYMIZhw/7D7NkLadq0OdOnf2nZn5qayqxZC3jnnfYAODg4MmvWAt56qxWff96bTz75jPnzv2ft2lXExcVm6poPop4QIiIiIhnREp3WT/N6iDw1Hh7NHqvXQlaLj4/n1q1bVKnyKgCNG7/JwIGfAXcCghkzviI+/haJiYlUr/6a5byAgDrY2dlRqlRpbty48cBrhIevZ+TIsdjZ2VGnTj22bAmnVau3AfDzq4qrqxuurm64ueWmVq0AAEqXfoEzZ04/sL4HqVq1mmXuiZIlSxMVFYW3t88jfjpw4cI5zp49Q69e/wIgLc1EgQKelv316welO97f/079Zcq8QKlSpfH0vHNs4cJFuHo1mnz53B+5hr9SCCEiIiKSAbN6Qli9P0dj6DsUeR6NHDmEkSPHUrbsi6xZs5IDB/Zb9qWfpDHjfyPOnDlNZORFy0N8amoqhQsXtoQQf23Hzs4OR0cny2uTyfjA+uztHUhLSwMgJSU53b6/tmtv//C2MmI2Q6lSpZk2bc599+fKlSvdeyenO/UbDAbL67vvTSbTY9XwVxqOISIiIpIRLdFp/fTdidi83LlzkydPXg4dOgDAunWrqVzZD4CEhNt4enpiNBrZsGHtY7UfHr6ezp27EBa2krCwlSxfvo5r164RFXXliesrVKgQJ04cB2Dr1k2Zas/e3gGjMfOBRPHiJYiNjeHIkcPAneEZZ8+eyfT5WU09IUREREQy8GcPCD3IWiuzVscQsTlJSUm0bNnE8v7tt9sxYEDoXyZ+LELfvoMB+PDDELp06YS7uzvly79MQkLCI18vPHwDY8dOTLctIKAO4eHryZ+/QKbayKi+d97pwKBBn7NixRJq1vTPVFvNm7ekY8e2vPhiuUzNC+Ho6Mjw4f9hwoSxxMfHYzKZaNPmHUqXLpOp62U1g9lK/0VOTTURG/vov0AiIiIimbV1xnguHNrDm5+PIn/RktldjjyGVV/05UbkOYpXqk6dj3pldzkiVi8q6jw+PiWyuwzJQe73O+HllSfD4zUcQ0RERCQDZnPaHz+t8m82wl8npNR3KCKSE2g4hoiIiMjDKISwXpZpPfQdisiDnTlzmmHDBqXb5ujoyIwZ87KpogeLi4vl44+73rN94sQpT7yCxdOkEEJEREQkA5pPwBbouxORzClT5gXmzv0mu8vItHz53K2q3rs0HENEREQkI1odw+opSBIRyVkUQoiIiIhkwPIAm811yBOwTAmhb1FEJCdQCCEiIiKSoT8eXNPSsrcMeQLqCSEikpMohBARERHJiDndD7FCf4YP+hZFRHIChRAiIiIiGfhziU71hLB6yiBEbMb169cYPLgvbdq8RefO7fn3v3tw4cL5x2prxIhQtmwJB+CLL4bx229nAZg/f/ZDzw0Kqp3u/Zo1K/nvf/8DwLJlYaxduyrDcyMi9vHLL4ceq2Zrp9UxRERERDKgP6LbAMu8HvoSRWyB2WymX78+NG7clCFDRgFw6tRJYmJuULx4CQCMRiMODo/+qPv55wMtrxcsmMN773V+7DpbtAh+4P4DB/aTK5crr7xSKdNtPu595TTWfwciIiIiT83d+QTUE8JaWaIHzQkhkuVWxNxiaczNLG2zpUdemnvkyXB/RMQ+HBwc0j3kly37IhER++ja9UPy5MnD+fPn+frrxUydOpkDB/aTmppCy5atadGiFWazmfHjR7N3788ULOiDo+Ofj8TdunWhW7eebNmyieTkZDp1akepUqUZPHj4I9/HrFnTyJXLlXbtOrB48XcsX/4D9vb2lCxZiv/7v+4sX74EOzs7NmxYS69efShY0JtRo4YSFxeLu7sHffsOxsfHhxEjQnFycuLkyRNUrFiJH3/cwVdfzcbDw4O0tDTeeecfTJ06Bw8Pj0euMbsohBARERHJiFmTQlg9yxKd2VyHiGSJs2fP8NJL5e677+TJ48yf/z2FCxdh+fIluLm5MXPmfFJSUggJ+YDq1V/j1KkTXLhwnoULFxMTc4P27VvTtGnzdO2EhHRnyZJFzJ37zQNruRtU3HXzZhz+/gH3HLdw4VwWL16Bk5MTt27dIk+ePLz11j8sIQXAp5/2onHjN2nc+E1WrVrOxIljGDVqHAC//36VqVNnY29vj5tbbjZuXEubNu3Yt28PL7xQ1qoCCFAIISIiIpIhTWpo/fQdijw9zT3yPLDXwrPm61uBwoWLALB3725Onz7N1q2bAbh9O57IyIscPHiABg0aYW9vj6enF35+1R77es7OzumCijVrVnL8+K/3HFemTFmGDh1A7dp1qF27zn3bOnr0MCNHjgHgjTea8tVXkyz76tZtgL29PQBNmzanb9/etGnTjtWrl9OkSfP7tpeTKYQQERERyZCWd7R6lt4s+g5FbEGpUqXZunXTffflypXL8tpsNtOrVx9q1KiZ7phdu358qvXdz5gxEzh06AA//rid+fNnM2/ed490vouLi+W1t7cPHh4F2L9/L7/++iuDBj36UJHsptUxRERERDLw5/OrHmCt1d0JKfUNitiGV1+tRkpKCsuXL7FsO336FIcPH0x3XPXqNVm2LAyj0QjAhQvnSUxMpHLlKmzevBGTycS1a9eIiNh33+vY2ztYzn0SaWlpXL0ajZ9fVUJCehAfH09iYiKurm4kJiZYjnv55YqEh68HYMOGtVSsWCXDNps1e4uhQwdSt259Sw8Ja6KeECIiIiIZuTshpUII62UZjaHvUMQWGAwGRo0ay8SJ4/j663k4OTlTqFChe4Y5NGvWgqioK3Tu/C5msxl3dw9GjRpHQEBd9u/fS/v2rfH29uHll1+573WaN29Jx45tefHFco81MeVdaWlpDB06kNu34zGbzQQHtyVPnjzUqlWbgQM/Y8eObfTq1YdevT5l5MghfPvtAsvElBnx9w9k5Mih98xlYS0MZiuN9lNTTcTGJjz8QBEREZHHtG78EK6eOU79rp9TpHzml1GTnGPJ4B7EX/8dnxfL07DHwIefICIPFBV1Hh+fEtldxnPt+PFfmTTpv0yZMjO7SwHu/zvh5ZXxXCHqCSEiIiKSIc0nYO3MWh1DRGzIggVzWbYszCrngrhLIYSIiIhIBv5coVNPsFZLwzFE5AnExcXy8cdd79k+ceIU8uVzf+b1dOjQiQ4dOj3z62YlhRAiIiIiGbk7J0SaHmCt1Z8Bkr5DEXl0+fK5p1uGU56cVscQERERyYB6QtgADccQEclRFEKIiIiIZOjuA6yeYK2fvkMRkZxAIYSIyFN06/doEmJvZHcZIvKYLOGDQgirpYkpRURyFoUQIiJP0c4FU9i3dGF2lyEij8usnhBWz6w5IUREchKFECIiT5ExOQljcnJ2lyEij0s9IWyHvkMRmxEQUJ1OndrRoUMbBgz4jKSkpGd6/UWLvnmia27fvhV//6qcP38u64qyIgohRESeInOaWX9BFbFiZvWEsHoajiFie5ydnZk79xsWLFiEo6Mjy5aFpdtvNBqf2rVNJhOLFn37RCFEePh6KlaszP+zd+5hclR1+n+rumeSkDtIJuLGKCw+XERYZQ0RTDTsJMvVRJOFn/ssG1aEX2BFLj8UREkgAd1HUJZlV4lZNaCIJpKABE3IcEnCRbmFAHILEEiAmcltZjLX7q46vz+qT3VVT1+quutUV3W/n+fRzDTd1TVdXadOvef9vt+NG9cX/O8q9z8KsEUnIYQoRAiTM19CYgzbO9YBLMcgRBnrXu7A/S+1B7rNsz85GWcc2+L5+ccffwK2b9+O5557BitW/BRjx47FO++8g5Urf4NbbvkBXn31r0gkEvjGN67Apz99Ih588A/YtOkR9Pb2Ys+e3Zg9+zT8279dCABYv/5BrF59D9LpDI455lhceeXVSCQSaG39PM4++8t45pm/4AtfmIU9e3bj0ksvwvjxEzBnzul4883t+OY3rwQA3H//GuzY8RYuvfTKgvvb39+Pbdu24rbbfopvf/tyfO1rFwHAsP3/9a9X4ac/vR3PP/8s0ukU5s1bgLlzv4L+/n5cc82VOHCgB5lMBl//+iJ8/vNfqO5DDxmKEIQQohi29iMkxrAaI/YIHkRC6pZMJoOnnnoC06Z9aEfe4QAAIABJREFUDgDw+uuv4s47f4vDDvsIfvMbK5Przjt/i3fe2YHLL78Ev/nNvQCAV155GXfe+VuMHDkSF1xwHj73uVMwcuQotLU9hJ/85OdIJpO4+eYfYMOGP+K0087EwMAAjjnmk/jGNy4HAKxbdz9uu+0OTJgwAf39/bjzzp/jkku+iWQyiQcf/AOuuuo7Rfd5y5bHMG3adHz0o1MxfvwEvPrqKzjqqKOH7f99992L0aNHY8WKO5FKpbBo0dfw2c+ehEmTWnDTTT/E6NFj0NXVhYsuWohTTpkJTdNUftSB4kmEmDVrFkaPHg1d15FIJHDvvfeiq6sLl19+Od577z185CMfwa233orx48dDCIEbb7wRjz32GEaOHIkf/OAHOPbYYwEAa9aswU9+8hMAwKJFizBv3jwAwEsvvYRrrrkGg4ODmDlzJq699tpYfYiEEFIMIQSEyYkvIfFF3sCatd0NUjmyHKPGu0FIPXLGsS2+XAtBMTQ0hIULvwrAckKceeaX8OKLL+Doo4/FYYd9BACwbdtWzJ9/DgBg6tSPYfLkD2PnzncBACeeOA3jx08AAMycOQvbtm1FIpHAa6+9ggsuOC/7HoOYOHEiACCRSOALX5hVcF8OOuggfOYzf4/HH9+Mj33s48hkMjjiiL8tuu8bN67HggXnAgBOPXU2Nm5cb4sQzv1/+umnsH37djz66MMAgL6+XuzatROTJrXgjjv+Gy+88Dw0Tcfu3buxb99eHHLIhyr8NMPHsxNi5cqVOPjgg+3fly9fjunTp+PCCy/E8uXLsXz5clx11VXYtGkTduzYgQ0bNuCFF17AkiVLsGrVKnR1deH222/H73//e2iahi9/+cuYNWsWxo8fjyVLlmDp0qU4/vjj8fWvfx2bNm3CzJkzlfzBhBASKkLw5oWQGCNFRC6ix5dcNQYPIiH1gsyEyGfUqFGeXj98wVuDEAKnnXYm/u///fdhz29ubkYikSi6vTPPnIu77vo5PvrRj+H0088q+ryenm48++zTePPN7dA0DaZpzREvueSbw/ZfCIHLL78K06ZNd23jwQf/gK6uLvzv//4KyWQS8+efhVQqVe5PjhQVB1O2tbVh7ty5AIC5c+di48aNrsc1TcMJJ5yAnp4edHZ2YsuWLTj55JMxYcIEjB8/HieffDI2b96Mzs5O9Pb24oQTToCmaZg7dy7a2tqC+esIIaTGCMFiDELiDZ0Q8YflGIQ0IscffwI2bPgjAODdd99BR0c7PvrRqQCAp5/+M3p6ujE0NIjNmx/Fpz51PD7zmc/i0UfbsH//PgCWYNDe/kHBbR900EHo7++zfz/22E+is7MDGzeuxz/8w5yi+/TII22YM+d0/P73D2D16j/g3nvX4bDDPoIXXnh+2HM/+9npWLt2tR1S+e6772BgYAC9vb2YOHEikskknnvumaL7GGU8OyG+9rWvQdM0nHPOOTjnnHOwd+9eTJo0CQBw6KGHYu/evQCAjo4OTJ482X7d5MmT0dHRMezxlpaWgo/L5xNCSF1AJwQhsUbQyh9/hOsfQkiDMG/eAtxyyw9w3nnnIJFI4Nprl6C5uRkAcMwxx+Laa7+F3bs7MXv2aTjqqGMAAF//+iJcfvm/QwgTiUQSV1zxbUye/OFh2z777Hm48spv4EMfOhT/9V93AAC++MVWbN/+GsaNG1d0nzZuXI9//ud/dT02c+YsbNy4HrNmtboeP+usuWhv/wD/9m//DCEEJkyYiO9//xbMnn0avv3ty3HeeefgqKOOwdSpH6vmY6oJnkSI3/zmN2hpacHevXtx/vnn4/DDD3f9d03TmOFACCEFEEJw8Y2QOCNPYJNiYlxhMCUh9cdDD20e9tinP30iPv3pE+3fR4wYge98Z3HB1x966CR8//u3DHv81FNn49RTZ5d9v/nzz8X8+ee6Hnvxxa34p3/6asn9loKFE5kPIf8Gia7ruOiiS3DRRZcMe80dd/yi5PtEHU/lGC0tVtjIIYccgtbWVmzbtg2HHHIIOjs7AQCdnZ12XkRLSwva23NtWtrb29HS0jLs8Y6OjoKPy+cTQkg9YLXo5M0LIXGFzR3rANvNwqNICAmeAwcO4Nxzv4zm5hE48cTP1np3YkFZEaK/vx+9vb32z48//jiOPPJIzJo1C2vXrgUArF27FqeeeioA2I8LIbB161aMHTsWkyZNwimnnIItW7agu7sb3d3d2LJlC0455RRMmjQJY8aMwdatWyGEcG2LEEJij8jZuQkhMUTewFJMjD8cigkhAE4//SxcccW3A9ve2LFjcc8992LZsv+wH+vu7sLChV8d9r/u7q7A3jfOlC3H2Lt3Ly65xLKAGIaBM888EzNmzMBxxx2Hyy67DKtXr8Zhhx2GW2+9FQAwc+ZMPPbYY2htbcWoUaNw0003AQAmTJiAiy++GPPnzwcAXHLJJZgwwWqLsnjxYrtF54wZMzBjxgwlfywhhISNECYdwITEGFtE5HkcW3JCMA8iISQcxo+fULB7B7EoK0JMmTIF999//7DHJ06ciJUrVw57XNM0LF5cuPZm/vz5tgjh5LjjjsMDDzzgZX8JISR+cAWVkPhCJ0T8sY8hRQhCCIkCFbfoJIQQUh5hmpz4EhJjBFsr1A88hoQQEgkoQhBCiGooQhASX+zGCnRCxBWWYxBCSLSgCEEIIQqxMiE48SUkrlB8qAdYjkEIIVGCIgQhhKhEgE4IQuKM7YTgeRxXeOwIqT9aWz8f6vuZpolbb/0h/uVf/gnnnXcOLrjgPLz//nsAgDvv/LmnbXh9XiNQNpiSEEJI5dAJQUi8yWVC8DyOLYLHkBBSHW1tG7Bnz26sXHkPdF1HZ2cHRo4cBQC4665f4Lzz/q3sNrw+rxGgCEEIIQoRQlCEICTOsLNC7GGXVULUMeLV1Rj5yj2BbnPw6HMxdNTwjorleOON1/DDH34fQ0ODOOywv8E111yHcePG4f771+D++9cgnU7jb/7mb/C97y3FyJEjceONSzB69Gi8+uor2Lt3Ly6++Bv44hf/oeC29+7dg0MO+RB03SokmDSpBQDwk5/8F4aGhrBw4Vfx8Y8fjsWLl+Gaa65ER0cHUqkUFiw4F1/60peHPe/CCy/Gt751Ge6663cAgLvvvgsDA/342tcuwqpV9+C++36PRCKBj33s47j++u9X+ElGF4oQhBCiEmH/HyEkhgiuotcBPIaENALLli3GZZddhb/7u89gxYqf4he/+Bm++c0rMXPmF3H22fMAAMuX/w8eeGAt5s8/FwCwZ88e/M//rMA77+zA1VdfUVSEmDWrFRdffAFeeGErPvOZv8ecOafhE584CosWfQP33vs7/PKXd9vPtcSP8RgaGsQFF5yHL3xh1rDnffDB+0X/jl/96pdYtep+NDc348CBA0F9PJGCIgQhhChECBPC5MSXkNginRAUE+ML3SyEKGPoqPkVuRaCpre3FwcOHMDf/d1nAACnnXYmvve9bwMA3nrrTfzsZz9Bb+8BDAwM4LOfPcl+3YwZX4Cu6/j4xw/Hvn37im5/0qQW3H337/Hss0/jueeewTe/eTGWLv0BTjzxs8Oeu2rVPdi06VEAQGdnB3bu3Inx4yd4/luOOOJI3HDDd/H5z38Bn//8Fzy/Lk5QhCCEEMUwXZ+QOoBiYmzhkSOksbnpputx000348gjP4EHH/wDnn/+Wfu/NTU1OZ5ZerRobm7G9OknY/r0kzFx4sHYvPnRYSLEc889g2ee+QvuuOMXGDlyJP793y9EKjU0bFuJRMIljDqf88Mf3ooXXngejz++CXfe+XOsXHkPksn6um1ndwxCCFGIMClAEBJnpIhIJ0SMYUkNIXXPmDFjMHbsOLzwwvMAgD/9aR1OOOHTAID+/j586EMfQiaTwYYNf6xo+6+99ir27NkNwOqU8eabb6Cl5cMAgEQiiUwmAwDo6+vF2LHjMHLkSLzzzg789a8v2dtwPu/ggw/B/v370N3dhVQqhSee2GJvu7OzA5/+9IlYtOhS9Pb2YmBgoKJ9jjL1JakQQkgEoQWYkBjDFp11A4UkQuqHwcFBzJt3uv37Oed8Fd/97hJHMOVHcM01iwEAF1ywCBdeuBATJkzAMcd8Ev39/b7fb//+ffiP/1iGdDoFADj66GPxla/8EwDg7LPn4V//9Vx84hNH4ZprrsPatffin/95Pj760ak45phP2ttwPm/x4mVYuPDr+PrX/xWHHjoJU6d+DIAlQtxww/fQ19cLIQTmzz8XY8eOrfRjiiyaiOlVNZ020NXl/wtECCFh8uvLz8PoiR/C3Ot+VOtdIYRUwG+vvhBDvQfw6S/9H3yy9exa7w7xiRACd33jqwCAMYdMwpev/88a7xEh8ae9/R1Mnjy11rtBIkSh78ShhxYXT1iOQQghCmGLTkLijQyW5XkcU1zHjceQEEKiAMsxCCFEJUIwmJKQWMM8gTjjkiB4DAkhZXjzze1YuvQ612NNTU342c9W1miP6hOKEIQQohAhBBffCIkzzISIN87jxkNISGAIIaBpWq13I3COOOJv8ctf3l3r3YgVlVwfWY5BCCEqoROCkFiTCzPkHWw8EY6feAwJCYJkshl9fT0UZwmEEOjr60Ey2ezrdXRCEEKIQpgJQUjMsds71nY3SGXITA/rFx5EQoJg4sRDsX//bvT2dtV6V0gESCabMXHiof5eo2hfCCGk4RHsTU9I7JHnMR1NcYXjLyFBk0gk8aEPfbjWu0FiDMsxCCFEFYKp+oTEHvs8rvF+kIpwRULwIBJCSCSgCEEIIYqgE4KQ+MPzOO6wHIMQQqIGRQhCCFEGnRCExJ/seUxbfyzh+EsIIdGDIgQhhCjCDkTjJJiQ2GKfvibP47hDQYIQQqIBRQhCCFEGV1AJiT88j2ONS3jgMSSEkChAEYIQQhRhp+pzBZWQ2CLPX66ixxPXceMhJISQSEARghBCFJGb/HLmS0h8YVlVvUA3CyGERAOKEIQQogq26CQk/vA8jjfuHp212w9CCCE2FCEIIUQRbO1HSPzheRxvnO4HHkJCCIkGFCEIIUQVXEElpG7geRxTRNFfCCGE1AiKEIQQogiuoBISbwQ7K8Qe+xhqGg8hIYREBIoQhBCiCLs7hjBrvCeEkIpgnkAdYB03TdMYTEkIIRGBIgQhhKjCFiFqvB+EkIpwOiFYjhFP5HHTNI2DMSGERASKEIQQogh71Y1OCEJiCkWI2GOXY3DKSwghUYEjMiGEqIJOCEJijTsSgidynNE0jUISIYREBIoQhBCiiFwwJZ0QhMQSx7nLPIF4wnIMQgiJHhQhCCFEFWzRSUiscZ26Js/jWCJFCJ1TXkIIiQockQkhRBEMtSMk7ogCP5FYwnIMQkhIpP+wA+m2XbXejUiTrPUOEEJIvSLy2/tpWu12hhDiG7eQyLKqOOIqx6CURAgJAdE1BC1t1Ho3Ig2dEIQQogo6IQiJNy4hsXa7QapH03VGQhBCwkEwgqYcFCEIIUQRbuGBVyNC4gadEPGHTghCSPgIDjdloAhBCCGqcN7AMNSOkHjDUzimSBFC59IkISQcBDjelMGzCGEYBubOnYuLLroIALBz504sWLAAra2tuOyyy5BKpQAAqVQKl112GVpbW7FgwQLs2pUL5bjjjjvQ2tqKOXPmYPPmzfbjmzZtwpw5c9Da2orly5cH9bcRQkhNcbf048WIkLjhdD/QCRFT5NCrabwnIISEgwCnfWXwLELceeedOOKII+zfb775ZixcuBAPPfQQxo0bh9WrVwMAVq1ahXHjxuGhhx7CwoULcfPNNwMAtm/fjnXr1mHdunVYsWIFrr/+ehiGAcMwcMMNN2DFihVYt24dHnjgAWzfvj3gP5MQQmqAyUwIQmINT9vYw3IMQkjoCIZClMOTCNHe3o5HH30U8+fPB2AN6E899RTmzJkDAJg3bx7a2toAAA8//DDmzZsHAJgzZw6efPJJCCHQ1taGM844A83NzZgyZQqmTp2Kbdu2Ydu2bZg6dSqmTJmC5uZmnHHGGfa2CCEkzjidEBQhCIkfzISoB3LlGByHCSGhQCdEWTyJEDfddBOuuuoq6Lr19P3792PcuHFIJq0On5MnT0ZHRwcAoKOjAx/+8IcBAMlkEmPHjsX+/fvR0dGByZMn29tsaWlBR0dH0ccJISQMXn1sPZ64W00Z2LAWnYSQeMHuGLHHdkLoGsdhQkh4cLgpSVkR4pFHHsHBBx+MT37yk2HsDyGEhErnW6+j/fWX1WzcVU/OqxEhcYNupjpAHjdNq+1+EEIaBsFyjLIkyz3hueeew8MPP4xNmzZhaGgIvb29uPHGG9HT04NMJoNkMon29na0tLQAsJwMH3zwASZPnoxMJoMDBw5g4sSJaGlpQXt7u73djo4O+zXFHieEEOUIAWGqsVm7jRC0chMSO+hmij+2BmGtuwkhsvkQhBCiCF4uylLWCXHllVdi06ZNePjhh/GjH/0IJ510Em655RZMmzYN69evBwCsWbMGs2bNAgDMmjULa9asAQCsX78eJ510EjRNw6xZs7Bu3TqkUins3LkTO3bswKc+9Skcd9xx2LFjB3bu3IlUKoV169bZ2yKEENUIYapb4XQKD7wgERJrBE/iWCLgDKYExSRCiHqE4LyvDGWdEMW46qqrcPnll+PWW2/F0UcfjQULFgAA5s+fj6uuugqtra0YP348fvzjHwMAjjzySJx22mk4/fTTkUgkcN111yGRSAAArrvuOlxwwQUwDANf+cpXcOSRRwbwpxFCSHlUWuZUOSGeXXs3PnLM8Zj8iWMD2yYhZDgulxRvXuOJcIsQAgB9EIQQpQjwmlEGXyLEtGnTMG3aNADAlClT7LacTkaMGIHbbrut4OsXLVqERYsWDXt85syZmDlzpp9dIYSQQBCmqbAcQ40T4tXH1kOYBkUIQkKEmRDxxD5qdEIQQsKC3THK4qk7BiGE1CtCiFDyGoJ8D2ufeXUjRDXscFMH2E4IOeXlcSSEKIbBlGWhCEEIaWwU3tA7HRaBvofKHAtCiI3zPOM5F1ccLTrB+wJCSEhwrCkJRQhCSEMjhAlhqgqmVLOKKhR29CCEOKEIEXdyHTp19wOEEKIKlmOUhSIEIaShUVmO4bz+BHkDI0yTE2lCwsB9EtdsN0gV5AVT8s6AEKIclmOUhSIEIaShEabCC4WCcgy5Ha7KEqIelmPUA9njpltTXh5GQohy6IQoC0UIQkhjIxR2xyjxW8XbpAhBSHi4zjOec3EkV45RH06It595HNv+eG+td4MQUop4DzOhQBGCENLQKC3HcGw3sNyJ7DbD6OhBSKMjmAlRB7jLMeJ+HN/d9gze/MvmWu8GIaQU7GJWFooQhJCGRpgKO02Ior9UvsmsmMFgSkJCgC06Y489vmt1MuUNqa00IaQKWI5RljoZkQkhpDKsThOKWnQKFZkQpvwhkO0RQorjzoSo4Y6QyskPpoz5gWQwMSExQIDnaRkoQhBCGhohhF3ioGDjuZ8DEjqYCUFIiLicEFx9jiNC1Fc5hhACJp1whEQcQSdEGShCEEIaG2FmcyGCv1q4VlEDK8dgJgQhYcFYyvpB07XyT4oBwlQXpkwICQiWY5SFIgQhpKGxhQLVIkRQ25dOCEUlJIQQB06xj8JfPMnPhIi9E4LlGIREHpZjlIUiBCGkoVFa3qAg1E6laEIIccNcyvgjD5tdjhHz5Ukrx4iCGCGRRrAcoxwUIQghjY0sb1AwqVPhhGA5BiEhwkyI+DMsmLKG+xIAVkcnfhcJiTQsxygLRQhCSEOjNugx+BsYOfmMe7gaIfGA3TFijy1C6Nlf430gVXZ0IoRUDx2r3qAIQQhpaHI39aqdEAFvkxc3QpSjJNeFhIp91KQTIu7Lk4JOCEJI/KEIQQhpaOSKkpIaW9cNTEDbz26TLdoIUY9beIj5zWujIp0QugymrOG+BAC7YxASceQYQ+G6JBQhCCENjS0OKO6OwWBKQuINLfDxRAZR1lUwJZ0QhEQXkfcvKQhFCEJIY6MwE4LBlITEG/eKM2eUsUR26KybFp2CpUGERBl7sai2uxF1KEIQQhqaXDCl2nKM4JwQDKYkpBbwnIsrshxDK/O8eMByDEIiDssxPEERghDS0NgihOoWnQFvk9ZwQkJAgZBIwsUeh2U5RsyPI50QhEQdOiG8QBGCENLQKHUWOLcZlMghxQeWYxCiHFdnBd74xRO7Rafm+j22mCZAIYKQ6MJMCE9QhCCENDYKgx6VZEKwHIOQ8MiKh5qm85yLKcIWIepjymvyGkBItGE5hifqY0QmhJAKka0u1bS8dJZjUIQgJG7Is0zT9dh3VWh0tDopx4DCEkJCSAAwmNITFCEIIY2NSieEM7ehyu1vXnk73n72ydw2WY5BiHqy562u61zViin5mRBxx+6QRBGCEE8Iw0R6zVswO/pDekPHj7xuFIUiBCGkoVHZ8tJVjlFlkOSuF59D55uv2ts0GUxJiHJsK7/OcozYkleOEffjqLSjEyH1yIAB860eiA/CFyHohigORQhCSEOTm9CpuFIEdyUSdhiZKR+oanuEEC84bmBjfvPa6ORadMb7OKrs6ERIXaLQ8VrkDQv+SNxQhCCENDZhteis8uInhGkLEUFsjxBSHqeVn+dcPBlWjhHzw2hfq/h9JMQbNdQgeJ4WhyIEIaShCatFZ9UihGla/eFNBlMSEhrZ04yZEHGm3soxVIYpE1KHhO2EYDmGJyhCEEIaGntCqjgTotqLn8iWYtjb5ASUEOXIGz6rOwaJJdIIUSflGOFbywmJOSLvX+XvRxXCCxQhCCENjdL6WpcTorrtC9OEMIXiDAtCSEE0jTX4MUXkOSHifk9gKgxTJqQeyXUVC+sNi/xMXFCEIIQ0NCrLG9xOiCq245h0Ki0fIYS4cHbHIDEl7xiKuN8VMJiSEH/YToiwyjEYTOkFXlUJIY2NQmeBc6WqmlUrl1uD7dkICY/s+abrOs+5uGLnUspgynjfFTAXiJAKYTBlpKAIQQhpaGxngZJyDOf7VH4hcjkhmIxOSGjYTghN54pWTMmVY9RJdwyV1yxC6hG7HKMGJ3/MxxuVUIQghDQ0KjMWXCun1YgQjkkne8QTEibO9o6cTcYSu0VnfZRjCLrhCPFHyMGUzKX0BkUIQkhDYwcWKb6pr8oJ4RBKaMUlJDyEsxzD5DkXR3Juljorx6AQTYg3Qm/RGVxntHqGIgQhpLFRGPQY1E2Lc9LJ7hiEhIgj1DDuK+iNjhQh4n4UeQ0gxCeht+gs8jNxQRGCENLQqLW2Olp0VrFq5Vr5ohWXkPCwnfw6V7RiiivXw3qghnsTACzJI8QfthMi5PfL/5m4oAhBCGlYnCtJKqzWru0HkQnhKMfghY0Q9QhHJgRXnmOKPG66Jh+o2a4EAUvyCPFJ6C06i/xMXFCEIIQ0LM6VJCXOgoDSiVzdMeyQSl7ZCFGNnQmh0QkRd+xyjJgfRnbHIMQntSzHIEUpK0IMDQ1h/vz5OPvss3HGGWfgtttuAwDs3LkTCxYsQGtrKy677DKkUikAQCqVwmWXXYbW1lYsWLAAu3btsrd1xx13oLW1FXPmzMHmzZvtxzdt2oQ5c+agtbUVy5cvD/pvJISQgridEMFP6IJyWuTKMQST0QkJE2cmRNzvXhuUugum5DWAEH/Icz6sxRuWY3iirAjR3NyMlStX4v7778fatWuxefNmbN26FTfffDMWLlyIhx56COPGjcPq1asBAKtWrcK4cePw0EMPYeHChbj55psBANu3b8e6deuwbt06rFixAtdffz0Mw4BhGLjhhhuwYsUKrFu3Dg888AC2b9+u9q8mhBDADqUEQmjRWY0TwjHpZCgZIeHDTIg4k5cJEfNlSnbHIMQntTzl4z3cKKWsCKFpGkaPHg0AyGQyyGQy0DQNTz31FObMmQMAmDdvHtra2gAADz/8MObNmwcAmDNnDp588kkIIdDW1oYzzjgDzc3NmDJlCqZOnYpt27Zh27ZtmDp1KqZMmYLm5macccYZ9rYIIUQlQrVa7dp8AJkQjmBK3hARoh77Rk9jd4y4Yg+VdVOOQSGaEF+E3qJz+FuT4XjKhDAMA1/60pfwuc99Dp/73OcwZcoUjBs3DslkEgAwefJkdHR0AAA6Ojrw4Q9/GACQTCYxduxY7N+/Hx0dHZg8ebK9zZaWFnR0dBR9nBBCVKO+HCMYp0UujNKEyVUwQkJH1zXmsMSV/HKMGItJQgiHtZzXAEI8ITWIsE4ZlmN4wpMIkUgkcN999+Gxxx7Dtm3b8NZbb6neL0IIUY7yYErXm1VfjmGawr6Ksh6YEPXYeQK6jjjfvDY2zmMYbweBc99NXgMI8UboLTqL/Exc+OqOMW7cOEybNg1bt25FT08PMpkMAKC9vR0tLS0ALCfDBx98AMAq3zhw4AAmTpyIlpYWtLe329vq6OhAS0tL0ccJIUQ5AbXQLLp5M3gnBK24hISJbNHJYMq4Ig9bLhMixjiFB34fCfGGGPZDSO8HnqclKDsi79u3Dz09PQCAwcFBPPHEEzjiiCMwbdo0rF+/HgCwZs0azJo1CwAwa9YsrFmzBgCwfv16nHTSSdA0DbNmzcK6deuQSqWwc+dO7NixA5/61Kdw3HHHYceOHdi5cydSqRTWrVtnb4sQQlQiFIsQ7veqfNXKGURGEYKQ8JAlGBpbdMaY+umO4RK2WY5BiCfs+VJNyjFCes8Ykiz3hM7OTlx99dUwDANCCPzjP/4jvvjFL+Jv//Zvcfnll+PWW2/F0UcfjQULFgAA5s+fj6uuugqtra0YP348fvzjHwMAjjzySJx22mk4/fTTkUgkcN111yGRSAAArrvuOlxwwQUwDANf+cpXcOSRRyr8kwkhxEL1hM4lPFRxIXIJD7Yrglc2QtRjnWc6W3TGF3ncdBlMGd/j6CrHoAhBiDfkaVOLcz++w41yyooQRx11FNauXTvyL0D/AAAgAElEQVTs8SlTpthtOZ2MGDECt912W8FtLVq0CIsWLRr2+MyZMzFz5kwv+0sIIYERVHBk8e07nRbBOCFMwWBKQsLCtvKzRWdssXM96qAcQ3lHJ0LqEZH3r/L343nqhfiPyIQQUiHuCZ2Cm/qALHnCGUbJcgxCwiN77slQQxJD8rtjxHjsZDkGIRUgQlYh4jvEhAqvqoSQxkV5i86gnRDCrlGnCEGIeuRZpmk6O9LEFHukzIoQcR453e49fh8J8YQ86UPLhCjyM3FBEYIQ0rAoD6Z0OfKqb9EphJmbeHICSoh67BadWpwX0BubunJCqBXOCalLatqiM77jjWooQhBCGhb1mRABBVNKJ4QQOUGCE1BC1OPME6DwF0sEHMcQiPVNgeprFiF1SejBlOyO4QWKEISQhkX5qpLLCVFFOYYjjNIpSBBC1GKHGup6nO9dGxs7XDT+5RiqSwgJqUtCdkIIOiE8QRGCENK4BJTZUHzzTidEFeUYju4YUF1CQgixsVfRdToh4otUIeqhHIOZEIT4JmwnRECh5PUORQhCSMPiWklSUY7h+jkAEUKY6nMsCCE57PtXjXPJmJLforOasbjWuMZ/M75/ByGhEnqLziI/ExcUIQghDYvy+lrnSlUVE0ZnDoTbXcGVMEJUIpwtOin6xROHkOT8PY6wOwYhFSDnX6E5IZw/x3jAUQxFCEJIwyIU19e6MicCcUIIroQREib2DWx2FZ0TyhiS1x0jxiqEuxwjvn8HIaESuhOC5RheoAhBCGlY1Jc2BLP9QsGU1W6TEOIF9w0sz7n4YR+zOhCSVAvnhNQnIbfoLPDWZDgUIQghjYtia6sQApqekL9UvqGs40GI/GBKTkIJUYmzO0b2gRruDamG3DGs7X5UhcnxnxDfZE+V0ARIUfQX4oAiBCGkYVG+qiSEPfGt5uJn2k4IEVjHDUJIefJFiDivojcquWDK+JdjmE7hnE4IQrwRcotO92JRSO8ZQyhCEEIaFtXlGEII6AGsoBbtjsFJKCFqsZ38dELElrxgylgLSRQhCPFP6C06nT/HeLxRDEUIQkjDorxFp8sJUcWE0VbxhTvskhc3QhRTP6vojYqrw0nM4fhPiH9E6E6IIj8TF/EfkQkhpEJCKcfQqq9DNk1HMCVbtBESGizHqCPqwAkh6IQgxD+hOyHYHcMLFCEIIY2LGUYwpZz4Vr59ZzkGuBJGSHjYeQIUIWJLfiZEjI+h6/sX47+DkFAJvUWn82eep8WgCEEIaViUt+h0lWNUs53CTghe3AhRi4DbCcFzLn6IYSU18cXVoplOCEK8EXY5huu9a/CeMYEiBCGkYXGXYyjKhNACDKY0GUxJSKiYLMeIPXYwZQD5PDVGuXBOSD3CcoxIQhGCENKwqM5XEBCApkHTtKq2b7foFIKTUEJChE6IOkDYKkT299rtStUoLiEkpC6paTBlnAcctVCEIIQ0Ls6LgwInhBVMqVmT36rKMZzuB8PxMC9uhCglv71jrO9gG5P8cow4H0OTwZSE+KemLTrDecs4QhGCENKwuF0FaoIpg3BCOCebpmE4/gOvboSoJHcDm7XyqxAriVqkkFQPbhbF1yxC6hJ53oR1ygiqEF6gCEEIaViEamur7YTQq3ItmEVECK6EEaKYvBadnFDGj2HBlDE+hE4RjOM/IR4Rw34I6f3Ce8s4QhGCENKwuHuuqwqm1LKlyFVs3yFgmEbG8TAnoYSoRIqHbNEZY/IyIeJcjuHOMYrv30FIqITeopPBlF6gCEEIaVjCaNFplWPoVYkcLseGwUwIQkIj3wnBcy62BNGpqNawRSchFcBgykhCEYIQEjjP3PsrvLb5oVrvRnmcwoCiTAgNsjd9MCKESRGCkNAQeSIEz7n4MewY1nJnqoTdkQipAHmqhJTpI4r+QpxQhCCEBM6ul55D++sv13o3yuK0tpoKVpXsYEq9ukwI52tN0xlMyZUwQtTiLsfgqlYMsUtqNNfvcUSwOwYh/gn7nGc5hicoQhBCAsc0DXcXh4jiEgYUlWNoug5oWpUiRLFgSl7dCFGK3VlBK/08EllEvggRZxhMSYh/ZDVGTVp0cp5WDIoQhJDAEYYZiwmSamurgIAGWCUZ1YgQrjpglmMQEhbDWnTynIshecGUMT6GJoMpCfFP6C06i/xMXFCEIIQEjmka7rKBqCIUryplyzGUOSFYjkGIUqTbiJkQMcZ2s9RBSY1LOOf4T4gn7O4YYTkhFLts6wSKEISQwBGm4eriEFVUtzuzW3TqwTkhzEzG8R94cSNELfWTJ9CoiPxjGGPYHYMQ/9hTvZp0xwjpPWMIRQhCSOCYhqkk6DFoXBM6FatKMpgSVTohHHXAJiehhISH7eSPv5W/YcnLhIjzMXQL5xz/CfGGHQpRq3cmBaAIQQgJHCFMV3ZBVBGKyzGkEyLIcgxhZByP8/JGiEqEMO1zOPtAbXeI+MY+ZLLDSYxvC1xhxAwmJsQbIu9f5e/HcgwvUIQghASOMAyYRvRXaVR3x7BbdGoBlmOYDCYjJDQEcucwctZ+EiekEyL7W4wPoatdM50QhHhDnjcsx4gUFCEIIYFjmvFwQiB7Q69V6VQoihBWZ4xAgykzBR8nhASPJTo4nBBcfY4f9iGLvwrhcsWxHI8Qb9iZELVo0RnOW8YRihCEkMARphGLCZLdPz6RVHJDbwdTalpV23dlQjgDP2M8mSYkFgiRbXBDJ0RcEch1KZKPxBbndZXjPyEeCdsJwXIML1CEIIQEihACwjRj0aJTihB6IqGuRaeuW5PfKq5D7kwIR4tOrsoSohSrpEqHXEVnCVQMEQIabB9ErO8JbOFc12Mh9BMSCcJu0VnovckwKEIQQgJFToziMEGSN/e6nlDTohPZyW/VTghnOYZDhGA5BiGKsZwQdREo0Khkcz3qwQlhX7MSaq5ZhNQlNW3RyfO0GBQhCCGBIrMgXGUDUcXhhFASTGlaq6jV9qd3hZGZRsHHCSHBY51iWl20d2xUBLJlcfWQCWFKJ4Qi9x4h9YgdTBlWJoRwWK/Cecs4QhGCEBIoZoycEHJfNV13dZ0IDGHaq6hBOSEEMyEICY9si06NToj4ImQmRPbX2u5NVeScEGpyjEhxutrfQ+++3bXeDVIJobfoBKDXwYCjGIoQhJBAkTfJcciEkDcUmionBJB1QuhV5Tc4RQjDyNi24jgIPSTaHNjTiTeefKTWuxFZRB1Z+RuV7ChfFyU18jpi5RjF9++II0/c9RM8u/buWu8GqYTQW3SKuhhvVFNWhPjggw/wL//yLzj99NNxxhlnYOXKlQCArq4unH/++Zg9ezbOP/98dHd3A7CsisuWLUNrayvOOussvPzyy/a21qxZg9mzZ2P27NlYs2aN/fhLL72Es846C62trVi2bBntjoTEGNsJEYNyDFcwpYpVJemEsH6pfDOOMVEYhlU+AmZCkOp56+nNePLXy+NRPlUThMsJwflJDMmOw3VRjuHKhOD4HybpoUFkhgZrvRukEsIOpsx2dkZ1lbh1T1kRIpFI4Oqrr8aDDz6I3/72t7j77ruxfft2LF++HNOnT8eGDRswffp0LF++HACwadMm7NixAxs2bMDSpUuxZMkSAJZocfvtt+N3v/sdVq1ahdtvv90WLpYsWYKlS5diw4YN2LFjBzZt2qTuLyaEKEVmQhRapTfSaTVlDxXiCqZUsF+WGK5ZSeZVXPxcwZSmAV2XIkR8J9MkGkjxISoixP733sG+XTtqvRs2wrbyU4SIKzLXo77KMZgJETamYURmnCQ+CdsJAeREiDgPOIopK0JMmjQJxx57LABgzJgxOPzww9HR0YG2tjbMnTsXADB37lxs3LgRAOzHNU3DCSecgJ6eHnR2dmLLli04+eSTMWHCBIwfPx4nn3wyNm/ejM7OTvT29uKEE06ApmmYO3cu2traFP7JhBCVyIlRoXKM9f95A154YFXYu1QcO+SrOpGgKMLMBVNWI0LktejUdD37OK9upDqkY0lEpHzqmXt/had/f1etdyOHEHBEGsZ6Fb0eEb1piJ5UmSeJrJPFuxNCGAJmR3/1Oxg0zmBKOiFCRZhGZMZJ4hPHKR/KvMkpXvOaURRfmRC7du3CK6+8guOPPx579+7FpEmTAACHHnoo9u7dCwDo6OjA5MmT7ddMnjwZHR0dwx5vaWkp+Lh8PiEknpglnBB9+/agb/+esHepKLlyjKSaFp2ussBgnBBCCGhZJwQvbqRapDMpKit8mXQaRmqo1rthI7Ip55pG4S+KZNp2Ib3+3TLPyt64SzeLh6VJc3sX0r9+HaIvXe0uBoq7hJDfxTChEyLGiCI/q3w/OiHK4lmE6Ovrw6WXXorvfOc7GDNmjOu/uZKjCSENjTCK39SYRiZSgZVOaysUrCpZNzBWMGVQTggAuUwI2nFJlQgjAyA6QbLCNGBkMrXejTxyVv56Ev4evuNmvPTQ/bXejaoQQwYwVPq7mwsXlQ942PCQYT0vFa0x1tUdg+N/qFgiBD/zOOIS7EIYw3NvQSdEKTyJEOl0GpdeeinOOusszJ49GwBwyCGHoLOzEwDQ2dmJgw8+GIDlcGhvb7df297ejpaWlmGPd3R0FHxcPp8QEk9yTohCIoQRqYmTvDCp67kurHmvFpwTAnCIELy4kSqJWpCsNdGPjgghhAlN13JOiBrvT5DsfedN7H77jVrvRnWYAig7dOeVY3g5itltRq0DhbwW0AkRPqZh2KItqZzn//BbvLP1L+G+aehOCEdbYJ6mRSkrQgghcO211+Lwww/H+eefbz8+a9YsrF27FgCwdu1anHrqqa7HhRDYunUrxo4di0mTJuGUU07Bli1b0N3dje7ubmzZsgWnnHIKJk2ahDFjxmDr1q0QQri2RQiJH1J8KBRAaRqZSNkZcxM6NZkQwsw5IaoTIdyvZSYECYqotdQVpgEzSk4Ike2qIO9fIySiVotpZDDUd6DWu1Edpii/0ijFZh/hovaYGzURwlmOUUffxTggTCNSwdpxZfuTj2LXi8+G+6YuJ0QY7weWY3ggWe4Jzz77LO677z584hOfwJe+9CUAwBVXXIELL7wQl112GVavXo3DDjsMt956KwBg5syZeOyxx9Da2opRo0bhpptuAgBMmDABF198MebPnw8AuOSSSzBhwgQAwOLFi3HNNddgcHAQM2bMwIwZM5T8sYQQ9dh2RSEgTNO+YQYAMxMtEQKOCZ0SC7hsDRdgMCUAuzuGihIS0ljknBDR+C6ZhgEjSquN+ZkQNd6dIDENA0N9vbXejeoQKCsUWM0xtLwHyhBZEYLdMWpF1FxaccU0MuGX3LmcEGEEUyIrQrAcoxRlRYgTTzwRr732WsH/tnLlymGPaZqGxYsXF3z+/PnzbRHCyXHHHYcHHnig3K4QQmKAswxDCBNa1nBlmiaEEJFKl5YTOqsco0zCeiXbR/bmRdPUlGNwEkqqxHYuRWRybRrRckIIIWwBwvq9fs45I5PBUG89OCHKPEd2ONF8lGPIp0RNhHB0x4jSedIIWN0x6uf8rxU1EXPCdkJAlmMIahAl8NUdgxBCyuG8SDtDnHIBeNG5iOcyIRS16DTN7Cpq9U4IZ/ivxkwIEhBmifKpWiBMM1I3V3YnBbvNTe32JWiEkcFQf2+8b6yEKJvbIDucuH4vh+2EqGLfFCCECWiadc2K83GLGUIIdscIiJp8jjVzQqCurhlBQxGCEBIozpsZp+tB2u+iFOwksv3jNU1TUustnRCaplW1giqEgJbIGddkOQZFCFItuW420TgvrdyYaOwLAHkSO/IE6uPGL+dMM5Ea7K/17lSOCY9uBc1dklF2u9Esx4BpOXM0XW/4crzB3p7QroHyfaKSnRNnauOEcO5ASO8nyzGoQhSFIgQhJFCcwoPzgm0a0VpxBWBNMAMIjiyGXLWyyjGq2I5pIpHMiRA5J0SEPksSS+R5GZVVVWGaEWvR6RAqrV/rAqfbJNYlGaYoLxQIke1w4j2YMqoiRK5bixaZc7YWDPX34vff+/fQAg7l4klUugiVovPN12Ckgy8vDQJZkhu62811zofhhMiqENQgSkIRghASKE6bnTPszozgRdya0OnZcgwFEzqB3A1MNU4I04QmwygB6DLsk04IUiV2OUZEzkvTNCJVe211uMmtoteL8OdciYxzOKXw0KJTwMqEcDzgYcPZf6MoQmiaMuE8LqT6+mCk0+jv3h/K+8nFk6g7IQYOdONPt16PHc//uda7UhA5rtfUCRFqdwwGU5aCIgQhJFBcmRCFnBARudkBnBM6TUk/eDvLQdOquu4JIewwSgC2IBG1HvYkfkjnUlQCY3PjRFTcEPmhhvVBXTkhyrbohMMaLR/wsF1Eb4wVjnKMehHEKkGOD2HNJ8J+v0pJ9fUCQiA9MFDrXSlIzeaBzjEijHNa+BtuGhWKEISQQHEKD+6Qysyw/15rrAmdptAJYc1+g3BCOEUIneUYJCBMOxMiGueldEpFpSTDDjX0Y+WPAU6RZ7AvxiKElxadchxGPZRjCMu9p+mRE0jCxJ5PhDROSFdnmE7OSkrTMqkhAIBpRmP8zCfs42YTuhNCOuhCer+YQhGCEBIozou0WeDnqNisgdyErtoWmqW3X73TQggTuiOYUmMwJQkIEcFyDCBCTohsi07bzl8n55zzeA/FWYTwmgkhbwj8bNf5b0TIdceoLuw47oTtmKrFIsrrj7dhzZJv+npNemgQQHTG83xq5nQLe9xmOYYnKEIQQgKlWHcMqXxH6uIoJ3SqQr7si091crgwRZ4TgpkQJBjscoyInJdyP6LSplNA5Nrson6Ev7oqx/CQCQFYnYqsB8ofQ/v+PmL3+cI0sx2XGrtFp5eb2aG+XqxdeiW6PthZ/fuZ4TvGevftQX/XPl9h3tIJEZXxPB95vQnd6ebqjhHSGE4nRFkoQhBCAsXdHaNAMGWUyjFErhxDxQ293aJTr85pUdQJ0cCTUBIMua41tT8vRbZtJBCdcgxLg3C0d6wTEcKol3IMT04I5G4IkBMlSr8mmk4Iu9OH3tjBlPa4lSk+bvXu242ejvex//1dVb+ffVOfbWsbBmYFbc0zQ9lyjKg4yfLIOWLDzoQo8rOy95PidUjvF1MoQhBCAsV0dMQoVJoRJSeELUJoajIhhGlmJ77Vt+gsnAnBqxupjlqs8BWjkGhZa0S2ttd2QtTJjNL5+aZi3B1DOhVKj4XZcT6nQnjYbjRFiJwTIjotOtODA6GLhrnyiOLvm3NfVr9vhUK2VSP3289nm4l8OYb/vymYN86dx6HMm2wTLMsxSkERghASKM6bebNAOUZUJk6A7F6hV53ZUAq5/aqCKQWDKYkaRK1WpgpQqHyr5th5Atkb2IjdlFaK8/MdjHM5hu1YKPWU7HP8uFmiKkIIxWHKFfCnHy/Btj/d6/n5e3e+jdRAv/37G08+gvuW/T9f7+kl4DDI/AFXvlVIY2UlIoodTBlZEaL6Y/Lmnzdh7dIrKxcTQnRCsByjNBQhCCGB4nQ/FOyOEZEVTgCA6QymVOCEsFt0VmedFaYJPZkrx9AZTEkCwg6CjIA46A6yjcY4IVDfTojmUaPjH0zp/LcQshzD/tVLOUbe9iOCEGbumhWBcxYA+vbvw0D3fk/PFUJg/Y+vx6uPrbcf6+l4H93t7/m6nsmxwigxTgTZicEpkIaVt1BJW9DYiBBVHJPu9vfQ0/G+r79RCAEkQiyps0vAqEKUgiIEISRQzCIX65x9MhoTJwAw7aRxRfW1tpUbVV348ssxZCYEbX7RYvBAD1Z9ZxH27dpR613xjLDLMWp/0+8SIaLkhADqsEWn9VmPGj8htiKEECI3vy9XjoGckOSnHCNqbTCtttJ6pDIhTCPj+YbQNAxkUkNI9fflHrNdkt5vKr2UdwYZhl0LJ4SRSWff2/tYmOuOEZHxM48gFqNy4lLa+4ucQmQAp82BPR3o6fyg9JPohCgLRQhCSKC43A/O0gxp+46SQu/MhFAgjrgzJ6oJhRDQdEcwZbY7RlRWwohFX9deDPR0obujzOQkQuTOy9p/l5w3IVEJppSZEI4HarczASJv0A4aPxFDfb2RuaH1hcfEe2Fbo324WWyHReW7pwLLCaFBj1B3DNMwPN9UyhtHw3EDaVSQE2ALDCVeYwTphCjSelwlMnTTz/7LYMpIzbMcCA/iUTnk98RI+xAhTAHIdZwAhrq/rPolnrpnRfEnyOsGRYiSUIQghASK8+LivLGpRZ/tctirSorCg6yJvZYt96h8+2Z+MCXLMSJJkEFoYSGiFEwZwXIMqxpDs9s71ss5J2/QRo2fCGGaSDtq9GODU3gocz+u+RWSIpsJYbn3oKspIfS/P8KXE0KKD84byEpyAuwyshKvEQHkD+S/HxCe+F9VOUaE5llOct0xzIpdsUYBIassAoAeXDlGenAA6YGB4m+XnfoBDKYsBUUIQkiguJwQhYIpI3CzI5GrSspCvmQ7tSpFjmLBlLy4RYsga5DDIkqtc11jR2Q+Q2s2ad/D1sk5Jz/fkWPGAQBSg3EXIUplQshjqBV/zrDXeNhuDcgJ53okSkWEafpqW5lzMKSHP+ZjbuDlBt0IsjtGDQTSSvY/k4p6dwznIlVln6P8vvgSIQCHCFHR2w7bh1J5JLnuGMG8X71CEYIQEijuFYPhFkYRYp/tsjja76mY0AXlhIAQrmBKTWd3jCgSZBp7WETKCeEsx4jIZyiyQiLqzAkhv6NNI0dav6ej8Xn7wqMIIbItOrNLk56OoYioE8IStvXIdMfwW+NfaBW7EvHWSzlGbrvBZkKEVbpWyeciyzGieg1y7lelJXeFhKzyLxKBOiGMTKb0cWE5hicoQhBCAkWUKccAohNOaWU2ZFt0qnJC2Mn6wZVjaLrMhODVLUp4mRhHjVx3jAiIEK5gSp+rXIoQIs8JUSczSvkdbRoxCgBgZFK13J3KcA7ZpW4ssqF0vtwscmw1onW8ZcclTdMj4crxEhDpRJZhVF2O4eE19nhsBtsdI4jteUH+bX4E2cxQxJ0QBRam/JITsnwcB1c5RkVv68IqQSrz/tkcmnoRrlVAEYIQEihFyzGMwg6JUjz6sx/hnef/7Pm9D+zp8HVhsid0ipLGRUBOC2Ga0J3BlBHrE08sjAomjbVGCoVRKJNyhb8pEnK2rPxvbH/qMe8vsEMNdfvXekCOx8msEyIqQaC+cByMUkNhbpz0UY7hwwnR9cEuV7cHlThzjKLgKPRbSpHLhMiJXpWUY3gJOAyiHWT+tqyfQ3JCVFDCGvUWnYU6pvnFrCSYUjrasj9Xi5FJlx4zZSYEnRAloQhBCAkUp8uhWI231wvkzhefRedbr3t6biaVwv03fgtv/tn7DYbsXgFlSeNy+1U6LYRwOyGksFEvd0R1QjEnhBACH7z2UiRuGvKJlBMihHKMXS9vRccbf/X1Gg1a7va1ToQ/uxxDOiH8TOijgudMCNjjMOCxpEYeZg8ixPpbr8dfH36w/DYDQHmOkU/8ZsoUquevqBzDQzBlJU6CYrjmMiGJzPb++/hcZIvOKIjKhQgiW8NuXVpxMGVFb+vCzGRKv79djsFgylJQhCCEuBg80IM//mgx+vbvrej1hXIgAP/p0qZpWgnKHi80mdQQjHQKQ73ee94L07TqaxXd0AtXOUYV28nup5xEWz9Hp088sShmEd63awce+q8b0bH9lVD3Z/dbr2Pjf3+/dIK8WX5FMSzcTgg1+2Nm0vZE3Qsu+zvqZz5pl2OMkuUYMRchShwYAeEux/ByF5LdXrn7fGGaGOrrxVB/b/ltBoAwzWg5IfyWYwTVHSP0TIjc+4TWHaOiYMqoZ0JU73azxRmfTgj7jjewcowS3yunE4IUhSIEIcRFV/su7H7rdex/752KXu92QhS+4Hi5QJo+6/4qWTVwBlOqWOEUZk4Nr7ZFpyWWWEN2rq1o7SehJEexFT1p1U6F3AZx99tv4P1XtmGor/gNkrQWR6E7hulyTqm5KTYyGbtu2hPZPAF7Mlkn55yR54SISgaHL5yHokx3jKx8m/3dy7a9lWPYq7IhiXi2sK2rcu/5w28pRSErfSWtKL2IH0G2THaNTSEda6OCMpVcMGXtx/NCBJMJUUF3jIBbdBqZTOn3Z3cMT1CEIIS4sIOjKlSpXauZRS7cXiZPufAhb4FpldRP2sGUijIhZDlG1atWjkR0AEo7epDKKTbpLVQHHQbl+qlbnWrkZL72NzRCcTmGME0I0/DnhLBX0bNOiMD3qjbkd8eIYzmGa8wu9/V1lmN4OYoeyzHsm76QRBwhTEBXec3yh+GzHKNwd4xKginLCwxBdityOyHCucGvrDtGNpgyAqJyIVwB5RVnQlQSTCkUlGNkip+DLMfwBEUIQoiLiurtHIgiLTqdNxVeLpB+Ow3YKyw+LmyWtVXL9lxX4ISQF6IqPXk5S7gsx1AXpkkqp5gbx0hZ4kPYN3pGgVVHJ7Wocy5FEPXCpbdvbdOPE8IKAnQ4ISKw+hwEZiYDTdOQbBoBIKbBlF5bdGbHyVw1hvdyjLIiRNoSIcL6/JzBlNbvtf0++nUx5JwQw4Mp/XyGXpyPRoDlGO5OXyGJED6dHEIIZLKfa1SdEEGED9sOET+ifsBOCE9ZKAymLAtFCELqgKG+XvR0fBDItuwbpkrr9Yq4H/wGU5a7gRr2fJ/9ygFnMKWPwDI/2NbZ6oIp7TpgXZZjSIW9Pm6I6oViFuFyjgRl+1NGUBQ+c1pUE0S9cCnkmOLHCSGLe+vPCWFATyahN1ldd2LfotNXMKWXbXsUIVLypi8kEccRTAkouGb5pOJMiELBlBW06Cx1ExhoOYZigbQQhk+Bx0in7C93FETlQjj/lkrdbpWIVhAAEj7O/1Lvn80rK7kPwh5u6ueioQCKEITUAS+uX4OH/vumQLZVtRPCMAqu0rjsjB4uqpVmQvi6eREC0HXoiiZ01vay2WytW/4AACAASURBVPpVbNpKRM+tftnBZHRCRIpi30G7xKlWTogi50Qt6pxLEUT7tlLI45IZ9JMJIVxmpih0JAgC08hATySRSDZZv8fRCeEc/0qOhVJI8nFX4DUTQgYBhuWEyA9KrbUTwueNfulgSv+ZEKXmB5WIG8Vw5VtFNJjSKa7GojtGxU6ICkR94VAFqpw3uRbUiooQLMfwAkUIQuqAwd4DvrpClELaJKtxQiSamrM/F+uU4aE7hn1D5zETIi0Dwvw4IUz3ClnQkwsRTItOu2xEOiH06KSjkxz2pNHMFyFSrn9D258yKeLCldNS+0mrq0WnEieE9Tn4y4SAKxOiXla1zEwGejInQsS+O0aJodAqi3M+UH7TwmsmRKpG5RiRcUJIW7q3a1EhQbaiFp0e8h6CzYRwjJVSAEmn8e62Z6redvH39Pe5yHwS67W1H88L4c6EqGwfzYpECEBLBJMJ4SnXgsGUnqAIQUgdYKRTgU0ic06ICoMpTQN6dmJbzAnhZVJQbhV32PMrmMgMa6EZ8CqnEMKyzlbZTtPZShSAdVPETIjIUew7mAt7DdsJUfp9g0gqDxLXeKHgpi4nyqS8r2ZKoVL+WidOCMPIQNcT9lgdx2BKr5kQAFxjp7dgSumEKP00uyViaMGU1jUlV1oSlUwIf9lNwjSGuRkqC6YsX45hBNGis8BYufPFZ/Ho8lvQs7u96u0Pf7+c5d/r2Cy/i4mmpkiM54UIoqwllwnh7Zyz50kyE6LKQG/n9bToNT0rfGp0QpSEIkTEaX/jr/GcHJBQMdJpCNMMRPGv9oZJmCYSTaVFCC+rJqbP/ag4mFJXt6okyzEsNbwKESKvO4YdpsmLW6QonglRm2BKs8xkzXV+RsEJEUByeimcgqbXcErrFHMKlYHvVk3IOSGS2d9jOM9wXEZKdQqyx2H5/z6CKct1IKokE2L7U4+hv2uf5+e7d8sENB165Mox/GVCOH8WFZVjlF90sF0aQZRjuJyc1s/poQHr38GBqrc//P38j4VyTGseNToWmRCVCs2+nRDyFA7ojtdbOQZyrZ3r5JqhAooQEaa/ez82/OdSvPP8U7XeFRJxpM1bToiC2FbFFwjTsEUIt+o93M5Ycj98OhtyEw6/LTpV1teKqgUDuU/O/dQ0veoSDxI8xTq6RNYJ4fOcVI2p2gnh+Bw8d8iwMyGisfIcFDITQtN16IlEPBc7nAJByUgIRzmG5jGfR1EmRHpwAE/86qd46+ktnp6fz/ByjFo7IfwJCM5z0B4XlZVj+F+YKLotc/hYWU7krQanYOrVDSqdEM0HjY6sE6La3B8hhKc8kLwXWf8G1KLTHbJeSoQARYgyUISIMOmBfgBAKvtvVHjlkT/i/Ve21Xo3iINMgDc5gTgh7HKMIt0xvDgh/HbHqCgx2VpVsoM0g3ZCmMKhhlcoQmQnmS4nhCzNoBMiUhSb9OYyIWrjhPAiQkRh0ionqImmZiWCjXMM8poLYeUJ6K6SjHrANAzbBWF93tFcOS2Jc/wrW44hp7teyzG8bTfjs5uUXDWveMEg2x1DWY6RTzy1KnRguFaR3aWfvsox7LIOs+hnYOaJBdVQaKyUY1RGQdaPc5+9CsQ5J8RBkRjPC+HMS6pkH52fi+eMJdsJEVA5huFBIHIGU1KFKApFiAiTqdHEtRwvbfwD3vzLplrvBnEQZPBdtd0xTMOAnkhA07Ti7To9ZUJUVo5RUYtOXdUqZxBOiGyPe013OCGy26zyYkqCpawTIuxgyjIihDvxvfaTVjlGJEeMUDKJdk4YvVuo850Q9XHOmRnLCQEAejIZy3IM4TETQgjhMkJ4Em89t+jMOiE8W+ary5DIb9dc6++j33KMQk6IirpjeMiz8btvpSg0VpYbX6vB+X3y6uRIZ79bzaNGRyJouBDVZkK4Wrt6dkJk/1XhhCi3D9QgSkIRIsLkVqSj1b/bSKfi2c6rjrFFiEDKMfy1xszHyllIQEskilrvPGVC2Dd0HkWIqoIpZTmGgkwIGXwZiBPCGUxZuhzjwO6O0G96G51iFuGKWooFQKF2eE6cImE0yjGyIkTTCKXBlIA7Sb4UdiZErkdn4PtVC0zDyoQAgESyKabdMRw/lzouzvZ8mubtnkCoESFkjkDFTsO8MOVaOyEM2wlhehJE3GUGqayTwX8XCy83s3aZh7LuGOoWCl2OEa8CV8pyQjSNOigWmRCVzDFdTgjPmRB55RhVqgLOOWkl5Rj9Xfsw1N9b1T7UCxQhIkytLLzlMNKpyO1To5NrqxlEOYb/oC0npmlA13XoemJYjbedFeFBpfdaXiGEyNYJVuCEMKXVWlF9rd2iE94swIU2YWdCuJ0QpfpPm0YGf/j+t/H6lrbK9ptURDEhrFBbujAwijgzJO6wtdpnHQiHEyKIOu58nOOjvBn0sFcOW23tV56DwsqESACIsQjhKsco8TQ4RAh4FIQ9lmNI4d/ruS3LgCoXIdxhyrUWxfyWdBl5TohKgwqFB0t8MWdaJUiHJ5Cbv9hzFAXjeiUhvVIQGxHxTAgpflYyx3Q5RHwHUwbjhHCVYxQ79rbwOXy8efRnP8Kza35d3U7UCRQhIozfi1sYCCFgpNPxnLDUMXZdaoDBlBVPkgwDWiJhtZDMszAmmkZYP3u4QJoeykJMw8Dqay/G209vgZGuYMKRra+1HQYBTuhyNyvVlU7I7Wi6bk+krUyI4iUe6aFBZFJD6O/eX9F7ksooJoTVLhMiXi06bSdE8wgl5QHOscFzdwwzv41vfYgQRsZwlWNEaZ7hGa8tOvPLMYJs0Zn2W46RFSEq/LztYMqsIF1r8dDtcPRyXXeLB84xyI/w6Gy7Wex9vYRXekUYBhJNze7t2uOr2kwI02OL0YyrHMObMyVsTNNA0sc8MJ/KyjHkHCqYTAgvwZSihBNioKcbgwd6qtqHeoEiRITJBLi6HRRmGXuvsvc1Mnhk+S3Yu/PtUN83LtjdMfLs9717d2PPju0+t+UOi/KLaZrQsys1+ascyWbrIu7F+u3sA17McppJDWGgpws9uzsqSsJW2h3DceGrqhzD0R1D13PdMSyRp8jnkp2MeF/tJUFQzL2TO6fCLY8pV47h/P5EoYZY7o8lQqhwQvgPppS+Wtv+XicFvvnlGLEssXQOfyUzIeByQpQbil03b+XKMbJjrfdgSut7V+nnLWzhPFotOoFKnBAp36+XeHJCOOYQ1WKapi1CiKg6IYYGoekJJJrdYkmUMA0DyRFZEaKCc8BVzuO13FSeIvKON8BMiOLBlMiKEMPnfkY6Fan7ulpCESLC5CaQ0anrrpUw0t/dhZ3bnkHn9ldDfd+4UMy98MIff49Nv7jN37aqFJqEaUDTE9D1hGuCZDpXEjxMnLy0qHL+3RW16LRDvoK3WtsOhqyVu9TNS+bR92C8XLhvvC1COLtjZFdmi5WPSFtmZtDrjRYJAlE0EyJYJ4RZIhHe9by8Fbti/11PJCIxYZWfW7JZTTmGq0Wn13Mjr0VntatoUUG26ASARFNMyzG8OiHsOwJ4E4Qd2yrnYPPthEhV6YSQOUORLMfwEDjt+Lud123A302p28VVphwjkEyIjN31y+6OIcVlFSJERZkQQ2gaMTJXNhLBXAjTyDgcJZVkQji/P15fH2yLTsPLd9bujjH8FM2khuLpPFMARYgIY6RSOCg5DkYqOl/W3IpeuPtk31RFSJCJCqZp5i6KeeUY6YEBDPR0+dpeYMGUuj5sopBsthRw4WWy4rLdFf6+yTIU07Gi4tsJ4ShzCDITwl2OUXria7zWBXPHgSLbyYkQdjmGLUIU3qY8X7yv9gLtr79ceds4AgBF+91X2/Y2n4f+60Y8d/89ZZ9XrhxDruglmpoj4YQwDeu7nmhuVh5MmU55b9GpOdv41osTIpOxW3TqyaZ4ToqdYkFJDULkjBDWA2W2W/g9CiGvuWYm40nErjoTQgrnEQmm9B84nYaevZkflglRQYtO63XFyjGCy4QQpml1/dITENlxqtz4Wg3uG12P5RipISSam21xMQphw/nIbA09kajouLhbdFaaCaG+HKNYMKVV0p6K1OJyLaEIEWHEQBqn/82FGD8wvta7YlOr2uZcHWUwJ24mlfJ1gxZlnJ9J/ueTSVuKq7wp9bS9alt0mib0hJ7tjuF2QshyDC9OCC8pyLk+3WnHfvsRIUyrzEFFdwynEwJa6QlaxgSMwv/dWY5hr35JQaKYCCHLMTy2IRzo6cKG25Zhx7NPeHo+KUyxlbeg2y0f2N2OA7s7yj6vnPghJ++JpqZIOCEsF5VulQeoaNGZ9p8JYa1ooQ6DKS3HGpA9/nF0Qngsm7AanDi6Y5Q7hJ4dFnBdW718ZzNVihAQIlvipyhM2Sf+yzEyaB41yvo5nfb0+lce/RP6u9xOQZezsmg5hrU9IUTV2RkyyFVPJGCasuxOXXlypU6IZPMIhxOi9mN6PpYIkYSeSFbYotN6TaKp2fuYZQbrhHC7MUrsQ4FyDNMw7Gw9QhEi0ohBAwktgUQmUetdsamdCDHkev9qefI3y7Hp5/7KFKKKS4TIGxDlKs1Qn/d2QPbKToVWPmEY0DQ9W47hzoRISCeE7wCrIiKE/X1MVdQdw7bMKbC22jcrzlC7YmQEYBR+b2cwZa47RulgSmn59SpCpAb6Xf+SyihWgyztukGNm5lUyraBl6JczbIUuBJNzZGYsMobYz2RVLK6KCePejJp1+aXI3v21V0wpdMJEdvuGF4dC3ZKnBSFvZdj+BMhyl97cpkQVXTHcJUQRscJ4eXvNzNpNI8aDcAqUytXjjF4oAdPr16J7U89lve+jmyBYuUYTpdGlaUJ0uFpla6Z2f2XYoS6YEpN03yIEKk8ESJ65RhCOiGSyaqCKZtGjqqiO0Z1Y7gnl4occ/KGGyMlM2RiON4qoKwIcc0112D69Ok488wz7ce6urpw/vnnY/bs2Tj//PPR3d0NwJosL1u2DK2trTjrrLPw8ssv269Zs2YNZs+ejdmzZ2PNmjX24y+99BLOOusstLa2YtmyZXWzyhAEIpUdhIrcnNSCoG3FXpE3VUHZxfv27UFPx/uBbKvWOG8w8j8feZM+1FfY6l9we7bF0H0BM40MXn+8reyKgmkaWduiVY7xyPJbsO2P91rlGE3eA5Nc5RjpwhdT5/cx147LRyaE0p7rOSeEphfPbxBCAIYJUVSEcLTozM+EKBtM6e1GS06kaRGsDrssKr8cI+AUdSM95KkTjlnGHWRGrBxDZNv7JpJJNcGU2cnjiIPGeO+OkXVLyZvYepmjODMh9GSy4vK7WuJyrpU8LHnlGOWTKa1/dXgQIZxOxPLzIq/dMV7f0obB3uHX7fzuGIG69yrAf4vODJpGHWT9nE6XFTGk2Nq3b0/e++bKO4uWY3gJEPSILCPQ9EQuBFth62U5ViVHjPQsJhipISSbm6HltRKNEjlHSWVjvFmJCCFJyDHc99vm7YPPcgwHGcfCGfEgQnz5y1/GihUrXI8tX74c06dPx4YNGzB9+nQsX74cALBp0ybs2LEDGzZswNKlS7FkyRIAlmhx++2343e/+x1WrVqF22+/3RYulixZgqVLl2LDhg3YsWMHNm3aFPCfGF9EOjuA1L59u02tginTthMiuJXEwd76aJFjpEqUY1TihEjLGlf3Z93++l/x1G9WoPPN0uGg1oqBDj1hBVPu2bEde95509OkwYmXftC2EyKVytXj++6O4aivVZEJIcsxil34TFgXLC/lGNIJoXvMhPDohJDfIWauVIezHMN5bDIBtlsWpmm1SfZwrMo6ISJWjmFbdZOVWXXLbj87jowYPdZ7OV62s4J9E1snIoSRye+OEcOVOY+OBeuQOcoxym43+29CLzv/Mvw6ITyUY/R37cNT96zA2888Puy/DeuOUWsnhOOGzJvDMY3mkVY5hplOu8SBQp+fvDb1lhIhigVXGxn72l7teGKaVutxPaHnAohDKMdIjhjpakdaikxaOiGs87rWY3pqoH/YwpgzE6KS8GH5NzWPHOW7RaeKTIii53GR7hjFQuQblbIixN///d9j/Hh3JkFbWxvmzp0LAJg7dy42btzoelzTNJxwwgno6elBZ2cntmzZgpNPPhkTJkzA+PHjcfLJJ2Pz5s3o7OxEb28vTjjhBGiahrlz56KtrU3BnxlPzFT0RIhcCFNtnBBBqYdGagjpwYG6qMvKlCrHCNAJIbeR6u8r+XppqZYtJDOpIQz197lqOL04DlytvIqJEKncgG4HU2b/fevpLWWFJmGarqTxQFc5HZkQVjBlkb9Zig/FnBCu7hjSUqxbJSRlRAivq722E8JHdkg+e955E51vvlbx6+sBe6IrhOs7njunqh9v5PlezhUmTNPRSq5IJoQsx0hGpBzDNKAl9IpXyQDrJu+NJx8peC4bmQw0PYGmUaN8tei0OtwoGCNqiDCNXHeMOgimLFeOYUdClMvncW4rqXtzQvi40S3khPjgtZfwwoOrHc+xxuFCodJRC6Z0WdP9OiEyaVd4YqHXy/Gub3++CGEgOWJk9ufCn7twPicgJ4SeSNrjZk7kVVCOkf2bmkaM9FxKkhkaQqJ5BHQ9GpkQbT/5Dzy75leux6wxvnKhWZ43TaMO8t2iU1PRHSPvb+jdtxt9+/e6umM43y/IBYl6oKJMiL1792LSpEkAgEMPPRR79+4FAHR0dGDy5Mn28yZPnoyOjo5hj7e0tBR8XD6fZMnenGi1nxvaOIN4wpyMBZ0Jkc7ebNWDG8KVCZF3Y5JJV+KEKHzDJPMChvpLb0umSOu61fYvPTSIVH9vduVCBlMGlAnh+D7K5wvTwGDvAWxZ+d946y9bSr9JfshXgBM6O8sBWsn8BmSE+99i23FkQsiciaItOh3lGF7O0yC6zzz/h9/hmXvvqvj19YBZZEKe6yqUqfo7ZgtvZY6Vu9a6dDBlsrkpGuUYhgldT1RVHvDuC0/jyV8vx4HO9mH/TeYgJJtH+ijHkGKi/UhF+xU1XE6IuLbodB4Kjy068+3RBbFFCK28CJEeQvNI66a6WNmgEyl+Oa9v7zz3FF5uW+faJgAMHuge9vpcCWE0RLFyIkI+RiaNphGjAE2zHF1lMiHk2Nm3b7frbzUNw1HeWcQJkXG4Jaoc34RhlYpZmRCy9FNdebLpKMfw6hjIZMsxZCZEtTkY1dK3b08BB4sl5iQSlWVCuMsxvP19wlleBQQaTJn/nX3iVz/FU/f8LwB5zSjihKAIASCAYEpnPTUJFlmOoSm6xgjTRPvrL/u6iDknvipqdoshJ4xB2cWNehUh8j4fO5iyQG1pue3l37hIB0S6v3R4oZlNuNcSCWvCJQRS/X3uYEov5RiZ0pMTwF0e5LxQpwb6XPtcDBnyJe8wUgN9vlualto2AMsCqKG4BTAjnRClyzHgWP2yXBF68UwI2X5QCE+dUWx1vop2wOnBAc/lH3EhPTiA3159IXa9/Lyn5xdqNyeEgJlJI9GUbUtX5bgpb1DKHVfDg4jnbNFZLOvF8Nh6MAhMQ1qekxCmUZFgkx6wvoOFWnAamTT0ZBJNI0Z6/67Kkq2su6se2tgKIezabKC2LTqrel8pECS0ko5RkS2psSjdLhnI5SxoSR0QpW/0M0NDaD4ou7LvxwnhyIdJDw3CSKfs95HfsYGeQiKECTjLMWLmhDAzaSSSyWwYasq92FCoHMNx0+YUZUwj43BCDH9fIQSEGaATIruCr+kJ+zNX2R1Djt/J5hGe991I5wdT1lZYzqSGkBlyj7OmkbGF5kqOifxcmkaO8u7IDrxFpwFoGhJNzcOu54MHetDfvQ+5rkqA2wlhXbeFadT8+ESBikSIQw45BJ2dnQCAzs5OHHzwwQAsh0N7e271ob29HS0tLcMe7+joKPi4fD7Jkl0Z1Uw1Ik/7G3/FhtuWYd+uHZ5f48ofCHHlJBecF1wmBGANGHHHVY6R9/lkfJZjmEZu4p8f8Gg7IQbK3NgbhnWR0XVbDBjqOwAhhC8nhKdyDEcmhPOClpbdHgZLCyZ2yFd2QveXVSvxyB03l903AMNahg3fuPVPuU4WMpCyaDBl9njoeoFgyrxt9nftG9aS1cvNlh386qHjQtFtDA3WTdtbSd/+PRjqPYC9777l6fmFhDN5TjoT4avBq53Tff4UtysD2WDKAhMiI5PB6u9eXLA2XQVWMGXC7tpQySQtF2I8/LssMyeaRo603UJl9yk7mdQ0DSMOGlNW2IwDwjQBIZCwyzGSMDNpCNNEf/f+0Pbj/Ve24Z5vfQ07X3y2sg04W++VuLGQ7gEgW6JdbilU/udkdu5VYmw20in73PaXCZFxPSZMMxdsm5bzkwIiRN41q9ZOCL+ZEEY6Db2pyXLfpNP2XCDZPKLgyr1zzinDKUW23E12xyg0vtkurzIdNLxSyAmREyHUlmN4HQczqSEkmpojkwmRSaXsPDeJqLpFZ84J4ZyvlmRYJoTvtwUA9O7djZ6OD2BkQ32tVtJ57bhTQ5bz2JklVMAJ4fxbGpmKRIhZs2Zh7dq1AIC1a9fi1FNPdT0uhMDWrVsxduxYTJo0Caeccgq2bNmC7u5udHd3Y8uWLTjllFMwadIkjBkzBlu3boUQwrUtAnuFVBNqRAh5A+5rldxrf9yACTK9X04cgHpxQji6Yzg+H2GatlLstRzDPqaaNrwco9+HuyBhrRxKMUBeDBPJJisYrMiqv3tfMrla22LBUwW6YwA5wSRdoOXkzheftSeCdshX9n0O7G5HvwcnxP733sXq715S8ubUVSphC/AFrn6ZMpkQrnKMXJu5fBFCCIH7b/wWXtu0wXWDlfHQitC2+FexyptJDXq+sYsLgwessXGg25s7xnCFr7rbtzU7EuGrwfBYOuNaYSzyXDsToqmpoDCY6u/FUO8B9BQobVCB7Kyj2yKE/0mqFN0KfRetVdgmJEeM9CWYyfOuefSYsuVo1RDWqrb8XJ3lGEIIvPXM41iz5JuhtOrdt2sHHl3xYxjpNHr37q5sIwLWLDZRrmxC5JwQlgpRGtthUbp1s7xGNh+UFSF8dMdwruLK1WL73M7+O1BAhLBKCHPuvVo7ISrpjmE7IRxllMkRIwoGMDrHOWntz5WRSWdlIRFCOgmKuyX8IAVMpwMxl0OlMphyhL8WnSNG5rpj1FCEMLPzz/xFEGcwZUXdMbKfhSyB8uQsDMgJ8eff/QKP//qntptHTw7/GzJDQ0hJEQIYNt44r//skOFBhLjiiitw7rnn4u2338aMGTOwatUqXHjhhXj88ccxe/ZsPPHEE7jwwgsBADNnzsSUKVPQ2tqK733ve1i8eDEAYMKECbj44osxf/58zJ8/H5dccgkmTJgAAFi8eDG++93vorW1FR/96EcxY8YMhX9uzMjelKgSIezJmo8wulKr7iqxVw8COGmdf8NQHTgh5Gei6brr83H9nV5FiOxrClnd7BIHhxPC3HkAZrt7wmoaVomDntCHTWb1RBK6rnvuJ96UtVIWO+45q2Yqrxyj3/WvpL9rHx6542bseO5JAHKVMydCWPbB8jcnckXm/7P35vGWXdV54Heme88d31iTqlSaBwRCNmAI0DI2bWI3dmLHdvrXTjodJ7GTtENsx+7GbYjjOMHY2ISm44kOxjYGKwIkBEIDAo1oLo0lqeZ5fvN9dzzz2flj77XPPueeO7xXr0oC1/r99FPVq/vuPfcMe6/1re/7VlbvmIoUE2LI5kfgw4gRnaqBJpeQ6CmzyygI4DtddFYW00wIbxwmxPmDfIFgQrzenbmNDLfDi4BxJTqxokHOTqagQuV8181QkUsNG5c7jpxJyjEGGFPS8zOuf8L5Bje11WUnbz3JfThkryA5hlm0+2jCg4LWCAAolis8wbwA0Vtdwe0f/gUcfOLBC/L+atD9kIzo5FKh5rnTiIJgTY2J9cb+x74l/7zu+ytmQu6mDR9VyRgSMcZoOUbKEwIYuDbTuilZTmthQii+WtQtzo7vc9vNvvWURsbqb5TpGCNGbKpB0jTdtASVPUiBBbnTMYJ+JkTyO2KtzQMhwvRrNkSOIdamZBTzBZyOoY7oHOPYqehXPSEuxIShcSMaYI6dMvhclyeEYIiU+ISVsZiFfSDEmj8WAGdGuu0WYuGno5v9Xjqh7/F1YYQcA7jkCwEA5qgXfOpTn8r9+ec///m+n2maJoGHbBAAkY2bb74Z99xzz6jD+NsZEb+Jdaan6IQbFVScrCUBUDulF3NCBnW1NkKPq36H7w0mhOi0lisDUdZx5RiyYLLLCJye3DAApbBXPCHCR89Cq1jQf/pq+TMWJ/O0s0wE3TRT7tLDIo5CWHaJTzEZyITon44BKHKMzOcTgCJ9LVia2gqM9zyQzCMcInVIwAOkTMT6nuIxPSF09TgFcBIribechNLrpM/FGEwIOXHhPECI0HO5zjEMpf/Bd3skTIjxKOrSsb3T6psln8gxzpcJkfaA0QVQ1/c68TmGVRiY7MRRwoSgiR7qs5CwCi4OCMFNbc1EjrGOwkEWdHlyjDCCITwh4iiSXdnhB8V44Qo+2vNCyRW6K0sIXAfPfOkvUKzUcMX3v+uCfA6QdEjV6RgA5HfLApe+08PyyaPYdsNbNuwYnNYq6pu2onHmxJoaIamIBUCkY3xPiDGIEIkcQzwLA95bghDEhBjjflWfpTji9yPtI3JUspxCFiJwevL9gWQ6xhuGCSGA19D3RhaVCSPSFHIMP8VqGAVCdFYWxfskBTodQ/9nJXIGYDyAaFgwKp51XbLGJBPzQnlCaBpMqzCefFXci6b1xhjRGfr563Ac8QlFumnKZ6GzsojDTz+GWz74MyPrHNrX1uT1sUEjOkkeJeUYGUkJTYOTH6EJBOKSHGNgnLcx5aW4cEFTMXTNuCCLCSWYa6Glpovc7045hroovh4ghNNu4uhzI6Y2rCEoYSmWqxmTyuT6rJkJIVHm5D0SdoEixwhisDCdtgV2fwAAIABJREFUBPFCRozozCz2HJzQx9OOhhyEAAYDXiElAb6f2gzkvZ0BIbIFCnWV5NQJQBYnw4KAGH8MvwWNxjTxT8z5EmuRY+jKn9NyDLp2fq+L0PPkZj4WE4Kmz6wT5IvjWN5v30u+ELQ+jFN40khMqUHOjG+TY+nOcw1LdVKGXC9VOzvKmFJ6tWT2GSlryjF5vBARS931eXhCeIM9ISLRhaXCZBy/FPKEAHixuZZxx2sJem5Mq4BXH/jaBfkMCinHEABzPwiRvt6HnnoYD/7xxzdUpuF1WihW64KVcp5MCH0MOYbChRiXCaFJEGIQEyIttRrIOGIMT/3N/4+5g3sRBUHfWhBk7ll1jchKMhhNdHqDGFPGcZTIIkYcC+3jhlnIkWPkex8QMF6emlGYEPx1lpS+9f9enyfEeTMhRF6jdPAv6HSMkBe64xo40r1jpKZjvP4gRJYdKaenKWOYj7/wNF65/04+2nJE0FQfmowy1rmX0ojM39cQcRzD67QRuI6cspS9NqkmjmRCpFHP0M/P0f+2xiUQ4o0cYj03NPOCaIeoQ7qWLoRKfbqonhAbKcdQQYjXQY5xdNfjeOLzf7JhUxhUJkSo+kPILk11DSAEdW37k6o8TwgWxkkRLSKORSEhZlWroQt36bFctINAghCDQIF4ABOCmApZY0q6jxJPCMEwyqDvo5LiQLzvsCKGErLUOLWcZJbRaM6I5UoZkukYWsqYEpqeer/EhLSL0Hdh1yYGHqPbbmH51DH59+g8QT71fIUXqWAdFIHrYPHowQ15r0SO0RydYMuOXnp2Pa2TxTXoxoeFmsQMY65I2qpdGpjEys6klW8YO8xf4UKEnI4hiuL1MSGGGVPy5LFQqQLAGqQVxIS4cMaUdLzVmc0Di33GGM7ue+W8JU+0nkpPCPF/YvxkfWSc5ioYY2sa9Twq3E4LpVodZqEoR2avNVgMnsVqI4AFxhRLCG20MaU6dUP9eyaiDBNiULc99FwcfvpRHH7mUQCAXanxtxXXITuCPJ2jZEGIWExK0rHZ3jmWv9JaY3XuzHCpoRJ86tXwUZkUtBbqxIRQ5RjF/CkQBLRObLlMMiGkoe6Qz1U9FfhrztcTIpRMCCYMEbNGohsZtFaNa+BI+0JqOsbrOHZZ7lOZCV3yPBqGfF4I/BxnbU3AGTFtapz9lNYGYk2tY/n0Oi0wxrjxt+dCN62+UdKpvJGYEBk5xrBpdn8b4xII8QYOmophaKZcvJkbgnU2pvhfjxwjfANOxwhcZ01J2evNhKAEc+R0hTFDBSHinAWuMjUD3+mOPcMbACyae66yC6QcQ9koQtYHQjBRSKi0bgoux9DHQuijKJTHMWixJtCFaHDU/SemQj8TIl2gsDjtCZF93aCQHeKhcgxl48v+TA01icwDKQjMSBlT6tB0Le0J4RMTooPQ81CqTw48xle/9XU89Ke/L/9+vnKMFAjxOjMhDj31CL756f+0IYwM0sazOBpZgKnJNKAyITZYjqFMMBmWxEjTPLs0UDcbS08IntBln0t6fi4Wu4WkXFQUr4dCLQHrHJZIHAbQDRN2lReB46z/ZF4LcLZZ4DoXRGtN57g0MTmQvbR49CAe/JPfw8KR/ef1WQkTQnhCCBCK9qTs9Sb2h7+BppxuW2VCrBPkYkyMq8QIOQZLsd3GNqYclwkh5Rj5zzatwSunjgPgYBbAn1HGWJKLyck3ChMi06xgMYOu6dB7wA9v+zlYCxvvwfP4X/0RXvja34z12jhlxjtCjiHWRMNUpmOMkmOIc1rbtAW91Yb4WVpqkQte9BlTnqccQ0jFdMNAHEep97twTIjxDRxpX0iP6Hz9PCHUPDsrQSLzYbr2jgQhRq8vyYhXU/59ZKip2CjAckCo43LdTpvvUxmAKAV8p5gQSrNojTXU9zpQcQmEeAOHJjZVXTNkIhs+fg7BXeONixsV6zGmfN3kGIpxk1rIuZ0Wvvyb/wpn9+4e/73E9y3VJ18XEIL0n90NAiHCIIBuGFyXqYIQYrErT/IRuuO4uicmeuQ8LIp8xuARE0It7KNYjpIFBCWdMTnKKhu6bozvCRGGKJRK8s/5x5t8X9/pSZ0gMRV8p5e6XyRV21PkGLreB5hkC+nW/Dk0zpyQfw9GgBAHHv82ju56HIBgQsj3z5NjqDB5DgjBckZ06v3TMVJyDF8BIXIKSLfdhNftJHPpz1OOoRYR2ZFcFzucdhMsjjakY60ypUZJMiRFOKNBlnKM8sZMx0gxIYas3YmBVxlRGA5g2WQ6igOZEBfTmNJIkujzMKbMl2OE0E0LdrUOAHDHkVYwSE+IQpkXj94FYEPQcZfqkwN9XKggPV8WXVIMppkQgevA0Mw+EMQVYNxGfe/Q9xH6HuxqDWaxeH5yDG20HCP0XAkOSp320PcV/zfJd2EQCMGPW47oHLBP0RrcnD8DACgKEIwz+AIJNNOIZPUZp+LHd3ocJGIxoGvQxaOhexsPQjjNxtisF9WMd1RzQfrUmCYMs5BiMHKflnxPCMOyULDL8nwnTIjB4zeT990gOUYU8j3YMMCiSK7jmqZtaC7cW13BiZd3cd8B0W1njI3MmWgPNgsF6Prr7wkR+WpOkAYhtIyfAu2t49xztIaT79SamRBjPP55oTKSvE4LhpTKqFNuku9Me0Y/E2J8Y8oTLz2LL/3Gv8TqudNrP+DvkrgEQryBQ2cJE0JSeDsBWHv8BW/Y4kWUyzV5QigdtYtqTEkUb8ZSm0m3sYwoCNBaODf2e1GhVZnedFFcwLNBRVqvsXFMCMMqwMwY0BEgUSEQYpwFnlgVxIQIBF3U98DiiOvLAz/5nDDtCUEFM3lCZEM3zYGeEKwXINqTnJMoDGBYBb7JD7jXVBAicB0JQhBQohoFAarBaXI/ZT0h+OvSz8Tzd30RT932Wfl3f4Qc4+ATD+LwM4/xvyhyj5FMiHAwE0J9H4hjViUCUo7R6yDwXNi1OqBpuSM6faeXGuEqk991ou6qZ0DouTj63JM4Qt//IscgU9L1hNtpSTDHaY0CIQYxITIjOs9z3RzXXVuVY4CxAbppYUxJ8oesJ8QYsqONDJYd0bkeOcYAQzR6P8M0URQghDcWCK1Mx6jwYvNCTMigfbg8McUnn+QUVgQkny/ANsgTYnv5Ovzkzg8h6qXXgY1mQhD4b9fqMAv2+RlT6uAgxJDuZuC5cm/Igrd5IX14xmRCkNRqkGyQnh9ar4tCjhEFQQpwihQmhFm0oWmaLH5e/Pp/x7f/+OOS1UFT09gAL6H1BotjeN2ObJbMH9mP1bkzA18fR1HiKTOC/p/IgCxpTJkwFoqI80Z0+iK/EVMiojBEHCcgmqbrA9a2jWVCxFHMpWJCTqqyRjcShNj/2AN47HOfRuD0pBwDQO4YUjVUOcYbYURnqjGh7B8pJoS4H4gJMU6jjMYsSznGWkZ0EjNhqH9MfqjeLG63Dd2yxHSM5Bz3rWM5LNgwYyw9LJrzZxH6Hp65/c9fd++XCxWXQIg3aDDG5Caja4Ys/lkQA/74C8tTX/wMvvO5T+f+WyLHWAMTwn99mRD8c5XCM88scdR7Se3tLJ+8cJHpTnTee6ujTXjGiSjgxTp3wc9hQkzNABg9IYPr3ZIRnUBSMNF5rs5sBsDPN4sZ7xgpIAQVNbrYrCmog6grm3jf99jXQPjNk2AO31RoDBIf5TWICZGeBkLHrRagqiSjjwkhnMZHyTG8XicFWAWOML4cUJwFriM/V9P0BBXP60anmBD9G01ajsGXbJqUkceEYHEMt9OCWbRhFe1carf0tJDU9USTvB69eVaOceA7D+C1b9+95veh6K2uYNdX/mpda4yUymwQCDG1fScArosfFnSP9nlCBOQJISjY57neRGMyIVRjSiAfNGZxeiRmNtGh+/yieULEMXTdkEXx+YzozJukRCM6pRxjDE8gFieTqS44E0LTFC+XHPBQfO75ejPI6RjEhBBdxao5CUsvgnUzIAQxITYIfKHC2q7W5WSFdUUMOaJzWGEReK58DgCsQ46R/7KsJ8SgQje7T9D9F4dBaq+Rsjjfh1W0UazWZfHTXVlCe3EOAGfC6ZGQ5m0w4953OUBNx/X0bZ/F7nu/MvD1URQqa94oOQZN7OEFXByGktVgFgdNx+D5DTHMQs9NTXcZ5JnQZ0x5ngW5BEiFHIPW+0K5zKdCbVDB316aBxhDt7Es5R/A6GKbGgnGG0WOEfQ3fxhjynnk140xtiZPiEh4ZSRMiHFGdBITAugfTzZeqOwzr9vJnY5B61iKBZHJLQeZx+cF5ewLRw5IZu33WlwCId6gEUcRDE0kCJqZ3Kx+xA3swvFQsebcGcwd3JtbWKzHdEwt9C62J4ScCa18rpdjljjOewFAdXoTgIRqerGCOtMb6QlhFgpSYyk/R/GEAIYnkJ2VJdz2az+PBWHoZ2W6tgTyVGc2ib/3EvBBlWMwpWAWIIRhFWTSpWoq+yKIU//n4/OE+c+AjSb7c7pH1KRPnWBBHXtJ68w4jVNkk+LQc1P3WNIhzi90A9eRn8v3oSFMCPVZFl0tvlmz1O/omi5nw0NOx1CYEKnxuZwiaxbt/GJGFOjZ0beq2dZaQk2kA8+D1+uivTQ/luwmL87sfRn7H3sA84f3rf1YBpiSrjVi0RGUIMQICnwyEi6d9EowoLRBcowxja0ilQkx4HPJ8Z3uq4FMiIsmxwih6XoCJq6DgZEF1lLvL9YUs1CEWSiOtfYzZbJCcc2GluNH6LkwC0X53fPAQ2kOvAbQPS/6PCEE6GPowhvETd8rtHdslCkngRp2tQ7rPKdjaMqIzmdu/xyeuu2/pV7CGEPoOrKIHU+OMZ4x5bjTMbLPj8qECJXrrDIhDKuAUn0CrpBjeL2OfD41TZNyXS2HPXc+QdeajtnvdYbmDmljyhFMiCBjTBkEiKIQmqbBsAoD5RimYELQcdF5znbUs8cFKL4R582EEKPHhRwjVpgQwMblw+2lBQAcdCKQhT5/WNC9Y1oFBYR4gzAhPGICEXhEXhfca4mu1Tggp2xOrcW8mB4RneQYa39mXMUTAowp0zFUOUZmHVNBD/GRoe/LfHDUPeN1O6hMz6I8OY1zB/es+Zi/G+ISCPEGjSjwoUsQwkgST1/sPGOyIXzXge90cwveYJ1yjEEGa8H9JxDt25jCWg2i1JOOUi22KBlbC/U6YUIIECLjPn2hgxbkjfKEoITFsAoZTwj+PaUnxBDqcWv+LKLAx/xhbniWTarI6FEyIXrdBHxQ5RiUJOk6NIMvL1bRlh1ETp808h29BfjAxP8TAyJr4EYTZgorKgCDEUyIQEHmkcOEyG4mgevKDpH8/sgvkhhjYowTiXZ1OZ9aBQ1kKHRaotbGR1vw//RVbkRLv6Mns+HlRI8B86cBrg217FI+EyLD5EhT/NfeqVcTjtBz4Xd5wtwVbuZrDbrfFtYx5WKjmBBetw0whsrULAqlymhPiLaPmjUtk+VoDXKMV755F/Y8eM9YxxWNea3o/hv2uUzqnInym29MebEmnrCIm79RZzlYY9HLGBvOhBDz3QGuyx9LjqGM6CxKJsSFkGN4sIo2LDvp+GbDk0yI8wMDJC3eSDMhqPERu8l6G0eh3GdHMUAYY3jp7i+l/HPyguQYxVqde0KslwnBkhGdLGZYPnUMi0cOpF5C7C4CdzSMlmPIomWkHCMzHWPQKOnMPiFBiDBIeegknhAezEIBdm1C5idqgaZpOnQmJi4Fawd6GWMDrxEBRHKvdJ2h4BOXY6zVE0LIMUJfegToYvRl9tpQfqM+F2kmRD6zMpHHpdfj9Yb0q5FyDMGEoHHmG8So1Vcj3DT5HjitVVFsEwgxSo6hGlPmr+cXM1LeReJeymOwOMq+OpZvmRizLM2L1zKiE+gbmTluuO1mitWQNz6VnmUCdaUHBSBztSjwYVENNeKe8Tpt2JUaKlMzY40J/26MSyDEGzSiwJcJgWpMSRsO88bbeGjzyNtwqGO4lgQg9INkw80UgPHBJuITF6BDJB5UW3YPFBCiN15ylHo/ZRQa0D+H+0JHsMFMCNJMGlaBm1yR9kycJ8n4GEI9po4gGWdlp2PQeU6YEN0EfGBK8UxIt57IMcxiUWpmNcOAZuj5G6pkVsTys2mhz240ex+6F6tzZxAFfgpAyHpCyGMV0Te+j8WpEZ2SCp0FITwHYEz+nIr3PDkG13Mnmz8Hw0mO0f+100wI8XyveIAXg6146VGfyojOQcaU8lwUuBwj1xOCnv0cE7/1gRAqE8KRyURrYW7N7wUk99vi0QMjXtkfG+UJ4Sm69dLE5Eg5hvVCB+/e9PflPZiVYwxaNwHg+AtP4ciz43lopI0ph4AQUYYJkZOsxdLxXTAhBhhTkhZ7WEjjvPMIFkfQDF0C3d4aO/4pk78c4ISATYB34ccypkTCZCoIT4gLAUKEwrfAKgomRK4cY4M9ITLGlKYmkmelyaEWv6M+d/Xcabz6ra/h6PNPDX2d9IQgOcZ65T4ZT4jA6fUl63QPm5IJMeb7AtDM4UwIyqGIITNQjkHrI3mLKMaU6topmRA+MSEmZX6SAiF0HZYpCn9v7R34xaMH8Y3f+3+wfLLf5JxAiMB1EYUhoiAYupampmOM8ISQDAYypgwC6dMyqHsfBj6MQsKESIMQnNafN0Un8YQYDyAZFmkZgZBjSOnqxjDcAH6Nt5pX4OapW2FoVoYJMSYIUSy+MZgQyvpLa5m8bjpNxwhTz6s/BriaNKc4+2Y8ECJtTJnXCxoVTrspWcWAuIcz9x595+rUbPKLWjr3C30/mY4z4p5xu20UqzWUJqZSYM33UlwCId6gEQVBWo5BDxolB954iwttko2zp/r/Terj18CECPxc6iGLxajGNfhVjBt0fMVaXR4DRdL1HD8po42+tmkrgI0DA8YN1RNiWEeGMYYnv/AZyU4YFAkTIu0WLLXolSrMoj10Eghpz6iAk+j+IDlGr5uWBIk/p/wLRMFsFmyZvBtiU80z2WGBOBdBLGUB3MCqkLrXeqsreP6uL+LorsdTqDIAmcCnQYgEKJDyA0+RYyjFfW12c+p1FLSJZpk3eQV+X/GgJQyGUUwI+WfxHLFVLzFJUzwhpDHlgNFPAE9GLLvfE4LFcQKiKOMMDTGmbz1dSRW0cZqr8vquxTBWDc/hCffi8cNrTqTGGZ86ThBoZ1frvBgYYUwJJ0LBKCUJ+aARnTkgj9tpobU4nnwlDDwF0Bgtx0jW6zwmBC/6STrVJ8dQn50RbIiX7r4dD/x//3nk8Q+LOIqg64aUrhAjZtxI6etzR3RGsvC2q3V4Y3lCxPL5LdhlQNPGSpbXGoGY4GDaNOFnsBxjXBBk6cQRfOUj/yea82dTP1fp7IAixxA5B/OT+1D1EhplTDl34DUAnEo+LNx2C7phwLL587JuuQ+DZEIg5gw0+o+C3ttSjClH0rHHHNEZuI7sPGu6MdKYcmLLZQBUT4gwdazqqGSVCRHHcVqCo2kwBGDE1pFzdRvcj6q9NN/3b3S9WRxJIHY4E0Id0Tm8UO5nQgSi2aCY0WbeI/J9mJYlr1/guSk5kTFIjiE9IdLTitYTtJ9ZsQUTBcRRJD8zjwXDYgbWXPs+2l6ah6XxfdjSizBMIxdQOL3nJTz75b9M/S7t20ZKjvF6jujMY0Lw49GkJ0SEXpPn3+WpmTGZEDwv1K3xmRDycdcxnhwrJ5zWKuqbt8m/k1RYvffoGlQmBViRI8eIAn8kc4rCEyBEeWLqEhPiUlzcCAMfhiYSBM1A5IsON1HvxgAhoiCQCXDjzMm+f6OHZ00jOsMgPwGmomlMcGQtQQUhMSHCDWBC6KaJytQ0NE276CBE6HIDsigIhmrgvG4bR559DGf2vDT0/bhm0oIpiki6LuqmZNfqQ5kQ2SkhiSeEYEKQMeW06gnRb6iY7lAQE8KWNGbuLq3nd0wUJgRtVjQPWl2sl08d48fQ7SAKAsmyoM8CeMElNaTDjCnJaVwU98SOSc+1DmUB5/d6iONY/nueHCNbPHCixWBPCJbjCUGyK7aqMiESYEcX0gwVzMnzx7Dsch8oEniu3JXpHlHlTufDhNB0Hd1GUoCQkdpag57r0HPROHtyxKvTQef/fJkQkjJeFUyIEZ4Qmh/D1Kw+DTKZCmeBPQpyo4/DYGTxBvCknOjcazGmzCuQyAiSntXs1BrV82RUt3r13Gm0F+fWT60H76TqYo2w7NKavQ/SkwbyRnQGsuAuVmtj+wERk0nTOUvjQjEhLJUJkVOY+2v0QFo5fRxOaxW777sz9XNap5MRnWlPCATJOiXPkaaN1GyfIxCiMdx02e20YNcmoGkazKLNJRPr8I9hMUt03nHy7Kt7Ov0sMabUhM/HkJAgxHAmhO86cq8cVAzTMWiahqnLuL9M0gn1M0wIT/7fsAoo1SYQBQF6jeUUcKJreuIfFKw956Lnymn1s0BVdpA0DFSkiGowMXVnXE8IdTSsWeDNhdD3pMkff48MCBEk0zGALAhhQBtkdL2BIzppXdx+aht2tq8Ei6P+9VXpaseHmvD/Yh9Yd23siPbSAiydH6+lFyUTNHv8p3Y/j4NPPJRmQsrpGAUprxvFTFlPNM6ewhd/9Z+gvdgPYKkR+p4Et6kRkpcf0p43uW3HWNN3VJkuMB4DpTUvmiHnOaKzOrM5ycFy5Bi0RyYghDLRTJFjyJG+o4wpO20UK5wJETi9i+bNdDHjEgjxBg1VjgEAzPN50ScennHQb9WYbTWTyKvd0TUxIfwBxpTSq2Ljx8hQB04WSX4/CLFWTwjT4gu8XZt4HZgQLqrTnK7lCBTYaTf7FlNHGlINTzhDxRMCSIpIkipwN/iJod4XbjcNUMgRnUOYECkZgQAkEiaEITcgq1js84TITVYUT4hIoW3yMUgKCHGSgxBer5NClemzKGi0ou/2sHT8MKLAl5q90Hf5Bs6YYBXwjaI0MQXDstJGi0ph4zvdhIJbrSEOg77r1ufBoDAhcjtwKTCH/9lZ5vdFvOJm2CXkuiz+PFSOUeTTMTKgSLZLyBhDGPgJyDeE4j8oQs/jBVq5kipA1suE8HtdKY1ZPHpI/pzFsXz/uYN7cM8nPpL63rHK8lgHCBEFvgTrJBOiVkepNjF6kkLAYGqWIscQxpR+ICdQZM1jAf58r4U5EvpeqogZFPGQJJlCTscYwISgTi8w2juoI/w/yFhtPUG6a4AzR9YqO1DBsPwRnRk5xhieEIwlIzoBXkBu1JSIOI6x+7474K22wLyIyzFI+54DcHprZEIQuHz8xadTs+ZlEaenQQhT5ByasrbTe1SnZ4eCQnEUSSNZFYgcdFy0n9P0gnWNB47FtdE1QBnHrHYNqTBIyTEYL0wGMhHp6wsmBBvEhHB6KIjni+jlua/zXJh2CVuuexMmt+2Q0ySiMJTPlaZpcu3lTIgi7DpfA1fnTqffUNeS/TdYe0VFz1UesKo2JCQFXPgcZYPFsTTp03Q93+tJiYQJUZBAqtNsDCy2gSS/yZuOoRkGB3+GyDGMESwNxhjOHXhtKCuVPq/oWbDiDBNCGg4n9y9r+UAMsOba7unO0jxMnedxBb3I858ccMbrdjgQohqR+56Up1CTIi/PeumeL+Phz/zhmo5LjdWzpxCHoZTuDorI91AolaCbpsy7Up4Q4np3V5ZQrFRRqk+ON0Y+DAQDhq9Z+x6+D/d84iNDr9+Z10Qjb50jOuM4htdpo1SbkPupKsd49YGv4dUHviaBF1vknvnGlB4s24amG0PXvEiwpOwKZ0IAo82xvxvjEgjxBo3IT4wpASAOwrTUYQzGASXhlelZNOfPphYsKqwKpcramBBic+SFoSLHoGO7IHIMwYSo5skx1jMdw5eJT3lq5qKCEHHEtYR1Qcvsrq6AMYZv/O6Hsffhe1OvdeTYouELMx9hZeXIMfjmrWkaZ0IMk2MoiYduGLKzIZkQvZ50bjcsK+0JAcVMUnpCJJMcOBOiIn7O9efZjiugMAJCpnRMLNFhSu7dFcGE8AQTopDDhAA45VXTdayeOYn7/st/wJFdj0tAi18H/p58OgZ5QpBGOe1xQOE7PWnqWBG6vyzo0M+E0IYyIdSxnJTEeav8WkXLvVw5hpYnxwgCQWsVBYWgdmePz88wQ6IgABgbW6eYF4HvypGgBBKUJqbQOg8mxNT2K1CqT2LpxGH586PPPYG7/uOvoLe6glOvPI+VU8dSBq9q4bae6RivfuvruOcPPgJAMCE0DcVKDcVqHaHvDV0rtQAwdBOWIsfoNpYRhYnUheugfZx+7UUJPKjmiOOBEBwI1nQjNTI5G1EYApqWjM/L84RQHN+BfE+IkkiA8opi9X1oHc2jeI8bpLsGOM15vSCEXa33gWlxHIMxJpPfYrXGO9Gj9j+WjOgEgGK5MlbHbpxonD6O3ffdie7d+3ELe/f4TIgxQRC304Zucpbcvke/KX/eWeZAEQEB0htCFEBamHxfoufXNm0bWiQsnzyKwHVQ33IZnNWVoV1xt92U+zmBXOuakCE8ITRdSxXAKSaEl2ZCaNBwdt9ufO0//xru++Rv9bFEASTgrjF8RGegMCF0wxwqx7CKNm649QP4+x/9Q3m+OTCeNFlkA4E8IQQQ2zyXLvg0TZfgtbaOlCthQuSAEMo17qVMA/ufRVUWwb0SxpNj6JYpr3+v2RDFdj4QSiM68z0hhMFhrhwjc2wD7sfFowfx7T/6XSwcGSx7jeMItlGBHuvQmQEWx31Tj0LfkyxNCGNX1lkjE2J5AUWL5zOSCZFzXqR81ktLeQhw4d/bQBwGeOmeL6eu49KxQzi7/5V1+0XQZ48CDELfgyEaIbR3MIUJQcyXzvIiShNTKJarY633NOGI8pz20jxWTh0bun6QwTEjk+E14nZepwXGGOzahASd1OkYR597AsdfeoY3OBUPtDgKc4wpA5hW/zS7bNAeQ54QAL4nfSEugRAXKaLAz6UoHniSf0xJAAAgAElEQVT826Kbl74ZwwwTIvbDRIqB8WQPVGxsueZGsDhOIZc0Fqo0MSm7oWN9jzApeOOUHEN0sdfIhGBxPLLokZu0lGMkr1dp2+Pq30JBdQSAyuT0BQEhHv7MH+a63dNCObFlOwCg11iB73Thdlp9ul1KEEYt9lGGCaHOGicwYZQcQ6UlG1ZBGX9ETIie3Gw5HbkrzSj5h4nrL6djJIWNWSxi8rLLYRaKKNZq0HQT8ZDpGAiSDZ5GManJHZlped0OwgwTQgUhDAGanNm3G2AMvcZKLrigabrc0Er1ST7WUtWWK3/2e12ZwJFJ0TCmAX//BIQYlwkBX/y/FWCwMaXeJ8cwrILcAM2CjYJdRuCkqbSpySGeK4uwZPrM2un0RCU3C7a8bzddeS26y4vrciX3el0UyxVUZzenns/5w/sRRxGWThzBiigeVADNT01FGe0JEYVhau1rzZ9Dr7GMwHPhtlsoVqrQdV0mzIOAPMaYHJVnCjpt4+xJ3PlbH8LxF59Jpg9YFk68vAsPf+YPZddYffbGASGiwBMjJgupWezZIMO3LDiZOm45olPIMZTElFglNF1nGBOi11yR99h6JTgAEEexvMcLpfKaJTV0jHat3nduYkWPDiB1TYcxShiS6RgAUKhU1yT/Gxb0rFhNHUWtBLNgp2jnqeOIY/huD7phIPS9sZ4rr9tGqT6Bqe070VHAobmDezG1facEHmnvsAx+7+pR8oXp/qxv3ppbJDDG8MyXPodHP/spAMA177wVjLGhGma305bnP+lwr0PGE0PKMVRZW1qOkfaEUGUlrfmzfVIV/r75xpRBJs/wHUeCG+oUp8aZE3j53jvk2hK4jiIHSaaRxGGI0HUFi6yayOOEGSOxwSh3o4JU03QJ/GvxOE6b6aD7182RY3g5cgz+XdPX/qE/+wRe/dbXxXEl0y2GhdpcSDMhjIEGjHxfUzwh3OQaGENGfqvSj0FABZCwdoblR3EUoWry7rYBbqydeP3wvOjorsdx7x98FJ3lRZmfs7Y/dm4NcCZEscDfj3tCWMl5CdNMCCCda6hj7AFu/tg4ewqvfvMunHjpWflzt9NCHIbrXqdpPRgHhLBoTHjGE0L1AGnOn0F5YgrFCr//R8lBuaSO72vbbnwrtt7wFgDDWQLkbRT6rljL14ZCECu5VFeYEIYpm7HdlSW4rabMg4p2VZ6DfmNKDs6YVkFKNfOCznOxUpVMiN7qJRDiUqwzDj75MO7++If7HrDFowexcuoYDj75UOrnJMdgfM9BHIRpCcYYxT4lcDM7r4alF9FZTsblkZa9VJ8EGBtbBy4nMZhmOqldo2EmAMwf3od7PvGbuOt3fnWoHjRbJOUZU2b/nBenX30RR597MrVYlyenN2xUphrzh/fh7L7dfT+nxbi+eav0o3BaTVTNKdn9plgLCGGmjCl95ef8e5aqE3A7rYHn2eu0JdpKhjuAyoToyuK2UK7wQjbHy4CM9XTDkF17q2Bj2w1vwf/2yb9AsVwdyISQcgzVE0Is9KSd6zUb/LxoGrxuG3EYSH0doCSa4POyC6WyLFLdTisNLrgJDbY2uwXv/T9+CVd83zvF3HoPLIzBgrhPjkH3mSzORoAQqi7w9MsvYOFQeuIDC2OgIJZi6m6J/+t+cl44E4JGdHI5hmp0Sd0zAmXMYhGV6VnEUZTaoFWGQOgqIETO9JlxIxDO/mbRlkDL7JXXgjGWKn7GDd/polCuoJxxhSYAaun4YTRO84k/g0AI+rPbbuHp2z6Lxcy4zygIcOe//zc48ux3+N/3rCBqJprybmNZajvJSG5gohrE0Bi/NpbBCzqiv/dWVxLNvWXJ70PXhIANwyqMzYQg0DEaNh1D+B8kz3IeEyJM6XPVIoLAt/LkVOrveaHuLRvKhMgUPk5rFY9//o8Hmo7SvWxX633nRnXm56/h1/S1b92Nr3zklwbvH4wl023Ax3SOy0QYFU6rCVMroOCbMGHBsm0YlsXlJBkvF9/t8ZGx5MszBhvD7bRgV+soVusyqY2CAIvHDmLrdTfJ19E5lyBEnKSGXrcDs2ijVJ8URUL6PvKdLg4+/iCqM5vw3v/9X2Nm59UAhksyuCdEhgmxnjGwqRGdyb2blmNkPSF4FEoVzFxxDZx2f/HCBhhT3vcHH8Xu+78qXxe4vRQ9m/atfY89gFfuv1MCvoHrpvYmqWcPA8kiMwvFhMXo8z29RHIMwYQgYzxNkWPQqM61xFA5RrctnxEVzFEBqCjwcXbvbpzbzz1AyJhzFAihNhfsWk18Xic9BSIrxxDnQpWFqXKMrC6fIkv9H9Skoukj6prCgjg1+pRFEaoWByH0WEccxcoIZL7frpw+ATDG1z+Xf/bhRx7G7vvuGHpO1GgvLaBg8PvEIjlGjmGnZEKoIISXASEMU+416qhs2sNUedZagph73ojJQrwJVoRVLEmAUb0m0zuuhGWX4LZbKE/OyLxlFMBLTAhN0/CBD/0m3vIjfw9Avr8JRaCaVWvaQGbToCBT6tLEVJ8cA4wh9D2ZX5qFovwuYeDJnWPu4B4pCzYtC/oIJoQnQQhFjnGJCXEp1hvVmU0InB6WThxJ/ZwWwFcfuCtVIPHpGAZYQdC4/TANPKxBjjFV3oaf2vnLiOaSpIUSnNIEX1jH6ULEUQQWR7zgNQsZOUbSxR6kn1QjCgI89KefwOq5M+itrgxdeJLEMn9EJxXfo6hcu++7A7vvuwORoEwBvJAMnN55O+mrQR3EZk5BQZ9TKFVQmphCd3UZbrOBv7v9n2JLb1vqtRKEGEeOUVA9IfjCRt0UALDrE9wAb8A58rptmTymmRBkTNmVTAi7WuNJ3ojpGDT2j841yTMGJiuhyoSggsESTAj+nVaEH8TMzqvlpjOQCWFZcnwWwBPf0HPlJiKZEKK4v+adt/IOs0Duw4dPI/ja0dS94fd6icxpTUwI/t1LTzro3L0n/b0jhhDiWRIMES0CIibcvXv0Poocg4wpM54QZqEgu5tmoYja7BYASMkiskyIaANAiNDjXQ/Vk2P2imsArL0oZYzB73EQoqS4Qoe+j9VzfMrPyd3PyQJVNVIjvw6zaCNwe2gtzuHeP/goDj31MI4893jqc7qNJbidFhpnToI5IcJvnsSMyws8DkIsoTxFIAQvmLIGrjKU9VgHT8iJ8g5AkWNY8meyoySSutkrr00MtIYESeI4E2K4ppQzIdLrghqcCaHnyjEk2DZBTIjsxBhH3n9dAUIUylW0F8/PE0KCEDmeEHMH9+DYc0/mjhbkx0hMiAmEvpdmClEBZCSeEABw+JlH4TtdKfPKRtYTolCu8G7iBoy/c1qrmCjwe87QTJhFG5qmwSqWEHgOFo4elGaPNJGDJviMQ10m7wXV/2Lx+CFEQYAt179Zvk7TNBiWBdPg96da2HrdNuxqTa6zWWCou8IZnje9/8dxzd95n1wX6efZCH1uxijlGOQJsS4mhJDK6FqKmTeMCUFAbnVmE+xKLf+Zlp4QCRMi9H00589iVZk0FrhOip5N9xgBnnLt8tJMCF0BIah7aliFDBOiiGK1Dk3TJBNiYtsO8R10uV8aQh6wlhgGQridtpQaOikmRLJvNOfPgjEmi1vdNHhzYRQIESRsJNpv+O+rsoN8Y0pN16VUMtVRNwZ4QoTqawYDJMQGSRX03ziO4AsHpLFkHEeoCCYEl2MkxpR0/VuCydpbXZFMCK0XYel4IiccFnEco9dYlmNyC1KOQSCEYFcwJnNC7UBHHiPt/xS6YcjnoCPMH1kcy3Wgz2dkzHBHyDEaZ06is7wom308nxLGlHECHm266jr87O/+KW79+Q/h5h/9KZm3jJQfh6EEZoDE+2sQE4LFsax3fN9Z13AMeo7LCghhKAARfU6vscxBCFvxCRFLyEtf/xJevPv2hLlsjgAhxHm2qzVYpTIMq/A9OSHjEghxkWLzNTcAmiYpuBRuu4Xy1AzcdgvHX3ha/lx6QhTFwhxEaWR2DXKMamEKuqaDNZJNPmFCcIRtHNdVaSpEeiaVSqQeTzB6Q+wsLyD0PWy/6RYAw6lUYUaOoT64vtOVEw2GMSGiMETj3Cl0G0sIPE9OkiiLTucgSUYUBDi5+7k1UepogydatxpyXJht87E7qyvwllqw9CKswEy91mnyczKq60ad0ex0DN4Z76ceA8DZfa/gyC5elDHG4HbamNy6XV5b2vjomlNHDQCmdlyJxukT6ftRghAkx0jG/qnAAMA3x1xPCHq/MOkyGBbv5BJt/shzj0PTdWy7/s3yPVLTMZRN2BBMCAqv00LoeZLiKpMOLU1nNYs2N65c9sAaXtqk0unKZ4funSztv39EpyY3ooo5CdPV01TqMEavJ+5/kUjrsY6WzxMHwyFn/sS7gkZ0EuuAtXxMdafFdyY5RlGOoVWdrOk50TQNoefKQtauno8xpcKEAD/31LXrjDHxIfteLI4lEyJwHQSei8aZE2BxjFJ9MsUYyGNCcBO9Hg4/9Qh6zQYqU7PoZAwT6bjcdhOsJ0aHCRlMb3UFvcaKLKhoPPBAOYay/hmxDsM0+TogWDCJHCO5PymJo/fcfPX16DaWRp5/ntwJJsRQECIQIB51GAd4QqjTMZREnZ4P6sJkZUlf+egv4cRLzwBITCm3XvcmtJfWL8fgRpkqEyK9phPwOGi/CBUQAkizPyKFCg4k15SKlWyDIDmopHAF+HUKXAcP/envn/cEFqe1ikkFhLAs/vyYto3AdfHS3bfjuTv/GkACRhOwOI4khNZtu1qTuua5g3sBTcOWa29EPN/jJnoQgK8ogExYsrAjh3aacOT1umCM4e6PfxiHnn5EMh7oWSHgbhATwu3wa5gwIfLlJ2OF8ISArkkAt1CuZJgQ/H1NRY4B8ElIfEJKzjNNjRQjYUL0Vjmoot57qsyCKP9et4PmHAcN6DgC102DEGJ/jIJA+kWYhQJC30cc8/3PtCzouo5itcaBY02TMk7OhCD5l7Xm+5ByFBr/qYbXbUvj7JQcQ7nfiJlB50LKMUZMY/AdLifiIEQ1OR+pEZ3JezDGuARY7Otm0eYgRBilfk/9nSjw8eQXPiPlBolUZAQTQvFXYG2f7/13HuGsiChWmBD8/iHQTPWEAEQuKZgQtl4Z26jXbTfBGIMh6M+cCZGsza2FOZzc/RxC30MchijqJVR2R4j2EkjvwbCSJoBmGHKPoQkUXq8jAav1MyHa8r3y4rHPfRrPf/ULvNlXKMCy7WQkOjEhREPKKtq46h3vRW12szQv90aMP1bNhYHRIITX7UjQwScmxBryeSAB40r1yYwcI52zt5cW5Gh0AAjDgK9N4M9Sc+4M4iiCWShySfuQEZ0EQhQrNWia9j07pvMSCHGRoliuYnLbDswfTpvfuO0mLrvxZmi6nkqSOQihQxMgBIIozYQYZzqG6FgQxVIdF0QUxbJ4gMfRgRO9lQpDlQKnFqTjsDSoO7r5mhsAjAIh8uUYUeAjCoIEhBiSlDXnTiMOQ8RhiM7ygkxIKlO8yzdIkvHK/V/Fo5/9FOYP7cv997xQO0VZ3V0oQYgSypPcFDNsiOvECqlEjChgeRRYihQ7RRQ66Vnj/NpTwkcTMvY+ch9euvt2AKKjGUewq3VUZzZxz49M4eK2mzKxn915NULfg7OinLMwI8dQdOZ9IIRu9CU+/D0SJoTaxaB54nsfuhcnXnwGt3zwZ1ARSRKdS/neCv3cLBST8WmWBbfdQuh7kuJKRZaWASEskejACQE3Sgz5NE0YU6aZENkxnX1MCCCRpmgFmFoBjdPH5b+zMIYf8vekbp7BTKz6fD0wHPKBUJkQ3GeC5BjRK8u41nkTLMtOmBDFIipTM9ANI3Uf0vHZ9cm0J8QYExcGBYEQ1G0kt2vdNAeOnYyjMDcxpOKqKJgQAE8CqPt93XvfD3FCoOlGmgkhr80sAsdBe3kB1ZlNmL3q2r5ksLuyBB06B/sECGEyoVOdOwPf6cprPMoTgpJOgGu0CcQr1Sex4y1vk7IOekaBJMlwO21YdgmTYnxfayHtDZMNormaVmEoYBGHIQzFRTxPjsHiWI7NBdIFQJCRHakgRHP+rBifyrvCnZUllCamMLF1O7orS2I8dP+xxXE8sCNJ4/4SOUa5z+uH9olB1FvVEwJI72uye2qmmRAALyQGsSu4J0SyRlz1jvfiPf/4X+Hcgddw8IkHc39n3HDbTUwWNsu/Fyy+llmie9hbXVEMiokJsUX8nRcBu+74PJ6+7bO8YAuCtIGdABDsah1xFCFwHcwf2ovpHVeiWK4iuPs4wqfn5GeSD5WlJ3uR2+XvQYCu3+3AbTexevYU5g/ulYwHWpOtIl+DBoEQib6a5x6SCbGe0a7kCaEnEoqJrdv7jCk5sC78FMTPqzOzsKs1+L1u/z1JZsAKE4I8vag5wATjUaVnR2GYkn3RtQs8p28fpCIk9DxFjuHL58aQe/cEtpWuhl2qyXwFmiaBf0Oz4I2a3JMJylEYYyljXMYYvG5H5lROBoT41n/9GJ6/62/QzBSwo8wf1fNRqk/JaUHErhkkx4jDEGBMNlisoo3Ad6UBpm6aPJ9Qfmfl9AkcefYxHHvhKfHegyUbQJITpeQYXgSUDLBFF2yuizgKUTX5XqQzHRo0eb+qjQ4A6K4uyxy4ZPDnYBymCq1tmvBjsfSiWL/5edl9/x147HOflswNGuVJtUDo9zMh5DGtpH0vNF3vu4ajgnJQV8ox+kGI0PfRWpxTmBC2mNCVGILzYzP7fpfyj5HM3zCUTCL6PU03cv1NAA4ykZyOyzGwLiZEsVKFYVmS5aD6dVC47SbPOws0kSphQsRhKK+xlFMOYUKonhAAl4JckmNcivOKLde+CYtHD8pkiKhRpfokSvVJ9FrJDUau55otOlRBxIEIAKiYYB5f1PxeFw98+nck8g5w87Yze3fzDq2mwYjFe/SSRTjok2OM7kJQcZv7ACnAwzjjQ6kg2HT19QBGgBBUJJWrgKbJ45BdTxobmTM+bP93voXnv/qFxLUYfKGgjiRRjfOYEK2Fc9jzMDeXXDo5oEuWEyoY0szQq2mjM4s2ylPcFDNuiu9nlFOLjJpoDxqNFmWuCf9ZejoGkHQGacN1WqvoNRuIgiDRnlVrmL3iGlSmN0kmQxSGiKMIrhhPBEDKNnpLCt22z5hSh2YkaLcamqH3FZ+HnnpE3jcs7Jdj+N0OXrz7duy85Z24+Uf/QaqLQqgyQEZVpjwnlCBsvf4tsvOdMCHIEyK9DJrFIjdr7QVAECNwxLNSn4Tv9OR9RwVqmJ2O4WXlGLpMfC29iIJuY/GYMnYyiODHYj6864ExBhMmnKiDUA9huokEIzGm1AUIIZgQwoW7YFYST4iCDd0wUJ3ZnJJEBI4DTde5eZ/nJiCfYBr5Tg9PfvEzY0kD5Hv6Hu/miWtdKFeg6ToqkzMDQYgH/+T38Z2/+KO+n9PzUyhVEkMmAULYtQlc/tZ3AODFmF2rZ5gQNLlkBr7bQ2eJgxC1mc3oriymwK/O8iL+3s5fwlRvGswR49Z0fvyLgkJLtGTLLkE3jJSJpBoqE0JnOnSTr7nliSm875//Mn7oF38NAC887GodtdktCQjRbqFYrWHTVdcB4KaBg0J2SQsFGIUComHGlFEoR9wCQJ6RYRxHvCsr58on54e8Q6hYDBTNPq3fqua4OrMJtdktYHGMe//wo/jmp/9T3+ft+vJf4qE/+0Tu8dK9rCsjOoE0wy0BIYYwITRNYfUoIIQ0pjQRH2uBffEYLLOImZ1X47Ibbx4IQnBPiHRc++4fQrFSTXlhrCc4EyIBISzSg9slBK6L7uqKnAIkxyRvSjMhTu1+HoeeehivfvMufP1jv45vfPzDaC2c47IH34NdraWYPKtzpzEr1nA4If8PwK0//yE5HtDUCnJd8DptbLOuxPQjGnQY8Htdef1bC+fQbSyJMdAJqFOZmh0ox1C7igD3DALWC0KwxHNHASHc1qoseLgfg+IHoTIhKkJmlS18+jwhEraP014FY0yCNElRwrvtC0cPyHWa7tOsMSW9PgoCBJ4jvUBC35PNHiq8J6tb8YNb/yGuqt+MUk104zVdMk51TYfbHq7Pz4bX60pwUc0zyMSYciryA9GEB9PCkf04uutxNM6dSr3f0NHbSjjNhgSWgWTPUadAREpuoOY3AMnsMtMxMn4PBH4FriNGhXPAIxrAhJByDIXRyNwAkUgxmBMhjmNUzInk+2rJGO8sCNFrrICJ6Rglo4o4DMfqYDutVZiaJQtmS7ehm6Zcm71OGyyO0Th7Uvy7ACE8GgXd7wkhv6PwK6D8b2bnNWjOnxvbNLpx5iRu+/V/hsbZUwkTIscTorVwlpuAr65IUISYpYAKQhh9vyuZVkO8Jpx2UzbMKDRdR6k+kevtAvCclxpNgddbJxNiVd63VknxhDD7wRSzoIxZDnwQCqH6Ckk5xhBjSq/bhilkWgAuMSEuxfnHlmtvROh7WDl1HADN+o1h1yZQqk+mEivmEQjBE0gWxtJ3QatacuFpnD2F+cP7ceLlXfJ3n7vzr/H8nX/Ndfx2Sc6RJqoxkGinS2MyIY4+9yTO7X8FAD1AZvoBGtM0c89D92Dl9Ak+D7lQxJTo/g1D+ALPlZpBUzFjoySsNoQJcez5p7D3kftx8uXnoEGHDtGdl8aU5DrbnzC9+PXbYZgW7FodKyePIW548G87KAuWQaEeR7arGaSYENMIXAdRg1+LolFKLTJOa1Ven0H6u2XhkWBXawoIQdMxEtlJwoTgKLbbagKMSV08wEGId/+jX8QP/cK/AwA5GtPrtgHG5Mzy+uZtMIs2nIZyzSQTQqFJ6ulzTaHrRmqcWuh7eOb2P5ejxpif6C1pRCfXd0e45cd/FpqmSeoeADGthX9Pda62aRUwtX0npndcienLr5TnRRZVg5gQBRux5wMC6Isd/nuVqRn4vS4CtwfDsiQzx3edVPEWuE5qk4QGQNd5gqFpKBj9IEQgQAh3tQkWxtA1A2HsI9QCaOJ2U70l9EeWMeFMyu4fsZxsq4Qt174J2258q9wga5u2pDwhfLcHyy5zwyjfleeFvs/SiSM48sxj2PdYMtJvVEg5hrjWlFBUpmdz5RiB62D+8D6c3L2rz4xRghA5TIiZnVdj6rLLoZsWpndcwWnmXRWE4L9bnpoBGMPqudOozmxGdXazGCOZPOfechO2UUE5LEsmREHnCcayoOYT0KRpGorVeqprmAoVhFCZEBNTUuIEANe990fwtp/8OfFeiSeEXa2jOj2Lia3bcWbvy+itrmDXHZ+XVGEKulamVYRhFREOG9EZBPz5sYYxISIxHUOXf6egpLxQrsCwCimZEZmNUle4s7yE6vQmKf9ZPXsKzXOn+2Rsi8cO5o9EBMBEkSCnY5AHgbKejiPH4H4ZVNgme5QKbMYLDtAK8Ob3fhBv/V9+GjM7r0ZneTHXeJR7QvSnSuWpGXRXVsYC3QeF02piorAJTsjvhYLJnx+zaKPbWJLAidNelXtAwoTo8qlbq8vQNA0v3/sVhB43SLvvk78l/VO4JwR/trsrS/A6bVSmZ/naEcQSQNt8zY3SENfSC9Lzw203UdOnoHsMBaMEr9eRfiethTl0V5ZQmZyR102em4FMCH7t+pgQ65FjKMaU1N2c3LJdHjcgWAi23fer1ZlNcs3re64ZxLotPLliJkEVklzQ/iGLEkH5Xzx2EDOXXwXL5vs5Y6xPjgHwPCoKQ2FmlzAhZLNHdLUrFb4GVopTsmkEPfGEAACv2cbJ3c+NNSEsjmMETg8TW7m0Q32WiFVWnpxOTamxShU0zp7kDYl2E2f3vZLa1zXhu5Ans2wvLWDvI/dxr59mB1sqV8h/o7wkJcdQCuOk8cXXMKL2J7R+8oRIPledQkfFrm4YfX4VjTMnwRiTaywxGhljYH6Ms2cFa7kXInYDlMwqIpskMAUJeGYZLiTHYBpDweDsItUfCOCjphtn00CO01yVk5WAZESnkem2U/1AgCHVBX3TMTKFfrexDEesbyRnbS+O12hYOX0cLI6wfOLI0BGd1Ax1Oy14vQ5nBRRt+azQ/anlgBByvR8ixyA/lqntO1M/L9UmBu4J/Of8Ofbd3lhMiH2P3I9dX/kr+fdesyEbItnpGEByHwNiNLrYf6IwYUKoWSbJKYft3163k2q2ERNiLdLw74a4BEJcxNh8zY0AgIUj3B2fFr9SfaKPahOLxEaz+QLEwjhxya9asuiXZlPHOAWQtNPt5UX4To+jtOK1Rphc7sB1U3T1YXpMFsd49kufwwt33cbfxyKzwBxjSmCgVGT55FG8cNffYO/D96K9tIDq7GYUSmWYheJAem0Uhpg/vE8i5ny2rmBCiOS0OjvYE6KzNA8whjN7XsJ7rvgZ/E9bfhpAkvgYVgHFaq1v9E0chTi7bzeufuet2HzNjVg+eRTsXBfsXA/xmeGaNanV1bS+brJMXIo2KqITEbeE54VeVuibLgLXkYlC3oLP4hgvfP02lCenccXb3s1paZqG9gIvOMmwEhAdB02D224hjmOZoHWWF2UxZFfrqY4EjR+S9FnBINB0HTOXXwWvqdA4+4wpDZnEZDdpLTNSa+XU8ZQR2plXXpKO0obSyZ3acQWmLrtcfB8VhChgorgJV1XfmjK4MgoW3vw//wR+/Dc+ngIFSmN4QhhhskmyXgBNN2DXJvh0jB4v4s1CEdA0nNv/Gm77tZ+XGsvAdfhoR2WcmqZpMmmwdBsLx5RJDWGMMOb3tNtqImjz+yeIfQTMhy5OFXcgF6j6sR5qXj1B9AUIUTBK2HnLD+ADH/pN+fa1TVvRXpxPxsWJdYFokgRAWkXeeSGa5smXd/XRSNtLC7mbIJ+OUZTXmq5PZXo25cxNsXjskHzvA9/5VurfCEggTwiAd1ybc2cws/Nq6IaJW//pv8Fbf+ynUazUUuwEAogoKYgCH7XZzbJwUyVvfkMYm+j5aYEAACAASURBVMZFxF1+/gu6DbtaR4lVUdTLKdmPXR0y5lYBIbRYkwk1gZwUV7393bKLnnhCJOMKt9/0fZg/vA/Pf/WL2P/oN/HYn386tc7StTIKBZgjmBBEW5UmsznUzziKoRuJf0tKjiGAarpXQjG2lMWx7IT3mg3EcYxuYxnV6U2YvGwHShNTmBGSrRTFmTG0F+fhdVq5cqxkso4pPxdIr+tue5Qcw5P6egDSdBVQRvYZprxeb/nBn8DlN79dmqgunzoqz5289xlLLRHxkoPopUVUJmewub0J/ufT024oQt/DPZ/4COYODWa26F0GSy9gyePJOxUhVtFOSaic5mqy3yld6vbyIsAYbvnxf4hr3vU+/Niv/w5+6Bd/HX6vi1OvvgAAwhOiLr4fB60rUzPKRCvxPRVjRyqynNYql7BZYpymXoTf68rnyHe6WD51DJXpmdT3qkzNoLO8mNsZd5p8uhEx0mjNWI8xJYuReEIIk8raZg6EkcQyy4TQFCaEnHqTZTjFCrgh/q6CKk5rVT4fZH6smyYi38fSiaPYdPX1crJPHAZgcdTHCNRFI4fkGIaQV9E9K32rbH6eSlYNE1t34IrvfxdnTSmjnc+98hIe/eyncOTZtPluXpDMinILVyneaE0qVmvyulh2CcVyJcUUisNASmkBZVRmzvU+/NQjeP7OL8BpNrAtvBxv7r1NyndlXmcmxbaaGySga9oTIlLlmqaVMuhNgRBiHTYMs2/M9zd+7zdwdt8rcDst1KwZuVZFbgBd07Ha4c8fc0Jobf67ofDSNAUTwjATuRvAc1Gv2QIYENj8O9pGNS21Dnw8+YU/wyv3p0fDOq1VWHoip6hWpjF7xTV9vgMrQsrZL8fwZL5H5wZIAJzuyqLM+7Zc9yYAQHNuuPSPglhAK6ePI444cJ3nVbaqMLIDp8dlg0VbmhonTap+BoFll6Dp+lA5BgHY1LyksOuTA/cEt5UnxxheyJ/cvQtHdj0ucx2VwWMpzCf6HlPbr1A80IryXg4VOYa6ieT66mXC67RhK+at1elZRGEwUFb03RqXQIiLGOWJKZQnp2UiQAuCXZtArTIru0oAEPsCMRQgBCLGFxtdA8qm7F54EoTgSf3KqWNggrLbnDuTAiHMKHnwg0znMnSdvhF2ydz3eQSuI4sDM0+OoQAPJBXJxsEn+BjShSMH0F6al4UBZ4HkMyFeuOuLWDp+GO/4B/8YAH94EzkGP57SxBR0w+gz6gp9P4WOTpW2YNreJr8DRXlyOrVxAcDyqeMIfQ9br7sJM5dfhfbSPMK20O0vDZ+kQVrdyW07+rq8qkkWGRuawpCyYNjoCXYBUQQnRdFNC3McRRIwOvHys1g+cQTf/xP/K8wClx5su/FmHH3+CbA4lmO+AG4EZFdqcNtNaVAGcBDCVQxw1DCEjpLOITEhAC7J8DvdZIENYxx4/Ns4+twT8vPk2LccTwi1K7F04ojUIgMAgljei7qyyV/zA7fKl2RBiKtKb8E7Zn8Uhp50VGg8qaZpKRAia0zZL8ewU88Kc0NYto1iuQK/14Pv8iJe0zRYdgnn9r+COAxk95yotyYlvpoGQJNJgw4d3mozYQhEDBELEcUh/FYb/iq/HiHzOUPCi6GbpvhMHZZehAbuX0CeEKzL1wvL7O/41Wa3iAKyKZ7jnji+YkqOwWUtBbRF18ZprabAktN7XsJd//FXUjPHAV5chp4Lq5B4QhBTpTo9C6e12qd9nD+yH5quY8db3obDzzyWAkHpXi+Wq/w4C0Wc3vMyGGOSRn7F978LU9t3otjHhHBg2eUURZZkAgBSvhB+S6wfZhVhSxTcho3Nl1+PH93+z/BTV/xbWI8niY1drY0lx9AiyCSkPDHV/1rGsM28UhpwuZ2W7MhedtMtiMMQx198GjM7r8bCkf14+Z4vy99VqdpUtAwKMvDSDQ4I5o/ojKDpCXCndjKpM2jZJZi2je7KEr7627+MQ089LOU9TrOB3uoyWByhMjOLYrmKn/3Yn+Cm938QQNrYzmk2OKOJsVzKrZRyGUOYEGKPdAdQb4mRQ3r6uUN7cd8nfwtup50aDyivl/j/9OVXAQCWTxwFi2Pc9Tu/ir2P3Ke8c5JARruXET58BuXJaRSDItDyU5MZKFbPncbKqWN9zwtF4LmwY75GNDx+Pi1NAJV2KQUA9poN+A6fBGUVbVilMnynK4GKy268Ge/9J/8a9U1bMbPzKkDTMHeQT+EpVmsoEgghCsnK9Kz87pLJQUCyDlgal6TR/lU0+XEWDJuDH8pz1F6cw6bqFalnYOt1NyH03D4jboCvKxzwTjPl1i3H0DXpeG/ZZWmqSOBnKOQOSWjyHNiSCZEDQmgKCBFxTwgqNJxmQ0q/qDNa1Saxw+GMu8nLLpeTfeR0jj45hoU4DJP9QoCKYdYTosivnW2UYRYKeN+/+FXUN23lwL/YuhYO8q593ljwbNDzNLHlMgDA4onD2PWVv4LX7aAn8iC7UpNruWWXUCiVJQBLTZ9tN9ws31N6M0QhnvziZ2QeAAAt0W1vnD0FMxZyA8EmpX1ZNfk79OTD+NZ//ZjwOEmzQiwxuYpFkZQnlqemOdgjuuyq/I/W4axkY+UMH+987sBrmNBn8MEdv4AJh7NM2md4Ie1FPYSakCsJECKq8/vB1Ll0xjALYn3l98Xmq2/gMk4AXkHIOI0qBwtFNM6eAotjzB/elwL0ndYqSiWRY+kaJmcuw+VvfUcfoyELQvjNDg4+8ZCQPyhMCHFM0zuulOeFpAkzV/B9dFw5GZ1TmvRRm90svMqS/YcFMewT6d8jk8Y4DPhoVQU8yoamaahMzQyVgTbOnESpPpliHgDoY5Kr4bSbEizgTAgtxYSIowi7778zxTrsNpYROD1p3Oq0VvuZEIocozq9KW20K64rByH4PaNrOiYmt+FtMz8Cw+QgRF5jgMLttmVeAADXvueH8YEPfSTlK/W9EJdAiIsc0zuulIsIIXe2XcNbFt6KLbhcorWM/B/ImDIScoyCDq1gyO4FbQx+r4vWwrkUzbtx9iQKpbIEBQqaLTd6mm9NG82RXU/g/k/9Np91DJ4Q3PsHH8WzX/5L2R2iMKwCR/GVBSh2A/hMFBI5TAjf6eHY80/CskvoLC+gtXAuASEmJuE0VxEFfsqbobOyiP2PPYAbfvDv4qp3vFd+dpYJUSxxHXxWjkHo7c5bfoB/f8NGUS9xc0BlsZ7cuoMzHZSkj5KnzdfckHggLPBjY4sup+yt5idNdByzV16L1sK51EYTuA7viheKqH8nxPX1d6BsJguNt9LEt//497DnoXvksQHJhIw9D34Dd3/s/wZjDGf2vAy7NoGr3pkU51e/81Z0V5awcPQAwsBLufHbNT6mTUWMO8sLKJyMcMPEO2VCRqGLkWMElhGDAOAghM50MEskaUGMF+++HUeeeQwAZztoA+QYmq6nuh1Lxw+jNrlF/t3Uk0VWN7nsQTdNXPmO98ifq4CJYVkoGCXomg4dSUdF7QqomxaBKcmIzn5jyqKRTNyIurxTVRCJv9dpyyJXTSxbojALXAemXZKJL5dRaLLAADgbgiYLIAIiFiLWYvjdLoIWv9aRFsGPHGhBQo/VdF36FhgxH83GYqbICfpBiLqgyH/z//0dfP1j/xe8bhuFUikZRRoo3XWrADDGPRBMCyeFzCv0Pez68l8CAM7seVn8jD+HsegacyaEkGNU+PkjT4Ws58rC4X2Y3nElbvjBDyBwHSyfOAIWx1g9dyolx9A0DaWJKQnwTO+8KvU+fMRewk4glgf5CQC848nPnyGL5ziKEHf59y4ZVURtV56/zZuvgaEZ8GIH7FhSnIwrx9CipANHnjNqsNNdXNO6HlPhtDSGo2R8yzU3wiwUoZsWfvhf/jqu+P534fAzj8oOY8KEKIqipR+EYHHM/V6UUWaGaeH0ay9hz0P3pGV/cQTdSABDtZPpNBuSbmwVbcwd2ovQ93Bm725JL3Y7LckAomkodM3ke4hQJUFuqwnf6aVAHakX1okJkR4JGUehBEwHJZyBGHdIa87Jl3dh6fhhHHnmsSQBNhMmBBXOhVIZtdktaJw9iW5jGb3GMs7u4/JDxuI0W6rNz3m9tgkmE2uVF2H/Yw/gvk/+llzvaSLNkrInq+G0VlGx+FrU9PleZWgWwkfP4Lr2TenXNlfhdbvynBTLVfjdjvwMksEAyWjepeP8mVGZEDTiuDI1mzQL6N4V3WlmazB0E4GTgBAW+NpVn9iMxtmT6CwvJHIKzcKbGjcjejkp/ra/+ftgFoqpiV/q95ayAiRU/GA9IIQix9DAQWEyVaQCi7NjVE8Ivh9wA01iQmSea5VhIT6nu7KE6e2JfFQyhcQeMBttxZvq70JBtzGxZbtkQqjsRzVoRB8f0cnlVfzvaSaEXRDXHGkQA2EMlARTVjBRzx14Ld/0WQkCecuT07DsEg4+/iD2P/YAXv3W13HgiQdRqk9iavtOlIt1vGXqVljFsgQEDcvCde/hxsDbblRACMPgDMcowrHnnsCrD3xNPgdNMbpy4egBuT+RPxkVWTRGEwBOv/oC5g7ukb4m/HMFE6LA96w4DuXr68KHhq53r7EsgRJa/3QjbV5Jhe65A6+ibAoWWnQlAKB9moMQ9vQk3KCDqONBc4Q0sybYnRpnCsn1VRSGm6++Hpb4jj1NNKPqW1NyDMr/3XZLjvUExHpQFaB1zZJrExXQxUoVxUpV7qVlAVh4jSaeuf3P4TvdlDGllunSd1aWhP9QHcVyFYVSRebIo4LOLR07rfUqQzc+uIqr3euxc1tyX5hWQTJq7vrtX8GzX/oL/p30fhACALZcdxPmDu0ZeA83zp6UBs5qlOoTfLpIzu+57ab0bfGdfjnG0vHD2H3vHXKt4sw+fo5bC3O8aRfHck+j3E8FzirTszI/NgtF+f5R4En82q5O4MpNb8V19bej6KRHz6uyIAoar0xhFW1sVcYqf6/EJRDiIsf05VeiOX+Wm8SIUVUlvQKd6ZiwNsmCLxRdMjkdI2TcmNLSOTARxmARS22eC8cOYvHYIZmAxWHIqUMCFCgaJVmAkkaRXktFNyXpu+74PFZOH8ex55/E0vHDfOqApLkLFE9Z1LtzC+j6/L2Xj/SbOB5/8WmEvoe3/eTPAeCJcm2TwoRoN/HK/Xfh7o//hlxIzu3n89FvuPUD8n144s0fXL8n6JBlXnT4Thes6Sldfr7w3/T+H8cP/vNfhqXx71qxJlOF8WVveivcdlOi4wAwf2gf6lsuQ6k+Kbtk3jJPqNmSg3j/KvzP7eM+EU4Ph595TH6u7/S4Zn37lQhcJ5WIEwMFMaA3QuyoXI+SUUOs8e+88NoenNv/inRdn9gm5BgicVg6cQTdxhK8bgedlSV+Dhu+lENc/tZ3wCwUceTZxzkTIlOIO+1WinXSWV5EfaGEa2q39Lt3i05NIhtKEsfJbTtgaCYijbNz3FZT0jwBDjSQzrywaiBW2CO6YaY2i6UTRzC7Iyku61MJIGGYJq5/7/vxkx/9ZKqrbBaKSSfNKiSFeWQoiYHy3ZXF3K7WAU0bakxpG0kn3V9ti+5jBYHrYOn4YcwI+nZBASFIBhO4LgoKwMenOGgpvefmHdfi6HNPgDEGnWmIEQOGhqDbQ9AWSWu9Ai/oQY91WcxD09IgBEsACCCZhKOGZAEszqG3uoKlE0eFJ4SN0FOM0Aq2BG7qm7dh+0234NhzT8LvdfHyvXfwe2XLZTh38DWsnD6B2z/8LzB3aK9kMajTMQqKJwQANBfO4uATDyGOIkRBgMXjR7Dl2hsxIUC21sIcTr7yPO7+3Q9j7sAeDtqI96LrXp6c7mMWFKs1eL2uTFp8twerVJYyM4AzIXTDQHWaj+lsnDmJ9tI8TFFY2UZVemqYegH1EmcotfUmEDJJHSYQD+BTTMKHT4OJgpR5EUJDXIeQpTwhskEJeEWr805LlBhtGZaFm3/0p/D2n/pHKE9O46p3vBdepy0p/QRCJJpS/vnN+bN48gufQRT4OPzsY/jqb/8yOsvzCYvo77wPXq+DF+76G9zx7z8ku/Ox8ITIyjEaZ07gwOMP4vKb385B06ItAY+5Q3vRW12RxnY08YkSU/WaqUyIlLyg3cQz//3P8ch/+y/yZ3J6hWBCFCUTgq8rbrvFvWmqdbiddi71O+tNQvTdg088mHRVTauPCQFwevrqudNS17x84igHmzMjOlmb3yuV8gyKwkOEuSHmDu7F0vHDcr2nUaUrZ07mdvmd1ioq5gQYGFoBT3pNzUQ830PNF+uVpkHTdTitBvxeVxaDhXIFXq+D9uIcCuVKihkGAFOXXZ6MMK7UuEFcocj3d03jMiFqFvgR/55E7y8Jg8Cug9bCHJf0CCnnzLarMH94P9pL89h6/ZuhGwbqhVlo0KQ5LsDX5x03vx0nXt7V51PAJyRMpn5mFuyRnhBHnv2OBOdlqIwFxv0ZLLuUMg0NXUeCo/yUahKoIIZIlpnDpBxD/D3kkqPZK68FAPRaqwkTggBpATJzEOIyyYQgg1crY2BoWFxGEPoezGJJ7tW019NaXDDExBRWSP0+ghiaACFM3UJt01b4vS5WBhisthfnced/+LdyFG2hXJHXob55G/Y/ej/mDryGN73/gzCsArbYV+HNk+/BpLVJ3ne12S140w/9GN73C/9OePIoTAbDgO/0EEcRmnNnsCo6/vTcLxze1wdCEN1c9XKiPGr+8D6Z69E6Rp4QcRjK19fEukOAXLexjK3XvVnkCARCpMeHErjWOH1CHtO0vhnxkoP2HM8br3r3rfCiHpb2H4S/0ETMYrCayDn0AgLPk8dlWBZKE1OobdqKgjCXbcccKJ2obU6BEI3TJ/4He+8dJcl1nXn+wqd3lVmZZbpcd1V7iwYaDe8JEIYOBMGBKAqivEYcSaMVtRLlONJwRyNpZmV2pDOa2dEZHZkZruhFSqRE0IMAAQgA0Q2gvS3vsrLSRsTbP17Ey8iqapDzj7SHy3sODrqrKzPci/vu/e53v6taVaNMoUZ1hURCPg8t3dV9C+OZvpGJHqHkmCPf+VAzCiE2aULomsFEbYpS35hkQtSqajpYsq/4v8yECH1utiJZNNH3xluQ67wyvEv9TMYEcv22G+uRka5bgxCDu/Zfcw37nsfK9OVNehAgY1Ph+1u2LTeqK6qFot2UY7OjhcGQHRYCQo3VZeU7q3NXu6DPBiZEdDpGslBUxS3T6YIQbrvLhIhncqRS8vmZwsQwbWKe/K7Xv/J5PvJLP6naWVr1GrWleVVA+m6274EQ/8RW2DYuBdOuXKRZXZVii0EwnDDTNIKJBUsXzssPhCCED7R9yYIIf9b2ZD9bsYydSDF76iTz504xvP+ICprseEKh5LYep1ldkZWylcVAzT4ELKSzr68sMXvmVU5/7QsUx3bQrq9z5htfpjA0Qt+o3IClWKCtULyV6cu0qzX0tLyOKy88x/Mf/4ueXt65M68RS2eZvOkulez2tGOsLjP9+iu06zU54giJUsczOdW7CBuYEI1acI2SCWHWdAkMnJGJQm1BOth0qczYkePKqSfNXE+VfHD3AQCunpBURt/3mTv7GuVAwyOWSpPq62f5vAQp/OUW3omAFTFX59mP/Clf+7M/UtXadn0dJ5FUzjIqxOa2AoGqQD254AwSN9O0EwEDpuFi2o5KCjL9A2i6rsR6QpBofWme9eUFyulxOv/tVfxgVrTpGoweOsaFF57umY4BkEwW8atNBUSl+vqpLc3jtGxsI7FJoFExIapymkgUpEiXKhi6hSdcMDUaS/L4fQFgo+s6hW1jFMd2EH+mgftUF/HXI9MxmmtVaotzFCpSrEpoEIun1bnoholh2QqwCk3TtIhOiK2oiYar90zHCM1JZZjKXM9t5cfkurdsVcnamgnRDRjdtYbqiwWZCA7tOSh/NxbHTiSpTO1RwVanKcexhe0YmmrH6J7PyO6jLF++wMplKbSk2SaaoSNcn/q83PCdXIZWZx1TmF2BRF3vXmsAQkRH70bZFqGliiUKw2MceuidaLoUD9smdjA1u1MyIVqtQEHcUNW3VLGfAw+8ndb6Gv/wx7/Nib//FFM3383uO+6nvrzIsx/5U3zXZea1V5TydXQ6RnivUgXZv/78x/6Cp//yT5g78xpLl84FPcW7SOYKGJZFdW5aVWmvnnwRK55UgVqYyIeMpKg5yTQIwaWXvsnn/uDD1JeXsGNxBQ7JhCStrunqyZf45Ic/wD/80b/HDu6jqVvotW5QkhCB4GgsuK/NkDqclkKynQ5iro73wgL+2QAEbnm4uosvPOj4ESbEZhAi/L60VVBVwihItv9Nb2X3HfcDMLhbgoMXnpesmTAoNzcwIc48/UXOfOOLzJ5+lasnXsR3XZprVXUexx57kkf/zR/w1l/+XeKZrBpfJzw/EHcL2jE8D+H7fPm//QFOIsnxd/9wcLwuoBaCjaHG0eypE5hOrCex3JoJ0Z3Q0lxbZfnqRZYvn0f4Pl/9sz/iC3/82/IQge+zlCaE9H0hGJofHoOgpcPrdPjKn/4hJ7/wGSDKhJDrOBRrW1uY5fK3XgAksLmRCQFh+9yMUp/vtjv0jugU1UCo1sngBAkiTU8lGgtBUBsmRcL3lIBw1JrVVZJmFi+OEqY1hAkND8M3cPQ48XSWWDpLfXWZ+uqyaiXLlCosXjzL6uzVHhaEd3oV/8KaauPTNE29iyEbLJ7JSSA4vHafoMgR6PkkAiHTRpvq3LSk/gcjaAt9w7TrNeorS6RLFdLFcne6R7MXbBi77jjteo3p177V8/PG2upmEMJx3lATwvd9nv/4X6jn3P2HQDNUMSHkmkn1dRO/TqtXFHJw9wFGDx2T99s0sWLxze0YImzzkECE12rhux2ylSGsWHyDJoT87pAVk0oXcZIpEtk8vuuqBG7TqGrTUkUnK6KCHyZ2oS+2A2DZdM1ePR5XKCaEoVkcevCdoGlcDQTEN9rcmVdZX1pQFV87nmRg1352HL+Tu3/8AwghsONJdt5yDwAxM6n+HzJw0qUKpu0weugGoPueh1pMUYbSuee+xvrKovJZ8+fPdHUMgpaFmGJCdKvKoUkQorcdI9SEiI7yDWOD6vwMbrtNs1Yl1VciNzgS0bfqHdEZbZMNQQhfeHgvLqr9Nzc6QnpkCFOYLJ86S9Nbl/E3kv3Tw4QwLVJ9JRK5gvq+amsRT3hkYqWeZH/p8nlK41PEMzkF4EJvO4aWtqHtI4RQuljFsUkF6jvJNHbQIhVOv5P3qReEyNtlsqtphtJT1JYWaFRXFXCUKpRYX1rA931OPvXZa+rCSWbAQg/AkekPQYhu0u/OyL0wlxvsvhO2TWliiqG9h3joF/4PMkEL0FaaEACVnfsAuPrqy5v+rTo/g+92rglChPdwozXWVlU80G6sb2JChCBEuBdHW7NXZ6cVkB7u5bmBYXID28gODJPu68e05YSlkAlhOTH1nkY1IeLpPMl4wB7zTfJuH3flHqc9vcqLf/MRhO+p4uPc6VdBiO9K5sNG+x4I8U9sYX/W4uXzNNZWZVARBAMJM0N9dZmZUydUO4ZiQngi0o4RPLaWR3OtSiyTZWDXPs4+82Waa6uUd+wmWxjk5v63ErczquKhazrNpSrPfezPWb5ykbHrjm9y/vWVRZUM3Pp9P4lhOrQb6xRGxhkIXghTTceQG8nFF5/F1GwyI8MISyNf2sa3Pv9JXvrMX6vvXbp0nr6RcXTDUKPooiBExRilfkUGbdXZaYTvM/PaKwzs3NeTHEsKY9iOUZd97EG/fKFdBCGDRCEEzjkX204SS2clUyCo9KTMXiZEPJMjPzyq6LcrVy/SadQp7+giujd9349SKAWBnQBxXgYKtdNXOPONLwGoCkO7sY4dTyhnGVLYINALcGIqqDM0A0Mz8LPyOTt6gtEjN7LvnoeJpbPEkmmcRIrW+hpCCGoLc+gYxD61woS/m/G2HHEq1tqIhkv7T06wZ/Q2pWweTcQn2cdR+y6VLJfGJxHLTXQMbM1Rkxai91oyIVaIZ7I9z8G0bWw7juu1wdRprUhK+a1P/hTjR28mXRogN7CNB97/a1D3EFfX1ffLUV5+cM9kj2G+HNzbhAkejA4f5obimzcpQ0ctShEN6cK61xUFNCO9c7FUmmJskFJsWCZwwRhOedDNwpQxPYEnAi0VIfuwFQ3PtChPSrr07jsf4IZH30u2Mkx1fiYY3dYIWp26mhRR8ABgYHwPmq5z4VkZEBqOhWabGJrBykWZBMUKOVpuHUMzSWYlCJEulkml5J913wDh91YgtwAhdMPkoV/4MAfuf7sSEyt2ysRbceJaglZDqlhrmqaCmFRfP33bxtl1x/3MnXmV3MAwR9/xHgamZJAQVnAWL57t0TkxNzAhEvk+0DSl1N9YW1UU0Gx5EE3XSZcqVOem1dg3IYRKnKC7+W8FQoRB7Il/+DTTr77EyvQl7ERSJbCpvpJat+m+fvluJpKszc8o2iyA2dZxkffRacl7GFJvw3c1FqmaiuVgmsnCitTjaXp4uocr5FjXMMgK2QJRC78vbRVU1d2JtAv5l9bwpwP9HdtmeN8RLr74DL7ndZkQSpdH+rrZQOx4+rWXe6prUdE0gEx5gNL4JMtB253wPbRoO4bvsXjpHCvTlzn8yOMqcQ1ZKWHyAai1tHjxLNn+gR7/YDkxOR0g0oazNjfTEyyGs+Trq8vMvP6K0kqKagVouqHac8IAszAs/er68iJf+r9/j7PPfoUzz0hBvk5Q9Y769+3HbiOWznLmmS8F398FIdgAQgjf49LLz6lrWbhwpmdEp+j4EK4HS4qXggQzQi2VcP+szs8qAH0+oq3SWq/xqX/3i5x99iskzSx61sEVQbIldDXeL2UVSOT7SGTzrC8tsHT5vAJ5R4/cSHOtyuypE2ofBfC+Mo37xatKuM1JphWYF1b9Fasq2jbZ8pQmhJYKXl5/hwAAIABJREFU1n+jw9rcNJnSgOrhj7LU0sV+0v0DZC35feG6Dm1o9wEMy1L7KkgGZKO6SiLTC85ZtqPAzK1s9tSJnrHSykLGgia1duwA+E31dRO/cM8N7bq3PsHeex5Sf3e20noJvxdA1+jUu2OZw6JJp9mrCWERCCgXZKIVj4jqRn8vNCMiApwbGFZrNkzsQl9sm8G+IzQ1eUz4QgpxBiCEZThsO3AdfcNjnPrqF1TvftRWg/MIW3adRJJjjz3JTU/8COlSmWOPPcmxx9+nzjME4h0r0cOEiFrYVqPrBppuKpFLw7I5/9zXVduDFYvjux3FEhANT9176J2mBZKVNHvqpNLHCAEZy4lJXaxmQ/nYWCqDFYsHTL9F9Zx233E/UzffLY+TTLMyfZmrJ1/C9zzW5meUwKttxBAI5lqXENPrNAPGK7ZOrJgnmymTiuXlBJsQhNCtYNS7fOaJbJ78wLYeEGJ9fZlVY5H+RoUJ9uJ12vi+z/KVixS2jVHesVvpQgghaKyudNkN6cBvt+Ve8sDP/hp773lIFSOcZErdSwOT4tgO2QYX0b7SDYNM8G6mUn2sXL3I+tK8AjJThSK1pXlmT53g2Y/8Kee++dVNa8brtAO9Da9HiDRT3qIdY1G+v0knq/Y903bIVYa4+8c/QG5gmJue+FEGdx/YJNgcWjydJT88ypUT/8js6ZM9hcxriVJCt8V2KxCiWV3FcqLtGL0jOkPQOHxPQ9AwLI6051ZJWwX1PsczOR75pd+S/q9U5l/87n8jVxnqCu3aUSZEtx0jnsmQyUjANu6kiYk4mqYx89Q3aa5VyQ+NcPYbX8Ztt5k5dQLDsigGhd/vZvseCPFPbMl8H04yxdKl8zRDEKLRBSEa1RUuvfgsthVsWLFAYd9HVirCdgxk4NMM+omPv/uHOf7Ej7DztvsYPXwjA/kdDCd3khUFaHkK+Lv6/POc+IdPs/O2+5gKEG9V5QLWl5dYW5yTFcS/r3LzuJwm0bdtgh033cmuO+4nVewPplR0mRC2EcNIxNAck6HJAxRHd6ig0m23WJ25TN82mUQM7ztCLJVRitqJZI6b+t/CzrTUbpDVqEs0a1UGAmQ0NMO2cYPjNtdW1eboJNOU/KAiVHcRcw0GFweZKB2SAWUkQEpZ2U06BUO7DzJ39nXajboCE4oBWAJSaCuTK9P0uroTQhMsv3SKRK5ALJVRn2sFtNmwz3jp8nkuvfwcH/31n2F9eQkzFldBnbJS0EtvxJm4/hYOPfwY7/jQ70kNgGSKVr3WVSo3U5gNnd3ZG0k0gyC44UqasCtIGQUV/PaALVqSlJVn+rVvYcXi5AaGiXUi1fpWbxCpmxae59KsrioHGzXHTtBxWxKEWKtRmpgi0z8g580HlQuxGvSsd3zEvAzawlFeQgguvPANqY9RkM9Oi5vQ8Rnr2894ej9aR9t0XHX8gIJsWLYCIbRORBV6w8zsmJXG1C0sw1HzxuW1b2jHsB0cI0FHa+H6MnCyYnGsYK2Vd+zqJmUHjjF++GYypQr7EzfT+vx5Os0GdiwRCTo1BnftZ/zgcXUMW49RGp9k4awMFg3HxnAsdM2kPic3wVhfXlVIU2n5roweuoHj73if/IyvS8R9vbuWTN5YtGhoz0FsPUY8eO59sSFqC3MqaQ+Bm3Ds7aEH38nuOx/g9h/6Gdlr3l9RAUbf6HYWL55VInWxVIZMfwUnmVJTTAzT7Kl6tmpVpWOjqrr9A1TnZyRQETK4vkMQImQ5REV1o8KUIe0apN+p7NzHI7/4W2TKg5taV2qeDGDMukbHbxGrBABCMwyYgzG3tSpiST6XuVdO8Pk//DCi6eLi4gkXEYAQumn1XIeyRsiEyKvzjp6n+4UruF/sMofGj95Ea73G2We/skETIhAma6yzeFH6njNPfwmzoXNs5C1oaJuqiyB7hNcWZiV12veDBKLbjhEmjUN7D6nPhIy5setuUtW48vadTGaOcGvpHYoSHbWNc82r89NSS8Y0Wb5yUYHJCxfO9IjJhecSVvE3ghD5oTFAAk+XXvommf4BVqYv02k2WJufkSOEI+9+fmiUY489GdGEiLZjdNvCsgOyNWj+7OuUJqYwbYeFC2cQQigfEbbfANjEVSLgrq4rhsiiYkLMUBqfDPQZuknh5ZeeI72cYPrll0iaWYxiCqELfOGj+bqKB9JWnkQ2TzyTY+7sa/huRwLHTZfhvYekeKUQZPsGVMuQqLuIhQa5kvT/Tk8bmvyzmmQRuXbR8hQTQk8H7Mh6i7WFWXKlATU5wzZiCsRIFfvJlgfI2jKZ27inGZZNcXQ7c5F3s7m+hvC9Hk0IkD43yoRYvHiWr/73P+Jbn/sEawtz3QRJCMWUDP8enWIRjstM9ZWoLc3j+75s0dkAAERNtfdcWMMP9ih8ulqkuqbG1SYLJSWC12k0INI2ZvqB78x0ta6gW2HdpAlh2cH4V43S+GSECSETOyUq7UZYOCHgHI7nDECIbbuPYNoORx/9foTw+czv/MomBkpINw+p5ht909Qt9zB+Xe8eJf8fV/50IyMx0cOE0BVdf+L6W6gtznH6608BXRHLcARylwmxWZjSisWZuvlu6itLrE5fUfcKumySxYtn1blomiaB7PkZVcVO5ouMH72JPXc/CMCRRx4n01/h7//Tb3H++afxPY+RgxJQtY04nuHT7NTw19v4TXlumm2gxU20lqCvOIZZSJIdCuIqzaZRXVEg790/+Qscfcd7sJwY8UBItLa+yLm+s9T7Xfbmb2L19EXW5mdw2y0Kw2P079hFfWWJ2sJcwLBrK/0PLWAVhzFZ38iEnKiWD5kQKazA95iaxcDkPt7yK7/L+PU3q2ejGyZZW77rxdKobA2urqh9N1ko4baaSsw01JAJbfb0Sf7i535QiYyGLAXZ2ijf+ZkTr/Dib/wJa1enMYNXJ6Ynlei6sSHO7p+Y4p6f/N97imMbbWDnfubPvs7f/scP8fzH/0Ke24UzPPfRP8NOpJSgatQUuL22qtqjm7U1hO/TrFUVONlqrCMQakR8u1GnOnsVO56kvrJEp9lQU3D6J3ZSnZsme9rmeOkR1cYiGi6dj57tKf4AvZoQgbkRodl4OqdawnVXV+1VnXNLDO45yPWPvpd2Y53zz32NmddPUBqf+q4TodzKvgdC/BObpmlKnLJZXSWezqrKR9xIUV9e4tLLz1HcJnvOsXQEAq/VQbRcGvVVLrzyrPy3th8IzaSx4wkmj9/JsceeJJZKk05JJxHTZTtGiKwuvX4KJ5Xm6Nu+T1V7wgAzWSgqp5jvG0LM1MnHZXBZGt9BqlDkhkffC8ttbGIKhFiduYKp29Jp2zq0fXKDw6r6uXz5AkIIJSq367b7ePuHfq9bLXSkAynHx9ANg7X5aaZfk3SsygYQwrRs1cM+d+51lZgMb9tP2pAbYnNhhcXXZNCXScr7EKXdJq18z+g9gMEd+zmUu4P5k6+yfPkCViyuErHQdE9nqTWDr/n4JkyvnyFtFrj9fT9NcWxHTztGuMHnh0dZunyBU1/7AmvzMyycP9XDhPCRztAclBtXZWQ35ck9siodOGonkaRVX1etGGGF4mr9NM0B0PKO1AUItQFqHVUBiDoxU9iYusXapSvE0lmShRJZu3sfxIYg0rRtWutrW9JnASwrTrtdlwF0q6MYLlGLinf6l2VwFVY8mmurnHvu62w/dhsGQRUkboLrU6zI52p513bC3fFelkq+dbdL9TM3bHSxQGzS8ExM21HrM74BYLGcGDEjiXA0OqKFrceUMCVIwCo0928u4H7mAulSmXJ8DO+8FEcyY11WgKZLwbSBiQi1ruVRHN1BdVoGhkbcQTMNYvEUJoEwXyZJOwAhksluRV0JVgkdHUO1Y7S8Oqa4NnME5AjI/tiIGlnV5wwyd/Z1xdAyIu0YIAPC69/x/Wrj1zSNyZvuZOy642w/dhvNWpWTX/gMVjxBaWKKVKHEu/7df+5poRrs38nQwF45IjYAITTdUPczE4wQrS3MqUp7NEAuT+6mOLZjy/UVTbL23vMwVjxBMl9Q7UOZ/i5VfXj/Ee77qV8ikStw+w++n+GpQz3ftdoMEuHlNlYhTeWgbNMSqh0jACHWqohlCWD59TadZgN3rY6nuXi4ARPCIJHNb2pxkt8nn1/cTDP76gmseIJsJIkXNVcxLQCG9x6mOLqdFz75V5z4+09hWDaJXEE9q9lTJ/Fdl+LYDpq1KuPpA4wZu8jYxS2ZRIVh2fq0fPViIEwp2zEms9fhLaxz9eRLFLaN97wXIaDWPzFFZXIvdjxJdmAbpdgIlfg42eIWgWFOqtYLzw+qj7OkSxVi6WxPUn7xxWd6PhdqQgDBFIg6Qgg1MShkQlx44RmShSL7738bvtvh/PNPI3yfvpGJnuA3Wxlk9PAxtt9wG2m7TwrGbtGOkS0PKhAsN7CNvpFxGZgL0U1I17qBp1bt+svWkjw3J5li8dJZOs0GzbVV0sUypYkpqZ0SVM5rL17gxtJD7MzfQNxMoWdtnGQaT3SkDw8YY2lLTtOKB7R+gGJmhPYffgtt2VVJ1Nj8GO7fXpTV8YYLApKdNAOp7RSTw+ocYxuYENFrp+0pXSEtKX1pbXYO3/PI5iJrs+Wp8X7pvn52334/pYx8HhuZEACliZ0sXTpPZ71B/Q//kcZzsvK/uR0jppgQr37p7/j0v/8gF154muc//hd87Nd/mrPPfmXLMbsbR2nasaANrK8f33WpLcjRxBsBgKg5yTSt2hqdT53H/dq0LJ5sYkLIc0sVikpIu9OsYzkxxTQxAt+bSklfnfg2TIgw6c5VhmU7qd3bjqFaRqPPKUjeVetMoN8xfvBGQArbPvJL/55kochzH/vzHu2l1YgIom6ab5gIAthBwmRrzrWZEMFzlJoQXV8zebNsuz3//NcxnZiqoneZEBuEKSOaEH0jE1SmJNPQeKXG/UPvU+caPsfV2atqzwIJjqzNzyowc+PI2ESuwJt++lexYjGe++ifAXI/0E2TuJ3GNwUtv46ou92WRkeX+iieQK955Ke2YySCVuJciZzdT8KQPtJJpFTymcoUEUKwvraAnU5jHZV7UO3UFZYunQekD64E79Hs6ZMKYLXNuJqABxve0ch1Ocl0T+tlIpkjU6r0PIMoEyJmpZSeSchuC5kgFwLx6YXzpxG+z4V/fIZOs8Hzn/hLfM/jlc99EpCFOPnMMir20k5W2RU/SvVvT6KF6WTdJZnvMiH+V23PXW/murc9QXnHbi699E06zQaf/4MPo+k6973/g1sm5vFMDk3T+MZf/hc+8sGf5Gt/9ke8+tRnaQTt5+G716qvU1uck0L0QqjnsXvvXWjoVOemWV9exI4n6RuZYG1+Fr0JCSvT1SyZqeOfreJf6dWfiIVCvY7TnY7RbqmpfbFUN9ej6ar2rbzRz8T1t1LesZv88CjP/vV/Z/nqRfUOfLfb90CIfwYrjU+xfPk8KzMyGQwrbbqmM/38CzSqK5RGpMPQTA0MDc0TtKs15i6d4tWv/R0Aotmhtb5GPLW5Sp2MByIqOND20HJBpV1PsP3Y7fh/P437lESZLSdGLJ2hf2KnBCEW5xjIBA7LTPHmn/s35AZkZVMIQed/nmFwfhDhe3iuS3V2RiaRjmRpiLZHbmCYVk0msCEjIqSSarreW6EPpkNk7SKlgR1U52aYee0VsuVBRT8LLaQgry8vUluYUwHRoDUhz03vsHDyVV79jBSwSsaCYCd8+R2DgcqUmgsfWp9RYSp7lNYrsyxduRAoCm94PYK+76q9ymq2ykp7jpSZpTiyneLYdlZnr9Ju1HtAiMLwKGvzMz2UVCvChGhkZLLhDBTA0RndeUQJOobmJNO012vUgh7jZEw+21eWv4a4s0+ObG24amMXtQ4Tx6RTi16n3pKOMaaniGdypEtltUnJe9S74VUm97B8+QJr87NbMiEs08H12rRbdQzdpDQxtel3xEpQOUyYiCuB2Gpwfa99+fP4boedt92nWmW0uAEdvws+1N1N3xmaasfQTPTAlUkmxObpGIDq39Y7mlTZbsh2no3OPp7N4xgJ9HQMT3OxdQcrFqc4up2pW+5hIjKNRCw2EQtNMqUBCQ4F5xudPKMymJan/ihaHsWx7QR6pBhxBwwNJ55WApZWKqGYEMlE5P5HnpOlOYj1DsLRaXkNFQhfy3KD2zh48EGEpdHO+PQ5gzhejG0FeQ/C93IjABe1g29+lNuefL8alznz+its23+dGle10a4r3MfNI+/ASSTldJbaKrFUlyae6R9Q1bmx626iv7SdTLob7PaNTPDmn/s3PWM3Q4uOXh0/ehNv/ZXfZd+9b0HTNN70r36Zffe+Zctzyg+Nku8bUsEeQLUdgBCuQEvb6PFgDap2jK6SfggSGB15DX7DxaWjQIgdN97eQ/nusUjvvN2yKI5MqHuhEsm6qwJQTdc5+vb30FhdZv7cKW75/p8glkqrYOzSy88BcOAByVorJaSvPnL7O9hx052bDh8G78uXL6iZ77g+Rwr3EDvjM3/ulNLJCW3k4PXsufsh4pkcR97yOPf+1C+i6zoJOyOB9cxmJkQxMczN/gO0/+NLtP7qdbxOm0x/hXg629OTffnl54Hu2osG0k4yxfnnv85ffeCHufTycxJkCqpwwvcYPXSMwpAEVU597QvyuKPb1VhSQPUh37D/7Tww/EM4Iqb8TTTBM21HrftsZSgAlk+rajX0MiHEUrd9oL1SC+7TDTTXqkpINF2qsPO2+2jX1zn51GfxXJfMovRbu0uy6qxlbWLpDK7oIKpdkCNlFgIxVrmHJXIFYm6QxC222HH8DnTdxFkz5blE/feFGjeV3sqUfaR7LxUIETIhou0YfkQTQt7/ajAlIJ2J+IKWx+TNd7H92G3EMzniVga9I2OUjZoQIEEr4Xtc+NyXMJrgPytbJN5IE+K1L/0dxdHtPPqb/xdv/9DvsfuuB4mlMhx+5F1AMDXrS3/HF//L/9llLIRMiEg7BnQp3BsBgKjFUmmcpgVNj8b0Ap/+d7/I2txMVytI13AbDexECjuRlFMvAmHKqNik4UlAIBHEHPGg5SRsfdpqRGd4j6C7/sNJWOHfRdtTjNhwFLNqnQmT1U4XbLDjCQ49+E6WLp3jvBKg9VlbmCUfvCt2PLklQBq1kF1oCZv84Ah2Ikk+ADBDC4s5puOQ8/u4f+gHiRspkoUiYwGrItM/QKZUQUPH1Hr39S4TwsAwLTTdoDQxRW5gG7F0FrOmkbWLmMHEnKiuRhSEyBTlBIr63AIJM7PlVCI7nmDHjXdIBoNmkTsfp688hmOlEDY0vTq6rxEzApFX21D3F0+gpSwwZetPrjTE8dIjTGr7Nx0nm6vQ9pt4nQ6xVJrEhAQh3KurzJw6gRWLk60Mk60M46TSEoQIRg9buiNbrsM27I0gRIQJEW29TCQ2x2eaYZAJmBCi4bL7jgcAWXQRviA3k8LSHQXqrcxc5vTTT/HFP/kPfPw3fo75s6+TyBWUJk9uYFiOx0yllTDySEK2LGcX5D3zTB9Rd3vaMf5XLZHNs/fuh9hxXD6r5z/xl7Qb69z63n+pAPSNZjkx7v6JX2BHUCBJF8ssXDjN4ukz3Df4A2Ta8v74vkujtorf6SgwImf3s2txD2OpfazOTrO+tECyUCRTljmO1hTYeky1E4dFH7He6++yQYtKMt/XbcfotBSoGE9nlI8WTQ/TD0QtzSxDI7LweOeP/JyMG4WgMvndrwcB3wMh/llsz90Pkurrl/SrdKanAi3WOsSzeQoDwctm6mimQTLbh99s0+7UWW8FEzSW6gjfx0mnNx0jbsmfWUIK3Gi5oKpuJJg6egf+yWX8i3KzSxfLDO4+SCLfR31lkbWFOfrsoLJVdxWCCsjgu+6SXE9gajarM1cw/GAzsw35X8tToMXq9GUWL50jlsps2R8N4ER6s4eyO1mZvszs6ZObWBAgE8t2Y13NQC/v2C034BOrrFrLLK9PY/imStRjwX0IqzRaOQ5rnU36B3pQeDTmXJYvX9ja2bU8sDVe1r/BGfMEDaMBvrwnYe/W4sWzEoQIhJwKAXXYdzsqsDcdp9sX/uY9tA/GsHJJtLi5iY0AXSX06sIsmqZRqkgwp+mvkyyUZAtDw1Pzqal1cBIp3vTTv6KYIjK5Cdt+0sQzWYojEwz074REEOBsOHY42tTrtHuqot7Li3gnl5Qw5Xp1EduKURrfCoRoQdxAH0vjX16X1OagXeL1r3yOytRecgPDKgAmbkpdj3DqQOPac5SdZBo0rYeuqrWESoajlR4hhAqqaLpKr2Fo76FNFaFUX4l8foh4uYBnCqyACWHaDjc+/r6ecZ9ivYNY75DI9uEYcfRARd6KxRUFOAz2ZDBpykCmKZkQhhZQUBNxMDRsJ4Gl27h0cFJpOr5MdGJO9x2PVkds3UHUOoi4TsdvoYs3dumappFupTGG07h9OnmnzO2VdzI8PaDumaZpmypJW1l+aFS1voxEtAI2HbPuo626xFIZWrU1qWMTWU/RqQr5yjB3lN/Foezt3/b40G3JsRNJ8oMjOOuWTIqQ4MXGqQE91vLQkhauFjBJzC77QEta3cA/eFdTfSUMy2bh3GkFQjhGnGx5EEMY1NYW8PAQHZ/Rw8d6pvpETTQ9NYEgbRV6/GuU0h5lQ/Rv38nhh9/F8Sd+hNHDUlSvMDyGpuucefqL5Aa2MbjrgLwPjgx6B8o7N4GtEIBsqTRLAQih6zqiJo/b58jAa3BXLwgxsHMfR9/2hPx8Jqf8SsyU9zdt964XUXeZrO3G8zto42n06Ra2HqcwPNaTgGYrQ0rINWz/iIK/hx9+F3vvfQQ7kWTh/GkSmZzSmwCpjZCtDKKbJgvnTxHP5OQ41mAMsp1IdYGq6SaaoEu5Z3OlMWTwZCtD7LvvLUzdei/pUkUBHeFkDNKWaskBcNdkK0b4bM48LfUn0qUypbEdDO87wit//ynOf/UrVJxxfENgtAPgNOPgpDISwApEL4UmmRDJXEEls6XxSRX4ivUO5R27eOwDv4/mBeyZyNrxXpjHxCSb7gIIqh0jACFERBNCtCOaEAEToq8igfhUMtK/3fIob9/Fze/5cTRdRwRTj7TBJLT8TftqCEwvPCtbA9J6jlJs25btGJ12i/XlRVZnrjB2+EbseIJUocTRtz3Bo7/xh4wcOiZHDS7O8+pTn+VikGBffuUFNfnAUSCEvO7lq5LttlEUMmpOKkPWk7GJX5P3f+nSedxA8wpTw2+2u9O8snncdova4qwSvBNCYPjyecasZHBNNkfe8m48t6MmzEQtBCFKAUsg3IdCfQq1LzU9yXYkkgCFIFosSJI7vet47OjN5IdGeP5jfy4p5ovz+K7LxPW3yGveok1M1N1upZau0KYpLPq37+Tx3/qTTazB7Tfcyn3v/yDxdJaMnydrl7iu7z7JzL3xDkD691SprISAw2OBfC577nozw3sPY1gW973/g+y752E0XWd432FiujxP3Q3EaqMgxLYx9ed0fwXh+6Re1bhz8N3XpLHvvPVe0DRG8/vQXlrluqNvJZvtB1uC+AApKydBLUNT7S4AWsqSe7mlk8n1kzDTxLTNwHgqVVDFg1gqg+6YNLUG2qrHlVdeYGDnfgzTRNM0ytt39TAhTN2WMXSk5TpqUU0Ig+65xWIZNpqp2STN4Hk1PEYPH+OGdz7JyMHrEXMNnG+1GEpIdmFlag8IwfOf+EvimRxuu02q2M/N7/lxdR2m7VDYNq7260J6iLxToaO1u6Lbg3FErdMDQoiVFp2/uaCe+XdqQ3sOgabx2pf+jkx5cMsiV9QGdx/ghkffy/GHfoB7s0+QnU/hf2uJvFPGWgz9kpDvIxpzp19l9vRJxvrkXtcXG6Q6d5X15QWS+T7F/owZCckcDYt84XVsuJ6+kQne+W//k8x9AiaEED7rq7K4EU/mIkwID9Mz1JozA+mkVKHIfT/1QY685d097eDfzfY9EOKfwex4gluf/CkSdk6KPjW7FdKsVeS+wffChUB7wNTB1CgMjGJqNrG+PA2/JkXQZuVmFa0Ghpaw5c9iXkAJz8oNoDSwnWQ9Ab5QPU13/Mi/5qYnfpRkrhCM0GuT9oIgodWlaQKqmq0LjcHEdpYunZOOE9BC0cy2LxNLZBCwdOmcRK03BCihWX6gH6BDyRxkfXkBt93apAcBMLjrAK31Gi988q+w40nyQ6N4Ly3Auot/MEPLb5BOFNhxUFar40GQrMYd9Sdk9WRDP1d4LzJuDrfd2oT4C19Ay0eLmdSXF1mbn8XLBNXLxaYKyhfOn6bdrOMEwnz54VHuHXwvR8sPcOPjPyR7SMPpGLqGOZghfc8uubnFTSXYFDUnmaJVW6M6e5VkoUgqEQSRti435YAJETrFaLVOWaRKFjfTMhEQYDcN9OH0pt8BGYiHQXk0afS+OYf3/AKGZuAJl7bbJJEubGJwgAQhtJyDPpiUTrzaVolrc63KrtvfJH8vDIDDuedhML5+7Y1r8qY7ufHx9/X0NodCTkDPeFKanmpBoOGpgHDbges3n7MQaC1fqsTbciTmVnRe0fHlsds+ej3op0bH1uM9TAhVcWr7XbZQy5OzpYMKhpVIgKFjWzEs3cbTPOxEUgUzPVoPzSgIEaOzXMO3BW2/papx1zLRkFR/bTiJKFkYmknaKqB3NETbY5s1xa2jj11TvVp9z1ob9z+dZGTwIKbtbEpao/eS9Q60PFLJIs012Y4RJkRCCDJ+2FdskdSyaOse2uIWa3gLMwNthPKO3XJyw/84jffM7Lf/IEGAFzNwzaCP+d47lR/WkqbU39FRFV7DsqlM7mHx1dNydCcCW49z5KF3Y2gG6/UVrHSyC6hdy5oeeiWJEGITCBEN1KIgBMipGZPHu8yG4uh27v+ZXyM/PMr40ZvQDYP7n/wl1ZITTZJB+jjvpUXJXBgaDSZTeBIUDJKbrFVCR7WuAAAgAElEQVQkk+3fFPCJ1Rbemd455gCOJpOwuOhNatyvz2B5Fl+d/Sj+AfmsB7NT9I1uV77kUPkeDuXvAmSVrX9CJmPR8W0DO/dx5OHHueueH0c3DEV5jaWzJPJ9AevBVH6qLwK6mLZDtjKo3r9Q7FMsRu5L2Jbh+bhfvspATgZ+ucoQTiLFscee5G2/+h8UuCCqbUiasioa2UO89RZOMkV5x27SpYpqMQnp64ceegzbs/GeuoKu6Ri3d9tXtKzNwNReNMdSfk8rxsg4RQZ27lMJe3FsR/eYwfMylgJf0HDVv2l5h6DLT4FL4T2DbvWalicTrfDP4boNqr97br2fhz7wYSwRJI8pa1PLhb8gQVJ9OHj+G/7dSaSkmrzVT0dr0/IaTGauU8BK9Nzqy4tK+2Fg92Z/ous6yUIfixfPUZ2bZv/9bwNg/sIpTj39DwDd6RiFIlmrSOuSTADeqB0jlkpTsuX6sYXDgTe9HdOw1Qh1vRQn0UmRDlrUwqkwc2de67IbWt39xRJdX73v3kd48Od/k9t/+Gc37Y+GaaFrRoQJ0W3H0E1T/b5oe2hZWybGG5gQWDI+3OhzdF3n2Lvex/rKIs99/M9ZnZOtGKXxSfYO3MZoYnOVtfOJc7ifu6z+bgZTF0z/2nuKYdlKwd8O1slQchJeX6M4Psn2Y7fLynRfCSucJKNrys9pmsbRt79H+cDyjl3qng7vv454wEowWkHrsO2wM3MDCTurxjtDV6vCcR1SRq4HTIlaulRm9NANjBTkORcK26RAccygFeh9ZWJFyUbQNBWPgAQhALB04kYaU7fVNUfNMRJ0RABCBAULN+mT8KXuwNC+bhtgecduaovzXHrpm/L6MNGcru7bRiZEPJtn331vYfTwMQxhKI2yUEui51rNoOgXMxANF90w2HX7fdiJpIoRU0GxbuqWezlUuIsj8bvYedu9vPVXfocHfvbXqUztJVXsV+2Zt/3g+xUwMZragxAC8275HBp+Das/A3WXod0HmbjhVrLlAfxzVfyTy3Q+fq4nl/h2ZnxhkX1jcr+bPH7nt2XuhOZ+6SqWb7EneSO5+SC29UJQQASTKzQuv/ICV199ieGsfJ+LiWGqs9OsLy2SLBQpju3gju//aXRtAwspAgRvNAWwi/B/gqVlySiL6YkuE6LhYrgG881L+Lrf09qRKQ+w795Htoynvxvt/x9X+f9B6xsY4+HRH2M8uQ/RcBXSvT17mFjT6Y5+MzQwdVLZIqZuMXjgMIWhUWpiFZa6aOtGswKRJKMhX1wtboKjM773mBphScNFuD7iW8uIU1WFXhqahVO3ulTlSGDsX12XgbvlsS25k7mzryuxlZAJIdoe8UwOO5Hk4ovPsnz1EjsKR2n/8YmtnVCQeBvjGVLNoNdM01QPWtRGD91AYds49ZUlto0fwv/qDN7Ts2gjKcq3HiK9bYCYnVZMEJugghBlQtCrVQBdB5Mw06TMfA/VD+hOGEk4rK8ssTY/g1FKgAZioYmTTJEpD0pqtBCUVyv456vE0znydpmJxH4SbpKbv+/H2XnrfRJsiBk9jlWLm1u2H4xlDnBr4e1c+MdnSBfLJOw0Hb9FvJDrfq7hdp1k2++pcgFdlgQQNyQIIVba4HcDyI1MCPeZWa4bkqMC48ksoiNHRom1DmKtjeZr+Pj4wiUe38zGkfe5jZZ1upWc1bbShEjmiwzvC+jCUSYEqDXxRu0Y2fKg1L6IBr4tD90MhCmjTIhocld3MR2pvD+8t1cXAJBggSfk+o+ZSphyk0XuqZjrVldjRlJWa8PAV+sG+pptSKply0PTNPIVyRiyUgk0Q8MwTGwrgTAEdqKrCdEDtETaOizdoTW7zOLCBVzRQvffeKNWSU7eQavE8YTHuieDbVHrMJTYwYA53jsKbgvz5xvQ8dm99y6uf/S9vYBPzz1y1Yaci5elJkStK3Tqn6mifeIqxfQI2cog4oLcjMVKu4di/EZ2/aPvZf+b3iqP1fE3vdvXtJaHZuvYfYEey/793eAvrHrFzJ7Ea3DPQfQ1+feG3cAxEgxtl5Tcg297TAqXfZvzFg0XUhYNUSNt5XuZEG8AQmxlpfFJ3vzIz7P34H0ApL2MOv+Nn/deXsT93CVEtU1+eIzl6Uu47XbAhOiu5Yee/LVNrTXus3O4nzzfU+kWHV/pl+i13msWi006GcFSe4Z1q4brtxkrH0DXdTl20kgyGT9EnyuD29zANgZ3H8B0Ykr0LDT/XJX4N5rc+45/zaGH3gnAvnsf5ujbnlD+szA0iqGZFEe74qXlyg4GxyWQLeouBK1hIlBxD8FA0fFxP34e75k5xpJ7OfauH7wma0+sddDSdk9y0vIatJZWSRXLGJbF/T/zq/RtGydbHlQtRPm+IR4c+zFGUnuoDXawDsjqK4YGSZP9b3orqYGyCpb1oRQGBjE9Sd+2cTLlQYb3HdlEBfZnu+rxfsBK0CeDMX+lmNwXAhG2kYPXc93bnlCsDto+BFpRouWp6rqWlNemeZqc8BQkdFrWVtMZ1P1Yact9LB+0iWyR/JUn95B3KpjDWRbtOQYSE5sq1VO33IPbafOPn/ofxLN5VcTYaKlCiZnXJauiMiHjg8qufWrKjGUHYwstmxsqDzG8IvUq3qgdw0mkKcW24eNj6hY7j99NMt9Hu1WXE5TKcVJGllww9aI4up1kQfb9q3aMnj2o9x4VhscYOXB003HHjD08PP4Tqr0o1DFpra/1svOaHjgmJM1u4hO2zpi61A3bwuf0T0yx584HeP3Ln+fVpz4LQCZbZm/iRnakDm/6fbHY7GkxCtkHuqtLXZcLa5s+EzVbOKy051hyZ6SfmWtw83t+jJEDRzEsm+sf/BfynHO2XJeRPca/tIb71emenw3s3KfGhGqNYMy8a3Oo7052Vo73+KjC8JhkVWblMxIRthME6zvwrbf8wE9RTozJf6h1ZIEibtLyAyaEkVX7QPQ9JwAhNNuAVfkcbGKb2D+0fcWiU+0mpSRpq4CGzvDe7r0P24nPP/91xo/ejOZpYBkSiIBNIISmaRx55HFyA9swfIO6K5+JY25mZEztkawXfSgJzd77HWrbpBNy7Q3s2kclNUElMc72G24jlsoonYU7fuhnZbGHYAx3sE5H8ntxCxrOgQE6povI2xKo8QSJRI5bvv8npPhqcCxxdR3v6e+wQNDx8E+tMpLfi2k7TNxwy3f0Of9KDf+1FdwJ+f7E9ITSXQMQ+KDJUbQXXvgGhm+QaCfB1EkbeWZePUG7sU4y34emaQxv7+p/hfGeegffoEDWPaBgYS4Yz1xzu36i6WF4Bh2tDUOJb/tufTfb90CIfyYTKy1ZTVtsSSeYdRCWRsGu9P6ioaEZGjTli2SlE5Qn97Cwdgl9Vf4sSg9X3x8mSGGPqa1Lh1pz8c9VJcMCoO7iPjOL97WZruq9M4AmusFMTwJ3dV1WtceTDMQnOP+Nr5HOSkem2bp0nm2ZYOUGtskZ8rZDJTchN57q5gqnqHcgZqDvzmO2dYYTO+kb3b6lsrym6xx55HEA9nMM75tzaGkL8/YhdMOkb2oSrel3g7Xw3BUIEUyTWN1wHusdRDD6tJIYJxcR1ot+3khJLYFGdYV0fxkt56iK49CeQyycP82e3HEKM2m8FxfRaq5E1NHofOIcw89kSV9xoOn2bnAQMCE2O7acm6c/PkLaLJAulrH1BC2v3q1oxQ3ZwhBJvsTaBpQ28gwTZopYJqt+XytLMGVjFcs/uUxxpUiuOET/yTTup893K2brLrR9zLiDEY9haCb++TX8mW5QLDwf1tpoOVupPYu1DpqmY2gmU7fe0616hkyIWG/VJXx+whN0/vos3uluNdafb+A+N6cCX2FIsGlLYcrofW24TN1yD8fe9eSWayxcO1rCRE/Y12ZCRKqM0aBncHQP+aFRzFgvE0K0PZl4hMnPSou+viBITiVlQuJBZWw3Ti6LHUvQCUb39VCnm64KiBwzScxMsbI6I9sx3O8QhMjYGLkEn7n8nzmXClSx1zry3z3x7TfYwK/k0wNMbqE70L1H3XWYtvO0amu0amvKZ4VB78TkDYwdvlH6pvCzS9ce2Re1cXZToL8b7FQ3Vyi2PLeACWEWgjWQMNX6CynpWszoeS+G9h5SFaZld07SUOshQGldMyEQa23a//UkftC7r8UNGlqdbLy/h+Ks/Lb+nYEQ3itLuJ+5qNgf/kwdbB19exax1OwJPEOf5881mLj+FoTv43XaaLrRW9WZaeKdXO55dmI1XBeR34uCcBvOVVTbKsG9+PI3mWteoqDLamUsk2V7+hC6ZmC6Bjo6+cFtZCtDvPu3/2uPqGn0vIv5EcpBFXryprsYO9JV8h/MTPK20X9FqdQFIW5MvZk9cclgCFkQ0AUhtIwFLQ//5UW57hwdo6lds40GkP4sbfXQtBvUsLCVDkE8k+PN/9tv8Oaf/82eY2odgfXIOH3vvh7N0NB3ZNGKMeUfNKsbjumDwT610iKRK/DWX/4dsuXBnnYMADFT7+rMBKwE41AR8+0TGIcCMCf4jB1PsPfuh7r6Iy2vW92NMiFsQ1bXg2Q6ZOdpWXsTPZxqOwBlAr+9BZPv0P3vIOuUMAczVG44LHUBNoDL6fMWD23/CfAl2/FaVc9UXz/C9yWbJ6DjV6b2SP9JL+MhbeZJaGkpMr1hqkPUKrntWLrD9Lr0g46eIJ7K4gs5KaaZkuuvYEkauqZpjB2RQpAhuNHTUnuNKvxGy1slYiKBFtzncL9qrlVV/CGEkPGUo6MlLcRqG+/V5S4wasr2gGsBn4ceehd9o9u5evIlqSNwXhYPrE5v7CHaHjS9LgOx48uRoIDeAf+1FTofOaPGMG5ltudQd6t8S3sGYqasfEf2rRCs1fKO1GQJztlfbNL52Dm8p2cRkWTM1G01oSNcL2Y7EPNMd+Nk7+VF+PI89/7LX8IMNXrme8/T/dwlOv+PfL5a1VVJuKhKlp4et2h6MnaxNEcyeqFHMyjKhFCxk6BL0297uF+fQSy3sDJyPYZjHRNj/RiayfC2fbIAFPjl3OAImfIgY9fdxM3v+THZOu3oahTopvctYrqnybGhgC42s1XMdSnYqg0k5f2OMpoD355y8mTLgziJFCk7h607JOysjE+C6yoMj6qinH9pTQmMW20TZ0Qm64kn9pJ750HJHqWXwSrW2pC10QaTPRV/f6lJ53OXEN7mgke4h2fiRd7+od/HfLqKd2Jp0+9tNO+VZXB0Yg9M8tLql7iyfopWIQpCCKxYXLVRjRT3owkwDvaho1MuyP0jF0z3YkPxqvf/bxBnRJgQ8zNn8PG64DfSX+gd2H7nXdiTfbDSRiy3ZHtv59rP/LvRvgdC/DOZSoJXW3LTihvomYBRkA2SJ1OTm7GhK0enWQaVyd2sNGfRXY24kd6SCbGp/8o2IGnhn16VdOBdsoouqm2oyhcgaWeDw8vgRR8LEoUw4Gm4iKUW2mCS+HXbMHSL0eQusn0D3WPYhuwNFUJVM3bffj96mJtuTP4B6q5M+CZzkLXYkz/O4M6t6d0ge78efOcv4jQszPtGsL9/F3p/UOkI6fxhAlMPEOCWK4GYrC3ZCxvOQ9Q6GENJGn6NwdyUqkQIIfAvrKnNwM52k9Z0KQAhgg1peN9his4Q+/O3yTFAqy21qet78zLQEyAu12QQsSHh1uLmZsSa7rlWUuMURsaxhU3Tq5MKQAjVwrDYlEEJbG43CYMkU6PUPyGrasH3ajlHVnyjgZQQksXgw5tv/2n0uTb+XKM3wev4DB04TGnHFMIVdD53CffLXQVusdoGEXx/WHGrtinpQ7x19P1MHr6t57uwZFWnx4J17L+2LGl9ERDCe3EB76mr3Xucj8m1HQpTRpkQjd7NpLx9l5ogsskiibqZimPpjqqw9dzTSELmR5gQ173pcWKpNJYdp+gMdZkQbV9WURwDmi7t/3mGgWkZHKf7+8GQFSdLd9ATNpquKzXujWwPLfARN77pPRiawVprgY7fQvs28W+YqGtpGzuWYN1dJTksq9Gi1ukGoVuAhT3f853+XiQYSepZMl6e7c4Bil5FrrHg8zv23cK+Ox6WIGcIfi58exBCeD7e0zN4Ly92zymgmvqXa2/ch9ry0BwDrT8umSGmrnqsw2qwZEJ0gbD4sy32FG7CpcP8qhScE0GCq2WdayYE/tV1xHJLJru+gJiJM5An7WzQUgjul9afQCw38U6tbFLhVt8538D9nOx5D32QmK6jVRLofY6syEWDwfD+zDUoDI+y+w7JctIMQ/oLU0Mrx/G+OYf7Nxfw/rE7NjP021G/qe5twuwBIYQvYK2NXcrgJFO8/NmPMte4gNUwEGttYsksOzKH8TUfDY18bojK1B68M6u4Hzu3afSZOnZkrXkvLeI+M6uq/OXsdsmESMk9R3R8GdQF4KCYrstoJ2aovUHL2BKEWGxKEHwso959/3x103mELDAtbXcF6yzJpLH1WI+Yq6brPQmx8rV93Z+Z92zDejSi2WFHQIhKwE7bCERFqnDC9RHzTbRhSVlXQGjCwhjPQCrYF7agDQNy/cdMedxQE8LQ0HQNLWXJ90cIyYRwgiLGhqRIVNsSzAnem60ScKtuognJQrTLWfW5qPmXayS9FPuKt6kEfyvLpStsTx+iMDSGZcp4SbNM1S6TCNo8RNvDwiZupnnsN/94E7smaomOBHyKdwasuHVXTtnQNC6//BxVsYwv/C7LCBg7chwdA9vZwIRIW5vA/GuZ2PBOWXaM64v3MzJ8gDt++GflL7lCttY4BlpSiju7n76A/3IwptTS0Sxjk88Jq/OmbXPXj/6c1DkaGsN7MXinW15PgquKFm1fPsNgv/R1X7bqBYWWNwKGLd+i4dbQkhbWQ2Ow1sF7br57jLAQFLJm1jp4L8xLcMDUIWniPhOZfBJZt+EatjvBGFSny1TyXlvB/9aS/P2wDWluAxNivoGYayDqnS7QkbJUYqgnHVpet4ASggDYumyDsfTu+7nBx4d+wvu6LOZh6mSu28597/+g0nCwhmS8vXf7XXQ+e5H277+M++wcuq7z8M98mFvf85Mybml5UhDT1GVR6hqAunB9NKFR9+S1aFsB30tNua8lNrBLI+eczw5y14/9PKLjqVZOsdjE/ZsLdD55HpAgUQg+uV+4gvvUFbl2mp70oYBejEvGa7hvRp/dWgctbaHlnR5/5p9axX9pces1pfbyDo4Zx385ANtfXtz8u9FrXmigleIYMYvVvhpfmftr7GJvfmQnkli2XIPjlcNg6xiHZSx985ue5O0f+j2pR0FvHtVtx9hamLL3RIL2D8DrdGjrHUQIjIU+QoCWsFSe5b22TPtPX8N96uo1vvS7074HQvwzWc8G1PBkxS1I1MxbB4Nev+DxmFqkeiN78OqGDEyzdlGpXqvvFkImb5FigmbrmHcOYdw+iHnfNoxD8qXzr64r1M5a1tB0g3xyQFbTSr0otH810IMYTKINJKAUY0/5FgbGg7YJu4vg0vYZ3H2AdKkiZzWvtnquu+d86y4kLTRdw7yxQt4us2fnG1RXhSB9wUbL2ei7e3tLFbsgdBC+kBtu05Mbua5Bxt6iHaMjz6GSoJQeUT/3T6/KCkBQobVy3XudLlXQcvK7hBCUd+xiID2JEIL2iCEp5YEzNW+s4PzLA+ijaXnshrup6k/akkHHBjZEeM+OHHsbkzfdheEatEWT3JA8T7XJND20UlCZ2RjIhz2YpTiZZElWX1dbcp3FDVnJigaQdVch597zQeCy1tl03+x0CjMRlyBPtd3Th94FOWy5qSZNRLVNxstiahZ2o1tlEK4fiLBGXJIm0WYhBF4QnPSwPZa7iRcElbqmi24aZJ0y7kfPK9Re3dOgdeWNLPxeLeeQrMj3pDK6a/MvRjfaqNhd8POiNsDdg99H2gsC73YQYDiGZEBV27KSAugxOR0DN6h8Be/RnT/6rxGWptqBIKhghkDlnDzXtc4Kbb8lRequob0CyM3d1CBuEEtnuP2HfobxO+/oXkNAt/6OQYit9EeiFq5DWyfuJTheepiDhTsYvNSPuLLe4wf9SzXwZSUXQ9sShPAvrtH+rydVAC1WJNAlllvdc2nKKk7nI2fofPr8lq0lwhfyWm0D40gJ6weC5xu+k1swIcR8A//kMlrS5OXFL1Gry8qMH66/cJ1vFRBuXKsxg+zOMQxX72U/1aVWjFZJIBaauJ88j/uV6eBaW73A17mqHB+3U7ZWCV8gFpvo/Qm0QhDoRwI8da+DtXrggUe5c/z7GIxPyIAqaaEPpyTjwdDUZ6Ngkai2EU0Xf7GpgjB9KCmT4nCNBsmA2ZfkwQ98mMHdB9BHZLuWf2WdtJsmZiSpleU9eeBHfpn0RQf3Y+fk6LMN1FSxxd7hfn0G78vTdP78FKLjYTUDBkutd/2KJemb/el1tGJcAqLBGtcytlw7cw0ZrGdsRFVWojp/fXaztkjQ7kPW7jIh4ibJSj/xWJqhfZJm7Z1a2ZSMK1+Y6QKjmqVLECA0K5L45Gy5729kmKgAuCNjAl+gTwTFggBM0QKdh5DNs2kvCL+r5UmNGttAtHzp7wMQ2LixIkfRnVwOWgdNCZ52fFW5DNeFlrG7e9kWCbgIWkb0/ri6/k1FgOA6d2eOMZDfYtRz06XztxeZOLWNo8U3saP/ehXoa7pGX9CGUxgY6fl+DSmM+0Ymlluy5XVXAGCtd0CAnUhw+VvPU12aYbU9j1PrtpDkh8d4ePtPcHj2KO2/PNXVOMg73zETouv75LVbTZOJ9EGOTz7a7S0PJ+Q4BvrOnARoTa0Lem/RjuGdWqH9+y/R+fQFRLVNPJPjwf+3vTMPsqO67/339HL3be6dVaNttAshhG0wi+3EFggwi1kMSRzHCcT247mcUJiUX9mhynbhgP94JE6l/MrPxI4fzkscygkInnHYZYQxq0EWSMJCCKGRNDN3tnvv3P1293l/nD6n+y4zGgkxksjvU0Uxurdv9+nuc37nd37b+R934fc3fBbI1T0jr1/G+/7mMw11D3q3qHEj9U+125WLky2j8cB+8JoN0zFRtmcQjMahDUahrUrCfjnryTd5L2lhPLIeG4b11GGwsAHzmiHo5/SCDxe92i0djBB6VfTtCPMKDvPpGmBzOEdcI4LGmudjh3tRYIdKcA4UgLgJbXFUyTg9FoLF67C5+x7d+VfU6tK9opRAm6OEFxvgDQf265PQ1iQRvHkDzLN7Va0Mcc9CHiffCcLZmxO6jWvks/55L+wXhKxR0ZJw+9L0LEYfV4bJSAhe6zznsK6g55jzO2Jk/ZiKg3hPX5OxwxmvwDlYBB8TWyNbjx6E9chBMd6na+I/Ob6SLTUxXJnjZCuwth8Btx0hI+IB0RbfPKHmlw4Rf6pvFhteDZ+4CeuxYWWI4JzDenFMpaFxzsEnqmDdQgcePGMTEn2LPCMEE7sXRdIZ6GYQH7jqD5EOD4D1hoFEQBjTxyqIpXu8d+2PdpDpGKowZWP2tFXe/IcddLwivl3eM2NhQ/w7FRAGrIrVZkB7v0NGiAWEWw5q338d9q6pZiNEwwFChlhYmBq0FQmhWMiJXWdKMWSDUZihMJb8/oUAgHRkUfvWeG5Ou7+zI6hD64vAOKcX+saMFx5/yBemeqiMSKoLyVCP+K0MrZLhR+7gYH1hUe357B5E7Bi6izIdQ/eUjFwNSzedi2u/+V0EwlEl5OSE20TZS03Q1qcBQ4N2eHaLu/2bcfDxCvQL+71ttFyaUhzcHUF4SeRiqVDrZKDZo+cIow2LmYiuGIBRYap2hbxnZ1gsZkNpL3Q63t3nKbUVC5puoC89hJKVA+sOAQ3HS09wDUwsFQTP18WE0JKOocmFg2/xxS3HW8iNVoVwrNoY+MDZWHmeu4OAv3iSGxHSujjkPiOEnIB4vg6WDHi5735Lec4rkAZAhSXK56AwNLGNrJwEi144mbwP6fkQSr5nqPCHpwkFmDVN8KwrCF624Lw9I45t9bi653HckGSWCABVG2nWh4sHPgv+zgzsl7LN958Jdkx5aXpW065xJmpAi7rbZcr9zYsNWE8dgvXKuOhXDOK/kvAWQmfe9mNV0f8MuaaShSlDujLw6L+3CGxRVIR4G2Kc87qjFJGe5avAQkazklETOawwNaXgFxtTsJy6+n7We5txFQJ3kl129ocRjMeAkK6MjMAxGCGOGgkh+po2GEUkH0RIj+KVySfEdxPVJg+7ki8DUbB0EHyyfTJ23hbbY/KsuG+1uJ+uNSnSzjsz4lkeLMJ5s72gojLquHVZpBxRMkKG3oZ1LxLC7a/xP9qEVTdfg4FNIl/UGRF1cljILWZpOe3RTK7xTKUFhHRlSGqLLogY0LqCQuZz77qN+/fDetIrHMcnq6J2xSJ3oXCkJOR+d0gp+ur5uNEJgBe1ow/X0ItBdDf6wYsNsKgJ/YJ+mH+4ShhLpUGxZHmFvfJ1WL8aQePf3lRGOOWJl9dyZT1LBBBLd+PiL38d53zh86p+TlwXXszwJnfRV6gL485ikZLEW0K+VV+TfaUqijCygQh4tiJ23ZGRINP1prbAcoTxdLQs+lXUJ3PlXDVeAUsFxftwuNg1intFF1U7ZBHGTEilH7CwDi0SQNCIom/lOvB8DdZDB2D/ttljx/M18a6M2VUuFQIeEpXzWarZc8htd4cjXcwBXDoFhlwl2+KdC+nNYoSQxk4W1IG6qI0hHR/aGV1g/RFYz4yIRUTYaN82sGp7Rhm1k0y7bHWyFfF9ItC5zzfEHKed3Q3EDDTu2yeKTcvvqxbq//w7OLumUF9lwuY20tFFyusNDWqLTrWNnt/I0Unn8MGnq2Aynx3uGHQ4gvE4aqUi9mz7BaYao2ATdTWuWdlGyIlAS4XAD5fgyO2nU0Hxbo5SU4dXLa8oquzXst8fKHmGN/msgzr09WmYnxoSuplcRLakYziHirAefgcsHoCzLyee26PENH0AACAASURBVIEZaNsmgB05aJsy0M/tbX8HhZa/ZQqOq0Mqw0CLE8LZmxeGQ7eGWdUqqh2J9I/0A3VHRVSpLYdlfagjJWhrUwh8bi20gSj0szKAzuDsdWsUSUeS7hXkVM+qIBaA3HK86KVhNypgcVTUt5Bh/sWGkl/O3hyctwvQ16TAYgG1WNRjIi1K7lbgj0piEUOlPwK+cSopNeDsmRZ5/md3jrhhpgb9gz3QP9SDwBfPgLY8IaILcnWgbClDtoqWdJ+T3+jjHCmh8cSw6Gvus6zYRXDwJicF4BpecnVhXFV1tnxjU+qHZUsZCtR1dk8LuVkX45JPVMUCOl93nSSOL/qvuRaUlK/2s6OwX8qK9UWxIQyVXS1zkvx/h0gI1R7uvVfz6iGwZXFhiHgrD5Qs2M+MeLK2INZS0nl65iVX4+rb/6fQrQCAAR//4lcQiETA3O9ZwQJLi3ev9UWUPqXaIZ25MVPsHmM5Qu6FdCF/Zot6UpEQbgeL6t429Cm/EcKtu7I8IfqihrY0yvc7ZIRYQPh4RQicAwUvLcEVjixswHAVQGZqMC5aDPNaN79Vdy2jg1GVN7rqExej6pSQjrTvz64WXN1e6Geb4AzrQuAfEYtK1hsGH57BhouuRCLULZQyGYImvS/jFeHtc4Wktj4lwltHStDO6ALipiiG5R7rTNdgPT8qJoFOyoFqb8MrhqUzsJ5QW06fxBkrw35mBNqqJLR1Xe0HRLzoAs21iKJsufnf7jVSwWZjiFs8j0VNYbH21VdQHgB3kgh1u1t/xhKi6JgSrOK+ksEezDg5L+x0uCiUT92zbsuc+yYvGLz31ZQ7Ji3OvWEgXxcGhLIFIxlR1XObFM+4UAiVoaFiCeFZESk/cqHOG44yQohzNEdCyOejf2QALBWA8THRz/jBmaYIGxjMi9iRv5VGhvGKuHfXgMHiAWCmIfLiATj+6v0NLhR037lYd0iMl/15L2TOfZe8YXvKdb4uvHQhA6g7WGaugx4JQtuQFovWckNMwAENLBY4qhHCcT0IjDGvUGFV1DOp/3gP7FcnYL84JhbYEcPzmkdEMVeZKygXLWox41f6ASBiQD+nB4HPrBaf627Ew0y92WPqFrIEXKOUxT3PZN0B10VYptxJo1UhaXo3hYZafPlhcRN8zOc9OppxQYbvumHh6vNyA43793tbrBYbQNgAy4TBwFC1S3ir8Cq4KTwu6jqFugil7BJyh3WH2xaBgJfrK/+vFmkNx1PkAK+2RMQQ4aOt0SE+D2PTc4iYbkSXO7ZChhcJMVkVUQqpILqXrcS6yy4XP3KVPQCeEa2lAK+UD7KWhvKAoEWxLzfAIgbYYFQYI9amgIoNZ6oq0jl898gnq2DpoNp+WRYcZpmQUJpNzXuGReHdRSognnXVgv2ya6AbrwhFMWYKj+vimDAC5WpNXkTANRaNlIGqDcc1YGuDzakD6p36+7ChgaVD4OMVaAXhgY+uc/PeJ4V3TVsSE4q3X/5x7lt4eNENAFQILc9WPHnd8n9AeIZRd0SdhZjbJuZbpNtczGtue+WCSnqu/M8bcOVSxIuEQMiLEHDcMcTHyqKOzZOHxLvL19V7mhVT9jl3EdJihJCeOBnt5rwzI8Lx00HPYeHLYUfEEIafDmHDIkXRETIkoIn50XLEohbCA2x8fFAsQtxi1Ooa0pPpS1tDUBfzQoeaEHysLFKeGBO6Q0hvXvS670objCLwJ2vBBqOwth1WxmxnuAgUGjA+NYT4pzbAiTEkzW5vty3GPE1W5mIXmvvsXAh5L3ZIEDtQNFwjRALpJUMoTo6jHCgLWSv1Abdv6B8Ui04uF8CpoGjDHIbg1jY1GSFco7ZaWHWQU9KoD8BNx/CMENazI0DUhPmZ1Qj86TogoKPxH2/BeTMH/aMDMC5arLzXvCX6QeHKB3EtV65JI0BrBKkbceAcEGOmbHtGCK07DNYbVgYaVC2R8hX3FvRyDAOuE6s7BMc1MCsjZ3fI02dkmy1HbEvuf47ulvPaioQwQE+3zBEBDc7vcqIY98aMcgwBQh6boYjaIQO+5218YlDpP/KZA1Bp02LXoQlh/F3cXmOq6TwfHxSyPxME8nXlpOITIlUEDccrgOkam2TkgP3qOJzfTqLxb2+qtJqGU4PD7Pb+VqgDDheyIdy8BTvn7s54cjwXG54BPxFoiiJxDsyINllc/C0/d+fXViMEgrob0el4x3GodAyggxFirkgIuHKOiXnNvHoIiBhwfpdTxnTVB6XTK+Ntjc40zZPzMrqBQYzRsuucdFPkWF9YGK98NRl4STgLWcwUf8u1lXT2zZbqpnYEdQsNJ3xrMb9z2NXd9TO6wAYi0M/ta0ujfL9DRogFhEuBM1YRizx/WFdIB4ua0NzCiSxsKM+4VAy0Zd4OBLphwBxMoT/TYS9Zqax0+/LYAy3KNmNC2NVEHqi2vgs8V8faTR+HUdPUQGFRUxk1nPGKUoAAgJk6An+yBoEvboD5yWXKewNDAx+vwn5lHPazo95OH4bWHoZpudsc+gsA9YSFYtnBGmi/mAWCGoxLlnQsXtW0IJeLeilsZMXjZEBMYHJhJwVJ1FACSYaAqagEd5LXY0GE4klV6EpaNaXCblQ0DH7kwzClEWKq1jTpsi6f0A43vxNExcLS6WCE0M4Qxg/nrbwQcH5l03/PEUMsKLMV1P/jLdS//zoaP90rjB4R0/P2FBuuEcIViK2REPK6y+MIfP4MaKu8+0HcVBNYq+EA8E0u4xWvVgfchW6h7nm/p1ojIZprQrDusAizfGcGrD/ivRs3JNAPk4sBADhUhr40Af2cHoAD9hs58LItJpOIPueOG/L8qv+rXGdb9OO6A+3MtFh8j5aF4Up6V6OGGC9SYXP7jvDKuMaDgLf9lrY41tyHdU15v1m/r9q1W8gSQJNSKhcrWiqIcCKldtLoFJqp7k0W1muBxQKeUh8z1WKZlxqo3bPLS2sBxL0UG17tGp8C6+wrwHm7AFtGHxQbYDFvwf1OcTccOOBdJpyDIloBAU14w8crasyyTEh4sFtz0CeaFQ9/uCofLas+4rw9A2gQyuNMo2O1dPls/ejn9sK8boX3XkKuocfm3qJfRk34x11K5qe7/bc1R7tV0Qrp3mLAn2JUtsCiBrS+CAL/fQP0M0VOsbNnWj1PXnK9gFM14ZV3z+O4W2iytDCgsf6I8lgpo6I7ju2Xx4UxIWYKz8tMoylKgHWFxLspeCllCOli7Enj2oGCuI9MSKQOyPlNLU6b+xnLhEQax2RVGKUMDYiZnkLbEwZLh5qL39Vsr9p8sSHqIMiIhIEokAyIRbIcc9M+I4T7npxdIm2GDUTB3DoJCOpNRmDWFVRGEzVfVeym4mOydgQihnr3rRECKnIuWwEfKcHZMQHntclmWTsbco6W5+5yo+akAb/UrAA7B4uqsKWU6019kjEganZOx/AXoZRGTlmXx0UbjKraUSxstBXL8xshRDSd3p6GYosQ6aZ5IBnoaISQXlvjgj6x6NnvhpqPinoe2vI4GGMILk6LeUjKK42pd60+y9fFvKTNbVDltgPk62rMIGKI58yFQ0QWwW64/g7pjJBGUG1NUkTv5OqiboYvNXIulB7k04n4VFVsZ70qCfv1KddQ1C6nZJST/D1ipqg/ZTngo2XoqxLKyBn4o1XQzu6G+dk1MM7rU+kFMFhzCoYsJKszYZCQkRDp5j7blo4hF4DuGK5YIh1DtdX1LnPOVUqs1F1YT0hEcfnw634y0pB1h71o3ELdW1QX6s3pZtM1ocu6erKSR9LItVa8RDYQEXLTF92AoA4zFEbVjYTwO+20JXEhayRu2hSTIfyTNfCxCrQ1qXlvIynTM5w3c+KDkqXkjubO/a1Game0DG0oDsRN2O580HBqcHSI6Ekf0sHjj4RQ+l3NFlFT7nVULSgN0Ja6aS6ubqHaB9eYK89/sCh0mda6Zkzs9gNTfKdkadz0dOXpmjCISON+RyNEQxlj+JGyiKIyhMFNWxQFHy17OsB4RaViAM3OVwBeFIt8NYwBDld9R3P7OOuPQKbnKaRTICJ2rpP9UJPrIFcm2/sLsPd7RbWVMdS9ppHx0oeaIyHcCPCBKAJ/vAbaEjeqcJ5Fud8PkBFiAVFeEjenSlvs65gtXvEmXC+63wgBAIEVGeh5p61Ka6dIiFYjBOB5glgyoM5t75gQE7C01kUM4Umv28Lj19NcoI8lg03CnGkykqEC7i5c7NeFVZ8tjrZ7JWRbI945tJ6wEJQtOzxwzuEcnIG2ItG+s4Qk3GzMENdoqEr4ss2AzwMhQxtjPmvtZFVM6n7LPwNgahj60AVY9gFRPIslTOFtytVUBXmxMPB5Af0eQb8Aao2EYAwsE2rOhZeLh9UpIKCpyadJ2fTXD4kYYPEA+EgZ/EgJbFkcPFcXofaugQKACGeXobSYJRLCFz7MQt4in8UD3kLW0LximFI5mHYVoslqs9EqEVCLbJhac/0IVwGWBjeYmvcMc3VoA9EmS7qauGTf8y3KUXeEoiE9MbunxDa4YUP0j7oz637V3BbhncoIIa85UhIFAqOGCBuFMDKwmOntpBBxoz5Klog0cftOY7wIWxoGfZEQcsJR6J4Co/mMEE2REFWfUiqNaqkAUouWHjUSglvCws46REKo52ho0PrCSnF39hdEgTG5CAbUuFRbu/q9Fu6Y527ajqq1sjgKHtPx1swO8dueqNoyUdUhyNWV4VTef5Pxo9zwFpsTPi+XbLs03jCImitdoaZaBE24hpq2SIiYCW2wg1yuiToI/sKCfgVMebk7GCFkoTe/J5yFDDFuo0aTYs9LlkqDY4wpGW7v9p4/z1bEO2g4YhEqPXLTNSBhepFqg1Gh0Nc9j6G2Siwq7RfGgLgJ4yP9YjxaTnPIsauYOdO+4rqLY8KoIRd6VVsoaIYGNhBVqVp8pi6ME2bLs+0JiWiurM/YlAh4xeF6wuL55r3tWVW7ZcqHXHgYDEgGoPWEVQ0JYbiqu3K7Lq4XEAZxhHQR7SDvMaCL1Cj1/oLeuPDvCuJPjXPfP2OsyQjhT0WQaULI11URXefAjDDGtXoNW2Aq+sYLx4btpdGo1Ca5oG84yiAho7GajNOA68HrYITwGzNlRJXF2wzKxscWiZS7uNlW96EpEgLuWGmJhOBTVRFp0uuTZ8lAU4Fqfw0eAGCDMeHt3CsWPc5oWdTzkHNRt9tH5AJdY56XU0Vc1sR14oE5IyFkTRnP6eJug+lwgDEMrNuIMzZfjkXnfagpZY1PyCg/05MJIX9fmJ8Rgi2OevWyJmtgaWGEkLnhnYylTZ5UQxNjo+a4YfRc7IYgj40HYF60GJr/+TOmUiNVe2RtD9dRIL3m/qgL1htW4wuAOEbqaBWZHjCjIiEAV45XbfG+3UKozBQ7oekfHWhbtGu9YXF8sSH0sqjrOCk1vLQduVDL1z09QI6tmPs+woZYKMPtXwaDvkEYIfSNYv5u0lvDwgjRYO4z6aAvq2NlxJI79yvjweDsURBt51DGcm/har8+JYwu0hHpX7S7WwyzxTHxvl29uX/jRuiRQHs6xrQvFTYko5TcuVPO39IIIXfFigeUvqatTIpIund88++wa3gwmJA9yUBHo4v+wR4YFy2GNhDxxnY8IJ5b3GzT32aLhJD6BZzm1HLWL87rHHQjM+qO2F1ioiLmvw5zuvjDFwkBz9EoDULSAez4I0Ld9EgZ4ap2TpO119zC99bjw2JbWuU4Ff+XWxEHe1Nee/zzQMs6RqVRTrU/k/crp4wRYvv27bj00kuxZcsW3HPPPSe7Oe8JfKzs5ZrZHMy/CGn1ivsJ6cKL29tsAJBVVf1hUgC8hb2MhDCYKlblRxkhUkExscdNz2AgJ+WIISYA1/LfaoToBOsOC0ulXCiMVQDmKpI1u8lb4q+wrn4vw01bKxxnK2L3g6XNxpimaxueN52lg8JAULbElpi+RRvgeQdV2F/UFIIyFfD2zOYAG4yK3S6CGhhjOPf6P8MZm0UoNtM1VehShetmQsrLBzQbIRAzPUWvwztnmZC4tswpy7uFBOOiii6XuactyqY8F4sY4l1GDZh/sArmxSLvWtbdYKkgOBw4bwgFTwnEkCG2jJXKf649fFj1ibi35aY/HYP1hIGEKYrBTbrPztdfnLiBbeZr2K0fFCGTvlBDyHxk02dM8feJ/kiT51gKaW3I7Qv+cGF41eW1DWkReTRWFs+mQ37koUMHsWvXTnXfTUppxAAbiIi810MlaIMxcU/S8xYR+7cDQoFF1IBTrqm+wxdF8JDzAh557P+J44OuB1wDmM+oaFkWuHvrPGE0LyY6REIgpPv6cxDpxUthye08ZwsFljtjzJKOIb4zlaeSc67CbJ23C16flIvSwVjTvwHPaCAr63M3zF9LhxD4wnrMNKagmwHovT5Fealv0e+mc7HFURE++5Zvy04lg0JwJspwbLEDgrYspgxhLBXwxl13SNxrMqAMoupcs0RCtCEXFTMNoUhnmg2uyjAnjYvuwttfKE4aGWQBQf95/bvrcC6K0jaNbTc6Cj4PoJOtqGgBlnZljfv+NJ+RRBuMij44UvYWjP1haOtS0D/QjcCfrm3yREpjmjivVIaqQk5G3CgxqWOFDbHnuvsbbXFUeKRqtoiiSZhtkWxqPqo7XgisjJYIaGIbt0wIHBzOlGuwl+2Wc2WhDmeiqvJ4WU9YpTRqy+PifvN18JybUiUVzIFoc8SAz4gn3wMzNc9L615PhvhyzmFNepE2MtUBUV8EVs0Wc5Z7DTmXKg/dUYwQSjfwRUIA7iKkannzlD+qoMdbPAPt8wKLGs1GFTmG3f5/cOYIcqwk/t0SCQEIWRG4cS308/rUwsypWeDczcU3fd7QTpEQbo51U5sTnnyR94eIF1HCNAZtdRLO/rwwoI2VmyLDVEScNBD5akIoI0TBrXfUEnXRilok+SI/URI1IaCJ3cnOue5zWPaBD4tFlRsJwcerniHNneNYyGcIP0pxSp6viR1ZesOivoHliNoUmZByCDkHZjqnY7h9GpqoZSMX5fZLorihtujoi+G2ulgyBVAaJ6qWcAT4oqO0IZG3rtKiWgrtOYyj7lRbIiFcXW607BZCFfdhXr4M+gqvvpY63jWWONkKeElEZ7GoIa4rC5z6DJK1ySIQNrwIYreApLYsJnY141zIgmQQ2mAM5h+vFpGMQEskhAEzHIITcGXWLPMC51wUioarL8ZMYcCVW2HOAefcqyki5wuLiyLvAPjhkogGMzVwzlXULJ+uwZH3PhBpMnac9akboMfC7RGD0zV33aD7an5Z4rm6Rk3ZXhEJIeo2yOhrbVDUZYLDhTxLBpRepIyFnZwZAIwP9ULfkG52QEn9wt0h47XXd+CwNgVtZUIVklZtdyMtWVdI6VaazwihjCcHi+AJQ8wX4xURcdXdYX0SdCN/fJEQIpKwKuSXbFvMFEYR/zbzZcuNdnW3FW6RwbzUEOuSYkPos3LNImuNBIIIJ7ugp9x2udFSFmw4Jpq2ZQag0ij/K0VCzOF+Xzhs28Ydd9yBH//4x+jr68P111+PzZs3Y9WqVSe7aSeMeiWLidj9YGtCaiLT4l1w1uSBhgO91gOWbX4dDncwPTWJxJoEjNU62PgL6jvGAkhlLocTtTGdfQixFR+FpoWRL2yD3ZhAKLEUgcQmVJOHUOr9LeoHnkY4EgHzJfQ73QU49RK0/gi08STsM/NqAOq8Dyyrwe7Jg6MCbSIOZ6gAPdiL6sE6DN1AJLIEqdTlcJwipqa3gruF8ZzeEpyiG2Ib1kUYWEBDOHkmIlgqQlNDBvKFX6KS2wVnaBo6y4BlXaGrObCHxqDlYtCy3oRmH54BH5qBnugDy86+eLBWZRGY7EE6sgHlvjdR5U+CL6lAi8cQKXwAscSFmE7/FmzqEPpwJUrlnSgO/Rp65WWwKgOWWwhPrEJkIgHObEys/xlyq/8TDCZSI1fC0JMwjAwCgUHUam+jvuIdkU+bD8EZmoEO8eyMwThCbw1iJrkNTtYTKvY6UdRQh7hn3ehCV+pK1OvDyPX+As5MAel8FMHkcuSsR9FYm4U+/jKcFVVEDg3BrHYjx7YhWtsE0+zD9PTPUV++D7xoQW/0AGt1YDXA2AtADbDXZ8GrNlh3BLWpgyhs2Y5wbhWiQ2fCNJYj49wAhBlyg08Doy8inFyHQD6Nypq3kMtu99q9JAceqkDriYJbHJyXofEuwHDgDOWFopixAcvG9P46tBVVZCJnIG1fC9suYHf+O+j92HNoNII4ErwcZt1BKqshPLgRhdiv0UiNg0/FgaEJsLgJzUnCHhK5j3qoF2xah7V2DGa9D8nS76PWOwqr9y1E2DLU4wdRdh4XxzNA1/vBsgy8z4Y9JHLfWU8YLBAS/W3iebCKCdvm2LZtCqWSA8PYjiRn4vug1x+dtTPCIxABtCUJaFNRGCujiLy5Vnga0UBu8VNwkgwlbQfKfa8hlj0HoaElmO5j4HwKh8Z7MMa6EdeHUY+MAX/AwRq/AbJAvVHH66/tQIgFsXy9huLgizD2ZpBMboGuRRGKDCFQy2B6+mHUZt6BMzQF3clA74pAS5uwukroX1lHanAp+DMWqtV9KGV/C2/FKOC5GuyhKehmGiwbRDi8HvH4hajXj2Aqdj+coZxYjKUC4IMlpPMpOIeyyA89Cwdl6O/8CixiwMmX4Qzlocd7YA+NQ6uKccqrFuzucbBlIqRZP/As7N4JaGlvHA+cW4BumpiM/wz2kAiT17u6vfccEHIwmbwY2vIEihMvoJ59FAwMzlQRztAMrKEyCnwbjDfSiA18AEZPDyKF1QgOLwKLB2CnqsilH4M2EIaWjcNelwOfqkLP9in551QrcIZyMGp9SFQvRig0BACo1d5BPv8kwBiSiYugB0Mo9D+H2BENlcxrOBz7XwjvW4N4/KNgTIe9IgtesWEE+5CqXwVHs5Bb8jhY/jkwx+0/k+JajZ4IzKESuMZQfvtJxBMJhHuXIbR3EUqlV1HMvwx76Ri0eBxa1jPM2GsnxSIhHXTzWE2waVPIGubK6ZUi5F8bjELLurslBB1MrtiP7BslDIUHYK7lMCZ/A5zLEI9fCD3IkSs/iurK18SuJGYawdwK6EYXqpV9aKw+AFYLA7oFrORgiQicoTygM9QGRjATfA6hxgokxy4Gum3Yy6egHfg1rNgYZpY/B2d3BanUZTDNftGegAV7SGzZp8dEH3R6CnBqJbB4APr4S7DNGvaseg3ODg3rz9wIvVgT9+n2kXj+9xCYzKCyeh/qxQJC3asw0/Mb1GMj0BelYU9NQRt7Fk56GlpvDDxhg4cr0Ja6fRQNVIZGMROtwz4QRPcQg6br0GdeBpthsFdNgM80xCImVAKrhqFnU3jzjQB280l8MrAFiwGwgI7y1YdQD78ElC3YQ5PQJpJwevPQFkdVvQzmRhKKvu3JlE44qMIZmoYVDcI4EocODfZQFmx/EHjZRrL+CXCzjoJ2PxpDb6Oc/h3K2huIvX0ewpnV4EMlaNEEtGwUjBkIhlbCyoygyvZBz3ZjZuZZ1Gr7sX7do7CrRexf9gD2Tr6DkBbEhoHF0MMmajyOqVf24KyzLoNtj6Bc2YVk4hNoNA4hX3sMjZWHUKq9jtquQ4gHz0dwzQD08Zdg2RYwkAerO9Czok6CafYilj0L1a6DyFm/AXN3X3SSJThLC9APvwAW0GHpE2CrGMK5w0gmt6Bc/i2Ky56DXZiC9spTcBaVoPUnoWVdQ0SQIRpYAW28G9XYMBrOLtiNvJBtpQyQNWEnx8QC3+HApIVo4ULU6mWMjb6C7u5uVU/JKQiZovSd3jy4URUReVETetarO+UsFfNApvBnwFQV+jK3IHdvGKXRnagtGoPWSAiZWEpBy4YBMCQSv4dAYBlyuYdhWZOwbRvvNN5AakUUqWQPnKUzSByogBlV5LteAUoc9pnjQFEDM4OifcWXwMqizZy7+hGPIlaso14/jNoZe4Se1adBr7p6jIRpCAVXAGCoN0aQSl4CK1lGTvtP6NmM2H0qMwqtxx0vuRqYEQRfWYNZeR1RfQiOUUa+70VYQ1lo2WehWSE4kyWEkosQ3XAh8vseQjWWRW9iAjX9UWSzL0E3kkilr0A9Porp/C/BE1VRYDMrjABmYBFMI4Nq9U04jmvsk7rfdAw8VgUyOlgoAmdoGlo2Lp5FLAN79RSKZQdvNPajb3kXlnYvAW+UwHpC0LNdcJaW4dTy0A/8Ck4oByzXESvmEB84H+Xya3B4HdHoB1FJ7ke5Zw8w9gQWb/klGtYvkT10AUxzMbRsRIyj4Apw2Jiaeh1v7t2FIEysGuqBnkiAGxY4KmBxE/r0y+qRR6PnIBLZiLGxh+DwMTj2GHL5R2EYGaSSl0DTwrDWZ4VTbUkMTqykUiQOv/x/kR0fwxnrz0RlzRhmCnUs1geAoTJ0ow9I2t58WXoRTl8esBzoWa++hs0mgTUc2vgLKBWLCAyVoVUAtp0hXv4oTMShdYdQ7t6LmrUNTlwU59XCSfCLymA9EYT6FyE0tgjVxQdRjr4uUot7wgDn4PEqtH5vrgGASOQsRKPnoFDYhlrtbTgZMe9BY9ALL4MVGOzBPHLjkzhQPQxthQZ0r0doqAb9yHNesfyqmCe0eBJ8ZVnI47SQa2IecWAPjaIWPYLiwG9gFnuRyV6L+NSHYK+awfT40wBvjnS1VmeBhgMj+xLsRA6c18FgAOtsRGdySCR+D5XKG8it/QVQsaBnRfFWOz2BVPSTMCMD4LAwlX8KyJSRSW1EqWcX6vY24CCDM+TWIxmOQWNx8GoNztIZ1Ic5QsEkJuz7YA0dBkI6rInt2LVqB+yZpbi0sR6l8nbUawe9d7duHJoTR4/z52hYk8jnH1f3EwgsRip16axzyOkI46dAGc5XX30V3/ve9/CjH/0IAPCDH/wAAHDzzTfP+ptGw0YuV571+1ONsb33Y7T6ceBblAAAD9JJREFUbTB24h63YfRCK0dRD7zd9h07cj5ia76K6u++j8bgEyfsmn44TyCV/D+oN3ajXP4mGJs7BJE5g0g8+VVEPr4ModUZjIz+N1Srb7wnbQMA7mTAtPZ9hR07DU2fAucMtv1xGPrTAGsJz+cMPaPfw/iB/XDO+99gE8uBgAUk957Qd+i1KQWmzahnyBBGKLwelcorLQdqYPU4eCgPQAeQADDderpZse0YgtY5sK3XwaMTAA8A7Ftwxg2w1DfBTGHJtSsp6OHcUc42PzhPAKiAcwfF8RVwgjNIpUbd+zQRjX4IxdLzx3ROu5aEFiiAMQ67moAWnDnu92LbOhwnCNM8NnliTqxDqfR52MghPHg3YNSg2d0IjKxDuXcntIAv1NLWYVVSCMaOss+1bSDQOBeN0DQA39iodAHho79nuxqHHiy19+cONBprEI18E5r2K1Rr329vi2MCjSBYsNjh1+8tVuNalA+dj1jkn6H1vd70neMYaGTXQouXYEYPuI1lSL5zMfiim7D7rZ9hcN3WY+gPOoDNsK010I17Acj7NcB4HJxNA44BgEPTBsB0DbZ9sO0sjmPAsgwEAvP3YnCuwz7wURhDvwIwt/w8sWgwjF5Y1ujx/dyJwh5ZC2QOQw+NtX3NS6vBIl0AexXALMW7jgeuwxnZBG2RkIsMveDInrjzz3ZZzjA5vgyWHcLg4BfR23smjozcBMYWcE/3RggAA1wZrVfS0OPnoFp/EZp2dFnN2CJEo9dAYxdj+uALYMm7oWmd+1y12otQaAqABcbS4DwP2T91azlCyTNQym8DjLm3k2O1NHhgGpjnWGSsD5y396e246wQTDuDevDwvM57IgkWbkL6hXOBj/XBXhKGNTKDQuF2WOl9s/xCh65nYNvvfT+dD4zFANsB1+Y33/F6FNDrYHrncayzPtizvDPGMuB2Dpilny00AXMz6o2nAQCx6I0oFn4C6A1wroHzjQhYOixjB6Adff48Ot0A3J1BuAnTOB8ck7Dt3Sfg3O8C24Q9dgb0AQdgr81+HNcQfvsSVIaeANj8CiUulDwGAMsyUZlZh4g5AT02An3sbNi9ewF27OvCRuPDMM3OcxXjIWjWGtT5Xuju3G7baej61DzOGwRjDgyj89hpNFIwzc6y2zC60ajnwDTv2WtaH87c8Og87ujUoqdnjuj1U8EI8cgjj+CZZ57BnXfeCQDYunUrdu7ciW984xuz/uZ0M0L824++h7Pq/wntXT9uDrjlXaWSzf3bFXAAaE+96HSW0w0G5m15c7RjfUoP56zj5+IDDnDWdMyJgoGLdJB5nFu9S868f3dom2q/7AaY3/mPqd2zPDtg/u9Aepy5v59yBk08FXWO2e5z7nOjo1I7/98334P3TNmcdyaLKvt/M69329LeTr9x33rHZzvf9h1ru47GiTzXe03b+Gl9x3P0W9UHgaZ+6J0TKp10rmfhf0+zvctZr/0eySCg8737720+88VC4pcdbd/5npefY3l2XjTgfGcTv0zk4Py9y2I96r3jxI5H5ko1Jdfcq/se0XHPM8x3njnHjds/55p32n5zDMd615GtWljtp9N7a3vuCyhrj/Vacx1/tPdwtGv559Q52+CTUf7316pPzHmOVtlxHHJ3rvHZdi1xYLN+fgzX4eDzvt4xnZuJJyavNOu7Oca5aVadG+3vuNN9HYt+33r8u5lHj/bbpjUDl2kdRx9Dfr2vkx7tdxTNeV2upDKOaL3YfPOP510A9VRhLiPEKZGO8V8BgzmIOVVoeDcW1hZVwacrzH58pwWPXwhp6DwN+D87/g7fuQXtLerchk6/nKeQUoex5g9bP29SaN+NoJ/lHLOesuWeW4+b7Xd8rmPmO6W3nlAly8F7RnO9k6MuhZvPz1u/m+O+53U95n7EZz2mvd+xluN9v2+bAOT48H/e+pujtVP+2z0Pn7+6NXsfYp0+bDm2w+qh6fhjaIc6rPW5ic+ar9B6rXm0dV7MJgPm6mPyd0eTKS3vsvUn/ufe9ixmaVbbNVq/7NCP1HWPRQ4dy1jvcN5jfl6dHpL/b3kjxyNLO40dPy3tPuqtH60NrWNkHm3teIj/OcznvuczG87RnuMeRnNds9Nz5839vUk+dnp2rXOI/7fzbdNcxx6l2bOedz59arb3dqyfz9EO3nqtDr/vqIPMJUPnasdR+lnHse//ovUdzjIHA757m+XaTTLxWGXCHG3qeMxRztN2C619+wRyDLd6dKkwV1/s9Hz8Z+5w9jn1uw4N63jY7HpKc4vmM47mkPdz4mvDnPqVf27qcMzR5pLW8at0z7n6pPzYv95rkaNNOuwszW75bZHXTjsDxNE4JYwQfX19GB31wkLHxsbQ19d3Elt04rn+z28BcMvJbgZBEARBEARBEARxmjB4shvwHnBK7I6xceNGHDhwAMPDw6jX63j44YexefPmk90sgiAIgiAIgiAIgiBOIKdEJIRhGPjGN76BL3zhC7BtG5/+9KexevXqk90sgiAIgiAIgiAIgiBOIKdEYcrj4XQrTEkQBEEQBEEQBEEQ/xWYqzDlKZGOQRAEQRAEQRAEQRDE+x8yQhAEQRAEQRAEQRAEsSCQEYIgCIIgCIIgCIIgiAWBjBAEQRAEQRAEQRAEQSwIZIQgCIIgCIIgCIIgCGJBICMEQRAEQRAEQRAEQRALAhkhCIIgCIIgCIIgCIJYEMgIQRAEQRAEQRAEQRDEgkBGCIIgCIIgCIIgCIIgFgQyQhAEQRAEQRAEQRAEsSCQEYIgCIIgCIIgCIIgiAWBjBAEQRAEQRAEQRAEQSwIZIQgCIIgCIIgCIIgCGJBICMEQRAEQRAEQRAEQRALAhkhCIIgCIIgCIIgCIJYEBjnnJ/sRhAEQRAEQRAEQRAE8f6HIiEIgiAIgiAIgiAIglgQyAhBEARBEARBEARBEMSCQEYIgiAIgiAIgiAIgiAWBDJCEARBEARBEARBEASxIJARgiAIgiAIgiAIgiCIBYGMEARBEARBEARBEARBLAhkhFggtm/fjksvvRRbtmzBPffcc7KbQ5wmfP3rX8cFF1yAK6+8Un2Wy+Vw00034ZJLLsFNN92EfD4PAOCc42/+5m+wZcsWXHXVVdi1a9fJajZxijIyMoLPfe5zuPzyy3HFFVfg3nvvBUB9ijh+arUarr/+enzqU5/CFVdcgX/4h38AAAwPD+OGG27Ali1bcOutt6JerwMA6vU6br31VmzZsgU33HADDh06dDKbT5zC2LaNa665BjfffDMA6lPEu2Pz5s246qqrcPXVV+O6664DQHMf8e4oFAq45ZZbcNlll+GTn/wkXn31VepTxwAZIRYA27Zxxx134Ic//CEefvhh/PznP8e+fftOdrOI04DrrrsOP/zhD5s+u+eee3DBBRfgsccewwUXXKCMWtu3b8eBAwfw2GOP4dvf/ja+9a1vnYQWE6cyuq7ja1/7Gn7xi1/gvvvuw7/+679i37591KeI4yYQCODee+/FQw89hK1bt+KZZ57Bjh07cPfdd+PGG2/E448/jkQigX//938HAPzsZz9DIpHA448/jhtvvBF33333Sb4D4lTlJz/5CVauXKn+TX2KeLfce++9ePDBB3H//fcDIH2KeHfceeed+NjHPoZHHnkEDz74IFauXEl96hggI8QCsHPnTixbtgxLlixBIBDAFVdcgSeffPJkN4s4DTj33HORTCabPnvyySdxzTXXAACuueYaPPHEE02fM8Zw9tlno1AoIJvNLnibiVOX3t5ebNiwAQAQi8WwYsUKjI2NUZ8ijhvGGKLRKADAsixYlgXGGJ5//nlceumlAIBrr71WzXlPPfUUrr32WgDApZdeiueeew6c85PTeOKUZXR0FL/85S9x/fXXAxBeROpTxImG5j7ieJmZmcFLL72kZFQgEEAikaA+dQyQEWIBGBsbQ39/v/p3X18fxsbGTmKLiNOZyclJ9Pb2AgB6enowOTkJoL2f9ff3Uz8jZuXQoUPYs2cPNm3aRH2KeFfYto2rr74aF154IS688EIsWbIEiUQChmEAaO43Y2NjGBgYAAAYhoF4PI7p6emT1nbi1OSuu+7CV7/6VWiaUFOnp6epTxHvms9//vO47rrrcN999wEgfYo4fg4dOoR0Oo2vf/3ruOaaa3D77bejXC5TnzoGyAhBEKcxjDEwxk52M4jTjFKphFtuuQV//dd/jVgs1vQd9SniWNF1HQ8++CCefvpp7Ny5E/v37z/ZTSJOY7Zt24Z0Oo0zzzzzZDeFeB/x05/+FA888AD+8R//Ef/yL/+Cl156qel7mvuIY8GyLOzevRuf+cxnsHXrVoTD4baaf9Sn5oaMEAtAX18fRkdH1b/HxsbQ19d3EltEnM5kMhkVwpXNZpFOpwG097PR0VHqZ0QbjUYDt9xyC6666ipccsklAKhPESeGRCKB8847Dzt27EChUIBlWQCa+01fXx9GRkYACCVuZmYGXV1dJ63NxKnHK6+8gqeeegqbN2/Gbbfdhueffx533nkn9SniXSH7SyaTwZYtW7Bz506a+4jjpr+/H/39/di0aRMA4LLLLsPu3bupTx0DZIRYADZu3IgDBw5geHgY9XodDz/8MDZv3nyym0WcpmzevBlbt24FAGzduhUXXXRR0+ecc+zYsQPxeFyFhBEEIPKqb7/9dqxYsQI33XST+pz6FHG8TE1NoVAoAACq1Sp+/etfY+XKlTjvvPPw6KOPAgAeeOABNedt3rwZDzzwAADg0Ucfxfnnn0+eIqKJv/qrv8L27dvx1FNP4e/+7u9w/vnn42//9m+pTxHHTblcRrFYVH8/++yzWL16Nc19xHHT09OD/v5+Ffn33HPPYeXKldSnjgHGqXrPgvD000/jrrvugm3b+PSnP40vfelLJ7tJxGnAbbfdhhdffBHT09PIZDL4y7/8S1x88cW49dZbMTIygkWLFuHv//7vkUqlwDnHHXfcgWeeeQbhcBh33XUXNm7ceLJvgTiFePnll/HZz34Wa9asUbnWt912G8466yzqU8Rx8cYbb+BrX/sabNsG5xyXXXYZ/uIv/gLDw8P4yle+gnw+j/Xr1+Puu+9GIBBArVbDV7/6VezZswfJZBLf/e53sWTJkpN9G8QpygsvvIB/+qd/wg9+8APqU8RxMzw8jC9/+csARA2bK6+8El/60pcwPT1Ncx9x3OzZswe33347Go0GlixZgu985ztwHIf61DwhIwRBEARBEARBEARBEAsCpWMQBEEQBEEQBEEQBLEgkBGCIAiCIAiCIAiCIIgFgYwQBEEQBEEQBEEQBEEsCGSEIAiCIAiCIAiCIAhiQSAjBEEQBEEQBEEQBEEQCwIZIQiCIAiCIAiCIAiCWBDICEEQBEEQBEEQBEEQxIJARgiCIAiCIAiCIAiCIBaE/w/vNXlVmeXN0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "SMxd41ruVRZm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "16e63fa9-6434-4dfd-85db-36d6d4247b5f"
      },
      "source": [
        "plt.figure(figsize=(18, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "\n",
        "\n",
        "loan_train['ApplicantIncome'].hist(bins=10)\n",
        "plt.title(\"Loan Application Amount \")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.grid()\n",
        "plt.hist(np.log(loan_train['LoanAmount']))\n",
        "plt.title(\"Log Loan Application Amount \")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "pY6BIHplVRZq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "7ecc0fa2-30af-41ad-c711-67f0f41ea654"
      },
      "source": [
        "plt.figure(figsize=(18, 6))\n",
        "plt.title(\"Relation Between Applicatoin Income vs Loan Amount \")\n",
        "\n",
        "plt.grid()\n",
        "plt.scatter(loan_train['ApplicantIncome'] , loan_train['LoanAmount'], c='k', marker='x')\n",
        "plt.xlabel(\"Applicant Income\")\n",
        "plt.ylabel(\"Loan Amount\")\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uvSLVJyxsNptiYnz/Ve7kyZOqqakx/0lMTPR7XMePH9dZZ52ls846S59++qmeeeaZJh+v1LRrNFj+rpcLL7xQf/vb3/TRRx+ppqamySvk+Pt/EwAgPEhEAAC89OzZU9dcc40ee+wx9e3bV48//rjXagBPPvmkuYKEp9mzZ+vDDz9Uamqq7Ha7Jk2a5PW53W5XcXGxUlNT9eSTT57x+4ceekgHDx7UmDFjNHv2bM2ZM0c//elPm30c7ir4w4YN04YNG7RmzRolJCRIkgoKCnTq1CldeeWVGjZsmDIzM82nqJdeeql+/OMfa/To0V5TUIYPH66EhASzsz58+HAZhuE1dN3fdidOnKhf/epXuvvuuzVkyBBdddVV5goNTbF9+3Z16dJF06ZNU+/evc1/rr/+etXW1prFA8855xzFxcVpzJgxmj9/vpYuXWrWc5Dqh8o/9thjGjFihA4cOKAHHngg4L4vvvhirVixQvn5+Ro6dKimT5+uQ4cOqXv37srOztbcuXM1bNgwvfjii0pLSzN/N2DAAE2ZMkUTJkxQamqqKisrdeeddyo5OVlXX321rr76ag0cOFB33nlnk89HS/g6Him016O/66IxaWlp+vzzz3XOOefowgsvNN/fv3+/brzxRqWkpCgjI0P33nuv+vfv73M7KSkpuvjii81/3nrrLb/Hdc899+jFF1/UkCFDlJOT0+zCjcFeo03h73r593//d82aNUu33XabJk2apKFDhzZp27Nnz1ZWVpZSU1NZNQMAIsRm+Ft3CQAAWM7bb7+tBQsW+Ex0ZGVlKTExUXfddVeEWwYAAMCICAAAAAAAEEEkIgAAAAAAQMQwNQMAAAAAAEQMIyIAAAAAAEDEkIgAAAAAAAARE9vaDWiJuro61dZaZ2ZJhw42S7UXZyKG1kcMrY8YWh8xtD5iaH3E0PqIofVFeww7duzg8zNLJyJqaw1VV59o7WYELSGhm6XaizMRQ+sjhtZHDK2PGFofMbQ+Ymh9xND6oj2GvXv38PkZUzMAAAAAAEDEkIgAAAAAAAARQyICAAAAAABEDIkIAAAAAAAQMSQiAAAAAABAxJCIAAAAAAAAEUMiAgAAAAAARAyJCAAAAAAAEDEkIgAAANAqDMPw+xoAEJ3Cloj47LPPdM0115j/DBkyRE899ZSqq6s1Y8YMTZo0STNmzJDT6ZRU/wfPsmXLNHHiRE2dOlUHDhwIV9MAAADQygoK8pWTk2UmHwzDUE5OlgoK8lu5ZQCAcAtbIuK8885TaWmpSktLtX79enXt2lUTJ06Uw+HQyJEjtW3bNo0cOVIOh0OStGvXLn3++efatm2b8vLytHTp0nA1DQAAAK3IMAy5XE45HMVmMiInJ0sOR7FcLicjIwAgykVkasabb76p/v3769xzz9WOHTs0bdo0SdK0adO0fft2STLft9lsGjx4sFwul6qqqiLRPAAAAESQzWZTXt5K2e0ZcjiKlZgYL4ejWHZ7hvLyVspms7V2EwEAYRQbiZ1s2rRJV111lSTpyJEj6tOnjySpd+/eOnLkiCSpsrJSSUlJ5m+SkpJUWVlpfrcxHTrYlJDQLYwtD60OHWIs1V6ciRhaHzG0PmJofcTQ+kIVw6Ki1XI4ir1ek4SIDO5D6yOG1teeYxj2RMTJkyf1yiuvaN68eWd8ZrPZWvSHTW2toerqEy1pXkQlJHSzVHtxJmJofcTQ+oih9RFD6wtFDN3TMTzNmZPJiIgI4T60PmJofdEew969e/j8LOxTM3bt2qWBAwfqnHPOkST16tXLnHJRVVWlnj17SpISExNVUVFh/q6iokKJiYnhbh4AAAAizLMmhN2eocpKpzlNw7OAJQAgOoU9EbFp0yZNmTLFfJ2WlqaNGzdKkjZu3Kjx48d7vW8Yhvbt26cePXr4nZYBAAAAa7LZbIqLi/eqCeGuGREXF8+ICACIcjYjjCnnEydOaNy4cdq+fbt69KgflnH06FHNnTtXhw8fVr9+/fTII48oISFBhmEoNzdXu3fvVteuXZWfn69Bgwb53f6pU7WWGsoS7UNv2gNiaH3E0PqIofURQ+sLVQwNw/BKOjR8jfDhPrQ+Ymh90R5Df1MzwpqICDcSEYg0Ymh9xND6iKH1EUPrI4bWRwytjxhaX7THsFVrRAAAAAAAALiRiAAAAAAAABFDIgIAAAAAAEQMiQgAAAAAABAxJCIAAAAAAEDEkIgAAAAAAAARQyICAAAAAABEDIkIAAAAAAAQMSQiAAAAAABAxJCIAAAAAAAAEUMiAgAAAAAARAyJCAAAAAAAEDEkIgC0K4Zh+H0NAAAAILxIRABoNwoK8jV//jwz+WAYhnJyslRQkN/KLQMAAADaDxIRANoFwzDkcjlVVLRaOTlZZhLC4SiWy+VkZAQAAAAQIbGt3QAAiASbzaa8vJXq3LmjiopWy+EoliTZ7RnKy1spm83Wyi0EAAAA2gdGRABoN2w2mwoLH/R6jyQEAAAAEFkkIgC0G4ZhaP78eV7vuadpAAAAAIgMEhEA2gV3TYiiotWy2zNUWemU3Z4hh6OYZAQAAAAQQdSIANAu2Gw2xcXFa86cTGVn55k1IyQpLi6e6RkAAABAhJCIANBuLFy4WPHxXeV0fivp+wKWJCEAAACAyGFqBoB2pWHSgSQEAAAAEFkkIgAAAAAAQMSQiAAAAAAAABFDIgIAAAAAAEQMiQgAAAAAABAxJCIAAAAAAEDEkIgAAAAAAAARQyICAAAAAABEDIkIAAAAAAAQMSQiAAAAAABAxJCIAAAAAAAAEUMiAgAAAAAARAyJCABRxTAMv68BtB7uTwAAIIU5EeFyuZSZmanLL79cV1xxhfbu3avq6mrNmDFDkyZN0owZM+R0OiXV/2Vk2bJlmjhxoqZOnaoDBw6Es2kAolBBQb5ycrLMzo1hGMrJyVJBQX4rtwwA9ycAAHALayJi+fLlGjNmjF566SWVlpZqwIABcjgcGjlypLZt26aRI0fK4XBIknbt2qXPP/9c27ZtU15enpYuXRrOpgGIMoZhyOVyyuEoNjs7OTlZcjiK5XI5efIKtCLuTwAA4Ck2XBs+duyY9uzZo5UrV0qSOnXqpE6dOmnHjh36/e9/L0maNm2abr75Zi1YsEA7duzQtGnTZLPZNHjwYLlcLlVVValPnz7haiKAKGKz2ZSXV///G4ejWA5HsSTJbs9QXt5K2Wy21mwe0K5xfwIAAE82I0yPIT766CPl5OToxz/+sT7++GMNHDhQ9957ry677DKVlZVJqn9CMmzYMJWVlemOO+5Qenq6UlNTJUm33nqr5s+fr0GDBvncR11dnWprrfMUpUOHGNXW1rV2M9ACxLDtMwxDnTt3NF/X1Jzy6uQQQ+sjhtYV6P6EdXAfWh8xtD5iaH3RHsOOHTv4/CxsIyJOnz6tDz/8UDk5Obrkkku0bNkycxqGm81ma9FfQGprDVVXn2hpUyMmIaGbpdqLMxHDts093NvTnDmZXk9ciaH1EUNrCub+hHVwH1ofMbQ+Ymh90R7D3r17+PwsbDUikpKSlJSUpEsuuUSSdPnll+vDDz9Ur169VFVVJUmqqqpSz549JUmJiYmqqKgwf19RUaHExMRwNQ9AlPGcc263Z6iy0im7PcNrTjqA1tHw/qypOcX9CQBAOxa2ERG9e/dWUlKSPvvsM5133nl68803NWDAAA0YMEAbN26U3W7Xxo0bNX78eElSWlqa/vCHP2jKlCl6//331aNHD+pDAAiazWZTXFy815xz95z0uLh4nrgCrYj7EwAAeApbjQipvk7Evffeq1OnTql///5asWKF6urqNHfuXB0+fFj9+vXTI488ooSEBBmGodzcXO3evVtdu3ZVfn6+3/oQknTqVK2lhrJE+9Cb9oAYtn2GYXh1ahq+JobWRwyty30/umPY8P6EdXAfWh8xtD5iaH3RHkN/UzPCmogINxIRiDRiaH3E0PqIofURQ+sjhtZHDK2PGFpftMewVWpEAAAAAAAANEQiAgAAAAAARAyJCAAAAAAAEDEkIgBErYYlcCxcEgcAAACIGiQiAESlgoJ85eRkmckHwzCUk5Ol3Nz7W7llAAAAQPtGIgJA1DEMQy6XUw5HsZmMyMnJksNRLKfTycgIAAAAoBXFtnYDACDUbDab8vJWSpIcjmI5HMWSJLs9Q4WFD8rp/LY1mwcAAAC0a4yIABCVPJMRbnl5K2Wz2VqpRQAAAAAkEhEAopR7OoYnz5oRAAAAAFoHiQgAUcezJoTdnqHKSqfs9gw5HMWaP38eyQgAAACgFVEjAkDUsdlsiouLl92eYU7HcE/TiI+PZ3oGAAAA0IpIRACISgsXLpZhGGbSwZ2MOPvss1RdfaKVWwcAAAC0X0zNABC1Go58YCQEAAAA0PpIRAAAAAAAgIghEQEAAAAAACKGRAQAAAAAAIgYEhEAAAAAACBiSEQAAAAAAICIIREBAAAAAAAihkQEAAAAAACIGBIRAAAAAAAgYkhEAAAAAACAiCERAQAAAAAAIoZEBAAAAAAAiBgSEQAAAAAAIGJIRAAAAAAAgIghEQEAAAAAACKGRAQAAAAAAIgYEhEAAAAAACBiSEQAAAAAAICIIREBAAAAAAAihkQEAAAAAACIGBIRAAAAAAAgYkhEAGizDMPw+xoAAACA9cSGc+NpaWk666yzFBMTow4dOmj9+vWqrq7WXXfdpYMHD+rcc8/VI488ovj4eBmGoeXLl2vnzp3q0qWLVq5cqYEDB4azeQDasIKCfLlcTuXlrZTNZpNhGMrJyVJcXLwWLlzc2s0DAAAA0ExhHxHx9NNPq7S0VOvXr5ckORwOjRw5Utu2bdPIkSPlcDgkSbt27dLnn3+ubdu2KS8vT0uXLg130xBmPM0ODufpTIZhyOVyyuEoVk5OlpmEcDiK5XI5OUcAAACAhUV8asaOHTs0bdo0SdK0adO0fft2r/dtNpsGDx4sl8ulqqqqSDcPIVJQkG92ICWZHcmCgvxWblnbwnlqnM1mU17eStntGXI4ipWYGC+Ho1h2e4Y5QgIAAACANYV1aoYk3X777bLZbLrpppt000036ciRI+rTp48kqXfv3jpy5IgkqbKyUklJSebvkpKSVFlZaX63MR062JSQ0C28BxBCHTrEWKq9zWUYhmpqTsjhKFbnzh1VWPig5s+fJ4ejWHPmZCo+vqtlO5KhjGE0n6dQKSpaLYej2Ot1S89Je7kPoxkxtD5iaH3E0PqIofURQ+trzzEMayLimWeeUWJioo4cOaIZM2bovPPO8/rcZrO1qFNRW2uouvpES5sZMQkJ3SzV3pbIzs5TTc0pFRWtVlHRakmS3Z6h7Ow8OZ3ftnLrmi/UMYzW8xQK7tEhnubMyWzxiIj2dB9GK2JofcTQ+oih9bWlGBqG4fVne8PXaFxbiiGaJ9pj2Lt3D5+fhXVqRmJioiSpV69emjhxoj744AP16tXLnHJRVVWlnj17mt+tqKgwf1tRUWH+HtbjHlrviSH1Z+I8Nc6zJoTdnqHKSqc5TcNzKgsAALA2pqkC7VPYEhEnTpzQN998Y/7366+/rvPPP19paWnauHGjJGnjxo0aP368JJnvG4ahffv2qUePHn6nZaBta+xpNh3IM3GeGmez2RQXF+9VE8JdMyIuLr7dJ2oAAIgGFKcG2q+wTc04cuSIZs2aJUmqra3VVVddpcsuu0yDBg3S3LlztW7dOvXr10+PPPKIJGns2LHauXOnJk6cqK5duyo/nyyoVTV8mp2Xt9J8LfHE343z5N/ChYu9hma6kxHt+ZwAABBNPEeGOhzF5t+BKE4NRD+bYeFU46lTtZaaUxPtc4A8FRTky+Vymn+IuDvdcXHxWrhwcVj2GYn5haGOYWucp/YuIaGbjh493ui1whxVa2hP/y+NVsTQ+oih9bWlGBqGocTEePN1ZaWTP3+D0JZiiOaJ9hj6qxFBIiKCov1CayiSnbpIdejDEUM6v5G1evUDqqr61xnXyv79H2jQoItJCllAe/t/aTQihtZHDK2vrcTQc4SoGyMigtNWYojmi/YYtlqxSrRvDf/wCNcfJlafXxip84T6a8XpbPxaOXbMZdlrCAAAK6I4NdB+hXX5TiASmF+IYNlsNhUWPqiamsmzpwUAACAASURBVFNnXCu5uSu0ZMkiriEAACLEV3FqSRSnBqIcUzMiKNqH3rS2SMwvJIbW564R0di1whxVa+A+tD5iaH3E0PraUgyZpto8bSmGaJ5ojyFTMxD1WAYTwfJ1rdTV1XENAQDQCpimCrQ/JCJgecwvRLAMw9D8+fMavVYmTBjDNQQAAABEADUiYHnML0SwbDab4uMbv1b27/+AawgAAACIAGpERFC0zwFqbZGYX0gMrc9dI6Kxa4U5qtbAfWh9xND6iKH1EUPrI4bWF+0xpEYE2gXmFyJYvq4VriEAAAAg/EhEAAAAAACAiCERAQAAAAAAIoZEBAAAAAAAiBgSEQAAAAAAIGJIRAAAAAAAgIghEQEAAAAAACKGRAQAAAAAAIgYEhEAAAAAACBiSEQAAAAAAICIIREBtEOGYfh9DQAAAADhQiICaGcKCvKVk5NlJh8Mw1BOTpYKCvJbuWUAAAAA2gMSEUA7YhiGXC6nHI5iMxmRk5Mlh6NYLpeTkREAAAAAwi62tRsAIHJsNpvy8lZKkhyOYjkcxZIkuz1DeXkrZbPZWrN5AAAAANoBRkQA7YxnMsKNJAQAAACASCERAbQz7ukYnjxrRgAAAABAOJGIANoRz5oQdnuGKiudstszvGpGAAAAAEA4BUxE3HrrrUG9B6Dts9lsiouL96oJkZe3UnZ7huLi4pmeAQAAACDsfBarrKmp0bfffqujR4/K6fy+mv4333yjysrKiDUQQGgtXLhYhmGYSQd3MoIkBAAAAIBI8JmIePbZZ/X000+rqqpK1113nZmI6N69u6ZPnx6xBgIIvYZJB5IQAAAAACLFZyLi1ltv1a233qrf//73uvnmmyPZJgAAAAAAEKV8JiLcbr75Zr333ns6ePCgamtrzfenTZsW1oYBAAAAAIDoEzARsWDBAn355Ze68MIL1aFDB0n1w7hJRAAAAAAAgKYKmIgoLy/X5s2bmUMOtCLP4pKNvQYAAAAAqwi4fOf555+vf/7zn5FoC4BGFBTkKycnyywYaxiGcnKyVFCQ38otAwAAAICmCzgi4ujRo5oyZYouvvhidezY0Xx/7dq1YW0YgPqkg8vllMNRLEnKy1upnJwsORzFstszLD0yglEeAAAAQPsUMBExZ86cFu2gtrZW119/vRITE/XEE0/oyy+/1N13363q6moNHDhQBQUF6tSpk06ePKmFCxfqwIEDSkhI0MMPP6wf/OAHLdo3YHU2m015eSslSQ5HsZmQsNszlJe30rId94KCfLlcTvMY3KM84uLitXDh4tZuHgAAAIAwCjg1Y/jw4Y3+E6zf/e53GjBggPm6sLBQt912m15++WXFxcVp3bp1kqTnnntOcXFxevnll3XbbbepsLCwGYcDRB/PZISblZMQnqM83FNO3KM8XC6nOQUFAAAAQHQKmIhISUnRkCFDNGTIEA0aNEgXXXSRhgwZEtTGKyoq9Nprr+mGG26QVN8BeeuttzR58mRJ0rXXXqsdO3ZIkl555RVde+21kqTJkyfrzTffpEMC6PuaEJ48a0ZYjTuxYrdnyOEoVmJivDnVxMoJFgAAAADBCTg1Y+/eveZ/G4ahHTt2aN++fUFtPD8/XwsWLNDx48cl1debiIuLU2xs/W6TkpJUWVkpSaqsrFTfvn3rGxUbqx49eujo0aPq2bOnz+136GBTQkK3oNrSFnToEGOp9uJMkY6hYRiaP3+eHI5izZmTqcLCBzV//jwVFa1W584dVVj4oGU77kVFq82pJu7XkTgW7kPrI4bWRwytjxhaHzG0PmJofe05hgETEZ5sNpsmTJigNWvWaP78+X6/++qrr6pnz55KTk7W22+/3aJG+lJba6i6+kRYth0OCQndLNVenKk1Yti5czfZ7RnKzs6T0/mtsrPzVFNzSp07d5PT+W1E2xIqjY3ymDMnMyIjIrgPrY8YWh8xtD5iaH3E0PqIofVFewx79+7h87OAiYht27aZ/11XV6fy8nJ17tw54E7fe+89vfLKK9q1a5dqamr0zTffaPny5XK5XDp9+rRiY2NVUVGhxMRESVJiYqIOHz6spKQknT59WseOHdPZZ58dzPEBUW3hwsVeK0q4pzZYdSSEZ00I93QM92vJ2vUvAAAAAAQWMBHx6quvmv/doUMHnXvuuXr88ccDbnjevHmaN2+eJOntt9/Wb3/7Wz344IPKzMzU1q1bNWXKFG3YsEFpaWmSpLS0NG3YsEEpKSnaunWrLr30UjojwP9peC9Y+d6w2WyKi4v3qgnhLsYZFxdv6WMDAAAAEJjNiEDFO3ciwr1851133SWn06mLLrpIhYWF6tSpk2pqarRgwQJ99NFHio+P18MPP6z+/fv73e6pU7WWGsoS7UNv2gNiGDqeozwaex0uxND6iKH1EUPrI4bWRwytjxhaX7TH0N/UjICJiIqKCuXl5em9996TJKWmpuree+9VUlJSaFvZDCQiECrBdoqJofURQ+sjhtZHDK2PGFofMbQ+Ymh90R5Df4mIgMt3Llq0SGlpadq9e7d2796tcePGadGiRSFtIBAJDXNu7tcFBfley2G6axgUFORHvI0AAAAAEO0CJiK+/vprXX/99YqNjVVsbKyuu+46ff3115FoGxAyvpINq1Ytl8vllMNRbH7uLpzocjnPSF4AAAAAAFomYCIiISFBpaWlqq2tVW1trUpLS5WQkBCJtgEhYRiGz2TDsWMu5eaukN2eIYejWImJ8V6rOVA4EQAAAABCK2CNiIMHDyovL0/79u2TzWZTSkqKsrOz1a9fv0i10SdqRCBYnskHN89kg2EYSkyMNz+rrHRSIyJKWSGGrVXI0yqsEEP4RwytjxhaHzG0PmJofdEewxYVq2zLSESgKXwlGwIlKTwRQ+tr6zEsKMiXy+X0SpLl5GQpLi5eCxcubu3mtQltPYYIjBhaHzG0PmJofcTQ+qI9hi0qVvnll19qxYoVmj17tmbOnGn+A1iJuzPnKScnS3V1dWYSwm7PUGWl05ym4VlTAogEf9OIqFkCAACAaBEb6AuzZs3SDTfcoHHjxikmJmDeAmhzPDtz7pEOniMg4uLivUZA5OWtNN9nODwiyfP6cziKzWuUmiUAAACIJgGnZtx444167rnnItWeJmFqBoIVaLh7sHPyiaH1WSGGwdYsaa+sEEP4RwytjxhaHzG0PmJofcHE0Mq1w/xNzQg4IuKWW27RmjVrNGrUKHXq1Ml8f+DAgaFpHRABDZMN7ifPnq89WeXmRvTxNY2IEREAAADtSzTXDguYiPjkk09UWlqqt956y6vT9rvf/S7sjQNCiWQD2rpA04hIRgAAALQPnrXDJHn9vdBuz7DUyIjGBExEvPTSS9q+fbvXaAgAQOjZbDZqlgAAACDqa4cFrBFx5513Ki8vT7169YpUm4JGjQhEGjG0PivE0MpzASPBCjGEf8TQ+oih9RFD6yOG1hdsjQir1g5rUY2IY8eO6YorrtCgQYPUsWNH8/21a9eGpnXtFB0NNAXXS/vCNCIAAABEc+2wgImIOXPmRKId7Uo0Fx1B6HG9AAAAAO1LtNcOiwn0heHDh3v9ExMToy1btkSibVHJs+hITk6W1wXmcjkVYKYM2hmuFwAAAKD98VU7zG7PiIraYQFrREjShx9+qBdeeEFbt27Vueeeq8mTJ2v69OmRaJ9fVq0R4dmZdIuWoiPRrjXm4nG9hBbzKa2PGFofMbQ+Ymh9xND6iKH1BVsjwqpTtP3ViPCZiPj73/+uTZs26cUXX9TZZ5+tK6+8Ur/97W/16quvhq2hTWXVRIRk7aIj7Vlr/Q+f6yV0+EPb+oih9RFD6yOG1kcMrY8YWl+0x9BfIsLn1IwrrrhCb731lp544gk988wzuvnmmxUTE3AmB4Lgq+gIw+zRGK4XAAAAANHEZ2ZhzZo16t27t2655RZlZ2frzTffpOMTAg2LjlRWOmW3Z3jVAADcuF4AAAAARBufq2ZMmDBBEyZM0IkTJ7Rjxw49/fTT+vrrr3Xfffdp4sSJGj16dCTbGTV8FR2RFBVFRxBaXC8AAAAAok1QxSrdnE6nXnrpJW3evFlPP/10ONsVFKvXiLBq0ZFQsOrxt2aNCCuer7Yo2ufitQfE0PqIofURQ+sjhtZHDK0v2mPYrBoRjYmPj9dNN93UJpIQVtewE9meOpUFBfle0wrc0w8KCvJbuWVtV3u+XgAAAABEF6pPIqIMw5DL5fSqceCugeByOal5AAAAAABRzmeNCCAc3DUODMOQw1Esh6NYkpSePtOsgQAAAAAAiF6MiEDEPfDACkkNEw62/3sfAAAAABDNAiYitm3bpkmTJmno0KEaMmSIUlJSNGTIkEi0DVHIMAw5nU6VlBR7vV9SUiynk6kZbg3PA+cFAAAAQLQIODXjgQce0Nq1azVgwIBItAftgq9ONZ1tqb6Yp8vlNKequOtoxMXFa+HCxa3dPAAAAABokYAjInr16kUSAiEVH5+g9PSZXu+lp89UfHxCu68RQTFPAAAAANEu4IiI5ORkzZ07VxMmTFCnTp3M9ydNmhTWhiH6rFq1XMeOuZSbu0I5OYsafGrTggUN37MOwzC8kigNXwfLXcxTklcxT7s9g2KeAAAAAKJCwETE8ePH1bVrV73++ute75OIQFMUFORr69bNKi/frzfe+IvKy/erV69eOnLkiJKTB6mkpFg2myzZ2Q71VAp3MsKdhJCseV4AAAAAoDEBExErVrCSAVrGPd2gvHy/kpMHqbx8vySZSYiXX96l++5brLi4+LB3tkM1csHz9+6pFFJ9wsA9lcJuz2jW9t2JDE85OVkkIwAAAABEhYCJiJqaGq1bt05/+9vfVFNTY75PggLBajjdwNP27bsVExMTkU52OIpAhnoqhWdNCPc23K+l0I6MCHVSBgAAAACCEbBY5YIFC/TPf/5Tf/nLXzR8+HBVVlbqrLPOikTbEEVsNptyc89MXi1ZsigiHeBwFoH0TEa4NTdhYLPZFBcX75XIyMtbKbs9I6QjRgoK8s3zIH2fACkoyA/J9gEAAADAl4AjIr744gutXr1aO3bs0LXXXqurrrpKv/zlLyPRNkSRuro6TZgwxuu95ORBYXnS35hwFoEM9VSKhQsXeyVn3G0P5UiIhtNJsrPvUUnJWnM6iXu/AAAAABBqAUdExMbW5yri4uL0ySef6NixYzpy5EjADdfU1OiGG27Q1VdfrSlTpmj16tWSpC+//FI33nijJk6cqLlz5+rkyZOSpJMnT2ru3LmaOHGibrzxRn311VctOS60IYZhaMmSRWaNiIqKatntGebrHj3iItLpDeXIBbeGUykqK52y2zO8Rl40t63+XreE5ygLh6NYiYnxKilZq/T0meb5YXQEAAAAgHAJmIi46aab5HQ69etf/1oZGRmaMmWK0tPTA264U6dOevrpp/X8889r48aN2r17t/bt26fCwkLddtttevnllxUXF6d169ZJkp577jnFxcXp5Zdf1m233abCwsKWHx3aBM/pBi+/vMusCWG3Z2jy5Ct1zz33RqQdvkYutHRaRiSmUoRaY0kZqb6toZqyAgAAAACNsRkR6Gl8++23+sUvfqGlS5fKbrfr9ddfV2xsrPbu3as1a9boySef1O23367Zs2crJSVFp0+f1qhRo/TWW2/57cidOlWr6uoT4W5+yCQkdLNUe0Nt1arlOnbMZXbY6+rqtGTJohYViwyWvyKQTZme4SuGViv86Hk+GhOKKSttVXu/D6MBMbQ+Ymh9xND6iKH1EUPri/YY9u7dw+dnAWtEHDt2TEVFRSorK5MkjRgxQnfeead69PC9Ubfa2lpdd911+uKLL/SLX/xC/fv3V1xcnDndIykpSZWVlZKkyspK9e3bt75RsbHq0aOHjh49qp49e/rcfocONiUkdAvYjraiQ4cYy7Q3HMtcnjz5rRyOYnXu3FGFhQ9q/vx5cjiKNWdOpuLju4a909unzzmaMydThYUPymazqahotTp37qj4+HidffZZQR2zlWLoi2EYXue+sPBBzZt3t9asKTK/U1S0OiqTEFJ0xLC9I4bWRwytjxhaHzG0PmJofe05hgETEYsXL9b555+vRx99VJJUWlqqRYsWac2aNQE33qFDB5WWlsrlcmnWrFn67LPPWt5iD7W1hqUySFbJeIVjmUtJys7OU03NKRUVrVZRUX3NELs9Q9nZeXI6vw1V833KzFwgwzC89pWdnSebzabFi7ODOmarxDCQzp27mee+uvqEampOe30+Z04mIyLQZhFD6yOG1kcMrY8YWh8xtL5oj6G/EREBa0R88cUXyszMVP/+/dW/f3/Nnj1bX375ZZMaEBcXpxEjRmjfvn1yuVw6fbq+01NRUaHExERJUmJiog4fPixJOn36tI4dO6azzz67SftBy1llmcuWtKHh63Aes6eG22nN+gsLFy72KkxZUlKs9PTQFdsEAAAAAF8CJiK6dOliTsuQpHfffVddunQJuOGvv/5aLpdLkvTdd9/pjTfe0IABAzRixAht3bpVkrRhwwalpaVJktLS0rRhwwZJ0tatW3XppZdG5dPYtq6xFRWaWkfBl3AUiwyFcB6zW0FBvtexus9Fa65MYbPZvIptLltmnWKbAAAAAKwrYLHKjz/+WAsXLtQ333wjqX50w8qVK3XhhRf63fDHH3+srKws1dbWyjAMXX755eZoirvuuktOp1MXXXSRCgsL1alTJ9XU1GjBggX66KOPFB8fr4cfflj9+/f3uw+KVYaPYRhKTIw3X1dWOkO6zGVzi0WGUzDH3JwYWuXYrVRssyWsdB+iccTQ+oih9RFD6yOG1kcMrS/aY9iiYpUXXnihnn/+eTMR0b17dz311FMBExEXXnihNm7ceMb7/fv3N5fs9NS5c2etXr06UHPQBM3tXPoaudCSDrOvZS4lhe3Je1OOPxzH7OZ5rA5HsblSRVtJQkiNT1kBAAAAgHAIODXDrXv37urevbsk6amnngpXexAizZ0K0PDpfShrBrjrErg7ue4OejiW7mzK8YfzmN3aQn0MAAAAAGgLgk5EeGrtOf3wryXFF32NXAhVzYBwPnn3TDo05fg9jzk3d0VY6iS01foY4dCWinICAAAAaHsC1ohozM9+9jO99tprYWhO01AjwjfPzrdbU6YCWK1mQMMlR+vq6jRhwhiVl+83vxPo+FetWq5jx1xe21iyZJHXEp7RWiMiVMK19GsoRftcvPaAGFofMbQ+Ymh9xND6iKH1RXsMm1UjIiUlpdHOkWEYqqmpCU3LEDbup/qeiYimdHitVDPAcwSEVH+cS5Ys8kpCuN/3VyPi2DHXGdtwJwtakohpjfoYraGxOHgmXNp6MgsAAABAZDRrRERbwYgI31o6IiIU+4/kiIrGjjc5eVCTRkQEc85aEkOrjTJpjta+7oIR7Znn9oAYWh8xtD5iaH3E0PqIofVFewz9jYhoVo0ItG2RKL7oT6BCkeGoIdBYMcjy8v1NOv5wF5S00iiT5qIoJwAAAIBASEREoXAXnPQnUKHI5q7mEcx+GxaDTE4e1KTik+2poGS4cA4BAAAABNJh6dKlS1u7Ec1VV2fou+9OtXYzgtalS8eItXfUqDEaN26C11KZ48ZN0OjRl4V1v+79uJMRhYUr9e67ZeaKFC+9tMlMSowbN8FMUiQnD/Jqb1M0HAGyefMOuVxObdmyydxPoOP3tQ3PttpsNnXuHKuamtNev2uvT/sbHru7uGegc9jaInkfIjysEMP2MBWrJawQQ/hHDK2PGFofMbS+aI/hWWd19vmZz2KVsL7WmgrQWKHM3NwViomJMYftOxzF5ufp6TO9hu839S/swRaDdK/i0FjnIJhtFBTkq6bmhLKz8xpdVaM9dTQaWx1jyZJF2r//g6gvygkEYoXVYwAAAFoTiYh2JhRP6QJto7Hh+ePHj9aOHX9RTEyMcnNXeCUppDN/29S/sDdMBLg7wZ7tCtQ58LcNzyknNTWn1KNHnLZu3WzWoWhsqc9oFWh1DPd0GKnxOADRjNVjAAAAAqNGRDsSivoMwRSi9PxLd0VFtXr37qMDB8o1fvxo1dbWavz40V7bLCkpVnb2mfUkmlpXwJ0waOx1oNoV7u/5GkXi7lDPmZMph6NYDz64SuXl+806FO4pCc1pt9V41txwOIqVmBhvxjsvb6ViYmLO+D7QXgS6P7gfAAAASES0G8F2xFu6jcamOEybdp0k6cCBcvXte7YOHCiXVD8l41e/ukNSfTKipX9h95ckCUXnwGazqbDwQa/3ysv3Kykpod11NFgdA/CN+wMAAMA/ilVGUGsWI/FXRLIpHfFgtuFZKNNmsyktbYKczmq9916Zua1f/eoOLV9eoPHjJ57x2ebNO5o1XSSYQpjjxk1QYeH3HYSm7MswDGVnZ+mdd95u9PPmtNuq3Emed9/9Pm5tqSClP9FeFKg9aOsxtPL9ESltPYYIjBhaHzG0PmJofdEeQ3/FKhkR0Y6E4ildsNsItM233nrDY+SC92fNWe4xmBEPdXV1zV5a0t2xKCpabU45SU4e1OJ2W1HD6TeVlU7zvLeXcwD4wv0BAAAQGCMiIqi1M16heErX1G3UjyK4RyUla73er6qq0pYtL+qTTz7Rb35T/1l6eoaGDk1VScnaZj09bGzEw6ZN2xUTE6O6ujpNmDBGW7ZsOmNpSafTqbS07/fVWDE5m82msrI9Gjp0iO67b7mWLFmkLVs2KTl5kG655f9TcvKgNrdMZbi4z0Vy8iAzyeMeKRMXFx/2JWJbqrXvQ7RcW46h1e+PSGnLMURwiKH1EUPrI4bWF+0xZPlOnPGUzrOSu2EYWrZsVcDlM/1tQ/I9usKdtEhPz9CyZSt177336De/WasDB8o96kXUfyZ9vxxnc6ZnNBzxMGHCGG3fvltLlizyKi7pHkFRVrZH776754xtNLb6xcKFixUf31VO57dmHQz3sqTup5xWXaayqaupBLNKCdBecX8AAAD4x4iICGppxqslS2/6ekr3yivbdfjwIU2ffqvXkpZlZXs0atSYoLbh70mfzWbToUMHNXDgIC1bttKsGVFd7V0XYsuWHWZNiXHjJjTpqaE7CeBOiqSnZ2jz5u166aVNKi/f71XL4v/9vz96rerw8ccfatOm5/3WlfDUtWsnfffdKbMOhntbzWl3W1FQkK+XXtpkHq+/a8BTU6fjtBXRnnluD6wQQ6veH5FihRjCP2JofcTQ+oih9UV7DBkREQUKCvLlcjnNBIC/J/e+NHxKJ0lDhw5TSUn93OVg1rtvzpO+xvbb8Pvu/buTEcHyPC9xcfFKT8+QZKiwcKW2b9+tpKQE87sN22mz2cyRIA5HsTmyI9gCntHQ0fBcCUVSUNcAAAAAALQExSotIBRLb7qd2RFv+pKWjXXAG7ah4WvPaR85OVkqKWl5IbeG52XBgkWSDJWUrJXTWa2cnEVe329s+6FcZi/QOWiLQrGsKQAAAAA0hc2wQm/Jh1OnalVdfaK1mxG0hIRuzW6vZ/LBLVSdRcMwlJgYb76urHQ2e1RCMKM1QjG6w7PtDc9LevpMSTYz2dHwKb/nOWvqefUVw1AeU2to6TVgJS25D9E2EEPrI4bWRwytjxhaHzG0vmiPYe/ePXx+xogIiwjlk3tPjRV4bMrIhOaM1li4cLFX293H5u6wN2VkQWPnZdmyVYqPj/dKKLif+nsWkwzVMnuhHLHSGlp6DQAAAABAU1CsMoJaUowkFEtv+tqmuyPuuaRlsNv2LFjpcBR7FYZs6vQOqemFE32dl+zs+72W5GysmKTNZtOePe9o0KCLzbb+7Gfj/RbfbCyGzT0HbUEorgGrifaiQO0BMbQ+Ymh9xND6iKH1EUPri/YY+itWyYgICwjVk/uG3MtkBho5EMx2QjFao6kjCwKdl8ba6amgIF/HjrnM5TwNw9CSJYsanU4RTA0Mf+egrdaPCNU1EKy2eh4AAAAARA6rZliAr86ipBZ3FkOx3r2vof1N3Y7ncQWzikVLzktTVovwrP/gebyeCQt/5+CBB1a06foRobgGgmH1OhoAAAAAQoOpGRHUkqE3o0aN8Rom39hUg+ZqyTKUoR7a7z6uwsLvRxds3rzD5zaae16CnU5hGIZeemmTeTxXXHGFFiyYJ4ejWMnJgzRu3ARJ8nkOnE6namq+U0nJWvN8uL/r/n1bmPoQ7qVIG57H1jwP0T4Erj0ghtZHDK2PGFofMbQ+Ymh90R5Df1MzWDUjgqK1KmpznnR7PoH3fB3O1UF8tSPQahHBtMnfOViwYFFEj6mtinRsfYnW+7A9IYbWRwytjxhaHzG0PmJofdEeQ3+rZpCIiKBgLzRfnfRIaO6+m/I7X532Hj3idOyYy2uZTV/LboZCUzrGwSYsfJ2D9rQ8pj9t4TxE+//w2wNiaH3E0PqIofURQ+sjhtYX7TFk+U4LKSjI9ypA6e4sFxTkt+l9Bzu0319BymPHXBErnNiUAqCGYSg7+x6v32dn39NowcrGXrM8Zj3OAwAAAACJYpVtSlMKKFp534EKUkaicGKwhS4Nw9AVV4zXe++VKT19ptasKdLs2XNUUrJW775bpi1bfNevcP++4agOz1EY7WV6BucBAAAAgBuJiDakqatGWG3fnlMy8vJWek2JcO+jrq5OMTHeA3VCse/Gpk00fbUIW4N/BxbOFU+shPMAAAAAwI0aERHUlBoRrTWP3te+fY2ICLY2hOcT8fT0DEmGSkrWmp+np89Ubu4KTZx4mXr0iFNp6ZaQHVNLl42sn5qRpZKS7xMn6ekZWrYs+ARNa9b9aEvawnmI9rl47QExtD5iaH3E0PqIofURQ+uL9hhSI8JCWnMeva9919XVNVoroik1JdxPwNPTM1RSUuyVhDj77LNVUrJWAwcOUHn5fh075lJdXV1IjtlfTQqXyxnUPmw2m5YtW+n1XlOSEO5t+HvdXnAeAAAAAJCI6/R+JwAAIABJREFUaEOaUkAxnPtOTh4kSUpOHiSHo1gTJow5o+PenA6+zWZTfHy813v/8R8DdfToUUnS119/rYEDk7V9+27ZbLaQFOn0LHjpcBQrMTG+yatwUGQRAAAAAEKHREQb4mseveeqEQ07v03tDPv6vee+t2/fLbs9Q+Xl+yVJ5eX7zTZ5tjVQB7/hvurq6vTSS5u83mtYD+KnPx0tSY0mNQIdq79j82y7FHxxxIbJoZqaUxFLDgEAAABANApbjYjDhw9r4cKFOnLkiGw2m/7zP/9Tt956q6qrq3XXXXfp4MGDOvfcc/XII48oPj5ehmFo+fLl2rlzp7p06aKVK1dq4MCBfvcRzTUiGptH39JaB8H83r2vxmpFSGr0+57fq6ioVkxMzBnbbjjiwp3k8Cc5eZC2b9/d6PaacmwLFizyWqFBaloRTs9tn332WTp69HiTzjvalmifi9ceEEPrI4bWRwytjxhaHzG0vmiPYavUiOjQoYOysrK0efNm/fd//7f+9Kc/6X//93/lcDg0cuRIbdu2TSNHjpTD4ZAk7dq1S59//rm2bdumvLw8LV26NFxNa/Mam0ff0loHwf7eva/sbO+pCNnZWcrOvsfr+41NWZgwYYxZU8Lzuw1HXDT0q1/N1MCByV7vlZfv15IliwIeq79jczqdZrubO91l4cLFXkkL9wiLYAtd+nsNAAAAAO1NxFbNyMjI0PTp05Wbm6vf//736tOnj6qqqnTzzTdr69atWrJkiYYPH66rrrpKkjR58mTze75E64gIXzw72G7NqXXg7/fBrG6xbNkqGYahJUsWmR383NwVmjBhjNdIh4bTNNzLc7p/5zZw4CAdOHDmCImGIyc8p4c0HDEiyeexPfDAihaNJPHUlBi2dASLVbSFlTCaItozz+0BMbQ+Ymh9xND6iKH1EUPri/YY+hsREZFExFdffaXp06frxRdf1M9+9jOVlZVJqu+wDBs2TGVlZbrjjjuUnp6u1NRUSdKtt96q+fPna9CgQT63W1dXp9pa6zxh7tAhRrW1dS3ahmEY6ty5o/m6puaU305fw05hXV2dunTp5Pf3ubn3y+l0qrDwQUk6Y3+SNH/+PO3bt0+DBw9WYeGDZpKhsW27t/fAA4VasGC+iopW65JLBmvq1Kmqrq7WmjVFXvv/7ruT5vc81dScUl5ertk2d+d+/vx5io+PV07OEp/npimdZX/fDTaG7nYVFa3WnDmZKix88IzXbbmzHizPa6VhPJYsua+1m9eoUNyHaF3E0PqIofURQ+sjhtZHDK0v2mPYsWMHn5/Fhnvnx48fV2ZmphYvXqzu3bt7fWaz2VrUGautNSyVQQrViAhPc+ZknjEiomFNidzcFYqJiVFdXZ0mTBgT8PeZmQtkGPXnNjv7Hq/vz549R5JNJSX1IyHuvTdXTue3PtuWm7tCVVX/ksNRrJqaU+rRI07JyYP0/vv7NGLESC1dulyvvfaa18iHzMxf6/778/Xqq696ve+57/qkxEqv0Rv1n/s/Nn/ntrFz1nAUQ1NimJ2dp5qaUyoqWm0mVez2DGVn58np/DaobbRlhmF4xdYzHnZ7ho4ePd4mky3RnnluD4ih9RFD6yOG1kcMrY8YWl+0x7BVakRI0qlTp5SZmampU6dq0qRJkqRevXqpqqpKklRVVaWePXtKkhITE1VRUWH+tqKiQomJieFsnqUEu7RnQUG+cnKyVFdXJ6fTaS6/uWrVcnPqRHLyIFVUVHv9vq6uztyP5/5KStYqPX3m/03TkEpK1qqkpFi/+tVM5eWtNDvr9977fR2Gw4ePKj29fttLlizSffctM/f14IOrVF6+X+npGerevYcmTRr7f69n6vDho+aSoRMnXmau1uE+1vopIoa5bfdKHZ5TSNLTZ/o8N74G/6xatdzjnFWb56ygIP+MuhgNY+Lvtb/VOlq7dkQo9h+KpVEBAAAAtD9hGxFR3zm9V+edd55mzJhhvp+WlqaNGzfKbrdr48aNGj9+vPn+H/7wB02ZMkXvv/++evTo4bc+RHvja2lPSV5Le7qLNpaV7dGQIUPNOgvukQW9e/cxV6KIi4tXcvIgffDB+1qyZJFyc1doyZJFiouLl8vl1P79H5g1ISSppKTYqz1uBQX5Ki1dryFDUrV//weaOPEyjRjxUw0ZkqoPPnhfF1/8E51//gVex1NW9o6GDk1Vefl+9ezZS4ZhaMqUiSov369evXqpurra57EuWLDIqy15eSv0wAMrlJw8SO++W6YHHlhhHluPHnF+6zMUFORr69bNKi/fr7KyPTKMOnXu3PmMc+Y+x56/C2YFkoajRHJystSjR5yOHXO1Wu2IUNaucMfGszYHSQgAAAAA/oQtEfHuu++qtLRUP/nJT3TNNddIku6++27Z7XbNnTtX69atU79+/fTII49IksaOHaudO3dq4sSJ+v/bu/u4qOq8f/yvMxhgKijIjbu5PbbaruuSoRRwXRUruTURtXKvx+61v90eJUPyuFZ3t5TEAA0Qjayu9NpVgWprt+9e195lJaIm1qp5k4AGg3VVW97tChjIjDeAMvP5/UHnOIeZ4XaYmTO8no9Hj5xzzpz5zHlzd97z+bzfo0ePRnFx8XANTbPkNpg9uzf0fGy1CpSXb0NtbbXdORYteliVtJBnSJSWbsXhw4eUx0ZjPWJiYmG1CqUwpS05EVBYuAF79uzCxYvNCA8PR2dnBxoajDAa6/H445l4++2/oKWlBW1tx1TPP3GiBp2dHQgLC8fFi80oL98O4GaByszMf0dBwQa79wbAbrlIYmI8Zs2KVxIHMTFxkKTurhuzZsXbzSaxbVFqew0cXS8AuHixGSZTm9IpxDbh030NNtqdH4Bqm+0xtkU4HT13OG/i+zP2gby+s2QLkxFEREREROSM27pmDIeR1jXDGdubx5KSYphMbapOF7YMhiwUFqrrH9h+mn3zuGXYseMvuHjxIqKi9GhoMCr/DwsLw8WLF1XH9+xwYSsgIBCdnR3K44kTw/D11xcdHiuPsajI/kZW3dFjGQDYvc+MjGVYv17d1UPmaMlAb9dANmVKFKqqDkGn06GoKA8BAbdi5crVfZ6/t5kHtskAZ2MbLkPtvuLoPD2TLd68PMPX1+KNBIyh9jGG2scYah9jqH2Mofb5egw93jVjuDARob7ZBbpnCzhLQmRkLEN5+Tbo9dHK8gyr1YrIyPEOjn1CmaXQk8GwDEII1f7GxjaH5wGAadNiceJETb/fU0bGMowfP97hMoGe7zciIthubIWFG5Gfn4Nx44LwwgvPKfuamkwOb4ydXQNben00fvCD2Sgv3/ZNEkRCcHAwNm26WQPC0fmddeAQQqjG7mxsw8VVr6/FFqW+/gN/JGAMtY8x1D7GUPsYQ+1jDLXP12PYWyLCb926devcNxTXsloFOjpueHoY/RYYeItLxyuEwO7dFUoxxblzE7FiRRauXXP8xTxtWixu3LgOo7EeZrMJDzyQiKSkOUrxUFu1tTWYMiUKkyd/BxcuXFC2d8+o2Ij339+vWsqwe3eFw/MAQGPjBYfbnamtrcaUKXokJCQBUNejmDlzNhISkgF0J116Lqeora3G7t0VqKyswGeffYr29pvdKbqvUZJdO1Nn18BWc3Mzamur8fOfL4fFIlBW1n3NbZ/n6Pw9b+5tb9Zraqp7fe5wceXrz549R/U8SZIwd24S4uPvc+mYXcnV34fkfoyh9jGG2scYah9jqH2Mofb5egzHjAlwuo+JCDdy9ReafNMnT/PftGmj0yREWFg4Dh06gIULH0ZMTBzKyrZh06aNaG5uRmhoqOqGXXbx4kVMmBCiWoYRExOL/furUF6+DZmZWaio2IfduytgNNYjKioa4eHdNR8AYOrUGFy82DyojgyxsdNRXf0Rdu+uUG50S0qKkZ+fg8bGC1i/fh0qKt4FAEybFoOJEyequrEAQHt7+zfFOD9TrpHtDbe8fKOysgJ6fTQWLnwItbXdMzdCQkLxr/86xS6J8tFHH6G2ttqmlkUWdu2qcnh+R3ouZxjIc11hOF7fUbLFm/n6D/yRgDHUPsZQ+xhD7WMMtY8x1D5fj2FviYhhK1ZJ7uGoa4Fcy0EWFhaOjz/+FCkp92P37go8+GCa6hzf+c7taG/vQGdnBywWi2rfqVMNqsfl5dsRFhaOmJg4ZTp+aup8AMC8eWn48MOD3yxjmIWammq788n8/Pyc7tProxEU1N1ZQn5fBQUblIQHANxyi79yfFzc92G1Wh3WqJCXoPTsMCJfO7kTSUHBBmzatFGZNZKaOh+rVuUoLU97Sk2dj1mz4p12MHGmP91PhpOnX5+IiIiIiIg1ItzI2RogZ3UE+vO4P8UWgZvFJENDQ9HS0mK3X54h4Ex3XQigvHyb8rio6DllHFarFTqdTvn3889vgNlshtVqxSuvqGtNhISEoLW1FVFRegQEBCizEGQZGU9g/foSALB7b70VxXTE9obbWUeIntdTfiy/dkbGMkiSpGoZalv009F5+tJXjIebp1/fk3x9Ld5IwBhqH2OofYyh9jGG2scYap+vx7C3GhE6p3vILUpKipGXt1pJAsiJhZKS4j7395xm39jYhrCwcIevI7eodJSEkM/b80bUz88P9947DQbDMpSVbVOSEDExcaokhBBC6cIhj3nPnl0oK9uKY8cO271Wa2sr9PpovPfeAcTGTlftMxiWobx8u9ISUv60XrZv30EnV7I7SdHz/6WlW1XXz5GeNR10Op0yc8BguJmEMBiy8POfL/9mactW5OfnqM47kBv5vhJNw01ryymIiIiIiMh3MBHhQUIIZX2+fLMsJxbMZhOsVmuv+wHYTbNftOhhp683Y8ZMREZO6nU8tiwWC6ZPnwGr1araXllZpWpRKSdG8vJWw2KxYO/eSmX2haPZC3p9NFJT58PPz09VMLGbBINhGYKCujs6yAkJWVLSHKfjv+UWfxgMy7Bv30HExMTB3z/A7lxygqc/srPXoKjoOQQHd1/joqKNeOGFF1FZWYXMzCyXLGXoKxFFRERERETka5iI8CB5fX5mZhZKS7ciIiJYmd1QWLhRqW3gbL8kScjOXoOCgg3KDXHPLhK2XnmlFGlp6U73BwQE2m0rL9+GV14pVW3LzV0Nq9WqSqTI3TuSk+9zugTk8cczAXTPzjCZ2pCbuxq1tdXIyFiGpiYTMjOzvln+IGHlytV2sz3kZRl6fTQyMpYp5zUYspCR8cQ3LUK7r0NsbNw310LCqlU5qgTOQGYcyNdYvt7yf4WFG5GdvcbuXAM5d1+JKA2vmiIiIiIiInKKNSLcqLcaERERwcrjpiaT3VR9Z/tLSophNptQULAB+fk5KC3ditGjR2Ps2LGqbhey0aNHO+yQ0bNGxIQJIbh0qVV5HBISAqPxCyxYkIza2hplVsPKlauV5ENf5ESCn58fVqx4EkePHsbly911JIKCgrFjxy4kJc3BuHFB2LFj1zd1JkxKEqCkpBi7d1cgNXU+JEmCydQGQEJwcLCSbAgKCkZ29hrlWNtx6fXRmDcvDdnZa/ocqzO2MZSvvW0NCtsx9IejGh+2iSZyPV9fizcSMIbaxxhqH2OofYyh9jGG2ufrMeytRgS7ZniYfCNqKy9vtd3NraP9AJRP1AFg7NhxCAzsTjS0t7djypQoVdcLf39/VRJCp9Mpyy5skxBhYTdbcMpaW1tx5523QYju443GelitVuzZs6vfxSONxnoEBgaio6MDe/dWYtaseJSVddedCAkJRW7u0zAa62EwLENu7moEBwerbsizs9dg5crVSoFIeczyfttrZjab7MZlNNZj1qz4QRVm7Pkc22Uz8mvbzuDo72s46nrCJAQREREREfkyv3Xr1q3z9CAGy2oVmuq72rNPbM9ik7t2VSk3t2azCQ88kKjMcnC0PyEhCQkJScq2I0c+RFdXl3L+njMiLBYLJkwIQXBwEK5evepw6v+ECSFoafm6x7YJ6OjoQFfXDbvzNzc3IzQ01OEsC0fi4r6P9vZrOHPmjGoZSXt7O2pra2AwLAPQXRxSr49GQkKSXTFJ23872idJEh54IBG7d1eguflmQkWvj8Zrr72p6nTRH/LsirlzkzB6tD/a268jPz8HQUHBSkHMTZs2oqamesCzGeSvAdtaGWazCXPnJjEZMUx8vV/zSMAYah9jqH2MofYxhtrHGGqfr8dwzJgAp/tYI8KD5M4Mtjevck2IoKBg6HS6Xvfb1iuwtXTpE05f89KlVjQ1NfW6337bpV7fh7NOHI5cuXK51+PLyrahrGzrkJYnCCGQn5/jcEaE3OmivyuS+qrjUFCwQXX8YJIQcqJJrpPRn04f3mooNTOIyHP4vUtERETuxKUZHiYXPLT9NL/ncoTe9jtauvHOO3914zu4aerUGJw585Vd4kJu1bl27RqUlm5Vlmf0ZigdKSRJQl3dx3YFM0NCQlFX9zEA9LuWg22ip7R0q7KEIjMzS6nLYct2WU1/xuko0QQM7f17iitqZhCR+/F7l4iIiNyNMyK8QM8bzv48lj/Vz83t/kTdYFiGCxcuYcqUKIdFKt3h5Mlah7MnumcirMHatUUIDQ11mITo2bFj9+4KpTMHoP50rq9P7qxWqzLzwrbDRmtrC65cuYxnnnl6QJ0pHM06KSjY4JLZDLYdOWxfazB//HvyE02tdwDhp8E0Umn9e5eIiIi0iV0z3MhVVVHlT6/GjQvC++9XARA4e/Ysrl27hvb2a8oMCm8I7ZQpUQgMDIS/fwCio+9BXd3H+OKLz1QzFfz8/GCxWJTHt956K65du4aMjGWQpO6uGGazCUFB3Z1D+vPJnaOuGbYGsvRDCIEHH0xU1bQwGJahpuY4AAmVlVUe/xTRGz7R1EoHkJ7fh95w7WhgfL3CtLt54nuXMdQ+xlD7GEPtYwy1z9dj2FvXDM6I0BjbT6/27NmF2tpqnD79Fb7++iKuXbvqcAaBJ0yZEgWDYRlOnWqAEAInT9airGwbrl69gpaWFoSGhgIAJk4MUyUhAOC7370DAPD2239FWdlW7N5dgdLSrTCZ2mAy9e+Tu+zsNdi376DT8Q0kCZGb+7SShPj5z5fDYFiGsrJtqK2tQWxsnHLsUGYzDIW3fKLpaOaItyUhevKWa0fkSVr83iUiIiJtY9cMN3JFVVRJkjB3bnenjMrKCgDod8eKoQoJCcUXX5yHydSGEydqej324sWLiImJRWzsdHz00TGYzSYAULpYyGP+0Y9+gjNnzqjeg9w69Nq1q8pz9Ppo6PX3IC/vWeXGsbdOFXLBSttuFLb625lCkiTU1BxHVJQesbHTsW3br5WkRExMHF599XdOu3q4i+3XxFA6eAyVVjqA2H4fesu1o4Hx9QrT7uaJ713GUPsYQ+1jDLWPMdQ+X49hb10zuDTDjVw59UYIgYiIYJecq38kAAIxMbHo7OxEQ4PR6ZFRUXq7/Xp9tN0yiccfN+DYsSNoaDAiLCwc9fWfITd3NcrLtzk8r3xzCED13puaTHZJCPlTbdt2oABUjwe6PKOv13X0nJ7jGs4b255fE32Nz9WvbVszo7Bwo91jb7mpd/R96MlrRwPn69MY3clT37uMofYxhtrHGGofY6h9vh7D3pZmcEaEGwUEjEJnZ5fyeLA3po4+vXIHf39/dHVZ8NVXX/Z6nBAC//EfP1PVVLjjjrtw/vw51XEnTtQqsx/+4z9+iurqj/DBB1XKNluhoaH405/ehiRJqqUSgP0nd5Ikobr6OPT6aBQVPffNjIZoxMbGITh4vDKrIigoGPHx9/X7/Q/kE0O5RoW8X45ZdfVxzJ49p9+v2V+eno1ge83lGxd5psFAr/Nw65l59vS1o4Hz9U8P3MlT37uMofYxhtrHGGofY6h9vh7D3mZEMBHhJiUlxaisrMB9980d0o2p7adXYWHhyvIFd7BYLLjjjrvQ1NTo9Jjx4yegtbUFX331d1V3jAsX/ul0vf3SpU+gqGgj1q17Bg0NRoSEhNgtN2lvb8eePbvw2Wf/p8yYMBiyEBsbh7KybXY3jrNnz1Eez549BwkJSUhISEZ8/H3KH9r9/QPb9povX74C7767V5nK7+iGVQih1LWQ98vP1+ujXX6D2/MTzV27qnod33CxveYABnyd3cX2B763XDsaGF//pe1unvjeZQy1jzHUPsZQ+xhD7fP1GPaWiBjlxnGMWLYF8To7b9hNfR3IzAhJkhAUFAyDIQvHjx/DxYvNmDgxDF9/7Z6WnXV1J53uGzVqFL71rW/h2rWrqjaegYGj0dHhvI5Fbe1xSJIEf/8AhIWFO5wRERISAqOxXlneYTBkoaioe5mGfE16a3sq/1u+1j0f90Y+f2ZmFjZtegEmU7uyRMTZ68r7S0u3KpXoh2uas+345PP3Nr7h1FfrWW/jTdeOyJO09r1LRERE2sYaEW4ihEBRUR62bNmsbBvMjalt606z2Qyr1YpXXtk+HEMeMH//AFy/3mnXOjQkJBQmU5tddwxbU6ZE4dSpBixd+oTq/Vy4cAn5+TkoK1PXjWhsbINOp1Nepz+JhaG2aRRCYMKEMcrXXF9JDHfXHXB3TQqtclYjgtdOO3x9PeVIwBhqH2OofYyh9jGG2ufrMWT7Ti8gSRI2bXpBtW2gSYierTvLyrbinXfecvVQB+369U4A9q1DW1tbYLFYMGFCiNPnnjrVgIyMJ3DkyCHV9vz8HFRXH7c7PilpDhYtehB5eauVbXJioaSk2O54V7RpHMgnhvL5bcmvO1x86RPNntdpuPOlvnTtiIiIiIi8HWtEuIkQArm5q/HRR8eUbQNdg+6odac7a0QMVW/LMwCgs7MTp041ICpKj0WLHkZtbTVqa6vR2HgBQHc3jrq6/8Nvf1uOM2dOo739Gg4dOtCvOgyuatPYn3VcrDswNMNd6NPX1+KNBIyh9jGG2scYah9jqH2Mofb5egx7qxHBGRFuIN9IbdmyGZmZWWhqMiEzM0v16Xx/2a5hH27uvlluaDAiKkqPqqpDWL/+OdW+kJAQNDQYMWnSBLS0tCA0NBSPProUBkP3dYyICO6z3Zyja+fOmg2ZmVmsO9AHV8xcISL3c/csJiIiItI2Fqt0A/nGdPnyFcjNLRxSQTxHU/6Hy3D9ITl+/AR8+9vfRkOD0W7fzJnxAGD3HltbW1WPFy9eAp1OB0A9xt4SC86WSwxHMiI7e42qzoAccyYheufuQp9ENHRDrb9DREREIw+XZrjJ7NlzsHBhGjo7uwAMrj2a7afDoaGhaG9vd9jq0lvYFpO01dHRAbPZBIvFYndjefbsGezc+TZ27nwHmZlZqKjYh8rKCrtOGidO1KCt7RL27KlUbXe29MFVyyUGMn2KdQcGR/7e2LTp5uyVXbuqXHb9fH0K3EjAGHqPwbYrZgy1jzHUPsZQ+xhD7fP1GPa2NIOJCDcaPdpfNV75k6P+VuuXJAnV1ceh10djxoyZuHzZjDNnzqj2e5OeSYjQ0Ilob++uCmvbQSMwMBC/+MVTMJtNOHPmNLq6LPjJT36KoqLu5RlvvPGqw5aeFy92tyw1GLJQWdl7YsH22smf2sk1I4KCgvudEPL1HxbeQE4a1dRUK9tcWVuDMdQ+xtB7DLb+DmOofYyh9jGG2scYap+vx7C3RATbd7pRz/Ys/Z3O2jM58dxz63H5shkVFe/iH/8479b3MFCRkZOQnr4IQgDl5dv6PF6vj8a8eWlYtSoHAJRP1qKi9A6XchgMWSgq6v904IEkfhwda9u+k1yv58yVwsKNdo+Hmozw9TZJIwFj6H0G2q6YMdQ+xlD7GEPtYwy1z9dj2Fv7TtaI8BDbonwAVDdcen00Vq5cDZ1OB6vVivz8HOXmWgiBy5fNKC3dCj8/Pw+/C+ciIyehsfEC0tMXKWv+jx79EEZjfa/Pe++9A6r3FRQUDINhGYSAw0SEbY2I/tRh6O9yCWdJovDwiVixYlWv74EGlvCx5azQJzDweipE5B7urL9DREREvoFLM9zIduqNs+msoaGhOHPmNMxmEx54IBFJSXNQWVmhWmt7//0J2LNnF5qaGj38jm7eyPf8Y/PHP/7/EBMTh/Hjx2P27DnIz89R3kdzs/0yC1n3+2pS2jTOmhWP/furUF6+TamLIZ8jNDQUhw4dVE3bd8Ufvb2teb733qm47765/OO6F0Ntvzl79hzVMozB1FPpja9PgRsJGEPvMdj6O4yh9jGG2scYah9jqH2+HkO27/RSjtpJtrS0QK+PRmnpVkRGjofRWA+9PhoFBRsgSRJKSoqxdu0apKbO99Co1YQQCAgIgBACo0ePxlNPPY3MzCyUlW0DILBqVY7yKbfBsAypqfOh10erzhEYGIhf/WoV9PpoGI312L27Qqkh0f3cIOj10cq1ee+9A6rH48YFKX/oumKlkW27zZ6tQTdteoFJiF64qv0mC30SaQPbFRMREdFgsEaEG/VcA2R7kyaTb8ZtNTa2KX/M2S7f6GuZg7stXZqJ9etLIEkS5s1LgCRJqKysUj4Vz819GjU11ait7S5kNmbMWLzxxqtoaWlRzqHXRyMl5UFcuXJZVetBXqJie61iYuIQExOrvKarW8Y5WvPMGhF9c/R17U3tN319Ld5IwBh6n4Eux2IMtY8x1D7GUPsYQ+3z9Rj2ViNi2GZE5OTkYObMmViwYIGyra2tDY899hhSUlLw2GOPwWQyAej+g6WoqAjJyclIT09HQ0PDcA3La/ScztrUZEJmZpbD5ML8+UnIzX0aAFBQsMHtSYgJE0L6PGbKlCgUFT2H/PwcPPfcety4cR21tdXIy1sNq9WKvLzV38ySADIynkBh4Ubk5OShvv5z1Xnee+8Arly5bPcJuk6ns5s9Ehsbh/Ly7UP65N0ZZ2ueNZy3cxtHM328JQlBRMODs5iIiIhoIIYtEfHwww+jvLxcta20tBQzZ87E3r17MXPmTJSWlgIADhw4gNOnT2Pv3r34K6zCAAAgAElEQVQoLCyEhstW9Mr2JtbRdNZnny1GaGio6jl6fTRqa6tRVrYNublPQ5IkfPnll6pj/vVf/21Yx33pUmufx5w61YDo6LtRWroVe/dWKktK5CUmN9cP74Mk6fD88xsghMC6dc+ozjNp0gSHHRIcJQYACQbDMrvlE0O96XWWJCot3YqVK59iMqIPTOIQEREREVFvhi0RMX36dAQHB6u2VVVVYfHixQCAxYsXY9++fartkiRh6tSpMJvNvRY01KKSkmLVTay8ll6ubyCEwNq1a5S6B42NbcoMCb0+GjExcSgr24aIiGBcu3ZVde5PP/3EE2/JztdfXwQAGI31yMzMwnvvHVDtLyjYgPz8HJSVbYXJZEJu7tPKzX5jY5vdsT2TED0TA2VlWwGoEw6u+OS9tzXPwcG+t+a5Z4JgKAmD3pI4TEYQERERERHg5vadLS0tCA8PBwCEhYUptQGampoQGRmpHBcZGYmmpiblWK2zLeDX2XlD1aozMzNLWUsr3/wWFGxQLUUYNy4IAFBbW+3JtzEgctLBVmTkeAA36wU8//wG5f32PDY/P0dJAjhLDAgB1NQcVz3PVS3j5Faptp0bCgs3+lyNCGdtSgdbZ4PtN4mIiIiIqC9uTUTYckWrRT8/CePH3+qiEQ2vLVs2IzDwFmzevFkp4rd8+QpVF4bi4iK7Al9btmyGEAIzZnzfI+N2JCYmBrW1tb0ek5r6AD7++CTuvXcq0tLSUFy8Xtm3efPL0Ol0KC4ugtVqxapVK1FauhXLl6/A889vwqpVK7Fly2YEBNyiXJ+e16a7W8co1NZWK9dx5cqn7J7nan5+Os18zfVFCIHOzmsoLd2qXLOVK59SYhEcPHpQ19DZ17G3JCF8KYYjFWOofYyh9jGG2scYah9jqH0jOYZuTUSEhoaiubkZ4eHhaG5uRkhIdxHEiIgINDY2Ksc1NjYiIiKiz/NZLEJTn04///wL2Lx5s/I4N7cQJlN7r8+RP6H++OOTXtMpo6Pjeq/7Q0JC8PHHJxEaGoqPPz6ptOKUrVjxC9WshYCAW5GZmYXc3EKYzR3IzS1EZ+cNBATc2uv1CQwcozzPZGrv9/OGwtcq28rXbMuWzdiypftr0/aa+iJfi+FIxBhqH2OofYyh9jGG2scYap+vx9AjXTMcSUhIwI4dOwAAO3bsQGJiomq7EAInT57EuHHjfGZZhkwIgZUrn1Jt68+aedup7j1rLnhCSEgoTp0yOtwXFaXHlClRaG3tLm4p17uQa0bIdS961gvIzl6jSkzI0/n7Whow2OfRTcPR4cKVNSeIiIiIiMj3DFsi4sknn8SPfvQjfPXVV7jvvvvwpz/9CZmZmfjwww+RkpKCw4cPIzMzEwBw//33Y/LkyUhOTkZeXh7Wrl07XMPyCHlWw5YtmwdVwC87ew0KCjZg7VrP3GBLkoR77pkKgyELra0tGD16NAIDA+2O8/cPQFXVIdW2efPSlHoBct2LzMwsu3oBvbV+6+3G1htbxmnpRtzVHS5KSopVz5fPX1JSPOSxEhERERGRbxi2pRkvvviiw+2vv/663TZJknwu+WBLntWwfPkK5OYWDriAnxAC+fk5KC3dCoNhGX7/+9fR3u542rxOp4PVanXp+IUQqKs7ie9/fwZiYmLR0GBEZ2en3XEnTtQgKWmOapvZbFJ1wJDfe38TBq4upjjctDTenh0ubIuoAgOfGWFblFV+vqOirERERERENLJ5rFjlSJOdvQbBwaOVdfcDuSGXExkGQ/fNnLMkBIAhJyEiIyPR0tKCGzduKNv0+mj4+/ujvHy73fFRUdFoaKiHn58fLBYLGhqMMBiWoajoOac3tf29GdXaja3WxuvqDhe2zy8t3apcB9vzExERERERScKb54334cYNi6aKewy1GInVasWDDybixIkaAMC0abE4fforXLrU6vB4OTkwEPKMCkmScO+9U6HT+aG2thoGQxbKyrb2+tywsHBMnjwZlZX7XTYbwPZTe5knb2z7iqG3jbc/eiZIhpowEUIgIiJYedzUZPKq9+7rRYFGAsZQ+xhD7WMMtY8x1D7GUPt8PYZeU6yShkan0yExMRkxMbEwGJYhLm46Ll1qxZQpUQgMHK06dvz48QNOQgA3Z1QIIRAX931UVLwHvT4a7767o9fnGQzLUF//mZKEAFxTPHI4iik64qq6Du4aryu5ss6Gq2tOEBERERGR72EiQmOys9egsnI/ioqeg9FYD70+GjNnzkZHRzsmTgxTjmtra0NISOigXyckJOSbpQU5MBrr0dh4AQbDMly4cAlTpkTZHV9QsAE6nU65iZVvPId6A+6OG1tXFlgcyTfiPWtODLQoKxERERERjQxMRGiQfHMfHX0PjMZ61NRUIyNjmV3L09bWlkG/RmtrKyZNmoDy8m2YNq17BkZBwQZER9+NU6ca7I7/7ncnKTMw5BvS555brzpmoDei7rixta3rIJ9Tfk2z2TSg19DqjbgrZ4M4qjnhqEsKERERERGNXCxWqVGSJKGgYAPeeWcHTp6sxcmTtcP2WrGx01FYuBG5uavx9dcXle0ZGctw5MghNDQY0dHRgaSkOaiqOqR0+NDro7FqVQ50Ot2g6kW4upiis9dwVYFFd4zX1Vzd5SM7e42qxsRAu6QQEREREZHvY7FKN3JlMZKSkmK0tV3C0aOHYTTWK9tHjRqFrq4ul7yGIzExcTh37iwuXmxWtkVF6dHc3KzaptdHw2ist2sLOZgbfFcXU3T2Gv0psNifGLpjvK7QW/tOby+wORS+XhRoJGAMtY8x1D7GUPsYQ+1jDLXP12PIYpU+Rl5OUF6+3a5d51CSEGFh4QgICADQ3XHjySezoddHq46prKxCff1nqm1VVYfstu3bd1BZlhARETykm1tXFlN0xNV1HYZ7vK5iu3TCFXEiIiIiIiLqD79169at8/QgBstqFejouOHpYfRbYOAtLhmvJEm4//4E7N5d4bBew2B98cU5HDz4N1y82IwbN27gyJEP0dzcrDrGZDLh/ff3oba22mZbG95/v0q1zWw2oaBgAzZtutlBYteuKq+7ue05K2DXriqlZoTZbMLcuUmqMbsqht5CkiTMnZvk9XFyJV+L4UjEGGofY6h9jKH2MYbaxxhqn6/HcMyYAKf7mIhwI1d9oZWUFGP9+mfh56dDY+MFF4ysm9lsQnh4BD766JjdPoMhCzExcSgv34ba2moYDMtQWVkFk6kNZWXytixUVt68kd+9u0KVyHB0Y+9pkiShuvo49PpoZRbA3LlJMJtNCAoKRnz8farjfe2HhZyIqalRJ5G8LU6u5GsxHIkYQ+1jDLWPMdQ+xlD7GEPt8/UYMhHhJVzxhSaEQGVlBSoq3nFJEiIgIAAWiwVhYeE4ePBviI2NQ0dHOy5evFmUUq+Pxmuv/R5JSSnYv38fJk36Fl599XeQJAkJCUl22x54IBG7d1coNSL6mmXgabNnz1GNSU5G9ExCAL71w2Kgs0G0UvuiL74Uw5GKMdQ+xlD7GEPtYwy1jzHUPl+PYW+JCHbN0JjubhnFOHLkkKpIpSNywUhn/Pz8MG1aLK5cuYyUlAdx+fJl1NR8ZLfcw2isR3LyfUhNnY/KyiplHPL/e27T6XSYNy8Ns2bFa6Z7hFbqOrjSQLp8uLq7BhERERERjVxMRGiMfEM4dqzzCqSyvhIVFosFn332f2htbcHMmfEoLNyA+fMT7Y4LDQ2F0ViPWbPiAfTvpp1tHLWhP3GSi6PKrU17dtfQ6swIIiIiIiLyDCYiNMT2htDPz2/I5xs9ejQeffRxXLlyBWVlW1FWtlXZFxISgtbWVgBAS0sLQkNDMXbsOLsbzt6m64/EWQZa1FecbGdKlJZuVRIS7K5BRERERESDwfadGtK9LGMDoqL0sFgsDo/x9/e32zZlShSWLn3Cbnt7ezuuXLkCIdQtQKOi9EoSQtbS0oIrVy6rWlqWlBSr2lzK0/VLSooH/N7Iu9kmI2RMQhARERER0WAwEaExmzZtRECA46IfkiTh+vXrdttPnWpAdfVHiIqKtttXVrYV5eXbVdtmzoxHVJRetU2vj0ZBwQblse3sDDkZIU/XN5tNqoSF7b8dPSbvJ8fXlm0SioiIiIiIqL+YiNAQIQRMpjbU1tY43D9+/ATl3yEhoZg2LUZ5bDTWoaGhHgZDFhob26DXq5MS8naDIQvl5dvQ0GBU7Tca65GXl4Pc3KdRUlKsfEKemZmF0tKtiIgIVmoG2H5SzlkT2tezu0ZTk0mJO5MRREREREQ0UExEaI7zqfCXLrVi9OjRAIDW1hacOFGLxx83ICQkFBaLBXp9NAoLNyAvL8eukGV19UfIy8tBYeEGxMTEISwsTLVfr49GdfVHKCvbpsx46Gu6/kBmTdDAuWumibPuGpmZWV7bBYWIiIiIiLyXJDR8N3jjhgVtbdc8PYx+Gz/+1iGPt6SkGG1tl1TLKZYuzcT/+3+/Q3t7u93xTU0mWK1WrF27RikyKMvIeALr15dgzZpVeOWVUgCAwbAMQgjl/FFRetXsiJiYOFRWVqlaONqet+eMiP4coyWuiKEreKKdZm+FSbXEW2JIg8cYah9jqH2MofYxhtrHGGqfr8cwLMx5p0fOiNAA21zRqlU56DkrQqfT4e9//4fD5+blrYZOp7ObuSAnIbqf74eYmFjExMShrGybKskht+yUxcZOV8bUn+n6LHLoep6aacIuKERERERE5Aps3+nlbD/5BoDc3NUoL9+GmJg4VFS8p8x0OHLkQ9XzDIYsAAKlpVu/uTFV3zQePXoYQgjk5+egrGwrDIYsFBZuQGTkeJtzLENZ2bYeI7qZYHA0XR+Aarq+syKHTEYMHttpEhERERGRljER4cVsP/kWQqCo6DnU1BwHAMTExEKn06GgYAMOHz4Eo7EeMTFxiI2NAyApyYWMjCdQU1ON2tpqZGZmoaBgA5KS5sBorFeSDpmZWXj22WLk5+eoXv/w4UOqx3JiQr4Rzs5eo5qeL293tCxDvkm2XabBm+bBk6+17ZIXXk8iIiIiItICv3Xr1q3z9CAGy2oV6Oi44elh9Ftg4C0DGq8kSZg7Nwn79+9DRcU72LRpIy5c+CcyMp6AJOlQU3Mc8fH3oampCXp9NF555XdISEhGQkISzGYTgoODkZdXgAsX/vlNocqN0Ol0uHDhgmoGxc6d7yE5+T5UVlYgMzMLu3ZVwWRqw+7du1TjiY2djtjYOAQHj0d8/H3KGHuO2fbf1dXHldeW34/ZbEJQULByDi0ZaAyHi5zkqampVraZzSbMnZvEZEQfvCWGNHiMofYxhtrHGGofY6h9jKH2+XoMx4wJcLqPMyI0IDZ2Omprb95wSpIOZWXdswyEEHYzEwD1p+O2+61WK/burVSdPzn5PhiN9dDro/Hss+q2mnJxSnkmg8GQ9U2dir71HJttpw3eLA8eZ5oQEREREZGWMRGhCerigzdrOmxULYuw5eixXBNCTjrILTyNxnqEhoZiz54PsHbtGgQFBaOmphoxMbFKhwxH9R9644muDiNFf+tzEBEREREReSMmIryYfPNeVrbNQeHIgXdGsL2BffbZYkyaNEHZ19LSgtTUB1RJCoNhmeq5/f2k3ba2BQDVJ/byLA7eLA9NX/U5iIiIiIiIvBUTEV5MThx0JwTUN5i2tQEGIjt7DaxWq11hSgCqGRKOOjD09yaXXR3cg+00iYiIiIhIi3SeHgD1buXK1ZC7YGRmZqGxsQ0GQxZqa6uRl7f6m9ac/Scvz5BnJzQ2tjk8bqgJA9tkhKvOSURERERERNrHRIQXKynpbqkZHBystN7Mz89BUFAQMjOzBlUPwHZ5hnw+RwaT5LAlLytx5TmJiIiIiIhI+5iI8FK2dRbMZpOSNCgt3YrLl80oKNgw6KKP2dlrVOfT66MBQPX/0tKtg04c9Ozq0NRkQmZm1pDOSURERERERL6BNSK81GDrLPQsBOmsMKROp1NmRowbF4RZs+JtZlwEY9as+EF3YGBXByIiIiIiInJGEhr+ePrGDQva2q55ehj9Nn78rQMerxACERHByuOmJpPTG/nBtMyUExXO/j8U/U2KaMlgYkjehTHUPsZQ+xhD7WMMtY8x1D7GUPt8PYZhYeOc7uPSDC82kDoLtks55GPk5RFms8npcgjb9o+O/j8U7OpAREREREREPTER4aUGWmdBXv4gHxMREaw8l90qiIiIiIiIyFt4VSLiwIEDSE1NRXJyMkpLSz09HI9yVmeht24ZbJlJRERERERE3s5rilVaLBYUFBTgtddeQ0REBJYsWYKEhATcddddnh6ax2Rnr1HVVZATDb0VqnS0lIPJCCIiIiIiIvIWXjMjoq6uDrfffjsmT54Mf39/pKWloaqqytPD8rj+1llgy0wiIiIiIiLSAq+ZEdHU1ITIyEjlcUREBOrq6jw4Im1hy0wiIiIiIiLSAq9JRAyGn5+E8eNv9fQw+s3PTzes4y0uLrJrkblly2YmIVxouGNIw48x1D7GUPsYQ+1jDLWPMdQ+xlD7RnIMvSYRERERgcbGRuVxU1MTIiIien2OxSI01XfV1/vEjgSMofYxhtrHGGofY6h9jKH2MYbaxxhqn6/HMCxsnNN9XlMjIjo6GqdPn8a5c+dw/fp1VFRUICEhwdPDIiIiIiIiIiIX8poZEaNGjUJ+fj4yMjJgsVjwyCOP4Hvf+56nh0VERERERERELuQ1iQgAuP/++3H//fd7ehhERERERERENEy8ZmkGEREREREREfk+JiKIiIiIiIiIyG2YiCAiIiIiIiIit2EigoiIiIiIiIjchokIIiIiIiIiInIbJiKIiIiIiIiIyG0kIYTw9CCIiIiIiIiIaGTgjAgiIiIiIiIichsmIoiIiIiIiIjIbZiIICIiIiIiIiK3YSKCiIiIiIiIiNyGiQgiIiIiIiIichsmIoiIiIiIiIjIbZiIcJMDBw4gNTUVycnJKC0t9fRwRrScnBzMnDkTCxYsULa1tbXhscceQ0pKCh577DGYTCYAgBACRUVFSE5ORnp6OhoaGpTnvPXWW0hJSUFKSgreeustZbvRaER6ejqSk5NRVFQEdsh1vQsXLuCnP/0p5s+fj7S0NLz++usAGEct6ezsxJIlS7Bw4UKkpaVh8+bNAIBz587hhz/8IZKTk/HLX/4S169fBwBcv34dv/zlL5GcnIwf/vCHOH/+vHKu7du3Izk5GampqTh48KCynT933cNisWDx4sV44oknADCGWpOQkID09HQsWrQIDz/8MAD+LNUas9mMFStWYN68eXjwwQdx4sQJxlBDvvzySyxatEj5LyYmBr/97W8ZQ4357W9/i7S0NCxYsABPPvkkOjs7+fuwL4KGXVdXl0hMTBRnz54VnZ2dIj09XXz++eeeHtaI9dFHHwmj0SjS0tKUbc8995zYvn27EEKI7du3i5KSEiGEEB988IFYunSpsFqt4sSJE2LJkiVCCCEuXbokEhISxKVLl0RbW5tISEgQbW1tQgghHnnkEXHixAlhtVrF0qVLxQcffODmd+j7mpqahNFoFEIIcfnyZZGSkiI+//xzxlFDrFaruHLlihBCiOvXr4slS5aIEydOiBUrVoidO3cKIYTIy8sTb775phBCiN///vciLy9PCCHEzp07xS9+8QshhBCff/65SE9PF52dneLs2bMiMTFRdHV18eeuG7366qviySefFJmZmUIIwRhqzNy5c0VLS4tqG3+Wakt2drb44x//KIQQorOzU5hMJsZQo7q6usSsWbPE+fPnGUMNaWxsFHPnzhXt7e1CiO7fg3/5y1/4+7APnBHhBnV1dbj99tsxefJk+Pv7Iy0tDVVVVZ4e1og1ffp0BAcHq7ZVVVVh8eLFAIDFixdj3759qu2SJGHq1Kkwm81obm7GoUOHMHv2bIwfPx7BwcGYPXs2Dh48iObmZly5cgVTp06FJElYvHgxYz0MwsPDERUVBQAYO3Ys7rjjDjQ1NTGOGiJJEsaMGQMA6OrqQldXFyRJwtGjR5GamgoAeOihh5Trvn//fjz00EMAgNTUVBw5cgRCCFRVVSEtLQ3+/v6YPHkybr/9dtTV1fHnrps0Njbigw8+wJIlSwB0f1LHGGoff5Zqx+XLl3H8+HHle9Df3x9BQUGMoUYdOXIEkydPxre//W3GUGMsFgs6OjrQ1dWFjo4OhIWF8fdhH5iIcIOmpiZERkYqjyMiItDU1OTBEVFPLS0tCA8PBwCEhYWhpaUFgH3sIiMj0dTU5DSmzo6n4XP+/Hl88sknuPfeexlHjbFYLFi0aBFmzZqFWbNmYfLkyQgKCsKoUaMAqK97U1MTJk2aBAAYNWoUxo0bh0uXLvU7hvy5OzyKi4uxatUq6HTdf05cunSJMdSgpUuX4uGHH8b//u//AuDvRC05f/48QkJCkJOTg8WLF+OZZ57BtWvXGEONqqioUJYOM4baERERgccffxxz585FfHw8xo4di6ioKP4+7AMTEUQ9SJIESZI8PQzqh6tXr2LFihVYs2YNxo4dq9rHOHo/Pz8/vP322/jb3/6Guro6fPnll54eEg3A+++/j5CQEOj1ek8PhYbgD3/4A9566y2UlZXhzTffxPHjx1X7+bPUu3V1deHUqVP48Y9/jB07dmD06NF268cZQ224fv069u/fj3nz5tntYwy9m8lkQlVVFaqqqnDw4EG0t7er6juQY0xEuEFERAQaGxuVx01NTYiIiPDgiKin0NBQNDc3AwCam5sREhICwD52jY2NiIiIcBpTZ8eT6924cQMrVqxAeno6UlJSADCOWhUUFIQZM2bg5MmTMJvN6OrqAqC+7hEREbhw4QKA7j+8L1++jAkTJvQ7hvy563q1tbXYv38/EhIS8OSTT+Lo0aNYv349Y6gx8jUNDQ1FcnIy6urq+LNUQyIjIxEZGYl7770XADBv3jycOnWKMdSgAwcOICoqChMnTgTAv2m05PDhw7jtttsQEhKCW265BSkpKaitreXvwz4wEeEG0dHROH36NM6dO4fr16+joqICCQkJnh4W2UhISMCOHTsAADt27EBiYqJquxACJ0+exLhx4xAeHo74+HgcOnQIJpMJJpMJhw4dQnx8PMLDwzF27FicPHkSQgjVuch1hBB45plncMcdd+Cxxx5TtjOO2tHa2gqz2QwA6OjowOHDh3HnnXdixowZ2LNnD4Du6t/yz8qEhASlAviePXvwgx/8AJIkISEhARUVFbh+/TrOnTuH06dP45577uHPXTd46qmncODAAezfvx8vvvgifvCDH+CFF15gDDXk2rVruHLlivLvDz/8EN/73vf4s1RDwsLCEBkZqcwoO3LkCO68807GUIMqKiqQlpamPGYMteNb3/oWPv74Y7S3t0MIgSNHjuCuu+7i78O+uL8+5sj0wQcfiJSUFJGYmCh+85vfeHo4I9qvfvUrMXv2bDFlyhQxZ84c8cc//lG0traKn/3sZyI5OVk8+uij4tKlS0KI7sr+69atE4mJiWLBggWirq5OOc+f/vQnkZSUJJKSksSf//xnZXtdXZ1IS0sTiYmJ4tlnnxVWq9Xt79HXHT9+XNx9991iwYIFYuHChWLhwoXigw8+YBw15JNPPhGLFi0SCxYsEGlpaWLLli1CCCHOnj0rHnnkEZGUlCSWL18uOjs7hRBCdHR0iOXLl4ukpCTxyCOPiLNnzyrn+s1vfiMSExNFSkqKqhI4f+66z9GjR5WuGYyhdpw9e1akp6eL9PR0MX/+fOUa82eptpw6dUo89NBDYsGCBSIrK0u0tbUxhhpz9epV8f3vf1+YzWZlG2OoLS+//LJITU0VaWlpYuXKlUrnC/4+dE4Sgo1kiYiIiIiIiMg9uDSDiIiIiIiIiNyGiQgiIiIiIiIichsmIoiIiIiIiIjIbZiIICIiIiIiIiK3YSKCiIiIiIiIiNyGiQgiIqIRYN++ffiXf/kX/P3vfx/SeVavXo3du3cDAJ555hl88cUXrhieYtu2bU73JSQkoLW11aWvR0RERO7HRAQREdEIsHPnTsTGxqKiosJl51y/fj3uuusul50PALZv3+7S8xEREZH3GeXpARAREdHwunr1KmpqavDGG29g2bJlWLFiBQDg2LFj2Lx5M8aMGYMzZ85gxowZWLduHXQ6HaZNm4Yf/vCH+PDDDzFx4kS89NJLCAkJUZ33pz/9KbKzsxEdHY0DBw7gpZdegsViwYQJE/D666+jrq4O69evR2dnJwIDA1FcXIw77rgDf/3rX7F//360t7fj3LlzSEpKQnZ2NjZt2oSOjg4sWrQId911F1544QWH7+f8+fMwGAyIjY3FiRMnEBERgd/85jcIDAzEmTNnsHbtWrS2tsLPzw8vv/wyJk+ejJKSEhw8eBCSJCErKwvz58/HsWPHsGXLFowbNw6fffYZHnzwQdx9991444030NnZiV//+tf4zne+g9bWVqxduxb//Oc/AQBr1qxBbGzs8AaNiIjIh3FGBBERkY+rqqrCnDlz8N3vfhcTJkyA0WhU9tXV1SEvLw+7du3CuXPnsHfvXgDAtWvXoNfrUVFRgenTp+O///u/nZ6/tbUVeXl52Lx5M9555x28/PLLAIA77rgDb775Jnbs2IEVK1bgpZdeUp7zySef4L/+67/w7rvvorKyEhcuXMDKlSsRGBiIt99+22kSQnbmzBn85Cc/QUVFBcaNG4c9e/YAAFauXImf/OQneOedd/A///M/CAsLw969e/Hpp5/i7bffxmuvvYaSkhI0NzcDAD799FM8++yzqKysxNtvv43Tp0/jz3/+M5YsWYLf/e53ALpnfjz66KP4y1/+gi1btiA3N3cQUSAiIiIZZ0QQERH5uIqKCvzsZz8DAMyfPx8VFRXQ6/UAgHvuuQeTJ08GAKSlpaGmpgbz5s2DTqfD/PnzAQCLFi3Cz3/+c6fnP3nyJOLi4pTzjH9GEaQAAALuSURBVB8/HgBw+fJlPP300zhz5gwkScKNGzeU58ycORPjxo0DANx55534xz/+gUmTJvX7Pd122234t3/7NwBAVFQU/vGPf+DKlStoampCcnIyACAgIAAAUFNTg7S0NPj5+WHixImYPn066uvrMXbsWERHRyM8PBwA8J3vfAezZ88GANx99904duwYAODw4cOqWhhXrlzB1atXMWbMmH6Pl4iIiG5iIoKIiMiHtbW14ejRo/jss88gSRIsFgskSUJ2djYAQJIk1fE9H/e1vTcvv/wyZsyYgV//+tc4f/68kgwBAH9/f+Xffn5+sFgsAzp3z+d3dnYOeHw9z6PT6ZTHOp1OGZPVasUf//hHJbFBREREQ8OlGURERD5sz549WLRoEd5//33s378ff/vb33DbbbehuroaQPfSjHPnzsFqtaKyslKpfWC1WpXlDu+++26vNRGmTp2K6upqnDt3DkB38gPonhEREREBAHjrrbf6Nd5Ro0apZk4MxNixYxEZGYl9+/YBAK5fv4729nbExcWhsrISFosFra2tqK6uxj333NPv88bHxyvLNIDuZSVEREQ0eExEEBER+bCdO3ciKSlJtS0lJQU7d+4EAERHR6OwsBAPPvggvv3tbyvLGm699VbU1dVhwYIFOHr0KP7zP//T6WuEhISgoKAAy5cvx8KFC/GrX/0KAJCRkYEXX3wRixcvRldXV7/G++///u9YuHAhnnrqqcG8XZSUlOCNN95Aeno6fvSjH+Hrr79GcnIy7r77bixatAiPPvooVq1ahbCwsH6f85lnnoHRaER6ejrmz5+PP/zhD4MaGxEREXWThBDC04MgIiIi9zt27BheffVVhy0zp02bhhMnTnhgVEREROTrOCOCiIiIiIiIiNyGMyKIiIiIiIiIyG04I4KIiIiIiIiI3IaJCCIiIiIiIiJyGyYiiIiIiIiIiMhtmIggIiIiIiIiIrdhIoKIiIiIiIiI3IaJCCIiIiIiIiJym/8fKVjVdodO0v8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "SzMSvvT7VRZu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "7b20913a-d5ec-443a-bcbb-824d5e20951b"
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(loan_train['Loan_Status'], loan_train['LoanAmount'])\n",
        "plt.title(\"Loan Application Amount \")\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "4szirwLqVRZz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "a35ef96a-18e7-4170-ab44-60ae7346cc3c"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(loan_train.corr(), cmap='coolwarm', annot=True, fmt='.1f', linewidths=.1)\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbSHnGhqVRZ3",
        "colab_type": "text"
      },
      "source": [
        "In this heatmap, we can clearly seen the relation between two variables "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHz3QIhTVRZ4",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"4\"></a><br>\n",
        "# 4. Choose ML Model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tk_bw4tWVRZ5",
        "colab_type": "text"
      },
      "source": [
        "* In this step, We have a lots of Machine Learning Model from sklearn package, and we need to decide which model is give us the better performance. then we use that model in final stage and send to the production level."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AgeDZBSsVRZ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import ml model from sklearn pacakge\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-K9dR4WVRZ-",
        "colab_type": "text"
      },
      "source": [
        "First of all, we are use `LogisticRegression` from `sklearn.linear_model` package. Here is the little information about `LogisticRegression`.\n",
        "\n",
        "`Logistic Regression` is a **classification algorithm**. It is used to predict a binary outcome (`1 / 0`, `Yes / No`, and `True / False`) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables. You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as the dependent variable.\n",
        "\n",
        "![](https://www.analyticsvidhya.com/wp-content/uploads/2015/10/logit.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IUaq4U4VRZ_",
        "colab_type": "text"
      },
      "source": [
        "* Let's build the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ad9aPyoaVRaA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "logistic_model = LogisticRegression()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7tCwHCwVRaE",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"5\"></a><br>\n",
        "# 5. Traing the ML Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGgwprGsVRaF",
        "colab_type": "text"
      },
      "source": [
        "> ### **Before fitting the model, We need to decide how many feature are available for testing and training, then after complete this step. fitt the model** \n",
        "\n",
        "Currently, we are using `Credit_History', 'Education', 'Gender` features for training so let's create train and test variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8EUjGzsAVRaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_features = ['Credit_History', 'Education', 'Gender']\n",
        "\n",
        "x_train = loan_train[train_features].values\n",
        "y_train = loan_train['Loan_Status'].values\n",
        "\n",
        "x_test = loan_test[train_features].values\n"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tCRGBqYtVRaJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "7fd5b004-dc82-405b-dca1-0bd33888a5cf"
      },
      "source": [
        "logistic_model.fit(x_train, y_train)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zA-TcXqAVRad",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"6\"></a><br>\n",
        "# 6. Predict Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yF4zs4ecVRaf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict the model for testin data\n",
        "\n",
        "predicted = logistic_model.predict(x_test)\n"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "W8O6fI_EVRam",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbaf2454-e31d-4d0c-bd26-adb3f8cc3d1f"
      },
      "source": [
        "# check the coefficeints of the trained model\n",
        "print('Coefficient of model :', logistic_model.coef_)\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficient of model : [[ 3.316164   -0.3059193   0.09398266]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KqyONHzuVRar",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9ccf2ed9-7ff2-4a99-9d9f-8b2637daddd1"
      },
      "source": [
        "# check the intercept of the model\n",
        "print('Intercept of model',logistic_model.intercept_)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Intercept of model [-1.98307795]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "JK2wCKo6VRax",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "97842ae0-2c18-4b9e-f758-9e44a8950b8c"
      },
      "source": [
        "# Accuray Score on train dataset\n",
        "# accuracy_train = accuracy_score(x_test, predicted)\n",
        "score = logistic_model.score(x_train, y_train)\n",
        "print('accuracy_score overall :', score)\n",
        "print('accuracy_score percent :', round(score*100,2))\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy_score overall : 0.8094462540716613\n",
            "accuracy_score percent : 80.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "HvdhSP1nVRa2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "828b9a2a-c30d-4d1a-f01a-887dac159ec5"
      },
      "source": [
        "\n",
        "# predict the target on the test dataset\n",
        "predict_test = logistic_model.predict(x_test)\n",
        "print('Target on test data',predict_test) \n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Target on test data [1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0\n",
            " 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1\n",
            " 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mS6LCcsEVRa7",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"7\"></a><br>\n",
        "# 7. Deploy Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VczyeAJwVRa8",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "- Finally, we are done so far. The last step is to deploy our model in production map. So we need to export our model and bind with web application API. \n",
        "\n",
        "Using pickle we can export our model and store in to `logistic_model.pkl` file, so we can ealy access this file and calculate customize prediction using Web App API.\n",
        "\n",
        "\n",
        "#### A little bit information about pickle:\n",
        "\n",
        "`Pickle` is the standard way of serializing objects in Python. You can use the pickle operation to serialize your machine learning algorithms and save the serialized format to a file. Later you can load this file to deserialize your model and use it to make new predictions\n",
        "\n",
        "\n",
        ">>  Here is example of the Pickle export model\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "model.fit(X_train, Y_train)\n",
        "# save the model to disk\n",
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))\n",
        "\n",
        "# some time later...\n",
        "\n",
        "# load the model from disk\n",
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model.score(X_test, Y_test)\n",
        "print(result)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YLYwJcmsVRa8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle as pkl"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-F3nbNZYVRbA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save the model to disk\n",
        "filename = 'logistic_model.pkl'\n",
        "pkl.dump(logistic_model, open(filename, 'wb')) # wb means write as binary\n",
        "\n",
        "\n"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwkDDLfCVRbG",
        "colab_type": "text"
      },
      "source": [
        "### Now, You can check your current directory. You can see the file with named \"logistic_model.pkl\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUSC7U_uVRbH",
        "colab_type": "text"
      },
      "source": [
        "- To read model from file\n",
        "\n",
        "```\n",
        "# load the model from disk\n",
        "loaded_model = pkl.load(open(filename, 'rb')) # rb means read as binary\n",
        "result = loaded_model.score(X_test, Y_test)\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Pr5tzJHVRbI",
        "colab_type": "text"
      },
      "source": [
        "--- \n",
        "---\n",
        "\n",
        "<div class=\"text-center\">\n",
        "    <h1>That's it Guys,</h1>\n",
        "    <h1>🙏</h1>\n",
        "    \n",
        "        \n",
        "        I Hope you guys you like and enjoy it, and learn something interesting things from this notebook, \n",
        "        \n",
        "        Even I learn a lots of things while im creating this notebook\n",
        "    \n",
        "</div>\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "<img src=\"https://static.wixstatic.com/media/3592ed_5453a1ea302b4c4588413007ac4fcb93~mv2.gif\" align=\"center\" alt=\"Thank You\" style=\"min-height:20%; max-height:20%\" width=\"90%\" />\n",
        "\n"
      ]
    }
  ]
}