Assignment_Logistic_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rahul2661998/Churn-Analysis-Teleco-Customer-Dataset/blob/main/Assignment_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYsTFWXYjCxF"
      },
      "source": [
        "# <u><b> Objective </b></u>\n",
        "## <b>To predict the whether a customer will churn or not, based on the variables available in the Telco customer churn data. </b>\n",
        "\n",
        "\n",
        "### Logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level.\n",
        "\n",
        "### First, logistic regression does not require a linear relationship between the dependent and independent variables.  Second, the error terms (residuals) do not need to be normally distributed.  Third, homoscedasticity is not required.  Finally, the dependent variable in logistic regression is not measured on an interval or ratio scale.\n",
        "\n",
        "### However, some other assumptions still apply.\n",
        "\n",
        "### First, binary logistic regression requires the dependent variable to be binary and ordinal logistic regression requires the dependent variable to be ordinal.\n",
        "\n",
        "### Second, logistic regression requires the observations to be independent of each other.  In other words, the observations should not come from repeated measurements or matched data.\n",
        "\n",
        "### Third, logistic regression requires there to be little or no multicollinearity among the independent variables.  This means that the independent variables should not be too highly correlated with each other.\n",
        "\n",
        "### Fourth, logistic regression assumes linearity of independent variables and log odds.  although this analysis does not require the dependent and independent variables to be related linearly, it requires that the independent variables are linearly related to the log odds.\n",
        "\n",
        "\n",
        "### <b> In this assignment, you need to do the following : </b>\n",
        "\n",
        "* ### Remove correlated variables and run Logistic Regression\n",
        "* ### Also implement regularized logistic regression using the hyperparameter C in the sklearn implementation. Add details about how this hyperparameter affects the learning and performance of the model.\n",
        "* ### Evaluate your logistic regression models using metrics such as roc_auc, log_loss, precision, recall, accuracy and f-score. You already know these metrics from your assignments in Module 1. Explain your observations about these metrics results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ah_20SDKV7Ao"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import log_loss\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.linear_model import LogisticRegressionCV\n",
        "from sklearn.model_selection import cross_validate"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n35XDsC_iWQs",
        "outputId": "843b225a-49c2-4e1c-b8a3-0ca5778156b0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/Classification models/Copy of WA_Fn-UseC_-Telco-Customer-Churn.csv\")"
      ],
      "metadata": {
        "id": "1vH94wgQiIvY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "QU1p3hnuio1A",
        "outputId": "adc9ce8a-1e50-4f93-ba77-46d13393d0e1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08c59d15-4d2b-4ad1-a476-bcb4b78c7830\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08c59d15-4d2b-4ad1-a476-bcb4b78c7830')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08c59d15-4d2b-4ad1-a476-bcb4b78c7830 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08c59d15-4d2b-4ad1-a476-bcb4b78c7830');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O93ng0v8io4l",
        "outputId": "d9bcb194-621f-43f2-efd5-7a736b76e484"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "ImBMubV9io8D",
        "outputId": "ff1b2915-8c02-461e-f83f-9d218acd22d0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        customerID gender  SeniorCitizen Partner Dependents       tenure  \\\n",
              "count         7043   7043    7043.000000    7043       7043  7043.000000   \n",
              "unique        7043      2            NaN       2          2          NaN   \n",
              "top     7590-VHVEG   Male            NaN      No         No          NaN   \n",
              "freq             1   3555            NaN    3641       4933          NaN   \n",
              "mean           NaN    NaN       0.162147     NaN        NaN    32.371149   \n",
              "std            NaN    NaN       0.368612     NaN        NaN    24.559481   \n",
              "min            NaN    NaN       0.000000     NaN        NaN     0.000000   \n",
              "25%            NaN    NaN       0.000000     NaN        NaN     9.000000   \n",
              "50%            NaN    NaN       0.000000     NaN        NaN    29.000000   \n",
              "75%            NaN    NaN       0.000000     NaN        NaN    55.000000   \n",
              "max            NaN    NaN       1.000000     NaN        NaN    72.000000   \n",
              "\n",
              "       PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
              "count          7043          7043            7043           7043  ...   \n",
              "unique            2             3               3              3  ...   \n",
              "top             Yes            No     Fiber optic             No  ...   \n",
              "freq           6361          3390            3096           3498  ...   \n",
              "mean            NaN           NaN             NaN            NaN  ...   \n",
              "std             NaN           NaN             NaN            NaN  ...   \n",
              "min             NaN           NaN             NaN            NaN  ...   \n",
              "25%             NaN           NaN             NaN            NaN  ...   \n",
              "50%             NaN           NaN             NaN            NaN  ...   \n",
              "75%             NaN           NaN             NaN            NaN  ...   \n",
              "max             NaN           NaN             NaN            NaN  ...   \n",
              "\n",
              "       DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n",
              "count              7043        7043        7043            7043   \n",
              "unique                3           3           3               3   \n",
              "top                  No          No          No              No   \n",
              "freq               3095        3473        2810            2785   \n",
              "mean                NaN         NaN         NaN             NaN   \n",
              "std                 NaN         NaN         NaN             NaN   \n",
              "min                 NaN         NaN         NaN             NaN   \n",
              "25%                 NaN         NaN         NaN             NaN   \n",
              "50%                 NaN         NaN         NaN             NaN   \n",
              "75%                 NaN         NaN         NaN             NaN   \n",
              "max                 NaN         NaN         NaN             NaN   \n",
              "\n",
              "              Contract PaperlessBilling     PaymentMethod MonthlyCharges  \\\n",
              "count             7043             7043              7043    7043.000000   \n",
              "unique               3                2                 4            NaN   \n",
              "top     Month-to-month              Yes  Electronic check            NaN   \n",
              "freq              3875             4171              2365            NaN   \n",
              "mean               NaN              NaN               NaN      64.761692   \n",
              "std                NaN              NaN               NaN      30.090047   \n",
              "min                NaN              NaN               NaN      18.250000   \n",
              "25%                NaN              NaN               NaN      35.500000   \n",
              "50%                NaN              NaN               NaN      70.350000   \n",
              "75%                NaN              NaN               NaN      89.850000   \n",
              "max                NaN              NaN               NaN     118.750000   \n",
              "\n",
              "        TotalCharges Churn  \n",
              "count           7043  7043  \n",
              "unique          6531     2  \n",
              "top                     No  \n",
              "freq              11  5174  \n",
              "mean             NaN   NaN  \n",
              "std              NaN   NaN  \n",
              "min              NaN   NaN  \n",
              "25%              NaN   NaN  \n",
              "50%              NaN   NaN  \n",
              "75%              NaN   NaN  \n",
              "max              NaN   NaN  \n",
              "\n",
              "[11 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9017164a-8b88-4452-ab06-7179afe66b20\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>...</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043</td>\n",
              "      <td>7043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>7043</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6531</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>3555</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3641</td>\n",
              "      <td>4933</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6361</td>\n",
              "      <td>3390</td>\n",
              "      <td>3096</td>\n",
              "      <td>3498</td>\n",
              "      <td>...</td>\n",
              "      <td>3095</td>\n",
              "      <td>3473</td>\n",
              "      <td>2810</td>\n",
              "      <td>2785</td>\n",
              "      <td>3875</td>\n",
              "      <td>4171</td>\n",
              "      <td>2365</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>5174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.162147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.761692</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.368612</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.090047</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.350000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.850000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>118.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9017164a-8b88-4452-ab06-7179afe66b20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9017164a-8b88-4452-ab06-7179afe66b20 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9017164a-8b88-4452-ab06-7179afe66b20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting TotalCharges into numeric\n",
        "data['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors='coerce')\n",
        "print(data.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjrUmlxX2-da",
        "outputId": "e31eba0f-00fc-47c9-c942-aa2db7f43989"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "customerID           object\n",
            "gender               object\n",
            "SeniorCitizen         int64\n",
            "Partner              object\n",
            "Dependents           object\n",
            "tenure                int64\n",
            "PhoneService         object\n",
            "MultipleLines        object\n",
            "InternetService      object\n",
            "OnlineSecurity       object\n",
            "OnlineBackup         object\n",
            "DeviceProtection     object\n",
            "TechSupport          object\n",
            "StreamingTV          object\n",
            "StreamingMovies      object\n",
            "Contract             object\n",
            "PaperlessBilling     object\n",
            "PaymentMethod        object\n",
            "MonthlyCharges      float64\n",
            "TotalCharges        float64\n",
            "Churn                object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA**"
      ],
      "metadata": {
        "id": "R2ImQSVZl1WK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking continuous variables\n",
        "fig = plt.figure(figsize=(15,5))\n",
        "\n",
        "plt.subplot(1,3,1)\n",
        "plt.title(\"tenure\")\n",
        "sns.boxplot(data=data, y = 'tenure', x = 'Churn')\n",
        "\n",
        "plt.subplot(1,3,2)\n",
        "plt.title(\"MonthlyCharges\")\n",
        "sns.boxplot(data=data, y = 'MonthlyCharges', x = 'Churn')\n",
        "\n",
        "plt.subplot(1,3,3)\n",
        "plt.title(\"TotalCharges\")\n",
        "sns.boxplot(data=data, y = 'TotalCharges', x = 'Churn')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "XvmMSbY4io-Z",
        "outputId": "6675bacd-b6eb-4043-d6f9-4a08604b20f7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(20,15))\n",
        "\n",
        "plt.subplot(4,4,1)\n",
        "plt.title(\"gender\")\n",
        "sns.countplot(data=data, x = 'gender', hue='Churn')\n",
        "\n",
        "plt.subplot(4,4,2)\n",
        "plt.title(\"SeniorCitizen\")\n",
        "sns.countplot(data=data, x = 'SeniorCitizen', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,3)\n",
        "plt.title(\"Partner\")\n",
        "sns.countplot(data=data, x = 'Partner', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,4)\n",
        "plt.title(\"Dependents\")\n",
        "sns.countplot(data=data, x = 'Dependents', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,5)\n",
        "plt.title(\"PhoneService\")\n",
        "sns.countplot(data=data, x = 'PhoneService', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,6)\n",
        "plt.title(\"MultipleLines\")\n",
        "sns.countplot(data=data, x = 'MultipleLines', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,7)\n",
        "plt.title(\"InternetService\")\n",
        "sns.countplot(data=data, x = 'InternetService', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,8)\n",
        "plt.title(\"OnlineSecurity\")\n",
        "sns.countplot(data=data, x = 'OnlineSecurity', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,9)\n",
        "plt.title(\"OnlineBackup\")\n",
        "sns.countplot(data=data, x = 'OnlineBackup', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,10)\n",
        "plt.title(\"StreamingMovies\")\n",
        "sns.countplot(data=data, x = 'StreamingMovies', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,11)\n",
        "plt.title(\"DeviceProtection\")\n",
        "sns.countplot(data=data, x = 'DeviceProtection', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,12)\n",
        "plt.title(\"TechSupport\")\n",
        "sns.countplot(data=data, x = 'TechSupport', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,13)\n",
        "plt.title(\"StreamingTV\")\n",
        "sns.countplot(data=data, x = 'StreamingTV', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,14)\n",
        "plt.title(\"Contract\")\n",
        "sns.countplot(data=data, x = 'Contract', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,15)\n",
        "plt.title(\"PaperlessBilling\")\n",
        "sns.countplot(data=data, x = 'PaperlessBilling', hue = 'Churn')\n",
        "\n",
        "plt.subplot(4,4,16)\n",
        "plt.title(\"PaymentMethod\")\n",
        "sns.countplot(data=data, x = 'PaymentMethod', hue = 'Churn')\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "44qeNGeXl689",
        "outputId": "42b584d0-0400-4812-9e7e-65481e25dc2f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 16 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABZMAAAQwCAYAAACQdcAEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdfZxdZXnv/8+XZytYQGLEBAzatAo+oE6B1noOQuWparBHEWshIr+m/g5UaDkegZ6aCM2veESpqEXxEAEPBWPRmiKVRsQqPfKQKA1PekgRISkPkQCCFDRw/f7Y9+AmZDJDMjN7Zs/n/Xrt16x93fda614I1yyvude9UlVIkiRJkiRJkrQxW/R6AJIkSZIkSZKkic9isiRJkiRJkiRpWBaTJUmSJEmSJEnDspgsSZIkSZIkSRqWxWRJkiRJkiRJ0rAsJkuSJEmSJEmShmUxWXoWklSSX+v1OCRpIkry7iT/1KvjJ3lDkh+O1fklSZKk8ZLkPUmu7vU4pPVZTJYkaQpK8jtJ/k+Sh5KsTfIvSX5zc45ZVRdV1UGbOa6Dk3w7ycNJ1iT55yRv3dDx1/8DX1V9p6p+Y3POL0mTQZI7kvxHkkeS3Jvk/CTbb8JxvpXk/xmLMUrSRNWVQx9O8mC7J35fkr6tkTkxTqOpb/9DkSaSJFv1egySNCjJ84DLgE8COwMzgA8Dj/doPEmyRZK3A18CLgRmAtOBDwFv6cW4JGmCe0tVbQ+8FhgA/sdIdxzMu2M2Mrz/lTThvaWqdgBeDJwBfBA4r7dDkiYHi8nqC0lem+T77S+LX0ryxSR/2drenOSGrr84vqprvzuS/LckK9rsvC8m2a6r/QNJ7k7y70neu945t01yZpI724yQzyR5TmvbP8mqJB9Mcg/w+XH6RyFJI/HrAFV1cVU9UVX/UVX/VFUrAJK8N8mtSR5IckWSFw/u2GY1vC/JbS2vfjpJWtvTHsVL8ttJrm/59fokv93V9q0kC5P8C/Ao8FLg48DpVfW/quqhqnqyqv65qv5o/eMn+XY71L+2mXnvHMy9rf2dLT74eTzJt1rbSPL3SUnua78Djhmb/xkkafNV1WrgH4FXJrmsPdXxQNueOdhvA3n3C8AbgE+1PPmp1m/IPN/ah/sdcVyS24DbxukfgSRtsnbPuQR4JzA3yStGeK94apKftJrCuwePtzn3mUmen2RJkp8muY7O/TFd7S9LsjSdpwp/mOSIrrbzW77+Wjp1kWuTvLS1bei+eZf2e+LBdrzvpI9nZmt0+S+KJr0k2wBfAc6nM8PuYuBtre01wCLgj4HnA58FliTZtusQRwCHAHsArwLe0/Y9BPhvwJuA2cDvrnfqM+gUZPYGfo3OzL4PdbW/sI3nxcC8UbhUSRot/xd4IskFSQ5NstNgQ5I5wKnA7wPTgO/Qyavd3gz8Jp2ceQRw8PonSLIz8DXgbDr59+PA15I8v6vbUXTy4w7AdsBuwN+N5AKq6j+1zVdX1fZV9cX12r/Y4tsDLwJu77qOkeTvX23xY4FPd/8zkqSJJMluwGF08tzn6dx77g78B/Cp9bp359330Mnxx7d8eXxXvw3m+RH+jjgc2BfYc1QuUJLGQVVdB6yi80e2kdwr7tLic4FzkwwutbY595mfBh4DdgXe2z4AJHkusBT4W+AFwJHA3yTpzrVH0nnacCdgJbCwXduG7ptPatc7jc7TgKcCNdJ/XpraLCarH+wHbAWcXVW/qKovA9e1tnnAZ6vq2jb77gI6j3Hv17X/2VX171W1FvgHOkkfOjfOn6+qm6rqZ8CCwR3a7Ix5wJ9W1dqqehj4/+gk70FPAvOr6vGq+o/RvmhJ2lRV9VPgd+jcMH4OWNNmQUwH3gf8VVXdWlXr6OS2vbtnngFnVNWDVXUncBW/zJvdfg+4raq+UFXrqupi4Ac8fcmK86vq5nae57XY3aN5rW2Gxd8C36qqz44wf/8COK39TrkceARwLWZJE83fJ3kQuBr4Z+C/V9WlVfVoy20Lgf+83j5P5d2q+sVGjj1Unh/J74i/avnV+19Jk82/05kQNty9IsBftP+v/890JlAcsTn3mUm2BP4L8KGq+llV3QRc0LXfm4E7qurzLYd/H7gUeEdXn69U1XUtP1/Ehu/Ru8exK/DiNpbvVJXFZI2I61ipH7wIWL1e4rur/XwxnUdV/qSrbZu2z6B7urYf7Wp7EbC8q+3HXdvTgF8Blnc/9Qds2dVnTVU99iyuQ5LGTVXdyi+fxHgZ8L+Bv6aTNz+R5GNd3UNn9sRgHlw/b27opU8v4ul5k/Z9Rtf3u7q2728/dwV+NNLrGIGFdGbgvb99H0n+vr/dhA8a6holqZcOr6pvDH5J8itJPkvnibvBWW47JNmyqp5o3+9a/yBDGCrPj+R3xEjPIUkTzQw6dbLh7hUfaBPOBv2Yzr3v5txnTmvn7s6h3ffSLwb2bX9EHLQVnSWLBo3kHn3QR+lMmPunNtZzq+qMjfSXnuLMZPWDu4EZ3Wu50XlUGjqJeGFV7dj1+ZU2Q24kx92t6/vuXds/ofPo4F5dx/3V9jj1IP+qJ2lSqKof0Fkq6BV08uYfr5c3n1NV/+dZHvbf6dz0dtsdWN196q7tH7Zz/5dneZ4hJTkSeBfw9q4ZeCPJ35I0GZ1E5ymKfavqecDgY83d98jr358+2/vVkfyO8B5Y0qST5DfpFJP/nuHvFXdqy04M2p3Ove/m3GeuAdYxdA3iLuCf18u/21fV//vsrxaq6uGqOqmqXgK8FfizJAduyrE09VhMVj/4LvAEcHySrdpabvu0ts8B70uybzqem+T3kuwwguMuBt6TZM8kvwLMH2yoqifbsc9K8gKAJDOSPGPdUEmaaNrLO05KezFTW2/zXcA1wGeAU5Ls1dp+Nck7hj7akC4Hfj3JH7Tc/E4662detqHO7emSPwP+IskxSZ6XZIskv5Pk3CHOcS/wkiGu8TXAJ+nM3FvTdR7zt6R+tQOdIsaDbd36+cP0h43k0SGM1u8ISZoQ2j3nm4FLgP9dVf/KyO4VP5xkmyRvoLMExZc25z6zPUHyZWBBe9JkTzrrMQ+6jM699VFJtm6f30zy8hFe6tPyfZI3J/m1NinvITo1lSdHeCxNcRaTNelV1c/pvATkWOBB4A/pJNrHq2oZ8Ed0Xj7yAJ1F6N8zwuP+I51Hvr/Z9vvmel0+2OLXJPkp8A1cU1PS5PAwnZcjXZvkZ3SKyDcBJ1XVV4CPAJe03HYTcOizPUFV3U/nxvokOktY/HfgzVX1k43s83d03qT9XjqzO+4F/hL46hC7LAAuaG+hPmK9tjl0HvO+ur21+pEk/9jazN+S+tFfA8+hMzPuGuDrI9jnE8DbkzyQ5OzhOo/W7whJmgD+IcnDdGb8/jmdl0Uf09qGu1e8h0594d/prE38vvak30j23Zjj6SxNcQ+dpwY/P9jQ1l8+iM76y//e+nwE2HaEx17A0++bZ7exPUJngt7fVNVVIzyWpri4vrb6UZJrgc9U1eeH7SxJkiRJkjSMJPvTmcE8s9djkXrFmcnqC0n+c5IXtkep5wKvYmSzMSRJkiRJkiSNwFa9HoA0Sn6DzhrHzwVup/Oypbt7OyRJkiRJkiSpf7jMhSRJkiRJkiRpWC5zIUmSJEmSJEkaVt8uc7HLLrvUrFmzej0MSVPM8uXLf1JV03o9jl4y/0rqBfOv+VdS70z1HGz+ldQrvci/fVtMnjVrFsuWLev1MCRNMUl+3Osx9Jr5V1IvmH/Nv5J6ZyLk4CTbAd8GtqVT6/i7qpqf5HzgPwMPta7vqaobkgT4BHAY8GiLf68day7wP1r/v6yqCzZ2bvOvpF7pRf7t22KyJEmSJEmaMh4HDqiqR5JsDVyd5B9b2weq6u/W638oMLt99gXOAfZNsjMwHxgAClieZElVPTAuVyFJE5xrJkuSJEmSpEmtOh5pX7dun9rILnOAC9t+1wA7JtkVOBhYWlVrWwF5KXDIWI5dkiYTi8mSJEmSJGnSS7JlkhuA++gUhK9tTQuTrEhyVpJtW2wGcFfX7qtabKj4+ueal2RZkmVr1qwZ9WuRpInKZS4kjZtf/OIXrFq1iscee6zXQ9ls2223HTNnzmTrrbfu9VAkaVjmX0nqHXPw+KmqJ4C9k+wIfCXJK4BTgHuAbYBzgQ8Cp43Cuc5tx2NgYGBjM6Al9Yj5d2xYTJY0blatWsUOO+zArFmz6LzvYnKqKu6//35WrVrFHnvs0evhSNKwzL+S1Dvm4PFXVQ8muQo4pKrObOHHk3we+G/t+2pgt67dZrbYamD/9eLfGtMBSxoT5t+x4TIXksbNY489xvOf//xJncQBkvD85z+/L/66KWlqMP9KUu+Yg8dHkmltRjJJngO8CfhBWweZdP4HOBy4qe2yBDg6HfsBD1XV3cAVwEFJdkqyE3BQi0maZMy/Y8OZyZLG1WRP4oP65TokTR39krf65TokTS39krsm+HXsClyQZEs6E+cWV9VlSb6ZZBoQ4Abgfa3/5cBhwErgUeAYgKpam+R04PrW77SqWjuO1yFpFE3wvDViE+k6LCZLkiRJkqRJrapWAK/ZQPyAIfoXcNwQbYuARaM6QEnqExaTpVH0ug9c2OshjLnlHz16XM93zz33cOKJJ3L99dez4447Mn36dA4//HCWLFnCZZddNq5j0diaCv/9wPj/NyRtKvPv1GH+lSYW8+/UYf6VJhbz78i4ZrKkCauqeNvb3sb+++/Pv/3bv7F8+XL+6q/+invvvXezjrtu3bpRGqEk9SfzryT1hvlXknrD/DtyFpMlTVhXXXUVW2+9Ne973/ueir361a/mDW94A4888ghvf/vbednLXsa73/1uOk+pwaxZs/jJT34CwLJly9h///0BWLBgAUcddRSvf/3rOeqoo1iwYAHvfe972X///XnJS17C2WefPe7XJ0kTlflXknrD/CtJvWH+HTmLyZImrJtuuonXve51G2z7/ve/z1//9V9zyy23cPvtt/Mv//Ivwx7vlltu4Rvf+AYXX3wxAD/4wQ+44ooruO666/jwhz/ML37xi1EdvyRNVuZfSeoN868k9Yb5d+QsJkualPbZZx9mzpzJFltswd57780dd9wx7D5vfetbec5znvPU99/7vd9j2223ZZddduEFL3jBZj++IklTgflXknrD/CtJvWH+fTqLyZImrL322ovly5dvsG3bbbd9anvLLbd8ah2irbbaiieffBKAxx577Gn7PPe5zx3RMSRpqjP/SlJvmH8lqTfMvyO3Va8HMNH4NlVp4jjggAM49dRTOffcc5k3bx4AK1as4Dvf+c6Q+8yaNYvly5dz6KGHcumll47XUCWpr5h/Jak3zL+S1Bvm35Ebs5nJSXZLclWSW5LcnOSEFl+QZHWSG9rnsK59TkmyMskPkxzcFT+kxVYmOXmsxixpYknCV77yFb7xjW/w0pe+lL322otTTjmFF77whUPuM3/+fE444QQGBgbYcsstx3G0ktQ/zL+S1BvmX0nqDfPvyGXwDYSjfuBkV2DXqvpekh2A5cDhwBHAI1V15nr99wQuBvYBXgR8A/j11vx/gTcBq4DrgXdV1S0bO//AwEAtW7bsWY/bmcnaHFPh35/N+Xfn1ltv5eUvf/kojqa3NnQ9SZZX1UCPhjQhmH83zvyrXjD/Tg3m340z/6pXzMH9z/y7ceZf9Yr5d2yM2TIXVXU3cHfbfjjJrcCMjewyB7ikqh4HfpRkJZ3CMsDKqrodIMklre9Gi8mSJEmSJEmSpNEzLi/gSzILeA1wbQsdn2RFkkVJdmqxGcBdXbutarGh4hs6z7wky5IsW7NmzShegSRJkiRJkiRNbWNeTE6yPXApcGJV/RQ4B3gpsDedmcsfG61zVdW5VTVQVQPTpk0brcNKkiRJkiRJ0pQ3ZstcACTZmk4h+aKq+jJAVd3b1f454LL2dTWwW9fuM1uMjcQlSZIkSZIkSeNgzGYmJwlwHnBrVX28K75rV7e3ATe17SXAkUm2TbIHMBu4js4L92Yn2SPJNsCRra8kSZIkSZIkaZyM5TIXrweOAg5IckP7HAb8zyQ3JlkBvBH4U4CquhlYTOfFel8HjquqJ6pqHXA8cAVwK7C49ZUkSZImjCS7JbkqyS1Jbk5yQosvSLJ6vXviwX1OSbIyyQ+THNwVP6TFViY5uRfXI0mSJK1vzJa5qKqrgWyg6fKN7LMQWLiB+OUb20/S5PS6D1w4qsdb/tGjh+2ThD/7sz/jYx/rLNd+5pln8sgjj7BgwYJRHYskTWTm3zGzDjipqr6XZAdgeZKlre2sqjqzu3OSPek8dbcX8CLgG0l+vTV/GngTnZdPX59kSVXdMi5XIWlMmYMlqTfMv6NjzF/AJ0kTybbbbsuXv/xlfvKTn/R6KJI0pUyF/FtVd1fV99r2w3SeqpuxkV3mAJdU1eNV9SNgJbBP+6ysqtur6ufAJa2vJG2SqZCDJWki6sf8azFZ0pSy1VZbMW/ePM4666xntN1xxx0ccMABvOpVr+LAAw/kzjvv7MEIJak/TbX8m2QW8Brg2hY6PsmKJIuS7NRiM4C7unZb1WJDxTd0nnlJliVZtmbNmlG8Akn9ZKrlYEmaKPox/1pMljTlHHfccVx00UU89NBDT4v/yZ/8CXPnzmXFihW8+93v5v3vf3+PRihJ/Wmq5N8k2wOXAidW1U+Bc4CXAnsDdwMfG61zVdW5VTVQVQPTpk0brcNK6kNTJQdL0kTTb/nXYrKkKed5z3seRx99NGefffbT4t/97nf5gz/4AwCOOuoorr766l4MT5L61lTIv0m2plNIvqiqvgxQVfe2F0s/CXyOzjIWAKuB3bp2n9liQ8UlaZNNhRwsSRNRv+Vfi8mSpqQTTzyR8847j5/97Ge9HsqYSbJlku8nuax93yPJtUlWJvlikm1afNv2fWVrn9V1jFNa/IdJDu7NlUjqJ/2cf5MEOA+4tao+3hXftavb24Cb2vYS4MiWh/cAZgPXAdcDs1ve3obOS/qWjMc1SOpvfZ6Dt0tyXZJ/TXJzkg+3uPfAknqun/KvxWRJU9LOO+/MEUccwXnnnfdU7Ld/+7e55JJLALjooot4wxve0KvhjZYT6Lz8adBHgLOq6teAB4BjW/xY4IEWP6v1I8medAoYewGHAH+TZMtxGrukPtXn+ff1wFHAAUluaJ/DgP+Z5MYkK4A3An8KUFU3A4uBW4CvA8e1GczrgOOBK+jk8cWtryRtlj7PwY8DB1TVq+ksK3RIkv3wHljSBNBP+XerXg9A0tS1/KNH9/T8J510Ep/61Kee+v7JT36SY445ho9+9KNMmzaNz3/+8z0c3eZJMhP4PWAh8GdtttwBwB+0LhcAC+is4zmnbQP8HfCp1n8OcElVPQ78KMlKOo9mf3ecLkPSGDH/jo2quhrIBpou38g+C+nk6vXjl29sP2mieN0HLuz1EMbFaOZNc/DYqKoCHmlft26fwntgSY35d3RYTJY0pTzyyCNPbU+fPp1HH330qe8vfvGL+eY3v9mLYY2Fvwb+O7BD+/584ME22w1gFTCjbc8A7gKoqnVJHmr9ZwDXdB2ze5+nSTIPmAew++67j95VSOobUyj/StKEM1VycJtBvBz4NeDTwL8xRvfA3v9KGol+zL8ucyFJfSbJm4H7qmr5eJ2zqs6tqoGqGpg2bdp4nVaSJEl6SlsqaG86Ly7dB3jZGJ7L+19JU5IzkyWp/7weeGtbp3M74HnAJ4Adk2zVZmbMBFa3/quB3YBVSbYCfhW4vys+qHsfSZIkaUKqqgeTXAX8Ft4DS9KocmayJPWZqjqlqmZW1Sw6Lw/5ZlW9G7gKeHvrNhf4atte0r7T2r/Z1pxbAhzZ3nS9BzAbuG6cLkOSJEkasSTTkuzYtp8DvInOS0y9B5akUeTMZEmaOj4IXJLkL4HvA4OvkT0P+EJ7uchaOgVoqurmJIuBW4B1wHFV9cT4D1uSJEka1q7ABW3d5C2AxVV1WZJb8B5YkkaNxWRJ6mNV9S3gW237djprx63f5zHgHUPsvxBYOHYjlCRJkjZfVa0AXrOBuPfAkjSKXOZCkiRJkiRJkjQsZyZL6pk7T3vlqB5v9w/duNH2quINb3gDf/7nf86hhx4KwJe+9CXOO+88vv71r4/qWCRpIjP/SlLvmIMlqTfMv6PDYrKkKSMJn/nMZ3jHO97BG9/4RtatW8epp546qZO4JE0G5l9J6h1zsCT1Rr/mX4vJkqaUV7ziFbzlLW/hIx/5CD/72c/4wz/8QxYuXMhNN93EL37xCxYsWMCcOXO4+eabOeaYY/j5z3/Ok08+yaWXXsrs2bN7PXxJmrTMv5LUO+ZgSeqNfsy/FpMlTTnz58/nta99Ldtssw1vfvObOeCAA1i0aBEPPvgg++yzD7/7u7/LZz7zGU444QTe/e538/Of/5wnnvAFzpK0ucy/ktQ75mBJ6o1+y78WkyVNOc997nN55zvfyfbbb8/ixYv5h3/4B84880wAHnvsMe68805+67d+i4ULF7Jq1Sp+//d/f8L+RVCSJhPzryT1jjlYknqj3/KvxWRJU9IWW2zBFltsQVVx6aWX8hu/8RtPa3/5y1/Ovvvuy9e+9jUOO+wwPvvZz3LAAQf0aLSS1D/Mv5LUO+ZgSeqNfsq/W/R6AJLUSwcffDCf/OQnqSoAvv/97wNw++2385KXvIT3v//9zJkzhxUrVvRymJLUd8y/ktQ75mBJ6o1+yL/OTJbUM7t/6MZeD4G/+Iu/4MQTT+RVr3oVTz75JHvssQeXXXYZixcv5gtf+AJbb701L3zhCzn11FN7PVRJGjXmX0nqHXOwJPWG+Xd0WEyWNCUtWLDgqe3Pfvazz2g/+eSTOfnkk8dxRJI0NZh/Jal3zMGS1Bv9lH9d5kKSJEmSJEmSNCyLyZIkSZIkSZKkYVlMljSuBheZn+z65TokTR39krf65TokTS39krv65TokTR39krcm0nVYTJY0brbbbjvuv//+CZUEN0VVcf/997Pddtv1eiiSNCLmX0nqHXOwJPWG+Xds+AI+SeNm5syZrFq1ijVr1vR6KJttu+22Y+bMmb0ehiSNiPlXknrHHCxJvWH+HRsWkyWNm6233po99tij18OQpCnH/CtJvWMOlqTeMP+OjTFb5iLJbkmuSnJLkpuTnNDiOydZmuS29nOnFk+Ss5OsTLIiyWu7jjW39b8tydyxGrMkSZIkSZIkacPGcs3kdcBJVbUnsB9wXJI9gZOBK6tqNnBl+w5wKDC7feYB50Cn+AzMB/YF9gHmDxagJUmSJEmSJEnjY8yKyVV1d1V9r20/DNwKzADmABe0bhcAh7ftOcCF1XENsGOSXYGDgaVVtbaqHgCWAoeM1bglSZIkSZIkSc80ljOTn5JkFvAa4FpgelXd3ZruAaa37RnAXV27rWqxoeIbOs+8JMuSLOuHxbUlSZIkSZIkaaIY82Jyku2BS4ETq+qn3W1VVUCN1rmq6tyqGqiqgWnTpo3WYSVJkiRJkiRpyhvTYnKSrekUki+qqi+38L1t+Qraz/tafDWwW9fuM1tsqLgkSZIkSZrikuyW5KoktyS5OckJLb4gyeokN7TPYV37nJJkZZIfJjm4K35Ii61McvKGzidJU9mYFZOTBDgPuLWqPt7VtASY27bnAl/tih+djv2Ah9pyGFcAByXZqb1476AWkyRJkiaMjRQzdk6yNMlt7edOLZ4kZ7eCxYokr+061tzW/7Ykc4c6pyQJgHXASVW1J7AfcFySPVvbWVW1d/tcDtDajgT2ovNOpr9JsmWSLYFPA4cCewLv6jqOJAnYagyP/XrgKODGJDe02KnAGcDiJMcCPwaOaG2XA4cBK4FHgWMAqmptktOB61u/06pq7RiOW5IkSdoUg8WM7yXZAVieZCnwHuDKqjqjzXI7GfggnWLF7PbZFzgH2DfJzsB8YIDOknDLkyxpL6OWJK2nTUS7u20/nORWhnjXUjMHuKSqHgd+lGQlsE9rW1lVtwMkuaT1vWXMBi9Jk8yYFZOr6mogQzQfuIH+BRw3xLEWAYtGb3SSJEnS6NpIMWMOsH/rdgHwLTrF5DnAhe0++JokO7Zl4PYHlg5OoGgF6UOAi8ftYiRpkkoyC3gNcC2dSW7HJzkaWEbnD34P0MnN13TttopfFp/vWi++7xDnmQfMA9h9991H7wIkaYIb8xfwSZIkSVPNesWM6a3QDHAPML1tz+CZRYsZG4lv6DzzkixLsmzNmjWjNn5JmoySbE/nvU0nVtVP6Tzx8VJgbzp/7PvYaJ2rqs6tqoGqGpg2bdpoHVaSJjyLyZIkSdIo2kAx4yltFnKN1rksZkhSR5Kt6eTei6rqywBVdW9VPVFVTwKf45dLWawGduvafWaLDRWXJDUWkyVJkqRRsqFiBnBvW76C9vO+FreYIUmjIEmA84Bbq+rjXfFdu7q9DbipbS8BjkyybZI96Kxdfx2ddzXNTrJHkm3ovKRvyXhcgyRNFhaTJUmSpFEwVDGDTiFibtueC3y1K350OvYDHmrLYVwBHJRkpyQ7AQe1mCRpw14PHAUckOSG9jkM+J9JbkyyAngj8KcAVXUzsJjOi/W+DhzXZjCvA46nk3NvBRa3vpKkZsxewCdJkiRNMYPFjBuT3NBipwJnAIuTHAv8GDiitV0OHAasBB4FjgGoqrVJTqczQw7gtMGX8UmSnqmqrgaygabLN7LPQmDhBuKXb2w/SZrqLCZLkiRJo2AjxQyAAzfQv4DjhjjWImDR6I1OkiRJ2nwucyFJkiRJkiRJGpbFZEmSJEmSJEnSsCwmS5IkSZIkSZKGZTFZkiRJkiRJkjQsi8mS1GeSbJfkuiT/muTmJB9u8T2SXJtkZZIvJtmmxbdt31e29lldxzqlxX+Y5ODeXJEkSZIkSZoILCZLUv95HDigql4N7A0ckmQ/4CPAWVX1a8ADwLGt/7HAAy1+VutHkj2BI4G9gEOAv0my5bheiSRJkiRJmjAsJktSn6mOR9rXrdungAOAv2vxC4DD2/ac9p3WfmCStPglVfV4Vf0IWAnsMw6XIEmSJEmSJiCLyZLUh5JsmeQG4D5gKfBvwINVta51WQXMaNszgLsAWvtDwPO741+l77YAACAASURBVBvYZ/3zzUuyLMmyNWvWjPblSJIkSZKkCcBisiT1oap6oqr2BmbSmU38sjE+37lVNVBVA9OmTRvLU0mSJEmSpB6xmCxJfayqHgSuAn4L2DHJVq1pJrC6ba8GdgNo7b8K3N8d38A+kiRJkiRpirGYLEl9Jsm0JDu27ecAbwJupVNUfnvrNhf4atte0r7T2r9ZVdXiRybZNskewGzguvG5CkmSJEmSNNFsNXwXSdIksytwQZIt6fzRcHFVXZbkFuCSJH8JfB84r/U/D/hCkpXAWuBIgKq6Ocli4BZgHXBcVT0xztciSZIkSZImCIvJktRnqmoF8JoNxG+ns37y+vHHgHcMcayFwMLRHqMkSZIkSZp8XOZCkiRJkiRJkjQsi8mSJEmSJEmSpGFZTJYkSZIkSZIkDctisiRJkiRJkiRpWBaTJUmSJEmSJEnDspgsSZIkSZIkSRqWxWRJkiRJkjSpJdktyVVJbklyc5ITWnznJEuT3NZ+7tTiSXJ2kpVJViR5bdex5rb+tyWZ26trkqSJyGKyJEmSJEma7NYBJ1XVnsB+wHFJ9gROBq6sqtnAle07wKHA7PaZB5wDneIzMB/YF9gHmD9YgJYkWUyWJEmSJEmTXFXdXVXfa9sPA7cCM4A5wAWt2wXA4W17DnBhdVwD7JhkV+BgYGlVra2qB4ClwCHjeCmSNKFZTJYkSZIkSX0jySzgNcC1wPSqurs13QNMb9szgLu6dlvVYkPF1z/HvCTLkixbs2bNqI5fkiayMSsmJ1mU5L4kN3XFFiRZneSG9jmsq+2UtlbRD5Mc3BU/pMVWJjl5/fNIkiRJkiQBJNkeuBQ4sap+2t1WVQXUaJynqs6tqoGqGpg2bdpoHFKSJoURFZOTXDmS2HrOZ8OPgpxVVXu3z+XtWHsCRwJ7tX3+JsmWSbYEPk1nLaM9gXe1vpI0JWxi/pUkbSbzryT1xubk3yRb0ykkX1RVX27he9vyFbSf97X4amC3rt1ntthQcUkSwxSTk2zXFp/fJclO7S2oO7dHRp7xmEe3qvo2sHaE45gDXFJVj1fVj4CVdBa63wdYWVW3V9XPgUtaX0nqa5uTfyVJm878K0m9sbn5N0mA84Bbq+rjXU1LgLltey7w1a740enYD3ioLYdxBXBQG8NOwEEtJkkCthqm/Y+BE4EXAcuBtPhPgU9t4jmPT3I0sIzOm1YfoPOL4ZquPt1rEq2/VtG+Qx04yTw6b2Fl991338ThSdKEMBb5V5I0PPOvJPXG5ubf1wNHATcmuaHFTgXOABYnORb4MXBEa7scOIzOZLZHgWMAqmptktOB61u/06pqpBPlJKnvbbSYXFWfAD6R5E+q6pOjcL5zgNPprFF0OvAx4L2jcFygs2YRcC7AwMDAqKyDJEm9MAb5V5I0Apubf5MsAt4M3FdVr2ixBcAfAYNvaDq1a7m3U4BjgSeA91fVFS1+CPAJYEvgf1XVGZt1YZI0wW1u/q2qq/llAXp9B26gfwHHDXGsRcCiZzsGSZoKhpuZDEBVfTLJbwOzuvepqgufzcmq6t7B7SSfAy5rXze2JpFrFUmaskYr/0qSnp3NyL/n05lBt36/s6rqzO7Aeu8NeRHwjSS/3po/DbyJzpN51ydZUlW3bNrVSNLk4f2vJE1sIyomJ/kC8FLgBjqzJqAzu/hZJfMku7Y1iADeBtzUtpcAf5vk43RupGcD19H5q+LsJHvQKSIfCfzBszmnJE1mo5V/JUnPzqbm36r6dlvfcySeem8I8KMkg+8NgfbekDaWwfeGWEyW1Pe8/5WkiW1ExWRgANizPQYyIkkuBvans3j+KmA+sH+Sven8IriDzppIVNXNSRbTuUFeBxxXVU+04xxPZ7H7LYFFVXXzSMcgSX3gWedfSdKoGO38OybvDfGdIZL6kPe/kjSBjbSYfBPwQuDu4ToOqqp3bSB83kb6LwQWbiB+OZ2F8SVpKnrW+VeSNCpGM/+O2XtDfGeIpD7k/a8kTWAjLSbvAtyS5Drg8cFgVb11TEYlSRpk/pWk3hi1/Ot7QyTpWfH+V5ImsJEWkxeM5SAkSUNa0OsBSNIUtWC0DuR7QyTpWVnQ6wFIkoY2omJyVf3zWA9EkvRM5l9J6o1Nzb++N0SSNo/3v5I0sY2omJzkYTo3vwDbAFsDP6uq543VwCRJ5l9J6pVNzb++N0SSNo/3v9LIvO4DF/Z6CONi+UeP7vUQtJ6RzkzeYXA7SYA5wH5jNShJUof5V5J6w/wrSb1h/pWkiW2LZ7tDdfw9cPAYjEeSNATzryT1hvlXknrD/CtJE89Il7n4/a6vWwADwGNjMiJJ0lPMv5LUG+ZfSeoN868kTWwjKiYDb+naXkfnxSFzRn00kqT1mX8lqTfMv5LUG+ZfSZrARrpm8jFjPRBJ0jOZfyWpN8y/ktQb5l9JmthGtGZykplJvpLkvva5NMnMsR6cJE115l9J6g3zryT1hvlXkia2kb6A7/PAEuBF7fMPLSZJGlvmX0nqDfOvJPWG+VeSJrCRFpOnVdXnq2pd+5wPTBvDcUmSOsy/ktQb5l9J6g3zryRNYCMtJt+f5A+TbNk+fwjcP5YDkyQB5l9J6hXzryT1hvlXkiawkRaT3wscAdwD3A28HXjPGI1JkvRL5l9J6g3zryT1hvlXkiawrUbY7zRgblU9AJBkZ+BMOklekjR2zL+S1BvmX0nqDfOvJE1gI52Z/KrBRA5QVWuB14zNkCRJXcy/ktQb5l9J6g3zryRNYCMtJm+RZKfBL+0vgyOd1SxJ2nTPOv8m2S3JVUluSXJzkhMG902yNMlt7edOLZ4kZydZmWRFktd2HWtu639bkrljdI2SNBF5/ytJvbFJ+TfJoiT3JbmpK7YgyeokN7TPYV1tp7T73x8mObgrfkiLrUxy8ihelyT1hZHeEH8M+G6SL7Xv7wAWjs2QJEldNiX/rgNOqqrvJdkBWJ5kKZ215q6sqjPajfHJwAeBQ4HZ7bMvcA6wb7txnw8MANWOs6R7pogk9THvfyWpNzY1/54PfAq4cL34WVV1ZncgyZ7AkcBewIuAbyT59db8aeBNwCrg+nb/e8umXIg67jztlb0ewpjb/UM39noI0rgZUTG5qi5Msgw4oIV+32QqSWNvU/JvVd1N52UlVNXDSW4FZgBzgP1btwuAb9EpJs8BLqyqAq5JsmOSXVvfpe3RQlpB+hDg4lG7QEmaoLz/laTe2NT8W1XfTjJrhKeZA1xSVY8DP0qyEtinta2sqtsBklzS+pr/JakZ8aN6LXmbQCVpnG1O/m031K8BrgWmt0IzdN6OPb1tzwDu6tptVYsNFd/QeeYB8wB23333TRmqJE043v9KUm+Mcv49PsnRwDI6T+89QOee9pquPt33uevf/+47SuOQpL7gum+S1KeSbA9cCpxYVT9N8lRbVVWSGq1zVdW5wLkAAwMDo3ZcSZKkicDH9Cetc4DT6SzZdjqdJTTeOxoHdjKFpKlqpC/gkyRNIkm2plNIvqiqvtzC97blK2g/72vx1cBuXbvPbLGh4pIkSdKEV1X3VtUTVfUk8Dl+uZTFZt//VtW5VTVQVQPTpk0b/cFL0gRlMVmS+kw6U5DPA26tqo93NS0B5rbtucBXu+JHp2M/4KG2HMYVwEFJdmpv1D6oxSRJkqQJb3AiRfM24Ka2vQQ4Msm2Sfag8yLq64DrgdlJ9kiyDZ2X9C0ZzzFL0kTnMheS1H9eDxwF3JjkhhY7FTgDWJzkWODHwBGt7XLgMGAl8ChwDEBVrU1yOp2baoDTBl/GJ0mSJE0kSS6m8wLpXZKsAuYD+yfZm84yF3cAfwxQVTcnWUxnXeZ1wHFV9UQ7zvF0JlBsCSyqqpvH+VIkaUKzmCxJfaaqrgYyRPOBG+hfwHFDHGsRsGj0RidJkiSNvqp61wbC522k/0Jg4Qbil9OZbCFJ2gCXuZAkSZIkSZIkDctisiRJkiRJkiRpWBaTJUmSJEmSJEnDspgsSZIkSZIkSRrWmBaTkyxKcl+Sm7piOydZmuS29nOnFk+Ss5OsTLIiyWu79pnb+t+WZO5YjlmSJEmSJEmS9ExjPTP5fOCQ9WInA1dW1WzgyvYd4FBgdvvMA86BTvEZmA/sC+wDzB8sQEuSJEmSJEmSxseYFpOr6tvA2vXCc4AL2vYFwOFd8Qur4xpgxyS7AgcDS6tqbVU9ACzlmQVqSZIkSZIkSdIY6sWaydOr6u62fQ8wvW3PAO7q6reqxYaKP0OSeUmWJVm2Zs2a0R21JEmSNAyXeZMkSVI/6+kL+KqqgBrF451bVQNVNTBt2rTROqwkSZI0UufjMm+SJEnqU70oJt/blq+g/byvxVcDu3X1m9liQ8UlSZKkCcVl3iRJktTPelFMXgIMPqo3F/hqV/zo9rjffsBDbTmMK4CDkuzUZmQc1GKSJEnSZOAyb5IkSeoLY1pMTnIx8F3gN5KsSnIscAbwpiS3Ab/bvgNcDtwOrAQ+B/xXgKpaC5wOXN8+p7WYJEmSNKm4zJskSZIms63G8uBV9a4hmg7cQN8CjhviOIuARaM4tCnvztNe2eshjLndP3Rjr4fQl6bCvzvgvz+SpFF1b5Jdq+ruZ7HM2/7rxb81DuOUJEmSNqqnL+CTJEmSpgCXeZMkSVJfGNOZyZIkSdJU0pZ52x/YJckqYD6dZd0WtyXffgwc0bpfDhxGZ5m3R4FjoLPMW5LBZd7AZd4kSZI0QVhMliRJkkaJy7xJkiSpn7nMhSRJkiRJkiRpWBaTJUmSJEmSJEnDspgsSZIkSZIkSRqWxWRJkiRJkiRJ0rAsJkuSJEmSJEmShrVVrwcgSdJUcudpr+z1EMbc7h+6sddDkCRJU0ySRcCbgfuq6hUttjPwRWAWcAdwRFU9kCTAJ4DDgEeB91TV99o+c4H/0Q77l1V1wXhehyRNdM5MliRJkiRJk935wCHrxU4Grqyq2cCV7TvAocDs9pkHnANPFZ/nA/sC+wDzk+w05iOXpEnEYrIkSZIkSZrUqurbwNr1wnOAwZnFFwCHd8UvrI5rgB2T7AocDCytqrVV9QCwlGcWqCVpSrOYLEmSJEmS+tH0qrq7bd8DTG/bM4C7uvqtarGh4s+QZF6SZUmWrVmzZnRHLUkTmMVkSZIkSZLU16qqgBrF451bVQNVNTBt2rTROqwkTXgWkyVJkiRJUj+6ty1fQft5X4uvBnbr6jezxYaKS5Iai8mSJEmSJKkfLQHmtu25wFe74kenYz/gobYcxhXAQUl2ai/eO6jFJEnNVr0egCRJkiRJ0uZIcjGwP7BLklXAfOAMYHGSY4EfA0e07pcDhwErgUeBYwCqam2S04HrW7/Tqmr9l/pJ0pRmMVmSJEmSJE1qVfWuIZoO3EDfAo4b4jiLgEWjODRJ6isucyFJkiRJkiRJGpbFZEmSJEmSJEnSsCwmS5IkSZIkSZKGZTFZkiRJkiRJkjQsX8AnSZIkqe/dedorez2EMbf7h27s9RAkSVKfc2ayJPWhJIuS3Jfkpq7YzkmWJrmt/dypxZPk7CQrk6xI8tqufea2/rclmduLa5EkSZIkSRODxWRJ6k/nA4esFzsZuLKqZgNXtu8AhwKz22cecA50is/AfGBfYB9g/mABWpIkSZIkTT0WkyWpD1XVt4G164XnABe07QuAw7viF1bHNcCOSXYFDgaWVtXaqnoAWMozC9SSJEmSJGmKsJgsSVPH9Kq6u23fA0xv2zOAu7r6rWqxoeKSJEmSJGkKspgsSVNQVRVQo3W8JPOSLEuybM2aNaN1WEmSJEmSNIFYTJakqePetnwF7ed9Lb4a2K2r38wWGyr+DFV1blUNVNXAtGnTRn3gkiRJkiSp9ywmS9LUsQSY27bnAl/tih+djv2Ah9pyGFcAByXZqb1476AWkyRJkiRJU9BWvTpxkjuAh4EngHVVNZBkZ+CLwCzgDuCIqnogSYBPAIcBjwLvqarv9WLckjQZJLkY2B/YJckqYD5wBrA4ybHAj4EjWvfL6eTXlXRy7DEAVbU2yenA9a3faVW1/kv9JEmSJEnSFNGzYnLzxqr6Sdf3k4Erq+qMJCe37x8EDgVmt8++wDntpyRpA6rqXUM0HbiBvgUcN8RxFgGLRnFokjRlOZlCkiRJk91EW+ZiDnBB274AOLwrfmF1XAPsOLjupyRJkjSJvLGq9q6qgfZ9cDLFbODK9h2ePpliHp3JFJIkSVJP9bKYXMA/JVmeZF6LTW/rdALcA0xv2zOAu7r2XdViT5NkXpJlSZatWbNmrMYtSZIkjRYnU0iSJGnS6GUx+Xeq6rV0Zl0cl+Q/dTe2x67r2Rywqs6tqoGqGpg2bdooDlWSJEnabE6mkCRJ0qTWszWTq2p1+3lfkq8A+wD3Jtm1qu5uMy/ua91XA7t17T6zxSRJkqTJ4neqanWSFwBLk/ygu7GqKsmznkwBnAswMDDwrPaVJEmSnq2ezExO8twkOwxuAwcBNwFLgLmt21zgq217CXB0OvYDHuqawSFJkiRNeN2TKYCnTaYAcDKFJEmSJrpeLXMxHbg6yb8C1wFfq6qvA2cAb0pyG/C77TvA5cDtwErgc8B/Hf8hS5IkSZvGyRSSJEnqBz1Z5qKqbgdevYH4/cCBG4gXcNw4DE2SJEkaC9OBrySBzj3431bV15NcDyxOcizwY+CI1v9y4DA6kykeBY4Z/yFLUn9IcgfwMPAEsK6qBpLsDHwRmAXcARxRVQ+kk6g/QScHPwq8p6q+14txS9JE1LM1kyVJkqSpwskUktRzb6yqn3R9Pxm4sqrOSHJy+/5B4FBgdvvsC5zTfkqS6N0yF5IkSZIkSb0yB7igbV8AHN4Vv7A6rgF2HFzbXpLkzGRJktQHXveBC3s9hHGx/KNH93oIkiRNRgX8U5ICPltV5wLTu9aiv4fOckQAM4C7uvZd1WJPW7c+yTxgHsDuu+8+hkOXpInFYrIkSZIkSepnv1NVq5O8AFia5AfdjVVVrdA8Yq0gfS7AwMDAs9pXkiYzl7mQJEmSJEl9q6pWt5/3AV8B9gHuHVy+ov28r3VfDezWtfvMFpMkYTFZkiRJkiT1qSTPTbLD4DZwEHATsASY27rNBb7atpcAR6djP+ChruUwJGnKc5kLSZIkSZLUr6YDX0kCnRrI31bV15NcDyxOcizwY+CI1v9y4DBgJfAocMz4D1mSJi6LyZIkSZIkqS9V1e3AqzcQvx84cAPxAo4bh6FJ0qTkMheSJEmSJEmSpGFZTJYkSZIkSZIkDctisiRJkiRJkiRpWK6ZLEmSNEncedorez2EMbf7h27s9RAkSZIkDcGZyZIkSZIkSZKkYTkzWZIkSZIkSdKE45N5E48zkyVJkiRJkiRJw7KYLEmSJEmSJEkalsVkSZIkSZIkSdKwLCZLkiRJkiRJkoZlMVmSJEn6/9m78zDLqvLu+98fzWREVKBFpIFuFQdQROk0DsEgTuCE8hABETDySHiDAxGNoIk0GvJoxAk0IAoKaBgSHFpFEZwnhEawmSS0SLQJMiuigtLc7x97FR6aqq7q7jp1avh+rquuPmft6d51qu5afe+115YkSZI0KovJkiRJkiRJkqRRWUyWJEmSJEmSJI3KYrIkSZIkSZIkaVQWkyVJkiRJkiRJo7KYLEmSJEmSJEkalcVkSZIkSZIkSdKoLCZLkiRJkiRJkkZlMVmSJEmSJEmSNCqLyZIkSZIkSZKkUVlMliRJkiRJkiSNasoUk5PsmuTqJEuTHD7oeCRppjD/StJgmH8laTDMv5I0silRTE4yC/gosBuwDbBPkm0GG5UkTX/mX0kaDPOvJA2G+VeSVm5KFJOBBcDSqrq2qv4InAHsPuCYJGkmMP9K0mCYfyVpMMy/krQSaw86gDHaHPhlz/tlwI4rrpTkIOCg9vbOJFdPQGxT0lawCXDLoOPoqyMz6AimpRnxswNr8vOz1XiGMQmYf8fZjPgdMv/2jT8/K2X+Nf+ulL8/WhP+/IxqOuVg8+848/dHa8Kfn1FNeP6dKsXkMamqE4ETBx3HVJBkcVXNH3Qcmnr82dFwzL9j5++Q1oQ/P1qR+Xfs/P3RmvDnRysy/46dvz9aE/78TD5TZZqL64Etet7PaW2SpP4y/0rSYJh/JWkwzL+StBJTpZh8EbB1knlJ1gX2BhYNOCZJmgnMv5I0GOZfSRoM868krcSUmOaiqu5J8nrgXGAWcHJVXTHgsKY6b8fR6vJnZwYx//aFv0NaE/78zBDm377w90drwp+fGcL82xf+/mhN+PMzyaSqBh2DJEmSJEmSJGmSmyrTXEiSJEmSJEmSBshisiRJkiRJkiRpVBaTp5gky5Nc2vM1t4/Hui7JJv3avyaXJJXk0z3v105yc5IvjbLdzqOtIwmS7Jrk6iRLkxw+6Hg0dSQ5OclNSS4fdCzSZJbO95Ls1tP2N0m+Osi4NLW0PvH7e96/JcnCAYYkTRn+/mh1+Pd76rGYPPX8oaq27/m6btABadr4HfCkJA9q758PXD/AeKRpI8ks4KPAbsA2wD5JthlsVJpCPgXsOuggpMmuuofBHAx8IMn6STYA/hU4ZLCRaYq5G9jDQTXSavH3R6vMv99Tj8XkaSDJDkm+neTiJOcm2ay1fyvJB5MsTnJVkr9M8tkk1yT5l57tP9+2vSLJQSMc49VJLmyjoT/WCiOafs4BXtxe7wOcPrQgyYIkP0xySZIfJHn8ihsneXAbQXdhW2/3CYpbmuwWAEur6tqq+iNwBuDvh8akqr4D3DboOKSpoKouB74IvA14J/Bp4B0r9k2SbNvTt12SZOsBhq3J5R7gROAfVlyQZG6Sb7Sfma8n2XLiw5MmNX9/tFr8+z21WEyeeh7UM8XF55KsAxwH7FlVOwAnA0f3rP/HqpoPnAB8ge7KzpOA1yTZuK3z2rbtfOCNPe0AJHkisBfwrKraHlgO7NvHc9TgnAHsnWR9YDvgRz3LfgrsVFVPpUvu/zrM9u8AvlFVC4DnAO9L8uA+xyxNBZsDv+x5v6y1SZLG31HAq+juBlmf4fsmBwMfbn3b+XR5WRryUWDfJA9dof044JSq2g74DHDshEcmTX7+/mh1+fd7ilh70AFolf2h/dIAkORJdMXh85IAzAJu6Fl/Ufv3MuCKqrqhbXctsAVwK10B+RVtvS2ArVv7kOcCOwAXtWM8CLhpfE9Lk0FVLWnzcO9DN0q510OBU9qVvwLWGWYXLwBeluQt7f36wJbAVX0JWJIkaQVV9bskZwJ3Aq8EXjpM3+SHdCOe5gCfraprBhOtJqOquiPJqcAbgT/0LHoGsEd7fRrwbxMdmzTZ+fuj1eXf76nDYvLUF7oi8TNGWH53+/fentdD79dOsjPwPOAZVfX7JN+i+yVd8RinVNUR4xa1JrNFwDHAzkDvKPV3A9+sqle0gvO3htk2wP+pqqv7G6I05VxPd7FuyByck1yS+une9jVS3+SqJD+im97rnCR/V1XfmOggNal9CPgx8MlBByJNQf7+aHX593sKcJqLqe9qYHaSZwAkWSfJtquw/UOB21sh+QnA04dZ5+vAnkke0Y6xUZKt1jRwTVonA0dV1WUrtD+UPxe/XjPCtucCb0gbwp7kqX2JUJp6LgK2TjIvybrA3vz5zhFJUv8M2zdJ8mjg2qo6lm4quO0GF6Imo6q6DTgLOLCn+Qd0f8Ohm/bvuxMdlzQV+PujceDf70nMYvIU1x7ktCfw3iQ/AS4FnrkKu/gq3Qjlq4D3ABcMc4wrgX8CvpZkCXAesNmaxq7JqaqWtcS8on8D/l+SSxj5roZ3001/sSTJFe29NONV1T3A6+k6RVcBZ1XVFYONSlNFktPpbul7fJJlSQ4cbRtJ9xmpb/JK4PIkl9JNGXfqgOLT5PZ+YJOe928A/rb9n2g/4E0DiUqaGvz90Zrw7/cklqoadAySJEmSJEmSpEnOkcmSJEmSJEmSpFFZTJYkSZIkSZIkjcpisiRJkiRJkiRpVBaTJUmSJEmSJEmjspgsSZIkSZIkSRqVxWRpDJJ8Ksmeg45DkvotyTuSXJFkSZJLk+y4GvuYn+TY1dhuQZLvJLk6ySVJPpHkL5K8LMnhbZ2XJ9mmZ5t3JXneqh5LkqaTJMtbzr48yX8m+YtV2Hb7JC/qZ3ySNFX05NMrkvwkyWFJBlY7S3Jdkk1Wc9v79Zul8bL2oAOQpqMka1fVPYOOQ5JWRZJnAC8BnlZVd7eO67qrup+qWgwsXoXjrg1sDPwnsHdV/bC17wk8pKoWAYva6i8HvgRc2Y71zlWNT5KmoT9U1fYAST4DHAx8YLSNWv7dHpgPnDMegSSZVVXLx2NfkjQAvfn0EcB/ABsCRw40qtVzv36zNF4cmaxpJ8k/t1Ft30tyepK3JHlMkq8muTjJd5M8oa37qSTHJvlBkmuHRh+n85G2n/OBR/Tsf4ck3277OjfJZq39W0k+lGQx8KZBnLskraHNgFuq6m6Aqrqlqv53lLz33iQXJvnvJDu19p2TfKm93ijJ59tI5wuSbNfaFyY5Lcn3gdOAQ4BThgrJ7fj/VVU3JnlNy8nPBF4GvK+NGHnM0J0jbTT0pe3rsiTVjrNK+V+SpoHvAo9N8tIkP2p3epyfZFMYNv++C9ir5c+92vKTW46/Nskbh3ac5NUt51+a5GNJZrX2O5O8P8lPgGcM4JwladxV1U3AQcDrW41gVpL3Jbmo9W3/Du7r+34nyZdbDeGEodHMSV6Q5IdJfpzuzpENWvt1SY5q7Zf19FE3TvK1dCOjPwFkKJ5RcvDR6UZSX5Bk0xH6zW9McmWL/YwJ/WZqWrGYrGklyV8C/wd4CrAb3SgLgBOBN1TVDsBbgH/v2Wwz4K/oRuO9p7W9Ang8sA2wP/DMtv91gOOAPdu+TgaO7tnXulU1v6reP/5nJ0l99zVgi1YY/vckfz2GvLd2VS0ADmX4ERtHAZdU1XbA24FTe5ZtAzyvqvYBSHDHQQAAIABJREFUngRcvLLgquoHdCOU31pV21fVz3qWLW5t2wNfBY5pi1Y1/0vSlJVupPFuwGXA94CnV9VTgTOAf+xZtTf/vhM4s+XQM9vyJwAvBBYARyZZJ8kTgb2AZ7VcuxzYt63/YOBHVfWUqvpef89SkiZOVV0LzKIbYHYg8Juq+kvgL4HXJZnXVl0AvIEuvz4G2CPdXX7/RJdvn0Z3596be3Z/S2s/nq6fCl1/+ntVtS3wOWBLgDHk4Auq6inAd4DXjdBvPhx4auuXHzxu3yTNOE5zoenmWcAXquou4K4kXwTWpysG/2dy30W99Xq2+XxV3QtcOTRiA3g2cHq7Re9/k3yjtT+eruBxXtvXLOCGnn2diSRNUVV1Z5IdgJ2A59DltH9h5Xnvs+3fi4G5w+z2r+gu8lFV32ijLTZsyxZV1R/G8xyS7AU8DXhBG/mxqvlfkqaiByW5tL3+LnASXb/1zHR3k6wL/Lxn/dHy75fbXSp3J7kJ2BR4LrADcFHLqQ8CbmrrLwfOHq+TkaRJ6gXAdj13tD0U2Br4I3BhKzyT5HS6PvBddMXl77e8uS7ww5799faj92ivnz30uqq+nOT21r6yHPxHuukshvb1/BHiXwJ8Jsnngc+vyolLvSwmayZYC/j10LxHw7i753VGWKd3+RVVNdLte79b1eAkaTJpF9G+BXwryWV000+sLO8N5dDlrHq/ojdnXkHXQf7CKu7jPkmeBCwEnl1Vy9vtheOV/yVpMvvDirkuyXHAB6pqUZKd6fLjkNH6rL35cSi/h246oiOGWf8u50mWNB0leTRdHryJLg++oarOXWGdnYFaYdNq65/X7gIZzqr0o1eWg/9UVUPHX9m+XkxXrH4p8I4kT/ZZT1odTnOh6eb7wEuTrN9GpL0E+D3w8yR/A/fNh/yUUfbzHbq542a10RzPae1XA7PTPaSKdsvftn05E0maYEken2TrnqbtgatYs7z3XdoteK2jfUtV3THMeh8BDkiyY088ewwzYvi3wEOGif1hwOnA/lV1M0A7zqrmf0maLh4KXN9eH7CS9YbNq8P4OrBnugdSDc2Jv9WahShJk1eS2cAJwEdasfZc4P9r08CR5HFJHtxWX5BkXhvMsBfdVEMXAM9K8ti2/oOTPG6Uw34HeFVbfzfg4a19dXLwffm9xbVFVX0TeBvd34gNxvJ9kFZkMVnTSlVdRDcv0BLgK3Tzxf2GrpBxYLqHglwB7D7Krj4HXEP31NNTabeiVNUfgT2B97Z9XUqbT1mSpoENgFOGHsxBd1veO1mzvLcQ2KHt7z2MUNCoqhuBvYFj0j245Cq6+Tp/u8KqZwBvTfdAqcf0tO8ObAV8vD1kZOh271XN/5I0XSykm+bnYuCWlaz3TWCbljv3GmmlqrqSbu7Pr7Wcfh7d3POSNJ08qOXDK4Dz6Z4pclRb9gm6GsGPk1wOfIw/jwK+iG5wxFV00wp9rg1weA1wesubP6Sbk35ljgKe3Y6/B/ALWO0cfF+/mW46jk+3Ow8vAY6tql+P9s2QhpM/j4SXpockG7R5P/+C7qreQVX140HHJUmSJEmSppd2991bquolg45FmgjOmazp6MQk29A9eO8UC8mSJEmSJEnSmnNksiRJkiRJkiRpVM6ZLEmSJEmSJEkalcVkSZIkSZIkSdKoLCZLkiRJkiRJkkZlMVmSJEmSJEmSNCqLyZIkSZIkSZKkUVlMliRJkiRJkiSNymKyJEmSJEmSJGlUFpMlSZIkSZIkSaOymCxJkiRJkiRJGpXFZE0qSb6V5P8OOo5+SnJFkp0HHYckSZI0KEm2THJnklmDjkWSJI2dxWQNRJLrkvyhdSBvTPKpJBsMMJ6/SvKDJL9JcluS7yf5y34cq6q2rapv9WPfkjRRklSSx65k+ZgvnLW/Cc9bw3jenuQTa7IPSRovY81rgx5IMVycSQ5M8tMkv2399HOSPGS8j11Vv6iqDapq+XjvW5LWVG9+nG79TC/maU1ZTNYgvbSqNgCeBswH/mkQQSTZEPgScBywEbA5cBRw92rsa+3xjU6Sxl/rHP8xySYrtF/SisRzV3F/n0ryL71t/bhwlmRui+8Bubaq/rWqpvWdLZK0ovEuBCT5a+BfgX2q6iHAE4EzV3Nf9oslDVSS1yS5LMnvk/wqyfFJHraq+xnPfuZEXbBbmRUv5g36wqamHovJGriquh74CvCk1rRVGxn82yRf6y12JHlZG+3265bwntiz7Lokb0mypI0wPjPJ+j3LX5Lk0rbtD5Js1xY9rsVxelUtr6o/VNXXqmpJz7avTXJVktuTnJtkq55lleSQJNcA17Q/UMf0nmOSLyR5c0+cQ1c4Z7WrnD9r53txki3asickOa+NlL46ySvH5RsuSZ2fA/sMvUnyZOAvBheOJE0/rZDxvSTHtH7kz5Ps1pYdDewEfKSNEPtIax+xD9gu3h3fig+/A56zun3gJKcBWwJfbMf/R+AvgR9W1SUAVXVbVZ1SVb9t26zXzuUXrQhyQpIHtWU7J1mW5G1JfgV8svWfX9ITy9pJbk7ytBUvECbZKMknk/xv+159frRzkKSRJDkMeC/wVuChwNOBrYDzkqw7oJjG7YLdGsTghT6tMYvJGrhWPH0RcElrehXwt8AjgHWBt7T1HgecDhwKzAbOoev89v4heCWwKzAP2A54Tdv2qcDJwN8BGwMfAxYlWQ/4b2B5klOS7Jbk4SvEtzvwdmCPdtzvtjh6vRzYEdimLdsrSdr2DwdeAJwxzOm/ma6Y8yJgQ+C1wO+TPBg4D/iP9n3YG/j3JNuM/J2UpFVyGrB/z/sDgFOH3qw4QmGoILLiTpIcBOwL/GMrRnyxtfdeOFuY5L9ageO3SX6c5CnDBZVkrSSHt4tstyY5K8lGo51MO8an2+uhAsUBreBxS5J3jOUYSdZP8unW/uskFyXZdLTjS9JK7AhcDWwC/BtwUpJU1Tvo+pWvbyPEXj/GPuCrgKOBhwBDeXmV+8BVtR/wC9rdglX1b8CPgBcmOSrJs1pfudd76AZibA88lu6Ovnf2LH8k3Z1+WwEH0fWL9+lZ/kLglqr68TDfp9PoLmpu2879g6OdwzD7kKShu4+PAt5QVV+tqj9V1XV0uXIu8OrWdzwryamtf3pFkvkj7G9c+pmswQW7tnz3dmHtjrb/XVv7/aYsGiHeA5P8AvhG78W8DHNhM8lHk7x/he/BoiT/sPqfiqYTi8kapM8n+TVdJ/jbdFfoAD5ZVf9dVX8AzqLrrALsBXy5qs6rqj8BxwAPAp7Zs89jq+p/q+o24Is92x4EfKyqftRGH59CN43F06vqDuCvgAI+DtzcEuVQ8eBg4P9V1VVVdU+Lc/v0jE5uy29rMX+37WuntmxPuj8Y/zvM9+D/Av9UVVdX5ydVdSvwEuC6qvpkVd3T/ticDfzNWL+5kjSKC4ANkzwx3W3SewOfXtWdVNWJwGeAf2vFiJeOsOruwH/SFRn+g+5vwDrDrPcGugt0fw08Crgd+OiqxtX8FfB44LnAO/Pnu1lWdowD6EavbEFXtDgY+MNqHl+SAP6nqj7ebic+BdgMGOki1Vj6gF+oqu9X1b1VdVdrW+U+8HAHr6rv0g2geBrwZeDWJB9Idzdd2v7+ofV7f0vXL967Zxf3AkdW1d2tX/wfwMuSDN358ioeOCiDJJsBuwEHV9XtrfDz7dU5B0miqxGsD3y2t7Gq7qQblPb81vQyukFfDwMWAR9ZhWOsTj9ztS/YJVlAN/DjrS3eZwPXrUK8f003EvqFvY3DXdik+1u1T5K12rE3AZ5Hl9Mli8kaqJdX1cOqaquq+vvW4QT4Vc86vweGHsz3KOB/hhZU1b3AL+kS7JCRtt0KOKyNMvt1K2Jv0fZJKxS/pqrm0E238SjgQz3bfrhnu9uArHDcX/bEVXR/kIZGYbyKrtAynC2Anw3TvhWw4wrx7ks32kOSxsvQ6OTnA1cB1/fxWBdX1X+1i4EfoOvgD1cIOBh4R1Utq6q7gYXAnlm9W/KOalMX/QT4CTA0Gnplx/gTXRH5sa1ocXG76ChJq+u+/mlV/b69HOnB02PpA/5ymO1Wqw88nKr6SrswuBHdhcDX0A2AmE03cvjinn19tbUPubmnwE1VLaX7+/LSVlB+GcMXI7YAbquq24dZtsrnIGnG24TuLoh7hll2Q1sO8L2qOqdd7DuNP/cVx2KV+5lreMHuQODkNrju3qq6vqp+ugrxLqyq3/XUXUZUVRcCv6ErlNNi+FZV3bgKx9M05lwpmkr+F3jy0JuWbLdgbMWPXwJHV9XRo61YVT9N8im6W+l6tx2pIAzdSORepwNfS/IeulsbX7GSuB4DXD5M+7er6vkP3ESSxs1pwHfobos+dZR111TvRbd7kyxj+ELAVsDnktzb07ackUfxrczKiisjHeM0ur8tZ6R7QMun6f5D8KfVOL4kjWbFPuRY+oArbrMyo/WBR9xXG7jx9STfoBts8XG6OzW2re6ZJ2Pd39BUF2sBV7YC83BxbpTkYVX161U8B0la0S3AJq2Au2JBebO2HB7YV1x/hG2Gszr9zOur6ivAV9qo3+fQ3bl3NfA5/nzBbmi7AEMPWt2CblT16hruQuTKnAK8mm7qpVcDH16DY2uacWSyppKzgBcneW67NfowulvcfjCGbT8OHJxkx3QenOTFSR6S7iEnhyWZA/fN4bwP3S3gACcARyTZti1/aJKVTjfRbkm8BfgEcO4wneIhnwDenWTrFtd2STYGvgQ8Lsl+SdZpX3/Zc+uMJK2xqvofugfxvYgVbgMEfsf9H8i3sjsjxlLY2GLoRes8z6G7SLiiXwK7tTtXhr7WX0nhYnWMeIx2a/VRVbUN3S2SL+H+c0tL0ni6EXh0z/vx7gOO2Ace7vjp5uPcO8nD2/oL6G6NvqAVlz8OfDDJI9r6myd5ISt3Bt3zQ/4/RrhFuqpuoHsg97+3Y6+T5NljPAdJWtEP6WoFe/Q2JtmAbkqdr/fx2GPqy7bRxV8Hhi7Y3cKfL9gNbffQqtqgZ7+PGeGYY+m3r6y/PtyyTwO7p3vOyROBzw+zjmYoi8maMqrqarorYsfRJdqX0j0w5I9j2HYx8Dq6OZBuB5bSHkwC/JZu9PCP0j0V+wK6kcKHtW0/R/cU2DOS3NGW7TaGkP+D0ecV+gBdkfxrwB3AScCD2i0tL6C7neR/6a56vhfwQSOSxtuBwC5V9bsV2i8F9kjyF0ke29YbyYrFkOHskGSPNpXEoXQd/AuGWe8E4Oi0eemTzE73INRe66V7UN7Q16r2Z0Y8RpLnJHlyunmk76Cb9uLekXclSWvkw3S3P9+e5Njx7gOO0gcG+H/AP7XpI97S1nkdcA1dDvw08L6eO/Te1vZxQesXn083Z+jKYriBrrDzTODMlay6H13O/SlwE93firGcgyTdT1X9hu4BfMcl2bVdoJpL93/vZXR3ovXLyvqZa3LB7iTgb9vgurXasie0ZZcCe7fznE/33KZV8YC+fFUtAy6i+16dPZbpMTRzpJveVZIkzRRJrgP+b1Wdv0L70JzB84A76S6GPQNYQneL2/Oq6q/augVsXVVLk2xNd4veXLr51F7ee4wkC+lGXCynGwW9FDiwqn68YjytMHwo3VRDj6IrKJxZVW9v/wn4+TCn9Hy6h6A8tqpe3bPeOkO3KSb5FvDpqvrEKMfYh25uuznte3Am8OYx3u4oSZKkSSLJgcA/0I3ovYNudO3hVXV7658+tqpe3dadS0//cZi+7Hj0M58NHEk3v/J6dPM3f6Kq/q3tZ326B+7tTTev8/XA8VV1bFv+Croi+Ty6AvAhVXVukkfTTSm0LfBtuucybbSSeFc812fQTWsxGzitqt7Y1ns1XTF5l6r65pp8FppeLCZLkqS+WrGzLkmSJGlya8XvTwNblcVD9XCaC0mSJEmSJEkApHtO1ZvoRk5bSNb9WEyWJEmSJEmSRHvo66+BzYAPDTgcTUJOcyFJkiRJkiRJGpUjkyVJkiRJkiRJo1p70AH0yyabbFJz584ddBiSZpiLL774lqqaPeg4Bsn8K2kQzL/mX0mDM9NzsPlX0qAMIv9O22Ly3LlzWbx48aDDkDTDJPmfQccwaOZfSYNg/jX/ShqcmZ6Dzb+SBmUQ+ddpLiRJkiRJkiRJo7KYLEmSJI2DJFsk+WaSK5NckeRNrX2jJOcluab9+/DWniTHJlmaZEmSp/Xs64C2/jVJDhjUOUmSJEm9LCZLkiRJ4+Me4LCq2gZ4OnBIkm2Aw4GvV9XWwNfbe4DdgK3b10HA8dAVn4EjgR2BBcCRQwVoSZIkaZCm7ZzJkiafP/3pTyxbtoy77rpr0KGssfXXX585c+awzjrrDDqUKcHPXtJMUFU3ADe0179NchWwObA7sHNb7RTgW8DbWvupVVXABUkelmSztu55VXUbQJLzgF2B0yfsZCSNG/tBkjQY5t/+sJgsacIsW7aMhzzkIcydO5ckgw5ntVUVt956K8uWLWPevHmDDmdK8LOXNNMkmQs8FfgRsGkrNAP8Cti0vd4c+GXPZsta20jtwx3nILpRzWy55ZbjE7ykcWU/SJIGw/zbH05zIWnC3HXXXWy88cZTOokDJGHjjTeeFlc3J4qfvaSZJMkGwNnAoVV1R++yNgq5xutYVXViVc2vqvmzZ88er91KGkf2gyRpMMy//WExWdKEmupJfMh0OY+JNF2+Z9PlPCT1R5J16ArJn6mqz7bmG9v0FbR/b2rt1wNb9Gw+p7WN1C5pipou/Yfpch6SZo7pkrcm03lYTJYkSZLGQbpe/knAVVX1gZ5Fi4AD2usDgC/0tO+fztOB37TpMM4FXpDk4e3Bey9obZIkSdJAOWeyNI52eOupgw6h7y5+3/4Terxf/epXHHrooVx00UU87GEPY9NNN+XlL385ixYt4ktf+tKExjJVJNkCOJVuTs4CTqyqDydZCLwOuLmt+vaqOqdtcwRwILAceGNVndvadwU+DMwCPlFV75mIc/Bz76/JnKsmOsdI4+xZwH7AZUkubW1vB94DnJXkQOB/gFe2ZecALwKWAr8H/hagqm5L8m7gorbeu4Yexrem/P2XJj/7QatnJX3gjYAzgbnAdcArq+r2dgHww3R5+PfAa6rqx21fBwD/1Hb9L1V1ynjEOFlzsPlX6ph/x8ZisqRJq6p4xStewQEHHMAZZ5wBwE9+8hMWLVq0Rvu95557WHvtaZ3+7gEOq6ofJ3kIcHGS89qyD1bVMb0rJ9kG2BvYFngUcH6Sx7XFHwWeT/fwp4uSLKqqK/sZvJ+7pKmqqr4HjHQP4nOHWb+AQ0bY18nAyeMXnaSpwH7QGhmpD/wa4OtV9Z4khwOHA28DdgO2bl87AscDO7bi85HAfLqi9MWtD3z7hJ+RpAlj/h07p7mQNGl985vfZJ111uHggw++r+0pT3kKO+20E3feeSd77rknT3jCE9h3333p/j8Oc+fO5ZZbbgFg8eLF7LzzzgAsXLiQ/fbbj2c961nst99+LFy4kNe+9rXsvPPOPPrRj+bYY4+d8PPrl6q6YWhURVX9FrgK2Hwlm+wOnFFVd1fVz+lGyC1oX0ur6tqq+iNwRlu3r/zcJUnSTGU/aPWtpA+8OzA0svgU4OXt9e7AqdW5AHhYm9f+hcB5VXVbKyCfB+w6gaciaQDMv2NnMVnSpHX55Zezww47DLvskksu4UMf+hBXXnkl1157Ld///vdH3d+VV17J+eefz+mnnw7AT3/6U84991wuvPBCjjrqKP70pz+Na/yTQZK5wFOBH7Wm1ydZkuTkNg8ndJ3sX/Zstqy1jdQ+3HEOSrI4yeKbb755uFXGzM9dkiTNVPaDxscKfeBN23z0AL+imwYD1rAPPJ79X0mDZ/4dO4vJkqakBQsWMGfOHNZaay223357rrvuulG3ednLXsaDHvSg+96/+MUvZr311mOTTTbhEY94BDfeeGMfI554STYAzgYOrao76G7dewywPXAD8P7xOlZVnVhV86tq/uzZs8drtw/g5y5JkmYq+0FjM0wf+D5teqEaj+NMVP9X0uCZf+/PYrKkSWvbbbfl4osvHnbZeuutd9/rWbNmcc899wCw9tprc++99wJw11133W+bBz/4wWPax3SQZB26TvRnquqzAFV1Y1Utr6p7gY/TTWMBcD2wRc/mc1rbSO195ecuSZJmKvtBa2a4PjBwY5u+gvbvTa19UvWBJQ2W+XfsLCZLmrR22WUX7r77bk488cT72pYsWcJ3v/vdEbeZO3fufX8Azj777L7HOBm1J1OfBFxVVR/oad+sZ7VXAJe314uAvZOsl2Qe3UNILgQuArZOMi/JunQP6Vuzpw+MgZ+7JEmaqewHrb6R+sB0/dcD2usDgC/0tO+fztOB37TpMM4FXpDk4W1auBe0NknTmPl37CwmS5q0kvC5z32O888/n8c85jFsu+22HHHEETzykY8ccZsjjzySN73pTcyfP59Zs2ZNYLSTyrOA/YBdklzavl4E/FuSy5IsAZ4D/ANAVV0BnAVcCXwVOKSNYL4HeD1d5/kq4Ky2bl/5uUuSpJnKftAaGakP/B7g+UmuAZ7X3gOcA1xL9/DpjwN/D1BVtwHvphtYcRHwrtYmaRoz/45dhp5AON3Mnz+/Fi9ePOgwNMPs8NZTBx1C3138vv1Xe9urrrqKJz7xieMYzWANdz5JLq6q+QMKaVIYLv/OhM9+KpnMuWpNcoxmNvPv2Pq//v5LE2+q9xtWZB/4gcZaf5isOdj8q+nK/NsfjkyWJEmSJEmSJI3KYrIkSZIkSZIkaVQWkyVJkiRJkiRJo7KYLEmSJEmSJEkalcVkSZIkSZIkSdKoLCZLmlGScNhhh933/phjjmHhwoWDC0gTxs9ekiTNVPaDJGkwpmP+XXvQAUiauXZ466njur+L37f/qOust956fPazn+WII45gk002Gdfja+z87CVJ0kxlP0iSBsP8Oz4sJkuaUdZee20OOuggPvjBD3L00Uffb9l1113Ha1/7Wm655RZmz57NJz/5SbbccssBRarx5mcvSRq08f5P7Hgay3+INXXZD5KkwZiO+ddpLiTNOIcccgif+cxn+M1vfnO/9je84Q0ccMABLFmyhH333Zc3vvGNA4pQ/eJnL0mSZir7QZI0GNMt/1pMljTjbLjhhuy///4ce+yx92v/4Q9/yKte9SoA9ttvP773ve8NIjz1kZ+9pH5LcnKSm5Jc3tN2ZpJL29d1SS5t7XOT/KFn2Qk92+yQ5LIkS5McmySDOB9J04f9IEkajOmWfy0mS5qRDj30UE466SR+97vfDToUTTA/e0l99ilg196Gqtqrqravqu2Bs4HP9iz+2dCyqjq4p/144HXA1u3rfvuUpNVhP0iSBmM65V+LyZJmpI022ohXvvKVnHTSSfe1PfOZz+SMM84A4DOf+Qw77bTToMJTH/nZS+qnqvoOcNtwy9ro4lcCp69sH0k2AzasqguqqoBTgZePd6ySZh77QZI0GNMp/1pMljRjHXbYYdxyyy33vT/uuOP45Cc/yXbbbcdpp53Ghz/84QFGp37ys5c0IDsBN1bVNT1t85JckuTbSYb+B7E5sKxnnWWt7QGSHJRkcZLFN998c3+iljSt2A+SpMGYLvl37UEHIGnmGsRTw++88877Xm+66ab8/ve/v+/9VlttxTe+8Y0Jj2km8rOXNEPtw/1HJd8AbFlVtybZAfh8km1XZYdVdSJwIsD8+fNr3CKV1Df2gyRpMMy/48NisiRJktRnSdYG9gB2GGqrqruBu9vri5P8DHgccD0wp2fzOa1NkiRJGiinuZAkSZL673nAT6vqvukrksxOMqu9fjTdg/auraobgDuSPL3Ns7w/8IVBBC1JkiT16nsxOcmsNg/cl9r7eUl+lGRpkjOTrNva12vvl7blc3v2cURrvzrJC/sdsyRJkrQ6kpwO/BB4fJJlSQ5si/bmgQ/eezawJMmlwH8BB1fV0MP7/h74BLAU+Bnwlb4HL0lTWJKTk9yU5PKetjOTXNq+rmv5liRzk/yhZ9kJPdvskOSyVoM4tl3UkyQ1EzHNxZuAq4AN2/v3Ah+sqjNawj4QOL79e3tVPTbJ3m29vZJsQ9f53hZ4FHB+ksdV1fIJiF2SJEkas6raZ4T21wzTdjZw9gjrLwaeNK7BSdL09ingI8CpQw1VtdfQ6yTvB37Ts/7Pqmr7YfZzPPA64EfAOcCueEFPku7T15HJSeYAL6YbVUG7orcL3cgLgFOAl7fXu7f3tOXPbevvDpxRVXdX1c/pRmcs6GfckiRJkiRp6qiq7wC3Dbes1RZeyQPvEFlxvc2ADavqgqoqusL0y1e2jSTNNP2e5uJDwD8C97b3GwO/rqp72vtlwObt9ebALwHa8t+09e9rH2ab+0lyUJLFSRbffPPN43kekiRJkiRpatoJuLGqrulpm9em5Px2kp1a2+Z0NYch1h8kaQV9KyYneQlwU1Vd3K9jrKiqTqyq+VU1f/bs2RN1WEmSJEmSNHntw/1HJd8AbFlVTwXeDPxHkg2H3XIE1h8kzVT9nDP5WcDLkrwIWJ9uzuQPAw9LsnYbfTwHuL6tfz2wBbAsydrAQ4Fbe9qH9G4jaQr7xbuePK772/Kdl610eVWx00478Y53vIPddtsNgP/8z//kpJNO4qtf/eq4xqKV87OXJEkzlf2gidXqC3sAOwy1VdXdwN3t9cVJfgY8jq7WMKdnc+sP08QObz119JUG5OL37T/oEGYM8+/46NvI5Ko6oqrmVNVcugfofaOq9gW+CezZVjsA+EJ7vai9py3/RpujaBGwd5L1kswDtgYu7FfckqavJJxwwgm8+c1v5q677uLOO+/k7W9/Ox/96EcHHZr6zM9ekiTNVPaDeB7w06q6b/qKJLOTzGqvH01XZ7i2qm4A7kjy9DbP8v78uWYhSatkuubffs+ZPJy3AW9OspRuTuTba5jUAAAgAElEQVSTWvtJwMat/c3A4QBVdQVwFnAl8FXgkKpaPuFRS5oWnvSkJ/HSl76U9773vbzrXe/i1a9+NUcffTQLFizgqU99Kl/4QtdXvOKKK1iwYAHbb7892223Hddcc80oe9Zk52cvSZJmqpnQD0pyOvBD4PFJliU5sC3amwc+eO/ZwJIklwL/BRxcVUMP7/t74BPAUuBnwFf6HrykaWs65t9+TnNxn6r6FvCt9vpaYMEw69wF/M0I2x8NHN2/CCXNJEceeSRPe9rTWHfddXnJS17CLrvswsknn8yvf/1rFixYwPOe9zxOOOEE3vSmN7Hvvvvyxz/+keXLvYY1HfjZazLwNktJ0iBM935QVe0zQvtrhmk7Gzh7hPUXA08a1+AkzWjTLf9OSDFZkiaTBz/4wey1115ssMEGnHXWWXzxi1/kmGOOAeCuu+7iF7/4Bc94xjM4+uijWbZsGXvssQdbb731gKPWePCzlyRJM5X9IEkajOmWfy0mS5qR1lprLdZaay2qirPPPpvHP/7x91v+xCc+kR133JEvf/nLvOhFL+JjH/sYu+yyy4Ci1Xjys5ckSTOV/SBJGozplH8HMWeyJE0aL3zhCznuuOPonvcJl1xyCQDXXnstj370o3njG9/I7rvvzpIlSwYZpvrAz16SJM1U9oMkaTCmQ/51ZLKkgdnynZcNOgT++Z//mUMPPZTtttuOe++9l3nz5vGlL32Js846i9NOO4111lmHRz7ykbz97W8fdKjTip+9JEmaqewHSdJgmH/Hh8VkSTPSwoUL73v9sY997AHLDz/8cA4//PAJjEgTxc9ekiTNVPaDJGkwplP+dZoLSZpmkmyR5JtJrkxyRZI3tfaNkpyX5Jr278Nbe5Icm2RpkiVJntazrwPa+tckOWBQ5yRJkiRJkgbPYrIkTT/3AIdV1TbA04FDkmwDHA58vaq2Br7e3gPsBmzdvg4Cjoeu+AwcCewILACOHCpAS5IkSZKkmcdisqQJNTTJ/FQ3mc+jqm6oqh+3178FrgI2B3YHTmmrnQK8vL3eHTi1OhcAD0uyGfBC4Lyquq2qbgfOA3Zdg7hWd9NJZbqchyRJmjjTpf8wXc5D0swxXfLWZDoPi8mSJsz666/PrbfeOqmS4OqoKm699VbWX3/9QYcyqiRzgacCPwI2raob2qJfAZu215sDv+zZbFlrG6l9uOMclGRxksU333zzA5b72UuSpJnKfpAkDYb5tz98AJ+kCTNnzhyWLVvGcMXGqWb99ddnzpw5gw5jpZJsAJwNHFpVdyS5b1lVVZJx+4taVScCJwLMnz//Afv1s5c0UyQ5GXgJcFNVPam1LQReBwwlwbdX1Tlt2RHAgcBy4I1VdW5r3xX4MDAL+ERVvWciz0PS+LEfJEmDYf7tD4vJkibMOuusw7x58wYdxoyQZB26QvJnquqzrfnGJJtV1Q1tGoubWvv1wBY9m89pbdcDO6/Q/q3VicfPXtIM8ingI8CpK7R/sKqO6W1o89nvDWwLPAo4P8nj2uKPAs+nuyvkoiSLqurKfgYuqT/sB0nSYJh/+8NpLiRpmkk3BPkk4Kqq+kDPokXAAe31AcAXetr3T+fpwG/adBjnAi9I8vD24L0XtDZJ0giq6jvAbWNcfXfgjKq6u6p+Diyle+DpAmBpVV1bVX8EzmjrSpIkSQNlMVmSpp9nAfsBuyS5tH29CHgP8Pwk1wDPa+8BzgGupStifBz4e4Cqug14N3BR+3pXa5MkrbrXJ1mS5OR2gQ7GYc56SZIkaSI5zYUkTTNV9T0gIyx+7jDrF3DICPs6GTh5/KKTpBnpeLqLc9X+fT/w2vHYcZKDgIMAttxyy/HYpSRJkjQiRyZLkiRJfVRVN1bV8qq6l+4OkAVt0crmrB+ufbh9n1hV86tq/uzZs8c/eEmSJKmHxWRJkiSpj9pDT4e8Ari8vV4E7J1kvSTzgK2BC+mmFto6ybwk69I9pG/RRMYsSVNNm0bopiSX97QtTHL9ClO/DS07IsnSJFcneWFP+66tbWmSwyf6PCRpsnOaC0mSJGmcJDkd2BnYJMky4Ehg5yTb001zcR3wdwBVdUWSs4ArgXuAQ6pqedvP6+keejoLOLmqrpjgU5GkqeZTwEeAU1do/2BVHdPbkGQbugt12wKPAs5P8ri2+KPA8+nmq78oyaKqurKfgUvSVGIxWZIkSRonVbXPMM0nrWT9o4Gjh2k/h+4BqZKkMaiq7ySZO8bVdwfOqKq7gZ8nWcqfpyBaWlXXAiQ5o61rMVmSGqe5kCRJkiRJ09Xrkyxp02A8vLVtDvyyZ51lrW2k9gdIclCSxUkW33zzzf2IW5ImJYvJkiRJkiRpOjoeeAywPXAD8P7x2rEPQJU0UznNhSRJkiRJmnaq6sah10k+Dnypvb0e2KJn1TmtjZW0S5JwZLIkSZIkSZqGkmzW8/YVwOXt9SJg7yTrJZkHbA1cCFwEbJ1kXpJ16R7St2giY5akyc6RyZIkSZKkaW+Ht5466BBGdPH79h90CFNektOBnYFNkiwDjgR2TrI9UMB1wN8BVNUVSc6ie7DePcAhVbW87ef1wLnALODkqrpigk9lwv3iXU8edAjD2vKdlw06BEnDsJgsSZIkSZKmtKraZ5jmk1ay/tHA0cO0nwOcM46hSdK04jQXkiRJkiRJkqRRWUyWJEmSJEmSJI3KYrIkSZIkSZIkaVTOmSxJkiRJkiSpL3wA6vTiyGRJkiRJkiRJ0qgsJkuSJEmSJEmSRmUxWZIkSZIkSZI0KovJkiRJkiRJkqRRWUyWJEmSJEmSJI1q7UEHIEmSJEkarF+868mDDmFYW77zskGHIEmSevRtZHKS9ZNcmOQnSa5IclRrn5fkR0mWJjkzybqtfb32fmlbPrdnX0e09quTvLBfMUuSJEmSJEmShtfPaS7uBnapqqcA2wO7Jnk68F7gg1X1WOB24MC2/oHA7a39g209kmwD7A1sC+wK/HuSWX2MW5IkSVotSU5OclOSy3va3pfkp0mWJPlckoe19rlJ/pDk0vZ1Qs82OyS5rA2oODZJBnE+kiRJUq++FZOrc2d7u077KmAX4L9a+ynAy9vr3dt72vLntk7z7sAZVXV3Vf0cWAos6FfckiRJ0hr4FN0AiF7nAU+qqu2A/waO6Fn2s6ravn0d3NN+PPA6YOv2teI+JUmSpAnX1wfwJZmV5FLgJrpO9M+AX1fVPW2VZcDm7fXmwC8B2vLfABv3tg+zzYrHOyjJ4iSLb7755vE+HUmSJGmlquo7wG0rtH2tp/97ATBnZftIshmwYVVdUFUFnMqfB2BIkiRJA9PXYnJVLa+q7ek6zAuAJ/T5eCdW1fyqmj979ux+HkqSJElaHa8FvtLzfl6SS5J8O8lOrW1zugEUQxxMIUmSpEmhr8XkIVX1a+CbwDOAhyVZuy2aA1zfXl8PbAHQlj8UuLW3fZhtJEmSpCkhyTuAe4DPtKYbgC2r6qnAm4H/SLLhquzTwRSSJEmaSH0rJieZ3fNwkQcBzweuoisq79lWOwD4Qnu9qL2nLf9Gu61vEbB3kvWSzKObM+7CfsUtSZIkjbckrwFeAuzb+ri0Z4Lc2l5fTDcl3OPoBk70ToXhYApJGoUPQJWkidHPkcmbAd9MsgS4CDivqr4EvA14c5KldHMin9TWPwnYuLW/GTgcoKquAM4CrgS+ChxSVcv7GLckSZI0bpLsCvwj8LKq+n1P++wks9rrR9MNmri2qm4A7kjy9FbE2J8/D8CQJA3vU/gAVEnqu7VHX2X1VNUS4KnDtF9LN3/yiu13AX8zwr6OBo4e7xglSZKk8ZTkdGBnYJMky4Aj6YoX6wHntQFuF7TCxbOBdyX5E3AvcHBVDT287+/pCiMPoptjuXeeZUnSCqrqO0nmrtD2tZ63F/Dnu6SH1fsA1PZ+6AGo5mBJavpWTJYkSZJmmqraZ5jmk4Zpo6rOBs4eYdli4EnjGJokzXSvBc7seT8vySXAHcA/VdV3WcUHoAIHAWy55ZZ9CViSJqMJeQCfJGlijTBn3MIk1/fMDfeinmVHtHnhrk7ywp72XVvb0iSHT/R5SJIkSWvKB6BK0vixmCxJ09OnGH5+tw/2zA13DkCSbYC9gW3bNv+eZFabx/OjwG7ANsA+bV1JkiRpSvABqJI0viwmS9I0VFXfAW4bdcXO7sAZrVP9c2Ap3dz2C4ClVXVtVf0ROKOtK0mSJE16PgBVksafxWRJmllen2RJmwbj4a1tc+CXPesMzQ03UvsDJDkoyeIki2+++eZ+xC1JkiSNqD0A9YfA45MsS3Ig8BHgIXQPQL00yQlt9WcDS5JcCvwXD3wA6ifoBlj8DB++J0n34wP4JGnmOB54N1Dt3/fTPYhkjVXVicCJAPPnz6/x2KckSZI0Vj4AVZImxphGJif5+ljaJEnjazzzb1XdWFXLq+pe4ON001hANw/cFj2rDs0NN1K7JE179n8laTDMv5I0ua10ZHKS9YG/ADZpt0OnLdqQEW51liStuX7k3ySbtXngAF4BXN5eL6J7gvUHgEfRzRl3YTvm1knm0RWR9wZetTrHlqSpwv6vJA2G+VeSpobRprn4O+BQuuLCxfw5md9BN/eQJKk/1ij/tjnjdqbrjC8DjgR2TrI93TQX17VjUFVXJDkLuBK4Bzikqpa3/bweOBeYBZxcVVeM0/lJ0mRl/1eSBsP8K0lTwEqLyVX1YeDDSd5QVcdNUEySNOOtaf5dlTnj2vpHA0cP034OcM6qHl+Spir7v5I0GOZfSZoaxvQAvqo6Lskzgbm921TVqX2KS5KE+VeSBsX8K0mDYf6VpMltTMXkJKcBjwEuBZa35gJM5pLUR+ZfSRoM868kDYb5V5ImtzEVk4H5wDZVVf0MRpL0AOZfSRoM868kDYb5V5ImsbXGuN7lwCP7GYgkaVjmX0kaDPOvJA2G+VeSJrGxjkzeBLgyyYXA3UONVfWyvkQlSRpi/pWkwTD/StJgmH8laRIbazF5YT+DkCSNaOGgA5CkGWrhoAOQpBlq4aADkCSNbEzF5Kr6dr8DkSQ9kPlXkgbD/CtJg2H+laTJbUxzJif5bZI72tddSZYnuaPfwUnSTGf+laTBWN38m+TkJDclubynbaMk5yW5pv378NaeJMcmWZpkSZKn9WxzQFv/miQH9OcsJWnysf8rSZPbWEcmP2TodZIAuwNP71dQkqSO+VeSBmMN8u+ngI8Ap/a0HQ58varek+Tw9v5twG7A1u1rR+B4YMckGwFHAvOB+v/Zu/d4X+s5//+Pp4qoqLSnXzrYIfompPZUxmQ2kmoQhhJqZ5BGzDAYGYbkMDkTgymTyqGDUSSRLeVYapfOSemgnXSOUhnl9fvjeq/2p7XXce+11mcdHvfb7XNb1+d9nd7XZ3/263p/Xtf7el/AOUlOrKrbVva4JGm6s/0rSdPbmHom96rON4DnTkJ9JEnDMP5KUn+MJ/5W1Y+AWwcV7wYc2aaPBF7YU35U2/6ZwNpJNmj7WVxVt7YE8mJg5wk4FEmaUWz/StL0M6aeyUle3PP2QXS9JO6ZlBpJku5n/JWk/pjg+Lt+VV3fpn8HrN+mNwSu7VluaSsbrlySZj3bv5I0vY0pmQw8v2f6XuBqup4UkqTJZfyVpP6YlPhbVZWkVnY7A5LsC+wLsMkmm0zUZiWpn1Yo/iY5HHgecGNVbdnK1gWOBea37exeVbe14TM+BewK3AXsU1XntnUWAe9qm31/VR2JJOl+Yx0z+VWTXRFJ0vKMv5LUHxMcf29IskFVXd+GsbixlV8HbNyz3Eat7Dpg4aDy04ep56HAoQALFiyYsCS1JPXLSsTfI3DMekmadGMaMznJRklOaE+mvjHJ15NsNNmVk6S5zvgrSf0xwfH3RGBRm14EfLOnfO90tgd+34bDOAXYKck6SdYBdmplkjTrrWj8dcx6SZoaY30A3xfpGruPaq9vtTJJ0uQy/kpSf6xQ/E1yNHAG8IQkS5O8GjgYeE6Sy4Ed23uAk4ErgSuAw4DXA1TVrcD7gLPb66BWJklzwUS2fydtzPok+yZZkmTJTTfdtILVk6SZZ6xjJs+rqt7gfUSSN01GhSRJD2D8laT+WKH4W1V7DjPr2UMsW8D+w2zncODwsVRUkmaZSWn/TvSY9Q4zJGmuGmvP5FuSvDLJKu31SuCWyayYJAkw/kpSvxh/Jak/JjL+3tCGr2AcY9YPVS5JasaaTP5HYHe620KuB14C7DNJdZIkLWP8laT+MP5KUn9MZPx1zHpJmmBjHebiIGDRwBNM2xNOP0oX5CVJk8f4K0n9YfyVpP5YofjbxqxfCKyXZCnwHrox6o9r49dfQ5ekhm7M+l3pxqy/C3gVdGPWJxkYsx4cs16SljPWZPKTBwI53B9gnzpJdZIkLWP8laT+MP5KUn+sUPx1zHpJmhpjHebiQe0WD+D+K4NjTURLklac8VeS+sP4K0n9YfyVpGlsrAH5Y8AZSb7W3r8U+MDkVEmS1MP4K0n9YfyVpP4w/krSNDamZHJVHZVkCfCsVvTiqrpk8qolSQLjryT1i/FXkvrD+CtJ09uYbxVpwXvMATzJxsBRwPpAAYdW1afaLSrHAvOBq4Hdq+q2JAE+RTcI/l3APlV1btvWIuBdbdPvr6ojx1oPSZrpxht/JUkTw/grSf1h/JWk6WusYyaviHuBt1TVFsD2wP5JtgAOAE6tqs2AU9t7gF2AzdprX+BzcP/4SO8BtgO2Bd7TO36SJEmSJEmSJGnyTVoyuaquH+hZXFV3AJcCGwK7AQM9i48EXtimdwOOqs6ZwNpJNgCeCyyuqlvbE10XAztPVr0lSZIkSZIkScubzJ7J90syH3gq8HNg/aq6vs36Hd0wGNAlmq/tWW1pKxuuXJIkSZIkSZI0RSY9mZxkTeDrwJuq6g+986qq6MZTnqh97ZtkSZIlN91000RtVpIkSZIkSZLmvElNJidZjS6R/JWqOr4V39CGr6D9vbGVXwds3LP6Rq1suPLlVNWhVbWgqhbMmzdv4g5EkiRJkiRJkua4SUsmJwnwP8ClVfXxnlknAova9CLgmz3le6ezPfD7NhzGKcBOSdZpD97bqZVJkiRJkiRJkqbIZPZMfjqwF/CsJOe1167AwcBzklwO7NjeA5wMXAlcARwGvB6gqm4F3gec3V4HtTJJ0jCSHJ7kxiQX9ZStm2Rxksvb33VaeZIckuSKJBck2bpnnUVt+cuTLBpqX5IkSZIkaW5YdbI2XFU/ATLM7GcPsXwB+w+zrcOBwyeudpI06x0BfAY4qqfsAODUqjo4yQHt/duBXYDN2ms74HPAdknWBd4DLKAb3/6cJCdW1W1TdhSSJEmSJGnamPQH8EmSpl5V/QgYfBfHbsCRbfpI4IU95UdV50xg7Tam/XOBxVV1a0sgLwZ2nvzaS5IkSZKk6chksiTNHeu3segBfges36Y3BK7tWW5pKxuuXJI0Tkme0DP023lJ/pDkTUkOTHLdoGHhBtZ5RxuC6LIkz+1n/SVJkiSYxGEuJEnTV1VVkpqo7SXZF9gXYJNNNpmozUrSrFFVlwFbASRZBbgOOAF4FfCJqvpo7/JJtgBeBjwReBTw/SSPr6r7prTikiRJUg97JkvS3HFDG76C9vfGVn4dsHHPchu1suHKl1NVh1bVgqpaMG/evAmvuCTNMs8Gfl1V14ywzG7AMVX1p6q6iu4h1dtOSe0kSZKkYZhMlqS540RgUZteBHyzp3zvdLYHft+GwzgF2CnJOknWAXZqZZKklfMy4Oie929IckGSw1u8hTEONZRk3yRLkiy56aabJq/GkjRDOcyQJE0sk8mSNAslORo4A3hCkqVJXg0cDDwnyeXAju09wMnAlXS93g4DXg9QVbcC7wPObq+DWpkkaQUleTDwAuBrrehzwGPphsC4HvjYeLbnnSGSNLKquqyqtqqqrYBtgLvohhmCbpihrdrrZFhumKGdgc+24YkkSThmsiTNSlW15zCznj3EsgXsP8x2DgcOn8CqSdJctwtwblXdADDwFyDJYcBJ7e2YhxqSJI3Z/cMMJRlumfuHGQKuSjIwzNAZU1RHSZrWTCZLGpffHPSkfldhSmzy7gv7XQVJ0uy0Jz1DXCTZoA0tBPAi4KI2fSLw1SQfp3sA32bAWVNZUUmahYYaZmhvYAnwlqq6jW5IoTN7lhl2mCF8ALWkOchhLiRJkqQpkGQN4DnA8T3FH05yYZILgGcCbwaoqouB44BLgO8C+1fVfVNcZUmaNRxmSJImhj2TJUmSpClQVX8EHjmobK8Rlv8A8IHJrpckzREOMyRJE8CeyZIkSZIkabZbbpihnnmDhxl6WZKHJNkUhxmSpAewZ7IkSZIkSZq1eoYZel1P8YeTbAUUcPXAvKq6OMnAMEP34jBDkvQAJpMlSZIkSdKs5TBDkjRxTCZLkiQJgN8c9KR+V2FIm7z7wn5XQZIkSRImkyVJ0jRiMlOSJEmSpi8fwCdJkiRJkiRJGpXJZEmSJEmSJEnSqEwmS5IkSZIkSZJGZTJZkiRJkiRJkjQqk8mSJEmSJEmSpFGZTJYkSZIkSZIkjcpksiRJkiRJkiRpVCaTJUmSJEmSJEmjMpksSZIkSZIkSRqVyWRJkiRJkiRJ0qhMJkuSJEmSJEmSRmUyWZIkSZoCSa5OcmGS85IsaWXrJlmc5PL2d51WniSHJLkiyQVJtu5v7SVJkiSTyZIkSdJUemZVbVVVC9r7A4BTq2oz4NT2HmAXYLP22hf43JTXVJIkSRrEZLIkSZLUP7sBR7bpI4EX9pQfVZ0zgbWTbNCPCkrSTOedIZI0cUwmS5IkSVOjgO8lOSfJvq1s/aq6vk3/Dli/TW8IXNuz7tJW9gBJ9k2yJMmSm266abLqLUmzgXeGSNIEMJksSZIkTY2/raqt6RIV+yd5Ru/Mqiq6hPOYVdWhVbWgqhbMmzdvAqsqSbOed4ZI0gowmSxJkiRNgaq6rv29ETgB2Ba4YSBJ0f7e2Ba/Dti4Z/WNWpkkafy8M0SSJojJZEmSJGmSJVkjyVoD08BOwEXAicCittgi4Jtt+kRg7zZ25/bA73uSHpKk8fHOEEmaIKv2uwKSJEnSHLA+cEIS6NrgX62q7yY5GzguyauBa4Dd2/InA7sCVwB3Aa+a+ipL0uzQe2dIkgfcGVJV13tniCSN3aQmk5McDjwPuLGqtmxl6wLHAvOBq4Hdq+q2dC3rT9E1mu8C9qmqc9s6i4B3tc2+v6qORJIkSZohqupK4ClDlN8CPHuI8gL2n4KqTRu/OehJ/a7CkDZ594X9roKkldDuBnlQVd3Rc2fIQSy7M+Rglr8z5A1JjgG2wztDJOkBJnuYiyOAnQeVjeuJqS35/B66IL4t8J4k60xyvSVJkiRJ0sy3PvCTJOcDZwHfrqrv0iWRn5PkcmDH9h66O0OupLsz5DDg9VNfZUmavia1Z3JV/SjJ/EHFuwEL2/SRwOnA2+l5YipwZpKBJ6YuBBZX1a0ASRbTJaiPnsy6S5IkSZKkmc07QyRpYvXjAXzjfWLqmJ6kCj5NVZIkSZIkSZImSz+SyfdbkSemjrI9n6YqSaNIcnWSC5Ocl2RJK1s3yeIkl7e/67TyJDkkyRVJLkiydX9rL0mSJEmS+qUfyeQb2vAVjPGJqT5JVZIm3jOraquqWtDej2s8e0mSJEmSNPdM6pjJwxjXE1OTnAJ8sOehezsB75jiOkvSbDeu8exX9onW27ztqJVZfVKd85G9+10FSZIkSZKmpUntmZzkaOAM4AlJliZ5NeN8Ymp78N77gLPb66CBh/FJklZIAd9Lck6SfVvZeMezfwDHrJckSZIkafab1J7JVbXnMLPG9cTUqjocOHwCqyZJc9nfVtV1Sf4KWJzkl70zq6qSjGs8+6o6FDgUYMGCBRM2Fr4kSZIkSZo++voAPknS1Kuq69rfG4ETgG0Z/3j2kiRJkiRpjjGZLElzSJI1kqw1ME03Dv1FLBvPHpYfz37vdLanjWc/xdWWJEmSJEnTQD8ewCdJ6p/1gROSQHcO+GpVfTfJ2cBxbWz7a4Dd2/InA7vSjWd/F/Cqqa+yJEmSJEmaDkwmS9IcUlVXAk8ZovwWxjmevSRJkiRJmlsc5kKSJEmSJEmSNCqTyZIkSZIkSZKkUZlMliRJkiRJkiSNymSyJEmSJEmSJGlUPoBPkiRJkiRJ6oPfHPSkfldhSJu8+8J+V0HTlD2TJUmSpEmWZOMkpyW5JMnFSf6llR+Y5Lok57XXrj3rvCPJFUkuS/Lc/tVekiRJ6phMliRJkibfvcBbqmoLYHtg/yRbtHmfqKqt2utkgDbvZcATgZ2BzyZZpR8Vl6SZzIt5kjSxHOZCkiRJmmRVdT1wfZu+I8mlwIYjrLIbcExV/Qm4KskVwLbAGZNeWUmaXQYu5p2bZC3gnCSL27xPVNVHexcedDHvUcD3kzy+qu6b0lpL0jRlz2RJkiRpCiWZDzwV+HkrekOSC5IcnmSdVrYhcG3PaksZIvmcZN8kS5Isuemmmyax1pI0M1XV9VV1bpu+AxjzxbyqugoYuJgnScJksiRJkjRlkqwJfB14U1X9Afgc8FhgK7qeyx8bz/aq6tCqWlBVC+bNmzfh9ZWk2cSLeZK08kwmS5IkSVMgyWp0ieSvVNXxAFV1Q1XdV1V/AQ5jWe+364CNe1bfqJVJklaAF/MkaWI4ZvIg27ztqH5XYUqc85G9+10FSZKkOSNJgP8BLq2qj/eUb9DGUwZ4EXBRmz4R+GqSj9ON2bkZcNYUVlmSZo3hLub1zD8MOKm99WKeJI3AZLIkSZI0+Z4O7AVcmOS8VvbvwJ5JtgIKuBp4HUBVXZzkOOASuodH7e/DnyRp/LyYJ0kTy2SyJEmSNMmq6idAhph18gjrfAD4wKRVSpLmBi/mSdIEMpksSZIkSZJmJS/mSdLE8gF8kiRJkiRJkqRRmUyWJB7Oi5AAACAASURBVEmSJEmSJI3KZLIkSZIkSZIkaVQmkyVJkiRJkiRJozKZLEmSJEmSJEka1ar9roAkSdPJbw56Ur+rMKRN3n1hv6sgSZIkSZrj7JksSZIkSZIkSRqVPZMlSZIkSeoj74ySpP4w/o6fPZMlSZIkSZIkSaMymSxJkiRJkiRJGpXJZEmSJEmSJEnSqBwzeY6armPCTKTpPL6MJEmSJEmSNNPYM1mSJEmSJEmSNCqTyZIkSZIkSZKkUZlMliRJkiRJkiSNasYkk5PsnOSyJFckOaDf9ZGkucL4K0n9YfyVpP4w/krS8GZEMjnJKsB/AbsAWwB7Jtmiv7WSpNnP+CtJ/WH8laT+MP5K0shmRDIZ2Ba4oqqurKr/A44BdutznSRpLjD+SlJ/GH8lqT+Mv5I0glRVv+swqiQvAXauqte093sB21XVGwYtty+wb3v7BOCyKa3ozLIecHO/K6EZye/OyB5dVfP6XYmJMkfjr9/x/vLz76+Z/Pkbf42/Wjl+/v010z//WRODjb/qAz///prpn/+Ux99Vp3Jnk62qDgUO7Xc9ZoIkS6pqQb/roZnH746GMpvir9/x/vLz7y8//5nH+KuJ4uffX37+M4/xVxPFz7+//PzHb6YMc3EdsHHP+41amSRpchl/Jak/jL+S1B/GX0kawUxJJp8NbJZk0yQPBl4GnNjnOknSXGD8laT+MP5KUn8YfyVpBDNimIuqujfJG4BTgFWAw6vq4j5Xa6abFbfjqC/87swhczT++h3vLz///vLznyaMv+oDP//+8vOfJoy/6gM///7y8x+nGfEAPkmSJEmSJElSf82UYS4kSZIkSZIkSX1kMlmSJEmSJEmSNCqTybNckkrysZ73b01yYB+rpGkunZ8k2aWn7KVJvtvPemlmm6xYlGRhkpNWdjv9luQLSbbodz2mkuenqWd8Vz8luS/JeUkuTnJ+krckeVCb97AkX0lyYZKL2vd0zTbvzv7WfHro+fwGXvOT/KzN6+u5MMlWSXbtef+CJAf0qz7Tnec/9ZPfv/6YC22wlf1ujeXc0c59L1+Jao5Zkn2SPGoq9tX2tyDJIVO1v5VlMnn2+xPw4iTr9bsimhmqG0h9P+DjSVZvP+Y+COzf35pphpvzsSjJKsPNq6rXVNUlU1mfaWDOfyemmvFdfXZ3VW1VVU8EngPsArynzfsX4IaqelJVbQm8Gvhzn+o5XQ18fgOvq6vqbyZiwyOdn8ZoK+D+ZHJVnVhVB6/kNmczz3/qJ79/fTBH2mAr9d0a47ljPjCuZHKSVVekPsA+wIQmk0eqS1Utqap/nsj9TSaTybPfvXRPpnzz4Bntqs4PklyQ5NQkm0x99TQdVdVFwLeAtwPvBr4MvDPJWUl+kWQ3gCRPbGXnte/RZn2stqa3lYpFSQ5M8qUkZyS5PMlre2avmeR/k/yy9WxLW+fZ7ft6YZLDkzyklV+d5L1Jzm3zNm/la7TlHvA9H1SPDZL8qH3nL0qyQyvfqdXt3CRf6+lRd3WSDyU5F3hbkrMGHfeFbfr0JAva9M5tO+cnOXWsdZuBPD/1gfFd00FV3QjsC7yhxewNgOt65l9WVX/qV/1mijyw1/bDk3w7yWVJPp9lvb7Hcn566aDtDhmDkxzRtr0kya+SPC/Jg4GDgD1avNgjXW+uz7R11k9yQjunnZ9kQhLgM5znP/WT378+mQNtsJX9vdd77jgiySFJfpbkyiQvaYsdDOzQPps3J1klyUeSnN22/bq2/sIkP05yInBJe396hv7NuE2SHyY5J8kp7ffeS4AFwFfavh46qK7/nOSSts9jWtmQv9facZ2Y5AfAqUmOSfL3Pds6IslL0nOXUZI1k3wx3W/VC5L8Qysf8pzeF1Xlaxa/gDuBhwNXA48A3goc2OZ9C1jUpv8R+Ea/6+tr+ryANYDLgAuB/wRe2crXBn7V5n8aeEUrfzDw0H7X29f0fK1sLAIOBM4HHgqsB1xLd6V4IfB7YCO6C6RnAH8LrN6WeXxb/yjgTW36auCNbfr1wBfa9AeH+p4PqsdbgHe26VWAtVp9fjSwLK2B2LOvf+tZ/zxg057l3tWmT6drsMxr9R5YZt2x1m2mvTw/9fWzN777mvIXcOcQZbcD69P1bL2xxfD3A5uNtN5cfAH3tXPIecAJvZ9NOxfeAzymnZsWAy8Zz/lp0L6GjMHAEcB36c63mwFL6c63+wCf6Vn//vfAsSw7/64CPKLfn2W/X57/fPXz5fev75//rG2Drex3a9C54wjga+18swVwRStfCJzUs86+LPs99RBgCbBpW+6PLPtNtZChfzOuBvwMmNeW2wM4vE2fDiwY5lh/Czxk4N+u/R3y91o7rqUs+133IuDInn/fa+l+495/bMCHgE/27G8dRjin9+O1ot29NYNU1R+SHAX8M3B3z6ynAS9u018CPjzVddP0VVV/THIs3Ulhd+D5Sd7aZq8ObEIXhN+ZZCPg+Kq6vD+11UwwAbHom1V1N3B3ktOAbekSEWdV1VKAJOfR3f50B3BVVf2qrXsk3W1kn2zvj29/z+nZ907AC4b4nl/aU4ezgcOTrEbXCDovyd/RNXJ+2i5wP5ju/8aAY3umj6NrpBzc/u4x6Bi3B35UVVcBVNWt46jbjOP5qT+M75puWix9DF2s2xE4O8nTqmpGx7gJdndVbTXC/LOq6kqAJEfT/Ui+h7Gfn3qNFIOPq6q/AJcnuRLYfJR6PwvYG6Cq7qP7MT/nef5TP/n965/Z3gab4O/WN9r55pIk6w+zzE7Ak3t6Lj+C7mLn/9GdF6/qWXao34y3A1sCi9t5chXg+jHU7QK6XsvfAL7RU5ehfq8BLO75Xfcd4FPp7prdme63391t/wN2BF428KaqbkvyPEY+p08pk8lzxyeBc4Ev9rsimlH+0l4B/qGqLhs0/9IkPwf+Hjg5yeuq6gdTXUnNKCsTi2qY9723Qt/H2M5tA+v0Lj/c93zZDqt+lOQZdN/5I5J8HLiNroGw5zCr/bFn+ljga0mO7zY35sbhqHWbwTw/9YfxXX3Vksf30fVIpqrupLvQd3ySv9CNwWsyeeyGOkeGsZ+fVmY/WjGe/9RPfv/6Z7a3wSbqu9X7Gy/DLBO6O05PeUBhspDlz3FD/WYMcHFVPW2cdft74BnA8+kS/09imH/PJNv11qWq7klyOvBcuo5Fx4xxn6Od06eUYybPEe0qyHF0DzQZ8DOWXe14BfDjqa6XZoxTgDf2jCv01Pb3McCVVXUI8E3gyf2romaClYxFu6V7YMUj6W4DOnuEXV0GzE/yuPZ+L+CHo1RvyO95rySPpntI1GHAF4CtgTOBpw/sq42X9fihdlBVv6ZrvPwHQ/cIOxN4RpJN27bWHWvdZirPT31nfNeUSzIP+Dzd7ayV5OlJ1mnzHkzX8+aaftZxBto2yabpxkreA/gJ4zg/DTJSDH5pkgcleSzdsBqX0d0NtNYw2zoV+Ke2/1WSPGKcxzVref5TP/n9mxZmZRtskr9bg883pwD/1O4aJcnjk6wxju1dBsxL8rS2/mpJnjjMvmjLPAjYuKpOoxtq4hHAmozv99qxwKuAHeiGjxpsMT0PZ2xtpBU9p08Kk8lzy8foxlkZ8EbgVUkuoEu0/EtfaqWZ4H104wldkOTi9h66W3MuareJbEk3Lq00mhWNRRcAp9GdSN9XVb8dbgdVdQ/dCfpr6R5y9xe6xMVIhvue91oInJ/kF3Q/1j9VVTfRjYV1dDuGMxj5tt9jgVfSNbIG1/smurG/jk9yPssSzmOp20zm+al/jO+aKg9N9xCbi4HvA98D3tvmPRb4YYvXv6Ab8/Drbd7Dkiztef3rlNd8Zjgb+Axdb+6r6MZVHu/5acBIMfg3wFl0t+nu1863pwFbtH/fwcM3/QvwzPZvew7dhQIt4/lP/eT3r79mcxtssr5bFwD3pXug65vpOvdcApyb5CLgvxnHCAxV9X90zxj4UPvtdR4w8KDYI4DPZ/kH8K0CfLmnzXJIVd3O+H6vfQ/4O+D7rQ6DvR9YJ90D388HnrkS5/RJkTZwsyRJ01aSA+keNPTRftdFkqS5KMkRdA8H+t9+10WSJPWPPZMlSZIkSZIkSaOyZ7IkSZIkSZIkaVT2TJYkSZIkSZIkjcpksiRJkiRJkiRpVCaTJUmSJEmSJEmjMpmsaSfJfUnOS3JRkq8leViS+UkumuT9PijJIW2/FyY5O8mmE7TtLyTZYiK2JUmTKUkl+XLP+1WT3JTkpDGse2f7Oz/Jy3vKFyQ5ZJR1R43zwy2T5KAkO45WP0maDgZi5SjLvCnJw6agLlsl2bXn/fpJTkpyfpJLkpw8gfv62URtS5JWVpKNknwzyeVJfp3kU0kePMo6VydZr02vcExLsn2Sn7e8x6VJDlzRbY1xf/e3xZMsTPI3k7k/zX4mkzUd3V1VW1XVlsD/AftN0X73AB4FPLmqngS8CLh9rCsnWWW4eVX1mqq6ZOWrKEmT7o/Alkke2t4/B7hunNuYD9yfTK6qJVX1zxNTveVV1bur6vuTtX1J6oM3AeNKJo/UFh3BVsCuPe8PAhZX1VOqagvggHHsP0mG/X1ZVSYvJE0LSQIcD3yjqjYDHg+sCXxgrNtYyZh2JLBvVW0FbAkctxLbGlGSVQe1xRcCxmOtFJPJmu5+DDyuTa+S5LAkFyf53kCio/WoODPJBUlOSLJOKz89yYeSnJXkV0l2aOWrJPlI63l8QZLXte1vAFxfVX8BqKqlVXVbW2enJGckObf1ll6zlV/d9nEu8LYkZw1UvPWgu7CnLgva9M5tO+cnObWVrZHk8FbXXyTZbVI/VUka2cnA37fpPYGjB2YkOTDJW3veX5Rk/qD1DwZ2aL0t3tx6QJzUs/6XWky9PMlrB+98hDg9pCRHJHlJm746yXtbnL0wyeatfMg4m+SJrey8tq/NxvlZSdIKabHx9CT/m+SXSb7SErL/TNfB4bQkp7Vlx9IWfel4YmC6HngHAXu0GLgHXXt46UAdq+qCnvq+rScuv7eVzU9yWZKjgIuA/0jykZ519knymTZ9Z0/521v9zk9ycCt7bJLvJjknyY8H6i5Jk+BZwD1V9UWAqroPeDPwj0len+T4Fo8uT/LhoTaQZXfkDRnL27xtkvywxbVTkmzQVv8r4PqBfQ90PBuhvbpKko+2dvcFSd7Yynt7Si9IcnqbHmhv/xT40kBbvLXZ9wPe3OL+DkmuSrJaW+/hve+l4ZhM1rSVZFVgF+DCVrQZ8F9V9US6HsP/0MqPAt5eVU9uy76nZzOrVtW2dL07BspfDfy+qv4a+GvgtemGszgOeH4Lqh9L8tRWj/WAdwE7VtXWwBLgX3v2cUtVbV1VBwMPzrKhMfYAjh10TPOAw4B/qKqnAC9ts94J/KDV9ZnAR5KsMd7PTJImyDHAy5KsDjwZ+Pk41z8A+HG7y+QTQ8x/Ml0j/mnAu5M8atD84eL0WN3c4vXngIHE93Bxdj/gU61nyAJ6kiiSNAWeStdO3QJ4DPD0qjoE+C3wzKp65jjaose092OKgcBqwLuBY1u8Phb4L+B/kpyW5J0D8TnJTnRt8W3pejNvk+QZbdubAZ9tbfTP0t3dN2APunPK/ZLsAuwGbNfawwOJmkOBN1bVNq3enx3fRylJY/ZE4Jzegqr6A/AbYFW6OLcH8CS6C24bj7K95WJ5S8h+GnhJi2uHs6zn8yeAy9J1hntda3PD8O3Vfenu/Nuq5T2+MoZj3ILuvLFnzzFeDXwe+ESL+z8GTmdZJ5KXAcdX1Z/HsH3NYav2uwLSEB6a5Lw2/WPgf+h6Z1xVVQPl5wDzkzwCWLuqftjKjwS+1rOt43uXb9M7AU9O68UGPALYrKq+l+QJdAmOZwGnJnkp8FC6QPzTdoHxwcAZPfvoTRgfR3fSObj93WPQsW0P/KiqrgKoqlt76vSCLOvttzqwCXDpkJ+QJE2iqrqg9VzYk66X8kT7ZlXdDdzdet1tC5zXM3/IOA38aozb7439L+7Z5lBx9gzgnUk2oms8X74iByRJK+isqloK0Nq/84GfDFpme8beFoXxxcAHqKpTkjwG2JmuU8cvkmzZ1t8J+EVbdE26uPwb4JqqOrOtf1OSK5NsD1wObA78dNBudgS+WFV3tXVuTdfT+m+Ar7VjBHjI4PpJ0hQ5tap+D5DkEuDRwLUjLD9ULL+dbgiLxS2urcKy3sgHJfkKXVx9OV2beyHDx+odgc9X1b1t/YE8wkhObO3t0XwB+DfgG8CrgOXuGpQGM5ms6eju1kPsfi34/qmn6D66JO9oBta5j2Xf99D1ejhl8MJV9SfgO8B3ktwAvBD4Ht3YcXsOXr75Y8/0sXSN4OO7zY05KRG63sqXjXF5SZpsJwIfpWvYPrKn/F4eeGfT6oxfjfJ+yDid5YfTGM5wsX+oOHtpkp/T9cg4OcnrquoHY9yPJK2swe3boX6fhbG3RXu3OWoMTLLd4I21JMVXga+mG6LoGW39/6yq/x60/vwh9n8MsDvwS+CEqhoc44fyIOD2wb8BJGmSXAK8pLcgycPpErf3MrbY3Guo5QNcXFVPG2qFqvo18LkkhwE3JXkkw8fq4fbb2y4f3CYfHJuHVFU/bUMWLQRWqaoRH4gtgcNcaIZrVwtvSxsPGdgL+OEIqwCcAvxTz7hAj29jE23dcyvfg+huw74GOJPuNpXHtXlrJHn8MPX5Nd3J4z9YvpcIbVvPGLhdO8m6PXV6Y8/YSk8d/eglaVIdDry3qi4cVH41sDVAkq2BoYafuANYa4Rt75Zk9dZoXgicPWj+kHF63Eew/DaXi7OtB96V7bbyb9LFfknqt944Oua26AiGa2s+IF4neVaSh7XptYDH0vU+PoVuLNGBsZo3TPJXw+zrBLphLPZk0BAXzWLgVT37WbfdXn5Vuytw4GF+TxnnMUrSWJ0KPCzJ3nD/A0w/BhwB3DVB+7gMmJfkaW0fqyV5Ypv++4F4THeXx310PZmHi9WLgde1oUB78whXA9u06YFhQEczVDv9KLqLiF8c89FpTjOZrNlgEd1YQhfQjW100CjLf4HuSuS5SS4C/pvuyuFfAd9qZRfQXeX7TFXdBOwDHN32cQbdLXvDORZ4JUM8kbVta1/g+CTnsyzh/D66cesuSHJxey9JfdMeQnrIELO+DqzbYtUbGHroiQuA+9I9WOnNw8w/jS5B8r6q+u2g+cPFaYAnJFna83opYzNcnN0duKjdkrglXWNakvrtUOC7SU5bgbboUIaLgacBW2TZA/i2AZb07OcLVXV2VX2PLtFwRroHTP8vw1w0bA+wvhR4dFWdNcT879Ld/bKkxd6B27lfAby6tZEvpktIS9KEa3dMvIjuwaWX07Vn7wH+fQL38X90vZ8/1OLaeXTD+UDXCe6yFgO/BLyiPQRwuFj9BboLexe0bb28lb8X+FSSJXQJ6bH4FvCiFvcHOuV9BViHnoduSyPJ2O46kiRJWnlJDgTurKqP9rsukiRJ0lzXnlOyW1Xt1e+6aGZwzGRJkiRJkiRpjknyaboHru7a77po5rBnsiRJkiRJkiRpVI6ZLEmSJEmSJEkalclkSZIkSZIkSdKoTCZLkiRJkiRJkkZlMlmSJEmSJEmSNCqTyZIkSZIkSZKkUZlMliRJkiRJkiSNymSyJEmSJEmSJGlUJpMlSZIkSZIkSaMymSxJkiRJkiRJGpXJZEmSJEmSJEnSqEwma0ZKcnWSHdv0vyf5Qr/rNJLe+krSXJVkhySX9bseY5XkziSP6Xc9JM1NSTZpcWiVftdlKiT5fJL/6Hc9JGk8khyR5P39roc0lUwmq2+S7JPkwiR3Jfldks8lWXu826mqD1bVayagPvOTVGu035nkhiSfTbLaym5bkiZTkr9N8rMkv09ya5KfJvnrFmd/0u/6DaiqH1fVEyZiW63hXkl2G1T+iVa+z8ruo6rWrKorV3Y7kma31mng7iR3JLm9xeP9kqzUb62q+k2LQ/dNUP0G2rdHJFlzBbdVSR63MvVp21nu/FRV+1XV+1Z225I0kp7f+3cm+UtPfLwzySsmYX+7JTkvyR+S3JzkB0k2nej9rKzp9rtB05vJZPVFkrcAHwLeBjwC2B54NLA4yYP7WTdg7apaE3gS8DRg/z7XR5KGleThwEnAp4F1gQ2B9wJ/GuP6M7nH26+AvQfeJFkV2B34dd9qJGmuen5VrUXXnj0YeDvwP/2t0gM8v7VvtwYWAO8avECLoZI0q7WLdGu2mPgbWnxsr69M5L7axbejgLfQ5T02Bf4LWKmLhBPN+K/xMpmsKdcSH+8F3lhV362qP1fV1XQJgPnAK5McmOS4JEe1Xh4XJ1kwzPYOTPLlNj3Qu3hRkt+0K3/v7Fn2QUkOSPLrJLe0faw71Har6kZgMbBFz/oD696R5JIkLxpUl9cmubRn/tZD1Pf/JbkqyZ5DXf3r7fHReo58Psnits0fJnn0GD5mSXPH4wGq6uiquq+q7q6q7wF/Bj4PPK31tLgd7o8rn0tycpI/As9M8qgkX09yU4tP/zyw8STbJjmj9ba7Pslnei/6tZj1+iSXtzj1viSPbT3z/tDi7IPbsguTLO1Z9+okb01yQbpe1ccmWb1n/r+1ff42yWuG6BH3LeBvk6zT3u8MXAD8rmcbD0ryriTXJLmxnVce0eZ9J8kbej/MJOcneXHPsQ3E44ck+Wg7t9zQYvND27z1kpzUPqNbk/w4K9kjUdLMVFW/r6oTgT2ARUm2HCV+XJrkeQPrJ1m1xeKte9q1q7Z56yb5YouJtyX5Rs96z0vX822gZ/STh6nfdcB3gC3bepVk/ySXA5e3stcmuaLFsxOTPKqV/6ht5vx2XtljtH0n2TjJ8e2YbmnnkP/H8Oen9/esO2Q9euq9Xzv33J7kv5JkRf/dJCmj5Aqy7E7A25NcmwfeBbdOkm+3tvDPkzy2lW8FXFVVp1bnjqr6elX9pm1zcNwbqq38jnS5hdvaOWD13mXTDft5c1v2FT3rPqK1e29q7eB3DbRP0+Uhfprujr5bgGMZIi5Lw/GHjvrhb4DVgeN7C6vqTuBk4Dmt6AXAMcDawInAZ8axj78FngA8G3h3a7QCvBF4IfB3wKOA2+iuDC6nNVifC5zZU/xrYAe6q4rvBb6cZIO2/EuBA+l6yT281f+WQdvcGjiFLpF+9BiP5RXA+4D1gPOACb1aKmnG+xVwX5Ijk+ySllitqkuB/YAzWk+L3mGEXg58AFgL+BldUvZ8ul7NzwbelOS5bdn7gDfTxaCntfmvH1SH5wLb0N1l8m/AocArgY3pEhZ7jlD/3emSwJsCTwb2AUiyM/CvwI7A44CFQ6x7D/BN4GXt/d50vT967dNezwQeA6zJsvPJ0b11S7IFXa/Cbw+xr4PpEvdbtfpsCLy7zXsLsBSYB6wP/DtQIxyzpFmuqs6iiws7MHL8eEAcoounN1fVuUNs9kvAw4AnAn8FfAIgyVOBw4HXAY8E/hs4MclDBm8gycbArsAveopfCGwHbJHkWcB/0sXmDYBr6NrjVNUz2vJPaeeVY0fad7o7X05q25jfjvuYUc5PA/Ucth49ngf8Nd25Y/f22UnSiho2V5CuQ9d36O4EnEcXz8/rWfdldPmBdYAr6NrZAOcCm7ek7TOzYkMMvYIuvj2W7lzSe2fJ/0fXRt8QWAQcmmRgSLlP0+UtHtOOaW/gVT3rbgdcSdd2fSWjxGWpl8lk9cN6dI3ke4eYd32bD/CTqjq5jRP3JeAp49jHe1vvvPPpEiQD6+4HvLOqllbVn+iSvy/JA2/ruLldibsO+CPwvwMzquprVfXbqvpLVR1L14Nj2zb7NcCHq+rsdtXxiqq6pme7O9AlxfeuqpPGcSzfrqoftfq+k+5q4cbjWF/SLFZVf6C7gFbAYcBNrQfX+iOs9s2q+mlV/YVuSJ95VXVQVf1fGyP4MFqCtqrOqaozq+redhfJf9M1SHt9uKr+UFUXAxcB36uqK6vq93QN76eOUJdDWly9lS6pvVUr3x34YlVdXFV30cXroRwF7J1uzP2/A74xaP4rgI+3+twJvAN4WYv7JwBbZdkdH68Ajm/x9n6tt9u+wJur6taqugP4IMuS2H+mS3Y8ut1t8+OqMpks6bd0ww+NFD++CrwgycPa+5fTJZgfoHVe2AXYr6pua7Hmh232vsB/V9XP2x0qR9INdbR9zya+0dq3PwF+2Oow4D9b3e6mi4OHV9W5LRa+g67tOX+YYxxp39vSJWTeVlV/rKp7qmqs43GOpR4HV9XtrYffaSw7f0jSihgpV/By4PvtTsA/V9UtVdWbTD6hqs5qOY6v0OJRa1cvpEv2HkeXaxjvuPWfqaprW1v5AyzfSeM/qupP7ZzwbWD3djHvZcA7Wm/oq4GPAXv1rPfbqvp0a+PfPY76SCaT1Rc3A+tl6HF5Nmjzoec2ZeAuYPVh1hnK4HUHgvWjgRParSm3A5fS9brrTbqs167EPQz4KV1PYgCS7N1zG9/tdD3uBpLfGzPyOJ37AT+rqtPHeAwDrh2YaImQW+ka5pIEdL2Qq2qfqtqILi49CvjkCKtc2zP9aOBRA3GtxbZ/p8XFJI9PN4TD75L8gS4Bsd6g7d3QM333EO9HajAPF68fNaievdP3a4mJeXQX204aojH8KLoebQOuAVYF1m9JnW+zLKmzJ0Pf/TGP7pxwTs9n9N1WDvARul4o30tyZZIDhjlWSXPLhnTxZtj4UVVX0LVHn98Syi+gSzAPtjFwa1XdNsS8RwNvGRTHN+aB7cUXVtXaVfXoqnr9oFjZG18fEDNb2/OWdixDGWnfGwPXDNOBZDRjqcdw5w9JWhEj5QpG+60/bDxqnTJ2r6p5dB3MnkHXbh2r3hh9DQ+M7bdV1R+HmL8esBrLt4F7Y+iQbWtpLEwmqx/OoOux8OLewnZ1bhfg1Enc97XALq0xPfBavbrx4x6gNbKPALZPNx7mo+l61wm0EwAAIABJREFU670BeGRLOF8EpGfbjx28nR77AZsk+URP2R/pfmAAkOT/G2K9jXvmr0nXw+W3ox+qpLmoqn5JF7u2ZPihFnrLr6Uby603Lq5VVbu2+Z8DfglsVlUPp0s0T8W4lNcDG/W8H+mOjC/TDTUxeIgL6OJl71jzmwD3sizhfTSwZ5Kn0Q3BdNoQ27iZLin+xJ7P6BHVPbiF1uPjLVX1GLpE0L8mefaoRyhp1kry13Q/2r/BCPGjGRjqYjfgkpZgHuxaYN12F8ZQ8z4wKI4/rMY+pFrvOeEBMTPJGnTDVyzXVh7Dvq+la/sO1RlktLs3xlsPSVpZI+UKRvutPyZVdTbdcJ9btqIH5APohq0YrLcNvAkPzAWs0+Lj4Pk30905N7gN3BtDB8dh76rTmJlM1pRrtz2/F/h0kp2TrNZuWTuObmy5L03i7j8PfGDgluYk85LsNtSCbZy5veiuMt4CrEEXYG9q81/FspMAwBeAtybZJp3H5YEPy7uDblzQZyQ5uJWdDzwxyVZtIP0Dh6jKrukG+38w3djJZ1aVVxElAZBk8yRvSbJRe78xXVLiTLqE6UbpeWDeEM4C7kjy9iQPTbJKugdG/XWbvxbwB+DOJJsD/zR5R/MAxwGvSvfQ0ocB/zHCsofQjbf/oyHmHQ28Ocmm7YLcB4Fje3rKnUzX0D6olf9l8AZa2WHAJ5L8FUCSDQfGlU738KnHteEwfk/Xi2W57Uia/ZI8PN0D9Y4BvlzdkGvDxo/mGGAnuvg6VK9kqup6umGDPptkndZ+HhjD+DBgvyTbtTboGkn+PslaK3AIR9PF3q1aW/iDwM/bLdLQnVce07P8SPs+i+7C4MGtfPUkT+/Zzkjnp9HqIUkTbaRcwVeAHZPsnu5BqY9MMurQOu13/Gt74v/mdB0PBp7LdB7d7/11W8eyNw2xmf2TbJTuYYDvpHtYXq/3Jnlwkh3oxpL/WnVDhR7Xjmetdkz/StcBYzhj+d0gASaT1SdV9WG63m0fpUtS/Jzuat+zB49VOcE+RTdu8feS3EEXxLcbtMztSe6kC6ZPA15QnUvoxhk6o817Et0wGAPH9DW6MYy+Spc4/gZdL2J6lrmdLuGxS5L3VdWv6BIY36cbf3moceS+CryHbniLbegGx5ekAXfQxbGfJ/kjXVy7iK6n7g+Ai4HfJbl5qJVbY/N5tKdN0/Vk+ALdAzsA3ko3TtwddEmDwQ3YSVFV36FLEp9GN4TEQKN7uXNEG+vz1Kohxyk+nO4i5Y/oju8eugesDKz7J7oeIjsyTBKneftAPdIN9/F9uge9AmzW3t9Jd474bFUN1cNZ0uz1rda2vJbux/7HWfago5Hix0Ci+Ay6h1SPFGP3outp9kvgRlrSoaqWAK+le7jobW1f+6zIQVTV9+ku3n2dLhH8WJYNBQRdx4cj223gu4+073Z+eT7dQwd/Q9dpZI+2nRHPT2OohyRNtGFzBW1s9l3p2te30iWBx/JMp9vpkscXthzDd+me2fHhNv9LdB3Mrga+x9DngK+2eVfSDbXx/p55v6OLvb+lS3jv1+5ShK69+8e23k/adg4foa6j/m6QBmTo312SpoMkRwBLq+pdoy0rSbNZkv9HlyR/yAqOvylJkiTNGEmuBl7TLrANnreQ7g6YjQbPkyabPZMlSdK0lORFSR6SZB3gQ8C3TCRLkiRJUv9MWjI5ycZJTktySZKLk/xLK183yeIkl7e/67TyJDkkyRVJLkiydc+2FrXlL0+yaLLqLEmSppXX0d3K/Wu6cYinarxmSZIkSdIQJm2YiyQbABtU1bntAQznAC+kG0Pr1qo6OMkBwDpV9fYku9KN6bIr3bg0n6qq7dog40uABXQPPzsH2KaqbpuUikvSDNcewHYUsD5d3Dy0qj7V4umxwHy6cbl2r6rb2kPDPkUXf+8C9qmqc9u2FgEDw6y8v6qOnMpjkSRJkiRJ08ek9UyuqusHkhFVdQdwKbAhsBswkIw4ki7BTCs/qj3o7Exg7ZaQfi6wuD1c5zZgMbDzZNVbkmaBe4G3VNUWwPZ0TwDeAjgAOLWqNgNObe8BdqF7gNhmwL7A56C7k4Tu4Y/bAdsC7xm4m0SSJEmSJM09q07FTpLMB54K/BxYvz2xGLonT67fpjeke/rxgKWtbLjyofazL10ihDXWWGObzTfffGIOQJLG6Jxzzrm5qub1sw4txl7fpu9I0nsxb2Fb7EjgdLonzN9/MY/uSfMDF/MW0i7mASQZuJh39Ej7X2+99Wr+/PkTe1CSNIrpEH/7zfgrqV/megw2/krql37E30lPJidZE/g68Kaq+kN3N3WnqirJhI2zUVWHAocCLFiwoJYsWTJRm5akMUlyTb/r0KsfF/M22WQTjL+Sptp0i7/9MH/+fOOvpL6Y6zHY+CupX/oRfydtmAuAJKvRJZK/UlXHt+IbWo+3gXGVb2zl1wEb96y+USsbrlySNILBF/N657VeyBN6Ma+qFlTVgnnz5mynFEmSJEmSZrVJSya3Bzr9D3BpVX28Z9aJwKI2vQj4Zk/53ulsD/y+9aA7BdgpyTptrM6dWpkkaRhezJMkSZIkSRNtMnsmPx3YC3hWkvPaa1fgYOA5SS4HdmzvAU4GrgSuAA4DXg/Qxup8H3B2ex00MH6nJGl5XsyTJEmSJEmTYdLGTK6qnwAZZvazh1i+gP2H2dbhwOETVztJ/fDnP/+ZpUuXcs899/S7Kitt9dVXZ6ONNmK11Vbrd1WGMnAx78Ik57Wyf6e7eHdcklcD1wC7t3knA7vSXcy7C3gVdBfzkgxczAMv5kkzlvFXkvrHGCxJ/WH8nRyT/gA+SRqwdOlS1lprLebPn0/vwzhnmqrilltuYenSpWy66ab9rs5yvJgnaTDjryT1jzFYkvrD+Ds5JvUBfJLU65577uGRj3zkjA7iAEl45CMfOSuubkqaG4y/ktQ/xmBJ6g/j7+QwmSxpSs30ID5gthyHpLljtsSt2XIckuaW2RK7ZstxSJo7Zkvcmk7HYTJZkiRJkiTNaEk2TnJakkuSXJzkX1r5ukkWJ7m8/V2nlSfJIUmuSHJBkq17trWoLX95kkXD7VOS5qI5PWbyNm87qt9VGNY5H9m731WQpoXf/e53vOlNb+Lss89m7bXXZv311+eFL3whJ554IieddFK/qyfNSJ7/NBbG39nJ///S9Gf8XWH3Am+pqnOTrAWck2QxsA9walUdnOQA4ADg7cAuwGbttR3wOWC7JOsC7wEWANW2c2JV3bayFZyuMdj4K3WMv2Mzp5PJkqa3quJFL3oRixYt4phjjgHg/PPP58QTT1yp7d57772suqrhT5KGY/yVJsd0TSSByaTpwvi74qrqeuD6Nn1HkkuBDYHdgIVtsSOB0+mSybsBR7WHUZ+ZZO0kG7RlF1fVrQAtIb0zcPSUHYykKWf8HTuHuZA0bZ122mmsttpq7LfffveXPeUpT2GHHXbgzjvv5CUveQmbb745r3jFK+jagDB//nxuvvlmAJYsWcLChQsBOPDAA9lrr714+tOfzl577cWBBx7IP/7jP7Jw4UIe85jHcMghh0z58UnSdGX8laT+MP5OjCTzgacCPwfWb4lmgN8B67fpDYFre1Zb2sqGK5c0ixl/x85ksqRp66KLLmKbbbYZct4vfvELPvnJT3LJJZdw5ZVX8tP/n717D7OrrO/+//5wECyUiiVaJEbQoo+APlFG8FAsAioeUasgVTnoz+gjHqjWR9QqFEurReoj0oKxxIhFEAWVWiqiFlELYjiYAEoFpBhKIYJCPYAC398f+56wk8xkJpM9s+bwfl3Xvmbvex32d28yn1nc6173+s53xtzfNddcw9e+9jXOOKM3qOCHP/wh559/Ppdeeil/+Zd/yW9/+9uB1i9JM5X5K0ndMH83XpKtgbOBI6vqrv5lbRRyDeh9FiVZlmTZqlWrBrFLSR0yf8fPzmRJM9Iee+zB/Pnz2WSTTVi4cCE33njjmNu8+MUv5sEPfvDq1y94wQvYYost2G677XjYwx7GrbfeOokVS9LsYP5KUjfM37El2ZxeR/LpVXVOa761TV9B+3lba78ZeGTf5vNb22jta6iqxVU1VFVD8+bNG+wHkTStmL9rml2TdmhGma5zxjlf3PSx66678vnPf37EZVtsscXq55tuuin33nsvAJttthn3338/AHffffca22y11Vbj2ockzXXmryR1w/yduCQBTgV+UFV/17foXOBQ4IPt55f62t+c5Ex6N+C7s6puSXI+8NdJtm3rPQd491R8BkndMX/Hz5HJkqatffbZh3vuuYfFixevblu+fDnf+ta3Rt1mxx135LLLLgPg7LPPnvQaJWk2Mn8lqRvm70Z5BvAaYJ8kV7bH8+l1Ij87yY+A/dprgPOAG4DrgE8AbwJoN977APC99jh2+GZ8kmYv83f8HJksadpKwhe+8AWOPPJIPvShD7Hllluy44478pKXvGTUbY4++mhe97rX8b73vW/15PeafqbrlQng1QkSmL+S1BXzd+Kq6ttARlm87wjrF3DEKPtaAiwZXHWSpjvzd/wyfAfC2WZoaKiWLVu23nXszOjWdP3+58J335Uf/OAHPP7xj++6jIEZ6fMkuayqhjoqaVowf6c/v/+5x/ydG8zf6c/vf24yg2e/8eQvTN8M8Pdfs5X5Ozmc5kKSJEmSJEmSNCY7kyVJkiRJkiRJY7IzWZIkSRqQJEuS3Jbkqr62z/bdDOrGJFe29h2T/Lpv2Sl92+yeZEWS65KcmGS0eUAlSZKkKeMN+CRJkqTBWQqcBKyeGLOqDhp+nuQE4M6+9a+vqoUj7Odk4PXAd4HzgP2Bf52EeiVJkqRxm7SRyY7KkCRJ0lxTVRcBd4y0rB3HHgicsb59JNke2KaqLqne3bJPA0a/lbgkSZI0RSZzmoul9EZQrFZVB1XVwjb64mzgnL7F1w8vq6o39rUPj8rYuT3W2KckaU2ezJOkaWsv4Naq+lFf205JrkjyzSR7tbYdgJV966xsbetIsijJsiTLVq1aNTlVS5IkSc2kTXNRVRcl2XGkZX2jMvZZ3z76R2W018OjMrzET5oFdn/naWOvtAEuO/6QMddJwtvf/nZOOOEEAD784Q/zi1/8gmOOOWagtXRsKV5iLWk9zN/OHMyao5JvARZU1e1Jdge+mGTXDdlhVS0GFgMMDQ3VwCqVNGnMYEnqhvk7GF3dgG/gozLAkRmSxrbFFltwzjnn8NOf/rTrUiaNl1hLmo7mQv6uT5LNgJcBnx1uq6p7qur29vwy4HrgscDNwPy+zee3NkmakLmewZLUldmYv111Jo82KuNJwNuBzyTZZkN3WlWLq2qoqobmzZs3oFIlzSabbbYZixYt4iMf+cg6y2688Ub22WcfnvjEJ7Lvvvty0003dVDhpPNknqROmL/sB/ywqlZna5J5STZtzx9Nb0q3G6rqFuCuJE9tJwEPAb7URdGSZgczWJK6MRvzd8o7kx2VIalrRxxxBKeffjp33nnnGu1vectbOPTQQ1m+fDmvetWreOtb39pRhZPKk3mSOjMX8jfJGcDFwOOSrEzyurbolax7VcgzgeVtHvvPA2+squErS94E/CNwHb1jY6cZkrRR5kIGS9J0NNvyt4uRyY7KkNSpbbbZhkMOOYQTTzxxjfaLL76YP/3TPwXgNa95Dd/+9re7KG/SeDJPUtfmQv5W1cFVtX1VbV5V86vq1NZ+WFWdsta6Z1fVru0G1E+uqn/uW7asqnarqsdU1ZvblEOSNGFzIYMlaTqabfk7aZ3JjsqQNJ0deeSRnHrqqfzyl7/supSp5Mk8SZ2bo/krSdOCGSxJ3ZhN+TtpncmOypA0nT30oQ/lwAMP5NRTT13d9vSnP50zzzwTgNNPP5299tprtM2nNU/mSZrOZnP+StJ0ZwZLUjdmU/5u1nUBkuauy44/pNP3f8c73sFJJ520+vXHPvYxDj/8cI4//njmzZvHJz/5yQ6rm7iqOniU9sNGaDsbOHuU9ZcBuw20OEnTgvkrSd0xgyWpG+bvYNiZLGlO+cUvfrH6+cMf/nB+9atfrX79qEc9im984xtdlCVJs575K0ndMYMlqRuzMX+7uAGfJEmSJEnSwCRZkuS2JFf1tX02yZXtcWOb2o0kOyb5dd+yU/q22T3JiiTXJTmx3T9EktQ4MlmSJEmSJM10S4GTgNOGG6rqoOHnSU4A7uxb//qqWjjCfk4GXg98FzgP2B/vHSJJqzkyWZIkSZIkzWhVdRFwx0jL2ujiA1n3ZtRrr7c9sE1VXVJVRa9j+iWDrlWSZjI7kyVJkiRJ0my2F3BrVf2or22nJFck+WaSvVrbDsDKvnVWtrZ1JFmUZFmSZatWrZqcqiVpGrIzWZIkSZIkzWYHs+ao5FuABVX1JODtwGeSbLMhO6yqxVU1VFVD8+bNG2CpkjS9OWeyJEmSJEmalZJsBrwM2H24raruAe5pzy9Lcj3wWOBmYH7f5vNbm2a43d952tgrdeSy4w/pugRpg9iZLKkzNx37hIHub8H7V6x3eVWx11578d73vpfnPe95AHzuc5/j1FNP5Stf+cpAa5Gk6cz8laTumMFTbj/gh1W1evqKJPOAO6rqviSPBnYGbqiqO5LcleSp9G7AdwjwsU6qljRw5u9g2Jksac5IwimnnMIrXvEKnvWsZ3Hvvffynve8Z0aHuCTNBOavJHVnrmRwkjOAvYHtkqwEjq6qU4FXsu6N954JHJvkt8D9wBuravjmfW8ClgIPBv61PSRpg83W/LUzWdKcsttuu/GiF72ID33oQ/zyl7/k1a9+NccddxxXXXUVv/3tbznmmGM44IADuPrqqzn88MP5zW9+w/3338/ZZ5/Nzjvv3HX5kjRjmb+S1J25kMFVdfAo7YeN0HY2cPYo6y8DdhtocZLmrNmYv3YmS5pzjj76aJ785CfzoAc9iBe+8IXss88+LFmyhJ///Ofsscce7Lfffpxyyim87W1v41WvehW/+c1vuO+++7ouW5JmPPNXkrpjBktSN2Zb/tqZLGnO2WqrrTjooIPYeuutOeuss/jnf/5nPvzhDwNw9913c9NNN/G0pz2N4447jpUrV/Kyl71s2p4RlKSZxPyVpO6YwZLUjdmWv3YmS5qTNtlkEzbZZBOqirPPPpvHPe5xayx//OMfz5577sm//Mu/8PznP5+Pf/zj7LPPPh1VK0mzh/krSd0xgyWpG7MpfzfpugBJ6tJzn/tcPvaxj1FVAFxxxRUA3HDDDTz60Y/mrW99KwcccADLly/vskxJmnXMX0nqjhksSd2YDfnryGRJnVnw/hVdl8D73vc+jjzySJ74xCdy//33s9NOO/HlL3+Zs846i09/+tNsvvnm/MEf/AHvec97ui5VkgbG/J08SZYALwRuq6rdWtsxwOuBVW2191TVeW3Zu4HXAfcBb62q81v7/sBHgU2Bf6yqD07l55A0ecxgSeqG+TsYdiZLmpOOOeaY1c8//vGPr7P8qKOO4qijjprCiiRpbpgD+bsUOAk4ba32j1TVh/sbkuwCvBLYFXgE8LUkj22L/x54NrAS+F6Sc6vqmsksXNLsNwcyWJKmpdmUv5M2zUWSJUluS3JVX9sxSW5OcmV7PL9v2buTXJfk2iTP7Wvfv7Vdl2RmfKuSJEmak6rqIuCOca5+AHBmVd1TVT8GrgP2aI/rquqGqvoNcGZbV5IkSerUZM6ZvBTYf4T2j1TVwvYYvryvf1TG/sA/JNk0yab0RmU8D9gFOLitK0laD0/oSdK08+Yky1s+b9vadgB+0rfOytY2WrskSZLUqUnrTHZUhqSRDE8yP9PNgM+xFE/oSeozA3JrXGbo5zgZeAywELgFOGFQO06yKMmyJMtWrVo19gaSOjFDs2sds+VzSJo7ZktuTafPMZkjk0czaaMyPJiWprctt9yS22+/fVqF4ERUFbfffjtbbrll16WMyhN6kvqZv92qqlur6r6quh/4BL18BbgZeGTfqvNb22jtI+17cVUNVdXQvHnzBl+8pI1mBktSN8zfyTHVN+A7GfgAUO3nCcBrB7XzqloMLAYYGhqa2f9SpFlo/vz5rFy5ktlwsmfLLbdk/vz5XZcxEW9OcgiwDHhHVf2M3km6S/rW6T9xt/YJvT1H2mmSRcAigAULFgy6ZkkbyfztVpLtq+qW9vKlwPAUROcCn0nyd/RuwLczcCkQYOckO9HrRH4l8KdTW7WkQTGDJakb5u/kmNLO5Kq6dfh5kk8AX24v1zf6YlyjMiRNf5tvvjk77bRT12XMZZN2Qs+TedL0Zv5OnSRnAHsD2yVZCRwN7J1kIb38vRF4A0BVXZ3kLOAa4F7giKq6r+3nzcD5wKbAkqq6eoo/iqQBMYMlqRvm7+SY0s5kR2VIUnc8oSdJk6+qDh6h+dT1rH8ccNwI7ecB5w2wNEmSJGmjTVpnsqMyJGl68YSeJEmSJEnaGJPWmeyoDEnqjif0JEmSJEnSoE31DfgkSVPAE3qSJEmSJGnQNum6AEmSJEmSpI2RZEmS25Jc1dd2TJKbk1zZHs/vW/buJNcluTbJc/va929t1yU5aqo/hyRNd45MliRJkiTNeru/87SuSxjVZccf0nUJs8FS4CRg7f/QH6mqD/c3JNmF3v1AdqV3z5CvJXlsW/z3wLOBlcD3kpxbVddMZuGSNJPYmSxJkiRJkma0qrooyY7jXP0A4Myqugf4cZLrgD3asuuq6gaAJGe2de1MlqTGzmRpjpquIzMclSFptpuu+QtmsCRpVnpzkkOAZcA7qupnwA7AJX3rrGxtAD9Zq33PkXaaZBGwCGDBggWDrlmSpi3nTJYkSZIkSbPRycBjgIXALcAJg9pxVS2uqqGqGpo3b96gditJ054jkyVJkiRJ0qxTVbcOP0/yCeDL7eXNwCP7Vp3f2lhPuyQJRyZLkiRJkqRZKMn2fS9fClzVnp8LvDLJFkl2AnYGLgW+B+ycZKckD6J3k75zp7JmSZruHJksSZIkSZJmtCRnAHsD2yVZCRwN7J1kIVDAjcAbAKrq6iRn0bux3r3AEVV1X9vPm4HzgU2BJVV19RR/FEma1uxMliRJkiRJM1pVHTxC86nrWf844LgR2s8DzhtgadPeTcc+oesSRrTg/Su6LkHSCJzmQpIkSZIkSZI0JjuTJUmSJEmSJEljcpoLSZIkSZIkSZNi93ee1nUJo7rs+EO6LmHGcWSyJEmSJEmSJGlMdiZLkiRJkiRJksZkZ7IkSZIkSZIkaUx2JkuSJEkDkmRJktuSXNXXdnySHyZZnuQLSR7S2ndM8uskV7bHKX3b7J5kRZLrkpyYJF18HkmSJKnfpHUmeyAtSZKkOWgpsP9abRcAu1XVE4H/AN7dt+z6qlrYHm/saz8ZeD2wc3usvU9JkiRpyk3myOSleCAtSVPOk3mS1J2qugi4Y622r1bVve3lJcD89e0jyfbANlV1SVUVcBrwksmoV5IkSdoQk9aZ7IG0JHVmKZ7Mk6Tp6rXAv/a93inJFUm+mWSv1rYDsLJvnZWtbR1JFiVZlmTZqlWrJqdiSZIkqelyzuSBHkhLkno8mSdJ01OS9wL3Aqe3pluABVX1JODtwGeSbLMh+6yqxVU1VFVD8+bNG2zBkiRJ0lo66UyejAPptl9HZkjS2AZ+Ms/8laT1S3IY8ELgVe0kHVV1T1Xd3p5fBlwPPBa4mTVP+s1vbZIkSVKnNpvqN+w7kN63/0AauKc9vyzJhA6kq2oxsBhgaGioJqN+zX43HfuErksY1YL3r+i6BM1w6zmZd3uS3YEvJtl1Q/dr/krS6JLsD/xf4I+r6ld97fOAO6rqviSPpjel0A1VdUeSu5I8FfgucAjwsS5qlyRJkvpNaWeyB9KS1J3JPJknSepJcgawN7BdkpXA0fTmqd8CuKDdy/SSNkf9M4Fjk/wWuB94Y1UNT1P0Jnpz4D+Y3tUk/VeUzErT9YS+J/MlSZIeMGmdyR5IS9L04ck8SZoaVXXwCM2njrLu2cDZoyxbBuw2wNIkSZKkjTZpnckeSEtSNzyZJ0mSpLkmyRJ6V+HdVlW7tbbjgRcBv6E3L/3hVfXzJDsCPwCubZsPHxvTpn5bSu8Y+DzgbcNX9UmSOpgzWZI0uTyZJ0mSpDloKXAScFpf2wXAu6vq3iQfojfA4l1t2fVVtXCE/ZwMvJ7e1XnnAfvjoApJWm2TrguQJEmSJEnaGFV1EXDHWm1frap728tLWPOeIOtIsj2wTVVd0kYjnwa8ZDLqlaSZys5kSZIkSZI0272WNUcY75TkiiTfTLJXa9sBWNm3zsrWto4ki5IsS7Js1apVk1OxJE1DdiZLkiRJkqRZK8l7gXuB01vTLcCCqnoS8HbgM0m22ZB9VtXiqhqqqqF58+YNtmBJmsacM1mSJEmSJM1KSQ6jd2O+fYdvpFdV9wD3tOeXJbkeeCxwM2tOhTG/tUmSGkcmS5IkSZKkWSfJ/sD/BV5cVb/qa5+XZNP2/NHAzsANVXULcFeSpyYJcAjwpQ5Kl6Rpy5HJkiRJkiRpRktyBrA3sF2SlcDRwLuBLYALen3DXFJVbwSeCRyb5LfA/cAbq2r45n1vApYCD6Y3x3L/PMuSNOfZmSxJkiRJkma0qjp4hOZTR1n3bODsUZYtA3YbYGmSNKs4zYUkSZIkSZIkaUx2JkuSJEmSJEmSxjSuzuQkXx9PmyRpsMxfSeqG+StJ3TB/JWl6W++cyUm2BH6H3gT22wJpi7YBdpjk2iRpzjJ/Jakb5q8kdcP8laSZYawb8L0BOBJ4BHAZD4T5XcBJk1iXJM115q8kdcP8laRumL+SNAOstzO5qj4KfDTJW6rqY1NUkyTNeeavJHXD/JWkbpi/kjQzjDUyGYCq+liSpwM79m9TVadNUl2SJMxfSeqK+StJ3TB/JWl6G1dncpJPA48BrgTua80FGOaSNInMX0nqhvkrSd11iW+NAAAgAElEQVQwfyVpehtXZzIwBOxSVTWZxUiS1mH+SlI3JpS/SZYALwRuq6rdWttDgc/SG2V3I3BgVf0sSYCPAs8HfgUcVlWXt20OBf6i7favqupTG/2JJGlm8PhXkqaxTca53lXAH0xmIZKkEZm/ktSNiebvUmD/tdqOAr5eVTsDX2+vAZ4H7Nwei4CTYXXn89HAnsAewNFJtp1ALZI0E3n8K0nT2Hg7k7cDrklyfpJzhx9jbZRkSZLbklzV1/bQJBck+VH7uW1rT5ITk1yXZHmSJ/dtc2hb/0dtlIYkzRXmryR1Y0L5W1UXAXes1XwAMDyy+FPAS/raT6ueS4CHJNkeeC5wQVXdUVU/Ay5g3Q5qSZqtJpS/kqSpMd5pLo6Z4P6XAiex5txGwyMzPpjkqPb6Xaw5MmNPeiMz9uwbmTFEb56ky5Kc2w6sJWm2O2aC2y3F/JWkjXHMAPf18Kq6pT3/b+Dh7fkOwE/61lvZ2kZrl6S54JiuC5AkjW5cnclV9c2J7LyqLkqy41rNBwB7t+efAi6k15mxemQGcEmS4ZEZe9NGZgAkGR6ZccZEapKkmcT8laRuTDR/x7HfSjKweUCTLKI3RQYLFiwY1G4lqTOTlb+SpMEY1zQXSf4nyV3tcXeS+5LcNcH3nLSRGUkWJVmWZNmqVasmWJ4kTR/mryR1Y8D5e2s7SUf7eVtrvxl4ZN9681vbaO3rqKrFVTVUVUPz5s2bYHmSNH0MOH8lSQM2rs7kqvrdqtqmqrYBHgz8CfAPG/vmbRTcwEZmeDAtabYxfyWpGwPO33OB4XnnDwW+1Nd+SJu7/qnAne2k3/nAc5Js2+a3f05rk6RZb7KOfyVJgzHeG/Ct1m4Q8kV6NwaZiEkbmSFJs5n5K0nd2JD8TXIGcDHwuCQrk7wO+CDw7CQ/AvZrrwHOA24ArgM+Abypvd8dwAeA77XHscNTDknSXDKA419J0oCNa87kJC/re7kJvZsx3T3B9xwemfFB1h2Z8eYkZ9K7AdSdVXVLkvOBv26jMqA3MuPdE3xvSZpRzF9J6sZE87eqDh5l0b4jrFvAEaPsZwmwZOxKJWl2mWj+JlkCvBC4rap2a20PBT4L7AjcCBxYVT9LEuCjwPOBXwGHVdXlbZtDgb9ou/2rqvrUAD6WJM0a4+pMBl7U9/xeeiF8wFgbtZEZewPbJVkJHE2vE+OsNkrjP4ED2+rn0Qvy6+iF+eHQG5mRZHhkBjgyQ9LcYv5KUjcmlL+SpI020fxdCpwEnNbXdhTw9ar6YJKj2ut3Ac8Ddm6PPYGTgT1b5/PR9DqwC7gsyblV9bON+UCSNJuMqzO5qg6fyM4dmSFJG8f8laRuTDR/JUkbZyOOfy9KsuNazQfQG2AB8CngQnqdyQcAp7Xj4EuSPKRNA7c3cMHwAIokFwD7A2dMpCZJmo3GNWdykvlJvpDktvY4O8n8yS5OkuY681eSumH+SlI3Bpy/D283NgX4b+Dh7fkOwE/61lvZ2kZrH6nORUmWJVm2atWqCZYnSTPPeKe5+CTwGeAV7fWrW9uzJ6MoSdJq5u8Uu+nYJ3RdwogWvH9F1yVIc435K0ndmJT8rapKUhtZW//+FgOLAYaGhga2X0ma7sY1MhmYV1WfrKp722MpMG8S65Ik9Zi/ktQN81eSujHI/L21TV9B+3lba78ZeGTfevNb22jtkqRmvJ3Jtyd5dZJN2+PVwO2TWZgkCTB/Jakr5q8kdWOQ+XsucGh7fijwpb72Q9LzVODONh3G+cBzkmybZFvgOa1NktSMtzP5tcCB9OYYugV4OXDYJNUkSXqA+StJ3TB/JakbE8rfJGcAFwOPS7IyyeuADwLPTvIjYL/2GuA84AbgOuATwJsA2o33PgB8rz2OHb4ZnySpZ7xzJh8LHFpVPwNI8lDgw/RCXpI0ecxfSeqG+StJ3ZhQ/lbVwaMs2neEdQs4YpT9LAGWbEjBkjSXjHdk8hOHgxxWn6170uSUJEnqY/5KUjfMX0nqhvkrSdPYeDuTN2nzBQGrzwyOd1SzJGnizF9J6ob5K0ndMH8laRobbyCfAFyc5HPt9SuA4yanJElSH/NXkrph/kpSN8xfSZrGxtWZXFWnJVkG7NOaXlZV10xeWZIkMH8lqSvmryR1w/yVpOlt3JeKtPA2wCVpipm/ktQN81eSumH+StL0Nd45kyVJkiRJkiRJc5idyZIkSZIkSZKkMXlHVEmSNG3cdOwTui5hRAvev6LrEiRJkiSpc45MliRJkiRJkiSNyc5kSZIkSZIkSdKY7EyWJEmSJlmSxyW5su9xV5IjkxyT5Oa+9uf3bfPuJNcluTbJc7usX5IkSYIOOpM9kJak7pjBktSNqrq2qhZW1UJgd+BXwBfa4o8ML6uq8wCS7AK8EtgV2B/4hySbdlG7JEmSNGzKb8BXVdcCCwHaAfHN9A6kD6d3IP3h/vXXOpB+BPC1JI+tqvumtHBJmgXMYEmaFvYFrq+q/0wy2joHAGdW1T3Aj5NcB+wBXDxFNUqSJEnr6Hqai9UH0utZZ/WBdFX9GBg+kJYkbRwzWJK68UrgjL7Xb06yPMmSJNu2th2An/Sts7K1rSHJoiTLkixbtWrV5FUsSZIk0X1n8sAOpMGDaUnaQHZmSNIUS/Ig4MXA51rTycBj6F01cgtwwobsr6oWV9VQVQ3NmzdvoLVKkiRJa+usM3nQB9LgwbQkjZedGZLUmecBl1fVrQBVdWtV3VdV9wOf4IGrP24GHtm33fzWJknaAN4zRJIGq8uRyR5IS1J3zGBJ6sbB9F0VkmT7vmUvBa5qz88FXplkiyQ7ATsDl05ZlZI0S3gDVEkarC47kz2QlqTumMGSNMWSbAU8Gzinr/lvk6xIshx4FvBnAFV1NXAWcA3wFeAIb34qSRvNe4ZI0kbarIs37TuQfkNf898mWQgUcOPwsqq6OsnwgfS9eCAtSRvFDJakblTVL4HfX6vtNetZ/zjguMmuS5LmkJHuGXIIsAx4R1X9jN79QS7pW2fU+zZJ0lzUSWeyB9KS1B0zWJIkSXNN3z1D3t2aTgY+QG8wxQfo3TPktRuwv0XAIoAFCxYMtFZJms66nOZCkiRJkiRpKgz0niHegFrSXGVnsiRJkiRJmu28Z4gkDUAn01xIkiRJkiRNBe8ZIkmDY2eyJEmSJEmatbxniCQNjtNcSJIkSZIkSZLGZGeyJEmSJEmSJGlMdiZLkiRJkiRJksZkZ7IkSZIkSZIkaUx2JkuSJEmSJEmSxmRnsiRJkiRJkiRpTHYmS5IkSZIkSZLGZGeyJEmSJEmSJGlMdiZLkiRJkiRJksZkZ7IkSZIkSZIkaUx2JkuSJEmSJEmSxrRZ1wVIkiRJkrp107FP6LqEES14/4quS5AkSX0cmSxJkiRNgSQ3JlmR5Moky1rbQ5NckORH7ee2rT1JTkxyXZLlSZ7cbfWSJElSh53JHkxLUjfMX0nq1LOqamFVDbXXRwFfr6qdga+31wDPA3Zuj0XAyVNeqSRJkrSWrqe5eFZV/bTv9fDB9AeTHNVev4s1D6b3pHcwvedUFzuVvMxM0iQzfyVpejgA2Ls9/xRwIb38PQA4raoKuCTJQ5JsX1W3dFKlJEmSxPSb5uIAegfRtJ8v6Ws/rXouAR6SZPsuCpSkWcr8laTJV8BXk1yWZFFre3hfB/F/Aw9vz3cAftK37crWJkmSJHWmy87kgR9MJ1mUZFmSZatWrZqsuiVppjN/Jakbf1RVT6Z31ccRSZ7Zv7CNQq4N2aH5K0ljc5o3SRqcLjuTB34wXVWLq2qoqobmzZs3wFIlaVYxfyWpA1V1c/t5G/AFYA/g1uErPtrP29rqNwOP7Nt8fmtbe5/mrySNj3PWS9IAdNaZPBkH05KksZm/kjT1kmyV5HeHnwPPAa4CzgUObasdCnypPT8XOKSNkHsqcKfzJUvSQDnNmyRNQCedyR5MS1I3zF9J6szDgW8n+T5wKfAvVfUV4IPAs5P8CNivvQY4D7gBuA74BPCmqS9ZkmYNp3mTpAHZrKP3fTjwhSTDNXymqr6S5HvAWUleB/wncGBb/zzg+fQOpn8FHD71JUvSrGD+SlIHquoG4H+P0H47sO8I7QUcMQWlSdJc8EdVdXOShwEXJPlh/8KqqiQbPM0bsBhgaGhog7aVpJmsk85kD6YlqRvmryRJkuaa/mnekqwxzVtV3eI0b5I0fl3egE+SJEmSJGnSOM2bJA1WV9NcSJIkSZIkTTaneZOkAbIzWZIkSZIkzUpO8yZJg+U0F5IkSZIkSZKkMdmZLEmSJEmSJEkak53JkiRJkiRJkqQx2ZksSZIkSZIkSRqTncmSJEmSJEmSpDHZmSxJkiRJkiRJGpOdyZIkSZIkSZKkMdmZLEmSJEmSJEkak53JkiRJkiRJkqQx2ZksSZIkSZIkSRqTncmSJEmSJEmSpDHZmSxJkiRJkiRJGpOdyZIkSZIkSZKkMW3WdQGSJEmSJEnSXHTTsU/ouoQRLXj/iq5L0DQ15SOTkzwyyb8luSbJ1Une1tqPSXJzkivb4/l927w7yXVJrk3y3KmuWZJmA/NXkrpjBkuSJGk26GJk8r3AO6rq8iS/C1yW5IK27CNV9eH+lZPsArwS2BV4BPC1JI+tqvumtGpJmvnMX0nqjhksSZKkGW/KRyZX1S1VdXl7/j/AD4Ad1rPJAcCZVXVPVf0YuA7YY/IrlaTZxfyVpO6YwZLUDa8MkaTB6vQGfEl2BJ4EfLc1vTnJ8iRLkmzb2nYAftK32UpGOfBOsijJsiTLVq1aNUlVS9LMZ/5KUncGmcHmrySNafjKkF2ApwJHtKs/oHdlyML2OA/WuTJkf+AfkmzaReGSNB111pmcZGvgbODIqroLOBl4DLAQuAU4YUP3WVWLq2qoqobmzZs30HolabYwfyWpO4POYPNXktbPK0MkabA66UxOsjm9g+jTq+ocgKq6taruq6r7gU/wQFjfDDyyb/P5rU2StIHMX0nqjhksSd3yyhBJ2nhT3pmcJMCpwA+q6u/62rfvW+2lwFXt+bnAK5NskWQnYGfg0qmqV5JmC/NXkrpjBktSt7wyRJIGY7MO3vMZwGuAFUmubG3vAQ5OshAo4EbgDQBVdXWSs4Br6M11dIR3sZakCTF/Jak7ZrAkdWS0K0P6ln8C+HJ76ZUhkrQeU96ZXFXfBjLCovPWs81xwHGTVpQkzQHmryR1xwyWpG6s78qQqrqlvVz7ypDPJPk74BF4ZYgkraGLkcmSJEmSJElTwStDJGmA7EyWJEmSJEmzkleGSNJgTfkN+CRJkiRJkiRJM4+dyZIkSZIkSZKkMdmZLEmSJEmSJEkak53JkiRJkiRJkqQx2ZksSZIkSZIkSRqTncmSJEmSJEmSpDHZmSxJkiRJkiRJGtNmXRcgSf1uOvYJXZcwqgXvX9F1CZI0qaZrBpu/kmY781eSumH+bjhHJkuSJEmSJEmSxmRnsiRJkiRJkiRpTHYmS5IkSZIkSZLGZGeyJEmSJEmSJGlMdiZLkiRJkiRJksZkZ7IkSZIkSZIkaUx2JkuSJEmSJEmSxjRjOpOT7J/k2iTXJTmq63okaa4wfyWpG+avJHXD/JWk0c2IzuQkmwJ/DzwP2AU4OMku3VYlSbOf+StJ3TB/Jakb5q8krd+M6EwG9gCuq6obquo3wJnAAR3XJElzgfkrSd0wfyWpG+avJK3HTOlM3gH4Sd/rla1NkjS5zF9J6ob5K0ndMH8laT0267qAQUqyCFjUXv4iybVd1rMxHgXbAT/tuo51HJ2uK5h00/a7B7//ro3v+3/UZJcxHZm/U2AO/P6D33/XZvj3b/6av5PD3/9u+f13a/zf/5zLYPN3Cvj73y2//25N4/ydKZ3JNwOP7Hs9v7WtoaoWA4unqqjJlGRZVQ11Xcdc5HffLb//acf81ZTy+++W3/+0Yv5qSvn9d8vvf1oxfzWl/P675fe/4WbKNBffA3ZOslOSBwGvBM7tuCZJmgvMX0nqhvkrSd0wfyVpPWbEyOSqujfJm4HzgU2BJVV1dcdlSdKsZ/5KUjfMX0nqhvkrSes3IzqTAarqPOC8ruuYQrPicpkZyu++W37/04z5qynm998tv/9pxPzVFPP775bf/zRi/mqK+f13y+9/A6Wquq5BkiRJkiRJkjTNzZQ5kyVJkiRJkiRJHbIzuQPp+XaS5/W1vSLJV7qsay5KUklO6Hv950mO6bCkgdnYz5bkxUmOGmOdHZP86UaUOW5JDkvyiKl4r/Z+Q0lOnKr309Sbzb//09lc+Bto/m70+5m/s5z52w3zd1zbm7/m76xm/nbD/B3X9ubvDMpfO5M7UL25Rd4I/F2SLZNsDfw1cES3lc1J9wAvS7Jd14VMgo36bFV1blV9cIzVdgQ2KMyTTHSu9sOAgYb5+mqpqmVV9dZBvp+mndn8+z9tzZG/gebvGMzfOc/87YD5Ozbz1/ydA8zfDpi/YzN/Z1b+2pnckaq6Cvhn4F3A+4F/At6b5NIkVyQ5ACDJrq3tyiTLk+zcYdmz0b30Jlv/s7UXtLNe32jf+9eTLJj68jbKRn22dibupPZ8aZITk/x7khuSvLyt9kFgr/bv88+SbJrk+CTfa/t+Q9t+7yTfSnIucE17fWGSzyf5YZLTk6Stu3uSbya5LMn5SbZv7zcEnN7e68Fr1frWJNe09zyztW2VZMkIv1OHJTk3yTeAryc5M8kL+va1NMnLW41fbm1bJ/lkkhXtPf6ktT8nycVJLk/yuXZQoJljNv/+T2tz4G+g+Wv+av3M346Yv+Yv5u9cZ/52xPw1f5lN+VtVPjp6AFsB1wIrgL8BXt3aHwL8R1v+MeBVrf1BwIO7rns2PYBfANsANwK/B/w5cExb9s/Aoe35a4Evdl3vVH42emfiTmrPlwKfo3cCahfguta+N/Dlvm0WAX/Rnm8BLAN2auv9Etipb7s7gfltnxcDfwRsDvw7MK+tdxCwpD2/EBga5bP+F7BFe/6Q9vOvR/mdOgxYCTy0LXsp8Kn2/EHAT4AH93824EPA/+t7v22B7YCLgK1a27uA93f9393H1P2O+Njo73/W/g00f81fH5P7O+Jjo79/89f8NX/n6MP87fz7N3/N31mRvxMd7q0BqKpfJvksvV+6A4EXJfnztnhLYAG9f+TvTTIfOKeqftRNtbNXVd2V5DTgrcCv+xY9DXhZe/5p4G+nuraNNeDP9sWqup/emb2Hj7LOc4An9p05/D1gZ+A3wKVV9eO+dS+tqpUASa6kd8nKz4HdgAvaicJNgVvGUdtyemcNvwh8sa+WF4/wOwVwQVXd0Z7/K/DRJFsA+wMXVdWv2/sP2w945fCLqvpZkhfS+8P2nbbug+j9vmoGmc2//9PdbP8baP6av1o/87c75i9g/pq/c5j52x3zFzB/Z0X+2pncvfvbI8CfVNW1ay3/QZLvAi8Azkvyhqr6xlQXOQf8P+By4JNdFzIJBvXZ7ul7nlHWCfCWqjp/jcZkb3pnBkfb33308ijA1VX1tA2s7QXAM4EX0fvD+wRG+Z1Ksmd/LVV1d5ILgefSOxN55jjfM/T+KBy8gbVq+pnNv//T3Wz/G2j+rlmL+au1mb/dMX/Hx/xdk/k7e5i/3TF/x8f8XdO0yl/nTJ4+zgfe0jdvy5Paz0cDN1TVicCXgCd2V+Ls1c4SnQW8rq/533ngbNCrgG9NdV2DMMmf7X+A3+17fT7wf5JsDpDksUm22oD9XQvMS/K0tv3mSXYd5b1o62wCPLKq/o3epR6/B2zNKL9To/gscDiwFzDSHXUvoO/mCEm2BS4BnpHkD1vbVkkeuwGfVdPEbP79n0Fm5d9A89f81fqZv9OC+bvhzF/zd8Yzf6cF83fDmb/TJH/tTJ4+PkBvvpblSa5ur6F36cNVbRj+bsBpHdU3F5xAbx6aYW8BDk+yHHgN8LZOqhqMyfpsy4H7knw/yZ8B/whcA1ye5Crg42zAFRBV9Rvg5cCHknwfuBJ4elu8FDgl606AvynwT0lWAFcAJ1bVzxn9d2okXwX+GPhaq2FtfwVsm+SqVtezqmoVvfmPzmjf48XA/xrvZ9W0M5t//2eC2fw30Pw1f7V+5m+3zN8NZ/6av7OF+dst83fDmb/TJH9TvYmbJUmSJEmSJEkalSOTJUmSJEmSJEljsjNZkiRJkiRJkjQmO5MlSZIkSZIkSWOyM1mSJEmSJEmSNCY7kyVJkiRJkiRJY7IzWdNKkvlJvpTkR0muT/LRJA8aY5sbk2zXnv/7Rrz30iQ/TnJlkh8mOXqC+9k7yZcnWockTYYk701ydZLlLef2THJkkt+Z4joekeTzG7H9MUkqyR/2tR3Z2oYmuM8J/+2QpLEkua/l7tVJvp/kHUkm9P9hSYaSnDjBbS9Mcm2r4TtJHrcB2z4kyZsm8r5t+5ck2aXv9bFJ9pvo/iRp0JL8fsvqK5P8d5Kb+16vt0+ibT9qP0CSFya5ouXvNUneMPhPMD5r57E0EXYma9pIEuAc4ItVtTPwWGBr4Ljx7qOqnr6RZbyzqhYCC4FDk+y0kfuTpM4leRrwQuDJVfVEYD/gJ8CRwIidyUk2nYxaquq/qurlG7mbFcAr+16/Arh6I2ra2L8dkrQ+v66qhVW1K/Bs4HnAhAYtVNWyqnrrRtTyqqr638CngOPXXrie7H8IMOHOZOAlwOrOi6p6f1V9bSP2J0kDVVW3t6xeCJwCfGT4dVX9ZqL7TbI5sBh4UcvfJwEXDqToDa9lM9bKY2ki7EzWdLIPcHdVfRKgqu4D/gx4bZI3JTknyVfaqOW/HWkHSX7Rfu7dRl98vo0yPr11VpNk9yTfTHJZkvOTbD/CrrZsP3/Ztnl/ku8luSrJ4r59/WGSr7UzjJcnecxa9TylnYF8TKtnqLVvl+TG9vywNhr7wvbZJvQ/F5K0HtsDP62qewCq6qfAy4FHAP+W5N+gl6FJTkjyfeBpSV6d5NI2IuPjw50MSU5OsqyNsvvL4TdpV4r8TVt/WZInt5y9Pskb2zo7JrmqPT9stGxP8rok/9He/xNJTur7PF8EDmjrPQa4E/hp37YHJ1nRMvtDre2NSY7vW+ew4X0O/+1oz9/Z8n758GdLslWSf2lZf1WSgzb6v4ikOamqbgMWAW9Oz6ZJju/LnTcAJDkzyQuGt0vvCrqX9498S7J1kk+2vFue5E9a+3OSXNyOTT+XZOsRSrkI+MO2/trZ//aWdVclObKt/0HgMS3fj2/brZOXrf2Q1vb9JJ9O8nTgxcDxbfvHDH+etv6+7Xh5RZIlSbZo7Tcm+cv2OVYk+V8D/E8hSWPKKH0HGb0fYOus2wfxu8BmwO0AVXVPVV3b9rM6C9vr/v6Mi9rx57VJTkm7oqVl9kfacfjXk8xr7QuTXNLy9wtJtm3tFyb5f0mWAe9irTyegq9Rs5CdyZpOdgUu62+oqruAm+iF70LgIOAJwEFJHjnG/p5Eb9TdLsCjgWekd1bwY8DLq2p3YAlrjnw+PsmVwErgzHbAD3BSVT2lqnYDHkxvhB/A6cDftzOMTwduGd5RO3A+BTigqq4fo9Y9gD8Bngi8IhO8VFuSRvFV4JGtc/YfkvxxVZ0I/BfwrKp6VltvK+C7LdNup5e5z2gjNO4DXtXWe29VDdHLrD9O8sS+97qprf8tYCm9TuunAn/JyNbJ9iSPAN7XtnsGsHYHwl3AT5LsRm+E8meHF7RtP0TvBOVC4ClJXgKcDby0bx8HAWf27zTJc4Cd6WXyQmD3JM8E9gf+q6r+d/s78JVRPoskjamqbgA2BR4GvA64s6qeAjwFeH16V8Z9FjgQIL3Lq/cF/mWtXb2vbfuEdtXJN9Kb+u0vgP2q6snAMuDtI5TxInpXecCa2f9r4HBgT3oZ/PokTwKOAq5vI/TeOVpeJtm1vf8+bX9vq6p/B86lXQHYf1ycZEt6fysOqqon0Dvm/z99df60fY6TgT8f1xcsSYMRRu87GK0fYJ0+iKq6g14G/meSM5K8KuOb6mgP4C1tX48BXtbatwKWtatdvskDV7qcBryr/T1YwZpXwDyoqoaq6jhGyWNpQ9iZrJnk61V1Z1XdDVwDPGqM9S+tqpVVdT9wJbAj8DhgN+CC1mn8F8D8vm2Gp7n4A2Df1iEM8Kwk302ygl4Hxa5JfhfYoaq+AFBVd1fVr9r6j+eBS1luGsdnu6BdVvNrelN9/NE4tpGkcamqXwC70xsNtwr4bJLDRlj1PnqdrtDruNgd+F7Ly33pHRQDHJjkcuAKeicC+y+VO7f9XEGvc+J/qmoVcE+Sh4zwniNl+x7AN6vqjqr6LfC5EbY7k15H8kuAL/S1PwW4sKpWVdW99A72n9lquCHJU5P8Pr0O6u+stc/ntMcVwOVtnZ3bZ3l2kg8l2auq7hyhHkmaiOcAh7Sc/S7w+/Ry51/pHX9uQW9ajIvacWK//YC/H35RVT+j1wG8C/Cdts9DWfOY+fTW/gwe6Jztz/4/Ar5QVb9sfzvOAfYape6R8nIf4HPtChhaJ8r6PA74cVX9R3v9KeCZfcvPaT8vo3csL0lTZQtG6DsYox9gpD4Iqur/o3csfSm97F0yjve/tKpuaFdsn8EDfQT388BAin8C/ijJ7wEPqapvtva1s/SzSAO0WdcFSH2uoTeCbbUk2wALgHuBe/oW3cfY/35HWj/A1VX1tPVtWFW/SHIhvWC+HPgHYKiqfpLkGB6YBmM0t7R1nkRv5B/tMwyfwFl7+xrjtSRtlHYgeiFwYTsxdugIq93d1oNeXn6qqt7dv0IbMffnwFOq6mdJlrJmpg1n7/2smcP3M3Jub2i2D/syvfk+l1XVXb2rCMd0Jr2Rfj+k11mydtYG+Juq+vjaGyZ5MvB84K+SfL2qjh1nnZK0hiSPppd3t9HLnbdU1fkjrHch8FxGuJJifbunN0jh4FGWv6qqlq3V1p/94zViXiZ5ywbuZyzDfyM25O+DJNV3GrIAACAASURBVA3CiH0HrTN5NKMe11bVCmBFkk8DPwYOo6+PoI1W7r/R33j7CMbTd/DLcawjjZsjkzWdfB34nSSHwOobgJxA79K3X61nuw1xLTAvvZtRkWTzdjneGtKbmH5P4Hoe6CT5aXpzzr0coKr+B1jZLp8myRZJhm9k9XPgBcDfJNm7td1Ib5QfrNVpTm/E20OTPJjeKLu1R8tJ0oQleVySnfuaFgL/CfwPvXncRvJ14OVJHtb28dAkjwK2oXdAemeSh9MbMTdo36M3fca2LY//ZO0V2giQd7HuTVovbdtu1/6OHEzvEkDojWA+oLWN1DFzPr15+rcGSLJDkoe1qTN+VVX/RK8D+8kb/QklzUltbstT6E2hVvRy5/+0qdhI8tgkW7XVP0tvyom9GHl6nQuAI/r2vS1wCb2p3YbnQ94qyWM3oMRvAS9J8jutjpe2trX/XoyYl8A36E3Z9vut/aFt/dH+3lwL7DhcL/AaHshsSerSPYzQdzBGP8A60pvffu++puHjcFizj+DFwOZ96+2RZKfWyXwQ8O3WvgkP9Cf8KfDtdtXcz5IMX0myvixd3/G/NC52JmvaaAfUL6V3APoj4D+Au4H3DPA9fkMveD+U3k1GrqQ3x9Gw4TmTl9O7rPmcqvo58AngKnoHzt/rW/81wFuTLAf+nd70GMPvdSu9uZX/PsmewIfp/c/CFcB2a5V2Kb3LC5cDZ48wYkSSNsbWwKeSXNPyahfgGHrT8Xwl7QZ8/arqGnqX8321bXMBsH1VfZ/eZc0/BD7DJJz8qqqbgb+ml43foXegvc7UElV1ZlVdvlbbLfTm9vw34PvAZVX1pbbsZ8APgEdV1aUj7O+r9D7TxW309ufpHWw/Abi0/X04GvirwXxSSXPEg9O70dHVwNfozWM/PI/8P9K7Ou/y9G5O+nEeGMn2VeCPga+1Y9i1/RWwbXo3yvs+vTnwV9Eb7XZGy+6LWXfe+VG1TF1KL3+/C/xjVV1RVbfTmzrjqiTHj5aXVXU1vZN832w1/V3b9ZnAO9NuTN33fnfT6zD/XNvP/fQ62yWpa/czet/BqP0AIwjwf/P/s3fnYZpU5f3/3x92gwsgIwIDDiISARFhAhg1XwS/CLgM+lMEF0bC14kJBIkrkEQIhkSDxggm6igj4AKiiIwGF4ISxYgyqGHVMLLOhGVkX2S/f3/UaXgYuqd7Zrr76el+v67rubrq1KlTp/qCu2vu59Q53UJ6v6KL/+9oxz5HNwjiv4GX8MQRxBcBn6J7dr2Gx6d1u5cu0XwZ3dRCA2/LzabLZ1xCl7Ae6i26QeOxtDzy5Dc8JY2nNm/pzKo6tN99kaSJIslT25RDa9A9PM8bmJtOkiRJmqzaSOb3VdVrBjl2T1U9dfx7JT3OkcmSJGkiOqaN3riMbjTGN/vcH0mSJEma8hyZLEmSJEmSJEkaliOTJUmSJEmSJEnDMpksSZIkSZIkSRqWyWRJkiRJkiRJ0rBMJkuSJEmSJEmShmUyWZIkSZIkSZI0LJPJkiRJkiRJkqRhmUyWJEmSJEmSJA3LZLIkSZIkSZIkaVgmkyVJkiRJkiRJwzKZLEmSJEmSJEkalslkaQSSvDzJb/rdD0mSJGk4Sc5P8v/6eP17kjy3bZ+c5O/b9m5JFvXUuzzJbn3qpiRNKaP5t6E3tmvqMZmsvkrysiT/leTOJLcl+UmSP0ryjiQX9Lt/A6rqx1W19cq2k+Q77eH6niQPJXmwZ/9zSR5OsuUg552V5GMre31JWllJ3pJkQYtbN7a49rKVbHNMH0aTXJvklWPVviT1ajHn9y1O3txi3FP73a/R0hLCj/Y8wy5O8ne9darqqVV19XBtVdW2VXX+mHVW0pSwKsfdpZ9Tk8xIUkl+uVS9DVv+4NoRtntMki+NcnclwGSy+ijJ04FvAycCGwCbAn8HPDDC81cfu96Njarauz1cPxX4MvBPA/tV9U7gPODtveck2QDYBzhl/HssSY9L8h7gX4B/ADYCNgf+DZg1xtddYyzbl6Qx8Nr2vLcjMBP4m/G4aDrj8W+8/+15pn0ZcHCSfcfhupI0lL7E3TH0B0m269l/C3BNvzoj9TKZrH56PkBVnVZVj1TV76vq+8BDwGeAl7RvFu+Ax0aufTrJOUnuBV6RZJMkZyZZkuSaJIcNNJ5k5yQ/TXJHGz33qSRr9RyvJH+R5Kokdyf5cJIt20jpu5KcMVB/kFfyrk3yviSXtFHVX02yTs/xD7Rr/m+S/9eu9bwR/E5OYalkMrA/cEVVXbq8v2BJGi1JngEcCxxSVd+oqnur6qGq+lZVvT/J2kn+pcW9/23ba7dzd0uyKMl7k9zS4uNB7dgc4K3AB1rM/1YrvzbJB5NcAtybZI0kRyT5bYvZVyR5/VJ9fGeSK3uO75jki3RJ72+19j8wjr82SVNcVS0GvgO8MMm32zPr7W17+kC9dK8e/2OSn7fn0LPbgIKB47u2Z9Q7kvx3eqaGaOcel+QnwH3Ac5fuR5I/bfHx9iTfS/KcVp4kn2ix+a4klw4kL5Ls02Lp3elGH79viHu8BvgvYJue643o2Tc9I/LSjaI7I8mp7ZqXJ5nZU3fHJL9sx77Wnr99xVrSE4wk7iZ5U5KLe89L8p4kZ7ftk5P8Wx5/s/gnSZ7dnm9vT/LrJC/uOXdZeYkhY9swz6lfBGb37B8InLpUnwe9bpK9gKOAN7d2/7vntOe0+7k7yfeTbNjT3uta/+5of1te0HPsxUl+0c77KrAOmrJMJquf/gd4JMkpSfZOsj5AVV0JvAv4aRvxsF7POW8BjgOeRvfQ+i3gv+lGNe8BHJ7kVa3uI8BfARsCL2nH/2KpPrwK2AnYFfgAMBd4G7AZsB1wwDL6vx+wF7AFsD3wDngscL8HeCXwPGC3kf5CgLOADfPEV8bfjqOSJfXfS+geGs8a4vhf08XSHYAXATvzxBEhzwaeQRevDwb+Ncn6VTWXJ76p8dqecw4AXg2sV1UPA78FXt7a+TvgS0k2hu4fBcAxdA/aTwdeB9xaVW8HrqeNVqmqf1qp34IkLYckm9G9YXY18AXgOXSJg98Dn1qq+oHAnwIbAw8DJ7Q2NgX+Hfh7urf53gecmWRaz7lvB+bQPSNft1QfZtElFd4ATAN+DJzWDu8J/AndII9n0D3f3tqOnQT8WVU9je65+AdD3ONWwEuBC4f/jQzrdcDpwHrAfNrvKN0Aj7OAk+l+B6cBrx+8CUlT2Qjj7nxgi95kKV0c7U3W7kf3LLsh3dvTPwV+0fa/Dvxzu95qLDsvAUPEtmGeU78E7J9k9STbAE8FftZzn0Net6q+S/cm4Vdbuy/qafctwEHAs4C16P6mkOT5dLH1cLq/FefQJbnXajH4m3QJ7g2ArwH/39K/e00dJpPVN1V1F91rcQV8DliSZH6SjZZx2tlV9ZOqehR4ITCtqo6tqgfbvGyfoxvJS1VdXFUXVtXDVXUt8Fng/yzV3j9V1V1VdTlwGfD9qrq6qu6k+zbzxQzthKr636q6jS6I79DK9wO+UFWXV9V9dMmNkf5Ofk8XmA+Exx7OdwK+MtI2JGmMPBP4XUvqDuatwLFVdUtVLaFL9va+afFQO/5QVZ0D3AMMNxf9CVV1Q4uNVNXXWtx9tKq+ClxFl7QG+H90Mf2i6iysquuGaliSxtg3071ddwHwn8AHqurMqrqvqu6mGxyx9HPpF6vqsqq6F/hbYL9007q9DTinqs5p8e9cYAFdsmTAye3Z8+Gqemipdt8F/GNVXdli+D8AO7TRyQ/RJaD/EEirc2M77yFgmyRPr6rbq+oXPW1u0kau3UU3QORn7V5X1gXtPh+hS1oMJEB2Bdag+7vwUFV9A/j5KFxP0uQx4rhbVQ8AX6WLryTZFphBNw3ngLNaTuF+ui+z7q+qU1t8+iqP5wr+iGXkJZqhYtuyLAJ+QzdI7cB2Xq+RXHcwX6iq/2nP12fweB7jzcC/V9W57e/Ix4CnAH9MF4PXBP6lxeCvAxeN4B40SZlMVl+1B9Z3VNV0uhEPm9DNxzmUG3q2n8PjD7J3tD8cR9HN40mS57dXWW5qD7r/QPctYq+be7Z/P8j+sibtv6ln+76eupss1c/e7ZE4BXhTumkz3g58r6puWc42JGm03Ur35sRQ8xdvwhNHw13Xyh47f6lEdG/cHMoT4meSA5P8qifmb8fjcX0zupHLkjQR7FtV61XVc6rqL+hmlPhskuvac+mPgPXyxDVAemPedXT/cN+Q7pn3TUs9876MbgTzYOcu7TnAJ3vOvQ0IsGlV/YBuhNy/ArckmZtuXRPoRp3tA1yX5D+TvKSnzf9t9/d0upF2v2d03qRb+vl6nfZ3ZxNgcVVVz/HlfcaWNLktb9w9BXhLktD9u/uMlmQeMNJcwTLzEs1QsW04p9K9AX0AT04mj+S6g1lWHuOxZ/k2gO8GulHPg8VgB21MYSaTNWFU1a/pXl3bjm608qDVerZvAK5pfzAGPk+rqoFRGp8Gfg1s1R50j6J7cB5rNwLTe/Y3W87zL6B7yJ9F902pU1xImgh+SveK31ALLP0v3UPtgM1b2UgMG/PbCLrPAYcCz2xTIF3G43H9BmDL5WxfksbLe+nextilPZf+SSvvfTbtfWbcnG5k8O/o4tsXl3rmXbeqPtJTf1lx7ga66Sp6z39KVf0XQFWdUFU70c15/Hzg/a38oqqaRfcq9DfpRrA9SXuj7yvAawc7PkpuBDZtSZ8By/uMLWlqWWbcraoLgQfpplB7C09O1o7UcHmJ4Swrfp9JN+Xb1VV1/XJed3mff5/wLN/i7WbAYgaPwZsvZ/uaREwmq2+S/GG6xZgGJsHfjO4btwvpvvWbnp4F8wbxc+DudAs0PaXNJbRdkj9qx58G3AXck+QPgT8fu7t5gjOAg5K8IMkf0L2mOGLt275TgY/SjfT41uh3UZKWT0sWfIhuruN9k/xBkjXbnPf/RDfH2t8kmdYW8vgQ3VxvI3EzgywYtZR16R6KlwCkW8Cvd4XrzwPvS7JTOs9rCeiRti9JY+lpdCPZ7ki3sN7Rg9R5W5Jt2vPjscDX2yvRXwJem+RV7Xl3nXQLm04fpI3BfAY4sr3GTZJntHnmSfJHSXZJsiZwL3A/8GibI/OtSZ7RXne+C3h0sMaTPJXuterLR9ifFfFTuvVQDk23IOssHp/mSJIGM5K4eyrd2xkPVdWKTtUzXF5iOEM+p7Zpj3anm85tea97MzAj3dzKI3EG8Ooke7S/Ce+lG0jyX3Qx+GHgsPb8/waMwVOayWT1093ALsDPktxLl0S+jC5o/YDugfSmJL8b7OT2cP0aujl+rqEbufF5usVDoJtI/i3tOp+jm9dozFXVd+gWTPkhsJDHFyN5YMiTnuxUum/6vrrUqzaS1DdV9XG6BUb/hi6pewPdSOFv0i0MtQC4BLiUboGSvx9h0yfRzct5R5JvDnHtK4CP0z3M3kw3b/5Peo5/jW4uvK/Qxf1v0i0QAvCPdInuO5K8b6T3K0mj6F/o5p78Hd2z4XcHqfNFurf0bqJb8PQwgKq6ge6NtaN4PPa+nxH+W66qzqIbpHB6e9X7MmDvdvjpdM/Jt9O9snwrcHw79nbg2nbOu+jmxh+wSZJ7ktzTzttgqeOjqqoepFtA8GDgDrq3977N8j1fS5paRhp3t2PkAyCeZAR5ieEs8zm1qhZU1ZOmchvBdb/Wft6a5BdLnz9Ie7+hi60ntrZeS7cw4IM9MfgddG9Rvxn4xgjvT5NQnjjliaTRlm6F2MuAtWvohaskSZI0RSU5H/hSVX2+331ZVST5GfCZqvpCv/siadWU5CnALcCOVXVVv/sjrSocmSyNgSSvT7J2kvXpRoJ8y0SyJEmStGKS/J8kz27TXMwGtmfwkYaSNFJ/DlxkIllaPiNZPVLS8vszutcUHwH+E/iLvvZGkiRJWrVtTTen57rA1cAbq+rG/nZJ0qoqybV0i/ENtbi0pCE4zYUkSZIkSZIkaVhOcyFJkiRJkiRJGtakneZiww03rBkzZvS7G5KmmIsvvvh3VTWt3/3oJ+OvpH4w/hp/JfXPVI/Bxl9J/dKP+Dtpk8kzZsxgwYIF/e6GpCkmyXX97kO/GX8l9YPx1/grqX+megw2/krql37EX6e5kCRJkiRJkiQNy2SyJEmSJEmSJGlYJpMlSZIkSZIkScOatHMmS5p4HnroIRYtWsT999/f766stHXWWYfp06ez5ppr9rsrkjQs468k9Y8xWJL6w/g7NkwmSxo3ixYt4mlPexozZswgSb+7s8KqiltvvZVFixaxxRZb9Ls7kjQs468k9Y8xWJL6w/g7NpzmQtK4uf/++3nmM5+5SgdxgCQ885nPnBTfbkqaGoy/ktQ/xmBJ6g/j79gwmSxpXK3qQXzAZLkPSVPHZIlbk+U+JE0tkyV2TZb7kDR1TJa4NZHuw2SyJEmSJEmSJGlYzpmsvtnp/af2uwuDuvj4A/vdBfW46aabOPzww7noootYb7312Gijjdh3332ZP38+3/72t/vdPa2gifr/PxgDpAHG36ljIsfk0WR816rC+KuppB9/g/x7oKEYf0fGkcmSJqyq4vWvfz277bYbv/3tb7n44ov5x3/8R26++eaVavfhhx8epR5K0uRk/JWk/jD+SlJ/GH9HzpHJkiasH/7wh6y55pq8613veqzsRS96EbfffjvnnXceb3zjG7nsssvYaaed+NKXvkQSZsyYwYIFC9hwww1ZsGAB73vf+zj//PM55phj+O1vf8vVV1/N5ptvztZbb83111/P1VdfzfXXX8/hhx/OYYcd1se7laSJw/grSf1h/J18HHkrjZ3h/v86/vUvoG743YjauvAnP+ahR+FPXv1GrmjnrLnBpmy29fZ865zvsuc+r2Xh//yal+yy85SPv1M6mTyRX+kz+Es89qA8mF/+8pdcfvnlbLLJJrz0pS/lJz/5CS972cuW2d4VV1zBBRdcwFOe8hSOOeYYfv3rX/PDH/6Qu+++m6233po///M/Z8011xyLW5GkVYrxV5L6w/grqZdfRoyfhb+5km1e+KJBj115+aWc/R8X8KyNns0795815eOv01xIWiXtvPPOTJ8+ndVWW40ddtiBa6+9dthzXve61/GUpzzlsf1Xv/rVrL322my44YY861nPWunXVyRpKjD+SlJ/GH8lqT9e+KIdefbGmxh/G5PJkiasbbfdlosvvnjQY2uvvfZj26uvvvpj8xCtscYaPProowDcf//9Tzhn3XXXHVEbkjTVGX8lqT+Mv5LUH897/h9yxaX/PeixtdZa67Ft46/JZEkT2O67784DDzzA3LlzHyu75JJL+PGPfzzkOTNmzHjsAfzMM88c8z5K0mRk/JWk/jD+SlJ/7PLSl/Pggw9yxpcfn1rkN1dezsU/v3DIc6Zq/DWZLGnCSsJZZ53Ff/zHf7Dllluy7bbbcuSRR/LsZz97yHOOPvpo3v3udzNz5kxWX331ceytJE0exl9J6g/jryT1RxJOmHsKF17wn+z1sj/idXu8jH/56N+z4bRnDXnOVI2/qap+92FMzJw5sxYsWLDMOi7A118T9fc/FX73/XLllVfyghe8oN/dGDWD3U+Si6tqZp+6NCEYf6WJx/g7NYwk/g5mIsfk0WR8V78Ygye/ZcVfF1BbNn8/y+bvZ9mG+/0c//oX8OzNthjVa26z2Yaj2t7ymCjx15HJkiRJkiRJkqRhmUyWJEmSJEmSJA3LZLIkSZIkSZIkaVhjlkxOMi/JLUku6yn7apJftc+1SX7Vymck+X3Psc/0nLNTkkuTLExyQpKMVZ8lSZIkSZIkSYMby5HJJwN79RZU1Zuraoeq2gE4E/hGz+HfDhyrqnf1lH8aeCewVfs8oU1JkiRJkjS1DTGg7fgkv05ySZKzkqzXc+zINmjtN0le1VO+VytbmOSI8b4PSZroxiyZXFU/Am4b7FgbXbwfcNqy2kiyMfD0qrqwqgo4Fdh3tPsqSZIkSZJWaSfz5MFn5wLbVdX2wP8ARwIk2QbYH9i2nfNvSVZPsjrwr8DewDbAAa2uJKlZo0/XfTlwc1Vd1VO2RZJfAncBf1NVPwY2BRb11FnUygaVZA4wB2DzzTcf9U5LGl07vf/UUW3v4uMPHLZOEt7znvfw8Y9/HICPfexj3HPPPRxzzDGj2hdJmsiMv5LUP8bgsVFVP0oyY6my7/fsXgi8sW3PAk6vqgeAa5IsBHZuxxZW1dUASU5vda8Yw65LGidvP+GcUW1vqsbffi3AdwBPHJV8I7B5Vb0YeA/wlSRPX95Gq2puVc2sqpnTpk0bpa5KmkzWXnttvvGNb/C73/2u312RpCnF+CtJ/WMMBuBPge+07U2BG3qODQxcG6r8SZLMSbIgyYIlS5aMQXclTQaTMf6OezI5yRrAG4CvDpRV1QNVdWvbvhj4LfB8YDEwvef06a1MklbIGmuswZw5c/jEJz7xpGPXXnstu+++O9tvvz177LEH119/fR96uPKSbJbkh0muSHJ5kne38g2SnJvkqvZz/VaetsDpwjaf3I49bc1u9a9KMrtf9yRp1TfF4+8xSRb3LDa9T885ztkpacxNhRi8LEn+GngY+PJotelgNkkjMRnjbz9GJr8S+HVVPTZ9RZJpbW4ikjyXbqG9q6vqRuCuJLu2eZYPBM7uQ58lTSKHHHIIX/7yl7nzzjufUP6Xf/mXzJ49m0suuYS3vvWtHHbYYX3q4Up7GHhvVW0D7Aoc0uZ6OwI4r6q2As5r+9DNCTewyOkcuoVPSbIBcDSwC91rf0cPJKAlaUVM4fgL8ImexabPAefslDS+pkAMHlSSdwCvAd7a1mKCbpDaZj3VBgauDVUuSStsssXfMUsmJzkN+CmwdZJFSQ5uh/bnyQvv/QlwSZJfAV8H3lVVA4v3/QXweWAh3Yjl7yBJK+HpT386Bx54ICeccMITyn/605/ylre8BYC3v/3tXHDBBf3o3kqrqhur6hdt+27gSrrX82YBp7Rqp/D4gqazgFOrcyGwXlsA9VXAuVV1W1XdTreAydKLmkjSiE3h+DuUx+bsrKpr6J53d26fhVV1dVU9CAzM2SlJK2yyx+DBJNkL+ADwuqq6r+fQfGD/JGsn2YJuUMXPgYuArZJskWQtuvzF/PHut6TJZbLF3zFLJlfVAVW1cVWtWVXTq+qkVv6OqvrMUnXPrKpt20iNHavqWz3HFlTVdlW1ZVUd2vNNoiStsMMPP5yTTjqJe++9t99dGVNtEZIXAz8DNmpvfADcBGzUtp0zTtK4maLxF+DQNpXQvJ63PIy/ksbVZI7BQwxo+xTwNODcNs3QZwCq6nLgDLqF9b4LHFJVj1TVw8ChwPfovhA8o9WVpJUymeJvvxbgk6S+2mCDDdhvv/046aSTHiv74z/+Y04//XQAvvzlL/Pyl7+8X90bFUmeCpwJHF5Vd/Uea1/MjdqXc84ZJ2mkpmj8/TSwJbAD3cLTHx+taxl/JS2PyRyDBxvQVlXPq6rNeqYZeldP/ePaoLWtq+o7PeXnVNXz27Hj+nM3kiabyRR/1+h3ByRNXRcff2Bfr//e976XT33qU4/tn3jiiRx00EEcf/zxTJs2jS984Qt97N3KSbImXSLjy1X1jVZ8c5KNq+rGNo3FLa18WXPG7bZU+flj2W9J48P4O3YGi79VdXPP8c8B3267y5qb0zk7pUnKGCxJ/fHFw/YZvtIwttlswxU+d7LEX5PJkqaUe+6557HtjTbaiPvue3zqtOc85zn84Ac/6Ee3RlVbsPQk4Mqq+ueeQ/OB2cBH2s+ze8oPTXI63WJ7d7aE8/eAf+h5HXtP4MjxuAdJk89Ujr8DX+S13dcDl7Xt+cBXkvwzsAmPz9kZ2pyddEnk/YG3jM9dSJqMpkIMlqSJaDLGX5PJkjT5vBR4O3BpW9gU4Ci6JPIZbf6464D92rFzgH3oFn66DzgIoKpuS/JhuoVIAI7tWRxVkvRkQ8XfA5LsQDe90LXAn0E3Z2eSgTk7H6bN2QmQZGDOztWBec7ZKUmSpInAZLIkTTJVdQHdqLbB7DFI/QIOGaKtecC80eudJE1ey4i/5yzjnOOAJ83JWVXnLOs8SZIkqR9cgE+SJEmSJEmSNCxHJkuSJEmSVshO7z+1310YF/1eNE+SpInCkcmSJEmSJEmSpGGZTJYkSZIkSZIkDctpLiT1zfXHvnBU29v8Q5cu83hV8fKXv5y//uu/Zu+99wbga1/7GieddBLf/e53R7UvkjSRGX8lqX+MwZLUH0/9witWuo3re7anavw1mSxpykjCZz7zGd70pjfxile8gocffpijjjpqlQ7ikrQqMP5KUv8YgyWpPyZr/DWZLGlK2W677Xjta1/LRz/6Ue69917e9ra3cdxxx3HZZZfx0EMPccwxxzBr1iwuv/xyDjroIB588EEeffRRzjzzTLbaaqt+d1+SVlnGX0nqH2OwJPXHZIy/JpMlTTlHH300O+64I2uttRavec1r2H333Zk3bx533HEHO++8M6985Sv5zGc+w7vf/W7e+ta38uCDD/LII4/0u9uStMoz/kpS/xiDJak/Jlv8NZksacpZd911efOb38xTn/pUzjjjDL71rW/xsY99DID777+f66+/npe85CUcd9xxLFq0iDe84Q0T9htBSVqVGH8lqX+MwZLUH5Mt/ppMljQlrbbaaqy22mpUFWeeeSZbb731E46/4AUvYJddduHf//3f2WefffjsZz/L7rvv3qfeStLkYfyVpP4xBktSf0ym+GsyWdKU9qpXvYoTTzyRE088kST88pe/5MUvfjFXX301z33ucznssMO4/vrrueSSSyZsIJekVZHxV5L6xxg8uV1/7AvH/Zqbf+jScb+mtCqaDPHXZLKkvpkIDxx/+7d/y+GHH87222/Po48+yhZbbMG3v/1tzjjjDL74xS+y5ppr8uxnP5ujjjqq312VpFFj/JWk6d47bgAAIABJREFU/jEGS1J/3HPQD1e6jW0223Clzp8M8XfMkslJ5gGvAW6pqu1a2THAO4ElrdpRVXVOO3YkcDDwCHBYVX2vle8FfBJYHfh8VX1krPosaeo45phjHtv+7Gc/+6TjRxxxBEccccQ49kiSpgbjryT1jzFYkvpjMsXf1caw7ZOBvQYp/0RV7dA+A4nkbYD9gW3bOf+WZPUkqwP/CuwNbAMc0OpKkiRJkiRJksbRmI1MrqofJZkxwuqzgNOr6gHgmiQLgZ3bsYVVdTVAktNb3StGubuSJEmSJEmSpGUYy5HJQzk0ySVJ5iVZv5VtCtzQU2dRKxuqXNIqqqr63YVRMVnuQ9LUMVni1mS5D0lTy2SJXZPlPiRNDVWTJ25NpPsY72Typ4EtgR2AG4GPj2bjSeYkWZBkwZIlS4Y/QdK4Wmeddbj11lsnVBBcEVXFrbfeyjrrrNPvrkjSiBh/Jal/jMGS1B+L7rifB++72/g7ysZsmovBVNXNA9tJPgd8u+0uBjbrqTq9lbGM8sHanwvMBZg5c+aq/V+KNAlNnz6dRYsWMRm+7FlnnXWYPn16v7shSSNi/JWk/jEGS1J/fOGn13MQMH29dUhGp83c059YPpHi77gmk5NsXFU3tt3XA5e17fnAV5L8M7AJsBXwcyDAVkm2oEsi7w+8ZTz7LGn0rLnmmmyxxRb97oYkTTnGX0nqH2Pw+EgyD3gNcEtVbdfKNgC+CswArgX2q6rbkwT4JLAPcB/wjqr6RTtnNvA3rdm/r6pTxvM+JI2eux94hBPOv2ZU27z4+ANHtb1V0ZhNc5HkNOCnwNZJFiU5GPinJJcmuQR4BfBXAFV1OXAG3cJ63wUOqapHquph4FDge8CVwBmtriRJkiRJ0oCTgb2WKjsCOK+qtgLOa/sAe9MNYtsKmEM3JedA8vloYBdgZ+DonrWeJEmM4cjkqjpgkOKTllH/OOC4QcrPAc4Zxa5JkiRJkqRJpKp+lGTGUsWzgN3a9inA+cAHW/mp1U2kemGS9ZJs3OqeW1W3ASQ5ly5BfdoYd1+SVhnjvQCfJEmSJEnSeNioZ6rNm4CN2vamwA099Ra1sqHKnyTJnCQLkiyYDPNhS9JImUyWJEmSJEmTWhuFXKPY3tyqmllVM6dNmzZazUrShGcyWZIkSZIkTUY3t+kraD9vaeWLgc166k1vZUOVS5Iak8mSJEmSJGkymg/MbtuzgbN7yg9MZ1fgzjYdxveAPZOs3xbe27OVSZKaMVuAT9LEttP7T+13FwZ18fEH9rsLmuKuP/aF/e7CoDb/0KX97oIkSdKEleQ0ugX0NkyyCDga+AhwRpKDgeuA/Vr1c4B9gIXAfcBBAFV1W5IPAxe1escOLMYnSeqYTJYkSRpHE/XLPPALPUnSqquqDhji0B6D1C3gkCHamQfMG8WuSdKk4jQXkiRJ0ihIslmSHya5IsnlSd7dyjdIcm6Sq9rP9Vt5kpyQZGGSS5Ls2NPW7Fb/qiSzh7qmJEmSNJ5MJkuSJEmj42HgvVW1DbArcEiSbYAjgPOqaivgvLYPsDewVfvMAT4NXfKZ7vXsXYCdgaMHEtCSJElSP5lMliRJkkZBVd1YVb9o23cDVwKbArOAU1q1U4B92/Ys4NTqXAisl2Rj4FXAuVV1W1XdDpwL7DWOtyJJkiQNymSyJEmSNMqSzABeDPwM2KiqbmyHbgI2atubAjf0nLaolQ1VPth15iRZkGTBkiVLRq3/kiRJ0mBMJkuSJEmjKMlTgTOBw6vqrt5jbdGnGq1rVdXcqppZVTOnTZs2Ws1KkiRJgzKZLEmSJI2SJGvSJZK/XFXfaMU3t+kraD9vaeWLgc16Tp/eyoYqlyRJkvrKZLIkSZI0CpIEOAm4sqr+uefQfGB2254NnN1TfmA6uwJ3tukwvgfsmWT9tvDenq1MkiRJ6qs1+t0BSZIkaZJ4KfB24NIkv2plRwEfAc5IcjBwHbBfO3YOsA+wELgPOAigqm5L8mHgolbv2Kq6bXxuQZIkSRqayWRJkiRpFFTVBUCGOLzHIPULOGSItuYB80avd5IkSdLKc5oLSZqEksxLckuSy3rKjkmyOMmv2mefnmNHJlmY5DdJXtVTvlcrW5jkiPG+D0mSJEmSNHGYTJakyelkYK9Byj9RVTu0zzkASbYB9ge2bef8W5LVk6wO/CuwN7ANcECrK0mSJEmSpiCnuZCkSaiqfpRkxgirzwJOr6oHgGuSLAR2bscWVtXVAElOb3WvGOXuSpIkSZKkVcCYjUwe4hXr45P8OsklSc5Ksl4rn5Hk9z2vXn+m55ydklzaXrE+oa2SLUlaMYe2GDwvyfqtbFPghp46i1rZUOVPkmROkgVJFixZsmQs+i1JkiRJkvpsLKe5OJknv2J9LrBdVW0P/A9wZM+x3/a8ev2unvJPA+8EtmqfwV7bliQN79PAlsAOwI3Ax0er4aqaW1Uzq2rmtGnTRqtZSZIkSZI0gYxZMrmqfgTctlTZ96vq4bZ7ITB9WW0k2Rh4elVd2Fa7PhXYdyz6K0mTXVXdXFWPVNWjwOd4fCqLxcBmPVWnt7KhyiVJkiRJ0hTUzwX4/hT4Ts/+Fkl+meQ/k7y8lW1K91r1gCFfsQZfs5akZWlf0A14PTAwDdF8YP8kayfZgu4tkJ8DFwFbJdkiyVp0i/TNH88+S5IkSZKkiaMvC/Al+WvgYeDLrehGYPOqujXJTsA3k2y7vO1W1VxgLsDMmTNrtPorSauaJKcBuwEbJlkEHA3slmQHoIBrgT8DqKrLk5xBt7Dew8AhVfVIa+dQ4HvA6sC8qrp8nG9FkiRJkiRNEOOeTE7yDuA1wB5t6gqq6gHggbZ9cZLfAs+ne526dyoMX7GWpBGoqgMGKT5pGfWPA44bpPwc4JxR7JokSZIkSVpFjes0F0n2Aj4AvK6q7uspn5Zk9bb9XLpXrK+uqhuBu5LsmiTAgcDZ49lnSZIkSZIkSdIYjkwe4hXrI4G1gXO73DAXVtW7gD8Bjk3yEPAo8K6qGli87y+Ak4Gn0M2x3DvPsiRJkiRJkiRpHIxZMnl5XrGuqjOBM4c4tgDYbhS7JkmSJEmSJElaTuM6zYUkSZIkSdJ4SvJXSS5PclmS05Ksk2SLJD9LsjDJV5Os1equ3fYXtuMz+tt7SZpYTCZLkiRJkqRJKcmmwGHAzKraDlgd2B/4KPCJqnoecDtwcDvlYOD2Vv6JVk+S1JhMliRJkiRJk9kawFOSrAH8AXAjsDvw9Xb8FGDftj2r7dOO75G26JMkyWSyJEmSJEmapKpqMfAx4Hq6JPKdwMXAHVX1cKu2CNi0bW8K3NDOfbjVf+bS7SaZk2RBkgVLliwZ25uQpAnEZLIkSZIkSZqUkqxPN9p4C2ATYF1gr5Vtt6rmVtXMqpo5bdq0lW1OklYZJpMlSZIkSdJk9UrgmqpaUlUPAd8AXgqs16a9AJgOLG7bi4HNANrxZwC3jm+XJWniMpksSZIkSZImq+uBXZP8QZv7eA/gCuCHwBtbndnA2W17ftunHf9BVdU49leSJjSTyZIkSZIkaVKqqp/RLaT3C+BSujzIXOCDwHuSLKSbE/mkdspJwDNb+XuAI8a905I0ga0xfBVJkjSZ7PT+U/vdhSFdfPyB/e6CJEmaZKrqaODopYqvBnYepO79wJvGo1+StCpyZLIkSZIkSZIkaVgjSiYnOW8kZZKk0WX8laT+MP5KUn8YfyVpYlvmNBdJ1gH+ANgwyfpA2qGnA5uOcd8kacoy/kpSfxh/Jak/jL+StGoYbs7kPwMOBzYBLubxYH4X8Kkx7JckTXXGX0nqD+OvJPWH8VeSVgHLTCZX1SeBTyb5y6o6cZz6JElTnvFXkvrD+CtJ/WH8laRVw3AjkwGoqhOT/DEwo/ecqpq4y8FL0iRg/JWk/jD+SlJ/GH8laWIbUTI5yReBLYFfAY+04gIM5pI0hoy/ktQfKxp/k8wDXgPcUlXbtbJjgHcCS1q1o6rqnHbsSODgdo3Dqup7rXwv4JPA6sDnq+ojo3ZzkjSB+fwrSRPbiJLJwExgm6qqseyMJOlJjL+S1B8rGn9Pppvbc+mkxyeq6mO9BUm2AfYHtqWbI/Q/kjy/Hf5X4P8Ci4CLksyvqiuWsy+StCry+VeSJrDVRljvMuDZy9t4knlJbklyWU/ZBknOTXJV+7l+K0+SE5IsTHJJkh17zpnd6l+VZPby9kOSVmErFH8lSSttheJvVf0IuG2E1WcBp1fVA1V1DbAQ2Ll9FlbV1VX1IHB6qytJU4HPv5I0gY10ZPKGwBVJfg48MFBYVa8b5ryTefLIjCOA86rqI0mOaPsfBPYGtmqfXYBPA7sk2QA4mu7byQIubiMzbh9h3yVpVbai8VeStHJGO/4emuRAYAHw3vYsuylwYU+dRa0M4IalyncZrNEkc4A5AJtvvvkKdk2SJhSffyVpAhtpMvmYFWm8qn6UZMZSxbOA3dr2KcD5dMnkWcCp7VWWC5Osl2TjVvfcqroNIMm5wF7AaSvSJ0laxRzT7w5I0hR1zCi29Wngw3QDIz4MfBz409FouKrmAnMBZs6c6SvhkiaDY/rdAUnS0EaUTK6q/xzFa25UVTe27ZuAjdr2pjx5BMamyyh/EkdmSJpsRjn+SpJGaDTjb1XdPLCd5HPAt9vuYmCznqrTWxnLKJekSc3nX0ma2EY0Z3KSu5Pc1T73J3kkyV0re/E2CnnURlBU1dyqmllVM6dNmzZazUpS34xV/JUkLdtoxt/2tt2A19PNBwowH9g/ydpJtqCb7u3nwEXAVkm2SLIW3SJ981f8biRp1eHzryRNbCMdmfy0ge0koZuSYtcVvObNSTauqhvbg/UtrXyokRmLeXxajIHy81fw2pK0Shnl+CtJGqEVjb9JTqN7dt0wySK6tT92S7ID3SCKa4E/a9e4PMkZwBXAw8AhVfVIa+dQ4HvA6sC8qrp81G5OkiYwn38laWIb0cjkXtX5JvCqFbzmfGB2254NnN1TfmA6uwJ3tukwvgfsmWT9JOsDe7YySZpSRiH+SpJWwPLE36o6oKo2rqo1q2p6VZ1UVW+vqhdW1fZV9bqeKd+oquOqasuq2rqqvtNTfk5VPb8dO26Mbk2SJjSffyVp4hnRyOQkb+jZXQ2YCdw/gvMGG5nxEeCMJAcD1wH7ternAPsAC4H7gIMAquq2JB+me90P4NiBxfgkabJb0fgrSVo5xl9J6g/jryRNbCNKJgOv7dl+mO71vFnDnVRVBwxxaI9B6hZwyBDtzAPmDdtLSZp8Vij+SpJWmvF3krn+2Bf2uwtjbvMPXdrvLkijwfgrSRPYSOdMPmisOyJJejLjryT1h/FXkvrD+CtJE9tIp7mYDpwIvLQV/Rh4d1UtGquOTXUTdeSEox2k8WX8laT+MP5KUn8YfyVpYhvpAnxfoFsgb5P2+VYrkySNLeOvJPWH8VeS+sP4K0kT2EiTydOq6gtV9XD7nAxMG8N+SZI6xl9J6g/jryT1h/FXkiawkSaTb03ytiSrt8/bgFvHsmOSJMD4K0n9YvyVpP4w/krSBDbSZPKfAvsBNwE3Am8E3jFGfZIkPc74K0n9YfyVpP4Y9fibZL0kX0/y6yRXJnlJkg2SnJvkqvZz/VY3SU5IsjDJJUl2XNkbkqTJZKTJ5GOB2VU1raqeRRfc/27suiVJaoy/ktQfxl9J6o+xiL+fBL5bVX8IvAi4EjgCOK+qtgLOa/sAewNbtc8c4NMreW1JmlRGmkzevqpuH9ipqtuAF49NlyRJPVYo/iaZl+SWJJf1lC336Isks1v9q5LMHuV7k6SJzOdfSeqPUY2/SZ4B/AlwUmvvwaq6A5gFnNKqnQLs27ZnAadW50JgvSQbr+j1JWmyGWkyebWBpAN0CQlgjbHpkiSpx4rG35OBvZYqW67RF+1aRwO7ADsDR/f2RZImOZ9/Jak/Rjv+bgEsAb6Q5JdJPp9kXWCjqrqx1bkJ2Khtbwrc0HP+olb2BEnmJFmQZMGSJUtWonuStGoZaUD+OPDTJF9r+28CjhubLkmSeqxQ/K2qHyWZsVTxLGC3tn0KcD7wQXpGXwAXtjnlNm51z22jQUhyLl2C+rQVvx1JWmX4/CtJ/THa8XcNYEfgL6vqZ0k+yeODKgCoqkpSy9NoVc0F5gLMnDlzuc6VpFXZiJLJVXVqkgXA7q3oDVV1xdh1S5IEox5/l3f0xYhGZUA3MoNuVDObb775CnZPkiYOn38lqT/GIP4uAhZV1c/a/tfpksk3J9m4qm5sAyluaccXA5v1nD+9lUmSWI5XRVrw9gFaksbZWMTfFRl9MUx7jsyQNOn4/CtJ/TGa8beqbkpyQ5Ktq+o3wB6t7SuA2cBH2s+z2ynzgUOTnE433dudPQMyJGnKc943SZo6lnf0xWIenxZjoPz8ceinJEmSNJr+EvhykrWAq4GD6NaQOiPJwcB1wH6t7jnAPsBC4L5WV5LUmEyWpKljPssx+iLJ94B/6FkAZU/gyHHusyRJkrRSqupXwMxBDu0xSN0CDhnzTknSKspksrSU6499Yb+7MKTNP3Rpv7ugVUSS0+hGFW+YZBFwNF0SecSjL6rqtiQfBi5q9Y4dWIxPkiRJkiRNPSaTJWkSqqoDhji0XKMvqmoeMG8UuyZJkiRJklZRq/W7A5IkSZIkSZKkic9ksiRJkiRJkiRpWOOeTE6ydZJf9XzuSnJ4kmOSLO4p36fnnCOTLEzymySvGu8+S5IkSZIkSdJUN+5zJlfVb4AdAJKsDiwGzqJb8OkTVfWx3vpJtgH2B7YFNgH+I8nzq+qRce24JEmSJEmSJE1h/Z7mYg/gt1V13TLqzAJOr6oHquoaYCGw87j0TpIkSZIkSZIE9D+ZvD9wWs/+oUkuSTIvyfqtbFPghp46i1rZkySZk2RBkgVLliwZmx5LkiRJkiRJ0hTUt2RykrWA1wFfa0WfBrakmwLjRuDjy9tmVc2tqplVNXPatGmj1ldJkiRJkiRJmur6OTJ5b+AXVXUzQFXdXFWPVNWjwOd4fCqLxcBmPedNb2WSJEmSJEmSpHHSz2TyAfRMcZFk455jrwcua9vzgf2TrJ1kC2Ar4Ofj1ktJkiRJkiRJUn+SyUnWBf4v8I2e4n9KcmmSS4BXAH8FUFWXA2cAVwDfBQ6pqkfGucuSJEnSsNraH7ckuaynbIMk5ya5qv1cv5UnyQlJFrZ1Q3bsOWd2q39Vktn9uBdJkiRpaX1JJlfVvVX1zKq6s6fs7VX1wqravqpeV1U39hw7rqq2rKqtq+o7/eizJEmSNAInA3stVXYEcF5VbQWc1/ahm/Ztq/aZQ7eGCEk2AI4GdqGb+u3onsWpJUmSpL7p5zQXkiRJ0qRSVT8CbluqeBZwSts+Bdi3p/zU6lwIrNemfnsVcG5V3VZVtwPn8uQEtSRJkjTuTCZLkiRJY2ujnrfubgI2atubAjf01FvUyoYqf5Ikc5IsSLJgyZIlo9trSZIkaSkmkyVJkqRxUlUF1Ci2N7eqZlbVzGnTpo1Ws5IkSdKgTCZLkiRJY+vmNn0F7ectrXwxsFlPvemtbKhySZIkqa9MJkuSJEljaz4wu23PBs7uKT8wnV2BO9t0GN8D9kyyflt4b89WJkmSJPXVGv3ugCRJkjRZJDkN2A3YMMki4GjgI8AZSQ4GrgP2a9XPAfYBFgL3AQcBVNVtST4MXNTqHVtVSy/qJ0mSJI07k8mSJEnSKKmqA4Y4tMcgdQs4ZIh25gHzRrFrkiRJ0kpzmgtJkiRJkiRJ0rBMJkuSJEmSpEktyepJfpnk221/iyQ/S7IwyVeTrNXK1277C9vxGf3styRNNCaTJUmSJEnSZPdu4Mqe/Y8Cn6iq5wG3Awe38oOB21v5J1o9SVJjMlmSJEmSJE1aSaYDrwY+3/YD7A58vVU5Bdi3bc9q+7Tje7T6kiRMJkuSJEmSpMntX4APAI+2/WcCd1TVw21/EbBp294UuAGgHb+z1ZckYTJZkiRJkiRNUkleA9xSVRePcrtzkixIsmDJkiWj2bQkTWgmkyVJkiRJ0mT1UuB1Sa4FTqeb3uKTwHpJ1mh1pgOL2/ZiYDOAdvwZwK1LN1pVc6tqZlXNnDZt2tjegSRNICaTJUmSJEnSpFRVR1bV9KqaAewP/KCq3gr8EHhjqzYbOLttz2/7tOM/qKoaxy5L0oRmMlmSJEmSJE01HwTek2Qh3ZzIJ7Xyk4BntvL3AEf0qX+SNCGtMXwVSZIkSZKkVVtVnQ+c37avBnYepM79wJvGtWOStArp28jkJNcmuTTJr5IsaGUbJDk3yVXt5/qtPElOSLIwySVJduxXvyVJkiRJkiRpKur3NBevqKodqmpm2z8COK+qtgLO4/HXSfYGtmqfOcCnx72nkiRJkiRJkjSF9TuZvLRZwClt+xRg357yU6tzId2qqxv3o4OSJEmSJEmSNBX1M5lcwPeTXJxkTivbqKpubNs3ARu17U2BG3rOXdTKniDJnCQLkixYsmTJWPVbkiRJkiRJkqacfi7A97KqWpzkWcC5SX7de7CqKkktT4NVNReYCzBz5szlOleSJEmSJEmSNLS+jUyuqsXt5y3AWXSrqN48MH1F+3lLq74Y2Kzn9OmtTJIkSZIkSZI0DvqSTE6ybpKnDWwDewKXAfOB2a3abODstj0fODCdXYE7e6bDkCRJkiRJkiSNsX5Nc7ERcFaSgT58paq+m+Qi4IwkBwPXAfu1+ucA+wALgfuAg8a/y5IkSZIkSZI0dfUlmVxVVwMvGqT8VmCPQcoLOGQcuiZJk16Sa4G7gUeAh6tqZpINgK8CM4Brgf2q6vZ03/p9ku4LvfuAd1TVL/rRb0mSJEmS1F/9XIBPktQ/r6iq3/XsHwGcV1UfSXJE2/8gsDewVfvsAny6/ZQkSZKkVc71x75w3K+5+YcuHfdrSmOlbwvwSZImlFnAKW37FGDfnvJTq3MhsN7AQqmSJEmSJGlqMZksSVNPAd9PcnGSOa1so56FTW+im9seYFPghp5zF7WyJ0gyJ8mCJAuWLFkyVv2WJEmSJEl95DQXkjT1vKyqFid5FnBukl/3HqyqSlLL02BVzQXmAsycOXO5zpUkSZIkSasGRyZL0hRTVYvbz1uAs4CdgZsHpq9oP29p1RcDm/WcPr2VSZIkSZKkKcZksiRNIUnWTfK0gW1gT+AyYD4wu1WbDZzdtucDB6azK3Bnz3QYkiRJkiRpCnGaC0maWjYCzkoC3d+Ar1TVd5NcBJyR5GDgOmC/Vv8cYB9gIXAfcND4d1mSJEmSJE0EJpMlaQqpqquBFw1SfiuwxyDlBRwyDl2TJEmSJPXZ9ce+cNyvufmHLh33a2rFOc2FJEmSNA6SXJvk0iS/SrKglW2Q/7+9Ow+3o6jTOP59CUqQsAoiCBiWoLIZISDIIiAyguMAiiziEnEGHBUEBwccZjCIIIqIgsoiA5FFQGQLyggIiYCyhZCFgAhCkH0RWQ1b+M0f9Tukc3O2u557c9/P89zn9unT3VWnuk6dqurqaulqSffk/+VzvSSdKOleSTMlbdzZ2JuZmZmZuTPZzMzMzGwgbRcRYyNiXL4+DLgmIsYA1+RrgJ2AMfm3H3DygMfUzMzMzKwLdyabmZmZmXXOLsDPc/nnwK6V9WdFcROwnKRVOhFBMzMzM7Maz5lsZmZmZjYwArhKUgCnRsRpwMoR8Wi+/xjlQakA7wAerOz7UK57tLIOSftRRi6zxhpr9GPUzczMzMxzSrsz2czMzMxsoGwVEQ9LehtwtaQ/Vd+MiMiO5rZlh/RpAOPGjevWvmZmZmZm3eXOZDMzMzOzARARD+f/JyRdAmwGPC5plYh4NKexeCI3fxhYvbL7arnOzDqgEyPRBtpgG/nWVyStDpxFufMjgNMi4keSVgAuAEYDc4A9IuLvkgT8CNgZ+AcwPiKmdSLuZmaDkedMNjMzMzPrZ5KWkrR0bRnYEbgDmAR8Ljf7HHBZLk8CPqtic+DZynQYZmbWvteA/4iI9YDNgS9LWg8/ANXMrEc8MtnMzMzMrP+tDFxSBryxOPCLiPitpFuBX0r6AvAAsEdufwVlVNy9lJFxnx/4KJuZDX15Ie7RXH5e0l2UOeh3AbbNzX4OTAEOpfIAVOAmScvV7iAZ6LibmQ1G7kw2MzMzM+tnEXEf8N466/8GfKjO+gC+PABRMzMbNiSNBt4H3IwfgGpm1iMDPs2FpNUlTZZ0p6TZkr6a6ydIeljS9PzbubLPNyTdK+luSf800HE2MzMzMzMzs6FL0ijgIuCgiHiu+l5ewOv2A1AjYlxEjFtppZX6MKZmZoNbJ0Ym1+Yrmpbzxt0m6ep874SI+H5145zLaC9gfWBV4HeS1o2IeQMaazMzMzMzMzMbciS9idKRfG5EXJyr/QBUM7MeGPCRyRHxaO1JqBHxPFCbr6iRXYDzI+LliLifMm/cZv0fUzMzMzMzMzMbylQmq/9f4K6I+EHlLT8A1cysBwa8M7mqy3xFAF+RNFPSGZKWz3WN5iuqd7z9JE2VNPXJJ5/sp1ibmZmZmZmZ2RCxJfAZYPsu02oeC3xY0j3ADvkaygNQ76MMZPsZ8KUOxNnMbNDq2AP4us5XJOlk4CjKPEVHAccD+3bnmBFxGnAawLhx47o135GZmZmZmZmZLVoi4gZADd72A1DNzLqpIyOT681XFBGPR8S8iHidcvWvNpWF5ysyMzMzMzMzMzMz67AB70xuNF9RTnhfsxtwRy5PAvaStISkNYExwC0DFV8zMzMzMzMzMzMz68w0F7X5imZJmp7r/gvYW9JYyjQXc4D9ASJitqRfAncCrwFfjoh5Ax5rMzMzMzMzMzMzs2FswDuTm8xXdEWTfY4Gju63SJmZmZmZmZmZmZlZUx2ZM9nMzMzMzMzMzMzMhhZ3JpuZmZmZmZmZmZlZS+5MNjMzMzMzMzMzM7OW3JlsZmZmZmZmZmZmZi22DSn7AAAgAElEQVS5M9nMzMzMzMzMzMzMWnJnspmZmZmZmZmZmZm1tHinI2BmZmZW89dvbdjpKNS1xhGzOh0FMzMzMzOzjvPIZDMzMzMzMzMzMzNryZ3JZmZmZmZmZmZmZtaSp7kws0FlsN7iDr7N3czMzMzMzMyGN49MNjMzMzMzMzMzM7OW3JlsZmZmZmZmZmZmZi25M9nMzMzMzMzMzMzMWnJnspmZmZmZmZmZmZm15M5kMzMzMzMzMzMzM2vJnclmZmZmZmZmZmZm1tLinY6AmZmZmQ0Of/3Whp2OQl1rHDGr01EwMzMzMzM8MtnMzMzMzMzMzMzM2jBkOpMlfUTS3ZLulXRYp+NjZjZcuPw1M+sMl79mZp3h8tfMrLEh0ZksaQTwE2AnYD1gb0nrdTZWZmaLPpe/Zmad4fLXzKwzXP6amTU3JDqTgc2AeyPivoh4BTgf2KXDcTIzGw5c/pqZdYbLXzOzznD5a2bWhCKi03FoSdLuwEci4l/z9WeA90fEV7pstx+wX758F3D3gEa0b60IPNXpSAxTTvvOGurp/86IWKnTkegrLn+tA5z+nTWU09/l79Avf/vbUM7f1nnOP80tMmXwIlL+Or825/RpzunT3GBLnwEvfxcfyMD6W0ScBpzW6Xj0BUlTI2Jcp+MxHDntO8vpPzS5/LW+4vTvLKf/0LMolb/9zfnbesP5x7oazOWv82tzTp/mnD7NOX2GzjQXDwOrV16vluvMzKx/ufw1M+sMl79mZp3h8tfMrImh0pl8KzBG0pqS3gzsBUzqcJzMzIYDl79mZp3h8tfMrDNc/pqZNTEkprmIiNckfQW4EhgBnBERszscrf42KG+XGSac9p3l9B9EXP5aBzj9O8vpP0gM0/K3vzl/W284/wwTi0j56/zanNOnOadPc8M+fYbEA/jMzMzMzMzMzMzMrLOGyjQXZmZmZmZmZmZmZtZB7kw2MzMzMzMzMzMzs5bcmdxhkkLS8ZXXh0ia0MEoLfJU3CBpp8q6T0r6bSfj1dd6m7ck/Yukw1psM1rSp3oRzbZJGi9p1YEIK8MbJ+nEgQpvOMm8eU7l9eKSnpT06x4ebzlJX6q83radYw1k/m2HpF0lrVd5PUXSuE7GqRlJq0m6TNI9kv4i6Uf5kBrrZ5LeKml6/j0m6eHKa58DG7aGSx3P+pfbZ9YOSfMqv73Ta+2mntbfJI2VtHMfxm9VSb/q5THaqlP35Dg9bQ9U22jZPvxxN+MyR9KKbWw3L+tYj0iaJukD3QmnTpg/bLHNQZLe0tMwuhGXAyXdJencXhxjSUm/lzSiwftvl3R+tg9uk3SFpHUr73erXS9poqTdc/n0WntJ0n812adr+7Dl9yHjPKbO+obfaUm/krRWu5+ly77b9iZfdSOcBfJWno/lmmz/FUn7tjquO5M772Xg4+0UaNY3okwU/kXgB5JGShoFHAN8ubMx63O9ylsRMSkijm2x2WigW51xknr64M/xQJ92JjeLS0RMjYgD+zI8e8OLwAaSlszXHwYe7sXxlgO+1HKrhY2mm/m3n+0KrNdyq0FAkoCLgUsjYgywLjAKOLqjEWuiF2XPoBMRf4uIsRExFjgFOKH2OiJe6XT8qhaldLfBbxjV8ax/uX1m7Zhb+e0d20a7qZWxQN3O5J78lkbEIxGxey/j1J961B4YwDbaXEod6wfAN4Dv9HN4BwF1O5Mbddr20JeAD0fEPu1s3CDv7QtcHBHz6mwv4BJgSkSsHRGbUNJv5cpm44HVuxtxgIj414i4M1827EymS/uwze/DycCh7cZF0vrAiIi4r919utgW6PfOZLrkrYjYOSKeabL9GcABrQ7qzuTOe43yJMiDu76Ro+aulTRT0jWS1hj46C2aIuIO4HJKYXEEcA5wuKRbJN0uaRcoBUSum57nYaErVYNYr/JW9UpvXg08UdIfJd1XuzIIHAtsnelzsKQRko6TdGsee//cf1tJ10uaBNyZr6fklbw/STo3f3iQtEle6bxN0pWSVsnwxgHnZlhLdonrgZLuzDDPz3VLSTqjzjkdL2mSpGuBa/IK5Ecrx5ooaXdVrqBLGiXpTEmzMoxP5PodJd2ocrX6wmy0WnuuAGrpvjdwXu0NSStIujTT+iZJG+X6CXlOp2Q+rFUkjwXWzrxxXK4bVS9/ddE1/46snOfbJW1XL+KZR07OuN2XeeUMlav8Eyvb7Z3HukPSdyvrX5B0tKQZeYyVVa5K/wtwXMZn7dz8k5mH/yxp6+4mcj/aHngpIs4EyMrkwcC+kt6S37OLJf1WZeTy92o7tvreSFpb0rTK6zG11/XKh1z/b1nuzJB0kfLqe56rUyTdDHyPRddikm4DkPReldE+a+Trv+Q5aVruS1osz9VKldf3Slop/y7KNL5V0pa5zWZ5Lm9X+X14V65foJwd0JSwYW+Y1PGsf7l9Zn2iUZ1H0qb5uzkjy6FlgW8Be2aZtGfWe8+W9Afg7EZ5Tw3aabn9Hbk8QtL3s046U9JCHUWS1pH0u4zTtEpdtG6dukmdrNFxauFsKul2QDRvDzSqY9QdLd2krvJWSVdJmi3p9Ay33rn6SMZ3hqRq3WU9SofyOJU256hM//sl/SPT/FRJa6m0Bf5P0lxJz0ua3CWMxST9IetXS1bWH0gZNDW5to9Ke+F4STOALSQdkZ/rDkmnVc7DFEnfVZf2Qr3fOEmnAGsB/6fS9mmrvVwnufYBLstta+kxTdIs4HDgVeC3tfwXETOA92devQvYCrg+43ZC5q25KnfanSFpCUk/lvSqpPuBjwPflbSxpKclPaRyx9GS+dkeq4Vf+wzARGC9PO4D+X34SyVPPZzpNVPSOSr9FN8E9snPdH6ez0uABfoe6qVDpsXJkqZmXjuysv6N0fAqI+unSBpNufh9cKbD1mr+HW+n7blQ+A3yVjU+n83wZkg6O8/XP4A5kjZr8LmLiPBfB/+AF4BlgDnAssAhwIR873Lgc7m8L2UEWMfjvKj8AUsBdwOzKFcaP53rlwP+nO+fBOyT698MLNnpeHfj8/Uqb1GuGP44lycCF1IuQK0H3JvrtwV+XdlnP+C/c3kJYCqwZm73IrBmZb9ngdXymDdSflTeBPwRWCm32xM4I5enAOMafNZHgCVq5y//H9PgnI4HHgJWyPd2A35eOccPUn4w3vhswHeBH1bCWx5YEbgOWCrXHQoc0enzPhT+Mm9uBPwKGAlM75LeJwHfzOXtgem5PCHzxxKZ/n/LPDMauKNy/Lr5q048uubf/6jkt3cDfwVG1tlvInA+pTK6C/AcsGGGdRtlZMmquf9KwOLAtcCuuX8AH8vl7zH/OzMR2L0SzhTg+FzeGfhdp89dJW4HUkbDdl1/e57b8cB9lLJnJPAAZQRCW98bYDIwNpePoVwdb1Y+vLWy77eBAypp+mvKqIGOp1s/nYsJlPJ9NqXM/wpwK6WC+07gxtyunXL/m8BBubwjcFEu/6L2HQLWAO7K5WWAxXN5h8r246mUs/7z30D/sYjX8fzXv3+4fea/9vLJPEodtva3Z66fQhkEU7fOk+XNfcCmuX4ZSl1xPNn2yvUTKPXKJfN13bxH43baaLJ+DPw7pd5d+81e6PcZuBnYLZdHUkYybkv322yNjvNryijM27Iu0ao90KiOUd3mjTSjcV3lRLKuSem4DmDFLp99JUobsNZWXSHP72PAPzINPkRpe4zM83s58HbgXuCnwNcoF6Ieo7R/fwnsn8ebk/GYCtxJtlu7xGFONV4Zzz0qr1eoLJ/N/LbEFOq0F2jwG1cNhzbby13i+WbgscrrxYFlcnlF4EngBBZunx1CyasvAjdlGu5HyecPAutn+lxMGRl7dcb1MOAZSv6ZCVxPyQ+PU/JQ1/DvzfP5CHB35XyOzrRfnPJ9uI4yuhrK3UsP5XZXUy4e1PLzRnleF+qHAH4PbNj1HAEj8rxsVCfNx1FGbZOf/ZDK/s2+403bnu2GX32daf7nStyqeexw4D+alYG+9XAQiIjnJJ1FaZzPrby1BeUqDJQCY1EeVTXgIuJFSRdQCqE9gI9JOiTfHkn5EbqRMpplNUphc09nYtszfZy3Lo2I1ykji1dusM2OwEaaP3J5WWAM8ApwS0TcX9n2loh4CEDSdEoB/wywAXB1XmwdATzaRtxmUkYtXwpcWonLv9Q5pwBXR8TTufx/wI8kLQF8BLguIuZqwYGsOwB71V5ExN8l/TOlwvaH3PbNlPxibYiImXlFdm/KqISqrYBP5HbXqowoWCbf+01EvAy8LOkJFrxlqqpe/rqhRbS2olS8iIg/SXqAMn3DzDrbXh4RkVfgH4+IWRnW7AzrnZSKwpO5/lxgG0r+fIVSIYJSAfhwkzhdXNludIv4DzbXRMSzAJLupKTJcrT3vTkd+Lykr1EaKJsB76Jx+bCBpG/n8UcBV1aOdWHUuQ1vEfRHYEtKPjuGUp6JUumG9sr9MygjLH5IqcSemet3oIzuqG23jMroqmWBn6uM6AxK47KmWs6aDajhUMez/uX2mbVhbpTpphrZnPp1nncBj0bErVDyGoDq3kTHpIio5b9mea9VO20H4JSIeC3DXOD3WdLSwDsi4pJ8/6VKnNpus7U4znsoI/53jIhHJLVqDzSrY9TTqK6yDZluEfEbSX+vs+/mlDbg/bX0kVSb5uJVyqCQ04EnKB3Ix1M6xu+nDHLZkVK/fwKYHBH3q9wxtlIljH+jDDTZICJebfFZoHRmX1R5vZ2k/6R0zq9AGURweb5Xr73Qzm9cu+3lqhUp579GwDGStgFep5y3ZnM/38L80eE7UjpXlwfOzX1/SelkPoZy8eVsSjvg0fwbk+G/TOn47hr+Oyh14FuAdeCN87kMpfP1QspgpaeBt2U8Xqx93mxffoCcQiPzaL22IMAqlM7zmj0k7UfpsF6F8v1vtG89zb7jrdqe03sQ/vaUdtJTsFC58ARlcFVD7kwePH4ITGN+w80Gxuv5J+ATEXF3l/fvUrk9+qPAFZL2j4hrBzqSvdRXeevlynLd2k6uPyAirlxgpbQtpZBudLx5lPJIwOyI2KKbcfsopaLwMcqP5oY0OKeS3l+NS0S8JGkK8E+UTqvz2wxTlB+dvbsZV5tvEvB9ygiDt7a5T71809Z2ee5PzXVHUK7qtiTpaPIWvEqjoXb817uE9XrGqVkl8dXIS74tPkM1nFbbDbQ7gQXmHctK2hqUEQEb0/g73s735iLKKNlrgdsi4m8qD+poVD5MpIz8niFpPCVP1XQtexZV1wFbUzrtL6OMgArgN+0eICIelPS4pO0pFffafHqLAZvXGoU1KlMhTY6I3bIxOKXy9nBJdxu8hkMdz/qX22fWG3XrPNlOaVe7v6XttNN6qu02W3YmN/IopbPyfZRRozWN2gNH0biOUU+jukqL3Vp6OSJuzGkBnqbUf5cGfhARh0qaQ+nIhnLHa808Fpwe4UFKR+dalDtnWnmpNhhC0kjK6OdxWVebQEnLN+JYCXNxgIj4RRu/cW21l7uY2yXsfSid5ptExKuSHqPcpfkaC06rW9vnRcrAj1r4xwKfiohtMuwPdQmv9tmChdtc9cKfQ7lwU8/KlHR8nTKv87cr79U+70hKOrbjjbSQtCZl9PWmOfBsIvM/czUtRnY9SJuatj1bhN8TI1nwQupCPGfyIJFXAX4JfKGy+o/MHw25D/NHF1nfuxI4QHpj7qH35f+1gPsi4kRK43yjzkWxZ/o5bz1P+TGtuRL4d0lvApC0rqSlunG8u4GVJG2R+79JZWL7emGR2ywGrB4RkymdJ8syf2TiQue0gQuAz1M6Yuo98f1qKg/vkbQ85facLSWtk+uWUuUptdaWM4Aja1dWK64nO7HyQsRTtVEbDdTNG11FxM0x/yEpk+rsVw13XUrH6N0RcXjMf9hZu24BPihpRZWHZuxNuRWqmbY+xyBxDfAWSZ+FNx4McjwwMco8W4209b3JhsCVlAdh1BrxzcqHpSkjYt7E/A7Q4eZ64NPAPTk66WnK7Y61EfntlvunU+aYrY7ovorKgzgk1b4LyzL/YTnje/8RzPrFIlvHs/7l9pn1UqM6z93AKpI2zfVLqzzkrFU9sDd572pg/wwHSStU34yI54GHJO2a7y+hfP5EA3XrZC2O8wylY/M7Wb+vadQe6G4do1Fd5TrygduSdqKMgu3qJmCb7JBbKH0kvZsy+noe5RzdSXlI526Ui/jLUjqK/1E9Dgt2Jl9BqdPeKqnew+eanf9ap+BTOdq65YMV2/yN6057GSh36AIjsoMbymd/Ijtyt6N02L6ZMsjrbSp3mG5CaQvV1D7rlZS66miVubbXpbTJf08Z5EUer+5zbCidtMt3Cf+dlGn3NqXcsVg9nyMoeepqFnx4X7W/Yl1KXqrlmQ1oXD+4ixz9TJmW5UXgWZW7A3aqbDcH2CSXP1EnHWp68x1vFn6jvHUt5fk8b4WF8v26wB3NAnRn8uByPOW2gZoDKLf5zgQ+A3y1I7EaHo6i3DozM28VOCrX7wHcoXJLzwbAWR2KX2/1V96aCcxTmbD9YEonxJ3ANJUJ90+lG6MpI+IVyo/jd1UeNjCd+U84nQicooUfwDcCOEfllo/bgROjPJ200Tmt5yrgg5Q5pl6p8/63geVVHngwA9gupy8YD5yX6XgjLW4FsQVFxENZwelqArBJpuuxwOdaHOdvlFsI79D8B/C1o2v+/SnlQWazKBcYxueUGt0WEY9S5viaDMygjK69rPlenA98XeWhEGu32LajcmT1bpQKyD2U+bZeovlTlenm9+ZcytX2q3LfZuXD/1Dm6PsD8Kcef7AhLCLmUEZ4XJerbgCeyUo/tF/uT6JckKuOxDuQ8uCZmSpTlnwx13+P0ii8ncE1ct6salGv41n/cvvMGlky2yW1v2Orbzaq82R9Zk/gpKzPXE3pLJxMmaZhuqQ9WVhv8t7plCkWZmaYn6qzzWeAA/P4f6RM51BXizpZw+NExOPAPwM/IfuimrQHulvHaFRXOZLSwTubMoXAX+t8nicpUytcnJ/nAkpH8BcpD+K8gPntkUuBtSnTW5xOGSV6DmXKhHm141DmUF6g0zciDqDUs2YoH3hccRrloXWT68TvGeBnlM69KynPxmilnd+47rSXq66iTA8Ipb4+LttPn6XUw/dnfgfwo7l9tX4+kTKFyJdy/WuUZxxMo4xA/hJwD6WD/ic0nkryLMr8x3t1Cf9p4F8pA1/m5nGhTEnxnYzfc8Cqeb43B8hO2LmUvDdK5WGB36JMH1LPb8i7IaM8ZPD2DP8XlDZJzZGUaTWnsuCo58uB3fI7vzW9+I63CL9u3oqI2cDRwO8zHX5QeXtLStnUkObfaWtmZmY2OKjM37ZsRPxPp+MynEgaR3m44tadjouZmZmZDS6SNgYOjojPdDoufSkHFz0XEf/b5vZLUi4CbRmL0PNZcoT611qdX48iMTMzs0FF0iWUUR/bdzouw4mkwygjPIbrVCFmZmZm1kRETJM0WdKIRakTlTIVy9ntbhwRcyV9kzKCeqER70PYipS7PpvyyGQzMzMzMzMzMzMza8lzJpuZmZmZmZmZmZlZS+5MNjMzMzMzMzMzM7OW3JlsZmZmZmZmZmZmZi25M9kGLUmHS5otaaak6ZLeL+kgSW8Z4HisKulXPdz38Iz7dEnzKsvflHRjl20Xl/S4pFX7JuZmZv1P0tslnS/pL5Juk3SFpHV7cJw+K98ljZb0qb44lplZX6rUB++QdGF/12slzZG0Yh8da7ykJzP+syX9qhZ/SV+U9Nlcnihp91yeImlcLl8habm+iIuZWXcMdNnbHZK2lfSByusJkkLSOpV1B+W6cS2OtUB9WtILvYxbr/a3RZc7k21QkrQF8M/AxhGxEbAD8CBwEFC34Jc0oj/iEhGPRMTuPdz36IgYGxFjgbmV5aOA1SS9s7L5DsDsiHikD6JtZtbvJAm4BJgSEWtHxCbAN4CVe3C4vizfRwPuTDazwahWH9wAeAX4Yn8EoqI/2noXZPzXp8R/T4CIOCUizmq2Y0TsHBHP9EOczMxaGZCyt4e2BT7QZd0sYK/K608Cs9s4VsP6tFlfcmeyDVarAE9FxMsAEfEUsDuwKjBZ0mQoV8okHS9pBrCFpE9LuiWvOp5a64CQdLKkqTmK4shaIDla4zu5/VRJG0u6MkfYfTG3GS3pjlweL+liSb+VdI+k71WO9QVJf87wfybpx40+XES8DvySBX8g9gLO65PUMzMbGNsBr0bEKbUVETEDuEHScTn6Y5akPeGNkRdTcjTbnySdmx0eB9K6fD9C0q15zNOyIxtJ60j6naQZkqZJWhs4Ftg6y/aDBzhNzMzadT2wjqSPSbpZ0u1Znq0Mb4xOO1vSjVnv/LfajpK+nmXizFrdNuusd0s6C7gDWL0aWL16cv5NrJTXB+e2B0q6M49/fteIS1ocWAr4eyWuhzT7sFnvXjHjeVfWl2dLukrSkrnNppp/V+JxtTq4mVkfalj2Slosy9uVAPL1vZJWyrLyZEk3Sbov67VnZHk2sXZwSTtmuT1NZRT0qFw/R9KRuX6WpHdLGk3p2D44y72t8zCXArvkfmsDzwJPNQujXn06tz0668k3VX5fRku6NsvbayStkevXzOPOkvTt/kl+WxS4M9kGq6uA1VU6Z38q6YMRcSLwCLBdRGyX2y0F3BwR7wX+RhkdsWWO/p0H7JPbHR4R44CNgA9K2qgS1l9z++uBiZRO682BI6lvbIazIbCnpNVVpqb4n9xvS+DdbXzG88jOZElLADsDF7Wxn5nZYLEBcFud9R+nlJXvpdx1cZykVfK991FGTawHrEUps5uW7xFxA/DjiNg0R5QsSbl7BeBc4Cf5O/AB4FHgMOD6HIFyQt9+ZDOz3svO2J0oo89uADaPiPcB5wP/Wdl0I2B7YAvgCJXp13YExgCbUcraTSRtk9uPAX4aEetHxAOV8N5D/XryWOAdEbFBRGwInJm7HAa8L+8QrI7g21PSdOBhYAXg8h4mwRhK2b0+8AzwiVx/JrB/JY5mZn2mVdmbg77OYX4/wg7AjIh4Ml8vTymPDwYmAScA6wMbShqrMq3QfwM7RMTGwFTga5UoPJXrTwYOiYg5wCnACVlvvT63ew54UNIGlD6DCyqfoW4YTerTN2U9+TqgdlHyJODnWcafC5yY638EnJy/B492J21teHFnsg1KEfECsAmwH/AkcIGk8XU2ncf8DtgP5T63ZiX3Q5SOCoA9JE0DbqcU9utVjjEp/8+idFw8nz8WL6v+vG7XRMSzEfEScCfwTkpl/vcR8XREvApc2MZnnAqMkvQuyg/azRHxdKv9zMyGgK2A8yJiXkQ8Dvwe2DTfuyUiHsrK+nTKlBT1VMt3gO1y9MgsSsfK+pKWpnSCXAIQES9FxD/64fOYmfWVJbOeOhX4K/C/wGrAlVm+fZ1SV625LCLm5l16kyl1zh3z73ZgGmUQw5jc/oGIuKlOuI3qyfcBa0k6SdJHKB0YADOBcyV9GnitcpwLsqP37ZS689d7mA73R8T0XL4NGJ317qUjovZckV/08NhmZl11p+w9A/hsLu/L/ItsAJdHRFDKv8cjYlbWaWdT6rSbU/oa/pDhfY7SX1Bzcf6/jcZ14JrzKR3Ju1KmlatpFUbVK8Cv64S5BfPL2LMpdXcoA+POq6w3q2vxTkfArJGImAdMAaZkAf+5Opu9lNsBiHJ17RvVDSStCRwCbBoRf89bUEZWNnk5/79eWa69rvcdqW4zr8E27aqNTn4PnuLCzIae2ZS7Obqj3TL0jfJd0kjgp8C4iHhQ0gQWLMfNzIaKudkZ+wZJJwE/iIhJkrYFJlTeji77B6XO+52IOLXLcUYDLzYIt249Ofd7L/BPlBHIe1A6Tz4KbAN8DDhc0oYLRCIiJF0OHECZWqi7uv4WLNmDY5iZtavtsjfrmo9L2p5yAW+fym6t+g7mAVdHxN4N4lHbp51+hF8DxwFTI+I5lRneoJTnzcKoejU7v9sNExb+3TFbiEcm26Ak6V2SxlRWjQUeAJ4Hlm6w2zXA7pLelsdYQeUBd8tQKtbP5hxBO/VDlG+lTJ+xfN4684lWO6TzgE9TRtld1g/xMjPrT9cCS0jar7YipxF6hnIr9Iicc24b4JYWx2pWvtc6jp/Keed2B4iI54GHJO2aYS+h8gTrZscyMxtslqVMGwELD57YRdJISW+lPKTpVuBKYN/KPJzvqNV/m6hbT87bpReLiIsot01vrPLgvtUjYjJwaMZvVJ1jbgX8pZuftaF8ON/zkt6fq/Zqtr2ZWS81K3tPp0x3cWFl8Fo7bgK2lLQOgKSlJK3bYp+69da82+5Q4OhuhNFuHfiPzC9j96FM+Qnwhy7rzeryyGQbrEYBJ+Xtbq8B91KmvNgb+K2kRyrzAAEQEXdK+m/gqqwEvwp8OSJuknQ78CfgQUoB2aci4mFJx1A6S57OsJ5tY7+7JL0I3BYRjUaSmJkNSjkybTfgh5IOBV4C5lDmRB4FzKCMbvjPiHhMUrP55E+jcfn+jKSfUR4o9RilM6XmM8Cpkr5FKfc/Sbk9e57Kw/smet5kMxvkJgAXSvo75SLdmpX3ZlKmt1gROCoiHgEeyTmQb8yRai9QBic07PBoVE8G5gJn5jqAbwAjgHMkLUsZAXdilsNQLhRuRRmU9BAwvvcffwFfAH4m6XXKFEkt69NmZj00gcZl7yTK9BZn1tmvoYh4MqfnPC+fiwTlQt2fm+x2OfArSbtQ7vaoHm+hB6C2CKNhfbqLAyhl/9cp04p+Ptd/FfhF1us92M0a0vwR72bWG5JGRcQLOTL5EuCM2jyeZmZmZmbdkVP6vBAR3+90XAZKrT6dy4cBq0TEVzscLTMbZiSNozwUb+tOx8VsMPLIZLO+M0HSDpTbsa8CLu1wfMzMzMzMhpKPSvoGpZ36AH0/8tnMrKm8kPXveJoHs4Y8MtnMzMzMzJVtDqwAAABTSURBVMzMzMzMWvID+MzMzMzMzMzMzMysJXcmm5mZmZmZmZmZmVlL7kw2MzMzMzMzMzMzs5bcmWxmZmZmZmZmZmZmLbkz2czMzMzMzMzMzMxa+n8Rx+PCGpp/lwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecBa11Yj9TUd",
        "outputId": "cd6cbc92-ea7c-4080-9a00-6a98ec93d900"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           0\n",
              "gender               0\n",
              "SeniorCitizen        0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "tenure               0\n",
              "PhoneService         0\n",
              "MultipleLines        0\n",
              "InternetService      0\n",
              "OnlineSecurity       0\n",
              "OnlineBackup         0\n",
              "DeviceProtection     0\n",
              "TechSupport          0\n",
              "StreamingTV          0\n",
              "StreamingMovies      0\n",
              "Contract             0\n",
              "PaperlessBilling     0\n",
              "PaymentMethod        0\n",
              "MonthlyCharges       0\n",
              "TotalCharges        11\n",
              "Churn                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "data=data.dropna()"
      ],
      "metadata": {
        "id": "KuqpBiWi-N6J"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data=data[['SeniorCitizen','tenure','MonthlyCharges']]"
      ],
      "metadata": {
        "id": "BalbyER6ipBt"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame({'feature':vif_data.columns, 'VIF':[variance_inflation_factor(vif_data.values,i) for i in range(vif_data.shape[1])]})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "y_qhjm0OipEk",
        "outputId": "8bb82c9a-6c3f-49f6-d713-b346c1b08303"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          feature       VIF\n",
              "0   SeniorCitizen  1.256633\n",
              "1          tenure  2.620398\n",
              "2  MonthlyCharges  2.928852"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a60baa28-0d3a-40c4-88ea-63e82353ac5f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>feature</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SeniorCitizen</td>\n",
              "      <td>1.256633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tenure</td>\n",
              "      <td>2.620398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MonthlyCharges</td>\n",
              "      <td>2.928852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a60baa28-0d3a-40c4-88ea-63e82353ac5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a60baa28-0d3a-40c4-88ea-63e82353ac5f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a60baa28-0d3a-40c4-88ea-63e82353ac5f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We removed TotalCharges after the VIF analysis\n",
        "\n",
        "VIF Threshold is 5 "
      ],
      "metadata": {
        "id": "8V-bgR8tCuE4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting Churn YES into 1 and Churn NO into 0\n",
        "data['Class']=data['Churn'].map({'Yes': 1, 'No': 0})"
      ],
      "metadata": {
        "id": "HpHmcZXtipHy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1aff8b9-bc6c-4ca1-9cc4-e665e9c590b4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data['Class'].copy()\n",
        "input=data.drop(['customerID','TotalCharges','Class','Churn'],axis=1).copy()"
      ],
      "metadata": {
        "id": "lu7JjZhbGc97"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=pd.get_dummies(input)"
      ],
      "metadata": {
        "id": "TAnL4z1iipLf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_2I9wLtipON",
        "outputId": "98c92f97-134e-46e0-c9f6-214196fdab8a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7032, 44)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalization"
      ],
      "metadata": {
        "id": "DKDJh2H6Olql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler=MinMaxScaler()\n",
        "scaler.fit(X)\n",
        "x=scaler.transform(X)"
      ],
      "metadata": {
        "id": "AB8kaH8TipRU"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting dataset into Train and Test"
      ],
      "metadata": {
        "id": "0u-aDA8GPlJt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42,stratify=y)"
      ],
      "metadata": {
        "id": "LQuKqd-UipTy"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BccTAkFw7lC",
        "outputId": "2815c7fd-bd86-4471-975f-7de0518d0e1a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4130\n",
              "1    1495\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W75GIZv9RyrH",
        "outputId": "9992bb7a-ffe0-4063-a9fc-f16f73cb69e8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1033\n",
              "1     374\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Our Model"
      ],
      "metadata": {
        "id": "bMHcivSwTN00"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logit = LogisticRegression(fit_intercept=True, max_iter=10000)\n",
        "logit.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3jWfOqsSt5m",
        "outputId": "7e1c37f1-754a-465e-a247-d51fc5492067"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=10000)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# value of coeficients\n",
        "logit.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ugRmc8KTYO-",
        "outputId": "baa217ac-9656-4ce9-f09c-418083e8d756"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.18869201, -2.25478919, -0.33846672,  0.01181811, -0.0111341 ,\n",
              "         0.01741424, -0.01673024,  0.1214331 , -0.1207491 ,  0.07721099,\n",
              "        -0.07652699, -0.21219236,  0.07721099,  0.13566537, -0.42694237,\n",
              "         0.55212298, -0.12449661,  0.25813143, -0.12449661, -0.13295082,\n",
              "         0.13184929, -0.12449661, -0.00666868,  0.03199212, -0.12449661,\n",
              "         0.09318848,  0.23487424, -0.12449661, -0.10969363, -0.10283324,\n",
              "        -0.12449661,  0.22801385, -0.08942293, -0.12449661,  0.21460354,\n",
              "         0.72041532, -0.05136029, -0.66837102, -0.14373712,  0.14442112,\n",
              "        -0.14295198, -0.09755733,  0.25370378, -0.01251046]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Intercept\n",
        "logit.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkPE2Gd-Ulxo",
        "outputId": "ab2d82c1-667f-4ee1-d69d-2591f7d1b095"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.01738815])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Lets Evaluate the Performance of Our Model**"
      ],
      "metadata": {
        "id": "DnDVnoJqUu2x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the predicted probabilities\n",
        "train_pred_prob=logit.predict_proba(x_train)[:,1]\n",
        "test_pred_prob=logit.predict_proba(x_test)[:,1]"
      ],
      "metadata": {
        "id": "caX_HYsjUqV4"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pred_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86p3WzPuVyNW",
        "outputId": "db0e5c83-043e-43a9-9647-aa1b4f962575"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.01626665, 0.26825958, 0.02856356, ..., 0.0388362 , 0.17753176,\n",
              "       0.78638912])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPbiA0T4VyQ8",
        "outputId": "8040e937-3e02-444b-9817-f2d2e5e2963b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.01983829, 0.59841625, 0.01055108, ..., 0.11538515, 0.03466955,\n",
              "       0.0086519 ])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the predicted classes\n",
        "train_class_preds = logit.predict(x_train)\n",
        "test_class_preds = logit.predict(x_test)"
      ],
      "metadata": {
        "id": "_zSGkd8vWzGx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_class_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcsWOANmW1ul",
        "outputId": "a42d9a71-b866-4f2e-9641-3b1dfcffd618"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_class_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJDrf_f3W1xh",
        "outputId": "1c6b102e-e035-4abd-b4ee-f15368b06872"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lets take a look at the Evaluation matrix\n"
      ],
      "metadata": {
        "id": "oglHvAn1YJY9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Train Confusion Matrix\")\n",
        "labels = ['Retained', 'Churned']\n",
        "cm_train = confusion_matrix(y_train, train_class_preds)\n",
        "print(cm_train)\n",
        "sns.heatmap(cm_train, annot=True) #annot=True to annotate cells\n",
        "# labels and ticks\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.xticks([0.5,1.5],labels)\n",
        "plt.yticks([0.5,1.5],labels)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Test Confusion Matrix\")\n",
        "labels = ['Retained', 'Churned']\n",
        "cm_test = confusion_matrix(y_test, test_class_preds)\n",
        "print(cm_test)\n",
        "sns.heatmap(cm_test, annot=True) #annot=True to annotate cells\n",
        "# labels and ticks\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.xticks([0.5,1.5],labels)\n",
        "plt.yticks([0.5,1.5],labels)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "Yy4r4rBgYIWk",
        "outputId": "612a5876-82ae-4007-c3eb-efc556a0858e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3706  424]\n",
            " [ 681  814]]\n",
            "[[913 120]\n",
            " [158 216]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Accuracy**"
      ],
      "metadata": {
        "id": "brZZMx-AtDgF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_train=accuracy_score(y_train, train_class_preds)\n",
        "accuracy_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2D_RGeF42Sb",
        "outputId": "dff64f4f-6030-4ce5-da42-3339df7cfbee"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8035555555555556"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_test=accuracy_score(y_test, test_class_preds)\n",
        "accuracy_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5-Lf2aa5VLS",
        "outputId": "3963dd4a-3a6e-4fe2-8ba9-2290a27823a9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8024164889836531"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Precision**\n",
        "Precision: The ability of a classification model to identify only the relevant data points. Mathematically, precision the **number of true positives divided by the number of true positives plus the number of false positives.**"
      ],
      "metadata": {
        "id": "t1nejQCWgNNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "precision_train=cm_train[1,1]/(cm_train[1,1]+cm_train[0,1])\n",
        "precision_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayvBhFQCW13q",
        "outputId": "3ce1090a-6c30-47ab-b48a-62fd231b21d1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6575121163166397"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_test=cm_test[1,1]/(cm_test[1,1]+cm_test[0,1])\n",
        "precision_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXqLySKZW2lQ",
        "outputId": "8c040eae-b407-44d7-8dfc-febfd0786091"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6428571428571429"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Recall**\n",
        "Recall: The ability of a model to find all the relevant cases within a data set. Mathematically, we define recall as the **number of true positives divided by the number of true positives plus the number of false negatives.**"
      ],
      "metadata": {
        "id": "c_8asu6Ch_Qu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recall_train=cm_train[1,1]/(cm_train[1,1]+cm_train[1,0])\n",
        "recall_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBns9OlqW2of",
        "outputId": "09b3e717-b1d9-4196-dd53-835952d7f0d7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5444816053511705"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_test=cm_test[1,1]/(cm_test[1,1]+cm_test[1,0])\n",
        "recall_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeUvY5IHkqsM",
        "outputId": "dfc7446d-5909-4a19-c80e-0a9c63181e0d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5775401069518716"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **F1- Score**\n",
        "**F1 = 2$ * $(precision$ * $recall) / (precision + recall)**"
      ],
      "metadata": {
        "id": "ii2_muEkmAoM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score_train=f1_score(y_train, train_class_preds)\n",
        "f1_score_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEyMNCSQkqvk",
        "outputId": "701ac653-981d-4f10-e857-595b8b6118a5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5956824002927186"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score_test=f1_score(y_test, test_class_preds)\n",
        "f1_score_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyRh_pa5kqz2",
        "outputId": "9f9effce-a29c-491f-c354-15e731bd13b6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6084507042253521"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Log-Loss**\n",
        "\\begin{align*}\n",
        "  Log Loss &= \\sum_{i=1}^{M} [ y^{i}log(P^{i}) + (1- y^{i})log(1 - P^{i} )]\\\\\n",
        "\\end{align*}"
      ],
      "metadata": {
        "id": "8R4E9j35n6ye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_loss_train=log_loss(y_train,train_class_preds)\n",
        "log_loss_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWfm_vS8kq5L",
        "outputId": "5472257f-f26b-4bfb-ca93-a812dd3740ba"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.7850110125665255"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_loss_test=log_loss(y_test,test_class_preds)\n",
        "log_loss_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9TnbPH1kq_A",
        "outputId": "d224454c-7c00-43ca-d0ee-ff02706acefd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.824360902257848"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC-AUC**"
      ],
      "metadata": {
        "id": "v4fL6D0cpmND"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_auc=roc_auc_score(y_train,train_pred_prob)\n",
        "train_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQBTR0vgphVX",
        "outputId": "d697d5e2-c833-46b4-abc0-f5986788feec"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8486424481929271"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_auc=roc_auc_score(y_test,test_pred_prob)\n",
        "test_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23xCGok6qwhJ",
        "outputId": "1be69d8b-8e2d-42b7-8c88-5277914598c0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333872579217378"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ut6Z2EFrLgYJ"
      },
      "source": [
        "## <b><u>Implementing Cross-validated Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAIV9X2gFfL2"
      },
      "source": [
        "logistic = LogisticRegression()"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8hlX3nyFYH2",
        "outputId": "fcd99585-b317-476e-fe2b-d5dd90064428"
      },
      "source": [
        "scoring = ['accuracy']\n",
        "c_values = {'C':[100, 10, 1.0, 0.1, 0.01]}\n",
        "scores = cross_validate(logistic,x_train, y_train , scoring = scoring, cv = 5, return_train_score=True,return_estimator=True,verbose = 10)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CV] START .....................................................................\n",
            "[CV] END ................ accuracy: (train=0.808, test=0.784) total time=   0.1s\n",
            "[CV] START .....................................................................\n",
            "[CV] END ................ accuracy: (train=0.801, test=0.822) total time=   0.1s\n",
            "[CV] START .....................................................................\n",
            "[CV] END ................ accuracy: (train=0.804, test=0.804) total time=   0.1s\n",
            "[CV] START .....................................................................\n",
            "[CV] END ................ accuracy: (train=0.807, test=0.788) total time=   0.1s\n",
            "[CV] START .....................................................................\n",
            "[CV] END ................ accuracy: (train=0.799, test=0.808) total time=   0.1s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxWSL1hnFuje",
        "outputId": "155fa230-caa6-4606-fb4c-bec46274afbc"
      },
      "source": [
        "scores['train_accuracy']"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.808     , 0.80066667, 0.804     , 0.80711111, 0.79911111])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXHA9l1yFw7r",
        "outputId": "316527cf-fe35-4ff7-fe81-f337c141df1f"
      },
      "source": [
        "scores['test_accuracy']"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.784     , 0.82222222, 0.80444444, 0.78844444, 0.808     ])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DEJJcBkF6fg",
        "outputId": "ca34d68e-cec1-46cd-99c1-1dedd708d49f"
      },
      "source": [
        "scores['estimator']"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[LogisticRegression(),\n",
              " LogisticRegression(),\n",
              " LogisticRegression(),\n",
              " LogisticRegression(),\n",
              " LogisticRegression()]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MMNQNSlGQ6h",
        "outputId": "ea433259-4334-4f5d-9d8c-df9e939c8078"
      },
      "source": [
        "for model in scores['estimator']:\n",
        "    print(model.coef_)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.61353698e-01 -2.32543877e+00 -2.67328835e-01  3.01670916e-02\n",
            "  -3.00751243e-02  8.44619447e-03 -8.35422722e-03  9.38188908e-02\n",
            "  -9.37269235e-02  9.61099897e-02 -9.60180224e-02 -2.08255364e-01\n",
            "   9.61099897e-02  1.12237341e-01 -4.63465724e-01  5.82328013e-01\n",
            "  -1.18770322e-01  2.50746004e-01 -1.18770322e-01 -1.31883714e-01\n",
            "   1.07877374e-01 -1.18770322e-01  1.09849152e-02  4.58264205e-02\n",
            "  -1.18770322e-01  7.30358688e-02  2.45881093e-01 -1.18770322e-01\n",
            "  -1.27018803e-01 -1.13148864e-01 -1.18770322e-01  2.32011153e-01\n",
            "  -1.29919540e-01 -1.18770322e-01  2.48781830e-01  7.86437377e-01\n",
            "  -6.60527322e-02 -7.20292677e-01 -1.16386873e-01  1.16478840e-01\n",
            "  -1.62340832e-01 -4.86955403e-02  2.12414022e-01 -1.28568240e-03]]\n",
            "[[ 0.18389995 -2.21324237 -0.03669391  0.01269997 -0.0132042   0.019843\n",
            "  -0.02034723  0.14162117 -0.14212539  0.10772027 -0.10822449 -0.19270102\n",
            "   0.10772027  0.08447653 -0.38902217  0.50768197 -0.11916402  0.21474504\n",
            "  -0.11916402 -0.09608524  0.13839154 -0.11916402 -0.01973175  0.03733892\n",
            "  -0.11916402  0.08132088  0.20614443 -0.11916402 -0.08748464 -0.13600572\n",
            "  -0.11916402  0.25466551 -0.06682544 -0.11916402  0.18548524  0.67875996\n",
            "  -0.0126614  -0.66660278 -0.14286645  0.14236223 -0.08957711 -0.16690762\n",
            "   0.22689881  0.02908169]]\n",
            "[[ 1.59835819e-01 -2.15330795e+00 -3.59636355e-01  2.18090385e-02\n",
            "  -2.20435248e-02 -1.13891010e-03  9.04423772e-04  1.47104666e-01\n",
            "  -1.47339153e-01  6.85360029e-02 -6.87704892e-02 -2.26254487e-01\n",
            "   6.85360029e-02  1.57483998e-01 -3.53182913e-01  4.68255669e-01\n",
            "  -1.15307243e-01  3.04532232e-01 -1.15307243e-01 -1.89459476e-01\n",
            "   1.39011346e-01 -1.15307243e-01 -2.39385894e-02 -8.12026186e-03\n",
            "  -1.15307243e-01  1.23193019e-01  2.67151442e-01 -1.15307243e-01\n",
            "  -1.52078685e-01 -7.75931445e-02 -1.15307243e-01  1.92665901e-01\n",
            "  -7.95691778e-02 -1.15307243e-01  1.94641935e-01  7.36849002e-01\n",
            "  -1.01624492e-01 -6.35458997e-01 -1.90461595e-01  1.90227108e-01\n",
            "  -1.43633701e-01 -8.85372816e-02  3.06480559e-01 -7.45440624e-02]]\n",
            "[[ 0.2776482  -2.17840561 -0.23518817  0.00315637 -0.00303328  0.04605571\n",
            "  -0.04593262  0.12092668 -0.1208036   0.07413422 -0.07401113 -0.22315623\n",
            "   0.07413422  0.1491451  -0.46308032  0.57939648 -0.11619308  0.29403785\n",
            "  -0.11619308 -0.17772169  0.12864589 -0.11619308 -0.01232972  0.04939576\n",
            "  -0.11619308  0.0669204   0.21731492 -0.11619308 -0.10099876 -0.1233489\n",
            "  -0.11619308  0.23966506 -0.09046232 -0.11619308  0.20677849  0.74024393\n",
            "  -0.09473537 -0.64538547 -0.14574782  0.1458709  -0.19929549 -0.13077545\n",
            "   0.29598754  0.03420649]]\n",
            "[[ 1.65309628e-01 -2.32885871e+00 -2.95665810e-01 -9.43725414e-03\n",
            "   9.68632821e-03  1.58390007e-02 -1.55899267e-02  1.02028802e-01\n",
            "  -1.01779727e-01  6.82846859e-02 -6.80356118e-02 -2.10428932e-01\n",
            "   6.82846859e-02  1.42393320e-01 -4.20441845e-01  5.46898934e-01\n",
            "  -1.26208015e-01  2.30581101e-01 -1.26208015e-01 -1.04124011e-01\n",
            "   1.46154345e-01 -1.26208015e-01 -1.96972557e-02  4.50802959e-02\n",
            "  -1.26208015e-01  8.13767935e-02  2.35876997e-01 -1.26208015e-01\n",
            "  -1.09419908e-01 -5.33099563e-02 -1.26208015e-01  1.79767046e-01\n",
            "  -7.09213913e-02 -1.26208015e-01  1.97378481e-01  6.70444764e-01\n",
            "   6.30891847e-04 -6.70826582e-01 -1.23932920e-01  1.24181994e-01\n",
            "  -1.27372895e-01 -5.83231660e-02  2.31012177e-01 -4.50670422e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "model = LogisticRegression()\n",
        "solvers = ['lbfgs']\n",
        "penalty = ['l2']\n",
        "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
        "# define grid search\n",
        "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
        "grid_result = grid_search.fit(x, y)\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "metadata": {
        "id": "U6M2foTW6ZqW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d42cbce1-817b-4329-de09-161f49cac568"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: 0.802948 using {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "0.802426 (0.013882) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "0.802190 (0.013965) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "0.802237 (0.014256) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "0.802948 (0.013697) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "0.797403 (0.013652) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfPdsiqkQbxA"
      },
      "source": [
        "From seeing the results from above i hace a intution that the lesser the value of c in my case will help me to create the good model.becase with lower value of c=0.01 we have higher value of score."
      ]
    }
  ]
}