imbalanced-fraud-detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jincy-p-janardhanan/imbalanced-fraud-detection/blob/main/imbalanced-fraud-detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxTaRgs5o3F9",
        "outputId": "aa4b3ffa-66a6-4933-8819-b13a8063858b"
      },
      "id": "QxTaRgs5o3F9",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import gdown\n",
        "# url = 'https://drive.google.com/uc?export=download&confirm=6gh6&id=1VNpyNkGxHdskfdTNRSjjyNa5qC9u0JyV'\n",
        "# output = 'Fraud.csv'\n",
        "# gdown.download(url, output, quiet=False)\n",
        "# !cp /content/Fraud.csv /content/drive/MyDrive"
      ],
      "metadata": {
        "id": "Drk5M4HTjnX3"
      },
      "id": "Drk5M4HTjnX3",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "cff41eaf",
      "metadata": {
        "id": "cff41eaf"
      },
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "c7f1c9da",
      "metadata": {
        "id": "c7f1c9da"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "6d023655",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "6d023655",
        "outputId": "01e30119-de29-4dda-98d9-c0c6a067300f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
              "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
              "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
              "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  M1979787155             0.0             0.0        0               0  \n",
              "1  M2044282225             0.0             0.0        0               0  \n",
              "2   C553264065             0.0             0.0        1               0  \n",
              "3    C38997010         21182.0             0.0        1               0  \n",
              "4  M1230701703             0.0             0.0        0               0  "
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              "      <th></th>\n",
              "      <th>step</th>\n",
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              "      <th>nameOrig</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C1305486145</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>C840083671</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>C38997010</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>C2048537720</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>M1230701703</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dce4c79b-f1a4-4fcd-b5b2-2e66b1f2d9db 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-dce4c79b-f1a4-4fcd-b5b2-2e66b1f2d9db');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# df = pd.read_csv('Fraud.csv')\n",
        "df = pd.read_csv('/content/drive/MyDrive/Fraud.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44b1d394",
      "metadata": {
        "id": "44b1d394"
      },
      "source": [
        "# Data analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae2e7ca0",
      "metadata": {
        "id": "ae2e7ca0"
      },
      "source": [
        "size, columns and datatypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "1b8dbfbf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b8dbfbf",
        "outputId": "59a215a2-a791-4ca9-b9b9-c10dc96a0759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   nameOrig        object \n",
            " 4   oldbalanceOrg   float64\n",
            " 5   newbalanceOrig  float64\n",
            " 6   nameDest        object \n",
            " 7   oldbalanceDest  float64\n",
            " 8   newbalanceDest  float64\n",
            " 9   isFraud         int64  \n",
            " 10  isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(3)\n",
            "memory usage: 534.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f69bbc20",
      "metadata": {
        "id": "f69bbc20"
      },
      "source": [
        "Null value-check"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "9d1e3354",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d1e3354",
        "outputId": "aa82ecf5-2b04-436c-c92b-837ae3a45f31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "step              0\n",
              "type              0\n",
              "amount            0\n",
              "nameOrig          0\n",
              "oldbalanceOrg     0\n",
              "newbalanceOrig    0\n",
              "nameDest          0\n",
              "oldbalanceDest    0\n",
              "newbalanceDest    0\n",
              "isFraud           0\n",
              "isFlaggedFraud    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "85c85bba",
      "metadata": {
        "id": "85c85bba"
      },
      "source": [
        "Identifying data stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "15f8b29a",
      "metadata": {
        "scrolled": false,
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          "height": 300
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        "id": "15f8b29a",
        "outputId": "a14028fd-04be-40bc-a3ec-c02415213493"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
              "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
              "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
              "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
              "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
              "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
              "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
              "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
              "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
              "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.433972e+02</td>\n",
              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.100702e+06</td>\n",
              "      <td>1.224996e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>6.038582e+05</td>\n",
              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.399180e+06</td>\n",
              "      <td>3.674129e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.327057e+05</td>\n",
              "      <td>2.146614e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>9.430367e+05</td>\n",
              "      <td>1.111909e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-578569b0-68cc-4bc5-a52c-9472a2ac7333')\"\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-578569b0-68cc-4bc5-a52c-9472a2ac7333 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-578569b0-68cc-4bc5-a52c-9472a2ac7333');\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": 7
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57631017",
      "metadata": {
        "id": "57631017"
      },
      "source": [
        "## Fraud and flagged fraud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "7a8f20ce",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a8f20ce",
        "outputId": "478ea8ca-4319-4153-b285-aa2fd26ae30f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of fraudulent transactions \t: 8213\n",
            "Number of non-fraudulent transactions \t: 6354407\n",
            "\n",
            "Number of transactions flagged fraud \t: 16\n",
            "Number of transactions flagged non-fraud: 6362604\n"
          ]
        }
      ],
      "source": [
        "print('Number of fraudulent transactions \\t: {}'.format(len(df[df['isFraud']==1])))\n",
        "print('Number of non-fraudulent transactions \\t: {}'.format(len(df[df['isFraud']==0])))\n",
        "print('\\nNumber of transactions flagged fraud \\t: {}'.format(len(df[df['isFlaggedFraud']==1])))\n",
        "print('Number of transactions flagged non-fraud: {}'.format(len(df[df['isFlaggedFraud']==0])))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "366652c3",
      "metadata": {
        "id": "366652c3"
      },
      "source": [
        "=> Highly skewed data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "a7271340",
      "metadata": {
        "id": "a7271340"
      },
      "outputs": [],
      "source": [
        "fraud_ratio = df['isFraud'].value_counts()/len(df)\n",
        "flaggedFraud_ratio =  df['isFlaggedFraud'].value_counts()/len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "bae962e6",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bae962e6",
        "outputId": "6c484c2a-4709-4209-8de5-64e57872f446"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fraud ratio \n",
            "0    0.998709\n",
            "1    0.001291\n",
            "Name: isFraud, dtype: float64 \n",
            "\n",
            "Flagged fraud ratio \n",
            "0    0.999997\n",
            "1    0.000003\n",
            "Name: isFlaggedFraud, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(f'Fraud ratio \\n{fraud_ratio} \\n\\nFlagged fraud ratio \\n{flaggedFraud_ratio}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "2e8809b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e8809b6",
        "outputId": "92ca3f9d-0f00-4f18-96b3-a5e37528358d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df['type'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "6f0ffc5c",
      "metadata": {
        "id": "6f0ffc5c"
      },
      "outputs": [],
      "source": [
        "fraudby_type = df[df['isFraud']==1].groupby('type')['isFraud'].count()\n",
        "flaggedFraudby_type = df[df['isFlaggedFraud']==1].groupby('type')['isFlaggedFraud'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "94e22de9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94e22de9",
        "outputId": "77a4badf-d417-4dc2-e522-b145f21f983b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fraud per transaction type: \n",
            "type\n",
            "CASH_OUT    4116\n",
            "TRANSFER    4097\n",
            "Name: isFraud, dtype: int64\n",
            " \n",
            "Flagged fraud per transaction type: \n",
            "type\n",
            "TRANSFER    16\n",
            "Name: isFlaggedFraud, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(f'Fraud per transaction type: \\n{fraudby_type}\\n \\nFlagged fraud per transaction type: \\n{flaggedFraudby_type}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c204cf2",
      "metadata": {
        "id": "9c204cf2"
      },
      "source": [
        "Only Transfers are flagged as fraud.<br>\n",
        "Only Cashouts and Transfers are fraud.<br>\n",
        "?? Do we really need data from other transaction types?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0be4482",
      "metadata": {
        "id": "b0be4482"
      },
      "source": [
        "### Fraud\n",
        ">In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7f2bf99",
      "metadata": {
        "id": "c7f2bf99"
      },
      "source": [
        "Analyze fraud data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "5e5034e1",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5e5034e1",
        "outputId": "1c4c9800-e887-4742-8b9f-05d2a0a48721"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  8213.000000  8.213000e+03   8.213000e+03    8.213000e+03   \n",
              "mean    368.413856  1.467967e+06   1.649668e+06    1.923926e+05   \n",
              "std     216.388690  2.404253e+06   3.547719e+06    1.965666e+06   \n",
              "min       1.000000  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%     181.000000  1.270913e+05   1.258224e+05    0.000000e+00   \n",
              "50%     367.000000  4.414234e+05   4.389835e+05    0.000000e+00   \n",
              "75%     558.000000  1.517771e+06   1.517771e+06    0.000000e+00   \n",
              "max     743.000000  1.000000e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count    8.213000e+03    8.213000e+03   8213.0     8213.000000  \n",
              "mean     5.442496e+05    1.279708e+06      1.0        0.001948  \n",
              "std      3.336421e+06    3.908817e+06      0.0        0.044097  \n",
              "min      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "25%      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "50%      0.000000e+00    4.676420e+03      1.0        0.000000  \n",
              "75%      1.478287e+05    1.058725e+06      1.0        0.000000  \n",
              "max      2.362305e+08    2.367265e+08      1.0        1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3903d2bc-c7ad-408c-9fff-803b6745924f\">\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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8213.000000</td>\n",
              "      <td>8.213000e+03</td>\n",
              "      <td>8.213000e+03</td>\n",
              "      <td>8.213000e+03</td>\n",
              "      <td>8.213000e+03</td>\n",
              "      <td>8.213000e+03</td>\n",
              "      <td>8213.0</td>\n",
              "      <td>8213.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>368.413856</td>\n",
              "      <td>1.467967e+06</td>\n",
              "      <td>1.649668e+06</td>\n",
              "      <td>1.923926e+05</td>\n",
              "      <td>5.442496e+05</td>\n",
              "      <td>1.279708e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>216.388690</td>\n",
              "      <td>2.404253e+06</td>\n",
              "      <td>3.547719e+06</td>\n",
              "      <td>1.965666e+06</td>\n",
              "      <td>3.336421e+06</td>\n",
              "      <td>3.908817e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.044097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>181.000000</td>\n",
              "      <td>1.270913e+05</td>\n",
              "      <td>1.258224e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>367.000000</td>\n",
              "      <td>4.414234e+05</td>\n",
              "      <td>4.389835e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.676420e+03</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>558.000000</td>\n",
              "      <td>1.517771e+06</td>\n",
              "      <td>1.517771e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.478287e+05</td>\n",
              "      <td>1.058725e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>743.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>2.362305e+08</td>\n",
              "      <td>2.367265e+08</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <style>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3903d2bc-c7ad-408c-9fff-803b6745924f 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-3903d2bc-c7ad-408c-9fff-803b6745924f');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df[df['isFraud']==1].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2944260",
      "metadata": {
        "id": "b2944260"
      },
      "source": [
        "Transactions that empties funds in one account"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "6068b427",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6068b427",
        "outputId": "490572fb-0cb4-4a51-9ca9-3e62d7fb0598"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8034"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "len(df[(df['amount'] == df['oldbalanceOrg'])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "474405e5",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "474405e5",
        "outputId": "3d2b072a-dbb0-472c-a44f-e290728c6a1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8034"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(df[(df['amount'] == df['oldbalanceOrg']) & (df['isFraud'] == 1)] )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c512dd6",
      "metadata": {
        "id": "2c512dd6"
      },
      "source": [
        "So all transactions that empty funds are marked as fraud.<br>\n",
        "But, there are 8213 fraud transactions in total. <br>\n",
        "=> Remaining transactions could be outliers?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "2ed8d213",
      "metadata": {
        "id": "2ed8d213"
      },
      "outputs": [],
      "source": [
        "df_outliers = df[(df['amount'] != df['oldbalanceOrg']) & (df['isFraud'] == 1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "64c5090d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64c5090d",
        "outputId": "7fed03d4-f9fb-433e-a61f-33dad260e0b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "CASH_OUT     25\n",
              "TRANSFER    154\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df_outliers.groupby('type')['type'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "c988f9e6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "c988f9e6",
        "outputId": "30477ddc-5f44-45c4-931f-8d797549bdd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             step         amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count   25.000000      25.000000      25.000000         25.0000   \n",
              "mean    56.880000  220121.416800   17031.663200      11950.7044   \n",
              "std     78.015127  158136.453984   69138.559766      59753.5220   \n",
              "min      1.000000   23292.300000       0.000000          0.0000   \n",
              "25%     13.000000   95428.320000       0.000000          0.0000   \n",
              "50%     19.000000  181728.110000       0.000000          0.0000   \n",
              "75%     38.000000  314251.580000       0.000000          0.0000   \n",
              "max    231.000000  577418.980000  340830.430000     298767.6100   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count    2.500000e+01    2.500000e+01     25.0            25.0  \n",
              "mean     5.806669e+05    1.185674e+06      1.0             0.0  \n",
              "std      1.613350e+06    2.341533e+06      0.0             0.0  \n",
              "min      0.000000e+00    4.061122e+04      1.0             0.0  \n",
              "25%      0.000000e+00    2.250277e+05      1.0             0.0  \n",
              "50%      1.139700e+04    4.070058e+05      1.0             0.0  \n",
              "75%      3.989313e+05    6.784196e+05      1.0             0.0  \n",
              "max      7.962205e+06    9.291620e+06      1.0             0.0  "
            ],
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              "      <th>count</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>2.500000e+01</td>\n",
              "      <td>2.500000e+01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
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              "      <th>mean</th>\n",
              "      <td>56.880000</td>\n",
              "      <td>220121.416800</td>\n",
              "      <td>17031.663200</td>\n",
              "      <td>11950.7044</td>\n",
              "      <td>5.806669e+05</td>\n",
              "      <td>1.185674e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>std</th>\n",
              "      <td>78.015127</td>\n",
              "      <td>158136.453984</td>\n",
              "      <td>69138.559766</td>\n",
              "      <td>59753.5220</td>\n",
              "      <td>1.613350e+06</td>\n",
              "      <td>2.341533e+06</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>23292.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.061122e+04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>95428.320000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.250277e+05</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>181728.110000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.139700e+04</td>\n",
              "      <td>4.070058e+05</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>314251.580000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3.989313e+05</td>\n",
              "      <td>6.784196e+05</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>231.000000</td>\n",
              "      <td>577418.980000</td>\n",
              "      <td>340830.430000</td>\n",
              "      <td>298767.6100</td>\n",
              "      <td>7.962205e+06</td>\n",
              "      <td>9.291620e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-51e31bc7-3b25-4cb1-9556-2df3d661f039');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df_outliers[df_outliers['type'] == 'CASH_OUT'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c2954fa",
      "metadata": {
        "id": "3c2954fa"
      },
      "source": [
        "Seems like most of the fraud cashout outliers are transactions in which Original account has 0 balance before and after the transaction; and Destination account has balance in the order of $10^5$ after the transaction.\n",
        "\n",
        "=> Outlier shows a pattern and thus cannot be ignored."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "acf4ed23",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "acf4ed23",
        "outputId": "2871ba16-4a22-4aa6-8d6d-b047f9e5a88e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  154.000000  1.540000e+02   1.540000e+02    1.540000e+02   \n",
              "mean   439.097403  9.565122e+06   1.928838e+07    1.006063e+07   \n",
              "std    213.665941  1.919552e+06   1.081729e+07    1.032873e+07   \n",
              "min      4.000000  1.231949e+05   0.000000e+00    0.000000e+00   \n",
              "25%    271.500000  1.000000e+07   1.227122e+07    2.455224e+06   \n",
              "50%    425.000000  1.000000e+07   1.595579e+07    6.359678e+06   \n",
              "75%    646.000000  1.000000e+07   2.370846e+07    1.493847e+07   \n",
              "max    741.000000  1.000000e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count    1.540000e+02    1.540000e+02    154.0      154.000000  \n",
              "mean     2.855618e+04    5.909128e+04      1.0        0.038961  \n",
              "std      2.048429e+05    3.562933e+05      0.0        0.194133  \n",
              "min      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "25%      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "50%      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "75%      0.000000e+00    0.000000e+00      1.0        0.000000  \n",
              "max      2.122337e+06    3.217684e+06      1.0        1.000000  "
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              "      <th></th>\n",
              "      <th>step</th>\n",
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              "      <td>1.540000e+02</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>9.565122e+06</td>\n",
              "      <td>1.928838e+07</td>\n",
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              "      <td>5.909128e+04</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.038961</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>213.665941</td>\n",
              "      <td>1.919552e+06</td>\n",
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              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
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              "      <td>0.000000e+00</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>1.000000e+07</td>\n",
              "      <td>1.227122e+07</td>\n",
              "      <td>2.455224e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>425.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>1.595579e+07</td>\n",
              "      <td>6.359678e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>646.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>2.370846e+07</td>\n",
              "      <td>1.493847e+07</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>741.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>2.122337e+06</td>\n",
              "      <td>3.217684e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdc34a47-463b-406f-9a55-8ed60fa5882c')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bdc34a47-463b-406f-9a55-8ed60fa5882c 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-bdc34a47-463b-406f-9a55-8ed60fa5882c');\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": 20
        }
      ],
      "source": [
        "df_outliers[df_outliers['type'] == 'TRANSFER'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "545126b0",
      "metadata": {
        "id": "545126b0"
      },
      "source": [
        "Here also, the outliers cannot be ignored because they show a pattern. \n",
        "\n",
        "Most of the transfers attempted to transfer an amount of $1 \\times 10^7$ to an empty Destination account and failed."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59d80d28",
      "metadata": {
        "id": "59d80d28"
      },
      "source": [
        "### Flagged fraud\n",
        ">An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "233db9fe",
      "metadata": {
        "id": "233db9fe"
      },
      "source": [
        "Analyzing flagged fraud data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "a814da8d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "a814da8d",
        "outputId": "6744ea66-b029-400e-8d5b-45ae2d9439c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count   16.000000  1.600000e+01   1.600000e+01    1.600000e+01   \n",
              "mean   537.562500  4.861598e+06   7.817869e+06    7.817869e+06   \n",
              "std    181.895196  3.572499e+06   6.972669e+06    6.972669e+06   \n",
              "min    212.000000  3.538742e+05   3.538742e+05    3.538742e+05   \n",
              "25%    415.500000  2.242749e+06   3.013980e+06    3.013980e+06   \n",
              "50%    601.500000  4.234245e+06   4.923043e+06    4.923043e+06   \n",
              "75%    678.750000  7.883451e+06   1.212835e+07    1.212835e+07   \n",
              "max    741.000000  1.000000e+07   1.958504e+07    1.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count            16.0            16.0     16.0            16.0  \n",
              "mean              0.0             0.0      1.0             1.0  \n",
              "std               0.0             0.0      0.0             0.0  \n",
              "min               0.0             0.0      1.0             1.0  \n",
              "25%               0.0             0.0      1.0             1.0  \n",
              "50%               0.0             0.0      1.0             1.0  \n",
              "75%               0.0             0.0      1.0             1.0  \n",
              "max               0.0             0.0      1.0             1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1afd32c-36e7-4296-8780-ce884ccd5228\">\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",
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              "\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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>1.600000e+01</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>537.562500</td>\n",
              "      <td>4.861598e+06</td>\n",
              "      <td>7.817869e+06</td>\n",
              "      <td>7.817869e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>181.895196</td>\n",
              "      <td>3.572499e+06</td>\n",
              "      <td>6.972669e+06</td>\n",
              "      <td>6.972669e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>212.000000</td>\n",
              "      <td>3.538742e+05</td>\n",
              "      <td>3.538742e+05</td>\n",
              "      <td>3.538742e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>415.500000</td>\n",
              "      <td>2.242749e+06</td>\n",
              "      <td>3.013980e+06</td>\n",
              "      <td>3.013980e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>601.500000</td>\n",
              "      <td>4.234245e+06</td>\n",
              "      <td>4.923043e+06</td>\n",
              "      <td>4.923043e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>678.750000</td>\n",
              "      <td>7.883451e+06</td>\n",
              "      <td>1.212835e+07</td>\n",
              "      <td>1.212835e+07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>741.000000</td>\n",
              "      <td>1.000000e+07</td>\n",
              "      <td>1.958504e+07</td>\n",
              "      <td>1.958504e+07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1afd32c-36e7-4296-8780-ce884ccd5228')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      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-c1afd32c-36e7-4296-8780-ce884ccd5228 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-c1afd32c-36e7-4296-8780-ce884ccd5228');\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": 21
        }
      ],
      "source": [
        "df[df['isFlaggedFraud'] == 1].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2188d10",
      "metadata": {
        "id": "f2188d10"
      },
      "source": [
        "*Attempt to transfer $\\implies$ Original and destination accounts are not modified.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "5af55884",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5af55884",
        "outputId": "1a43136d-7230-4637-cd3e-f401438cf57d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "len(df[  (df['oldbalanceOrg'] == df['newbalanceOrig']) \\\n",
        "       & (df['oldbalanceDest'] == df['newbalanceDest']) \\\n",
        "       & (df['amount']>200.000) \\\n",
        "       & (df['type']=='TRANSFER') ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "69c021d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69c021d8",
        "outputId": "851138c3-7916-4728-b261-397077f3b4a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "len(df[df['isFlaggedFraud'] == 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d872d15e",
      "metadata": {
        "id": "d872d15e"
      },
      "source": [
        "$\\implies 21 - 16 = 5$ missing values or outliers in isFlaggedFraud."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "c1c21a07",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "c1c21a07",
        "outputId": "285a20cb-e2bd-4992-d944-a26e8c2dbd1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           step         amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "count   5.00000       5.000000            5.0             5.0             5.0   \n",
              "mean   16.80000  237475.474000            0.0             0.0             0.0   \n",
              "std    16.11521  161578.156092            0.0             0.0             0.0   \n",
              "min     1.00000   18931.590000            0.0             0.0             0.0   \n",
              "25%    12.00000  133711.480000            0.0             0.0             0.0   \n",
              "50%    12.00000  271161.740000            0.0             0.0             0.0   \n",
              "75%    15.00000  342317.150000            0.0             0.0             0.0   \n",
              "max    44.00000  421255.410000            0.0             0.0             0.0   \n",
              "\n",
              "       newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count             5.0      5.0             5.0  \n",
              "mean              0.0      0.0             0.0  \n",
              "std               0.0      0.0             0.0  \n",
              "min               0.0      0.0             0.0  \n",
              "25%               0.0      0.0             0.0  \n",
              "50%               0.0      0.0             0.0  \n",
              "75%               0.0      0.0             0.0  \n",
              "max               0.0      0.0             0.0  "
            ],
            "text/html": [
              "\n",
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              "      <td>0.0</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "dff_outliers = df[  (df['oldbalanceOrg'] == df['newbalanceOrig']) \\\n",
        "       & (df['oldbalanceDest'] == df['newbalanceDest']) \\\n",
        "       & (df['amount']>200.000) \\\n",
        "       & (df['type']=='TRANSFER') \\\n",
        "       & (df['isFlaggedFraud']==0)]\n",
        "dff_outliers.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06e9dd7d",
      "metadata": {
        "id": "06e9dd7d"
      },
      "source": [
        "i.e. Failed transfer attempts from empty accounts are not flagged as fraud. <br>\n",
        "Again, the outliers has a pattern and must be retained."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d7dbf47",
      "metadata": {
        "id": "1d7dbf47"
      },
      "source": [
        "## Merchant transactions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d6c032a",
      "metadata": {
        "id": "9d6c032a"
      },
      "source": [
        "Given,\n",
        "> there is not information for customers that start with M (Merchants)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e6667e6c",
      "metadata": {
        "id": "e6667e6c"
      },
      "source": [
        "Finding transactions initiated by merchants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "6a6caa5f",
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        "scrolled": true,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       step  amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
              "count   0.0     0.0            0.0             0.0             0.0   \n",
              "mean    NaN     NaN            NaN             NaN             NaN   \n",
              "std     NaN     NaN            NaN             NaN             NaN   \n",
              "min     NaN     NaN            NaN             NaN             NaN   \n",
              "25%     NaN     NaN            NaN             NaN             NaN   \n",
              "50%     NaN     NaN            NaN             NaN             NaN   \n",
              "75%     NaN     NaN            NaN             NaN             NaN   \n",
              "max     NaN     NaN            NaN             NaN             NaN   \n",
              "\n",
              "       newbalanceDest  isFraud  isFlaggedFraud  \n",
              "count             0.0      0.0             0.0  \n",
              "mean              NaN      NaN             NaN  \n",
              "std               NaN      NaN             NaN  \n",
              "min               NaN      NaN             NaN  \n",
              "25%               NaN      NaN             NaN  \n",
              "50%               NaN      NaN             NaN  \n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df[df['nameOrig'].str.startswith('M')].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee8b8882",
      "metadata": {
        "id": "ee8b8882"
      },
      "source": [
        "$\\implies$ No merchants initiated transactions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "58022931",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "58022931",
        "outputId": "2c833266-9d81-4812-885f-93b5e76dbf28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  2.151495e+06  2.151495e+06   2.151495e+06    2.151495e+06   \n",
              "mean   2.443782e+02  1.305760e+04   6.821683e+04    6.183789e+04   \n",
              "std    1.426951e+02  1.255645e+04   1.989911e+05    1.969915e+05   \n",
              "min    1.000000e+00  2.000000e-02   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  4.383820e+03   0.000000e+00    0.000000e+00   \n",
              "50%    2.490000e+02  9.482190e+03   1.053000e+04    0.000000e+00   \n",
              "75%    3.350000e+02  1.756122e+04   6.088300e+04    4.965413e+04   \n",
              "max    7.180000e+02  2.386380e+05   4.368662e+07    4.367380e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest    isFraud  isFlaggedFraud  \n",
              "count       2151495.0       2151495.0  2151495.0       2151495.0  \n",
              "mean              0.0             0.0        0.0             0.0  \n",
              "std               0.0             0.0        0.0             0.0  \n",
              "min               0.0             0.0        0.0             0.0  \n",
              "25%               0.0             0.0        0.0             0.0  \n",
              "50%               0.0             0.0        0.0             0.0  \n",
              "75%               0.0             0.0        0.0             0.0  \n",
              "max               0.0             0.0        0.0             0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f601710e-0fb1-47af-a907-eacfbb1d253c\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.151495e+06</td>\n",
              "      <td>2.151495e+06</td>\n",
              "      <td>2.151495e+06</td>\n",
              "      <td>2.151495e+06</td>\n",
              "      <td>2151495.0</td>\n",
              "      <td>2151495.0</td>\n",
              "      <td>2151495.0</td>\n",
              "      <td>2151495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.443782e+02</td>\n",
              "      <td>1.305760e+04</td>\n",
              "      <td>6.821683e+04</td>\n",
              "      <td>6.183789e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.426951e+02</td>\n",
              "      <td>1.255645e+04</td>\n",
              "      <td>1.989911e+05</td>\n",
              "      <td>1.969915e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e-02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>4.383820e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.490000e+02</td>\n",
              "      <td>9.482190e+03</td>\n",
              "      <td>1.053000e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>1.756122e+04</td>\n",
              "      <td>6.088300e+04</td>\n",
              "      <td>4.965413e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.180000e+02</td>\n",
              "      <td>2.386380e+05</td>\n",
              "      <td>4.368662e+07</td>\n",
              "      <td>4.367380e+07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f601710e-0fb1-47af-a907-eacfbb1d253c');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df[df['nameDest'].str.startswith('M')].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b9721ab",
      "metadata": {
        "id": "8b9721ab"
      },
      "source": [
        "$\\implies$ 2151495 transactions were destined towards merchant accounts. <br>\n",
        "However, oldbalanceDest, newbalanceDest, isFraud, isFlaggedFraud - are all 0; i.e these are the missing data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "505ab101",
      "metadata": {
        "id": "505ab101"
      },
      "outputs": [],
      "source": [
        "df_missing = df[df['nameDest'].str.startswith('M')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "e4888143",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4888143",
        "outputId": "0a1cbf84-2371-4843-e3a1-3651a92eb706"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PAYMENT'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df_missing['type'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f41f8b1c",
      "metadata": {
        "id": "f41f8b1c"
      },
      "source": [
        "$\\implies$ all missing data are payments."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c38a6d9c",
      "metadata": {
        "id": "c38a6d9c"
      },
      "source": [
        "# Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0df765af",
      "metadata": {
        "id": "0df765af"
      },
      "source": [
        "## Fix missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b85b8a73",
      "metadata": {
        "id": "b85b8a73"
      },
      "source": [
        "Interpolating oldbalance and updating new balance with payment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "15684620",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15684620",
        "outputId": "e30f25c4-9cd2-436a-a263-af4f69a5eee6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2151495 rows updated with NaN\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "df.loc[df['nameDest'].str.startswith('M'), ['oldbalanceDest']] = np.NaN\n",
        "print('{} rows updated with NaN'.format(df['oldbalanceDest'].isnull().sum()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "b25b6b78",
      "metadata": {
        "id": "b25b6b78"
      },
      "outputs": [],
      "source": [
        "df=df.interpolate()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4c2f3c0",
      "metadata": {
        "id": "e4c2f3c0"
      },
      "source": [
        "Find if there are missing data after interpolate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "1ae30fc6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "1ae30fc6",
        "outputId": "c70023b5-3706-48b2-9603-d96cd928513d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   step     type   amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
              "0     1  PAYMENT  9839.64  C1231006815       170136.0       160296.36   \n",
              "1     1  PAYMENT  1864.28  C1666544295        21249.0        19384.72   \n",
              "\n",
              "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0  M1979787155             NaN             0.0        0               0  \n",
              "1  M2044282225             NaN             0.0        0               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29dcf1c7-0256-445d-86fb-fa01d1964a23\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>step</th>\n",
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              "      <th>oldbalanceDest</th>\n",
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              "      <th>isFlaggedFraud</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>C1231006815</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>M1979787155</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>C1666544295</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>M2044282225</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-29dcf1c7-0256-445d-86fb-fa01d1964a23 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-29dcf1c7-0256-445d-86fb-fa01d1964a23');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df[df['oldbalanceDest'].isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24d3cc3d",
      "metadata": {
        "id": "24d3cc3d"
      },
      "source": [
        "These are the 1st two rows in the dataset, and couldn't be interpolated. So let's assign oldbalanceDest for these to zero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "aac2387b",
      "metadata": {
        "id": "aac2387b"
      },
      "outputs": [],
      "source": [
        "df.loc[df['oldbalanceDest'].isnull(), 'oldbalanceDest'] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "df1d3c1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df1d3c1c",
        "outputId": "9cbecb27-e384-4fe1-efbf-3647f4b011b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fec1627",
      "metadata": {
        "id": "4fec1627"
      },
      "source": [
        "Now, we have fixed missing data in oldbalanceDest column. <br>\n",
        "Let's try to find payments that doesn't involve merchants to infer update criteria for newbalanceDest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "368fe1e6",
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        "colab": {
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          "height": 49
        },
        "id": "368fe1e6",
        "outputId": "726a6a34-6d1b-4615-c273-87ec1bef1d37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [step, type, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest, isFraud, isFlaggedFraud]\n",
              "Index: []"
            ],
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              "      <th></th>\n",
              "      <th>step</th>\n",
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              "      <th>nameDest</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
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              "  <tbody>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df[(df['type']=='PAYMENT') & (df.nameDest.str.get(0) != 'M')]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe689fff",
      "metadata": {
        "id": "fe689fff"
      },
      "source": [
        "$\\implies$ There are no payments that doesn't involve Merchants. <br>\n",
        "So, let's use a general update criteria for updating newbalanceDest.\n",
        "$$\\text{newbalanceDest} = \\text{oldbalanceDest} + \\text{amount}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "cce3e334",
      "metadata": {
        "id": "cce3e334"
      },
      "outputs": [],
      "source": [
        "newbalanceDest = df.loc[df.nameDest.str.get(0) == 'M', 'oldbalanceDest'] + df.loc[df.nameDest.str.get(0) == 'M','amount']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "4043f5a6",
      "metadata": {
        "id": "4043f5a6"
      },
      "outputs": [],
      "source": [
        "df.loc[df['nameDest'].str.get(0) == 'M', ['newbalanceDest']] = newbalanceDest"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e3ba1ef9",
      "metadata": {
        "id": "e3ba1ef9"
      },
      "source": [
        "Let's see if we need to update isFraud column. <br>\n",
        "(No need of updating isFlaggedFraud column since only TRANSFERs are flagged fraud.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "1a3b59b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a3b59b6",
        "outputId": "a989623d-ffb3-4e37-bd69-93ab66a7fd65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "len(df[(df['nameDest'].str.get(0) == 'M') & (df['amount'] == df['oldbalanceOrg'])])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37afe591",
      "metadata": {
        "id": "37afe591"
      },
      "source": [
        "$\\implies$ No transactions to merchants try to empty funds in Original account. <br>\n",
        "$\\therefore$ None of them need to be marked as isFraud. <br>\n",
        "So we have fixed all missing data.\n",
        "\n",
        "However, this means fixing missing data did not contribute to the classification goal. Still, only transaction types CASH_OUT and TRANSFER contribute to Fraud classification.\n",
        "\n",
        "So, it may be safe to ignore other transaction types. <br>\n",
        "(Try two models - one that ignores other transaction types and one that doesn't ignore other transaction types.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "4fb73334",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4fb73334",
        "outputId": "87e47ace-42ee-458c-84b4-cc99c62a0e6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  2.151495e+06  2.151495e+06   2.151495e+06    2.151495e+06   \n",
              "mean   2.443782e+02  1.305760e+04   6.821683e+04    6.183789e+04   \n",
              "std    1.426951e+02  1.255645e+04   1.989911e+05    1.969915e+05   \n",
              "min    1.000000e+00  2.000000e-02   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  4.383820e+03   0.000000e+00    0.000000e+00   \n",
              "50%    2.490000e+02  9.482190e+03   1.053000e+04    0.000000e+00   \n",
              "75%    3.350000e+02  1.756122e+04   6.088300e+04    4.965413e+04   \n",
              "max    7.180000e+02  2.386380e+05   4.368662e+07    4.367380e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest    isFraud  isFlaggedFraud  \n",
              "count    2.151495e+06    2.151495e+06  2151495.0       2151495.0  \n",
              "mean     1.161337e+06    1.174395e+06        0.0             0.0  \n",
              "std      2.614824e+06    2.614912e+06        0.0             0.0  \n",
              "min      0.000000e+00    2.000000e-02        0.0             0.0  \n",
              "25%      1.105357e+05    1.240366e+05        0.0             0.0  \n",
              "50%      4.304567e+05    4.436661e+05        0.0             0.0  \n",
              "75%      1.266756e+06    1.279647e+06        0.0             0.0  \n",
              "max      3.368401e+08    3.368669e+08        0.0             0.0  "
            ],
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              "\n",
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              "      <th>count</th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>1.161337e+06</td>\n",
              "      <td>1.174395e+06</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.000000e-02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>4.383820e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.105357e+05</td>\n",
              "      <td>1.240366e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>9.482190e+03</td>\n",
              "      <td>1.053000e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.304567e+05</td>\n",
              "      <td>4.436661e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>1.756122e+04</td>\n",
              "      <td>6.088300e+04</td>\n",
              "      <td>4.965413e+04</td>\n",
              "      <td>1.266756e+06</td>\n",
              "      <td>1.279647e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>max</th>\n",
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              "      <td>2.386380e+05</td>\n",
              "      <td>4.368662e+07</td>\n",
              "      <td>4.367380e+07</td>\n",
              "      <td>3.368401e+08</td>\n",
              "      <td>3.368669e+08</td>\n",
              "      <td>0.0</td>\n",
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              "            + ' 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": 38
        }
      ],
      "source": [
        "df[df['nameDest'].str.get(0) == 'M'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "982f4646",
      "metadata": {
        "id": "982f4646"
      },
      "source": [
        "The dataframe stats after fixing missing data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "20e38e93",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "20e38e93",
        "outputId": "d372990e-90c3-4918-f850-81fc3303e432"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
              "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
              "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
              "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
              "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
              "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.493403e+06    1.622113e+06  1.290820e-03    2.514687e-06  \n",
              "std      3.647435e+06    3.891930e+06  3.590480e-02    1.585775e-03  \n",
              "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "25%      1.271562e+05    1.814925e+05  0.000000e+00    0.000000e+00  \n",
              "50%      5.065537e+05    5.929597e+05  0.000000e+00    0.000000e+00  \n",
              "75%      1.533432e+06    1.678439e+06  0.000000e+00    0.000000e+00  \n",
              "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8de5831-c0c4-4bf8-a5e2-f6e982c92bc0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.433972e+02</td>\n",
              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.493403e+06</td>\n",
              "      <td>1.622113e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>6.038582e+05</td>\n",
              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.647435e+06</td>\n",
              "      <td>3.891930e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.271562e+05</td>\n",
              "      <td>1.814925e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.065537e+05</td>\n",
              "      <td>5.929597e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>1.533432e+06</td>\n",
              "      <td>1.678439e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8de5831-c0c4-4bf8-a5e2-f6e982c92bc0')\"\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",
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              "  </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-e8de5831-c0c4-4bf8-a5e2-f6e982c92bc0 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-e8de5831-c0c4-4bf8-a5e2-f6e982c92bc0');\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": 39
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6613589f",
      "metadata": {
        "id": "6613589f"
      },
      "source": [
        "## Remove unwanted"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9551ac6",
      "metadata": {
        "id": "c9551ac6"
      },
      "source": [
        "We do not need customer id to make predictions from the dataset. So let's drop columns nameOrig and nameDest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "c02fe931",
      "metadata": {
        "id": "c02fe931"
      },
      "outputs": [],
      "source": [
        "df = df.drop(['nameOrig', 'nameDest'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "ad00f9c3",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ad00f9c3",
        "outputId": "3ca2079e-3b92-4f7f-ef4c-e140af9a7724"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6362620 entries, 0 to 6362619\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            int64  \n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   oldbalanceOrg   float64\n",
            " 4   newbalanceOrig  float64\n",
            " 5   oldbalanceDest  float64\n",
            " 6   newbalanceDest  float64\n",
            " 7   isFraud         int64  \n",
            " 8   isFlaggedFraud  int64  \n",
            "dtypes: float64(5), int64(3), object(1)\n",
            "memory usage: 436.9+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52a1ee72",
      "metadata": {
        "id": "52a1ee72"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "b83d561b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "b83d561b",
        "outputId": "62efe7a1-aecc-4726-df4e-f8c3e8eb5c11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
              "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
              "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
              "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
              "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
              "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
              "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.493403e+06    1.622113e+06  1.290820e-03    2.514687e-06  \n",
              "std      3.647435e+06    3.891930e+06  3.590480e-02    1.585775e-03  \n",
              "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
              "25%      1.271562e+05    1.814925e+05  0.000000e+00    0.000000e+00  \n",
              "50%      5.065537e+05    5.929597e+05  0.000000e+00    0.000000e+00  \n",
              "75%      1.533432e+06    1.678439e+06  0.000000e+00    0.000000e+00  \n",
              "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d27978ea-10e4-4c32-bf6d-cc50f80b3567\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "      <td>6.362620e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.433972e+02</td>\n",
              "      <td>1.798619e+05</td>\n",
              "      <td>8.338831e+05</td>\n",
              "      <td>8.551137e+05</td>\n",
              "      <td>1.493403e+06</td>\n",
              "      <td>1.622113e+06</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>6.038582e+05</td>\n",
              "      <td>2.888243e+06</td>\n",
              "      <td>2.924049e+06</td>\n",
              "      <td>3.647435e+06</td>\n",
              "      <td>3.891930e+06</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.560000e+02</td>\n",
              "      <td>1.338957e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.271562e+05</td>\n",
              "      <td>1.814925e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.390000e+02</td>\n",
              "      <td>7.487194e+04</td>\n",
              "      <td>1.420800e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.065537e+05</td>\n",
              "      <td>5.929597e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.350000e+02</td>\n",
              "      <td>2.087215e+05</td>\n",
              "      <td>1.073152e+05</td>\n",
              "      <td>1.442584e+05</td>\n",
              "      <td>1.533432e+06</td>\n",
              "      <td>1.678439e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.430000e+02</td>\n",
              "      <td>9.244552e+07</td>\n",
              "      <td>5.958504e+07</td>\n",
              "      <td>4.958504e+07</td>\n",
              "      <td>3.560159e+08</td>\n",
              "      <td>3.561793e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "          const element = document.querySelector('#df-d27978ea-10e4-4c32-bf6d-cc50f80b3567');\n",
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              "            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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecf07451",
      "metadata": {
        "id": "ecf07451"
      },
      "source": [
        "Values of columns step, amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest vary by a very large scale. So we can apply mean normalization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "d1598741",
      "metadata": {
        "id": "d1598741"
      },
      "outputs": [],
      "source": [
        "cols = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
        "df['step'] = df['step'] - df['step'].mean() / (df['step'].std())\n",
        "df[cols] = df[cols].apply(lambda x: (np.log(x+10)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "d701af87",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "d701af87",
        "outputId": "a98c0998-0f96-4b92-bf78-d745542fd4d6"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       step      type    amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "0 -0.710067   PAYMENT  9.195190      12.044412       11.984842   \n",
              "1 -0.710067   PAYMENT  7.535980       9.964536        9.872756   \n",
              "2 -0.710067  TRANSFER  5.252273       5.252273        2.302585   \n",
              "3 -0.710067  CASH_OUT  5.252273       5.252273        2.302585   \n",
              "4 -0.710067   PAYMENT  9.365474      10.634990       10.305475   \n",
              "\n",
              "   oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
              "0        2.302585        9.195190        0               0  \n",
              "1        2.302585        7.535980        0               0  \n",
              "2        2.302585        2.302585        1               0  \n",
              "3        9.961379        2.302585        1               0  \n",
              "4       10.112316       10.499926        0               0  "
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              "      <td>-0.710067</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7.535980</td>\n",
              "      <td>9.964536</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.710067</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>5.252273</td>\n",
              "      <td>5.252273</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>2.302585</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.710067</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>5.252273</td>\n",
              "      <td>5.252273</td>\n",
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              "      <td>PAYMENT</td>\n",
              "      <td>9.365474</td>\n",
              "      <td>10.634990</td>\n",
              "      <td>10.305475</td>\n",
              "      <td>10.112316</td>\n",
              "      <td>10.499926</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 44
        }
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      ]
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    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "5ded8d19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5ded8d19",
        "outputId": "d89c5db0-1868-44b9-cdf1-57daf64e8992"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
              "mean   2.416872e+02  1.084252e+01   8.178673e+00    6.672674e+00   \n",
              "std    1.423320e+02  1.809876e+00   4.691891e+00    5.229820e+00   \n",
              "min   -7.100673e-01  2.302585e+00   2.302585e+00    2.302585e+00   \n",
              "25%    1.542899e+02  9.502978e+00   2.302585e+00    2.302585e+00   \n",
              "50%    2.372899e+02  1.122367e+01   9.562264e+00    2.302585e+00   \n",
              "75%    3.332899e+02  1.224880e+01   1.158362e+01    1.187943e+01   \n",
              "max    7.412899e+02  1.834213e+01   1.790292e+01    1.771920e+01   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
              "mean     1.205322e+01    1.275373e+01  1.290820e-03    2.514687e-06  \n",
              "std      3.762009e+00    2.795486e+00  3.590480e-02    1.585775e-03  \n",
              "min      2.302585e+00    2.302585e+00  0.000000e+00    0.000000e+00  \n",
              "25%      1.175325e+01    1.210902e+01  0.000000e+00    0.000000e+00  \n",
              "50%      1.313541e+01    1.329290e+01  0.000000e+00    0.000000e+00  \n",
              "75%      1.424303e+01    1.433338e+01  0.000000e+00    0.000000e+00  \n",
              "max      1.969049e+01    1.969094e+01  1.000000e+00    1.000000e+00  "
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              "      <th></th>\n",
              "      <th>step</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>6.362620e+06</td>\n",
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              "      <td>6.362620e+06</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.416872e+02</td>\n",
              "      <td>1.084252e+01</td>\n",
              "      <td>8.178673e+00</td>\n",
              "      <td>6.672674e+00</td>\n",
              "      <td>1.205322e+01</td>\n",
              "      <td>1.275373e+01</td>\n",
              "      <td>1.290820e-03</td>\n",
              "      <td>2.514687e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.423320e+02</td>\n",
              "      <td>1.809876e+00</td>\n",
              "      <td>4.691891e+00</td>\n",
              "      <td>5.229820e+00</td>\n",
              "      <td>3.762009e+00</td>\n",
              "      <td>2.795486e+00</td>\n",
              "      <td>3.590480e-02</td>\n",
              "      <td>1.585775e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-7.100673e-01</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.542899e+02</td>\n",
              "      <td>9.502978e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>1.175325e+01</td>\n",
              "      <td>1.210902e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.372899e+02</td>\n",
              "      <td>1.122367e+01</td>\n",
              "      <td>9.562264e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>1.313541e+01</td>\n",
              "      <td>1.329290e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.332899e+02</td>\n",
              "      <td>1.224880e+01</td>\n",
              "      <td>1.158362e+01</td>\n",
              "      <td>1.187943e+01</td>\n",
              "      <td>1.424303e+01</td>\n",
              "      <td>1.433338e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.412899e+02</td>\n",
              "      <td>1.834213e+01</td>\n",
              "      <td>1.790292e+01</td>\n",
              "      <td>1.771920e+01</td>\n",
              "      <td>1.969049e+01</td>\n",
              "      <td>1.969094e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6050165-ab17-4082-9c61-6641aec3ac4f')\"\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-d6050165-ab17-4082-9c61-6641aec3ac4f 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-d6050165-ab17-4082-9c61-6641aec3ac4f');\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": 45
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac3dd51d",
      "metadata": {
        "id": "ac3dd51d"
      },
      "source": [
        "Let's make df2 that ignores transaction types other than transfers and cash outs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "207092d1",
      "metadata": {
        "id": "207092d1"
      },
      "outputs": [],
      "source": [
        "df2 = df[(df['type'].isin(['CASH_OUT', 'TRANSFER']))].copy(deep=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "549b142b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "549b142b",
        "outputId": "79bee74e-2dd9-4693-97a9-9d3107fbc44c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2770409 entries, 2 to 6362619\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   step            float64\n",
            " 1   type            object \n",
            " 2   amount          float64\n",
            " 3   oldbalanceOrg   float64\n",
            " 4   newbalanceOrig  float64\n",
            " 5   oldbalanceDest  float64\n",
            " 6   newbalanceDest  float64\n",
            " 7   isFraud         int64  \n",
            " 8   isFlaggedFraud  int64  \n",
            "dtypes: float64(6), int64(2), object(1)\n",
            "memory usage: 211.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "8a731691",
      "metadata": {
        "id": "8a731691"
      },
      "outputs": [],
      "source": [
        "df2['step'] = df2['step'] - df2['step'].mean() / (df2['step'].std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "4124364d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4124364d",
        "outputId": "a1c4abd4-3610-4c55-ebb5-4e5dbae2e14e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
              "count  2.770409e+06  2.770409e+06   2.770409e+06    2.770409e+06   \n",
              "mean   2.386012e+02  1.192809e+01   6.367012e+00    3.169231e+00   \n",
              "std    1.416191e+02  1.231621e+00   4.114653e+00    2.662251e+00   \n",
              "min   -2.406858e+00  2.302585e+00   2.302585e+00    2.302585e+00   \n",
              "25%    1.515931e+02  1.132640e+01   2.302585e+00    2.302585e+00   \n",
              "50%    2.325931e+02  1.205100e+01   5.749266e+00    2.302585e+00   \n",
              "75%    3.285931e+02  1.263396e+01   1.034197e+01    2.302585e+00   \n",
              "max    7.395931e+02  1.834213e+01   1.790292e+01    1.771920e+01   \n",
              "\n",
              "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
              "count    2.770409e+06    2.770409e+06  2.770409e+06    2.770409e+06  \n",
              "mean     1.188522e+01    1.355135e+01  2.964544e-03    5.775321e-06  \n",
              "std      4.150795e+00    1.651896e+00  5.436687e-02    2.403183e-03  \n",
              "min      2.302585e+00    2.302585e+00  0.000000e+00    0.000000e+00  \n",
              "25%      1.176044e+01    1.269863e+01  0.000000e+00    0.000000e+00  \n",
              "50%      1.322802e+01    1.362685e+01  0.000000e+00    0.000000e+00  \n",
              "75%      1.436704e+01    1.456797e+01  0.000000e+00    0.000000e+00  \n",
              "max      1.969049e+01    1.969094e+01  1.000000e+00    1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-df223509-76bd-4dd9-86f9-f48d753307ca\">\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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>oldbalanceOrg</th>\n",
              "      <th>newbalanceOrig</th>\n",
              "      <th>oldbalanceDest</th>\n",
              "      <th>newbalanceDest</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "      <td>2.770409e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.386012e+02</td>\n",
              "      <td>1.192809e+01</td>\n",
              "      <td>6.367012e+00</td>\n",
              "      <td>3.169231e+00</td>\n",
              "      <td>1.188522e+01</td>\n",
              "      <td>1.355135e+01</td>\n",
              "      <td>2.964544e-03</td>\n",
              "      <td>5.775321e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.416191e+02</td>\n",
              "      <td>1.231621e+00</td>\n",
              "      <td>4.114653e+00</td>\n",
              "      <td>2.662251e+00</td>\n",
              "      <td>4.150795e+00</td>\n",
              "      <td>1.651896e+00</td>\n",
              "      <td>5.436687e-02</td>\n",
              "      <td>2.403183e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.406858e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.515931e+02</td>\n",
              "      <td>1.132640e+01</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>1.176044e+01</td>\n",
              "      <td>1.269863e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.325931e+02</td>\n",
              "      <td>1.205100e+01</td>\n",
              "      <td>5.749266e+00</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>1.322802e+01</td>\n",
              "      <td>1.362685e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.285931e+02</td>\n",
              "      <td>1.263396e+01</td>\n",
              "      <td>1.034197e+01</td>\n",
              "      <td>2.302585e+00</td>\n",
              "      <td>1.436704e+01</td>\n",
              "      <td>1.456797e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.395931e+02</td>\n",
              "      <td>1.834213e+01</td>\n",
              "      <td>1.790292e+01</td>\n",
              "      <td>1.771920e+01</td>\n",
              "      <td>1.969049e+01</td>\n",
              "      <td>1.969094e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df223509-76bd-4dd9-86f9-f48d753307ca')\"\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-df223509-76bd-4dd9-86f9-f48d753307ca 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-df223509-76bd-4dd9-86f9-f48d753307ca');\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": 49
        }
      ],
      "source": [
        "df2.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc71d66c",
      "metadata": {
        "id": "fc71d66c"
      },
      "source": [
        "# Data visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "f107308a",
      "metadata": {
        "id": "f107308a"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import norm"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1142e9dc",
      "metadata": {
        "id": "1142e9dc"
      },
      "source": [
        "> step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).\n",
        "\n",
        "Here we have normalized step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "ce00e3c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "ce00e3c1",
        "outputId": "12f1c233-c736-4aeb-a12d-28899612a1ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] =(16, 5)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot(df.step, fit=norm)\n",
        "plt.title('Frequency of transactions in each step (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.distplot(df2.step, fit=norm, color='y')\n",
        "plt.title('Frequency of transactions in each step (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "973f8749",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "973f8749",
        "outputId": "c03c8870-93eb-48d8-86cc-381cb3003ea8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x864 with 4 Axes>"
            ],
            "image/png": 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SlLIRo8fAbowmSvfoy92UK4zmWn0YzeSOY9zPlPSkHEx90kxOCCGEEEIIkZakZkgIIYQQQgiRlpL6nqGioiJdXV1tdRhCCJHWGhsbu7XWxbNPmX6knBJCCOvNVE4ldTJUXV3Nvn37rA5DCCHSmlLqap54nlaknBJCCOvNVE5JMzkhhBBCCCFEWpJkSAghhBBCCJGWJBkSQgghhBBCpCVJhoQQQgghhBBpSZIhIYQQQgghRFqSZEgIIYQQQgiRliQZEkIIIYQQQqQlSYaEEEIIIYQQaUmSISGEEEIIIURakmRICCGEEEIIkZYkGRJCCCGEEEKkJYfVAQghRLyEQiHOnj2L0+mkurra6nCEEEKIcSMjI1y4cGHCsKqqKrxer0URpSdJhoQQKWv//v0899xzAHzxi1+krKzM4oiEEEIIw7Zt29i3b9+EYevWrePee++1KKL0FNdmckqpP1VKHVVKHVFK/UwplaGUqlFK7VVKNSulnlBKucxp3eb7ZnN8dTxjE0KkvosXL46/vnTpkoWRCCGEEBNdvHiR7Pxibrj9v3DD7f+F7IJiLkpZlXBxS4aUUuXAl4H1WuvVgB24H/hn4Ota6zqgD/isOctngT5z+NfN6YQQ4m3r6uoiK68QpRTd3d1WhyOEEEIAoLWmq6uL7LxCcgqKySkoJju3kO6uLrTWVoeXVuLdgYID8CilHIAX6ADeDTxljv8xcI/5+kPme8zxdyqlVJzjE0KkKK01HR0dZOUW4M3Opb293eqQhBBCCAAGBwfx+/14c/LGh3lz8vD7/QwMDFgYWfqJWzKktW4D/gW4gJEE9QONgE9rHTInawXKzdflQIs5b8icvvDK5SqlHlJK7VNK7evq6opX+EKIJNfV1cXo6Cg5hYvJKVjE+QsXiEQiVoclhBBCjDfjzswpGB8WfS3NuhMrns3k8jFqe2qAMiATeN+1Lldr/V2t9Xqt9fri4uJrXZwQIkWdO3cOgNzCReQULsI/NiYFjBBCiAVhPBnKjUmGzNex97uK+ItnM7n3AGe11l1a6yDwNHALkGc2mwOoANrM121AJYA5PhfoiWN8QogUduz4cTK8WXiycsgvMXqRO3bsmMVRCSGEENDe3k6GNwunyz0+zOlyk+HNkmbdCRbPZOgCcLNSymve+3MncAx4GbjPnOYB4Dnz9a/M95jjd2i5g0wI8TaMjIxwurmZ4ooalFK4PZnkFi3m8OHDVocmhBBC0NLSSlZ+0aThWflFtLS0WhBR+ornPUN7MTpC2A8cNtf1XeAvgD9TSjVj3BP0iDnLI0ChOfzPgL+MV2xCiNR2+PBhIpEIxeW148OKy2vo6uqira1thjmFEEKI+BoaGsLn6yM7f/LtHjn5xfh8fQwNDVkQWXqKa29yWuu/1lqv0Fqv1lp/Smvt11qf0VrfpLWu01p/VGvtN6cdM9/XmePPxDM2IURqikQivPbaa2TnF5OVd7kPlkVVdTgcTl577TULoxNCCJHuzp8/Dxj3tF4pxxwWnUbEX7y71hZCiIQ6duwYvb29VNavIbZ3fofTxeKa5Rw+fJi+vj4LIxRCCJHOzp8/j81uJ3uKZnLZ+UXY7PbxToBE/EkyJIRIGeFwmJe2b8eTlUNR2ZJJ4yuWrgal2L59uwXRCSGEEHD69Gly8kuw2eyTxtlsdnIKSjhzRhpIJYokQ0KIlPHmm2/S1dlJ7eoNKDX59Ob2ZlJRt5oDBw7Q0tJiQYRCCCHS2dDQEBcvXiTP7OV0KnnFZVy8eFHuG0oQSYaEEClheHiYl156ifySMgpLJ9cKRVUtvx53hpfnN2+Wh7AKIYRIqNOnTwOQX1I+7TTRcc3NzQmJKd1JMiSESHpaa5555hkCgQBLr7t5wr1CV3I4XdSsuYm21lbpTEEIIURCnThxApc7g+z8wmmnyc4vxOX20NTUlMDI0pckQ0KIpHfgwAGOHz9OdcN6MnPyZ52+pKKWovJqXnzxRTo6OhIQoRBCiHQXDodpamqiYHHVlE25o5SyUbC4kqamJsLhcAIjTE+SDAkhklpPTw/PP/88uUWLqVi2ak7zKKWoX3sLDpebJ558kkAgEOcohRBCpLuzZ8/i9/spLK2cddrC0ir8fr90pJAAkgwJIZJWIBDgscceQ6NYsf7WGa+0XcnpzmD5utvo6uzk2WefRWsdx0iFEEKku0OHDmF3OMlfVDHrtPmLyrE7nBw+fDgBkaU3SYaEEElJa81zzz3HpUuXWLHhdjK82Ve9jIJF5VQ33Mhbb73F3r174xClEEIIAaFQiKNHj1JYWoXd7ph1ervdQWFpFUePHiUUCiUgwvQlyZAQIint2bOHgwcPsmTljRTM4SrbdKqWr6VgcSVbtmyRh9wJIYSIi5MnTzI2NkZJRe2c5ympqGVsbIyTJ0/GMTIhyZAQIumcOXOGLVu2UFhaxZIVa69pWUopVqy/jQxvFo8//jg+n2+eohRCCCEM+/fvx5XhuaqLdwWLKnBleNi/f38cIxOSDAkhkkpvby+PP/443uxcVqy/bcZutJvf2kPzW3tmXabT5abh5rvw+wP89KePEQwG5zNkIYQQaWxoaIimpiZKKutQtrn/9FY2GyWVdTQ1NckDWONIkiEhRNIIBAL89Kc/JRgKs+rmu3A4XTNOP9Tfw1B/z5yWnZmTx/INt9PR0c4zzzwjHSoIIYSYF42NjUQiERYvqb/qeRcvqScSidDY2BiHyARIMiSESBJaa55++mkuXbrEyg134MnKmfd1FJVWUb3S6FBh9+7d8758IYQQ6SUSibB375vkFZeSmZN31fNn5uSRV1zK3r1vEolE4hChkGRICJEU3njjDQ4fPkzNqvUULH77HSbMpmrFWgpLl7B161bOnj0bt/UIIYRIfU1NTfT3+yirWfm2l1FWu5L+fh8nTpyYx8hElCRDYlrt7e388pe/lPsnhOVaW1v5zW9+Q1HZEirrr4vruowOFW7Fk5nDE088yfDwcFzXJ4QQInW9+tprZHizKCpb8raXUVS6hAxvFq+9/vo8RiaiJBkS09q2bRv79++ntbXV6lBEGhsbG+PnP/85rgwv9Te+a8YOE+aLw+lixU23Mzw8xC9/+Uu5f0gIIcRVa21t5fy5c5QtbbiqjhOupGw2ypY2cP7cOflNFgeSDIlpDQwMANDZ2WlxJCKdbd68GZ/Px4oNt+N0uRO23uy8ImrX3ERTUxNvvvlmwtYrhBAiNezatQuH00Vp9fJrXlZp9XIcThe7du2ah8hELEmGxJRCodB4MiQPohRWaW5u5sCBA1TWX0du4aKEr7+stoH8kjK2bdtGf39/wtcvhBAiOXV2dnL06FHKahtm7fl0LhxOF2W1DRw9elQuUs8zSYbElJqbmxkbGyPT5eLEiRMEAgGrQxJpJhgM8uyzz+HNyrnmB6u+XUoplq29hWAoxObNmy2JQQghRPJ55ZVXsNsdVNStmrdlVtStwm538Morr8zbMoUkQ2Iax48fx+1w8P4V9QQCAc6cOWN1SCLN7N69m76+XurW3oLN7rAsDk9WDktW3MCxY8fkeyCEEGJWnZ2dHDp0iNLalTjdGfO2XKc7g9LalRw6dEhqh+aRJENiSp2dnZRkZVKeazzLpaury+KIRDoZGxtj165dFCyqIL+kzOpwKK9bhdvj5aXt26UzBSGEEDPavn07NruDqjj0flpVfx02u4Pt27fP+7LTlSRDYpJIJEJ3Vxf5Hg8ZTgcel1OuQIiE2r17N6Ojo1Q33Gh1KADY7Q4q66/n/LlznD592upwhBBCLFDt7e0cOXKE8qUN81orFOV0Z1C+dBVHjhyhvb193pefjiQZEpOcOXOGkdFRqvJzAViSl8fx48fleUMiIcLhMHv37iV/UQXZ+cVWhzOutHo5rgwPe/bssToUIYQQC9S2bdtwutxULlsTt3VULluN0+XmN7/5TdzWkU4kGRITBINBXn31VTwuJ8tLjB+ia8tLGR0dZe/evdJESMRdc3Mzg4OD89IV6Xyy2e2UVNZx4sQJhoaGrA5HCCHEAtPc3ExzczNVy9fiiOOjIBwuN1XL13L69Gmam5vjtp50IcmQGHfixAkefvhhmpubeUdlBQ7zAWFVeblU5OXy61//mkd//GN6enosjlSksv379+N0Z1BYWml1KJMsXlKP1pqDBw9aHYoQQogFJBKJsHXrVjK8WZTVroz7+spqV5KRmc3WrVuJRCJxX18qk2RI4PP5ePTRR/nJT36CLeDn42vX8I4ll3+IKqW4f+0a7qir5dzZszz88MO8+OKLhEIhC6MWqSgcDnPq1CkKS6uw2exWhzNJZk4eWbn5NDU1WR2KEEKIBWT//v1cunSJmtUbsNnjX37Z7HZqVq3n0qVL7N+/P+7rS2XW9VcrFownn3iC9rY2bl9aw/rKcuy2yTmy3WbjpqoKGhYV83LzWV555RUyMjJ417veZUHEIlW1tbXh9/vJLym3OpRp5RWXc/6scQ+d0+m0OhwhhBAW8/v9vPDCC+QUllBcXpOw9RaX19BWeJQXXniBNWvW4HbHr2leKpOaoTTX29vL+QsX2LikkncsqZwyEYqV5XbzwVUrKM3J5uCBAwmKUqSL6HN88out7057OnklZYTDYS5cuGB1KGIeKKX+VCl1VCl1RCn1M6VUhlKqRim1VynVrJR6QinlMqd1m++bzfHV1kYvhFgIXn75ZYaHh1m65maUUglbr1KKpWtuZnh4mJdffjlh6001kgyluRMnTgCggcgcO0cYDQZx2+1cvHSJvr6+OEYn0s3FixfxZGbHpTvS+ZKdXwQYsYrkppQqB74MrNdarwbswP3APwNf11rXAX3AZ81ZPgv0mcO/bk4nhEhj3d3dvP766yxesoycgsT3gJpTUMziJct4/fXX6e7uTvj6U4EkQ2lu5cqVLFmyhFfPnOPRfQdo9fVPO21Eaw60tfO9Pfs47+tn48aN5OTkJDBakeo6Ll7Em5NvdRgzcrk9uDI8kgylDgfgUUo5AC/QAbwbeMoc/2PgHvP1h8z3mOPvVIm8DCyEWFC01mzZsgWb3UHNqg2WxVGzagM2u4PNW7ZIr79vgyRDaS4/P58HH3yQj3/844wqG4/tf4ttTacmfZmGAwEe3XeAF5qaWVxRwZe+9CU2bdqEPQE3CYr0EIlE6Onuxpu9sJMhAG92njyIOAVorduAfwEuYCRB/UAj4NNaR3uIaQWiN7GVAy3mvCFz+sJExiyEWDhOnDjByZMnqVqxFleGx7I4XBkeqlas5dTJkxw/ftyyOJKVdKAgUEpx3XXXsWLFCl544QV2795NkdfLukqj/Ndas/lYEz2jY9x///2sXr06oW1iRXoYHBxEa02GN8vqUGbl9mTR3yc1Q8lOKZWPUdtTA/iAXwDvm4flPgQ8BFBVVXWtixNCLEDBYJDNmzeTmZNH+dJVVodD+dJVXDp/ii1btrBs2TLp4OcqSM2QGOdyubj77rtZvnw5L58+y6VB48GSe8+3cK63j02bNrFmzRpJhERc9PcbTTTdHq/FkczO7clkaGiIcDhsdSji2rwHOKu17tJaB4GngVuAPLPZHEAF0Ga+bgMqAczxucCkB69prb+rtV6vtV5fXJz4ewiEEPG3c+dOfD4fS6/biG2WzqcSwWazUXf9Rnw+Hzt37rQ6nKRi/d4TC4pSio985COEIxGaOrsAONrZRWVlJevXr7c4OpHKhoaM5NuVsfCTIVeGB601o6OjVocirs0F4GallNe89+dO4BjwMnCfOc0DwHPm61+Z7zHH79DSQF+ItNPT08OuXbsoqaglv2Th9H6aV1xKSeVSdu3aRU/PpOs0YhqSDIlJxsbGAMj1GD165WW48Y+NSY2QiKvocedwLfznJDjNGEdGRiyORFwLrfVejI4Q9gOHMcrE7wJ/AfyZUqoZ456gR8xZHgEKzeF/BvxlwoMWQlhKa83mzZtRykbtmpusDmeS2tU3oZSN559/XjpTmCO5Z0hMEu0uO89j3AyYm+Hh3MVLVoYk0kC0lsXpdFkcyewcTiMZkpqh5Ke1/mvgr68YfAaY9CtHaz0GfDQRcQkhFqbjx49z8uRJatfchNuTaXU4k7g9XpY03MipQ3s5fvw4DQ0NVoe04EnNkJgkL2zYiZAAACAASURBVC8PgP5R40r9wNjY+DAh4sXv9wNgdyz8mz7t5o2pwWDQ4kiEEEIkSiAQYPPmLWTm5C+IThOmU17bQGZOPps3byEQCFgdzoInyZCYpKCgAKfDQefQMACdw8OUlpZaHJVIdcFgEJvNhloAN6LOxmZ2KS+FjBBCpI+dO3fS3++jbu3C6DRhOspmo27tRvr7pTOFuVi4e1JYxmazsbi0lEuDg4wGg/SPjlFWtnBuEBSpKRQKYbMnR8tduxmn1AwJIUR66Onp4dVXX6Wkcil5RQv/AnFekdGZwquvviqdKcxCkiExpcrKSi4NDdPePwBARUWFxRGJVBcKhRb0lbZY0dor6VpbCCHSw9atW0HZqF29wepQ5qx29QZQNrZs3Wp1KAtacvzyEAmXnZ1NMBxmYMw//l6IeIpEIknRRA5AKUmGhBAiXZw6dYoTJ06wZMXaBdlpwnTcnkyWrFhL04kTnDx50upwFqzk+OUhEm5gYACXw0FBpnf8vRDxFIlExpOMhS5agxWJRCyORAghRDyFw2E2b96MNyuH8rqF22nCdMrrVuHNymHLli1yAW8ayfHLQyTc6Ogobocdt3mjuHQhLOItuZ6HYDxzK7liFkIIcbX27t1Ld3c3NWvegc1mtzqcq2az2alZ8w66u7vZu3ev1eEsSJIMiSkVFxczOOanzawRKi4utjgikeq01snzYF8zTEmGhLDWwMAATz755HjX/ELMp9HRUXbs2EF+SRmFiyutDudtK1xcSX5JGTt27JCL21OQZEhMKdqV9vFLXTjsdoqKiiyOSIgFxMyBkiZ5EyJFvf7667z11lscOnTI6lBECtq5cyejo6PUrr4pqc/3SilqV9/E6OiodLU9BUmGxJQyMjIAGAkEcLlc2O3JVzUskotSKulqWpK5cBQiFUSf9SX374n55vP5eOONN1hUVUdWXqHV4VyzrLxCFlXV8cYbb+Dz+awOZ0GRZEjMKJJcv01FEkumxCLZkjYhUp18J8V827FjB1prqhvWWR3KvKluWIfWmh07dlgdyoIiyZCY0oULFwCoLcxnZHSUrq4uiyMSqc5ms6F1klzdNX94JctzkYRIdcl0MUUsfD09Pezfv5/SmhVkeLPivr7mt/bQ/NaeuK8nw5tFac0K9u/fLw9ijSEluZhEa83+xkbKcnP4neoqFHDgwAGrwxIpzkiGkuPqbjRpk+ajQiwMyXLuEMnh5ZdfRtnsVNZfn5D1DfX3MNSfmOSkavn1KJudl19+OSHrSwaSDIlJRkZG6Ozqor64kCy3m/LcHM6ePWt1WCLF2Ww2dJK0+4+YyZDUDAmxMEjNkJgvfX19HDx4kNLq5bg9XqvDmXeuDC+l1cs5ePAgfX19VoezIMS1JFdK5SmlnlJKnVBKHVdKbVRKFSilXlRKnTL/55vTKqXUN5RSzUqpQ0qpG+MZm5je2NgYAJlOFwBel0u6YhRxZ7fbk+Ym6GjSJjVDQlgrWiOULOcOsfC9+uqroBSVy9ZYHUrcVC5bA0oZn1XEvWboYeA3WusVwPXAceAvge1a62XAdvM9wPuBZebfQ8C34xybmEZ7ezsAHpcTAK/LSX+/j+HhYSvDEinObrejI8nxdOyIGackQ0JYK5oMSTM5MR9GRkZobGxkUeVS3N5Mq8OJG7c3k0WVdTQ2NjIyMmJ1OJaLWzKklMoFbgUeAdBaB7TWPuBDwI/NyX4M3GO+/hDwqDbsAfKUUqXxik9MbWxsjC1btlCclUl1fh4AN5aXEQqG2LJli8XRiVQWrRlKhh81UjMkxMIQ7VpbmvuI+bBv3z5CoRDldautDiXuyutWEQqF2Ldvn9WhWC6eNUM1QBfwQ6XUAaXU95VSmcAirXWHOc1FYJH5uhxoiZm/1RwmEqS7u5vHHnuMocFB3r+iHrt5P0RxViYbl1Ty1ltvsW3bNnnSt4gLh8MBkBT3DUXCRs1QNGYhROIFg0GampoAOHr0qDSVE9ckEomwZ88e8opKycotsDqcuMvKLSCvuJQ9e/ak/XcnnsmQA7gR+LbW+gZgmMtN4gDQxiXgq7oMrJR6SCm1Tym1T7p7nh+jo6Ns3bqVbzz8MK3nz/Pe5XWU5mTz0snTvHTyNAA3V1exanEJu3bt4mtf+xqNjY1p/+UR8yuaWESSoKlcNEZJhoSwzsmTJ/H7/axevIj+/v7xR0II8XacPn2a/v5+SmtXWB1KwpTVrKS/v5/Tp09bHYql4pkMtQKtWuu95vunMJKjS9Hmb+b/TnN8G1AZM3+FOWwCrfV3tdbrtdbri4uL4xZ8ujh+/Dhf+9d/5fXXX2fVohIevHkDa8vLAOgcGqJzaAgAh83GpoYV/N66tWTbFE8//TTf+ta35CnGYt6MJ0PhJEiGpGZICMudP38eh83G7XU1AJIMiWvS2NiI0+WmqHSJ1aEkTGFpFU6Xm8bGRqtDsVTckiGt9UWgRSm13Bx0J3AM+BXwgDnsAeA58/WvgE+bvcrdDPTHNKcTcXLkyBGCgQC/v+FG3r+yniy3a8bpy3Nz+NS6tby7rpaOjg5aWlpmnF6IuUrGmiGn02lxJEKkr9bWVkqyMsl0ucj1eGhtbbU6JJGk/H4/x48fp7iiFlsa3Qtqs9sprljKsWPHxnsSTkfx7k3uj4HHlFKHgLXAV4F/Au5SSp0C3mO+B9gKnAGage8BfxTn2ASQn59PKBymMHPufekrpcjJyACgoCD129WKxIgmFpFwyOJIZhetGZIOFISwxujoKC0tLVTk5QJQmZvD6dOnCYUW/vlDLDxNTU2EQiGKK2qtDiXhiitqCIfDnDx50upQLBPXZEhrfdBs0nad1voerXWf1rpHa32n1nqZ1vo9Wutec1qttf6i1nqp1nqN1lq6t0iA8vJyNPDUoSMMzPGqwNnePrY3n8bldFJUVBTfAEXaSK5mcsYPLqkZEsIaBw4cIBKJsLzEKIOWlxQxNjbGsWPHLI5MJKOjR4/iyvCQW1hidSgJl1tYgivDw9GjR60OxTLy+PQ0t2LFCj70oQ/RMTjMD97cz+GOi9N2bRwIh3mh6RRPHjxMRlY2n/3c53C73QmOWKSqaGIRTqKaIUmGhEis3t5eHnvsMbZs2cKi7CxKs7MBqM7PJ8/j4YknnuAXv/gFAwMDFkcqkkU4HObUqVMULK5EqfT7WayUjYLFlZw6dYpwElyMjAe5+zfNKaW46aabqKur46mnnmLrcaOadE3p4knTbjnWxMmubm655Rbuuusu+SEo5lVS1QxJb3JCJFQ4HGb79u28/tprKODW2mo2VFaglALAYbfx+xtuYM/5Fn576BDHjh3jjjvu4J3vfCc2W/r9wBVz197ejt/vJ78kfZ/mkl9SzsVzJ2lvb6eysnL2GVKMnCEEYNz7c++99844jdaaoqIiPvCBD0giJObd+D1DSdCBQrT2SpIhIRLjtddeY+fOnSwvKuTBm9ezsboKh33iTxi3w8FtS2v47E3rqMrJZtu2bRw4cMCiiEWyiHYrnVdcanEk1ol+9nTtYluSITGut7cXgHyPZ8rx+V4Pvr4+eb6QiItk60DB4XCMX5UWQsTP6Ogou3btYmlhAZtWrSB7lubZ+V4P965poDQnm+3btxMMBhMUqUhGra2teLNzcbmn/u2TDlxuD97s3LTtkVGSITGuo8PoyXy6nuWKMr2EwmG6u7sTGZZIE5eToYVfMxQJh3BI7agQCXHixAnGxsbQaPpH59bRT8/IKDal6O/v59y5c/ENUCS11rY2svKs6wyq+a09DPl6GPL1cHDXFprf2mNJHFl5RbS2TXq8Z1qQZEiMa2lpId/rwTPNj7zSHONG1XS9ciDi6/I9Q8lRM+SUJnJCJMTq1au5/fbbudA/yPf27uPl5jOMTVPbM+QPsO3EKX7wZiPdY37e+973Ulubft0li7kZGRlhcGCArNxCy2IY6u8hHAoSDgXp777IUH+PJXFk5RUyODDAyMiIJeu3kpTmYlwgECAciRCKRHBMccNpIBQen06I+Xa5N7n5qRmKXm0DOLhrC1m5hdRdf/O8LDsSDsl9c0IkiNPp5K677uKmm27ixRdf5M0DBzh2qZNPrbuBnIzLTea6h0f4aeNBgpEIN998M3fccQeZmZkWRi4Wup4eo4zwZOdaHIn1PFnGNujp6cHrnfuzJ1OB1AyJcbfeeisDY34OtnVMGqe1ZueZc2R6vdxwww0WRCdS3XzXDMXzals4HJZmckIkWG5uLvfddx9f+MIXCEQ0zx09Tti8h9UfCvHMkWM4XC6+/OUvs2nTJkmExKz6+voA8GRmWxyJ9aLbIHr/eDqRZEiMKy0tJcPt5nDHpUnjBv1+LvT5KK+okCviIi7Gk6Ek6E0uEpFmckJYpaKigns/8hHa+wfYe74FgFfPnKNvZJSP338/xcXFFkcokkV/fz8Abo8kztFtEN0m6USSIQHA4OAg3//e9wiFgtxeVzNpfLbbzU1VFZw8eZJnn31WepQT885ms2G325OkA4WwdKsthIVWrVqFy+ViOGDcOzQUCJCXm8vSpUstjkwkk5GREZTNht0hF3ntDifKZmN0dNTqUBJOSnNBMBjkB488Ql9vLx+9bjVV+XmTplFKcfvSGhw2G280NuJ2u7n77rstiFakMrvDkRQ1QzoSwumUK4lCWMXn8xEIBCjOMr6HxZmZNHWex+/3456l620hokZHR3E63fKYBIzfeU6nOy2TIakZErz66qt0dnVxz+qVUyZCUUop3lVbzdryUnbv3k1bmnbBKOLHkSw1Q5GI1AwJsQBorY3/gIp5L8RchEIhbHa71WEsGDa7nVBo4ffoOt8kGUpzPp/PeKp3SRG1hQVzmue22ho8Tiebn38+ztGJdGN3OJIiGdIRaSYnhJXy8/PJysqktX8AgFZfP4sWLyYjI8PiyEQy0VpLrVAMpVRaXlCQZCjNBYNBFHBxcIju4eE5zdPc3cNYMEggGEjLL42IH4fdgdYL/360SCSCXa4mCmEZpRRVVUvoGBxCa83FwSEqKyutDksIkYQkGUpzxcXFfO7BBwnbHfy08S3O9/bNOP2rZ86x5XgT1TU1fO5zD8oVFTGv7HZ7ktwzJMmQEFZbtGgRvpER+sfG8IdCLF682OqQRJJxuVyEk+BB34kSDodwuVxWh5FwkgwJKioq+MM//ENCkQivnT0/7XSjwSBvnLtARUUFDzzwAB6PJ4FRinRgt9vQSdBTYSQSlmRICIvl5eWhgfb+wfH3QlwNt9tNKCgPko8KBQNp2QGJJEMCgFOnThGORFhfWT7tNB6nk1WLS+jo6GBgYCCB0Yl0YbPZk6LppdYam01On0JYKdrrVUGmZ8J7IebK4/EQCYcJh4JWh2K5cChEJBxOywvdUpoLBgcHeenFF6nMy6W+uGjGaW9bWoMNeP5Xv0qKH60iuSRLzZCORCQZEsJiIyMjKCDL5R5/L8TVyMnJAcA/KseOf9S4bzy6TdKJlOZpLhKJ8MunniIQ8PPe5ctmvQco2+3mttpqTp46xd69exMUpUgXNpstKZJsrSUZEsJqixYtQgNHL14afy/E1cjNzQUuJwLpLLoNotsknUhpnuYOHjzIqeZm3lmzhKJM75zmubGijMq8XH7961/T398f5whFOkmWbj2lmZwQ1quqqgJgX6vxzDvpTU5crYIC45Eio0PyWya6DfLz8y2OJPGkNE9zWVlZOOx29rW009Lnm3V6rTVvXmiltX+A7OxsedaKmFc2mw2SoGtt5NkUQlgueuHEbZZDyXAhRSwsubm5OJ1ORgZn//2T6kYGfTidzrTsiESSoTRXX1/P57/wBdxZWfzswCH2nm+Zdlp/KMQvDx3lldNnaWho4Etf+hKZmZkJjFakOqUUyfBzRmskGRLCYr29vQAsMX+8Rd8LMVdKKYpLShgemPmxIulgeMBHcXFxWpZtkgwJSktL+eIXv8iq1at55fRZjnRcmjSN1pqtx09yprePTZs28YlPfEKe9C3SWDKkbEKktqamJuw2G9eXG88XOnnypMURiWRUXlbGkK8nrWsWtdYM+bopL5++R+FUJsmQAIy+9j/2sY9RU1PDtqZTXBocmjD+zQutnOzq5n3vex8bN25MyysHIv6UUka1SxKQ74AQ1gmHwxw6dIi6wgJKsrKoyM3h4IEDVoclklB5eTmhYIDRofR9ZMjo0AChYECSISHsdjv3338/doeDPTHN5bTWvHb2PHV1ddxyyy0WRiiEEPNLKZWnlHpKKXVCKXVcKbVRKVWglHpRKXXK/J9vTquUUt9QSjUrpQ4ppW60Ov50NTg4yPDwMNUFxs3e1QX5dHV3EwqFLI5MJJtoxxsDvZ0WR2Kd6GdP105IJBkSE2it8QcCE3qWU0pRlJmJ3++Xq+FCiFTzMPAbrfUK4HrgOPCXwHat9TJgu/ke4P3AMvPvIeDbiQ9XAIyNjQGXO0/IMP9HhwsxVyUlJXg8Hvq7O6wOxTL93R14PB5KSkqsDsUSkgyJCU6dOgXAsqLCCcPrigpoaWmRh9qJuNJGzwRWhzEHydEFuJiZUioXuBV4BEBrHdBa+4APAT82J/sxcI/5+kPAo9qwB8hTSpUmOOy019fXxy+efBKA5u5uXjp5evwC3uOPPcbQ0NBMswsxgc1mo6amBl/3RatDsUx/90Wqq6vT9pER6fmpxbSCwSAAXpdrwvBMlxNAmiCIuEuKVCgZghRzUQN0AT9USh1QSn1fKZUJLNJaRy8TXwSiT/MsB2K73Gw1h4kEOXPmDN/61jfp6+nho9evZtAfoHNoiCUF+XywYQVtra1865vfpK2tzepQRRKpra1lbHjQkvuGQsEALpeLjRs34nK5CAUDCV3/6NAAo8ODLF26NKHrXUgkGRITuN1uwOhGO5Y/FJ4wXoh4iEQiyZFpKGXEKpKdA7gR+LbW+gZgmMtN4gDQRhXgVVUDKqUeUkrtU0rt6+rqmrdg01kgEGDLli088sgjZKD49Pq11BYWTJimYXEJn7zxeiIBP9/5znfYvn27XMATc1JfXw9A76XWhK87FAywbt06Nm3axLp16xKeDEU/c3QbpCN5YqaYoLi4GIALvokPIDvf5yMnOxvXFTVGQsynSCSCUgv/Go1SNmkmlxpagVat9V7z/VMYydAlpVSp1rrDbAYXvbO6DYi9w7jCHDaB1vq7wHcB1q9fLwfKNTp9+jTPPPMMfX193Fhexq1Lq8fvFbrS4pxsPrPhRrafPM2OHTs4euQI937kI1RUVCQ4apFMCgsLyc8voPdiC+VLGxK6bofTRWNjIwCNjY04PVkJXX/vpVby8/MpLCycfeIUtfB/dYiEKisrY/HixRxsv9x2tn90jLO9faxbv146UBBxZSRDC/8YUzZFOBy2OgxxjbTWF4EWpdRyc9CdwDHgV8AD5rAHgOfM178CPm32Kncz0B/TnE7EwfDwMD/84Q8Z6O/nEzdcx13L66ZNhKI8TiebVq3gw2sauNTZySPf/77UEIlZrVy5Al9XB+FQMKHrdThdBAIBdu/eTSAQwOFM3EXncCiIr6udlStXJmydC5EkQ2ICpRTLly+nc3Bo/Mp3W7/Rhnb58uUzzSrENQuHI6gkuIHTpmySDKWOPwYeU0odAtYCXwX+CbhLKXUKeI/5HmArcAZoBr4H/FHiw00vXq+XjRs3Eo5EaGxtJzjH750/FGJ/azsAt99xB45ZEighVqxYQSQSpq8zfe436+tsIxIOs2LFCqtDsZScHcQkHR0dFGZ6x6/QL87JGh+ern3Qi8QIhYLYkqApps1mlyvNKUJrfRBYP8WoO6eYVgNfjHtQYpxSirvvvpu8vDy2bt3Kk28F+ej1q3HZ7dPOMxoM8vMDh+keGeG+++7jhhtuSGDEIllVV1eTkZFBd/t5isqqrQ4nIbrbz+N2u6murrY6FEst/EuwIuFaWi5QnpMz/j7f48HrctHS0jLDXEJcu1AohM02/Y+chcJml2RIiES65ZZb+PjHP05b/wBPHzo6bQ3RWDDEEwcP0zs6yqc//WlJhMSc2e12Vq5cSU/HBSKR1K/5j0TC9HRcoKGhAfsMFxfSgSRDYhKX00VYX+4pSylFOBKRnuRE3AUCAez2hV9hbbM7CAQS2+OPEOnuuuuu47777uN8n48tx09OOc3Th4/SPTLK737ykyxbtizBEYpkt2rVKkLBAL6u1L8V0NfVQSgYYNWqVVaHYjlJhsQkefn5+EYvP8V7NBjEHwqRm5trYVQiHQQCAexOp9VhzMrmcOL3+60OQ4i0U19fj9vtnrZr+7DWeL1eamtrExyZSAV1dXW4XC66Ws9aHUrcdbWexeVyUVdXZ3UolpNkSExSW1tLW//AeDOEt8ye5WpqaqwMS6S4SCRCMBjE7lj4yZDd4cTvl5ohIRJt586dBPx+bl1aPeX422qrGRwcZPfu3YkNTKQEp9NpNpU7n9LPkotEIvR0nGflypU4k+ACZLxJMiQm2bhxIy6nk4ExP1prftvSxrK6OnlOg4irsTGjNtLhXPjNMZ1OF6Ojo1aHIUTa6evrw+tyUeD1Tjl+cU42DpuNvr6+BEcmUsWaNWsIBvwp3aucr7OdYMDP6tWrrQ5lQZBkSEySmZnJ8hUr8IdCBCMRRgIBbrjxRqvDEiluZGQEAKdr4SdDDlcGI6MjVochRNq57rrrGA4EONc7dbLT1NlFKBLh+uuvT3BkIlUsW7YMt9ud0k3lutrO4Ha7qa+vtzqUBUGSITGl0dFR7DYbdrN7bbkKLuJteHgYAKcrw+JIZud0ufGPjcmzhoRIsGinCB0Dg1OOvzgwhNPpZMmSJYkMS6QQh8NxualcCp7jI5Ew3e1GEzl5/pZBkiExpeGhIWxKYTcfgDk0NGRxRCLVRY8xV4bH4khmF40xmsAJIRJjYMB4CHhuxtQXTXIy3ASDwfFmt0K8Hddddx2hYIDeFGwq13epjVAwwJo1a6wOZcGQZEhMSWuNsjoIkVaSMRkaHJz66rQQYv75fD6effZZAEqyMqecJjr8F7/4hVysEG/b0qVLjQewtqVeU7mutrNkZGRIL3IxJBkSU1I2Gzr2vZLUSMTXwMAAKJUUzeRcGcbN25IMCZEYR44c4d///d9pa2nhAyvrKcnOmnK66oJ83r2sluZTp/jGN75Bc3NzgiMVqcDhcLBq1Sp62s8TCafOA7YjYeNBq6tWrZImcjEkGRJT8nq9RLQmbHYtmZk59VU4IebLwMAA7gwvyrbwT0vuDOP7EG2yI4SIn/b2dn72s5/htdn4zIYbWVO6eNpplVJsqKzg0+vWooJBfvTDH9Lf35/AaEWqWL16NaFQkN7OdqtDmTe9nUYTOelFbqKF/6tDWCIvL49QJDKeDMkDV0W89ff3j9e4LHSuDA8oJT+yhEiA4uJiysvKGPL7Cc3x2S+BcJjhQICldXVkZ2fHOUKRilKxqVy32URu6dKlVoeyoEgyJCbRWtPR3o5NKWzKOEQ6OjosjkqkOp+vH7c3OWoglc1GRoZXkiEhEsDpdPJ7n/oUbq+Xpw8fI6L1jNMHQmGeOXKM/IIC7r//fmxJUNssFh673U5DQwM9HReIRJK/V7lIxGgi19DQgN1utzqcBUXOEGKSs2fP0t7RQbbbjcNuY2lhAXv27CEYDFodmkhRWmsGBvpxe5IjGQJweTIlGRIiQXJycrj77rvxjY7S0uebcdrm7h5GAkE+/OEP4/Es/A5ZxMLV0NBAKBjA15X8F4R9XR2EggEaGhqsDmXBkWRITPLb3/6WDKeTTJcTgPWV5QwPD3PixAmLIxOpanR0lGAwmFTJkNuTic8nyZAQiVJXV4dSiqau7hmna+rqxu12U1VVlaDIRKqqq6vD6XTS3X7e6lCuWXf7eZxOp/QiNwVJhsQEoVCIphMnqC8qHO9Brio/D6/LxZEjRyyOTqSqaK9syZQMuTxeBgcH0LM02RFCXLv+/n5+9KMfobWmMHPmewuLMr34/X4effRRRkZGEhShSEVOp5P6+np6Oy4k9blea01vxwXq6+txOp1Wh7PgSDIkxrW3t/PTn/wEfyDA8pKi8eE2pagvLuTYsWP8+te/lmc3iHkX7ZXNnSQdKIDRo1wwGMTv91sdihAp7dKlS3zrm9/kUkcH96xeybqK8hmnf1dtNe9bsYyzZ87wrW9+E59v5mZ1Qsxk5cqV+MdGGPL1WB3K2zbk68E/NsLKlSutDmVBkk7GBd3d3bz00kscPnyYDKeTO+pqqSnIZ8/5lvFpbq2tJhSO8Prrr/HbN9/kne96F7fccgtut9vCyEWqiNYMJUtvcjDxwasZGQv/2UhCJKuuri6GhodZUVJMfXHR7DMAaxYv5vilLs73+ejr6yMvLy/OUYpUVV9fD0BPxwWy8+d2/C00PRcvAJc/i5hIkqEUFwgEGBoaYnBwkKGhofG/2Pdtra3YbTY2LqnkpqpKMpyTDwuP08ndDct5x5IKXj1zju3bt/PGG6+zaNFisrOzycrKGv9/5Z/0WiJmE23K4nQnT1IRjXV4eJji4mKLoxEida1evZrbbruNnTt3ku12cUdd7YwPAo9oza9PNHG+z8fdd99NTU1NAqMVqSYzM5PKykp6L7VS3XCj1eG8Lb0XW6moqJBnRk4j7smQUsoO7APatNablFI1wM+BQqAR+JTWOqCUcgOPAuuAHuDjWutz8Y4vGQWDwQmJzZXJTezrQCAw5TK8Lhdel5NMp5N1FeXcVFVBlts167qLMjP58JpVtA8M0NjSzkBfL62dnQwHAgRCUz+l2evxGIlRdvaEhOnK116vV7pATVPDw8PYbDbsjuRpyxybDAkh4uuuu+7C7/ezZ88eBv0B3r+yHtcUF9rGgkGeO3qCc7193HnnnfzO7/yOBdGKVLNs2TJ27NhB0D+WVBftAIL+MQb7uthww7utDmXBSkTN0J8Ax4Ec8/0/A1/XWv9cKfUd4LPAt83/fVrrOqXUi2jZVQAAIABJREFU/eZ0H09AfJbTWo/X4AwPD0/5f3BwkCEzyRmb5h6FaA9wmU4ni1wuahcVk+l0kul24XW5yHK5yHQ58Tpd2GzTX1Wbi7KcHMpW5UwYFjQfcjccCDIcCDASCDAUCDISCDAcCDDU001vRwfDgQDB8OQ++5VSZHq944lTNEnKzMwkKyuLzMzMCa8dDqnYTBVjY2M4nK4Zr/YuNA6ncfFA7hkSIv6UUmzatInc3Fy2bdtG78go966Z2EVw9/Awvzx0jEG/n3vuuYcNGzZYFK1INdFkqK+zjZLK5HpgaV9XO2B8BjG1uP6aVEpVAHcD/wD8mTJ+6bwb+F1zkh8Df4ORDH3IfA3wFPAfSimlk7T7jlAoNCGhmfK1WXszPDJCaJpaFbfDQaaZxBS6nCwpLsRrvjeGmwmOy4XD4loVp91OnsdD3hye6xAIRROnwIQEavx15yU621oZ9gcIT/PE8Qy3G29McjTTf4/HI7VOC1ggEEiqWiEAu92Id7raVyHE/FJKceutt7Jo0SIef/xxXjt7bsL4HafOMKY1n/3c51iyZIk1QYqUVFFRgdvtxtfVkXTJkK+zHbfbTXn5zB2PpLN4X1r/N+DPgWzzfSHg01pHf/m3AtG9Uw60AGitQ0qpfnP6mR8oYIHOzk4uXrw4KcmJJjjDw8PT1t7YbTYyXS68Tidel4N8rwdvXo7ZZM1ouuY1a2+8LqflCU68uBx2XA4P+d6ZEyetNYFwmJFAkJFgtJbp8uuRQJCRwQG6ens4HwgyGggwVfaslBpvrhdNoK6sbaqursbrTZ4b+FNJMBjElmT3ltnNmklJhoRIrGXLlqGUwu1wApfLWrfDgScjQxIhMe9sNhs1NTWcb223OpSr5uvuoKamRu7fnkHckiGl1CagU2vdqJS6fR6X+xDwEGDZA9W+853vTNs0xmW3U5abQ1npIrLd7vHkJprouOz2pGoKZDWjwHPgdjjIZ/Yap4jWjAWjyVLQTJwC+Eb/H3tvHuRIet5nPokjkZlI3AWg7q6uPtlzcrrn4r0mR6TJsSgpRIq2JVk213Ts0CH6kCVqHbvh2PCG7QjHrhm7G15TwZBEiiZpW/KKulaixSUVooZDTs/V0/dR1UedKNxnJoDM/SMT6Lq6u6qnqgBUfU9ERiYSWcBX1Q18+fve9/29DeYKJZYyGazl5U1/9sknn+RTn/rUTv8Kgi1gWRaSNFjCv/M5HtDgtUAwsBSLRZrNJkNBjeVKpXs+GdS4tHwT0zSR5QfXwAoE22F6eppLly5h1KoEtMEwIjDqVeqVEtPT7+v1UPqaLYkhSZKmgS8BzwMW8DLwj23bvnGfH3sv8JOSJH0cUHBqhr4ERCVJ8rnRoXFgzr1+DpgA7kiS5AMiOEYKa7Bt+8vAlwHOnDnTk7uQl156idu3b5PNZp1tZYVsNkvDMDDbbWZzeW7mC0QUhZiqENNUYqra3UeUAN59GvHpJWarTb5ed7ZanVytTqFeJ99oUDXWrt6HQyHiiQRDQ0MkEgkSiQTT09M9GrlAIHinPOQ8JRhAOn2DIuraQvbO42KxKBweBTtOJ+JYzC2R0gbjfqGYXQIQ0dIHsNXI0H8E/i/gp93HnwG+ATx7rx+wbfvXgV8HcCNDv2Lb9t+WJOk/Az+L4yj3d4Dfd3/k2+7jl93nv9uv9UJDQ0MMDa31mrdtm1qt1hVIK65AymaznM+sYKy6GfdIEkPBIOlQkOFQiHRIJ6kHN3XGEWxO1TRZLFdY6myVCsV6Y801IV0nMTTESVf0xOPx7l6sGvYXkiQNXISlM14R6e0btj1PCQaTTl8yzb+2zlB1H5fLZSGGBDvOyMgIfr+fUnaJ1PhgiKFSdgm/38/IyEivh9LXbFUMabZtf23V49+RJOmfPeR7/hrwTUmS/iXwOvAV9/xXgK9JknQNyOFMZAODJEnd+pP16Xu2bVOtVrviKJPJsLCwwI25Oc4tOKpdAuLBIMN6kHRIZzikkwrpBA64Y5pt25QN0xU9ZZYqFZYqVcqNu2mKiXicyaPHGB4e7kZ64vG4aAg7QPj9fqz25iYi/UrbHa/fP1jGD/uYnZynBH1M58bucmZtSfGVzAo+r1cIIcGu4PV6GR8fZyW3eap9P1LKZRgfHxf1Qg9gq3fafyJJ0hdxojk2juX1H0uSFAewbTt3vx+2bft7wPfc4xvAM5tc0wD2ZcGGJEndgv3VoUrbtimVSszPzzM/P8+tW7e4dOMG55fuftBimsq7UknePz3Vg5H3jkazxZ9cusydYpnaqgJ1TdM4duIkY2NjjI6OMjIygqIMlue/YCOOGNpot97PWEIM9RvvaJ4SDA7JZJJTp07x+tUrDGkakiRRNU3eXlzm9JkzhEKhB7+IQPAQjI+PM3vzB1jtdt+b/ljtNpVilicfPdnrofQ9WxVDn3b3/2Dd+c/gTDqDES/sMYZhUCgUNmz5fJ5CPk+lUtnghNZsW9SbzZ6Mt5dYtkXVbGKtS52q1Wq8/fbb3L59m2g0SiwWIxqNrtkikYhYBRkwFEWh2RwsV7aW+7kUEci+QcxTB4jx8XEuXLiAjZNZ0Wi2aFsW4+PjvR6aYB8zPj6ObVlUijnC8f6OQFaKOWzxmdgSWxJDtm0f3u2B7CdM0+Ts2bPkcjlH6BQKFPJ56o21NS0eSSKsKIQDMof0IJGhOGFFIaIEnPMH2GhBk2V+/vSTABitFqWGQanRoLh6v5Lh8twclXXOfpIkEdJ1orFYVyyNj49z6tSpzd5K0AdomobVbtFutbqW1f1O03Q+z8HgYLgK7XfEPHWwWFhYIKwqeNyavZim4vd6WVhY6PHIBPuZ0dFRACrF7ACIIceDrDNmwb3ZqpucBvwTYNK27c9JknQMOGHb9h/u6ugGlPn5ef7wD+/+aSQJTiSTpEeHHZGjBogoCrosi+LrLRDw+UjqPpL65jedLcui3DAoNhqU3P1MLs+tW7e4desW4Nxsnzx5UjRe7VM6/Z2aZgOvT+/xaLZGy3REuOhN1R+IeepgUSqViAYC3WwKZ3ExQKlU6um4BPubaDRKIBCgWuz/rNtqMUcgECAajfZ6KH3PVpdgfxM4C7zHfTwH/GdATDKbMDU1xS//8i/z1ltv8dZbb5HL5biSWcFotXhXWiauamiyqDPYKXweD1FVwbJtFkplrq5kyVSqgPNv8fjjj/Poo48KIdTHdKIrTaOOog2GGDIbdQB0fTDGewAQ89QBwufz0bDtbmQIoG3b+AYksiwYTDweD8PDwxQGRAyl02lx77MFtvqtccS27Z+TJOlvAti2XZNESOO+pNNpXnjhBT7ykY8wPz/PuXPnOHfuHH988QoAwYBMQlUZ0oMMaRqJoMZQMChE0gOwbZtio8FKtUa2WmOlWmOl5hw33QL8yYkJPvHBD/Hoo48SDod7PGLBVugUPBuNOjtV+txqOo0XT58+zdmzZ2ntcE2S2aghSZKIDPUPYp46QOi6ztytWyQ0FY8kUTYMyg1DLE4Idp1UKsX8wlvYtt3X2T21SpHj04/1ehgDwVbFkClJkopThIokSUcA4/4/IgCnfmVsbIyxsTE++tGPcufOHW7evMny8jJLS0ucX17GWO2WJssMaWpXHA0FHaEUPGB9cWzbplBvkK3VWKlUXcFTJ1u7K3oAQiGdVCrN9KNpUqkUx44dEyHhAaQjhsxGbcdes9U0OX36NC+++CIAr715bsdeG5zIUFDXxapb/yDmqQPEe9/7Xt58803KDQ8RVeEvb9wESeL555/v9dAE+5xkMknTNGgaDWRF7fVwNsU06jSNhrCZ3yJbFUP/Avh/gQlJkr4OvBf4u7s1qP2KJElMTEwwMTHRPWfbNsVikUwmw+3bt7l27Rq3b9/mVqG45mdVv5+PnTzG8eTQ+pfdd3zn8jXeWlikZVlrziuKwvGTJzly5AjDw8OkUilUtT+/iATbYzfEkM8vc/bsWQDOnj2LX93ZFWOjUSMcEpHHPuJfIOapA8PY2BinTp3i4sWLhJUA5xYWefqZZ4jFYr0emmCfk0qlAKhVCn0rhupl5x5SiKGtsVU3uT+TJOks8ByOi+UXbNteecCPCVZhGAalUolisdjduo8LBUql0ga3OXAiRaGAs+kHJDqUCGqMR8KUTZNSw+hGghqNBufPn+fC+fPouk4kEiESjRIOh53jSKR7HA6Hhb32AOH1etGCQcz6zoqhatHk5ZdfBkAN7+znp9mokR5J7ehrCh4eMU8dPFRVRcJZaJQkSSyOCfaEeDwOQKNShqGRHo9mc+pVx0gkkUj0eCSDwVbd5P7ctu0PA3+0yTnBOvL5PN///vcpFAqUXOHTMDZma9wVOgHGEnHnWAkQCtzdfN6Dl4Lz1PgoT407VpC2bWO225QahpMTbhh3j+s1Fgt5rjYMzHUNOyWcovyI23doeHiYD37wg6K4to8Jh0IYOxgZ2m3MRk00d+wjxDx18KhWq3g9Ts2GJstUKpUej0hwEIhGo3g8nq7g6EfqlRKSJImygS1y3ztDSZIUQAOGJEmK4dxjAoSBsV0e28Dyh3/4h1y9coVkMEg4IDOWTLjiRiYcCBBSAujywRQ620WSpAdaa8PdXkQdwVRuGJQNk3K9xlIhz4ULF4hEIpw5c2YPRy/YDqFQiMWVQq+HsSUsy8I0GkIM9QFinjq4WJaF5P5zeyQJ217ftlwg2Hm8Xi/hcIRGrX/Fd6NWEQ3ot8GDlsn/AfCPgFEcy1IJpzi1DPwfuzu0waRYLHL58mUeHU7x4WNHCIhIxJ5wL8Fk2za1ZpPf/vHr/PhHPxJiqI/RdZ3mnfleD2NLNA1hq91HiHnqgCJJErbbachGCCHB3hGLRckWy70exj1pVMvERVRoy9z3Tt227S8BX5Ik6X8G/p1t2yVJkv4n4Cng5b0Y4KBx584dbNvm3MIS5xaWCPh8hAIB9MDdlLiQGx0Ku9GigM/X1/aM/Ypl21RN040EmXejQp3IkNmkYhi0XSOG6vw8zWYTv1/Yl/cjwWAQ09xYN9ePNN2Gq53+SILeIeapg4uiOP3lAOrNpqgZEuwZ0WiU+cXlXg/jnpj1KrFDIjC+VbYatvhZ27b/F0mS3gf8NeDfAv8eeHbXRjagnDp1is997nMUCoW1JgnFIrPFIpWFpQ3rV36vt5tGt14wdY5V/8ESTJZlUzHdVLdu2tuqx+5+fVqEE74OE4nFmVpnqJBOp4UQ6mM0TcNqt2m3W3i9/R1RbbliSPQY6ivEPHXACIfDtC0by7ZptS3RV06wZ4TDYcxGDdu2kKT+KnmwbctxOxWfhy2z1TuOTnX6J4DfsG37jyRJ+pe7NKaBRpIkDh06xKFDhzZ9vt1uUy6X1wil1YLpyuIiZrO54ecOx2N8+sm9a571365cZ7ns5MP+x9feJKXrfOT4kT1576pp8hs/fBWj1drwXEjXGUomObLKRW61m5ymaQdKNO4nFEUBHKHhVftbDHUiQ50xC/oCMU8dIGzbZmZmBq/kVA0pfh8zMzO8733v6/XQBAeAcDjsGDw1GgTU/loUMxsNbNsWYmgbbPWOY06SpP8AvAD8G0mSAkB/SeEBoRO5sG2bhmul3Wg03F5Dy2uEUMDnI66pDAU1jg3tbX+h5UoFw3Vou72u59Fuo/h8PH9ogoVSmWytTq5W66ZClCsVPB4PXq/XsVV17VSj0ahYpR9wAoEAAO32RhHcb1ht53PaGbOgLxDz1AHi2rVr3Llzh5g7D5wZH+MvL11ibm6OsTGRHiTYXTpCw2zU+lAMOa6sQgxtna2KoU8DHwP+rW3bBUmSRoB/tnvD2l/k83lmZ2eZnZ1lfm6OzMoKzVWiR5NlEprKyUScIU0jEXQ2XZYPZJTD6/Hw7KG7jWnblkWh3iBbq5GtOtvK0iI3Z2a6PYgAgppGMpVicnKSqakpDh06JFbuB4hOCqO1SUSw32i7/++EVXtfIeapA8Qrr7xCMCATlJ3vjTMTY7xy6w4//vGPhRgS7Dod8xzTNdPpJ0xh8LNtttp0tQb83qrHC8DCbg1qkLFtm2w2y8zMDLOzs8zMzFAsOpEVxe9jWNd5YjhFQtMYckWPKupY7ovX4+kKRFY1U7Ztm1LDYGWVSMrksvzlzZv8xV/8BZIkMTI8zNThw0xNTTE1NSUK3vuYjrCwrPYDruw9liuGRA1a/yDmqYNDqVTi6pUrPDGSZrlSBZxMiiOJOBfOn+ejH/2oMFMQ7CododFs9J8Y6oxJiKGtI5Y1d4hz587x9ttvMzs7Q8X9ctZkmYlImKePHWEiGmFID+I5gJGe3UKSJCKqQkRVOJKId883223miyVuF4rcLpb40Q9/yF/91V8BkEommTp8mGeeeYaRkf7sHH1QGawoqJO2OVhjFggGF8MwuHjxIm++8QbXrl3DBh4ZTrN87Ub3msdG0lx6823+1b/6V5w4cYInnniCEydOiEULwY7TjQz1oQNqZ0xi8XfrCDG0A9i2zR/8wR9QrVY3PFdsNLDyNvl6fYOtth6Q8XpESvs7xWy1KRkG5UaDsmG6x477XM1srhGgy5kMy5kM7Xabn/mZn+nhqAUCgUCwGbZtYxgGtVqNxcVF3nrrLS5dvEiz1SKsKDwzOc6pdGpDX7npRJxfPPNuLiwtc/H6NS5cuEBAljn1yCM8/vjjJJNJNE1DPqAp6IKdw+/34/P5aBr9J4aaRgOv14ssy70eysAgxNAOIEkSv/Irv9K10+5sXZe4QoFbmRUMw9zws0HXUju8SQ+ioWAQxS/+iYr1Brl6fZW9tkGpYVA2TcoNY1PXOT0YJBKJkEwPc9R1muts4XCYqGhG1ndYbj8o6P+blM6N1N0xCwYZSZK8wKvAnG3bL0qSdBj4JpDAaeT6C7Ztm64pw1eB00AW+Dnbtmd7NOyBoN1u02g0qNVqVKtVarUatVqNer3ePV5zvlajVq+v+Wypsp/H0ineNZxkLBy+r5AZCYcYCYf4745Ocytf4OJShgvnzvH66693r/F6vWiqiqppaJpGMBhEVdXusaZp3cedc4qi4BGLlwKXjnlTqw8jQy2zIZx1t4m4094hZFkmlUqRSqXueU2j0Vhjo71aMGVzOa7cmV9zfVIP8veeOb3bQ+9r8rU6X/7hj9ec83g8DA8P31PohMNhUdg+gLRcUevxens8kgfj8ThjbA2A2YNgS3wBuAh07Jf+DfC/27b9TUmS/m/gszg9iz4L5G3bPipJ0mfc636uFwPuBc1msytaHri5AqdhGPd8Pa/Hg+r3o/h9qD4fMb+f0UgIdSiO6vej+n2EAwHGo5FtZ1F4JImpeIypeIwXThzlVr5AxTCpN5vUmy1336Sez7GUyVBvNambza5z6XokHCt9TdPQXMG0lU3MRfsXTdO6bRb6iaZpCHfdbSI+pXuIoigoikIqlcKyLObn57sGC+VSqXtdOqQzGY3wrvS9hdVBIaoq/I1HTjKTzXGzUKTcMLAsi3K5TCqVYnR0lOnpaWKxWK+HKniHdMWQZwDEkFeIof2CJEnjOL2J/lfgn0jOcupfA/6We8lvA/8CRwx90j0G+C/A/ylJkmSv7/48YNi2Tb1e39D7rlQqOZu7eHc/YSN7vaiyH9XniJu07EMNJlB9vu551e9zRY5zjez17snqtc/jYXpVXem9sG0bs93eKJjWH1fKFAp5Ftzzq11N16OpqrNIt64J+OrFO0VRxCr+AKJpGrlS7R2/jh5JUClkneNoAj2SeEev1zINwmEhhraDEEO7iGVZFItF8vk8uVyOfD7fPc4sL3cnlkQwyKOpJJOxKJOxiHCXW4UkSZxKpziVTmHbNoV6g5v5AjfzBa5cuMAbb7wBQCQSJpEYIh6PE4/HicVixGIx4vG4CBcPCIb7efAOwEqq1+t8Ro373BwKBoZ/B/wqEHIfJ4CCbdsdpXsH6Hg1jwG3AWzbbkmSVHSvX9m74W4Py7KoVCprxc2qDIXO482Eve7WtoZlmbFkAl2WuxGbjqhR/T4Uvx/fPkghkySJgM9HwOcjug0zulbbciJLq4RTo9mkZjapmCZlw6S4MM+d2Vlq5sZ0ednvJ7SqeXhHJK0WTcFgUKTp9RmqqtLKFt7x6xx94jkqRUcMPfmBT7zj12s1TVT1nQmqg0b/33X0MbZtU6vVNgidfD5PPpejUCyuyXuWgLCqEAkEOJmIMxGNMBmLoIvGjVtCkiRimkpMU3lybATbtlmp1riVLzBXKlFYyXDhzp0Nk40sy8SiUWKbCKVYLCaKDPuEjrDw+fv/8+D1O/9nhBgabCRJehFYtm37rCRJH9rB1/0c8DmAycnJnXrZbXHp0iW+/fu/T7lc3jT1yyNJpHSdtKpwZGSYkLKqbjUgE5SFwc9W8Xk9hLzO3+5BtC2LimF261/Lq47zy0vMzMywWaDR6/EQjoT52Z/9FFNTU7vwWwi2i6qqtJsbxW2vaTdN0WNxmwgxtE1eeeUVrl69Si6bJV8oYK678VZlP1FFIaUoHJ8YI6IoRFVnCwUCYnLZQSRJIqkHSepBTnO3yZ7ZalNsNCjUG+6+TrHRYOXOba5fvbohpSGoaY4wisd57rnnOHTo0F7/KgKcmjpJ8gxEzZDPFUP1ev/1mBBsi/cCPylJ0scBBadm6EtAVJIknxsdGgfm3OvngAngjiRJPiCCY6SwBtu2vwx8GeDMmTM9SaHTdZ3RsTFKpRLlUolKtbpmcc6ybRbLZQqNOkFZRpcdAaQH3GN3r7vCKDAAEdt+pJN6VzFMKqZJ1TApmwbVVY8rzSYVw8TcJDrn9XjQdb0bORK1IP2Doig0m/23INZsmqLP1jYR327b5PLly1y5cmXTlZtgQCahql1HuFAgQFD2o/h8+Dxe0WNoj/B6JCd/3e+jaflp2xYg4ZE8+D0eVqq1NYKoWqtRrdVYWFzk0KFDQgz1iFqthj8QGIiURr/srAALMTTY2Lb968CvA7iRoV+xbftvS5L0n4GfxXGU+zvA77s/8m338cvu89/t13qh8fFxfv7nf7772LIsarUa5XJ5zVYqlahUKpRKJebLJSorWVqb1MDIXi/BQABd9jMeCfP+6amB+KzuNW3L4nvXZ1gslV2RY9Bqb3Sd9Pt86KEQoXCE0XCYUCi0YQuHw6iqKv7OfYqiKLRbLWzLQuqThW7bsmi3mgRExtG2EGJom/ziL/4i7Xa7O3msLzYtFovMFQuUMyu019nueiQJXQkQklfbaMuEAwEmY1FRK7QNstUaC6VyN73ASTu4u+K2HlmWiYTDhJMpHjsaWVPE2jkWk05vqdVqXZHR7/jccdZq77x4VtCX/BrwTUmS/iXwOvAV9/xXgK9JknQNyAGf6dH4to3HjTDoun7fhtO2bXedT9cLp3K5TDab5eWbt5mMRZmKC+Oa9VxazvDq7TkmJiaYjMU2FTihUIjAgCz8CO5NR3C0Ws2+mbtarSaASJPbJkIMPQRer7d7Ez0xMbHpNZ1VuPUW2p19pljkWm6xW7Tq9Xg4nkzwxOgIk9GI+JLchGa7zaXlDG/OLzJXvOu+pyqKI2xGRhhf59bTORZfDP1PtVodiHohAK/Pj8fj2bTRsmAwsW37e8D33OMbwDObXNMAPrWnA9tjOv1TVFUlnU53z7daLfL5PJlMhm984xv8+PacEEOb8OqdeQKyzIc//GHi8TiRSETYa+9TOvcVrabZP2LIrWESkaHtIT6hu8TqVbixsbFNr+nYma6srPDmm2/yxhtvcHEpQ0xTeXxkmMdG0gRFcT9L5Qpvzi9wYSmD0WoxlEjwsY99jJMnTxKJRIQBwj6hUqniDwQffGEfIEkS/oAixJBg32DbNpVKpWsCtHqfy2Ypl8uszgdsCFv5TWk0WximyW/91m8BzndFJBxeY+DTcT0VbqeDTUdw9JOJQmcsYgF4ewgx1EMkScLj8eDz+ZiamkJVVV555RXytRrfvz7DD2Zu8rnnn96SQ81+5e2FJf7o4uXu45GREZ566ikSiUT37yfYH1RrVaKhwbED9ctCDAkGk7m5OW7evOkIHXcr5PM01wmckBIgoihMBjWiQ/E1hkBioW5zPvfcGcdKu16n0DHyqTco5LJcmp+nus6BUvb7HYGUSHTF0pEjR+7bwF3QH3QjQ25qWj8g0uQeDiGGdplO9CebzXa3XC7n7LNZqutqDoKBAOORMFFVJaUH0Q54HdFkLMJzhybI1+sU6g2ymQx/9Ed/1H1ekqRuj6GEO5kkEgkSiYSwzR4gLMuiUa/3TarBVvAFFFEzJBhIvv71r1MsFjec93k8jEXCTMWiTMaiJIKacJHbJpIkEVYChJUA65PoG80WK9Vqt1feXLGE2WyytLzM0vJy97qxsTFeeumlvR24YNt0BEc/RoZEmtz2EN9yu8S1a9f40z/9U3LZ7Iau3WFFIaoEOBoNExtJE1VVYppCVFGRff1vK7yXhBWFDx453H1s2za1ZpNCvUG+VndFUp18Zpn5W7eoN9eu0IRCOun0MJ/61KfQdX2vhy/YIo1GA9u28QcGZzXLLweoVksPvlAg6DNeeuklFhYW1qbEuRGizo16h067iNVRoYiiOIt1YrFpA2XDYKVacyJDdScyVGg0KDYMGuvmJ01VN02fu5/BhaB/6KbJ9VFkqC0iQw+FEEO7RLvdptlsYq6/OVcCpPQgQ0HN3YLENRX/APRW6QckSSLo9sOIayorlSorARnF78fv8bBYrmCusoU1DZNWs7mpFbqgf+hEWAYpMuSXFUorC70ehkCwbXRd59ixY5s+V6/X19YLucfLuRxXbs91exUpfh+ff89z+LwiVblDzTT5Dy//uOsk6/V6iUWjxEdGmVrV6LuzFzesg03XTa7ZP2KoMxYRGdoeQgztEidOnODEiRO0Wi2y2SzLy8vdbWlxkRurJhUJiGoqQ5ojkCbdFAXRl+guRqvF1UyWhXKZbLXGSq22xkJblmVSqRSPTh8hnU6TSqVIpVIj8qFlAAAgAElEQVREIsKZbxDo9OsZFDc5cMZqNBpYliVq1wT7BlVVGRsb29T4x7IsisUily9f5g/+4A+4mc9zZGhw6vx2m2srOdqWxc/93M9x6NAhQqGQ+G7Yx9ytGeqfNLnOWITQ3h5CDO0yPp+PdDq9xqIUWCOSlpaWyGQyLC0ucv32HC/fvE0wIPOuVJJT6RTDIf1A3tC3LIsb2RwXFpe5lnUmGVmWSSWTnDg8TTqdJplMkk6nhegZcBqNBnC3f88g4HNThEzTFBOPYF/RarW6DVk7/YY6x6VSiWLBSaObzReEGFrFzXwegD//8z8nHA6v2To9hsLhMLquC7vtfYDf70eSpL6rGZIkCf8BrzffLuLT2CNWi6THHnuse77ZbHL58mXefPNNXr98mVdvzxHTVE6lkjzq1hftd27lC5xfXOZyZgWj1SKoaTz9zDM88cQTjI+Pi5W2fUhXDPkG5wvc53PEUL1eF2JIMBBYlkWlUrmn0Cm7+5obqV2N1+NBDwTQZT/JgMzh8TEeG05v8i4Hl3ePj6L6/ZQNk0p2hZX5eSqGgbVJmnZQ0xyRdB/RpGmamO/6GEmSCAQC3d4+/UCr2RQNfR8CIYZ6RMdlrjMxrd8bhoGu6xSLRfK1Oj+YvcXZO/P88vuf39f/ye8Uinzj9bfWnAtHIhQKBc6ePcuVK1fQdZ1QKLRmL1zjBpumm+fsHSAx5HVXdpt9lC8uEGzGt7/9bS5euEClUtlwYy7huJjqARldlhmORwnJKUf4BGRC7l7x+fb13LMTjEcijEcia87Ztk292XQEkmFSMQ332KBimBQX5pm7Obsm7buDx+MhpOu8+6mneOGFF/bq1xBsA0VR+8xAwSQgFue2jRBDO4xpmvcUON29e9wpslyN1+MhGJAJ+v2kZJnDoyMEZT/BgExa3//pcqPhMD/16ClKRoOq0aRqmlSNBrm5MrfNJjXDYDMrBFmWu+KoI5A2E026ruMVZhV9h2k6NwIe7+B8JXlc4dYZu0DQr3QiDa1Wa0PUJ+D3EwrIxFSFqKq6jnHOXpflfT/n7DaSJKHJMposk9JtyoZx12WuXsdf9yJJEq22hbGuz5OqKIQjEUKhUI9GL3gQihKg2UdzQMs0UYUY2jaDc+fR53z729/mjddfx7jHh8IRODJB2c+EphKMRggG/ARlZzWu45AW8HkP9OTj8UicSA3d83nLXWWrGiYV03TEktkRTSbVYoGFlQxV06TR3LxDelDT+ORP/RSPPPLIbv0agm3Sia54BkiodkR1q7X5/zOBoF/40Ic+xIc+9CHASUnNZrNdt7hO77v5XJaLyytrnDd9Xg8RRSWmBhgNhzk6lGAoqB3oOWqr2LbNYrnC9ZUsC+WKY69db6xZBPV4PEQjEeKpNJOurfbqTTiC9T+qqlIr9U+/uVbTIBTa/+UUO40QQztEMpnsCqHnDk0wEY10BY7m9+PxiMljJ/CsstZ+UH/uVttyxZKzvT63wEwuj41NNBrdk/EKtkbHWXGQ8uMlyRmrtUmEVyDoVxRFuadbXKvVolAokM/nuyIpl8uRXVnh2o1Z/uLGLBFV4WgizpGhBJPRCN4efmb/25XrLJcrAPzH194kpet85PiRno2n2W5zM1/g2kqW69k8FcNAkiTSqRQjUyM8sk7sRCIRkakw4KiqSjtbePCFe0SraaKq8V4PY+AQYmiHeP755xkaGuI/fetbvDa3QNuyiakKYbdBXUQJiF5Ce4Rt2zRaLYr1BkW32d1iucxMLs/E+Dif+Zt/U4ihPqPt9oaSBkkMuWNtr+prJRAMMj6fj6GhIYaGhjb0ISqVSly+fJnz58/z2tWrnL0zj+zzcTgW5czk2IZamb1guVLBcD9/twvFPX//DjO5PK/dmWM2X6DVdqz2T5w4wSOPPMLx48cJBoM9G5tgd1EUpc8MFEzUA2C0tdMIMbSDHDt2jM//w3/I7/7u7/LazZsbbpI0WSaiBAgrAVcguZsaIKwoyEIsbQmnILXlCh1nK9UN59gwKNUbaxqvgtOA7L3vfS8/8RM/ISxN+5C7qTkigioQ9IL1TnPr9x23uWrtbkqQ2WpxObOC7PP2RAz1C2dvz3E9m+s+tiyLSxcvcvvWLf7qBz/oOsZ1nOKEY9z+QdM0mqbR62F0aZmGEEMPgbgr3GGi0Sif/exnuxNLPp/vph10tpVCgWtzC1sSSxPRCEn9YK4q2bbNjVyelWr1rthpOPvmur+dEggQjUVJptIcj8WIRqPEVu3Fl0N/c7cGwUYIIoFgd1hcXOTOnTtrhU6xSKlUolKtrqkX6hB07bR1WSYdDaOnk+iyTCggdx3ntAPe0+RnHn+Emtlxi3Oc4la7x+Xn57g9M0Ntk5rijmPcesEUDoeZmJggmUz24DcSbBVVVbHaLax2q+cGQFa7RbvdQtO0no5jEBFiaJfweDzdL7RDhw5teN6yLJaXl7l27RrXrl1jdnaWmmlSM00WSuXudclgkL/37Om9HDoARquFLMucPn2as2fPbnC52QuKjQb/5c23N31O13WOHj3K0aNHOXLkCOFweI9HJ9gVBkkLuTeOophcMCh85StfoVbbWOwd11SeGhshrmlrRE7QL4t61y3gkST3b3Z/w4O25dSxrhZK2WqNG9kcd+7c2XB9KpXiC1/4wm4NW7ADdBZam6ZBQO3tLXUnQiUWf7ePEEO7TK1WY2VlhWw2e3dzHzeMu6FVCYiqKjFVIaapxFSVmKYyEtJ7Mm6j1eL06dO8+OKLAFx4/bU9H0NUVfnvnz1DplolX6uTr9edfaNBpVLhjTfe4I033gAgpOsk3Fz3RCKxZhOdmPufThGxbVtIDEbKSGcVXaS4CAaFl156idnZWRYWFtxtnnq9Qa5WJ1erE9NUksEgaT1IKqSTDumEhKPZjmDbNhXDZKlSYblcYblSZalapVRvdK8JBjVGR8cYGRlheHiYw4cP93DEgq3QqQdzxFBvs3g6YkjUqG0fIYZ2kR/84Af8yR//MaurIcKu4HnXUJyYphJ3RU9EUXrqyrOegM/H2bNnATh79ixRuTeCIhHUSAQ3hnyNVssVR427Iimf48L8/JpUBEVR+OxnP8vo6OheDluwTTpiyLIsPANSOmdZTqqmcIMSDAqxWIxYLMa73/1uwLlBL5VKXXG0uLjI/Pw8V2Zudn8mrCiMR0KMRSKMR8MMBYN4RDT0gViWzXKlwp1iiTvFInPFMhV3AVQCEkNDTB07zsjISFf8iH5Cg0dXDBmNB1y5+3TGIMTQ9hFiaJe4evUqf/Inf8J0Is6TYyPEVJWIquDrI8FzPwI+H2alyssvv+w81vor7Brw+RgOhRjeZPIwWi3ytTq5ep3vXZ/h67/zO7z0+c+LL4g+5q4YGhxnto6lthBDgkFFkiQikQiRSISTJ092zxuGweLiInNzc9y8eZObs7NcWMoAznfvaDjEeDTCeCTMSDgknFJx5p35Upm5QpE7xRLzpXK3tjUaiXDk5EkmJycZGxsjnU4jy3KPRyzYCe6KofoDrtx9mqYQQw+LEEO7QKPR4Fvf/Ca2bRNRFUoNZzXIsm3CSoCAcDPbFWzbpmqalBoGJcOgYpgkVJXZfIH/57/+V/72z/98r4couAedVEZrgGyqrbZTRyfSMAX7jUAgwOjoaLeI/8knn+T27du8/fbbZLNZZnJ5ZnJ5ALySxGeeevxAu8ldW8nye+cubDCgSKfTPPLII4yNjaHrencTjqb7B113ShnMPhBDZsOpB+yMSbB1xCdyF7Btm0NTUywtLfHm/OKajtPgrKyFlADhgOMc190rCmG3cLWfUub6BbPdpuy6yXUET7l7bFJqNLDWTUayLJNKJkml0z0atWArdFZJOwJjEBBiSDBotFqtrn12pVLZsN09X8YwNu+dovj9BGU/Qb8fTZYJBQLEDnjBdjIY5KmxESqmSc1sUjWbVE2TpaUllpaWNlyvBALooRC6rhNy9+u3UChEMBgUwqnPURQFj8eD2eh9mpzZaODxeFAUpddDGTjEp2wXUFWVX/iFXwDu9m4oFosUCoXuvlAoUCwUWMwVqNXXrihIgK4ohANyVyidSqdI9chMoRdcXMowVyx2RU+pYVBvNtdcI0kS4VCISDTKZDRKNBolEokQXXWsKIpw+xoAOoKi3QPXwoelM1YhhgT9zHe+8x3ePneOSqWyxrRnNYrfhybLBP1+krLMVHKIoCyjuZbamiwTlB3xMyip3ntJRFX4yPGjG8632o57XLXpiKSK4TjGdsRSNZ/jzvIyNdO8p2Orqijous6Zp5/mfe97327/KoJt4vF4CAaD3ahML2kaNbRgUJj6PARCDO0yqy22JyYmNjxv2zbZbJZr165x5coVbty4QbPZpNxoUG40mFt17UESQ385M0uutjHsHAqFOH78OMeOHePIkSPCT3+f0IkMtdvNB1zZP7TdyFBAuG0J+hhZljFMc40Qkr1ejiUTnEwlORSLipqfXcLn9RBRFSLqg1fqjVaLmVyeS8sZrq/kaLkZJfVGg4CiiEWXPiYcDlPrAzFk1GtERJuRh0KIoT3Ctm0KhQLLy8tkMhmWl5dZXlpiOZPBWDVJKX4fqUiYhKYx5DqpDQW1A2dv+veeOU2+XmelWiPrbiu1GrlqlbNnz3ad7sKhEMlUinQ6TTKZJOUeC5/9waIrhpoDJIZazlhFGougn/ngBz/IBz7wAXK5HDMzM8zOzjJz4wbnF5c5v7iM4vczHgkzFNS66doRdy8LkbTjGK0WpdXp3o0Gy5Uqc6UyphsdSsTjHJ6eZmpqisOHDxONRns8asH9CIfDFOc3pkPuNU2jTiie6vUwBpJdm8UlSZoAvgqkcVopftm27S9JkhQHvgVMAbPAp23bzktOLtOXgI8DNeCXbNve++Y2O0C1WmVpaYnFxcVuzvDy0hLGKstnTZYZ0lRODcUZCgZJaCqJYJCg7BdpXYDX42EoGGRonSuKZdkUGg1XIFVZqdXILi/z49nZrnMPQCikk04PMzw8TDqdJpVKkUqlhINPn9KJrrQHqGao3Woiy7JISRD0PZIkdfuunTlzBoB8Ps/s7Kyzzcwwc3tuQ32r6vevqmu9K5LCSoCIEkD1i/lqNY6JT5OS0dggeEoNk5LRoNFc+x3n9XpJxOM8+dRTHD58mKmpKdFEfMAIh8MY12/0ehgY9SqRA2xk8k7YzSXNFvBPbdt+TZKkEHBWkqTvAL8E/Llt2/9akqQvAl8Efg3468Axd3sW+Pfuvm8xDOOu2FledsXPItXq3XCp4veTDGo8khxiSNfcG3wNVYS8HwqPRyKuqcQ1lWPJRPe8bduUGgYr1Ror1Sor1SqZpUVmbtzoTvASEIvHSafTXZGUTqdJJBLCHrnHdCNDrcGKDAlxLRhU1vccsiyLcrncrWldveXzeW4uLWOui9z6vd41JkARReGJ0WG0A/C5KDUanFtYuit6XBOfDYZJAZloJEpsLMnhaJRYLNata41GowRFjcfAE4lEaJoG7VYTr68393btVpOmaQgx9JDsmhiybXsBWHCPy5IkXQTGgE8CH3Iv+23gezhi6JPAV23Hm/KHkiRFJUkacV+n7/ja177GpUuXNpwPBWQeHxlmOhFnLBIWkZ49QpKkbm72kaF497xl2+RrdW4VCtzI5rmZz5PL5bh48WL3Gq/Xy8c//nGee+65XgxdwKrI0KCJoQOWvirYv3g8nm7PoUOHDnXP27ZNtVoll8sxNzfHzMwMMzMz1Go1mu12N425Q0RRODW8/1N1rmSy/OWq5rSr0XWd6elpDh8+zOjoKPF4HFVVxb3APqWTxtioVQmGe5PS2KhVAYQYekj2JNldkqQp4N3AK0B6lcBZxEmjA0co3V71Y3fcc30pho4dO4bH46FYLFIsFKhWq9hA2TB5a2GRtxYWHQvtQIBQwLEfDSsB57G7khZSAiIn+x1i2zb1Zouy6zhXNox1xyZlw9iwWuftGFu47nMjIyM9+g0EMKg1Qy0UIYYE+wTLsrhx4waZTIa8u2iUy2bJ5/MbIkJ6IMBYJExUVYgqClFVJeouRh2U+tbT46McTyYo1BvO1mhQrNfdY4O33nqLt956q3t9ICATj8WJJxLdqFw6nWZqakqIpAHnrhgq91AMlQEn4ivYPrsuhiRJ0oHfBf6Rbdul1R9627ZtSZLse/7w5q/3OeBzAJOTkzs51G3x3HPPrYkktFotyuWyI47crVQqde20M8UilYXFDa+j+O8Kpql4jKcnxvfy1xg4jFaL712fIVetUTZNyg2j67rTwevxEAqHiESiTLornZ0tHA4TjUbRNE2kJvQRXq8Xr9dLa9AiQ7ro5yDYH8zOzvKbv/mb3cd6QGY4pDOeTjmix90iiiLc53BbOygKYUVhcpP7T7PVpthoUOgKJEc0zVy9wnnz7vfcF77wBVKp/R9J2890BEijWu7ZGDrvLcTQw7GrYkiSJD+OEPq6bdu/555e6qS/SZI0Aiy75+eA1d7T4+65Ndi2/WXgywBnzpzZlpDaTXw+X3e15160Wi1KpRK5XI433niDc+fO0Wi2aDRbZCpVvJJHiKEHYLbbXF/JUV7lwBcKhXj66ac5fvy4yMEeYORAYKDS5KxWk0BApCQI9geHDx/mM5/5DK+88oqTBmc28XkcC+6JSASPR0QvtoPs85LUgyT1IG3LYjZX4I35eequEDp+/DjPPvusEEL7AF3X8fl83ehML2jUyni9XnT94LRg2Ul2001OAr4CXLRt+39b9dS3gb8D/Gt3//urzv9DSZK+iWOcUOzXeqHtsLoodWlpiStXrnD92jXMZhOfx8N0Is4Rd9tKL4KDTigQ4H94zzMslMtcX8lxI5tnsVzmu9/9Lq+++ionTpzgyJEjJNxUBNF0dXCQZRlrkNzk2i3RY0iwb5Akiccee4zHHnuMTCbDj370I1577TUuLWfwSBIxTSWhqcQ1jbimktA04pqG4hfW8h3qzaZTQ1WrkavVydVq5GoN8vU6tm0TDAb5wAc/yNNPPy1W8PcRHo+HWDxOvVLq2RjqlRLxREIsBD8ku/kt9l7gF4BzkiS94Z77H3FE0H+SJOmzwE3g0+5zf4xjq30Nx1r77+7i2HYMy7KoVCrk8/mu605nK+TzFIpF2qssn8OKwiOpIY4k4kyKZncPhSRJjIbDjIbDvH96iophcCOb53o2y5uvvcaPf/zj7rWBgEwsGiMWj3ddfDr7eDyOoggB2i/Iskz7Hl3Y+5F2qykaIQr2Jclkkk984hP8xE/8BBcvXmRhYYFMJkMmk+Ha7TmsVanJwYBMXFVdl0/NFUkqEVXBsw8XotqWRbHRIFt1xM5d4VOnvqq2yuv1MpRIMHL4MI8nk4yOjnLixAnRl2yfMpRIcGuud+v3jWqJidHhnr3/oLObbnJ/ieNmvBkf3uR6G/j8bo3nnVCpVMjlchvETj6fp1go0FoldsDpIRRRAiQVhaNjI47LmVtkGlNFpGKn0QMBHh8d5vHRYVqWxUq1SrHe6fHg5Glnbt/i2pUra3oRASiBgCOO3BTH9ZtY+d87ArJM1RycNLl2qyWstQX7Gr/fz+OPP87jjz/ePddut8lkMl1XuZmZGW4XitwuFNf8rNfj4ScfOcnx5NBeD3vXeGNuge9cuYZlb8zQ13Wdx06e7LrIJcQq/YFiaGiIy5cvY9sWkrS3/+62bVGvlEgmH9vT991PiCWKB3DhwgW+/vWv3/P5iBJgbCjOWCTCWCRMTFOFQ1wP8Xk8DIdCDIdCG57rOM8tVyrMFUvMFUvMl0osLC6ysLjR3CIQCPCrv/qrInq0R/j9ftr12oMv7BOEGBLsNwzDoFwub7qVSiXK5TKVcpl6o7HhZyVACwTQZT9BWUaXZRKatve/xC4yHNJ5ZDhFxTCpmk0qpknNbaZeqVQ4d+4c586dA0BTVUKhEHooRDgcJhQKbbqJ75D9QTKZxLIsGtUKqr63TXMb1QqWZZFMJvf0ffcTQgw9gKmpKT72sY9RLBa7E0KpVKRcrtButyk2DIqNDBeWMoDTZFUPOBOBLsuEAjJ6IHD3XEAmKMt4xYrRjmO22lRMg4phOptpUjGMu8emc359dAicdLpQKOzYbbsTVzqdFhPVHuLUDPUu53o7WFYb27ZEmpxgYLEsi/Pnz/OjH/2IQqFApVzeYKENToQnGJDR/X5iAZnxRAxdlp1zcsCd0/xosrwv0+JWMxwO8fHwiTXn2pZFrdl0BZJ5d2+aVA2TSmaZzNwcFcPYNKIUkGX0UIhYLMZ73vMejh8/LrJHBpCOEKmWC3suhmrlAuBEpwQPhxBDD0DTNN7//vdvOG/bNvV6vbta5oiku8flUonb5TLl5cya/Oru67pi6WRqiGcmx4U4egjKhsH3r8+wUK5QMUzMTepN/H4/4VCIUDTGhCtywuv2oVBIpMP1AYNkoGC5/9eEWBYMGrZtc+3aNf7sT/+U+YUF4ppGWg8yPZxas2DX2Ss+n7g5vw9ej8dtj3H/OaSTmVB1F+k6kaWOeFqYu8NXv/pVDh06xEc/+tE1jW8F/U/HFbBWysPI3rZ9qZbyAKTT6QdcKbgXQgw9JJIkoWkamqYxPHzvojXLsqjVamuFkrvPZDL8xY1ZLi5n+OsnjzMS3pjaJdiIbdu8Ob/I967P0AZOnDhBJBLZVOgEAgExkQ8Ifr8fa5Oo3cOiRxJUClnnOJpAjyR27LXbrmgTkSHBIFGtVvnGN77BzMwMAI8Op3n20DihQICAKOzfVSRJQpP9aLKfpB7snrdtG8PtSfRXs7e4cvMmX/7ylzl58iSf/vSnxULdgKAoCuFwuCtM9pJqKU84HBYp/e8A8e23y3g8HnRd3+D93hFJr7zyCv/fd7/L1159nWcPTfCBadGN+n7UzCa/f/4it/IFgsEgn/rpn2Z6elpMGPsAv9+/o32Gjj7xHJWiI4ae/MAndux1ga5oE2JIMEgUi0WWlpa6j99eXOLtReex7PM5ad1uVCjkpnev3gf9sug3tA3alkXVNCkbJuVOyrZhUO7szSYVw9g0dXtubo5qtSrmtgFieHiYucXMnr9vrZRj9D6L8oIHI8TQLtBut7vRn2Kx2E2h6z4uFimVy2sstwHeml/kvVOH8HnFZHMvliuVrmtRtVrld37ndwAn7zocDhOORIhEIt3an85xJBJB0zQhNPsYv9+/4TPRr4jIkGAQGR0d5Z//83+OaZpr5qX1251ikfLyyoYUbwkIdsTRKtE0FY8d6MyG24Uit/IFtz7VFTtuzdB6vF4voZBOOBxhfFVGw+pNGCsMJiMjI1y9eg2r3cLj3Zvba6vdoloqMvLk4w++WHBPhBh6CFqtFrdu3eoKna7gcfeVSoX1ZZI+jwddCRCSZUYCAY6HR9wVtwAhRay6bZWpeIx//IH3UFm10lY2DHczKWeWWZ67Q6VhbPg38Hq9hEOhTQVTIpFgZGSkJ7+TwMFJk2th23bfi9ZObZPoGSIYRGRZZmho6L4F153shUwmw/Xr17l+/Tp37txxTWkMVvtv3imW+NQTj+7+wNdhuI6Op0+f5uzZsxg96lP23avXWSxXNpz3er1MTk5y5MgRpqenGRoaEoty+5jR0VFs26JaKhCK7Y2ZQbVUwLYtcf/yDhEz+UPw/e9/n+9+97ubPpfQNN49PkoyGHSKKl2hI4pQdw6/10tMU4lp6j2vsSybatOk3DC6KQrzxRI38wVuFgqb/sznP/95RkdHd2vYggfQibJYVhvvHq2qPSwiTU6w37Asi5s3b7K0tEQmk2F5eZmVTIZSudy9xiNJDAWDxDWVhKaSCGokghpDweB9Xnn3MFotTp8+zYsvvgjAhddf68k4/tZTT5CpVslWnQas2WqNbK1Ovl7v9mICiETCpFJpkskkyWSS4eFhJiYmxL3BPqFz/1AprOyZGKoUVta8t+Dh6O87jj7lfe97H4lEgkwmw8rKCpnlZVayWdrtNlm3I7Umy90Jo9OVOxFUCSv7syt3v9Bst8nX65t2B1+dl60oAZLJVHdSGhkZESsrPaYTZbHbbeh3MWQJMSTYH9i2zYULF/hv3/kOyxmn3kH2+UhoKpNBlUQyQSKoEdc0oqrSV86nAZ+Ps2fPAnD27Fmicm8+j36vl9FwmNHwWkvltmW581GtK5CyiwvM3rjRnY/GxsZ44YUXOHr0qBBFA04sFkNRFMr5FUYO7817lvMrKIpCPB7fmzfcp/T3HUefEggEePLJJ9ecsyyLfD5/VyBlMmQyGa4uL1Obv5tQ4PV4SOlBRkIhRsLOFtdU8SX4ELTaFkuVCgulsrOVy+Rr9e7zEhCNRkmOjHI0mWRoaKgrfoLBoPib9xkdMdQRGv1MJzLkFQ2WBQNK12L7z/6M+fl54prG3zh1kolYBF2WB+L7MeDzYVaqvPzyy87j+2QL9AKvx8NQMLghcmbbNmXDYCaX569mb/Nbv/VbTE1N8cILLzA1NdWbwQreMZIkMTY2xlJ2Zc/es1xYYWxsbCA+r/2MEEM7hMfjIZFIkEhstO+tVqtrBNLc3Bxvz83x2tw84HyhD4d0RsN3BZIuHGTWYNs22VrtrvApVViuVLpN7MKhEONTh3n38DCplBPxSSQSYuV+gOgIi836cvUbHcEmaoYEg8r58+f5xje+0X0cCsjM5vNkazU3xTtAOBAgFJAJiDTvd4Rt2zRaLTdt29lKhpPGHQ7IlBoNZmdn+Y3f+A3+/t//+0IQDTATExPcuHGDdquJ17e79x/tVotqMcfEk4/t6vscBMRMvgcEg0GCweCaJmqWZZHJZLh9+zZXr17l6tWr3MzfrWUJKQHGw2E+cvwoWg9C/yldZ9ktCE2FdFLrrMH3ituFIj+YuclCubKmqWokEuGZZ59lenqa8fFxIpFIT8Yn2Dk6YsgeADHUGaOIDAkGlenpaT72sY9RLBa722yxSGVhaYP5jN/rdRuLyu4W6EkC9u8AACAASURBVAqmznnV7z+Qgsm2bapmc5WRj0G5scrYxzSpNAxa6535JImQrhOJRHh0YpJwOEwsFhPp2gPOxMSEE/krrBAd2t1/y3JhBdu2mZiY2NX3OQgIMbRLNJtNyuUylUqFcrm85rhSqVB2m69Wq1Xam9z81c0my9UqZruFxt6LoY8cP8JyxRFDf+upJ/b8/TuUGwbZWm1DH4ZiscgPf/hDXnvtLLoeIhRyNl3XN90Hg0Fx49rneNxaBNseADHkjtHTR/UTAsF20DSN97///RvOb9YaYvX+dqFAaSmDba+VTF6Ph2NDCT756Lv26lfoOd947U3uFEvdDIUOHo+HSDhMOBJlclV7h9V7XdfFnLQP6QiTUnZ518VQKbu05j0FD48QQw9Ju93m/Pnz5PP5NYLHETtljE36CwAEAzJBv0xQ9jMZ1NDjUYKy89jZO70bZK/3QK6yrefUcIpTwyks26bebFJ1ezdUzabTx6HzuFhgYSVDxTA3tVeVcCb/UCiEHlornkZHRzl8eI+qHQX3pPP/3V6/LN2PuGMUn1HBfsPr9RKNRgmHwwSDwW6/m2azSaVSwbKtDUIooirEVZWJ6MGK0E/FY0iSRK5ep9wwuucty8KyLXw+H6qqEo1Gu1bmkUhELKLsY4LBIIlEgmJ26cEXv0NK2WUSiQTBHrk57ieEGHpI5ufn+da3vtV9rMl+4qrKUEBmKjnUFTZBt6N3UJbR/H7RR+gh8UhS92/KAzL2mu02tdViyTSpGJ3jJtXlJWZu3OhG5HRd54tf/KK4sRUIDhiSJE0AXwXSOBL3y7Ztf0mSpDjwLWAKmAU+bdt2XnK+JL4EfByoAb9k23Zv/Jx3CNu2yefzLC4usrCwwNLSEstLS+Ty+TVNkBW/n7imciioEU8OEXedUqOqgv+ARjien5rk+alJAMx2m3zNcTHtOJjmMsvcvnlzTYq3z+cjkYiTSqUZHh5meHiYkZERwuGwmIP2CVNTU7x17u1d7Zln2zal3DKPP/bIrrz+QUOIoYdkYmKCz372s/zgBz/g0qVLNJotonGFp8bHSOnBvrIfPWj4vV4iqpeIqnTP2bZN07K4kc3x6u052pZFQJY58/TTPP/882ISEjwU61fIBQNHC/intm2/JklSCDgrSdJ3gF8C/ty27X8tSdIXgS8Cvwb8deCYuz0L/Ht3PxCYpsnS0lJX+CwuLrK4uIhh3I1qxDSNIU1lemyEuKa5okc9sDVBW0X2ekmHdNKhtat1nZqiNSKpVuPWtWucO3eue52qKAy7LR46IimVSgkToAFkamqKs2fPUivlCUa2ZnmtRzaab92PWrlA02ysqUUXPDxCDL0DpqenmZ6eJpvN8vLLL3P21Vd5e3EZcG7IVb+fgM+H4vOi+n0EfD5Uvx/F50Px+5y9z4finutcIyace9O2LBqtFo1mi3qridFs0Wi1qLv7RrPZfb7RatFotWm0mjSarW4kKBaN8olPfIKnnnoKRVEe8I6CvaKzCj0IKSSSO8ZBcL4T3BvbtheABfe4LEnSRWAM+CTwIfey3wa+hyOGPgl81XZU8A8lSYpKkjTivk5fcu3aNV599VUW5ufJ5nJdAS97vST1IKeG4qT0IEldJxkMIvsOZpRnt5AkCT3gpL9PxqJrnjNaLTKVKsuVKsuVCsvZFX506yattluTKEkMDQ0xMjrKc889x+TkZC9+BcE26bgBFlYWtyyGjj7x3Lbeo7jitGwRKf47gxBDO0AikeDFF1/kwx/+MOfPn6dcLlOv16nX69RqNer1OoV6nXq5SqPRoLlJTctqHKHkiiaf1xFLa8ST87zqX/vY7/UMhJCybBtjtWDZIGBa1JtNjK7IaXevW2+ksJ5AQEZVVFRNQ41EiWkaiqKgaRqqqpJMJjlx4sRA3HAfNFru50IagH8bj5sW1HrAZ1kwOEiSNAW8G3gFSK8SOIs4aXTgCKXbq37sjnuub8XQlStXePvcuQ0OcS3LomyY2P8/e/cdJtddH/r//ZnetxetulaSVWzLyLrYxjXBBuKGwQETA3ZMwEAoSX6B/HJzc28K5N7cPCk3CSQYAo9jahwMSXBy4+AfwTbYsi3JtmwVC0lWWUnby/T+/f1xzoxmd2ebtLuz5fN6nvPMmVO/c+bM+c7nfMsBEtkc/YkkXV6v/cfdS8hj9RqnwdHsMMaQLRSIZbLEMxniGavHuXjWeh/LZIlncxSK57+pojH09vXR199Pe3u7BkOLRENDA+FwhJH+blZ2bpuTfQz3nyMcDtPQ0DAn219uNBiaRX6/n127dk25XC6XKwdL0xn6kknS8RFS6fSkd6IdIuXAKeh20+D30xj002RXdajzzc/Tw7P5AoOpJIMJu/50KsVwKm0HNlYpzWTcLhc+n88OaEI024HMVIPP59PeeRaxXC4HgNO58C9LDjuNpTSrxU1EQsBjwK8bY6KVN5WMMUZEZlQfUkQeBB4Eav4H9tZbb+Xtb397uYe4qN2TaWk8Go0yGI1ysrefTHZ8xz8el4uw3fY1ZHerHfJ42NzaTFifhzfOUDLFsYFBK9ApBT5219vVbub5vF7CkQiRllbaIxEiFUM4HNae5xYhEWHDhvUcOnxkTtoNGWMY6e9m6yWbF8UN8MVg4f/rWILcbjdut5tIJDKj9YwxZLPZcolTOp0eNV4qhUqlUkSjUY729ZE8111e3yFCfcBPo99XURfceg3MsD540Rii6TQDlQ1GE0mG0ulRveoIUFdfR1NLK62h0ISBTMAuwfH7/VpHepkqtVtwuj01TsnUXPbD9CrbWqjFSUTcWIHQN40x37Mn95Sqv4nICqDXnn4GqOzHdpU9bRRjzJeBLwPs2rWr5g3LSj3E1dfXT7pcJpMpB0hnzpyxnn938iQDiSQDieSoZRPZLDd0ahWdsXafPM3+iny3xO12s2nDBjZt2kRHR0c50Cn11qeWlg0bNvDKK6+QjA0TjMxu6U0yNkw2nWLDhg2zut3lTIOhRURE8Hq9eL3eKTO1kmQySX9//6ihr7eXN86cG9NTkMsqSQr4WVNfz2Ur2kZtJ5PP88KpLvoTCQaSaYZTqVHPR/L5vDQ3t7Bx/YZyF6LNzc00NTVpcKOmJZ1O43A6F0UVxlLAlk6na5wSdTHs3uG+Chwyxvx5xax/Ae4H/th+/eeK6Z8Uke9gdZwwspDbC01HKpWip6dn9NDdTari3A55vTQH/LSEgjQHg7SEgrTV6EHcC90tl2zk8o52+uIJ+hMJ+uIJ+pJJUtlc+QHrwUCAtvZ22traykNra6u2YV1CSoHKcN/ZWQ+GhvvOjdqHungaDC1xgUCANWvWjKuqUSwWGR4ePh8g9fXR39/Pqb5eDnT30p9IlJdN5XI8+spr9MTiNDU20rJ6NdtaWkYFPcFgUItr1UVJJpN4PIvjz4DbY1UPSiaTUyypFrhrgQ8Cr4rIy/a038EKgh4VkV8BTgLvtef9G1a32kexutZ+YH6Te+GSyaTVbXZvL319feUutOMV13qvy0VzMMAljQ00hwK0BK3gJ+DRG1rT5XI4WFkXYWXd6JofiWzWCowSCfrjSfr6+9hz8uSoqnORcJhWOzhqaWmhtbWV1tZW/H7/fH8MdZEaGxupr69nqPccKztnt/vrod6z1NfX09g4vc4Z1NQ0GFqmHA4HjY2NNDY2snnz5vJ0YwyPP/44u3fvJuTxUOfz8p2XX2UgmeIDH/gAW7ZsqWGq1VIWj8dxexdJMGSnMx6P1zgl6mIYY36CVZu3mrdWWd4An5jTRF2kRCJBb29vOfDp7e2lt6eHREXg7nE6aQoGWBcO0dzeSnMwQEsoSNjr1ZtacyTo8RBs9LCu8XwpgTGGkXSavniSgUSC/kSS/p5uTrxxvNyjHEA4FBoXJLW1tWmQtMB1dnbyyv5XMaaIyOzUeDCmyEj/OXZcftmsbE9ZNBhSo4gIt99+O319fRw7doy83ZX1bbfdpoGQmlPxeBzXIikZcjicuD1eDYbUgnL48GG+/vWvV53ncTpZXV/HmoZ62sMhAh43AbcHv1sf51ArIkK930+9309ncyOpXI5kNkcim+VcNMap4RHODI8Qi8eJxeMcO3asvK7D4eDBBx9k9erVk+xB1dLGjRvZu3cvsaEBIo0ts7LN2NAA+VyWjRs3zsr2lEWDITXOgQMHOHbsGH67HVEsm+Xpp55i27Zt026rpNRMjUSjhJs6ap2MafP4AkSj0VonQ6my1atXc8sttxCLxUgkEsTjcRLxOIlEgmQyybGBQY4NDI5aR0TswMge7CAp6Dk/HvC4rfduz6J5hEOtlbrSTmZzJHJZktmcPWRJ5nIkyuN5kjlrvBoRIRQMEgwGCYZChEIhgsEgdXV1tLa2zvOnUjPR2dkJwFDvmVkLhoZ6z4zatpodGgypUU6fPs0/PvooK+siCNaF+D2XX8o3973C3z/8MB/7+MfxaneqapYVCgUS8TjNq4K1Tsq0aTCkFppgMMhNN91UdV6xWCSZTJ4PkiZ47YnHSQyNVO1mG8DldBDx+njXZdtoDgbm8NMsTmdGovzg4GHimeyoToYq+bxeK7iJ1NFqBzehCV79fv+i6FRGjRcMBmlvb2eo9wxrt1wxK9sc6j1De3s7weDiySsXAw2GVFk8Hudb3/wmQY+bd1+2nX967SAALaEgd126lUdffpXHHnuMX/qlX9I7g2pWxWIxALz+xfPnyusPMjxwttbJUGpaHA4HIbtkoa2tbcrlc7kciUSCc+fO8dxzz5WraBUKRep8Xnwu/ftQTcDtJuL1MpKyeuMTES655BKuvvpqWltbCQaDuPTYLRubNm3iJz/9KYV8Dqfr4joiKeRzRAd7ufzaa2cpdapEf5Gq7B/+4R9IJhJ84MorxvUetK6xgRs71/PjAwfYvXs311xzTY1SqZaikZERALz+xdNdr9cfpDsep1Ao6AMR1aKXz+cZGhpicHCwPJw9e5YTJ04AsCISZltbK1taWwh59dk4E2kI+Ll35w6i6TQHe/o42NPL4cOHOXLkCOvXr6e9vZ3GxkaamprKPY7p9WPp2rhxI8888wzD/d00tV9c+67h/m5MsajtheaABkMKsOo3Z9JpCsbQNTxCa2h0EWy2UOBs1Lp7n0qlapFEtYSdD4YWV8kQQDQapaFhdp8jodRcSKfTDAwMjAp4BgYGGBwYIBqNUvl0WLfTSUPAz7Xr1rCtvY3GgPZcNhMRn4+r167m6rWr6YsnONDdy7Gebk6+8Qb5iupzDhHq6upotIOjporXhoYGrZa+yK1duxaXy8VQz5mLDoaGes/gcrlYu3btLKVOlWgwpACrKP9XPvxhHn30UZ48fJi+RAIMIBBNp3ls/0H6EgluvfVW3vKWt9Q6uWqJKVWT8/gWTzDksQO3WCymwZBacIwxPP/885w6daoc8CTH3MgKeDzU+7ys9PvZ3lhPg99Pvd9Hvd9P0OPW6tCzpCUU5KaN67mJ9RhjiGezDCdTDKWsB5gPp9IM9/Vy5tQp0rncqHVDwWA5UOrs7GTnzp01+hTqQrjdbtauXUt335mL3tZw71nWrl2rD7KfAxoMqTKv18v73/9+nnzySZ566inCXi91Pi/fe/UgI7ks991336hnEik1W+LxOCIOXJ7FcxfU47XulGv32mohMsZY1XOGh6vOD3u9NAb8hDweQl4PPrcLp91QP1cokC86cWv1rVmXKxTJFYoYrM4ofG43oaJBBNwOB/3J1Kie5eKJBPFEglOnTtHX16fB0CK0adMmjh07RiaVKNcomKlMKkEiOsSmt1w1y6lToMGQGsPhcPC2t72NeDzO3r17SeVy5ItFPvCBD2ggpOZMIpHA4/MtqjvRGgyphczhcPCZz3yGdDpNNBolFosRi8XK46XXM9ERYv0DFAqFcdvwuV0EPR4rYLKDppDXS2soyJoGfczCRI4PDDKQSBLPZolnMsQzWeK5HPFMlmw+P255l8tFOBwmUldP5+o1hMNh630kMmpcq8wtTqU2PkO9Z2lfu+mCtjHUe3bUttTs0mBIVXX77bezd+9e8sUiV199NVu3bq11ktQSlslkLrqnnfnmtKsqZCfoglipWhMR/H4/fr9/0h7kjDGkUqlRQdLYwOlMNEq0r59isYjX5eTXrn/Lorp5MV8y+TyP7T9A0RicTieRcJhwpI4OO7CJjHkNh8P4FtmNIDUzbW1tBINBq1vsCw6GzhAMBqfVE6SaOQ2GVFUejwe3200ul2PHjh21To5a4nK5HA7n4rocOewqRLkxdfyVWmxEhEAgQCAQGPdnqxQovf766/zzP/8zfreLuy7duiD+vLeGQvTGrJLZ1nCI1lDte6P0uly8+/LtPH7wMOJ0cettt7F+/XoNeJYxh8NBZ2cnh4/8DGPMjM8DYwzDfWfZsnmTPnNqjiyufx+qJrTbTzXXCoUCssgu8iJWeqtVL1JqoSsFOdVKgsrj0Sgxu/t4gJV1Ee66dCuhBVJd6+bNnfTa1VTv3blwbtp1NjVy35Vv4vuvHeSb3/wmYFeFC4UIRyJVS4dK7zVoWpo6OzvZv38/yegQwbrGGa2bjA6RTafo7Oyco9QpDYbUlIoTPEVbqdnidrsxiyyoKBat9GrPPmohuZAgp5LP7SLk8RL0uFnp9xKqixDyeoj4vHQ2NZY7WVCTawj4+eCVV3BsYJBYOmO3H8oSj45wdqCfRCZLZqL2Q2OCpmrthzRoWlxKgcxQ39kZB0NDfWdHbUPNPg2GVFWFQqEcBPX19bF69cX1j6/UZDweD4X84qpuVkqvx6MPoFQLx9e//nVef/31Cec7RGgOBtjW2kxLMEjI6yXstTpHCHo82oPcLHI7nWxpbZlwfjZfIJ7NkshkiGWzxDIZ+uMJumNxTp86Neq5T2Pt3LmTu+++e/YTreZEQ0MDDQ2NDPeeY9XGS2e07nDvORoaGvURDnNIgyE1SjabZc+ePfz0pz8t3zV87LHH2LdvH9dffz2bN2/Wu1Fq1gWDQbKZ1AXVp66VbNp6ZksgsHiejaSWvquuuoqmpqZySVAsGiUej5OxO/ooGkNvPEFvPIFDhKDXS8jjJuhxE/RYAVHIa/UeVxoPeDy4tEToguULRRLZbLl0qDSeqBzP5khksxgzPgTyeb2E7NKiUumQdmq0+HR2buDlV/ZjisVpVws3xSIjA91csePyOU7d8qbB0DKXz+eJx+PE43EOHTrE87t3k0qnWVkXwRUM4HW52NzSzJ7TZ3jkkUdobW3h2muvY9WqVYRCIQKBgDboUxetubmZQj5PJpXAF6h9I+jpSMas57e0tEx851ep+XbJJZdwySWXjJueyWSIx+Png6RYbPT7aJRzw1GSyWTVEgmf203I48HvduF3u/C53Pjdbvt95au7PN/hWBw3NmaiUCySzudJ5XKkcqXX0ePp0ng+TyKbJZ0bXx1ORAgGAoQjEeqamlllBzmhUGhUO6JQKKSlz0tEZ2cne/bsITY8QKRxevlGbHiAfC6rVeTmmAZDS4wxhmw2Ww5wSkMikRg/LR4nncmMWn9TcxNv3n4Jq+rq+Na+VwB485pVXLmqg0M9fbxwuovvf//75eVFhIDfTygUImRfuEtDMBgcN+5y6SmnxmttbQUgER1aPMFQVIMhtXh4vV68Xi9NTU2TLlcoFMp5RGXgVAqekskkw4kEyWiMVCpFYZI2pT63C5/bjd9lBVB+lxu/p/Tebc0bE0zNVzU9Ywy5YpF0LkeyIphJVwtw8nlSuTzpXK5qO58Sp9NJwO+3euZrjFAXCIwLbkpDMBjUG4nLzPr16wEY6T837WBopP8cAOvWrZurZCk0GFoUisUi6XR6XDAzKtCxM6pEIkFugou1z21VhQi43bR4PKxtaSLodhPweAh63LSEgtT7/VXXdTocXLqije3trZyLxhhJZ0jmrKL9ZDZLMpsj0ddL/9mzJLNZchM0hvd5vQTtAKmUIVQLnkp3wxZLlSl1cTo6OnA6nQz3naWpfXG0TxvuO8uKFSu0AwW1pDidTurq6qirq5ty2dLNt2QyOa1hMJkgNTRcrrJXjcvpwO/2lAOogMfN6vo6NjQ2Uuf3zfjzDCZTHB8Y5MxIlGQ2Ww5sUrncpIGc1+sh4A8QCAYJBwK02V2Pjx38peAnENA8S00qHA7T1NzMcH83qzdPr9rbcH83TU1NRCKROU7d8qbB0DzL5/OkUqlxmUS1aclEwpqXTlft0U1ECHo8BNxuAnbPP4FIuFz3O1DxGnC7Z6UXIBGhoy5CxxT5ZLZQIJk9HyyVX3M5K3AaGeZcfx+JbI70BM9pcbtc5zOaYHDKjCgQCODz+fRu2yLk9XpZv349Z7tP03nZVbVOzpRymTQjg73svOmmWidFqZoRkXKJ00wad+fz+YnzvTHDmeEhDvX0AdAUDLKhsYENTQ2srq+rmqflCgVODY9wfGCQ4wNDDKestn0N9fVE6uppniCoGZuv6CMl1FzYsH49L738CsYUy49nmIgxRaIDPdpeaB5oMHSBjDFkMplJL+KjLvSJBKlUavI7Yg4Hfo/bqovtctLodrOyvg6/u6lcelN6DXo8+FyuBXsXyuN04vH7JyxpqlQoFkmWgqRSKVM2a1ddsKsqDPQz0nP+jl61RqYAAvh8vqoB1NjAqXLQu/u1t2XLFo4ePUp8ZJDQDLsenW/9Z0+CMVXbZiilJudyuYjYXUdPxRhDf38/R44c4fXXX2ffiRO8eLoLj9PJ2oZ60rkcPrebw7197D/bzenhEfLFIm6Xiw2dnVy/eTObN2+msXFhX1PU8rBu3TpefPFFEtHhKfO5RHSYfC6rVeTmgQZD05BOp3n88ccZHBycsrSmZGxd6Qa/F38ktCDqSi80ToeDsNdLeJoP8jPGkMkX7LrcuXJd7nGNWeMxhoeHOGdPn6jqHlSUQgUDBAJBNm3axA033DBbH1FNw44dO/j3f/93zhw9wCVXXl/r5EzIGMOZYwdoa2tj1apVtU6OUkuaiNDS0kJLSwvXXnstmUyG48ePc+TIEV7dv59cNssKt5sfHjmGw+PhzVdfzebNm1m3bp3e5FILztq1awEY6e+eMhga6e8etY6aOxoMTUOxWKSvt5ez586NC4BEoD0UZnVDHavr61gRCeNfor3oLBQiYgebLhqYuuSpJF8oEs9m6BqOcmp4mNNDIwyn0wDk8nlysRjRWAyvx0Nzc/NcJV9NIBAIsHPnTvbs2cP67Vfi8S3MLquHes+QiA7xjlvuXrAls0otVV6vl61bt7J161YSiQRHDh0qz9u2bRu33XZbDVOn1OTq6+sJh8NEB3pY2blt0mWjAz2EQiF9vtA80GBoGgKBAB//1V8ln88zMDBAT08P3d3d9PT00NPTw7mhIc7FYrxwqguX00FzMEhzIEBLKEh7OER7JIxnmZb61FI8k+VsNEpvLE5fIkl/IslQRbexLqeTFe3ttLW309bWRmtrK21tbdTX1+uf3Bq59tprefHFFzl56CU2venaWidnHGOKnDi4l3A4wuWXaz1upWrllVde4Wc/+1n5xmPA7ea1V19ly5YtWn1VLVgiwpo1azh+4tSUy8aG+tiwbq3+H5kHGgzNgMvloq2tjba2tlF/hDKZDH19feXgqLu7m5M9PbzW3QNYJ39rKEhHJMzKuggdkQj1fp+e4LOoUCzSG09wZiTK2WiUs9EYIymr1EdEaGpsZMX69Vxhf39tbW00NjZqI9kFprm5mWuuuYZnn32W9nWXEG5YWCV05954ndhQP/fcc492E69UDWSzWX7wgx+wb98+VtZFyu1H77p0K/984BCPPPII1113Hbfccov+RtWCtGbNGg4cOEA2ncLjq167JZtOkUrEWL16cfSuutjplWIWeL1eVq1aNa79QCKRoKuri1OnTnHq1CkOdnXx0hmrz/iAx0NHJERHJEJHXYSVkQgup/aCNl2JbJauYSvwOTMSpScWJ29XYYxEwqzp3Mi1a9awevVq7f54kXnrW99q3fV9+VnedOPt035S91zLplOcOLiX9evXc9lll9U6OUotSz/60Y/Yt28fV61ZxQ0b1vOdl/cD0BQM8MErr+DJI8f4yU9+Qjgc5rrrrqtxapUar/RfMTrUR/OKNVWXiQ5ZPShqMDQ/NBiaJblcrtyDXKkXudK4MYampib8fj8nT560HlyXzXK0f5Cj/YMAbG9r5fbtW2r8KRYHYwxf2f0imfz4DhHa2tpob2/H7/eTSqXo6upiYGCg3JOc3+8vD1oqtDD5fD5uu+02Hn30UU4efpl123bWOkkYYzi892mKhTx33nmnluoqVSPr16/nmWeeoTEQGNc21+10Um8/i6j0gEulFpqOjg5EhNgkwVBsqM96lElHxzynbnnSYKhCZXfZ1YKayvfJZJJUaV46TX6Sp1I7RMq9x9W5XLQ1NVodALisaT63i3XaQG7aRIQ7t29lIJEklbd6kkvn8tZ4KsnJI0dI561pk/F6PFZgVNHtdilQmmjc7/fjdrv1z/Ac27FjB0eOHOHlV16mvmUF9S0rapqerqOvMdTTxR133EFra2tN06LUcrZ582Y6Ojr4yRsnWREJj5p3ZiTKC6fPsHnzZlauXFmjFCo1OY/HQ0trK7Gh/gmXiQ/109LSgsfjmceULV/LPhjq6urise9+l3g8TjqdpjjB82vAuuvkc7vxuVz4XU7q3W7awyF8jfX4XVZX2j636/y4ywp0PE6n/nmeZRuaGtnQNHm3lEVjyOTzdqBkBUejxu1AKpXNkE4mys8xSudyk54HLqcTv99PU3MzDzzwgNZLnyN33nknp06d4vCep9j5c3fWrHe5kYEe3jiwh23btnHVVQv/gbBKLWUiwl133cUjjzzC1/e+TMTrJeBx8/KZc/zwyFHq6uu1Rzm14K3s6ODAocMTzo+PDLB9q9YWmi/L/l+c1+slFouRSqfZ1NzEqvo6q7SmotTG57Lea5ueMfvZqQAAIABJREFUxcUhYj/HyT2jLriNMeQKRdKVzzCyg6p0Ps+r57oZjMdpaGzEsUDasyxFXq+Xe++9l4ceeogDu59kx/W34nDO7yUrnYxxcPf/R0N9Pe9617v0poZSC8DKlSv5xCc+wbe++U1Od3UxnBJOD4+wceNG7rnnHgKBhdktv1IlHR0dvPTSS2RSSbz+0edrJpUkm05pFbl5tKD+yYnIO0TkdRE5KiK/PR/7bGlp4SMPPkg4HOb0yAipXI5ENkc8k2EknWYomWYwmaI/mWAomSKRzZIrFMo92KilwdilSLFMhgH7+x5MphhOpYlmMiQyWZK5HIPJJIPJFJs3beKBBx7QYGiOrVixgve85z1EB/t4fd9P5vV3V8jnOPDckwhF7rvvPv2DpdQCEolE+PBHPoLH46FgDJ2dndx///36O1WLwooVVtXv+MjAuHmlaaVl1NxbMCVDIuIEvgjcAnQBL4rIvxhjDs71vtva2vjoRz/KN7/xDZ4/1TWtP1wOETwuFx6XE6/TiccevC6rWlx5uv3e67LGveV51roepxOn/qG+IMYY8sUi2UKBbL5AppAnmy+QLRTI5MeMFwpkxrzPFopkC3lr+iRtvio5nU527tzJXXfdpR0wzJPt27dz88038+STT+IPRVi3de47VDDFIgdf+E8S0SHuu+8+Wlpa5nyfSqmZcblcuN1ustksnZ2denNKLRptbW0AJKNDNLWP7jEuGR0atYyaewsmGALeDBw1xhwHEJHvAO8E5jwYAmhoaOCTn/qUVUUqlyOTyYwa0un0uGnV5sXSaTLJFJlshmw2N619uxwOK1ByVQZV9ngpgCqNu5x4nNZr0OMh7PXidS3ONklFY0japXCJXI5sZdAyYQBTIGMHP9l8ftK2PSUigtfjwev1WkMoTMjnO/9+zODz+fB4PPiqLKPtg2rjpptuYmBggJdeegmfP0T7us1zti9jDD975TkGu09z5513snnz3O1LKXVxSgGQ1+utcUqUmr5AIEAoHCZhBz6VEtEhQuGwlnLOo4X0z24lcLrifRcw762VRQSPx4PH4yEcDk+9wiSKxSLZbHbaAVU2my0vk0inGcxkyMQSZDIZ8oXx3UiXuJwOQl4vIbebsNdLyOux3ns8hEvjXg/ueSrJMMaQzueJZ7LEMxlimSzxrDUeL41ns8Qz2UlL4TweN16PHYj4A3i9XiJTBCvV5mnvb4tfqdF0NBrlyEs/xeMP0Ni2auoVL8DpI/s598ZhbrjhBu0wQSml1Jxob2uju398MJSMDtOupULzaiEFQ9MiIg8CD4L1FN+FzOFw4PP58Pl8F72tfD5fDpay2SypVIpYLEY0Gi2/RqNReqJRjg72kKtS7cvrchH2egl63KOCpJDHeg17PQQ9nnK1vdZQaNw2svkC8awd1GSyxDKZcmATz2SI2yU9pQegVvL7fEQiEcKtbayIRKzxcJhIJEIoFBoVyHg8Hq3yoEZxuVzce++9fOUrX+Hg8z/iihtuI1TfNOk6obrJ54/Vc/oYbxzYw+WXX84tt9xyMclVSs2xwcFB4vE4AM8++yyXXnopoSr5llILUWtrK2+cOIExpnzD1hhDMj5M6zatkTCfFlIwdAaorDi5yp42ijHmy8CXAXbt2rVsejFwuVy4XK5pFZuWnpdUCpAqg6VYLEZ0ZITT0Six3n6KY4IWAS5d0cbPb+zk5s2d5em9sTj/9/Wf0R2Njdufx+0mHIkQaWxi7Zggp/LV7XZf9HFQy5vP5+O+++7jS1/6Eq899x9cceMd+AIT//nZuOPqaW97uO8cR/Y+zfr167n77rs1GFdqASoUCkSjUfr6+vjeY48BEPF5iQ4P89BDD/Gud72LpqYmwuGw/obVgtbc3EwhnyeTSpTzsUwqQSGfp7m5ucapW14WUjD0IrBJRNZjBUHvA+6tbZIWJxEpl0hN9oDIYrFIMpkcFTCdO3eOF154gRODw7z9kk2sa6xn98nTPHviFIFAgJtvvpmGhoZRQc5slHwpNV11dXXcf//9fPnLX+a1Z/+DK268HZf74h5Ml4wNc2D3kzQ1NfH+979f24YpVQOlPGl4eJiRkZHyMDw8zIg9LRaPl6tXh7xe2kJB3E4nd25fz3f3H+CrX/0qYHVyFA6Hqauvp76+nrq6ulFDfX09gUBAq1CrmikFPKl4tBwMpeLRUfPU/FgwOb4xJi8inwSeAJzA14wxB2qcrCXN4XAQCoXGVSvYuXMn33vsMb67/zX8bjepXI7LL7+cO+64Qxv0qQWhvb2de++9l4cffpjDe55i+9U3X/Cfmlw2w4HnfojX4+b+++/H75/+M6mUUtOTz+etToZisfGBzsiIFexEoxTGtI91OR1EvD7CXg9rggEiTQ1EfF7CXi8dkTDfe9XqY2llXYQPX3Ul3bE4sXSaaDpDLJMhGh3hVH8fsXSGwpiaEC6Xi7pIZFzAVBoPBoP4fD7tOVTNiaYmqxp3Kh6lobWjPF45T82PBRMMARhj/g34t1qnY7lbtWoVv/qJT/DjH/+YY8eOcf3117Nt27ZaJ0upUTZu3Mhtt93G448/zomDe1m/fdeMt2GKRQ698CPSyTgf/vCHaWhomIOUKrW4GWPKbVUzmQypVIp0Oj3pUF7GXqdaO1YRIez1EvZ6aPN52bRyBWGfl4jXVw54/G7XtG90BD0eOpsaJ/wMyVzufJCUzpwfH+jnZ2fPEk+nqVb33u1247M76PH5/fh8Pvx+P16vF7/9frLB4/FoCZQaJxKJ4HQ6SSWi5WmpRBSn00kkEqlhypafBRUMqYXD5XJx8803c/PNN9c6KUpN6Oqrr6a7u5s9e/YQbmimuWPdjNZ/4+A+hnrP8q53vYu1a9fOTSKVqrFCoTBhwFIKbsYFOZXjmcyUz99zORx43S689uMgvC4XzS4X3rowXlcDXpcLn8uF3+0m4vMS8XkJejw45ilIEBGCHquToBVU7ym2WDTEs+cDpVQ+RyaXJ523Hu+QyefJxGPER4YZyBdI29PGljiN5bCrrnt9Pvyl1ypBVGWAVRloeb1eLZ1aghwOB/X1DaQT59tipxMx6usbtL3bPNNgSCm1aIkId9xxB2fPnuXIvp8Qrm/BGwhOa92h3rOcPvIKu3btYteumZcqKbXQnTx5kkceeYR0Oj3lsl6X6/zgdBB0u2j0evCGAlZw47aCmfPLOUe9dy2BP28OhxDx+YjMsB1svlAsB0aZfN4erwig7PfpfJ5MNkMmmSBmPy8vnc9P66HfwUCAD3/kI5O2A1aLT2NjA91957vXziTjtLdoDYX5psGQUmpRc7lc3HPPPXzhC1/g8N6nuPy6dyAy+R+zXCbN63uform5mdtuu22eUqrU/GpqaqK5uZmuri4cIly9djXt4fC4QMbjcs5bCc1S5HI6CDk9hLwX1pFL0RiyVYKo08MjvHja6lS3rb2durq62Uy2WgDq6+s5eer8IzYzqTj19etrmKLlSYMhpdSi19zczB133MH3vvc9zh4/zMrOydu4HXv1eXKZNPd86AE8novriU6phSoUCvHRj36UF154gf/4j//ghVNdrGusp84u/ajzecvjM2mbo2aPMYZULsdIOs1IKkM0nbbG02lODA4TCAS47bbb2LFjh34/S1BdXR3ZTJpCwSodzGbSGvTWgAZDSqklYefOnbz88sucOLiXllXr8Xir9wo3MtBDz6mj3HjjjXR0dMxzKpWaXw6Hg6uvvppt27bxwx/+kDNdXZzu7SeTzY5azu102oGRtyJIOh8sBT1u/TN+AYwxxLNZRtJpoqlMOdCJpjOMZKzgJ18Y3ebI5/PSUN/Af3nzm7n55pu1F9clrBT4ZFIJBBk1Tc0fDYaUUktCqf3QX//1X3PiwF4277xu3DLGFDn68rNEIhFuuumm+U+kUjUSiUS4++67AesPejqdZmhoiOHh4XGvXb39ZHO5Ueu7HA67PY2X1lCQ6zesWxLthGZbJp/nmeMn6E8kGclkqnbp7fN6aWxqYsWKDrbW19PQ0EB9xas+u2/5KPUal00lx01T80eDIaXUktHa2spVV13F7t27WXPJDnzB0b1G9Z89SXxkkPe+971aPU4tefl8nlQqRTKZLL9Wjo+aVnpNp8mNCYQA8sUig8kkw6kUsUyGt6xbo8FQFZl8nmMDg0TTGYoT9MCXzmTo7+sjEY8zODCAPxAgEAjg9/sJ2OOV7ytftVe5pSUctvKobCY1bpqaPxoMKaWWlOuvv57nn3+erqOvsXHHNeXpxhhOH3mVhoZGLrvsshqmUKmL19PTw9GjR0kkEhMENSmy2fFBTYlDBL/HbXV37XIRcrtpDgbw19fhd7vwu1343O5yd9il926HQ6vLTSLi8/HRa95sPZupUCCdy5PK56zXnP2arxjPZUkPJBnpyZOyl5msG3Ovx3M+OBoTNAWDQS655BJ9YOciUg6G0slx09T80WBIKbWk1NXVsWPHDl7Z/yprt+7E7fECMDLQTWyoj3e+8536DAe1KBljOH78OM888ww/+9nPAKt6aDlYcbkIul00Bfz46sLWdJcVxFjBjAu/y43P7cLjdGpQM4dEpNxbXx3Tr/ZWCqLOB0s5Uvn8+WCqFEhlM6QScYbyBSvYyuYwwL/+67+yfft2rrvuOtasWTN3H1DNCp/Ph8PhIGt3f+9wOLSaZA1oMKSUWnKuvvpqXnrpJfrPnmTFus0A9J46hsfj4Yorrqhx6pSaGWMM+/fv55mnn+Zcdzdup4Or1qziytUrCXk8GtQsIZVBFNX7gKnKGEM0neGFU128cuggBw4cYM3q1dxw441s3bp17hKsLorD4cAfCJDLWtXk/IGA3qyrAQ2GlFJLzsqVK2loaKCv6xgr1m2mWCzSf/YEW7ds0bZC6qKJyDuAvwScwN8ZY/54Lvd39uxZHn300fL7XKHI86e6eP5UF26nE4/LKunxOB32qxOPy351uirGrene8rhr1LIurQI3r4wx5IpFsvkC2UKBbCF/frw8rUA2n68Yr5heOeQL5AqFUds/dfo03/jGN/jsZz9LfX19jT6lmkowGCSXSZfH1fzTYEgpteSICJdffjlPP/00+WyG2PAAuWxG2wqpiyYiTuCLwC1AF/CiiPyLMebgXO2zo6ODj33sY8TjcbLZLJlMpjyMfZ/JZEhmMgxlMmQyCbLZLLl8frqfrSJIqgispgiuzgdZVnBllWwszWp4xhj74aj5KkHLzIKXbD7PxK2DRnO73Xg9HjxeL157qLdfPR5PedrY95FIRAOhBS4YCDAQTSJAJKLdqNeCBkNKqSWps7OTp556ipHBXmKDfYgIGzZsqHWy1OL3ZuCoMeY4gIh8B3gnMGfBkIiwevXqC16/UCiUg6apgqlqwdVIJkM2dX7+2K6iq3E6HAQ8HoJul/XqcRP0eAh43ATd9qs93e+u7TOMivaDTxPZLIlsjuSY10Q2SzKXK0+bqJe4Sk6n0wpeSoFJIEhoGsHLRO+16tTSFQgEOHnqFACr2rfUODXLkwZDSqklafXq1TgcDqIDPUQHe2lvb9eGqWo2rAROV7zvAq6qXEBEHgQeBBZEI3an04nf78fvn0EjlEnk8/lJg6d0Ok08Hh819MViJJJ9FKsEUiJiB05uO1hyjw+g7PcBtweH43zg1BoKVU1joVi0A5gqgU1pmh3gpLLZqiU0TqeTYCBAKBymrrmFlaEQIXvw+XyTBi8ul/69UtOzc+fOcnf2O3furHFqlif9tSqlliSPx0NbWzuxoT7iwwNsfpN2nKDmhzHmy8CXAXbt2jXdmlCLhsvlwuVyzbh9Q7FYrBooJRKJ8+9jMc7E48QHBslPUL0v6PWwc2UHb169ips3d5anZ/MFnjt5ilfOdZOaoFtxj9tNMBgkFA7THAqxzg5ugsFgOdCpDHiWYlU/tbBs2bKFLVu0RKiWNBhSSi1Zra0tvPrqaxSLBVpbW2udHLU0nAEq66ytsqepKTgcjvIDRaf6PRpjyGaz4wKneDzOmTNneOb113n1XA8/v3EDG5sbOdjTy4+PnSCeybB9+3ba29vHBTehUEg7UFFKjaPBkFJqyWpubqZYLJTHlZoFLwKbRGQ9VhD0PuDe2iZp6RGRcrWzag8RPXr0KI//4Ad879UDBDxuktkcKzs6eP8ddyyIqolKqcVDgyGl1JK1bds2jh8/jsfjuagG6EqVGGPyIvJJ4AmsrrW/Zow5UONkLTsbN27kU5/+NLt37+bw4cPs2LGDnTt3akcDSqkZEzONXlEWql27dpk9e/bUOhlKKbWsicheY8yuWqdjIdJ8Simlam+yfEpvoSillFJKKaWWJQ2GlFJKKaWUUsuSBkNKKaWUUkqpZUmDIaWUUkoppdSypMGQUkoppZRSalnSYEgppZRSSim1LGkwpJRSSimllFqWNBhSSimllFJKLUsaDCmllFJKKaWWJQ2GlFJKKaWUUsuSBkNKKaWUUkqpZUmMMbVOwwUTkT7gZK3TscQ1A/21ToRSF0HP4bm31hjTUutELESaT80L/Y2rpUDP47k1YT61qIMhNfdEZI8xZlet06HUhdJzWKmlTX/jainQ87h2tJqcUkoppZRSalnSYEgppZRSSim1LGkwpKby5VonQKmLpOewUkub/sbVUqDncY1omyGllFJKKaXUsqQlQ0oppZRSSqllSYMhpZRSSiml1LKkwZCqSkTeISKvi8hREfntWqdHqZkSka+JSK+IvFbrtCilZp/mU2qx03xqYdBgSI0jIk7gi8AvANuAXxKRbbVNlVIz9jDwjlonQik1+zSfUkvEw2g+VXMaDKlq3gwcNcYcN8Zkge8A76xxmpSaEWPM08BgrdOhlJoTmk+pRU/zqYVBgyFVzUrgdMX7LnuaUkoptRBoPqWUmhUaDCmllFJKKaWWJQ2GVDVngNUV71fZ05RSSqmFQPMppdSs0GBIVfMisElE1ouIB3gf8C81TpNSSilVovmUUmpWaDCkxjHG5IFPAk8Ah4BHjTEHapsqpWZGRL4NPAdcIiJdIvIrtU6TUmp2aD6llgLNpxYGMcbUOg1KKaWUUkopNe+0ZEgppZRSSim1LGkwpJRSSimllFqWNBhSSimllFJKLUsaDCmllFJKKaWWJQ2GlFJKKaWUUsuSBkNKXQQReXaK+SdE5FURedke3jIHafixiOya7e0qpZSafxeSr4jIOhF5bb7SWCVN5XxI8z212LhqnQClFjNjzHQu8j9njOmvNkNEnMaYwiwnSyml1CJ1IfmKiKybswRdGM331KKhJUNKXQQRiduvK0Tkafsu2Gsicv1k64jIn4nIK8A1IvI/RORFe70vi4jYy1XeaWsWkRP2uF9EviMih0Tk+4B/zj+oUkqpeXEh+cqY9deJyDMiss8e3mJPd4jI34jIYRH5oYj8m4j8oj3vVnv6XhH5KxF53J4eFJGvicgLIvKSiLzTnj6jfEjzPbWQacmQUrPjXuAJY8wfiYgTCFTM+08RKQAZY8xVQBB43hjzmwAictAY84f2+NeB24EfTLKvjwNJY8xWEbkc2DcHn0cppVRtzSRfqdQL3GKMSYvIJuDbwC7g3cA6YBvQChwCviYiPuAh4AZjzBsi8u2Kbf034EfGmA+JSD3wgog8CXyUyfMhzffUoqHBkFKz40WsTMUN/JMx5uWKeWOrCxSAxyrni8hvYWV0jcABJs8UbgD+CsAYs19E9s/GB1BKKbWgzCRfqeQGviAiV2DlN5vt6dcB/2iMKQLdIvKf9vQtwHFjzBv2+28DD9rjbwPuFJHP2O99wBqmzoc031OLhlaTU2oWGGOexrpYnwEeFpH7Jlk8Xaovbd+R+xvgF40xlwFfwcpsAPKc/436xm1FKaXUkjXDfKXSbwA9wA6sEiHPRSRDgLuNMVfYwxpjzKEL2I7me2rB0mBIqVkgImuBHmPMV4C/A3ZOc9XSxb5fRELAL1bMOwFcaY9XTn8aq/oEInIpcPkFJlsppdQCdRH5Sh1wzi4B+iDgtKf/FLjbbjvUBtxkT38d2FDRCcM9Fdt6AvhURZueN9nTLyYf0nxPLSgaDCk1O24CXhGRl7Aykr+czkrGmGGsu2KvYWU6L1bM/lPg4/Y2myum/y0QEpFDwB8Cey869UoppRaam7iAfAWr1OV+u7OCLUDCnv4Y0AUcBL6B1e5mxBiTAn4V+HcR2QvEgBF7nc9hVbvbLyIH7PdwEfmQ5ntqoRFjTK3ToJRSSiml5piIhIwxcRFpAl4ArjXGdFdMF+CLwM+MMX9R29QqNT+0AwWllFJKqeXhcbtXOA/wOWNMtz39IyJyvz39Jaze5ZRaFrRkSCmllFJKKbUsaZshpZRSSiml1LKkwZBSSimllFJqWdJgSCmllFJKKbUsaTCklFJKKaWUWpY0GFJKKaWUUkotSxoMKaWUUkoppZYlDYaUUkoppZRSy5IGQ0oppZRSSqllSYMhpZRSSiml1LKkwZBSSimllFJqWdJgSCmllFJKKbUsaTCkZoWInBCRm+3x3xGRv5vFbcdFZIM9/rCIfH4Wt/0lEfnvs7W9Gez34yLSY3+2pirz3yUip+35b5qH9Py+iHxjrvdzIUTkpxMdAxG5SUS6Kt5fIiIvi0hMRD49C/sun9cLlYhcLiLP1jodSs0GzUtmvN8Z5SUL8ZomIutExIiIq8bp0Hyw+vYX3Dkz1sXmgxoMzZB9UqTsC0tp6Kh1uhYSY8z/NMZ8eKrlROTHIjLlcsaYkDHm+MWmS0R+WUR+MmbbHzPGfO5itz3DdLiBPwfeZn+2gSqL/SnwSXv+S/OZvhIR+SUR+dYUy0zrO7yINNwBxGZwDH4L+E9jTNgY81ci8lkRec3OFN4Qkc/OYVqNiGycq+1PtB9jzH5g2D5WapHQvGRqmpdMmY5FkZfMlP19psf8Nq6pUVo0H5xZWhdlPqjB0IW5w76wlIazlTNrfXdjqVjCx7EN8AEHJllm7UTz5/G43Ab82zztayIfA74+g+XHHjcB7gMagHcAnxSR981e8haMbwIfrXUi1IxpXjIPlvBxvKi8ZIH75JjfxnOVMzUfnJTmgzNljNFhBgNwAri5ynQDfAL4GfCGPe124GVgGHgWuLxi+TcB+4AY8A/Ad4DP2/N+GfhJle1vtMe9WHd7TgE9wJcAvz3vJqAL+E2gFzgHPFCxHT/wZ8BJYAT4iT3tX4FPjdnnfuBdExyHD9rbGAD+W+VxAX4f+IY97gO+YS83DLyIdQH/I6AApIE48IVJjmPlZ3/Y/rw/tI/dU8Bae946e1lXRTp/DHwY2Grvq2Dvb7hie5+vWP4jwFFgEPgXoGPMd/AxO23DwBcBmeD4eIH/A5y1h/9jT9sMJOxtxYEfVVkvbs9PAMcqzrv/1/5OMoAL+G3gmH0cDlZ+V5XfQbVjA6y3j13MPpZfGLO8A+vcar6A73CLvc1B4HXgvRXbnfD7q3IMPUAKWDXm/H0YGLI/82eBLnvej8akZ3OVbf4V8NeT/L4nO6/fDDxnH4Nz9jHz2POervjO4sA9WBnP40Cfnd7Hx3yWXwaO28fhDeD9FfM+BByy13uC8+f4uP3Y01fax8pb62ukDtMb0LxkOr+530fzkrnIS6a8ptnz34Z1DR8B/sY+Rh+25znt778f6/r1SUbnMXXAV+3tngE+Dzgr1v1Te93j9vdUue6PS/up8pvRfFDzwVnPB2ueISy2gckzsB8CjfaJ+iasDOQqrB/+/fa6XvvkPgn8BuAGfhHIMf0M7C+wLq6NQBj4AfC/7Hk3AXngD+1t3wokgQZ7/hexLjQr7XS9xU7Te4HnK/a3w/4heKp81m32CXiDve6f2/usloF91E5fwN7flUDEnvdjxlzwxh7HKp/9YftHU9r3X5aOFZNkYJMc14crjvvPY12cd9rb/mvg6TFpexyoB9Zg/bjfMcF58ofAbqAVaMH6A/O5idI5wfm0ccx59zKwuuK4vAfowLpg34N1YVgx9juotk+si9mf25/zBvuYVi5/NfDcTL9DIAicBh7AyqjeZB/TbVN9f1WOwXYgMWbaHwPP2OfHauA17ExgonOqYp4ALwEfm2D+VOf1lfZxcdnH8xDw65N8Z03A3fZxCwP/CPxTxXGKApfY71cA2+3xd2L9idpq7+t3gWcn2k/F9CgVf5J1WNgDmpdM5zf3+2heMhd5yZTXNKwAIAq8257/a1jnVukYfAzrj/gqrD+8TzI6j/k+8BDWta4VeAH4aMW6h7Gu4Y3AfzL9YEjzQc0Hx+2nYvoF5YM1zxAW22D/GONYUfFwxZdqgJ+vWO5vsS9YFdNeB260T7KzVNwJwrrATZmB2SdyAuismHcN5+983YQVGVdexHvtk9dhz9tR5XP5sKLvTfb7PwX+ZoJj8D+A71S8DwJZqmdgH2LMncyK9cb9YMcex8rPbo8/PGbfIay7IKu5+Azsq8CfjNl2DlhXkY7rKuY/Cvz2BMfoGHBrxfu3Ayfs8XHprLJ+tQzsQ1Ocmy8D7xz7HYzdJ1bmmweCFfO/NWb5zwH/fabfIVZm9MyYZR4Cfm+q76/Ktq8FusdMO07FnwbgQaafCfwB8AoT3DViivO6yvK/Dnx/ou+syvJXAEMV2x7GyiT8Y5b7v8CvVLx3YP0JXTvZfrDuvt4w2Tmiw8IZ0Lxkyt8cmpfA3OQlU17TsKpVPVcxT7D+4JeOwY+wgxv7/c2cz2PasEpu/BXzfwmrHUtp3Y9VzHsb44OhJOd/G/sq0q75oOaDE+6HC8wHtc3QhbnLGFNvD3dVTD9dMb4W+E0RGS4NWBfZDns4Y+xvznZymvtuwYqw91Zs99/t6SUDxph8xfsk1o+tVNR7bOxGjTFprCoWHxARB9aFa6I6qh2Vn9UYk8C681fN17GKN78jImdF5E/sRp+TOT3d+caYOFYx9Gw0PO6g4nuwtz2AdeezpLtivHRcp9yWPX6xaRx1XETkPrvHmNLedEvuAAAgAElEQVR5cCnWdzyVDqyLUWJM+irdyvl60jP5DtcCV405798PtFf7HFN8f0NYd5LGpr3yOEzrdyMin8TK3G8zxmQmWGzS81pENovI4yLSLSJR4H8yyfEWkYCIPCQiJ+3lnwbqRcRpb/serDuk50TkX0Vki73qWuAvK47fINYfkZXV9lMhjJWxqMVD8xLNS2D+8xJgymva2O/GYFWbpNp8xp+zbqxrW+ncegirhKjautXO2U9X/DZ2TrAfzQerp7sqzQcnpsHQ7KrMkE4Df1TxY643xgSMMd/Gqme5UkSkYvk1FeMJrEwKABGp/AH1Y92R216x3TpjzEQX0kr9WPVIOyeY//dYP9i3AkkzpsFihXNYmXEpfQGsotBxjDE5Y8wfGGO2YVWjuB3rxwijj9eo1Sb7EGP2HcIqKj6Lddyg4tgx+uIz1XbPYv0AS9sOYn2uM1OsN+W2sL7fsxMsO13l9IvIWuArWPW0m4wx9VhF5aVzatQ5xOjjcA5osD9fZfpK227HKq7eBzP+Dk8DT40570PGmI9XLDPR9zfWUWsRqbz4jTr3GP27qUpEPoRVr/ytxpiuSRad6rz+W6yqHZuMMRHgdzh/vKv5TeAS4Cp7+RtKmwYwxjxhjLkF61gfxvo+wTqGHx1zDP3GmAm7DbWPkQerxEAtfpqXjKF5Sdls5CUlk13TzmFVgQOsC3Hl+7HzGX1dPo1VMtRccW5FjDHbK9ad0XW8guaDmg9O9BkvOB/UYGjufAX4mIhcJZagiNwmImGseqp54NMi4haRd2M1Sit5BdguIleIiA+rqBcAY0zR3vZfiEgrWCeAiLx9qgTZ634N+HMR6RARp4hcIyJee/5zQBGrUeRkPZd8F7hdRK4TEQ9Wneaq55KI/JyIXCYiTqy6nDl7H2A1TNwwVbqruLVi358DdhtjThtj+rAymw/Yn+1DjM6se4BV9nrVfBt4wD7uXqw7Hs8bY05cQBq/DfyuiLSISDNW0fNsPr8giHUB7gMQkQew7oiVvAzcICJrRKQO+K+lGcaYk8Ae4A9ExCMi1wGV3VH+AvDvpbvNM/wOHwc2i8gH7XPbLSL/RUS2VixT9fsb+wGNMVmseug3Vkx+FPivItIgIquAT012kETk/Vjf4y1m6i51pzqvw/bnj9t3rz4+Zv2xxyKM9WdzWEQagd+rSFebiLzTzogzWNWlSsf0S/Zn3G4vWyci75lkP2Adox9NcrdPLV6al6B5yRzlJZNd0/4VuExE7hKr57ZPMDqYeBT4Nfucqcfq2AAAY8w54D+APxORiIg4RKRTRG6sWPfTIrJKRBqw/qRfCM0HNR+sdMH5oAZDc8QYswerN5kvYBVzHsWqZ1w6ud9tvx/EKib8XsW6R7BOwCexepsZ9TwDrIvOUWC3WMWOT2JF3tPxGeBVrJ5QBoH/zejz4BHgMia52BpjDmBdGL+FdRdhiNHF55XasX5cUayGdk9xPnP8S+AXRWRIRP5qmunH3u/v2em/EvhAxbyPYPWsMoDV8LDyLsKPsLqb7BaR/iqf60ngvwOP2Z+rE7jQ7ic/j3Wh3Y91vPfZ02aFMeYg1h+N57AuCpcBP62Y/0Osqir7gb1YF+dK92I1yB7EOpaPVMwb25XotL9DY0wMq/73+7DucnVjnWPeiu1N9v2N9RBWzzYlf4BVJeANrMx2qu5GP491V+tFOf+8ii9VW3Aa5/VnsI5bDOtP5D+M2cTvA38vVrH+e7F6ffJj3UXfjVUFqcQB/D9Yx2gQ6yL+cTsd38c6Zt+xf9+vYWXME+0HrLvwVT+XWtw0LynTvGT285IJr2nGmH6szgn+BOsYbLPTUfqj+RWsa/B+rAb5/4YVmBfs+fdh3aU/iPW9fhfr7n9p3SewgvV9VJyzM6H5oOaDs5UPijFTlfaq+SAiD2M1gPvdGqfjPuBBY8x1tUyHqg37DmA3sMEYE52D7T/MDM9zEfkp1jMnFsUDA+ebiFwOPGSMqclDCdXConmJmgtitf/qwur6+D+rzP8F4EvGmLXjVl5kNB9cfC42H1yqDyJTF0CsuqG/ivU8AbU8NWL1njPrGcCFMsZcW+s0LGTGevK2BkJqwdC8ZGmwq0w+j1XN6bNY7Tx22/P8wM9hlUy0YZVyfL82KZ11mg8uMhebD2o1OQWUL3p9WEXN36pxclSNGGN6jTF/W+t0KKUWJ81LlpRrsHoM7MdqT3OXMSZlzxOs6lpDWNXkDmG1Z1r0NB9cfrSanFJKKaWUUmpZ0pIhpZRSSiml1LK0qNsMNTc3m3Xr1tU6GUoptazt3bu33xjTMvWSy4/mU0opVXuT5VOLOhhat24de/bsqXUylFJqWRORaT0BfTnSfEoppWpvsnxKq8kppZRSSimlliUNhpRSSimllFLLkgZDSimllFJKqWVJgyGllFJKKaXUsqTBkFJKKaWUUmpZ0mBIKaWUUkoptSxpMKSUUkoppZRaljQYUkoppZRSSi1LGgwppZRSSimlliUNhpRSSimllFLLkgZDSimllFJKqWXJVesEKKXUXEkmk+zZswe3281VV12Fw6H3f5RSSi0M3d3dHDp0aNS0LVu2sGLFihqlaHma02BIRH4D+DBggFeBB4AVwHeAJmAv8EFjTFZEvMAjwJXAAHCPMebEXKZPKbW07du3jyeeeAKAjo4O1q5dW+MUKaWUUpYnnniCI0eOjJp28uRJfvmXf7k2CVqm5uw2qYisBD4N7DLGXAo4gfcB/xv4C2PMRmAI+BV7lV8Bhuzpf2Evp5RSF6yvr6/quFJKKVVrvb09bF3dzG/d/RZ+6+63sHV1M329vbVO1rIz13VGXIBfRFxAADgH/DzwXXv+3wN32ePvtN9jz3+riMgcp08ptYT19fWxsimC0+HQYEgppdSCkc1mGRkeoTkSwOlw4HQ4aI4EGB4ZIZvN1jp5y8qcBUPGmDPAnwKnsIKgEaxqccPGmLy9WBew0h5fCZy2183byzeN3a6IPCgie0Rkj/65UUpNpFAocO7sWdrqg7TUBfj/2bvz4Div887339M7ekNj31cC3HdSG2VriSTLSeRlnMRxYnsUl8cae6ZmcpO6cyf5496ZujV3alIzVZls5bETJXbsSPZYiq3VpCVKlEhKlERwE0lxAUDsC7E0Gt1A733uH90NiSK4ieh+u9HPp4oFdPeL7p8gAP0+7znnOaOjo0ZHEkIIIQC4fPkyGqjxOpfuy35+WUaH8iqX0+QqSI/2dACNgAv47O0+r9b6+1rr3Vrr3TU1Nbf7dEKIVWpiYoJYPE5LtZfmai8jw8MkEokbf6EQQgiRY+Pj4wDU+lxL92U/zz4m8iOX0+QeBi5prae01nHgn4F7AV9m2hxAM5C9XDsKtABkHi8n3UhBCCFu2cDAAADN1V5aqr3EEwkZHRJCCFEQxsfHsVnN+FyOpft8Lgc2q1mKoTzLZTE0BNytlHJm1v48BJwFXgd+O3PM48Bzmc+fz9wm8/hrWmudw3xCiFXs9OnT1Ja78DrttNf6MJtMnDlzxuhYQgghBCMjw9T73Hx0ebxSinqfm5GRYQOTlZ5crhl6h3QjhGOk22qbgO8D/xH4Y6VUL+k1QU9mvuRJoCpz/x8Df5KrbEKI1W1ubo6hoSHWt1QD4LBZ6Kjz8f7775NKpQxOJ4QQopTF43EmxidorPRc9VhjpYeJ8Qni8bgByUpTTrvJaa3/k9Z6vdZ6s9b661rrqNa6X2t9p9a6S2v9O1rraObYSOZ2V+bx/lxmE0KsXsePHwdgQ6YYyn4+Pz9PX1+fUbGEEEIIxsbGSKZSNFa5r3qsqcpDMpWSad15JNuxCyFWlXg8zttvv01nfQWV7rKl+9c3V+Ny2Dh06JCB6YQQQpS6S5cuAdBSXX7VY83VXuDDda8i96QYEkKsKidOnGBhYYG71jVdcb/FbGJ3VwO9vb2MjY0ZlE4IIUSpGxgYoNrrxGm3XvWY026lptxFf79MkMoXKYaEEKtGNBpl//79NFR6aKu5+orbjjUN2K0W9u3bh/RnEUIIkW/xeJyBS5doq736PSqrrcbL0OCgrBvKEymGhBCrxptvvkkwGOTh7R1XdOjJKrNZ+NTGFnp7ezl37pwBCYUQQpSywcFB4okEnfUV1zymo76CeCLB4OBgHpOVLimGhBCrwvT0NIcOHmRTaw3NVd5rHrerq4Eqj5OXX36JWCyWx4RCCCFK3YULFzCbTLQuM3shq62mHLPJxPnz5/OYrHRJMSSEKHrJZJJnnvkZFrPiwa3t1z3WbDLx6M5OZmf97Nu3Lz8BhRBClDytNWfPnKG9thybxXzN46wWM+215Xxw9qxM6c4DKYaEEEXvzTffZHh4hEd3rMFTZr/h8W21PnZ3N3LkyBF6e3vzkFAIIUSpm5iYwD83x9qmqhseu7apCv/cHBMTE3lIVtqkGBJCFLWhoSFee+01NrRUs7G15qa/7oEtbVR5nTzzs58RCoVymFAIIYSA06dPoxR0N1be8NjuxkqUSn+NyC0phoQQRSsUCvH0U0/hLbPx2Z1dt/S1VrOZL961jnB4kZ/85Cckk8kcpRRCCFHqtNacPHmSthofLofthse7HDbaanycPHlSpsrlmBRDQoiilEql+OlPf8ri4gJf2rMeh81yy89R63Px2Z1ruHTpEq+++moOUgohhBAwMjKC3+9n0y3MYNjUWoPf72dkZCSHyYQUQ0KIorRv3z76+/t5dMca6nzuT/w8W9rr2NFZz5tvvinTEYQQQuRET08PFrOJtc03Xi+Utba5CovZRE9PTw6TCSmGhBBF58SJExw6dIidaxrY2lF328/38PZOmqq8PPPMM7JYVQghxIqKxWKcOnWS9c3VOKw3P4vBYbWwvrmaUydPylYQOSTFkBCiqIyOjvLzn/+c1ppyHt7ecd1jXznRzysn+m/4nBaziS/dsx6HxcSPfvQjFhYWViquEEKIEnf69Gmi0RjbPsHFu+0ddURjMU6dOpWDZAKkGBJCFJGFhQX+6cc/xmkz8y/uWY/ZdP0/YZfnQlyeu7lOce4yG1/as57g/Dw//elPpaGCEEKI26a15u233qLa66Sl+tobgl9Lc7WXaq+TI0eOSCOFHJFiSAhRFJLJJD/5ydOEQiG+tGcDTrt1xV+jsdLDozs76evr45VXXlnx5xdCCFFahoaGGBsfZ1dXA0qpW/56pRS7uxoYHx9naGgoBwmFFEPimubm5njzzTdJpVJGRxGCV155hf7+S/z6rjU0VHzyhgk3sq2jnh1r6jl48KA0VBBCCHFbDh8+jN1qYXNb7Sd+jk1ttThsFg4fPryCyUSWFEPimvbt28e+fftkQbkw3MWLFzl48CA7OuvZ0n77DRNu5JHtnTRUevj5z/8Zv9+f89cTQgix+kxPT3P2zBl2rqnHZjF/4uexWczs7Kzn7JkzTE9Pr2BCAVIMieuYnJwEYH5+3uAkopTNz8/zs//9v6kpd/HQDRomrBSzycQX7lpHKpmQ9UNCCCE+kUOHDmEymbiju/G2n2t3dyMmk4lDhw6tQDLxUVIMiWtKJBMAchVCGEZrzXPPPUc0GuELd63Fav7kV9ZuVYXbwa/v7GJ4eJiDBw/m7XWFEEIUv7m5OY719LC1vRaXw3bbz+dy2NjaUcuxnh7m5uZWIKHIkmJILCsSieCfTU8PunDhgsFpRKk6e/Ys586d475NrdSUu/L++htba1jXVMXrr7/OzMxM3l9fCCFEcXrjjTfQaO7Z0Lxiz3nP+mY0mjfeeGPFnlNIMSSuob+/n1QqhbvKx8DAgGz2JfIuEonwwgsvUOdzc0d3k2E5HtnRiVnBc889J21NhRBC3JDf76fn6FG2tddR7nSs2POWOx1sa6+j5+hRWc+6gqQYEssaGBjAZDbTtm0dyWSS0dFRoyOJEnP48GGCwSCf3bUGk+nW25GuFE+Znfs2t9LX18fFixcNyyGEEKI47N+/H4A9G1pW/Lmzz5l9DXH7pBgSyxoYGMBTU0F5Q83SbSHyZXFxkcOHD7OuqYrGSo/RcdjRWU+5y8Grr74qo0NCCCGuaXJykhMnTrCrqwGv077iz+912tnV1cCJEyeWGl2J2yPFkLhKKBRibGwMX301VrsNd6VProiLvHrrrbeIRqN8alOr0VGAdHe5ezc0Mzo6KmvohBBCXNO+ffuwWczcs37l1gp93D3rm7FZzOzbty9nr1FKpBgSVzl37hxaa6ra0q0gq1rrGRwcJBQKGZxMlIJkMsnR996jq6GCWgOaJlzL5rZa3GU23n33XaOjCCGEKEB9fX2cP3+ee9Y347Rbc/Y6TruVe9Y3c/78efr6+nL2OqVCiiFxhYGBAfbu3YvT68ZdWQ5ATUf66saPf/xjFhYWjIwnSkBvby/BUIitHbnfXPVWmE0mNrfWcOH8eYLBoNFxhBBCFJBUKsUvX36ZcpdjRfYVupE7uhspdzn45csvk0qlcv56q5kUQ2LJiRMn+Pu//3uUzcLmz+xBqfSidVeFl42/dhejY2N897vfZWpqyuCkYjU7fvw4ZXYrXQ2VRke5ytb2OlJac+rUKaOjCCGEKCA9PT2MT0zwwOY2LObcn15bzCYe2NLG+MQEPT09OX+91UyKIQGkC6Gf/exnuGsq2P7Y/ZR53Vc8XtPexLZf/zQLkUW+973vSUtHkROpVIre3ot0NVRgNhXen6cqr5Mqr1PW0AkhhFgSDof51a/20VLtZUNLdd5ed0NzNS3VXn71q32Ew+G8ve5qU3hnGyLvJiYm+PnPf055XTVbH70Xq335nZK9tZVs+437iCcTPPXUU8Tj8TwnFavd+Pg44XCE9lqf0VGuqb22nIGBARKJhNFRxApQSv2RUuqMUuq0UupppZRDKdWhlHpHKdWrlPqpUsqWOdaeud2bebzd2PRCiEKwf/9+wuEwj+zoXJpVkw9KKR7Z0Uk4HJZW27dBiqESl0qleOrppzFZzGx48A5MZvN1j3eWe1j36V2MjY3x6quv5imlKBXZFu6FXQz5iMfjjI2NGR1F3CalVBPw74HdWuvNgBn4CvBnwJ9rrbsAP/DNzJd8E/Bn7v/zzHFCiBI2NjbGkSNH2N5ZT53PfeMvWGF1Pjc7Ohs4cuSIvC99QlIMCVxOJ/FojLnx6Rseq1MppgfTv2xOpzPX0USJmZiYwOWw4S5bfnSyENT60h3uJiYmDE4iVogFKFNKWQAnMA78GvBM5vEfAl/MfP6FzG0yjz+k8nkZWAhRUFKpFC+88DxlNgv3b243LMd9m9sos1l44YXnpZnCJyDFUIkzmUz8wR/8Ae3t7Zx74z3GzvVf89hUMsnZ199lsneIhx9+mPvuuy+PSUUpmJiYoKa8sIvscqcdm9UsxdAqoLUeBf4HMES6CAoAPcCc1jo7D3IEaMp83gQMZ742kTm+Kp+ZhRCF49ixYwwNDfPglnbKbBbDcpTZLDy4tZ2hoWGOHTtmWI5iJcWQwG638/jjj7N23TouvnWCoVNXbyqZjCc4/crbTA+O8Zu/+Zs8+OCDeZ0XK0rDzMw0VZ4yo2Ncl1KKKo+T6ekbj6SKwqaUqiA92tMBNAIu4LMr8LxPKKWOKqWOSvdNIVanhYUF9u7dS3O1ly3ttUbHYUtbLc3VXvbu3SvboNwiKYYEAFarla999ats2bKFS0dP03/0NFprABLRGKf2HWZufIovfelL7Nmzx+C0YjWKRCJEozG8TrvRUW7IW2ZjPhAwOoa4fQ8Dl7TWU1rrOPDPwL2ALzNtDqAZGM18Pgq0AGQeLwdmPv6kWuvva613a61319TU5Pq/QQhhgL179xKNRPjszjUFcXFYKcVnd64hGomwd+9eo+MUFSmGxBKz2cyXv/xldu/ezfCpC8yNp69oDhz/gNC0n6985Svs2rXL4JRitZqfnwfAU1b4xZDHaScgxdBqMATcrZRyZtb+PAScBV4HfjtzzOPAc5nPn8/cJvP4azp71UgIUTIuXbrEsWPHuHNtEzXlLqPjLKkpd3Hn2iaOHTvGpUuXjI5TNKQYElcwmUw89NBDAITnQ0sf6xvq2bx5s5HRxCqXHdZ32a0GJ7kxl91KLB6X9vJFTmv9DulGCMeA90m/J34f+I/AHyulekmvCXoy8yVPAlWZ+/8Y+JO8hxZCGCqRSPDcc7+g3OXgUxtbjI5zlU9tbKHc5eC5534hW0DcJCmGxFVcLhdKKaKLEQBiixG8Hq/BqcRql90wzmHgItSblc0om9wVP631f9Jar9dab9Zaf11rHdVa92ut79Rad2mtf0drHc0cG8nc7so8fu2OM0KIVengwYNMTU3zmR2dWC3X347ECFaLmc/s6GRqapqDBw8aHacoSDEkrmI2m7Hb7SRj6aveiVhc2miLnItE0sV3URRD1nTGbGYhhBCr38zMDAcOHGB9cxVdDZVGx7mmroZK1jdXceDAAWZmrlrWKD5GiiGxLJvNRiIzBSgZT2CzFe6+L2J1iMViANgK8Erbx2WvBmYzCyGEWN201jz//POYlObh7Z1Gx7mhh7d3YlLpzLK08fqkGBLLcrvdxBYiJOMJ4tEYLlfhLBAUq1N2/Y3FXPh/lqyWdEZZMySEEKXh/fffp7e3l/s3tRVHo58yO/dvaqO3t5f333/f6DgFrfDPOoQh6uvrWfAHWPDPL90WIpeKqRiymNIZZXGqEEKsfpFIhJdfeon6Cjc7uxqMjnPTdnY1UF/h5uWXXpJp3ddR+GcdwhA1NTXEwlGCM/6l20LkUjKZRCkwFcB+DTdiMqUzJpNJg5MIIYTItf379xMMhXh055qieI/KMinFozvXEAyF2L9/v9FxCpYUQ2JZ2Q3ETJkr4IWwoZhY3ZLJJCZVHH+SzFIMCSFESZiYmODI22+zo7OexkqP0XFuWWOlhx2d9Rx5+20mJiaMjlOQiuPMQ+RddvqPKbNQXE76RK6lUqmlEZdCl70ymEqlDE4ihBAiV7TWvPDCC9itFu7b3GZ0nE/svs1t2K0WXnjhBWmmsAwphsSy7Pb04sBYZq8h6SYn8qE4SiEhRKFYWFjgpZdeks6OIifef/99BgYGuH9zK84i2BD8Wpx2K/dvbmVgYECaKSxDiiGxrOy+QuFA6IrbQuSK1rpoqqHstFG5wiaEsQ4fPsxbb73F6dOnjY4iVpl4PM7evXup87nZ1ln8TaS2ddZT53Ozd+9e6YT6MVIMiWU5HA4AIguLKKVkZEgIIUTBWVxcBKTNvVh5hw8fJhAI8NC29qJqmnAtJqV4aFs7gUCAQ4cOGR2noEgxJJb10WlyNptNGiiInFNKQZEMtGRHhOT3QgghVp9QKMQbb7zB2sZK2mp9RsdZMW21PtY2VvLmG28QCoWMjlMwpBgSy/ro/impVEoWioucU0qRKpJpZ9mUUgwJIcTqc+DAAeLxOA9sbc/L671yop9XTvTn5bUe2NpOPJHgwIEDeXm9YiDFkFjW8PAwAA3rOojH41y+fNngRGK1M5lMFEktRCqVDpptPS+EMIas3xMrze/38+4777CtvZYqT37WS1+eC3F5Lj8jNVUeJ9vaa3n3nXeYnZ3Ny2sWOnknF8saHh7GVeGlsjm9aHBoaMjgRGK1M5lMpHRxjEBmR7CkGBLCWDJlVay0119/HYBPbWo1OEnuZP/bZHQoTd7JxbKmpqZw+rw4PE5MFjPT09NGRxKrXHZkqBimymVHhsxms8FJhBAgI0NiZfj9fo4fP872zjo8ZXaj4+SMp8zO9s46jh8/jt/vNzqO4XJaDCmlfEqpZ5RS55RSHyil7lFKVSqlXlFKXcx8rMgcq5RSf6mU6lVKnVJK7cxlNnFtsViMubk5yrwulFI4PW6ZJidyzmKxAB8WGoUsW7BJMSSEEKvHm2++iQLuXtdsdJScu3tdM4r0f3Opy/XI0F8Ae7XW64FtwAfAnwD7tdbdwP7MbYBfB7oz/54AvpvjbOIa3nvvPbTWVDTVAeBrqqW3t1dGh0ROZQuLRBE060hmMso0OSGMlW3us7CwYHASUewWFhY4duwYm9tq8DpX76hQltdpZ0tbDceOHSv535+cvZMrpcqB+4AnAbTWMa31HPAF4IeZw34IfDHz+ReAf9RpRwCfUqohV/nE8hYXF3njjTfwNdTgq68GoGVLNyaziV/96lfSVU7kTLYYKoaRoUQynTE7miWEMMZgZj3rpUuXDE4iit27775LIpHgju5Go6PkzR1rm0gkErz77rtGRzFULi9rdgBTwD8opY4rpf5OKeUC6rTW45ljJoC6zOdNwPBHvn4kc98VlFJPKKWOKqWOTk1N5TB+6bl06RJ/9Vd/xWI4TMeuTUv328ocNG9Zy5kzZ/jBD35AMBg0MKVYrbKFRSJZ+AV3dmRIiiEhjDM7O8v01BQms4nBwUEikYjRkUSRSiaTvHPkCB11PmrKXUbHyZtqr5PO+greOXKEZDJpdBzD5LIYsgA7ge9qrXcAC3w4JQ4AnV7xeEuXgbXW39da79Za766pqVmxsKUslUrx6quv8uSTTxInxY7H7sdbW0nvkZP0HjkJQNv29XTv2cGlwQH+8i//kgsXLhicWqw2xVQMZTNKMSSEcS5evAhA193bSKVS9PX1GZxIFKsLFy4QDIXYuab0JiTtXFNPMBQq6fO6XBZDI8CI1vqdzO1nSBdHk9npb5mP2ZX5o0DLR76+OXOfyLGjR4/y+uuvU7umhZ1feBBPdQUAodkAodkAkG5b2ri+g52fexBlt/KjH/2Iubk5I2OLVSZbWCSLYCqmjAwJYbyLFy9S5nFR192GxWYt6ZM5cXuOHj2Ky2FjTUOF0VHybk19Je4yG++9957RUQyTs2JIaz0BDCul1mXuegg4CzwPPJ6573HgucznzwP/MtNV7m4g8JHpdCKHTp48idPnYf19u7FYrdc91lXhZeOv3UUqleLMmTN5SihKgTXzs1cMI0PxTEbrDX5fhBC5EY/H6e3tpaKpFma6aRMAACAASURBVJPJhK+xhvMXzkuLbXHLFhcXuXD+PJvbajCXYFMck0mxubWGixcusLi4aHQcQ+T6//q/A/5JKXUK2A78V+C/AY8opS4CD2duA7wM9AO9wN8C/ybH2QQQDocZGBhYGg26GQ53GbYyO2fPns1hMlFqimmaXFKmyQlhqP7+fuLxOJUt6WlNVS0NBOeDjI2NGZxMFJuzZ8+S0poNLaW79GJDSw0prUv2IndOiyGt9YnM+p6tWusvaq39WusZrfVDWuturfXDWuvZzLFaa/1vtdZrtNZbtNZHc5lNpNlsNrq7u5nsHWLg+Ac3vKqWiMV5f99bxMJRNm/enKeUohQsFUNFME0uLsWQEIYZGBjg2WefxeqwU9GQPoGtbK7HbDHz1FNPMT4uk0rEzTt9+jQ+l4N6X+k0Tvi4Op8Ln8vBmTOnjY5iiNIbDxRXMJvNfP3rX2fHjh0MHv+AvndOXfPYRCzOiZfeZP7yDL/zO7/DPffck8ekYrUrpmlyyWQKk1Ky6aoQefbee+/x5JNPos2Kbb/xaUyW9O+grczO1l//NOFYlO9973ucPl2aJ3Xi1sTjcS7199PdWIlSyug4hlFK0d1YyaX+S8TjcaPj5J0UQwKz2cxv/dZvsWvXLkbP9hGPRJc9bnpwjAV/gN///d9n+/bteU4pVrtiKobiyRQWq4wKCZFPhw8f5he/+AW+hhp2fO4BXD7vFY97ayrZ8fkHKKvw8PTTT3PixAmDkopiMTg4SCKZpL3OZ3QUw7XX+UgkkwwODhodJe+kGBJA+qrAHXfcAcDs6OVlj5kdmcDtdrN+/fp8RhMlIjvlLF4ExVAimcJqkeYJQuTLiRMnePnll6lua2TzI3uw2G3LHmd3lrHts5/GV1/Ds88+Kx3mxHX19/djUorWmnKjoxiutaYck1Il2aJeiiGxpKEhvRB1IdNO++MW/PM0NjaW9FCyyJ1iaqCQkJEhIfJmcXGRZ599FrurjA3334EyXf89yGQxs/GhuzBZzDz9k6dLejNJcX0jIyPU+lzYLDLl2WYxU+tzMTpaervaSDEklkxMTADgrlp+uNhdWc74+Li0LhU5UUzT5GRkSIj8KSsrY/v27UQXwpw/fIzUDYqbRDzOuTfeIxGLc/ddd8vaPrEsrTWjoyPUV7gNzfHKiX4m5xaYnFvgnw6c4pUT/YZlqa9wMzo6UnLneVIMiSXZqwGemuXbbHtqKgkGg8zPz+czligRxVQMxZMp2WNIiDxRSvGlL32Jhx9+mMt9w5zad5h4NLbssdGFMCdfOoh/9DKf//znefTRR/OcVhSL+fl5IpEodQZ3kbs8FyIaTxKNJxmamufyXMiwLHU+F5FIlEBg+RlCq5UUQ2KJz5ceEQrPL/+LGJ4PYbVacblKt/2kyB2z2Yxi5dYM5fJqW3qanBRDQuSLUooHH3yQL3/5y4Sm/Jx48Q3CwYUrjgnNBjj+4gFioUW+/vWvc9dddxmUVhSDmZkZACrcDoOTFI7s92J2dtbgJPklxZBY0tHRgclkYm5satnH58am6OjokL1VRE4opbBYLEsbmt6uXF5tS6RS8nsghAG2bdvGN77xDVLROCdePEAkUxCFZgOcfPlNbCYL3/rWt1i3bp3BSUWhy57w+1xlBicpHNnvhRRDomRNTk6SSqWu2aXH6rAxeXmSRCKR52SiVFisluLpJicjQ0IYoqOjg2984xvEwtGl7qczQ2MkYnG+/e1v09jYaHBCUQxCofQFMk/Z8uc8pSj7vch+b0qFFENiySuvvILVYadxQ8eyj7duX09gLsB7772X52SiVFgslqJYM5RIaRkZEsJA9fX1AEv74sUjMex2+9J0byFuJBwOY7WYsZjlVDjLYjZhtZgJh8NGR8kr+QkQAJw5c4a+vj5at6275lqIisZafPU17N+/v+SuGoj8sFgsJFOFXwwlZWRICENZLBZsNttSMRQLR2U9q7gl4XAYu2yRcBW71SLFkCg9kUiE559/HndlOY0bOq95nFKKrnu2EYlGeemll/KYUJQKq8VaJCNDKWnXK4TBXC4X8Ui6q1w8IsWQuDWpVArzDfasKkVmkyJVBBclV5IUQ4Jjx44RCoVo27kRk+n6PxKuCi91Xa2cOnUKv9+fp4SiVJgtZpKpwt/fIJmUaXJCGM3j8RBdTF/Bji1G8Hg8BicSxURrjUKKoY9TKNlnSJSeDRs2YLVaGT/Xf8NfgFg4wszgGK1tbZSXl+cpoSgVZnORTJOTbnJCGK62tpbFuSCpRJLwfIja2lqjI4kiopQiVWIn/TcjpTVKlVaRKMWQoKKigkceeYTZkUlmhyeue+xAz1lSiST/4otfvOEokhC3ymwukpGhVEp+/oUwWG1tLfFIlPlpP1prKYbELXE4HETj0h3346LxBA5Hae29JO/mAoAtW7YA6d27ryeysEhdfb286YicMJvNRXGlLpXSsmZICINlr16rzLqPUruaLW6P3W4nGk+U3JSw69FaE0sksNvtRkfJKymGBMBS5xCr4/r99q0OO+HFxXxEEiXIZDKRKvCRIa01Ka1lZEgIg2X3vLPY0p0d4/G4kXFEkXG73QAsRuXnJmsxGkfrD783pULezQUAJ0+eBMDpu/4CVJfPi9/vZ2hoKB+xRIkxmUwFPzKUjSfFkBDGyk7lCQfSWz2UlZUZGUcUmey65/nFqMFJCkf2e1Fqa8Ll3VwwPT3NwYMHqetqxVVx/V+Apo1rsLvKeP6F50uu9aLIPZPJRIHXQkvFmhRDQhgru8FqYHIGKL0TOHF7ln5+pBhakv1elNrmxfJuLjh58iSpVIq6rtYbHmu2Wqhua2R8bJzx8fE8pBOlpBi6+2gphoQoCDZbelp3Iprea6jU1jmI21NdXQ3A9LxM/c/Kfi+y35tSIe/mgjvvvBO3283Ft06QiF1/7uzcxDRjH/SzceNGGhsb85RQlIr0yFCBF0OZj7JYWwhjJZNJIH2R7qO3hbgZNpuNCp9PiqGPmJ5fpMLnW7rQUCqkGBJ4PB5+7/d+j2hokXNvvIe+xgL26MIiH7z+LhWVlfzWb/2WnAyKFaeUorBLoQ9HhuTnXwhjzcykp8e5KsuvuC3Ezaqrr2dyToqhrMm5dMfgUiPFkACgvb2d3/iN32BmeIJLPWeuejwZT3D61SOQTPG1r3615HrQi/xQShX8mqECjydEyejr68PuKqNuTStmi5m+vj6jI4ki09zczGxwkXBM9hsKxxLMBhdpbm42OkreSTEkltx9993ceeedDL9/galLI1c8duGt4yzMBvjKV75CXV2dQQmFKBwyMrQ6KKV8SqlnlFLnlFIfKKXuUUpVKqVeUUpdzHysyByrlFJ/qZTqVUqdUkrtNDp/KRsYGKC8vhqT2YS3toqBgQGjI4ki09LSAsD4bNDgJMbLfg+y35NSIsWQWKKU4rHHHsNut+Mfm7riscD4NFu2bGHdunUGpRNCiJz4C2Cv1no9sA34APgTYL/WuhvYn7kN8OtAd+bfE8B38x9XQHpvvFAotNQB1VXhZXp6WrqcilvS0tKCSSmGpwJGRzHc8FQAk1JSDAmhlCIWi121+arFbl3a4E4IIVYDpVQ5cB/wJIDWOqa1ngO+APwwc9gPgS9mPv8C8I867QjgU0o15Dm2AA4dOgSAu6o889FHIpHgnXfeMTKWKDJ2u52m5mYGpBhiYCpAU3NzSXZllGJIXCEWi6G1xvKxTiIWm5VwOGxQKlEqtNYU+uyzbLxC73onbkoHMAX8g1LquFLq75RSLqBOa53dO2ACyM4NbgKGP/L1I5n7RB4dOnSIAwcOUN/dRkVjLQC1nc1UtTbw4osvcvz4cYMTimLS2dnJ+GyISNyYC77ReAKbzcY999yDzWYjakCOSDzB+GyIzs7OvL92IZBiSFwhO8XAZL7yR0OZTNK2VOSc1poCr4WW1gpJMbQqWICdwHe11juABT6cEgeATv+PvqX/2UqpJ5RSR5VSR6empm78BeKmxONxXn75ZX75y19S097E2nt3Lv0+KpOJjQ/cia+hhmeffZbXXntN3rPETVm7di1aawYm5wx5/Ug8ya5du3jsscfYtWsXkXj+f24HJufQWrN27dq8v3YhkGJIXCF7gqc/Nu9aay0nfyLn0iNDhV0OycjQqjICjGits3OrniFdHE1mp79lPl7OPD4KfHRCfXPmvitorb+vtd6ttd5dU1OTs/ClZHJyku9+97scPnyYxvWdrL//DpRJ0XvkJL1HTgJgspjZ/PA91HQ2s3//fv727/6O2dlZg5OLQtfS0oLDYadv3G/I6zusZnp6enjxxRfp6enBYTXnPUPfuB+H3V6S64VAiiHxMU6nE2+5l7mJ6aX7kokkwWk/TU0yG0TkViqVwlTgxZDJlM4nC7WLn9Z6AhhWSmU7wzwEnAWeBx7P3Pc48Fzm8+eBf5npKnc3EPjIdDqRI5OTk/zN3/wN/vk5Nj9yD917ti/NXgjNBgjNfrjew2y1sOH+O1h//x2MT4zz13/91wQCsh5EXJvZbKa7ey29E35SBlzkslstxGIx3n77bWKxGPbMJsL5ktKa3gk/Xd3dmM35L8QKgRRD4gpKKdavW8/c2BTZDV8CE1OkEknpJCdyLplMLhUbhSpbrMkUnFXj3wH/pJQ6BWwH/ivw34BHlFIXgYcztwFeBvqBXuBvgX+T/7ilx263o7Wmqq2Rqpab61dRt6YFX0PN0tcLcT0bN25kMRJjdGbe6Ch5Nzozz2IkxqZNm4yOYpj8lp+iKPh8PpKJBJr0lKBENL50vxC5lEwmC35kSCmFUkqKoVVCa30C2L3MQw8tc6wG/m3OQ4kr+Hw+du7cybFjx2jasAZXhfeGXxOYnGF6cIwHH3xQNgkXN7R27VrMZjMXRmdoqS43Ok5eXRidwWw2l+x6IZCRIbGMUCiExWpZWrthdTqW7hcilxKJBFZzYRdDABazNBQRIp8efPBBysrKOPXLgwRnrr/QfW58ivd/dRhfhY977703TwlFMXM4HHR1dXFuZKak1oNqrTk3MsOaNWtK+qKBFEPiKpFIBIvNunTbkpm/GolEjIokSkQikcBsKvw/SxaTiXg8bnQMIUqGz+fjiSeewGG3c+qXBwldoyBKF0JvUemr4IlvPUFZWVmek4pitWXLFuYXo4zNls6F37HZEPOLUbZu3Wp0FEMV/lmHyDuz2XzF4nCd0kv3C5FLsWgUq6Xwf86sFjOxWMzoGEKUlOrqav7VN/8ViVic6cGxZY+ZujQCWvOtb32L8vLSmu4kbs+GDRswm818MFw67fDPjUxhNplYv3690VEMJcWQuIrdbicRiy81UIhlRoRkEarItVgshq0IiiGbxSTFkBAGyM5QuNa6IafPSyqVIpEwZgNNUbwcDgdr164tmalyWms+GJ6he+3akh9BlWJIXGXt2rWkkini0fTJ3tSlURwOB83NzQYnE6tdNBrFZsAeC7fKZjXLtFEhDDAzMwNAWbln2cedmfuzxwlxK7Zu3UowHGV4evV3lRuenicYlilyIMWQWEZHRwder5dYOIrWmunBMbZu3YrFIs0HRe4kk0misRhltsL/OSuzWgiHw0bHEKLkZGcopBLLNzBJZkaEZCaD+CTWr1+P1Wrl7NDqnyp3dmgKq9XKhg0bjI5iOCmGxFVMJhNNTU2kkkl0KkUqkZRRIZFz2eKiGIohh81CeHHR6BhClJzsdJ7szIWPS8TiVxwnxK2w2Wxs2LCBcyMzJFfxxtrJVIpzozNs2LABm81mdBzDSTEklmW320HrpeYJcpVN5NrCwgIAZR/pZFionHbrUl4hRP6oG+1DllnrccPjhLiGbdu2EY7FuTR5/RbuxezS5BzhaJxt27YZHaUgSDEklmW1WtMLCDNvLFZr4Z+giuKW3cfKXVb4V6lcDhuxeJxoNGp0FCFKytRUevqSMi1f7KhMa/7scULcqq6uLsrKHKt6qtzZoSnKMnsrCSmGxDUopUBDtp+KqQj2fhHFLRgMAuCyF37h7XKkM8pGxELkz8WLF/nFL36Bp7qC8rrqZY+pbKnHWe7m6aefZnh4OM8JxWpgsVjYtGkzF8ZmiV9jbVoxiyeSXBibZdPmzbIWPEPOcMWyzGYzH20sKcWQyLVsMVQMI0OeTMZsZiFEbvn9fn784x9TVu5my6P3Yr5GC36r3cbWz34as93KD37wA2l0Ij6Rbdu2EU8k6R2fNTrKiusdTxd50kXuQ3KGK5b14Zqh1Ie3hcihQCCAzWrGbi38K1WesvTvQyAQMDiJEKVhYWGBRCJB85a1WO3Xv2Bid5XRuKGTSCQiU1nFJ9Le3o7b7eKD4Wmjo6y4D4ancbtddHR0GB2lYEgxJJa11L40UwxJtxGRa/Pz80tFRqHLjgzNz6/+vSiEKAQNDQ1YrVbmJ2/u5DRweYZyXzk+ny/HycRqZDKZ2Lx5C30TfqLx1bOBbzSeoG/Cz+bNW2TGz0fId0Isa3BwEGUyYck0ThgaGjI4kVjt5ubmloqMQme3WrBbLTIyJESemM1muru7mRoYXdpL6Fpi4Sizw5OsW7suT+nEarRlyxYSyRS9436jo6yYvnE/iWSKLVu2GB2loEgxJK4SjUbp7e3F6rBhtlpwuF2cOXPG6FhilQsE5vAWycgQgMdpZ25u9bZeFaLQ7Nmzh3gkxmTv9S/OjZ3rJ5VMsmfPnjwlE6tRa2srbreL8yOrZ6rc+dH0FLnW1lajoxQUKYbEVY4ePUoikcDmSJ+Y1nQ2cfHiRaanV88fBFFYkskkC6EFPM7iGBkC8DiszMvIkBB5k+18FQlef8PjSHBBumSJ22Yymdi4cRN9E35iq6CrXDyRpG9ijo0bN8kUuY+R74a4QigU4tChQ5TXV2PObH7ZvLELZVLs37+fxA2mJwjxSQSDQTQUzZohSGedD8qaISHyYXFxkaeffhqH20nL1rXXPbZ950aU2cRTTz1FLBbLU0KxGm3atIlEMsXAKtiA9dLkHPFEkk2bNhkdpeDIpRNBKpXi4sWLHD16lHPnzpHSmq33forB4x8AYHM6aFzfyalTp7h48SI7d+5k9+7d1NbWGpxcrBbZFtXFsmYI0i3AQ6HLpFIpucomRI6dPXuWQCDAls/suWE3OYfbSeedWzh/sIe+vj42bNiQp5Riteno6MBht3NxbIa1TVVGx7ktF8dmcNjt0kVuGVIMlahkMsnc3BwnTpygp6eHQCCA1WGnceMaGta24/R5loohgM47t1DRXMfE+QHeevttDh8+TEtrK3fecQfr1q3D6XSmN2oV4hMopg1Xs9wOG1prFhcXcbvdRscRYlXLnsDNT/mpbK6/4fHBqVnMZjNtbW25jiZWMbPZzNp16+g9/wFa66I9z9Fa0zvuZ+26DZjNy+/RVcqkGFpltNaEw2Hm5+eX/gWDwStuz8/Ps7CwgNbpbVUrmmrZuPNOqlobMZmXv8KtlKKyqY7Kpjpi4QiTvUNMXBjk2WefBdJ/MDxeD+XecrxeLx6PB6/Xu/Qve1tadIvlLC6m1wA4i6gYctrTfz4XFhakGBIix6qqquju7mbgbB+VzXV4ayqveax/9DKTvUNs2bIFp9OZx5RiNVq/fj2nTp1i3B+isdJjdJxPZNwfYjEaZ/369UZHKUg5L4aUUmbgKDCqtX5MKdUB/ASoAnqAr2utY0opO/CPwC5gBvhdrfVArvMVk1gstmyBEwwGCQQCzAfnCc4HSSavXuhnc9ixOR1Yyxy4G6uodDZjdzqoaKqjzOO6pRy2MgctW9bSvLmb+ckZgjNzRBfDxBYjzC0sMDU0S3QhvGz7U7vdjsfrwevxUl5efkXRlP3c4/HIlYsSky2GyoqoGCrLrKlbWFgwOIkQpeFzn/scf/8Pf8+pXx5kw4N3UtXScNUxk31DnD/YQ21NLY8++qgBKcVq093djSLdlrpYi6G+cT+K9H+LuFo+Rob+EPgA8GZu/xnw51rrnyil/hfwTeC7mY9+rXWXUuormeN+Nw/5DJdIJJaKm49/nJ+fZz44z/x8kNgyO2mbLRbsrjKsZXbslR4ammuwO8uwOx3Ylj46MOWguFBKUV5fTXl99VWPaa1JxhPEFiNLhVJ0IUwsnL49HZxj7PIE0cUwOqWv+nqXy7VskfTRjy6XS9ZqrBKRSASlwHqNkclCZLOmf6dkh3sh8qOqqorvfPs7/OCHP+DMq0fY8bkHrnh8dmSSc28cpaOjg6997Ws4HA5jgopVxel00tzSTP+En09vKs6W1P2Tfpqam2Wk9BpyWgwppZqB3wT+P+CPVXqy5a8Bv5855IfAfyZdDH0h8znAM8BfK6WUzs7lKkLJZJJQKLRskRMMBgnMzxOcnyccDl/1tcpkWipkbE4HNTXN2JwO7M6yKz5abIV5JV0phcVmxWKz4vRd+0qK1pp4JHp10ZS5PTo9SWxokFg4suxruN3uaxZN2c9lPVPhi8fj2CyWovr/ZLOki6F4PG5wEiFKh9vt5mtf/Rr//b//d+anZq94bP7yDACPP/44VmthvjeK4rRmTRdvvHGASDyBw1pcK0wi8QTjs0Huv3+X0VEKVq7/j/5P4P8CsmfDVcCc1jo7f2oEaMp83gQMA2itE0qpQOb4Kza3UUo9ATwBFNSmUSdPnqS/v/+KEZ3lps8opdIFTlm6yKloq6duqcD5cDTHYrcV1YnhJ6WUSn8vyhy4q3zXPC6VShEPR4guRq4onGKLERYXwwQmxoheihCPLDN6ZjZfUTR5vV527dpFQ8PVUyyEMeLxOJYiGhWCD0expHWvEPnl9XpRShFbvPIiWSwcwelySSEkVtyaNWs4cOAAQ5cDRddVbngqgNbp/waxvJwVQ0qpx4DLWusepdQDK/W8WuvvA98H2L17d8GMGr377rsMDAxccZ/T56F2TQvuivKl0Ryrw44yrf4iZ6WZTCbsLid21/WHeFPJZKZYShdKc+NTXL40QiAQIPCRDTJ9Pp8UQwWkGLv0FFteIVaLVCq1/N8MpdCplDGhxKrW2tqKxWxmaKr4iqHBywEsZnNBDSAUmpsqhpRSncBfAPcAKeBt4I+01v3X+bJ7gc8rpX4DcJBeM/QXgE8pZcmMDjUDo5njR4EWYEQpZQHKSTdSKArf/OY3mZycpL+/n76+Pi5dusTiXJCBnrO4K8spb6jB11CDr64Kyw32SBCfjNaa6GKEwPgU/vEpAuPTRBfTUxC95V661nTR2dlJZ2cn5eXlBqcVQqykT/g+JYpQthW/3X3lxTGHy0k4HCYWi0nnUrGiLBYLzS0tDE9P3/jgAjM8PU9zSwsWS3FN78unm/3OPAX8DfAvMre/AjwN3HWtL9Ba/ynwpwCZkaH/U2v9VaXUz4DfJt1R7nHgucyXPJ+5/Xbm8deKab2QyWSioaGBhoYG7r33XpLJJKOjo0vF0dD5AUbP9ALgrijHW1dJeV013roqHG5Z0PZJ6JRmwR8gMDlDYHKG+cmZpeLH6XSyNlP8rFmzhsrKSrmSX8CUUqSWaaRRyFKZP0/yc1Uwbvl9ShSn7O9cInrlFNV4LIbJZKKITh1EEWlvb+eNNwaJJZJLa0YLXSyRZHJugfu37TY6SkG72WLIqbX+0Udu/1gp9R8+4Wv+R+AnSqn/AhwHnszc/yTwI6VULzBL+o2saJkzQ5Ktra088MADxONxhoeHGRgYYHBwkKH+IcbOXQLSu2V766ooz/xz+rxygrWMZCJJcNpPYGKawOQMwalZErH04nWP18u6rm7a2tpob2+nrq5OvodFxGazEV+mJXwhiyfSeeUKdMFYyfcpUcCy05ynB8ZQmbV7WmtmBsZYs2YNdrvd4IRiNWptbUVrzfhskLbaa69xLiTjs0G01jJF7gZuthj6pVLqT0iP5mjSLa9fVkpVAmitZ6/3xVrrA8CBzOf9wJ3LHBMBfudmgxcbq9W6NEUL0p3mJiYmGBgY4NixY0z0DXO5bxgAu8vJHV96GHORdSzJJf/YZd7/1VsfzgdXio0bNrBp0yba2trw+XxS/BQxq9VKPJEsqrVD8WT6Z1EWaxeM23qfEsWlu7ubN998c2lrh3gkSji4QPenZR8VkRvNzc0AjM2GiqYYGpsNAR9mF8u72bPtL2c+/uuP3f8V0m86nSuWaBXTWuP3+xkdHWV0dJSRkRHGxsaW9ikxWcy4K334GqoxFVlnrVwr87qpXdNMaHqOBf88aM3Zs2cZGRmhubmZpqYmmpubaWxslD76RSi7H0gskcReJBcBovF0U0zZy6RgyPtUifnoPnOy55zINZfLRUVFBeOzQaOj3LTx2SAVPh8ul8voKAXtps46tNYduQ6ymg0PD7N//35GRkaW9hQymUy4Ksup6mjCU+3DXV2By+dByR/0ZTncTtZ/Oj3nNRlPEJyZIzTtJzjtZ2BkiLNnzy4dW1FRQVtbG5/73OfkRLVIZAvYcCxRNMXQYjRdDEnxXRjkfaq0xONxTB9ZEJ79XPb9ErnU2NjI6GCf0TFu2uXAIo1tch3oRm62m5wT+GOgVWv9hFKqG1intX4xp+lWidnZWS5evAhARVMtHbs24aool9GfT8hsteCrr8aXmR4B6YW0gckZLrx1HL/fTyQS4TOf+YwUQ0UiW1AsRuP4XMXx/ywcTZ90STFUGOR9qrQkk0lMH9mmIrtlRbLI1h6K4tLQ0MCZM2eKYvPVaDyBPxRml2wjckM3ezb+D0AM2JO5PQr8l5wkWoW2bdvGd77zHRoaGvCPXmag5yyhmTl0kXXPKlSJeBz/2GX63jlFbDHCjh07+KM/+iNpn11E3G43AAuR4tnAdCESw6SUFEOFQ96nSojW+oqucTqzhi8l+wyJHMruTzgdWDQ4yY1Nzacz1tfXG5yk8N1sWbtGa/27SqnfA9BaL6piWeVcIJqbm/nOd77DO++8wyuvvMLxFw9gsVkpr6/G11BDRWMtTp+naBaPGymVTDJ/eRb/UM1z8gAAIABJREFU2GXmxqcJTs+iU5rqmhq++ru/R0eHzJYpNh6PB4CFyMpNcYnGE9hsNnbt2kVPT8/SGp+VEorEcbldslahcMj7VAlpb2/nvffeIxGNYbHbmBpMb1kof/9FLtXW1gIwPb9Ic7XX4DTXly3Y6urqDE5S+G62GIoppcpIL0JFKbUGiOYs1SplNpvZs2cP27dvp7e3l76+vvS/oVMA2Moc+BrSxZGvsZYyjyx4g/R+QsEZP3Nj6c1U5ydnSCWTKKVoampix6fvo7Ozk/b2dtlUrEhlR4aC4ZUbGYrEk+zatYvHHnsMgLOnjq3YcwOEIjE8bs+KPqe4LfI+VUI2bdrEiy++SCwcwWK3MXFhkIrKiqWOrULkgs/nw2q1Mj1f+CND0/OLWC0WfL7i6HxnpJs9c/zPwF6gRSn1T8C9wDdyFWq1czqdbN26la1bt5JMJunv7+ett97iwoULXO4f4XL/CACbHrqb6rZGg9Ma78LhY0xcHFy6bbPZ2HnHHdx1113U1NTIaNoqYLFYcDmdKzpNzmE109PTA0BPTw8VzpUtlEORGJX1csWtgPxn5H2qZFitVtra2rjQm16PGw4E2bJxs4zUipwymUxUVVUyGwwbHeWGZoNhqqqr5HfiJtxsN7lfKaV6gLsBBfyh1no6p8lWGa01gUCAycnJpX8TExNMTU1dseDT6XXjrPDiqvBSXldlYOLC0bC+A7PNyoJ/nvDcPNHFCEeOHOHIkSM4ysqor6uj7mP/ysrKjI4tbpHH61nRkSG71UIstsjbb7+dvl2+so0ZQuE4bR4ZGSoU8j5VesrKypbW3sYjMVm/J/KiurqGsSLoKOdfiNDY1mR0jKJws93k9mutHwJeWuY+cR2BQIBnnnmG0dHRpf2EAByuMsp8Xho2dOLKFD9OnwezTPO6iremEm9N5dLtWDjK4tw8C/55FvwBZv1BRo6Pkoh9uN7E4/GwYcMGPv/5z8vIUZHweLwEp8eNjnFTkqkUi9HY0lonYTx5nyo9drsdtAatSaVS2Gw2oyOJElBVVcXZM2dIpfQVHQ0LSUpr5kIRNldW3vhgcf1iSCnlAJxAtVKqgvTVNgAvIOXmTThw4AADAwPUr23HVenF5fPirPBitcsf7U/KVmbHVlaDr6Fm6T6tNdGFcKZAmicwOc27777Lhg0bWLt2rYFpxc1yu91MjBbHHiGLmbba2bVOwjjyPlW6TCYT+mO3hci1iooKUloTjEQpdxbmVhDBcJSU1lRUVBgdpSjcaBjiXwP/B9AI9JB+k9FAEPir3EYrfgsLCxw7dgxPbSUtW9did5XJKEWOKKVwuJ043E4qGmupbKplbmyKQ4cOSTFUJNxuNwuRGFrrgv89yXa9k2KoIMj7VIkym82gNdkO22az2dhAoiRkC4zAQuEWQ4GF9EykShkZuinXLYa01n8B/IVS6v8B/qfWel4p9X8DO4G38xGwmM3Pz5NKpQhMTPPO/96LxWrB6fPirPDgqijH6fPgqvBiK3MU/MlfIdKpFOHgwtJo0OLcPItzQRYDwaV55H6/3+CU4ma5XC5SKU0skcRe4JvZhTNTMl0u6fhoNHmfKl3ZTbV1Zm8h2WRb5EN2D8P5xcJtVpnNJvst3pybPeP4ba31/6uU+hTwa8D/AL4L3JWzZKtAQ0MDf/qnf8rk5CSXL1/+sHnCyGUmLnzYHc1qt+H0eXD6MmuHKrx4ayoxW+QqV1ZsMcL89CyL/uCHhU8gSCr54QZ7FRUVtNQ1Urullrq6Ompra6mpqbnOs4pCkm16EYkliqAYSu9ZJI06Coq8T5WYeDx9UUJnJsvFYsWzabMoXl5ven+hlWz4s9Ky2bJZxfXd7BlHtt3ZbwJ/q7V+SSklO3vfBKfTSUdHx1UbwYVCoaUCaWRkhNOnTxOYnFl6vK6rlfX37c533IL13j+/clWDhHvuvof6+npqa2upra2VxbNFbqkYiico9GtZ0UwxJFeiC4q8T5UQrTUnT53EbLNitlhwVXg59f777Nmzx+hoYpWz2+047HaC4cIdGQqGozjs9nSTEXFDN1sMjSqlvgc8AvyZUsoOyErFTyiRSDA1NUV/fz/9/f0MDw+TSqVQJhPe2kp8DTXUd7UamrH3yElCM3MAnHj5TdyV5XTdvc2wPJseupupgVHmxqdYnAsSDAbp6emho6ODaDSKzWaTPYeKXLaYjSdSNzjSePHMiKS80RQUeZ8qIRcuXMA/66esPL1ur3ZNK5eOnmZ0dJSmJumbIXLL7fGs6L54K20hEsMt3U5v2s0WQ18GPgv8D631nFKqAfgPuYu1+sTjcc6fP8+JEye4ePEiiUQClMJT5aNpUxe+xhrKa6swF8j0oNBsgGQ8ffU7MGH8Vh2+hg+7x0UXw8yNTzM3fplLw0N88MEHQHox+6ZNm9ixYwfNzc1SGBUZq9UKQCJZPMWQRVrhFxJ5nyoRiUSCl156CafXjdWRviDRuL6D0TMXeeGFF3jiiSeks5zIKY/HQyg4c+MDDRKKxPF4ZK/Km3Wzm64uAv/8kdvjQHFsCGKgVCrF4OAgJ06c4P3330+PYJQ5qO1upaKxFl99NRZpsX3L7M4y6ta0ULemBYBwcIG58SlmRyZ57+h7vPPOO1RWVrJ9+3a2b99OVZX8QSgG2U5QiVThF0PJTDEk3asKh7xPrW6JRIKhoSF6e3s5f/48MzMzbH5kD8PvXwDAYrPSsWsT5w8d4/vf/z7d3d10d3fT1NQkv6dixXk8HoanJoyOcU2L0ThNDdLt9GbJZc0cOXToEG+99RaBQACT2UxNexO1XS1UNNSiCnSTrmJV5nFR5nHRsLadRCzO1MAok71DvPbaa7z22ms0t7Tw6Gc+Q2dnp9FRxXUU20ieovgyC1EstNZMTU1x8eJFent7uXTpEvF4HKUUnppKuu7eRlVL/VIxBFDX3UYsHGV6cGzp77/dbqezs5Pu7m66urqorKyU31tx21wu11JX0UK0GI3L1g+3QIqhHDl9+jSBQACAVDLJ3PgU8WiUubEp3JXluCrLcZa7UTKUf9sS8Xi6vfZsgIXZAKHZeRb9gaXHR4aHGRoakmJICLEspZQZOAqMaq0fU0p1AD8BqkjvXfR1rXUssw7pH4FdwAzwu1rrAYNiFyWtNZFIhIWFhSv+hUIhFhcXlz6/fPkywWAQAGe5m5qulvSMioYaLDbrss+tlKJ12zpat60jHokyNz6Ff/Qyl4YHl6ZT+3w+qqurcblcuN1uXC4XTqdz6fPsP7vdLkWTuCan00kkliCZSmEusPO4ZCpFJJbA6XQaHaVoSDGUI9/+9rcJhUJMTEwwMTHB+Pg44xMTjJ7pJZWZBmQym9LttCu9uCvKKa+vxlMtuwVfj05pZkbGCc2kC58F/zzh+dDS43a7nfr6ejZ2dlNfX099fT11dXXSaa4IZH8viuEERCmFJp1Z1iasCn8IfABk+9D+GfDnWuufKKX+F/BN0m26vwn4tdZdSqmvZI77XSMCF4qbLW5CmfsXFxaWftc/zmK1YC1zYLXbcFSX07C1i4qmOhzuWz+pszrs1HQ0U9PRjNaa8PwC/rFJ5sanmAzMkJgcJxaOkkwk/n/23jQ4jvS88/xl3UfWXagDKIA4SfAmmxRbLbkPd7ttHe2V5d2VR/Iq5IkJy2HPRIw9cqw84y/7YTfsT451xI5HksMfLMeEPfJIli2vvO6x1OpDzSa7yebRTRI8cBBAXaj7vnM/VFUSaLBFkARQVej3F5GRhaysqocEKvP9v8/z/p/7vl6r1WKxWpE74uiDYumDmxBPHy26QqNcayCb+mt80W39IMTQ1hFiaAeRZZnp6Wmmp6fVY41Gg0QiQTQaZWlpiYsXL1JIZojRHmB98n/75b4xUehHEnfDXPvxOfVnh8PBJ154gWAwSCAQwOl0ihvSgNLoDEr02v4XF7pOjI1GQwjtAUeSpBBtO+7/C/gPUvsC8jzwpc4pfwn8H7TF0Oc6jwH+O/D/SJIkKYqi7GbMu0FX5KTTabLZLJlMhkwmQz6ffyRxozcZkINuXKYgepMRg8mI3mREbzJgMBvRG41odqi3niRJWBwyFofMyMGpDc81G03qlSr1SpVauao+rldq1CtVqpUqhWyS1UcQTzabDafTuWETdvx7g67QqGyDGPI5ZWKZIgB+pxWf8/HK2ypCDD00YtS9y7RaLdbW1rh27Rpzc3M0Gg2MFhPe8RH802NCCD0A775hDj53hrXFFVLLMbLZLOfOnePo0aNYLBYcDocQQwNKt4Fiv5Uc3A+dtv03JsTQnuD/Bv53oOtD6wEyiqJ0R70rQNereQRYBlAUpSFJUrZzfu8tNx+SVqtFPp8nk8moYiedTquiJ5PJbGpiqtFqMVpMqoiRA25c5t0XN9uJVqdFK1u2nH3aqnhaiUaolcqbhKLRZMLpcOByuTYJJafTidVqFdnmAUDNDFUff93QiycmiWfaFS6//tyxx36/7lom0RR864iR9w7SarVIJBKsrq6yurrKysoKkUikPYDquMoNTYRw+D1iAL9FJEnCNxnCNxmiWW+QXI6ytrDCufPnOXv2LGaLhdFQiJGREXUTHZgHg2q13cDOqO//AZShM8irVqti9m2AkSTpJSCuKMoFSZKe28b3/SrwVYCxsd72jANYXl5mbm5ug9DJZrObBup6owGj1YJRNjM0PYrR2hYJRtmMSbagN4lSsIcRT4qiUCtXqBbKVAolqsUSlUJ7W4lFuD1/Z0MzcWhnmBwOB06nUxVMhw4dIhAI7NQ/SfAIdDN8lfr9M4W9pJsZEmJo6wgxtE0oikIymVSFz+rqKuFwWJ1Z0+p0yB4HgQPjeMaCOPxe4Sr3mGj1OlUYNep1knejZMJxVtdi3Lx1CzqVK7LNRmidOAqFQlit1h5HL/gglUoFGAwxZOpkcLsxCwaWTwL/kyRJnwFMtNcM/SnglCRJ18kOhYDVzvmrwCiwIkmSDnDQNlLYgKIo3wK+BXD69Omel9D95Cc/4caNG5uOS1oNDr8X94gP17APq8suTH22EUmSMFrMGC1m7D73pudbzRaFVIb0apx0OE4unqTZbJJKpUilUup5mUyGX/3VX93N0AUPoCs0usKjnxBi6OERYugxWVtb4wc/+AErq6tUOwMjjVaD7HbinQph87qweV1YHDYhfnYQnV6/ofdQs9GgkMyST6TJJ9LcjaxuGAw4nA6mJqf4lV/5FdGDok+4J4b6/7JkNAgxtBdQFOU/Av8RoJMZ+n1FUX5dkqS/Bf4X2o5yXwH+vvOSf+j8fLbz/I8HYb3QF7/4RZLJJGtra+o+kUiwlkiQCcfJhOMASBpNu1WBQ8bskLE4bMhuhzD2eUwURSEXT1FMZylnC5RyBSrZAuV8kfV/PharlSGvF+8HNtErr/9QxVC92eNINtONSaxP2zr9P+roc6rVKul0WhVC0J7tqZUrlHMFJEmi1WhSq1SxOGQMZtNHvsxgJ1FaLcr5IqVsvn3TyeSp5EvUK9UN5xULRZKpFK1WS4ihPqFUKqHVaAbCQMHcEUOlUqnHkQh2iK8DfyNJ0v8JvAv8Ref4XwB/JUnSbSAF/KsexfdQ6HQ6/H4/fr9/03OlUkkVR91tLZEgsjpPs9keVAUPTDD95LGBWAPUbzTqdW6+cZG1hXZyUafT4fF4GB4b3yR6xEz+4GA0GgGo9mGZXDcmIYa2jhBDj0koFOJrX/sa9Xp944zb2hpra2vEb91VF4ZD213H7LCps26OgFesGXpEWo0mibthCqkspUxb/JTzRZR1dfBWWWbI62Vo3yRDQ0MMDQ3h9XpxOp1ikWqfUSqVMBv1A/FdMHf6nAgxtHdQFOUnwE86j+eBM/c5pwL8r7sa2A5jsVjYt28f+/bt23C81WqRyWQ4f/48r7/+OoVkmkM//yQmmygx3irFdI5rPz5HOVfgxRdf5MSJE9jtdnHv2QNotVoMen3fiiGDXi8meh8CIYa2Cb1er/a1WY+iKORyuQ0CSRVKd5YBsA+5GT1+AM9oYCAGgr2mWW8QvrHA6vu3qJYqaDQa3B4P4yOjqtjp7sVM2+BQLBbVjEu/YzaKzJBg79K9b4XDYSRJQpZl8okMV17+KWf+51/sdXgDgdJSuPTD12hUa7hcLhqNBpFIBI1GI0x99ghGk5FqH5bJVetNNXMl2BqDMfIYYCRJwuFw4HA4mJra2N+gWq1y6dIlXnvtNd7/l7NYXQ7Gju9naDwk1hfdh3q1xuq1O4Sv3aFerTE5Ocmzzz7LxMSEmAHZAxSLRSzGwbgk6bVa9DotxWKx16EIBI9NPp9XHU9XV1dZWV2l1PnbljQSVpeD4Mg4ntFgjyMdHCSNxMxTJ0ivxigkMrzyk59sMPXpup6GQiGGh4eFqc8AYjSa+jYzZBQlcg/FYIw89ihGo5FTp04xNDTEP/7jPxKLxbj+k7cpnSwwfvJgr8PrO678f29QSGYAOHHiBM899xxer1dk0/YIpWIRn1Xf6zC2jMWoF2JIMNDUajX+7M/+jLW1NfWYwWzCFfIx7JnGNuRCdjnEWqFHpOt2CptNfW7P3+H69evquSMjI/zO7/xOr0IVPAImk4lKvdDrMDZRqTcxmR6vcetHDSGGdplWq0U4HGZ+fp75+XkWFxfVNUU2rwtH0Etguvd9KfqRydNHiM8vk4mscenSJS5duoQsy0xNTTE5OcnU1BQul3A9GlRKpRIW9+D8/swGnSiTEww0Wq2WmZkZNBoN8Xhc7YuTCa/RqNZp1Os0anVsXhd6o2gu/KjUylVVBOUTaQqJDPVqu+2GRqPBH/AzMzPT4ygFD4vJZKJUyvY6jE3U6k0sNpEZehiEGNpF3nzzTX70ox+pdrwWhw3fzBjO4BCOgFfcbB6Aa8SHa8SHoihU8iUykTiZyBrX5m5w+fLl9jkuF1/84hcZGRl5wLsJ+olms0m5UhmYNUPQFkMiMyQYZLRaLZ/97GeBdpYoEomopXLLK8ss3r2mnmu2y/inR9l3QlQtbJXb566QXApTKdybNBkaGuLw7EFCnTK5QCCAXj84GXHBPUwmE+lGH64ZajRxizK5h2JwRh57AKPRiNlsVsVQvVJVu1GbCiV0hsFw0uo1rUaTSr7Y+b8rqx28JUnCYrWi04k/60Gj+53ourQNAmajnkxeiCHB3sBgMGxylSuXy4TDYVZWVrh8+TIrV28xdnxW3Ke2QLPeYPX924RCIY783DPq2iCxsH3vYDKZhIHCHkGMGneRU6dOcerUKTKZDIuLi+1SuYV57py7AoDOoMcR8DI0EVKbhwraNBsNlq/eIr0aJ59Io7RaSJLEyMgIJ37uMOPj4+zbt0/46g8o5XIZANMAZYZMeh2VSr7XYQgEO4bJZMLv96PRaIhEIsRiMRq1uqhi2ALdbFAwGGRsbIyhoSExQN1jGI3GvjVQEGOhh2NwRh57CKfTyYkTJzhx4gSAKo4WFha4fec2N159G61Wg3dclHp1uXX2ErFbdwmFQpz4ucNMTEywb98+cXPZI3QzQ0b94FySTAYd1UoVRVHETLlgoGk0GqRSqU198tYSiQ0NxfUmI+IvfWtodVp0Bj1vv/02b7/9NgBms1ntd7e+DYTL5RKOqAOIyWSi3mjSbLXQ9knvqGarRb3RFGLoIRmckcceZr04ajQafPNb32TujYvIXhcm2dLr8HpO7PZdYrfu8txzz/Hiiy/2OhzBDlCtVgEw6gdnQGDUa2kpCrVaTYhywcBQr9e5fPnyhp536XQapWP7DGC0mjHbZTzjQcwOGxaHDYvThtFqFsJ/i5hsVj7xpZeoFIqUsgVK2TzlbJ5stkD02vvUyvdEpkajwe12qyLJ7/dz9OhRIZD6nK7gqNabWIz9IYa6ZXtCDD0cQgz1AEVRKJVKpFKp+265XA6A8I15Jk8f6XG0vefulTkAXn/9da6+dxWP24Pb7cbtduNyudTHBoMo3RhUarW2s5JhgCx89Z2BSr1eF2JIMDDMzc3xd3/3dxuOabRahvYF8U2N4Qh40IkF/duCpJEw2+W2sBzd2JC9Ua2RiawRu7NMcjlKIpEgkUiodttOp5Px8fEeRC3YKt2m7tV6A4uxP74z3bI90XD+4RBiaIdoNBpkMhlV4KTTaVKpFMlkknQ6rQ7+uhgtZoyyBfOQE9fkMCabFe++4R5F318cfv7jZKJrlPNFKvki4WSc+cUF1Tihi9VqVYXR+s3lcmGz2dD0SRpbsJnu90GvHZzfkV7XjvWD32WBoJ85fPgwv/3bv73ONW6FtXic+PwK8fkVTLIF2evE5nW12z34PKLP0DbQrDfIxpPk17r22mmqpXZ2SJIkAoGA6jA3NjZGIBB4wDsKek03+1Kp9c+6oUonMyQm6B4OIYa2mVarxTe++U1WV1Y2HNdotZhtVow2C0PTo5hsVsw2KyabFZPNglY4oH0oFme7RGM9iqLQqNUp5wpU8iUq+SLlfJF8vsjandtUrpTUbt/QtpB95pln+IVf+IXdDl+wBRqN9s1EO0BiSNcR193YBYJBQJIkQqEQoVBIPVatVgmHw6yurrKyssLKygoLi2EADGYjwdlJRg5OojeJAdbDUi2WWb12h8jcvQk8j9fDwf2zjIyMEAqFCAaDwl57AOlmXyp95CjXFWYWi1hi8TCIEfg2I0kSwUCA1ZUVNFoNM584iWvEh8FsErXW24gkSeiNBvRDbuxD7k3Pt1otqoUSK+/dInxjAUmS8Pv9PYhUsBWazfbNRKsZnO+IphNrq9XqcSQCweNhNBqZmJhgYmJCPVYqlbh79y7nzp/n5rvXWb5yE//MGKHD01gctp/xbgKAQirLynu3iM8vg9LOyH3sYx8jFAqJ9Rx7BFUM9VNmqCbK5B4FIYa2GUmS+PznP8/09DTf//73uX32EsOHpnD4PNiGXBjM4iK4kyiKQjlXIL+WJrkcZW1hheBwkF/7wq8xNDTU6/AEH0JXUGgGaMKgK9y6Qk4g2EtoNBpkWebY0aPodTref/99IjcWiNxY4Pinn8YZ7P319PZblykkMwBc+uFryG4H0x8/3uOoYG1hlWuvnFN/PnnyJDMzM5jNwoBiL3GvTK7+gDN3j24sQnA/HEIM7RBHjx5lbGyM73//+9y6eotl5SYAJtmKbciJbciN3etC9jpFidxjUCtXyK2lya+lyCfS5NfSaimCXq/n2Wef5fnnnxeNWAUCgeADtFotMpmMaqe93la7UCio50mShMUuY3bIWJx2LE57D6O+RyGVpdlZMJ6NJnoczT1sQy5GDk9TzuYpZwu8++67vPvuu/eet9vxrbPW7tps2+12sbZ1gOiWopX7KDNUFmVyj4QYIe4gDoeDr3zlK9RqtQ212MvLy8wvrALtm4zV5VAFkm8yJMTRz6CcL5JYDJNPpMivpdXGdpIk4fP7OXHsOKOjo4RCIXw+n7ixCHaMdUvSBIKBolgs8u1vf5tIJLIhs6k3GjA7ZKxBN0P2fVicNswOGbNNRjNA6/l6jUm2MP3kMfXnVqNJOV+glGlbbJeyeeLZFEt3l2isa9qp1+sZCYX4ja98RawhGgD0ej0GvZ5yH2WGyrU6er1e/P08JGLUvQsYDIYN9ditVov5+XnOnj3L3NwchVSGQipDZG4Rpdli+OBkjyPuX269eYn0akz92Wg08sQTT/Dkk0+KMrgBpls6MogCQ5S9CAYNrVaLLMsbjtn9HgIz+3CH/BgtYr3BdqLRabG6HFhdDvVYpVAiuRwhenNJLfUDsNts4poyQJgtZkrV/skMlaoNLGK90EMjxNAukM/nWV5eVrfV1dV7VsImAzavC7vPjW3IjSvo63G0bRq1OgaDgVOnTnHhwoVNNta94uBzHyO9Gie3liK/lqKQzHD27FnOnj2LzWZjdHSUsbEx1aJU9B4aDLrNBVsDpIaarXasojGiYNAwmUx8+ctfplarcefOHebm5pibm+PmGxcBkD1OhsZHCM5OoDeKa+h2UStXCF+fJ7EYpphp9xN0uVw89dRTHDhwgPHxcTGjP2DIVplStdzrMFRK1TrWD0x0CB6MEEM7zOXLl/nOd74DtBuwyW4n3qkQ9iE3dp8bk83al7NAjVqdU6dO8dJLLwFw8erlHkfURm804JsM4Zts28K2mi0KqQz5tTS5eIqFlbtcu3YNaGfkfu/3fg+7vT/q2wUfTndNV3OAnNm6sQoxJBhUDAYDBw8e5ODBgyiKQiwWY25ujhs3brBw4X3uXp4jsH8fI4emMdutvQ53YCmmc6y8f4v47WVarRYTExPMPvVJDhw4gNfr7csxgGBrWGWZ3Fqu12GolKp1bF5vr8MYOIQY2mG6pQihw9OMnzqMdkCa1+kMei5cuADAhQsX0Mv9mXbVaDVtYTnkZuTQFAC1cpWr//wGrUpd2EsOCF1B0WgKMSQQ9IJu489AIMCzzz5LJBLh1Vdf5erVq6xen8e7b5ipM0cxyWJh9lYpZfPcOXeF1Eq7tPvUqVM888wzeMVgdc8gyzLRlf4qkwuKzNBDI8TQDjM5OUkwGGRtcRWdUY/scSJ7nH1fk60z6CnWspw9exYAs6G/sytKS6GUy1NIZsgnMhRSWZ5//nlRcjAgdH9PgySG6p1YRSmmYFBRFIVSqUQymSSdTpNKpdQtmUqRz+W6J5JYXMU1PMTwrFjTulXWFlZUIQRw8eJFbt++jcfjwe1243a7cblc6mNhvT14yLJMoVJFUZSe/+4URaFYqWGziT5gD4sQQzuMJEl89rOf5e++/30WL15XjxvNJqwehyqObB4nRtnS8y/TINBqtSilc23hk8xQSGYppjI0G53GnVotk5OTPPXUUz2OVLBVumKoPkBiqCvchOAW9DuKorC0tEQ0GiWdTpNMJkmlUqTTaXX9ahejxYzJZsHic+KeGsFss2KyWTHbrBgsonfJwzB2fJbAzDjlfJFKZyvniyTyGVYiYWrlyobzjUblvZuGAAAgAElEQVSjKoy6WzAYZHR0tEf/AsGDkGWZVkuhXGtgMfb2XlCpN2i2WpvMUQQPRoihXWBiYoL/8Hu/R7VaJRKJEA6H1W35yk2UzqJxvdGA1d0WSL7JEDavq8eR9weKorC2sEomHKeQylJMZdUmnQaDgWAwyJEzBxgeHiYYDDI0NCRKlwYMVQw1BqeBaTdW0cNK0O+kUin+/M//fMMx2eNkaHpUFTqmzjYopdyDgCRJGK1mjFYzBDaXxjXrjU1CKRNZI/J+ZMN7/OEf/qEo+e5TumuSC5Vaz8VQvtye2BDrpB8ecRffRYxGI+Pj44yPj6MoCtlslqWlJTV1Xq/WyETWyETWqBZLHPr5J3sdcl+gtFrc/OlFtbketEXQsWPHOHbsGCMjI6Lb8oBjNBoBqDUGJzNUazQx6PWil5Wg7/F4PPzmb/4m77//PteuXyOTzlBIZpAkCZ3RgMFqxuKwIWlEZcJuotXrsDpttBpN8mtpstEEpUweAK/Xy8GDBzly5IgQQn1MV3jkyzV8jt6ajAgx9OgIMbRL5HI5VlZWWF1dVbdS6V7DUNntQPY6sXlcyF4nstvZ44j7B41Wy5Nf+BT5RJpCIkM+2d6/8847vPPOOwC4PW5CI2077eHhYUZGRtQBtqD/6a67qTX6ZyHqg6g1WugNokROMBh0J+I+85nPEI/HuXHjBtevX2fp3fam0WqxOG1YnDasTjtWlx2Ly45JlG9vC0pLoZwvUsrkKKZznX2ecjZPq9VCkiTGx8eZ/fgnmZ2dFSYLA4IqhkrVHkdyLwYhhh6eHRNDkiSNAt8G/IACfEtRlD+VJMkN/DdgHFgEvqAoSlpqX23/FPgMUAJ+Q1GUizsV326QzWZ57733uHL1KivLy0Bb+FhdduwjQwx3xI/V7RClCQ9AbzTgHvHjHvGrx+qVKvl14ujm/G2uXLkCtEuXDhw4wNGjRzlw4IBY5N7ndH8/9QHLDBkNQnALBgtJkvD7/fj9fp599lkKhQK3bt0iEokQj8eJxmLE7yyr52t1unsiyWXH0hFKRqtY7H8/FEWhki9RzGQppfMUMzlK6TylbJ5W814ZsNPpJOQP4DtyjOHhYWZmZkQGaACx2+1IkkSu3HsxlCtXkSRJiKFHYCczQw3ga4qiXJQkyQZckCTpfwC/AfxIUZQ/liTpD4A/AL4OfBqY6WxPAv+lsx8oCoUC77//PleuXGFxcREA2e1g4tRhHEEvstuBVqwx2Bb0JiPukB936J5AqpWr5BNpUitRbs3f4f3330ev13Pw4EGOHTvGzMyMWOPRh9wrkxucNUO1elNkHwUDjyzLnDx5kpMnT6rHyuUy8Xhc3WKxGNFIlNjtu+o5OqOe459+Btnt6EXYfUkmssZ7/3J2Q0m3zWYjGAjgP3QEn8+nbuLasTfQarXYbDK5Yh+IoWIVmyyLNdOPwI6NChVFiQCRzuO8JEnXgRHgc8BzndP+EvgJbTH0OeDbSttN4C1JkpySJAU77zMQ/PM//zOvv/66aojgHR9m34mD4maxixjMRjyjATyjAaafPE5yOcLChWtcuXKFK1euYDQa+dKXvsT09HSvQxWsQ6fTodVoBkoM1RtNDBYxoBHsPcxmM4FAgEajQS6XQ1GUDa5zZpsVu9+DwSzWaq7HZLPiDgXIxZNUi2UAarUakiRhNptxu90EAgHhQLnHcDpdZEuZXodBtlTB6RLGW4/CrkyRS5I0DpwEzgH+dQInSruMDtpCaXndy1Y6xzaIIUmSvgp8FWBsbGzHYn4UXC4XwWCQWCxGs9kksRgmeTeK1Wlru8S5HepebxKDqO1ELU1ItXsMtV3nclQKRfUcs9lMMBgUpQh9isFgGCgxVG00sYvZXcEeY21tjb/9739LeDXcntjrrGn1zYxh93lw+D1tdzTBJkyyhUM/fwZFUagWSmRjSbKxJKvxKDdv3gRAo9EwOjbGv/q1XxPlTHsEt9vN/M14r8MgU6wxOeLudRgDyY6LIUmSZOC7wO8qipJbX2OsKIoiSZLyMO+nKMq3gG8BnD59+qFeu9OcOXOGM2fO0Gw2SSaTRCIRotEokUiESCSyocTAaDVjddlxBocIHZkRtdePQK1c4e7lOQrJDMV0jkatrj7n8XqYmZgkEAgQDAYJBAJqba+gPzEYB0sM1ZstUeoi2HMoiqJmgzRaLROnDzM8O4lGK1wTt4okSapVuX96jGajycp7t1i6dJ1Wq0W+8/8r2Bu43W4ulSo0mi10PfqeNFst8uUKLpEZeiR2VAxJkqSnLYT+q6Io3+scjnXL3yRJCgJdOb0KrO8sFuocGzi0Wq1aF3z8+HEAms0m169f54033mB5eZlqsUy1WKaYyjFycApJGCg8NKVMnvD1efWmotFoOHbsGJ/85CcJBoNC+AwYRoORWn1wxFCt0RTlLoI9h8/n4/e/9vtcvXqV1157jTvnrrDy3i2CBybw7hvG4rSJa+sWUBSFQipLYnGVyNwi9UqVkVCIZ55+mkOHDglL/j2Ex+MBIFus4LFbehJDplBBURAuhI/ITrrJScBfANcVRfmTdU/9A/AV4I87+79fd/zfSZL0N7SNE7KDtF7ogyiKQiKR4Pbt29y+fZv5+fl2zbUkYR9y4xrx4Rr2YfO5xUXxEXEGh/jElz5LJrJGKhwnE17j0qVLXLp0CbvDzsz0DNPT00xNTWG19tb/X/BgDEYD9Uap12FsmXpDGCgI9iY6nY6TJ09y4sQJbt26xWuvvcbCxWssXryG2S7jGQviGQvi8HlEb6J1tJotstEEibthUstRKoX29ezAgQM8/fTTjI+PCyG5B+mKoVSh3DMxlCqUN8QieDh2MjP0SeDLwFVJki51jv0n2iLoO5Ik/RtgCfhC57kf0rbVvk3bWvtf72Bs246iKKRSKebn51lYWODOnTsUCgWgvdjUMzmCa9iHKziEzihsnrcLndGAd3wE7/gIAOVckXQ4Rno1zuWrV7hw4QIAPr+fqclJJicnmZiYEOuG+hCDwUitku91GFumVheZIcHeRpIk9u/fz/79+8lms2pvojvX7rDy3i30JgPuUIDggQkc/o/uICy1GiN6c4n0aoxGrY5Op2N6epqDBw8yOzuLLMu9DlGwg3SzMcl8mZkexZDMlzfEIng4dtJN7g3gw6ZAXrjP+Qrwb3cqnp0gk8kwPz+vbtlsFgCD2YQj4GX4+AzO4BBmu7gQ7hZmuxWzfZLh2UmUVot8IkMmskY6ssa5t89z9uxZAILDQaYmp5iYmGB8fByTSbgi9RqDwUBxQPoMNVstWooi+lcJ9iyNRoNMJkM6nd6wlcpl9Ho91WqVeqVG7PZdiukcpz73fK9D7hk337iouscB6PV6CsUid+7cIZVK4XK5cLvdOJ1OnE6nsD7eY5jNZmTZSjLXu8qGZK6ELFvFRO8jIhquPCLf+9731KxDF3fIT/DABO6QH4242PUcSaPB7nNj97kZO36AWrlKfH6ZyNwCkXCESDjCG2+8gSRJfPGLX+Tw4cO9DvkjjV6vp94cDDFU64g2IYYEg0yz2WRpaWmT4EmlUuTzG7O0kkaDSbZgki24x4OYZCsmmwWTbMXq+mi7op3+lRcoZvJUCkUq+RKVQpFCvkRy4Q6Vq6UNZgmSJGGz23G7XLg6m9vtxuVyMTY2JsrmB5ShIR+JbKJnn5/IlRka8vXs8wcdIYYekQMHDlCv10kkEyQTSarVKqmVGKmVGEgSZtmCyWbFbJcx262YOnuzzSqE0g7SqNUp5wqdrajuK/kCtQ90iLbZbHg8HjweD36//0PeUbBbGAwG6gPiJtfodJIXZXKCQeY73/kO7733XvsHScJkNWOULVgCLtzTobb4sVkx2ywYzGaxPuhD0BkNOPye+5YKKq0W1VKFSr7Y3golKvkS6UKeyFpsQ0bp4x//OL/8y7+8m6ELtolAIMCF5bsoirLr68IURSGRK/HEfjGOeVSEGHpEDh8+rGYSFEWhVCqRTCbVLZVKkUgkSCyGqVYqG17bvcH4p8cIzOzrRfh7BqXV4s75q+TX0lTyRWqVDwgeux2vx4NnbEIVPh6PB7fbLWb1+4xBygzVRWZIMOAsLCzw3nvvETo8zfDBKYyyWWQldoD1GTWCQ5uebzWbVApl7l65wblz5zhz5oyYnBtA/H4/tUaTTLGKS97dsvtsqUqt0SQQCOzq5+4lhBjaBiRJwmq1YrVa79sI9oNCKRKJcP36dXQGvRBDj4nSUojeXEKr0XD8+PFNgkfM3A8Oer1+WzNDPqdMLNNuuut3WvE5t2/tXl1khgQDzuuvvw5ANpakWW9gcdmxuuxYnXb0ZqNwPdtBFEWhVipTTOcpZnKU0jkKiQyKovDmm2/y+c9/vtchCh6SrhBZyxZ3XQzFu/c5IaIfGSGGdgGLxYLFYmF0dJR8Ps8bb7wBwOjR/T2ObPDR6LSMHJri7pU5ZmdnmZiYEHbHA4per1eNCTTbMBB78cQk8Uzb0fHXnzv22O+3nm5mSIghwaDy9NNP43A4iMfjxJZjRG4uqs/pjQYsTltbIDnt6t5gFtfWh0FRFGrlCsV0jlIm39m3H69vEm61WvH7/fhnD3Py5MkeRix4VPx+P5IkEc0U2D+yu86KsUwBSZJEZugxEGJoh2i1WqRSKSKRCJFIhHA4TDgSodix2/buG8buc/c4yr1B6OgM0ZuL/NVf/RUAHq+H4eAwwWCQ4eH2Xlib9j9dYdFotjD0eRPiRlOIIcFgMzExwcTEBNAetBeLRWKxGLFYrC2QYjFiixEilQX1NQaTkaknj+GbGv2wtxV0WL12h6V3r1Ov1tRjZosFv8/HwYkZfD4ffr8fn88n+uDtAQwGA0NeL7F0Ydc/O5ouMuT1irLtx0CIoW2g0WgQi8UIh8Oq+IlEItTr7ZkfSSNhddqRA24CnklktwO776Pbk2G70RsNnP7VXyAbT1JIZikkM9xamOfq1avqObIsq8KoK5LcbrcoBekjBkkM1YUYEuwhJElClmVMJhN6vR5FUSiXy6QzGXXNq0arxeyQMVhEG4KtYJTNmO0yjWQGpdW+XhiNBhwOB0NDQ4yOjhIIBITN9h5ieGSE23PXdv1zo5kC0wcO7frn7iWEGNoGvvnNbxIOhzcccwS8+CZD2IbcWJ024SC3w+hNRrxjw3jHhtVj9WqNYipLJpogdvsuN2/e5ObNm+rzL7zwAs8//9HtjdFv6HTty1FjAEwUmp3BTTdmgWCQmZub45VXXiEcDtPsrIczyRZsQ258B8ex+9zIbicabX8ZLDRqdQwGA6dOneLChQsbSs96Tfd+1Go0yScz5OIpcmtJbtya4/Lly0D7+hEKhfjFX/xF9u0T64cHnVAoxKVLl8iXq9h2qaQ0X65SKNcIhUK78nl7FXEn3wZeeOEFbty4QTgSIRaN0mg0yEYT5OIprE47Vo8d2e1Edjuwuh3ojSKVuRMoikK1WKaQylBIZimm2ls5X1TPMVssDHeyQ0ePHu1htIIPomaGWv0vhkSZnGAv8dZbbxGJxwgenFR7sxkt/d+8sVGrc+rUKV566SUALl693OOINqPRadfZbs+o96lcPEkunmL55iIXLlwQYmgP0BUk4WSeA6HdEUPhVH7DZwseDSGGtoHZ2VlmZ2eBdhO7rmNcNBpV1wvFbt1VzzfJFjxjQSZPH0HT5+VAg0A5V+D2W5fJxVMbZgY9Hg/T45MEAgG1PM5ms4nSuD6lm2VpDkBmqCuGRImLYC+QSqUwyRZ8kyEsThvaAcl46gx6tfn5hQsX0Mv9L+AkScIkW9AbDZhkK8nlKKlUqtdhCbaBYDCIVqslnMpzIOTdlc8MJ/NotVphnvCYDMYVb4DQarX4fD58Ph/Hjx9Xj+fzeVUcrays8P7775NfS3PohScHYgauX0mH41x/5TxaScPJ4ydU0eP3+4Wr3IDRFRbNlvKAM3tPN0ZRJifYC5hMJhIrK1z8h1cAsNjle1bbLgdWtx2zTe67pqs6g55iLcvZs2cBMBvsPY5oM61Wi3K2QDGdpZjKqVba6ysWjCPiXrUX0Ol0DA8Ps5LM7tpnriTzDA8PiyqFx0TcyXcJm82GzWZjZmaGVqvF2bNn+eEPf8i7//ATjrz4FLLH2esQNyC7HRSSmfZjT7vEr98I35jn9tnLmEwmvvKVrxAKhUTWZ4DpNnwcBDHUUtqZIdGkUrAX+K3f+i1SqRTRaFR1lItEIywt3VsLq9FqsThtqkByDQ/13X2rH8jFU2Sia20b7Y6ldqtT+itJEh6vl+nxybaVtt9PIBDA6RT/j3uFsbEx3jp7lkazhW6H19g1mi0iqQIff0qU/D8uQgztEM1mk0wmQyqVIpFIkEqlSCaTJJIJ0qm0enGslsok7kb67qYy/fHjFFLt2Y0Tn3mmx9Hcn8jcoup69I1vfAOD0YjH48br8eJ2u/F623uPx4Msy0Io9TndzFBXaPQzrY5gE2JIsBfQaDR4vV68Xi9HjhwB2mswU6kUV65c4fLly6ytrVFIZtRJMqPVzMd/7dO9DLvvUBSFy//0Gq0PlPoGg0FOnDjBkSNHhPDZ4+zbt4+f/vSnRNMFQt6dzVRG0wWarZZYb7YNCDH0mORyOSKRCMlkUhU+yWSSTCajCh4ArU6H2W7FZLcyEpzGbLditsuY7VYMokzukTj5y89RyRcp5wqUc/f2t5cWqLz3HopyL8Og1+vxeDybtpGREeHNL3hkhMAebCRJGgW+DfgBBfiWoih/KkmSG/hvwDiwCHxBUZS01P6F/ynwGaAE/IaiKBd7Eft20mg0iEajamaou+a1XC6r55hkCxaXHdnVNgJqGwII1iNJEqd/9UVy8STFVI5CKkspnVPbbfzTP/0TVqtVXccaCATU7JCYWNkbjI+PA7CcyO64GFpOZDd8puDREWLoMWg2m/zJn/yJ2k+oi+xxMnJkGotdxmSXsdhl9GajGDhtMxqNBovDhsVh2/Rcq9WiWiipAqmUyZFYat/g13P48GG+9KUv7VbIAoGgv2gAX1MU5aIkSTbggiRJ/wP4DeBHiqL8sSRJfwD8AfB14NPATGd7Evgvnf1AEo1GuXjxIu+++y6lUgkArU6L1eXAOepnpOOAanXZhQvqFjHbrJhtVpi6d6xWrrbXDKVzFFNZ4ukUC4uLtDo25ja7jSdOPsGpU6fweITIHGSsVitDQ17uruV4anbrr/M5H74x/N21HENer2jauw0IMfQYaLVavvzlL3Pr1i2Wl5dZXV2lXq9TSGaoFkvYvC5sQ25ajQa2Ibe4mewSiqJQLZTJr6XJraXIr6UppDJq6YJVlhkbHSUUCgl77T6im8mTGJxJg/XZR8HgoShKBIh0HuclSboOjACfA57rnPaXwE9oi6HPAd9W2r/4tyRJckqSFOy8z0BQLpe5cuUK71x4h/BqGEmjwTMWZN/4EWxeJyabVUzcbTMGsxGD2Ydr2KceU1otyrki+WSa+PwKr772Gq+++irj4+OcOnWKI0eOiKqFAWViYpJLFy/Qailotmg68uKJyYf6jFZLYSWR48QTpx4lRMEHEGLoMZmammJqqj0F1Gw2icfjLC8vs7Kywt3lZZbeva6ea5KtGCxG9EYDepOxsxk+sG8/1up04oa0DkVRaFRr1Cs16pUq9UqVWqVGvVrdcKxRqVEplqlXqkDb3WVkZIRjBw4RCoUYHR3F4XCI/9s+pNvscas3j16i7Zo9dGIWDD6SJI0DJ4FzgH+dwInSLqODtlBaXveylc6xgRBDa2tr/Of//J/VagazQ2biicM4g170JuFotptIGg0Wpw2zQ8Y17CO1EmPx4jUWFxdZXFzk//3hD/ndf//vsdk2Vz4I+pvJyUnOnz9PJF1gxLMzv79IukCt0WRy8uFElOD+CDG0jWi1WtXa+cyZMwBUKhVWV1dZWVkhGo1SLBYpFIsUMylKpdKHDqY0Wg0GkxGd8X6CaZ1w6goro6HvbE9/Fq1mUxUxtY6IqXUETb3aFTc1Gt1zqjX4kFl4g8GAxWLBKstYPU7sE3aGh4cZHR3F7/eLXjADQve7oB2Av2NtxyWo0Wj0OBLBdiBJkgx8F/hdRVFy6ydLFEVRJEl6qBSgJElfBb4KbXepfsFms3HmzBlWV1eJxmKUswWuvXIOAKPZhMVl76wLau+tTjtavRgmbBeNWl211i6u27qTdwAWq5WA38/o6Cgmk6mH0Qoela5AWYpndkwMLcXbRiYTExM78v4fNcRVbocxmUwbskfrURSFarVKqVSiWCxSLBY3PN6wZQpkiiVq1ep9PqVNVxTpTAYMHdFkslmxOtv9IoyyZVcyIq1mk1K20L7gZ3JUi+W2wClXO9mdKo36hw8izRYLVqsFl1XG6rG2hY7VumHrHrNYLMJff49Qq9UA0A9AI2J9Rwx9cL2gYPCQJElPWwj9V0VRvtc5HOuWv0mSFATineOrwOi6l4c6xzagKMq3gG8BnD59um9qKU0mE5/5zGeA9v0nn8+rVtqxWKxtoDC3yOo6kW+2y4wdP0BgRjhWPSor791i9dodKoWSekyv1+P3+5k+clS12fb7/cjyw68dEfQX1o6gXYpn+cTB0Qe/4BFYimcJiL+XbUOIoR4iSRImkwmTyYTb7d7SaxqNxibB9EExVSgUKBaLZNYylEv3Lr5anU7tE2Fxdhvq2TBYzI8kklqtFuVc4d4sVyZHKZ2nnCvcW/8hScg2G7LVisvhRpblDWLmg+LGbDYLV52PKNWO0DcOgBgy6tsxViqVHkcieBw67nB/AVxXFOVP1j31D8BXgD/u7P9+3fF/J0nS39A2TsgO0nqh9UiShMViQZZl8vk8JpMJg8GAVqu9l/GUJCSNNFBVB/2IpNFsuq/pdDoMBgMmk0n9PZjNwll2rzA1Pc1bZ89SbzbRb3N1Sr3ZZDmR4+NPPbWt7/tRRoihAUOn02G327Hbt2bZWC6XicfjxGIxdR8Nx4jeWrr3ngZ9Wxw5bVhcduw+N/ahjeKs1WqRWom2O2inc5QyOUrZAso6+3C3282+4RD+k358Ph9+vx+v14tOJ/7MBA+mKyyMA1CW042x+jMytYKB4JPAl4GrkiRd6hz7T7RF0HckSfo3wBLwhc5zP6Rtq32btrX2v97dcB8PRVGYn5/n8uXLhMNh4vH4vbV6Oi2yy4F7YhjZ7UD2OLG67GjF9fuxGTk0xcihKRr1ettyO5mhkMqwlkqxuLiotuHQ6XT4/X5GRkY4efJkX5VYCh6OqakpfvrTn7KSyDHhd23re68kcjRbrftWHAkeDXGV2+OYzWb27du3qSlXsVhUxVFXKEWXY0RuLgKw/+eeUM9VWgrXXzlPotON3Ol0EvIH8R1pCx6fz8fQ0JBwvhE8FsViEYNOu+Ndu7cDi7FdmlksFnscieBxUBTlDfhQ+8IX7nO+AvzbHQ1qB6jX61y6dIk3z54lHouhM+ixDbkYPjSlCh+zXRYZoB1Gp9fj8Hs29GhqtVqUMnmKqWxHJGW58O5Fzp8/z0goxCc/8QkOHz4sJhUHjImJCbQaDQuxzLaLoYVYBq1GI9YLbSPi2/URxWq1MjExseHL1K0h/+73vsetn76L2WlDbzRw+9xlEkthfumXfoknn3wSo1G4Dgm2n2KxiMU0GIJaiCHBIFCv13nllVc4f/485XIZq9POgZ97At/kKJoBKEf9KKDRaNqC1O3AP93OBDXqdWK37rL6/m2+853vIMsyTz31FM8884woIx8QDAYDY/v2sRCLP/jkh2QhlmFs3z4xAb2NCDEkUJEkCbvdzpe++EW++c1vEovFMDtkstEEn/jEJ3jmmWd6HaJgD5PP57EaB+OSZDa0re/z+XyvQxEIPpTV1VVeffVV9ediJsfiu9eJ3FzCZLNgki2YbNb2XrZilMWazd2k1WxSKZSpFIrtJuH59r6SL1EtlqiW2qXDhUKBf/nRv3Do0CF8Pt8D3lXQL8zMzPDyywsUKjXkbZroK1RqxDNFfvHMzLa8n6DNYIw8BLuKTqej0Wig0Wrb9eKSJPryCHacXDaL1zwYM12SJCGbDeRyuV6HIhB8KOPj43z9618nlUqRTqc3bKlEirX5lY2NgyUJk9WMUd4olCxO26Z1pIKtoSgKuViSUq5ApVCi0hU8hRLVYnnDuRqNBofDgdflwjU2gcvlUjePxyOcwwaM6elpXn75ZRaiaY6O+x/8gi2wEMuo7y3YPoQYEmziwoULJJNJLE4bWr2OwMwYZ8+e5amnnsLl2t7aV4GgSy6fY8I91OswtozNbCCbzfY6DIHgZ9I13BkfH9/0XLPZJJvNkslkSKVSZDIZ0uk0yyvLxG7f3XDuqc89j+xx7lLUe4d0OM7Vf/7phmN+v5/J/bMbxI7L5cJut4vM3B4iGAxitVpYiGW2TwxF01gtFoLB4La8n6CN+NYJNnHjxg0sDpvakXz06H5arRa3b9/ucWSCvUqlUqFWq2MbkMwQgN1sICfEkGCA0Wq1uN1uxsfHCQQCSJLE2toayUQSAKPFRGD/Pg7/wlNY3Y4eRzuYuIZ9HHzuDL6pUfSdUql4PE4qlUKn0zE8PMz4+DhOp1MIoT2GRqNhZmY/87HMxgzsI6IoCvOxDNMzM+JvZZsRmSHBJiLRKLLHodYrm+0yWr2OWCzW48gEe5VuhsVmGRxzDpvZyHxsDUVRRBmpYOCo1WrcuXOHGzducOPGDQqFAgD2ITfjTxzCHfIje5zib/sxkSQJ32QI32QIpaWQT6RJLkdIrsR4+eWXefnll3E4HMzOznLw4EEmJiaEc9weYmZmhkuXLhFNFwi6bY/1XtFMkXK1zsyMWC+03YhvnGADiqJQq1bRlO/N0NfKbVEkGkwKdgpVDA1QZshmNlCr16lUKqJZomAgyGazzM3NcePGDe7cuUOj0UCn1+Ea8RN6YhbXiB+DeXAmJAYNSSO1+/j53EycOky1VCa1HCW5HOXtCwL7C2MAACAASURBVO9w7tw5DAYD09PTHDx4kAMHDmC1WnsdtuAx6K7tmY9lHlsMzUfTAEIM7QBCDAk2IEkSL774Ij/4wQ8w22UMFhO3fvoukgLPPfdcr8MT7FG6s9Lb5bizG8gd4VYoFIQYEvQ93/3ud7l48aL6s8FsZPzYfgL792Ewm0QGqAcYLWaCByYI7B+nWigRnlsgMrfItWvXuHbtGgBPP/00n/rUp3ocqeBRkWWZ4eFh5qNpPnlw9LHeaz6aZjgYFEYaO4AQQ4JNnDlzhqtXr7K4tIQkSWSjCT796U/j9Xp7HZpgj9IVQ9YBEkNW4z0xNDQ0OMYPgo8moVCIbDZLMpkkk8lQK1dZvHiNxYvX0Bn0mO1WTDYZs92K2S53Nit6k1EIpW1AURRq5QrlXJFyrtDZilRyRcr5As16Qz1XkiScLidej1cslN8DzMzM8Nprr1GpNzDpH23YXak3WE3meeaZk9scnQCEGBLcB41Gw6c+9Sm+8Y1vUMrmsdlsPPXUU70OS7CHKRaL6LQaDAPUCLLbeLUr5ASCfubJJ5/kySefBKDRaJBOp0kmk+qWSqVIJBIsL65uWOyt0+sw2WXcI34mTh/uVfgDy603L5GLJynnCjQbTfW4RqPB5XIx7PXhOXAQj8ejbk6nE612cK6Fgp/NzMwMr776KkvxDAdGHm1SeSneNmEQJXI7gxBDgvsSCoXQaDS0Wi0+9rGPiQuzYEep1WoYBmzRsL4j3Or1eo8jEQgeDp1Ox9DQ0H0zmo1Gg6tXr/L6668Ti8Vo1BsUkhksjv4rzZHdDgrJdt8V2eNE7jPHO0VRKOcKFFL3XCdHRkZ45plnOHjwoLivfkQYHR3FYDCwEH10MbQQzWAwGBgdfbxSO8H9GazRh2DXkCQJrVZLq9Viamqq1+EI9jj1eh2ddrCsQvW6drxCDAkGmWazSSKRIBKJsLS0xLVr1ygUCkgaDa4RP0Pjw3jGghjMpl6Huonpjx9XhcaJzzzT42g2I0kSxz71c1QKJZJ3IySWwqyGw/z1X/81DqeDw4cOMzo6yvDwMG63W9gl71F0Oh2Tk5MsLC8+8nssxDJMTk4Kp8EdQvyvCj6Ubp24Xq/vcSSCvU6r1UIzYMsSNJ3vR6vV6nEkAsHWqNfrxGIxwuEwkUiE1XCYWDRKo9Fer6LVaXGFAoROHcQzGkBnENf+7cAkWxg5NMXIoSnqlSrJuxHWlsK8de4t3nzzTQAMBgOBQIDh4WGCwSDDw8P4fD4x+N0jzMzMcOPGDdKFMi754Qx30oUymWKFp0WJ3I4hvmWCD6U743337l1GRkZ6HI1gL2MwGKg3B0tU1Dr1/wbD4Jg+CD5aZLNZ3nvvPcLhMOFwmLW1NXU9kM6gR3Y78B8Yx+ZxInscWBw2JJGd2FH0JiOB/eME9o/TarYoZnIUkhkKyQyZZJbVC++oZgoajQafz8fw8DDDw8McO3ZMWG0PKF2L7YVY5qHF0EIss+E9BNuPEEOC+3L9+nX1pvnjH/9YXIQFO4rBYFDFxaBQF2JI0Of8+Mc/5p133tl0XKvXIXucGCwmJNq95ApJqJYqGMwmDGajcJHbIZSWQr1SpVauUCu39/VKFUmjwWg1I2kk8ok0rUaTVqtFNBolGo1y8eJFCoUCL774Yq//CYJHwOPx4HA4WIxleGLq4RwCF2MZHA47Ho9nh6ITCDEkoNlsEo1GuXv3Lnfv3mVpaYlsNotGq8HisFFIZfmjP/oj/H4/+/btY2xsjLGxMVwul7hZCrYFm81GvdGkXKtjHpDSnFypCrRjFwj6kZdeeonTp09TKBTI5/Mb9oVCgXwqTyofUcvk1iNJEgazCb3Z2BZHZmNHKJlUwdR9XmfQf6TvBYqi0KjW7omb8kaxc+9YlVqlCuvc+roYDAZkWcZpszE6G8BqtWKz2ZBlWd0Lm+3BRZIkpqamuPbeFVqKopZZP4iWorC0luXQkWMf6e/YTiPE0EeQUqnE8vKyKnxWVlbUkjij1Yzd52Zqdh/x+RU0Wg0nX3qO1EqUbDzFOxcvcO7cOQCsssy+sTFVIA0PD4v6ZsEj0XW1SubKhLyDIYaS+TKA6DEk6Fv0ev0D3acURaFWq324YMrnyRfy5GMZ1gqF+66RkzQSBlNbGOlNBvQmIwZTR0CZjOjXZZsMJiOaAbDQbzYabQFTqX7ovlGpdrI81Q125F20Wi3Wjpix+dyqqFkvcLp7kWHe+0xNTXHx4kVimSJB19bcGWOZIpVaQxhZ7TBi5LoHUBSFarVKsVjcsJVKpQ0/F4oFCoUiuWzbfUeSJGSPE9/MGHafB7vPjUm2qO+bWAoDYPe5sfvc7c9qKRTTWXLxFNl4koXlJbVTtlarxeF0IFtlrFbrfTeLxaI+FsYMgi4+nw+AeLZIyGvvcTRbYy1bxGIxi/JRwUAjSRJGoxGj0fjAMhxFUSiXy5sEU7FY3LAvJPOki9EPdVrU6XXozabNwslsQr9eRJmM6I0GpG1wV1FaLeqd7E19k7DpZG4qNRodcdO8T7YM2hkcq2zFZpWR/R6sViuyLKv79QLHZDKJ2XyByuTkJAB345kti6G78cyG1wp2BiGG+pBWq0WlUtkkbtYLnO6Np3us2bz/eguNTtu5qRjQGY2YvQ7cUyM4/B5sXhfah+yGLGnaAkr2OBk+2P5y1koVcvEk2bUU1UKJXKVMMpqhXqlRr9x/xgzaNxWL1YLVKiM/QDhZrVYMBoO4sexRXC4XdruNhUeop+4FiqIwH8syMSkWtAo+OkiShMViwWKxqBMYP4tarXZPIK27Z20QToUCuVSCUrF433uFJEnYhly4RwN4RoNYXfYt3QcURSGfSJNajpJcjqr9iD6IRqPBYrUiW624HG7kEXmDwFkvcsQknuBxsNvteD0eluJZnjwQ2tJrltayeD0e7PbBmCQcVIQY2kUURaFYLJLJZNQtm82qM2zrxc2HCQidXofeZETXETjWgBunKajOsOk7x7uzag8rdh4Fg8WEd3wE7/hmxzlFUWjU6upMXL3Snn2rd8oL6pUapUqV7FqB+kr7nA+zKtbpdG2BJFvV7JPdbsfpdG7YjEbjTv+TBduMJEnMzh7k3YsXaDRbfd9zKJYpUihXmZ2d7XUoAkHfYjAYMBgMuFyuB57bnQT8YLYpl8tx584dFi9cY/HCNUxWC+5RP+7RIM7gxhLVZr1BejVGcjlKaiVGrVxBkiRGx8Y4ffSEmrFZL3BE9kawm0xMTnLl0rtbWjfUUhRWEnmOnTi5S9F9dBFiaBtpNBpks1my2ewGwZPJZEhn0mQz2U0ZHK1eh9FiRmc0oDcZsDuH8HTKBNYLHIPJgN44GLXW65EkCb3RgN5oAMeDF5orikKz3lCFUk0VTVU101StVCmkE9Qjq1RLFZQPiCejyYTT6cT1AZHkcDhwOp3Isiya2/Uhs7OznD9/ntuRFLOhR+vSvVtcu7uGRpLYv39/r0MRCPYEGo3mZ2ad8vk8c3NzzM3NcevWLcI3FtDotOhNRkyyhdtvXSZyY4FWq4XRaOTA/v3Mzs6yf/9+LBbLfT5RINh9xsfHefvtt4lnigQeUCoXzxSp1huMj4/vTnAfYYQYegSWlpYIh8NqZiedyZBJpykUCpvONVrMGKxmTHYzwaAHk2zBKJsxWS0YZctH3oXng0iShM6gR2fQY95CVlhpKdQqFSqFEtVCub0vlqgUSqzEI9yev0OjtrFuXaPRqMJo/TY1NbWlGUzBzjAzM4PL5eL8zXBfi6FqvcGlhRiHjxxBlrdW9y0QCB4Pm83G6dOnOX36NI1Gg4WFhf+/vTsPjru88zz+fvq+pJZalyVZlo0tbAM2hx1i4yEhISGOYRZ2YUjIcqRCJpvZykxqK1uprdqq/WOmtmr+2K2tTG0CIceQSqaSmoTKcqVgJwMxWULAMWADPvCBbVmSLUutq+9Dz/7RrXbLkm9J3VJ/XlVd/etfX1/Zaj39+T3P73l49tlnGY9NQChA9ORpWltbufvuu+nu7sbpXFwHDqU2TAWb3qGxi4ah3qGxac+R+aMwdJmy2Sw/+tGPZvTwGIehsbOVpq52Ag11hdAT9OPQH+R5ZRwGb8CPN+CH8wxhz2WypZA0fmaE4RMDjERHGBkZmfa4tWvX8uijjy5A1TIbh8PBtm3beOGFFzg5NF61Eyns+eg06WyObdu2VboUkZrkcrno6enB5/cxES8chHR53NTX1+tEc6lqhVEq9fQNTfCxngs/tm9ognC4cCqAzC+Focvkdrv55je/yYkTJxgYGKC/v5+BgQFSqRQjfYOM9A0SqA8RjIQJNoUJRQore3v8Gpe8kOykJTE+QXx4jFh0jFh0lHh0jEwyXXpMQ0MD7e3tpcuqVasqWLEA3HLLLbz6yiu8svcYj3xqQ9V9ZpKZLH84cJJVq1ZedMpiEZk/fX19DPQP4A36AQg21nPkyBGGh4e1OKVUte7ulXx0+OBFH3cyOsGqNWsXoCJRGLoCTU1NNDU1cfPNhZParLWMjY0xMDAw7XLsWF/pOd03r2flzesrVXLNefv5V0uzBzkcDtra2li1/vpS8Fm2bBl+v7/CVcq5vF4vd33uc/z617/mveODbFzZVumSptn53nFSmRz33PPnlS5FpGZNTk7y7LPP4vH78AYL5wOt3HQdQ8f6eP6FF/jyY49VuEKR81uxYgV79+5lPJGmPjD7hE/jiTQTibQOui0QhaE5YIwpnXeyfv3ZwLNnzx5++ctf4vZ6aOpaVsEKa0/bmhXEhkepr6/n8ccfp7m5es9BkeluueUWdu3axavvHWdNe4SAtzqmsu0bnuCdo6fYunUry5bp8yxSKel0mr6+PjqvX1M66OUN+Il0LePokSNYa6uuV1lkyvLlhWm1+6MT5w1D/dEJAIWhBaIwdJWy2Szj4+MzLiMjI+zfv59QUwPX37ll2mKmMv+WX78Gf32IAzt38eSTT7J27Vrq6+tnXOrq6jSzXJVxOBzcd999PPHEE7y460Me2HZdxb/YpLI5nnvzIOFwmDvvvLOitYjUOr/fT09PD8eP9uKvD4Ix5NIZhk+cYuPGjRX/eyFyIe3t7TidTvqjE+edLGggOoHT6aS9vfrX3VsKFIbOw1pLIpFgbGxs1rAzVrxOJZMznjs1XXbHulVcc+sGnC79M1dCU9cybv7zOzj8xz0cOPwh6UQSOzl9/SZjDMFQiHBZQAqHwzNCk9YuWljt7e1s376dF198kT8d7udjPTPXsFoo1lpe3n2E8USar/7loxpeKVIFtm3bxqGnn8bhcuIN+jmx90PyuRy33XZbpUsTuSCXy8WyZW2cis6cgXjKwEiMZW1tuPT9cUHU/L9yOp1m9+7djI6OFkLO2Bhj4+PEJiZmzBgH4A348AR8eAJ+It3L8AT8eIN+PAFfYVazoB+XpzqG9QgEwnVs/NyfAYUvtdlUmnQ8STqRIpNIko4nySRSJOJJRgf6SB85PGMqbgCP10t9Xd20sNTR0cH111+vo5DzZOvWrRw+fJhX9n5Ie2NdxWaXe/vIKfb1nuEzn/kM3d3dFalBRKZbs2YN69at48CBAxiHoff9Q2zatImOjo5KlyZyUR0dnex9951Zh3Raazk9GmfDjVrHbqHUfBjq7e3lxRdfnPU+p9tFeFkzDctaaGhvJhgJa0jVImaMweP34fH7uNDyr7lMlvEzUUYHzjA6MMTE0AiZdJqhdJqhoaHS43w+H+vWrdORm3lijOGBBx7gySee4Jk3DvDlOzcSDvgWtIZjg6P8y7tHWbt2LZ/85CcX9L1F5PyMMdx7770cPHiQ5FiM+vp6duzYUemyRC5JR0cHu3btYjSeojE0fbTBWCJNKpNTsF9AVfUtzhizHfgO4AR+aK39+/l+zzVr1vDtb3+boaEhhoaGGB4eLmwPDzESHSHae4po7ymA4kKgIfzhEP76EIHitjdQ6A1yuLSm0GJgrSWfy5FLZ0lNxEmOx0iMx0iOxUiNF25PTk6WHu/xeGhqbqKluYWmpiaam5tpamqipaVFQWieBQIBHn7kEZ588kmeef0AD39qA54F+pxFY0l+/cYBWlqaefDBB3UgRKTK1NfXEwgEiMfjbNiwAZ9vYQ+WiFypqXOBBsfiM8LQ4Gh82mNk/lXNNzljjBP4LvBZ4CSwyxjznLV233y/dzgcJhwOs3r16mn78/k8o6Oj00NS8TJ4pHfG6zicTtxeNy6PB6fHhctT2HZ53Li87uJ18Xbp4inc53ZjHBpudakm85PkMhlymSy5dPbsdul2lly6sC+byZIvXrLF/dZOP3fI6XQSaWpiRXsnzRuaS4GnubmZUCikoXAV1Nrayhe+8AV++tOf8uwfD3L/betxzPNnJZHO8s//bx8Op5tHHnlUX7JEqtTU3+ZwOFzhSkQuXVtbGwY4M5Zg7TmnxA6OxTHFx8jCqJowBNwKHLbWHgUwxvwCuBeY9zB0Pk6ns7Sm0Lmy2SzRaJShoSFisRipVIpkMkkymZy2nRyNMZFMkUqlZnwBP1d5SHIWr91ez7TtaUHKezZsOVzORfWF3VpLvhhessXrfDpT2p4KMuUB5+zjM0zmZp7PVc7pdOLz+/H7fNT5/fgjDfh8Pvx+/7TrxsZGmpqaCIc1BLKarV27lnvuuYfnn3+e//vuET538+p5+33P5vP86vX9TCQyfOXxx4lEIvPyPiJy9aba1UQiUeFKRC6dx+MhEomUeoHKnRmL0xiJ4PF4KlBZbaqmMNQJlHe3nAQ+fu6DjDFfA74GhYWrKsXtdtPW1nbJyd1aSyaTmRGWprZTqdT0EJVMkkylSIyOkEqlyGQyF3x943DgLoYkT9CPvz6Iry6Evz6Ivy6Ivz6E070w/91TExUkx+OlYWjJiTjJ8Ti5VJpssSfnYkqhxe+jIRSeEWT8fv+MfVPbbrcmsVhqtmzZwujoKL///e8JB3xsXbd8zt9j0lqef/ND+ofH+eJDD2nCBJEqlc1mOXToEKlUCoD333+f7u5uVq9ejdOpIetS/Vrb2jjTd3zG/qHxJG2dansWUjWFoUtirX0KeApg8+bNF+5qqSLGGLxe7xVP0ZzP56cFpnMDVfntkdFRor2DDCSOTXsNj9+Hry5YCEj1Qfx1IXz1haDk9s48AhGKnH/YgbWWdDxJciJOajxGcjxe3I6TnIiRz+am/ez19fW0NDVR31k/a6g599rj8ainRma46667GB0d5XfvvUc44OW6FS0XfHxrQ+iyXv+VPR9xsG+YHTt2cMMNN1xNqSIyx/L5PEePHmXv3r188MEHpNNpjDG4/V5Gx8f4yU9+gj8QYMMNN7Bx40a6u7vVjkjVam1t5eCBA+QnJ3EWf0/zk5NEJ5Jc39pa4epqSzWFoT6gfKnd5cV9QmHYVzAYJBgMXvJzUqkUw8PDRKNRotEow8PDhctglNOHT0x77NTkEL66IKGmBpZfv5o1W248+1oTcfr2HSExHiM9UQg+k/mzkww4HA4aI410NLfStHY9kUiEpqYmIpEIjY2NmmhA5oTD4eD+++9nfHycF3Ydos7voavl/KH9szddc8mvvetQP7sO9bN161atVSJSIblcjng8TjweJxaLla6HhobYt28fiUQCl8dNc3cHLdcs58S7B8AYNn5uG9G+05w5epLdb+/mrbfeoq6+juvWX0ckEiEUChEMBkvXwWBQPUhSUS0tLUxay2g8RVNdAIDReJpJa2lpufCBPplb1fQNdRfQY4xZRSEEfRH4UmVLWtx8Ph+dnZ10ds5csDKbzTIyMlIKSKWwFB3mo49OMnyin+s/vQVPwMfIwBn2v/omk9k8zc3NLOvoKgWdqWudcyMLxe128/DDD/P97z/Jr/6wn8fuvJFI6OoWQj3UH+W37x5l/fr17NixY1GdfydSzaaGiMdisdJlKuCUb0/EJojH4qVhb+dyul1Eli9j1TUbiSxvw1EMMif2HAQKExg1r+igeUUH+WyO4d5TDB7tZdfuP533HFO/308wFKQuVEcoFJoRmKb2hUIhnb8hc27qfPToxNkwFJ1ITrtPFkbVhCFrbc4Y8w3gZQpTa//YWvtBhctastxuN62trbTO0hX73nvv8cwzz/D286/S3N1B//6jNDU388jDD9Pc3FyBakWmCwQCPPbYl/ne977Hr17fz2Of3oj3Cs+JGxpP8NxbB+lob9cU2iKXoLz3JpFIzOjFOTf45HK5WV/H7fXg9nlx+714Qj4iLWE8fi9unxeP31e8LtzvdLku+SCF0+2i9ZrltF6zvDBZTzZHJpkmm0yRSaXJJtPF68Lt4cQ4p6NDZJKpWRfdhkKbGQwFCQVD00JSeXgKBAKla42GkIuZ+j41EkuW9k1t67vWwqqqT6u19jfAbypdR63bsGEDTU1N/OyffkbfviOsXbeOB//iLzS9sFSVSCTCQw89xNP/+I889+aHPLBt/WX36CQzOX71+n48Hh///uGHdfRXao61lnQ6XQo35QFnWtiJx4nHYyTiifNO6GMcBo/PVww4HnzNYeq6WssCjhe334fHV7jtcM7/gQdjTGkGVsIXP4dwMp8vBKdUuuw6VQpQ8WSa0cEBsicK959vlliv10sgGCAUnB6Upi7n3vZ4POqRrjGBQACf18tI7Gxv6Egshc/rJRAIVLCy2lNVYUiqR0dHB3/z13/D6dOn6erq0tFyqUqrV69mx91388ILL/DGgZPctr7r4k8qstby4q4PGU+kefyrX6WhoWEeKxVZWPF4nMHBwVlDTiwWI17cTiYS5POzDyNzOB14fF5cPi9unwdPYx3B9qZC2PEVe3Wm7vP7cHnci/4LvcPpxBcK4Atd/MuotZZcOlMKTdlUhmy6bDuVZiKVZmRwnGwqQyaVxpYt6F3O6XQSCAYJFYPSub1NU5e2tjb8/qsbFizVozESYSx+NgyNxVM0NjZWsKLapDAk5+Xz+TS1sFS9LVu2cOzYMV774AO6WxvobKq7pOe9c/QUh/qj7NixQ7/nsuQ88cQTjIyMTNvn8riL4caD2+shtCxCo699RrBx+7x4fN5Ft37dQjPGlP7dLsXUkL3ysJRNpcmmC0Epm0qTTmWIjY/QP3iKTCo9bWZWgK6uLr7+9a/Px48jFRCJRDjV+1Hp9mgiTdvy9gpWVJsUhkRkUTPGcN9999Hb28tzbx7kK5+96aLnDw2NJ/jXPR/R07OGrVu3LlClIgvnoYceYs+ePRw6dIjBwUGg0OtR1xKhcXkrjR2tePwa+ryQyofs+etnf0w6niTad5qRvkFG+weZ6rNr72jn2p5ruemmmxasXpl/4XCYDw+eHW45nkizVqMUFpzCkIgsen6/nwcffJAf/OAHvL6vl0/fuOq8j7XW8tLuw3g8Xu6//wENAZUlqXwm0bGxMQ4fPsyhQ4c4fPgwp48UllZYdm0313xsw6zrzMnCyiRSHP7jHs4cK6woEgqFuOG66+np6WH16tWEQpe3ZposDuFwmGwuTypbiL3ZXJ5w+PzLRcj8UBgSkSVh5cqV3HLLLex65x02rmqjuX72Mf/7es/QOzTOfffdR13dpQ2pE1nMwuEwmzZtYtOmTUxOTtLf38+ePXt44403iPaeZs2WjTSv7NSQuAqw1nLqw2Mc3fU+Nj/JHXfcwYYNG2hra9P/Rw2YCj4TiTSY6ftk4eiQqIgsGdu3b8fj9fLbd4/Oen82l+eVvcfo7Ohg06ZNC1ydSGVkMhmi0SjHjx9n//799PX14fP56OrqIpNMse/Vtzjy1nuVLrMmHdj5Jz58/R1ymSwrV67E6XRy4sQJ9u/fT29vLyMjI2Szs0/3LYtffX1hvGQslSGWLMzSqIN0C089QyKyZASDQe644w5eeukl+qMTdESmNyp7PjpNLJnhoYfv1vA4WdRyuVxhsdKJiWnr+pTfnto+31TYHp+XUGMYl99LsEFfwCoh1BQuTN2dSnOi7yRHjhyZ9XEer5e6UIi6usICsVPXU5ep28FgUGscLSJTwSeWzJR6hhSGFp4+MSKypNx666387tVXefPgSf7t1vWl/ZOTlrcO9bNiRRcrV66sXIEilykej/PSSy8xMjLCxMQEE7EY6VRq1se6vcVprn0ePOEALe0RPD4vnoDv7Do//sJscUYHBCqua8O1dG24tnR7Mj9ZWtuocClb5yiZYiQZY3Bk+IILxPr8/lJwam5u5vOf/7zWUKtSU+eCxdNn/y8VhhaewpCILCler5ePb9nCzp07GYunCAcLM2Z92D/MWDzFPfd9osIVymJnjNkOfAdwAj+01v79fL5fJpPh2LFjRKPRcwqB+tYmmpYvI7K8jUBDHQ6ncz5LkXnmcDrwBv14gxdfS2gylyc2Mka09xTRk6eZGCpMpZ5KJkklk5w5c4ZYLEYul1MYqlIejweP2008lcEY8Ljd+r+qAIUhEVlyNm/ezM6dO9l/cogta5cDsO/EGUKhIOvWratwdbKYGWOcwHeBzwIngV3GmOestfvm6z0bGxv51re+RTwep7+/n/7+fvr6+ujr72P09DDjp4f5aPcH+EJBQk1hQk1h3F4vTo8Ll9uN0+PG5Xbh8pzdVq9Q9bKTk+QyWXLZHPnidS6TLW5nySbTxKJjxIZHSceTpec1NTXR2dlJR0dH6aIFWqtfMBgkUewZCgQvvtivzD2FIRFZciKRCJ2dnezvLYShdDbHkVMjbP7YrTpXSK7WrcBha+1RAGPML4B7gXkLQ1OCwSA9PT309PSU9iUSCQYGBujr6yuFpGPH+y/6Wk5XMRy5XYWA5HHhdBeuSwGqGJwKAcpdCFelbTcOp0MznpWx1jKZy5PLZslncsVAU9zOFsPMtJCTJZfJkS8+Jp8t3J/P5S/6Xs0tLaxbc20p/LS3t+Pzad2oxSgQCJDMFEJtMBCscDW1SWFIRJakDRs28NJLLzGRTNMfnSCXn+SGG26odFmy+HUCvWW3TwIfr1AtBAIBVq9eFJFe2wAACV9JREFUzerVq0v7stksqVSqdEmn0zO2z71OpVKkYiliqTHS6fQlzWBmHAaXx1MITGVhyekuD03TA5Q36McXCuDyuOfzn+WKWWvJZbKkYwnSiWSxR2ZmsMllsuSnQk1ZkJlaPPNCPB4PXq8Xr89Lnc+PLxjG6/Xi8/nw+Xyzbpfv8/l8miRhCQkEg/T3ngEMHV3NlS6nJunTJCJL0qpVhYVXTw5N0B8dx+V00tXVVeGqpBYYY74GfA1gxYoVC/7+brcbt9t9VSdi5/N50un0rEHqQtupdJrU+ATjxf3nCweuYjDyBv14QwF8wUDZth9P0H9JvbihyOWtyTKZnyQdT5KOJ0jFEoXtWIJUPEEmniQVS5LP5WZ9rsPhKISYUjAJ4Wu4tABTvk+901Ju9erV9PWdLG3LwlMYEpElqb29HbfbTe/QGP3RGJ3Ll+toqsyFPqA8VS8v7iux1j4FPAWwefPmi3cVVCGn00kgECAQuPJzGKy1pV6qdDpNIpFgfHyc0dFRxsbGGB0dLVxOnCaZSEx/sjF4A75iYArgDfkLgal47Q+HcLpcrNly47Sn5bJZkmMx0vHkOWEnSSaeJJ1Icq5AMEhDQ5iOzhWEw2EaGhoIh8PU19fj9/tLIcbtdmtYoMy522+/ndtvv73SZdQ0fTMQkSXJ6XTS0dHB6ZERBkfjbFm/sdIlydKwC+gxxqyiEIK+CHypsiVVJ2NMYbasS5gdK5PJMDY2Ni0klW8PnBggnz97Lo3b5+XabTfT3N0BFILX6cMnOPLHPeSyZ3t2XC4X4XCY1oZGGlasKoWdqcATDodxu6tzyJ6ILAyFIRFZspqbm9l9/DgALS0tFa5GlgJrbc4Y8w3gZQpTa//YWvtBhcta9DweDy0tLef9nE5OTpJIJBgdHWVkZISdO3fywb/+kWU93ay4aR1H33qPoeP9dHd3s23btlLgCQQC6s0RkQtSGBKRJav8i1Vzs05Mlblhrf0N8JtK11FLHA4HoVCIUCjE8uXLWb9+Pa+88gqvvfYapw4dx+FwsH37drZt26ZzckTksigMiciSdeONNzIxMYHb7a7IiewiMj9cLhd33XUXa9euZd++fdx00020t7dXuiwRWYQUhkRkyaqvr2fHjh2VLkNE5kl3dzfd3d2VLkNEFjH1JYuIiIiISE1SGBIRERERkZqkMCQiIiIiIjVJYUhERERERGqSwpCIiIiIiNQkhSEREREREalJCkMiIiIiIlKTFIZERERERKQmKQyJiIiIiEhNUhgSEREREZGapDAkIiIiIiI1yVhrK13DFTPGnAGOV7qOJa4ZGKp0ESJXQb/D86/bWttS6SKqkdqpBaHPuCwF+j2eX+dtpxZ1GJL5Z4z5k7V2c6XrELlS+h0WWdr0GZelQL/HlaNhciIiIiIiUpMUhkREREREpCYpDMnFPFXpAkSukn6HRZY2fcZlKdDvcYXonCEREREREalJ6hkSEREREZGapDAkIiIiIiI1SWFIZmWM2W6MOWiMOWyM+S+VrkfkchljfmyMGTTGvF/pWkRk7qmdksVO7VR1UBiSGYwxTuC7wOeB64CHjDHXVbYqkcv2NLC90kWIyNxTOyVLxNOonao4hSGZza3AYWvtUWttBvgFcG+FaxK5LNba14BopesQkXmhdkoWPbVT1UFhSGbTCfSW3T5Z3CciIlIN1E6JyJxQGBIRERERkZqkMCSz6QO6ym4vL+4TERGpBmqnRGROKAzJbHYBPcaYVcYYD/BF4LkK1yQiIjJF7ZSIzAmFIZnBWpsDvgG8DOwH/tla+0FlqxK5PMaYnwNvAGuNMSeNMY9XuiYRmRtqp2QpUDtVHYy1ttI1iIiIiIiILDj1DImIiIiISE1SGBIRERERkZqkMCQiIiIiIjVJYUhERERERGqSwpCIiIiIiNQkhSGRq2CM+cNF7j9mjHnPGPNu8XLbPNTwO2PM5rl+XRERWXhX0q4YY1YaY95fqBpnqanUDqndk8XGVekCRBYza+2l/JH/lLV2aLY7jDFOa21+jssSEZFF6kraFWPMynkr6Mqo3ZNFQz1DIlfBGBMrXrcbY14rHgV73xhz+4WeY4z5n8aYPcBWY8x/M8bsKj7vKWOMKT6u/EhbszHmWHHbb4z5hTFmvzHm14B/3n9QERFZEFfSrpzz/JXGmN8bY94uXm4r7ncYY75njDlgjPkXY8xvjDEPFO/bUdy/2xjzD8aYF4r7g8aYHxtj3jLGvGOMube4/7LaIbV7Us3UMyQyN74EvGyt/e/GGCcQKLvvVWNMHkhbaz8OBIE3rbXfAjDG7LPW/m1x+6fAPcDzF3ivvwIS1tr1xpiNwNvz8POIiEhlXU67Um4Q+Ky1NmWM6QF+DmwG/h2wErgOaAX2Az82xviA7wOfsNZ+ZIz5edlr/VfgFWvtV4wxDcBbxpjfAv+BC7dDavdk0VAYEpkbuyg0Km7g/1hr3y2779zhAnngmfL7jTHfptDQRYAPuHCj8AngHwCstXuNMXvn4gcQEZGqcjntSjk38L+NMTdRaG+uLe7/M+CX1tpJ4JQx5tXi/nXAUWvtR8XbPwe+Vty+C/g3xpj/XLztA1Zw8XZI7Z4sGhomJzIHrLWvUfhj3Qc8bYx59AIPT02Nly4ekfse8IC1dgPwAwqNDUCOs59R34xXERGRJesy25Vy/wk4DdxIoUfIcxVlGOB+a+1NxcsKa+3+K3gdtXtStRSGROaAMaYbOG2t/QHwQ+CWS3zq1B/7IWNMCHig7L5jwKbidvn+1ygMn8AYcwOw8QrLFhGRKnUV7UoYGCj2AD0COIv7XwfuL5471AbcUdx/ELimbBKGL5S91svAX5ed03Nzcf/VtENq96SqKAyJzI07gD3GmHcoNCTfuZQnWWtHKRwVe59Co7Or7O7/AfxV8TWby/Y/AYSMMfuBvwV2X3X1IiJSbe7gCtoVCr0ujxUnK1gHxIv7nwFOAvuAn1E472bMWpsE/iPwkjFmNzABjBWf83cUht3tNcZ8ULwNV9EOqd2TamOstZWuQURERETmmTEmZK2NGWOagLeAbdbaU2X7DfBd4JC19n9VtlqRhaEJFERERERqwwvFWeE8wN9Za08V9/+lMeax4v53KMwuJ1IT1DMkIiIiIiI1SecMiYiIiIhITVIYEhERERGRmqQwJCIiIiIiNUlhSEREREREapLCkIiIiIiI1KT/D1sRGuBIlWfEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] =(14, 12)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.violinplot(x='isFraud',y='step',data=df, palette='Pastel1')\n",
        "plt.title('Frequency distribution of fraud/step (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.violinplot(x='isFlaggedFraud',y='step',data=df, palette='Pastel1')\n",
        "plt.title('Frequency distribution of flaggedFraud/step (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.violinplot(x='isFraud',y='step',data=df2, palette='Pastel2')\n",
        "plt.title('Frequency distribution of fraud/step (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.violinplot(x='isFlaggedFraud',y='step',data=df2, palette='Pastel2')\n",
        "plt.title('Frequency distribution of flaggedFraud/step (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "f700e19c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "f700e19c",
        "outputId": "47cf5c90-5669-4a10-e511-a82cf2238f01"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] =(18, 12)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.violinplot(x='isFraud',y='step',data=df, hue='type', palette='Pastel1')\n",
        "plt.title('Categorical distribution of fraud/step (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.violinplot(x='isFlaggedFraud',y='step',data=df, hue='type', palette='Pastel1')\n",
        "plt.title('Categorical distribution of flaggedFraud/step (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.violinplot(x='isFraud',y='step',data=df2, hue='type', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of fraud/step (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.violinplot(x='isFlaggedFraud',y='step',data=df2, hue='type', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of flaggedFraud/step (df2 dataset)')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34c1aee2",
      "metadata": {
        "id": "34c1aee2"
      },
      "source": [
        "Both transfer and cashout contribute almost equally towards fraud.<br> df2 has better captured the distribution of transactions that contributed towards flaggedfraud."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "c7393d2f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "c7393d2f",
        "outputId": "d8a5f2a2-0ac4-4057-d226-6e074674a648"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (18, 12)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.violinplot(x = 'type', y = 'amount', data=df, hue='isFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of each amount (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.violinplot(x = 'type', y = 'amount', data=df, hue='isFlaggedFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of each amount (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.violinplot(x = 'type', y = 'amount', data=df2, hue='isFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of each amount (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.violinplot(x = 'type', y = 'amount', data=df2, hue='isFlaggedFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of each amount (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ef9d883",
      "metadata": {
        "id": "9ef9d883"
      },
      "source": [
        "# Feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9027a57",
      "metadata": {
        "id": "c9027a57"
      },
      "source": [
        "oldbalance, newbalance and amount are interdependent variables. So, we can combine them into a single a variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "8f445230",
      "metadata": {
        "id": "8f445230"
      },
      "outputs": [],
      "source": [
        "df['diffOrg']  = df['oldbalanceOrg']  - df['newbalanceOrig'] + df['amount']\n",
        "df['diffDest'] = df['oldbalanceDest'] - df['newbalanceDest'] + df['amount']\n",
        "\n",
        "df2['diffOrg']  = df2['oldbalanceOrg']  - df2['newbalanceOrig'] + df2['amount']\n",
        "df2['diffDest'] = df2['oldbalanceDest'] - df2['newbalanceDest'] + df2['amount']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "75a725ad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "75a725ad",
        "outputId": "bd0558f0-8f9e-4b80-c935-5f849e8cbfdf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (18, 12)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.violinplot(x = 'type', y = 'diffOrg', data=df, hue='isFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of diffOrg (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.violinplot(x = 'type', y = 'diffDest', data=df, hue='isFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of diffDest (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.violinplot(x = 'type', y = 'diffOrg', data=df2, hue='isFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of diffOrg (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.violinplot(x = 'type', y = 'diffDest', data=df2, hue='isFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of diffDest (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "9cb3b94b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "9cb3b94b",
        "outputId": "5b981a03-4d13-4347-88bb-e48c719e26a5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ],
            "image/png": 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tCVgkRMbz9pjA+4HbdaKXYSAfp6pgNk5/D59WskFEJOKoDCIiEmSq4SAiIiIiIiIiAadOI0VEREREREQk4JRwEBEREREREZGAC4s+HHJycmxpaanXYYiIiAwrGzZsOGatzfU6jmih8oiIiMg7nas8EhYJh9LSUtavX+91GCIiIsOKMeaA1zFEE5VHRERE3ulc5RE1qRARERERERGRgFPCQUREREREREQCTgkHEREREREREQm4sOjDQUREwk9XVxeHDx+mo6PD61DCXmJiIsXFxcTFxXkdioiISFhReSRwLqY8ooSDiIgExeHDh0lLS6O0tBRjjNfhhC1rLY2NjRw+fJiysjKvwxEREQkrKo8ExsWWR9SkQkREgqKjo4Ps7Gyd3C+RMYbs7GzdmREREbkIKo8ExsWWR5RwEBGRoNHJPTC0HUVERC6ezqOBcTHbUQkHEREJmSuuuOKcr5eWljJ16lRmzJjBjBkzePPNN6murmbKlCkhivCdrr32WtavXz9ofMFcn4iIiASeyiMXtr5LoT4cREQkZIZyQlyyZAk5OTlnnldXVwcxogvXP76+enp6iImJCXFEIiIiciFUHgkd1XAQEZGQSU1NBaCuro4FCxYwY8YMpkyZwhtvvDGk91dXV3P11Vcza9YsZs2adabA4Pf7+cxnPsOECRN497vfzU033cQzzzwDwJ/+9CcmTJjA7NmzeeCBB7j55psBOHHiBIsWLWLevHnMnDmT5557DoBTp07xoQ99iIkTJ/LBD36QU6dOnfczffGLX2T69OmsWrWKhx56iLlz5zJlyhTuu+8+rLXA2XcKjh07Rmlp6UWtT0RERC6NyiOhK4+ohoOEPWstbW1tpKenex2KiAzRr3/9a2688Ua++tWv0tPTw8mTJ8+8dt111xETE0NCQgJr1qw5630jR47k1VdfJTExkcrKSu666y7Wr1/P73//e6qrq3nrrbeor69n4sSJLFq0iI6ODj75yU+yfPlyysrKuOuuu84s69vf/jbXX389Tz75JC0tLcybN48bbriBH//4xyQnJ7Nz5062bt3KrFmzzoqhf3wnTpzgsssu43vf+x4AkyZN4utf/zoA99xzDy+++CK33HLLoNviscceO+f6RCQ8+P1+Tp48eeZCRkSGP5VH3has8ogSDhL2Nm3axLPPPssDDzxAXl6e1+GIyBDMnTuXRYsW0dXVxQc+8AFmzJhx5rVzVRHs6uri/vvvZ/PmzcTExLBnzx4AVqxYwR133IHP5yM/P5/rrrsOgF27dlFeXn5m+Ka77rqLxx9/HIBXXnmF559/nu9+97uA04v1wYMHWb58OQ888AAA06ZNY9q0aWfF0D++mJgYbrvttrNe/4//+A9OnjxJU1MTkydPPucJ/nzrE5Hw8Nprr7Fs2TIeeuihYVOVWUTOTeWRtwWrPKKEg4S9Xbt2AdDQ0KCEQwhs3bqV0tJS1SiRS7JgwQKWL1/OSy+9xL333ssXvvAFPvrRj573fT/4wQ/Iy8tjy5Yt+P1+EhMTLzoGay3PPvss48ePv+hlACQmJp65uOjo6OAzn/kM69evZ9SoUTz44INnho+KjY3F7/efmU9EIktvlerh1HZaRM5N5ZHgl0eC1oeDMSbRGLPWGLPFGLPDGPOv7vQyY8waY8xeY8xiY0x8sGIQkcDq7Oxk8eLFZ9qWiVysAwcOkJeXxyc+8Qk+/vGPs3HjxiG9r7W1lYKCAnw+H7/4xS/o6ekB4Morr+TZZ5/F7/dz9OhRli5dCsD48ePZv3//mY6eFi9efGZZN954I//1X/91pk3jpk2bAKfw8etf/xqA7du3s3Xr1iF/rt4Td05ODu3t7WfabYLTo/SGDRsAzpp+KesTERGRi6fySPDLI8HsNLITuN5aOx2YASw0xswH/h34gbV2LNAM/H0QY5Ao0PvjlODrzYYOt156JfwsXbqU6dOnM3PmTBYvXsznPve5Ib3vM5/5DE899RTTp09n165dpKSkAHDbbbdRXFzMpEmT+MhHPsKsWbPIyMggKSmJH/3oRyxcuJDZs2eTlpZGRkYGAF/72tfo6upi2rRpTJ48ma997WsAfPrTn6a9vZ2JEyfy9a9/ndmzZw/5c2VmZvKJT3yCKVOmcOONNzJ37twzr33pS1/iscceY+bMmRw7duzM9EtZn4iIiFw8lUeCXx4xobhYM8YkAyuATwMvAfnW2m5jzOXAg9baG8/1/jlz5liNSS6D+eUvf8nOnTv58Ic/zOTJk70OJ6KdPHmSb3/72yQmJp45GIoMZufOnUycODFk62tvbyc1NZXGxkbmzZvHypUryc/PPzPdWstnP/tZKioq+PznPx+yuAJloO1pjNlgrZ3jUUhRR+UROZcHH3yQrq4uvvGNbxAfrwq8IsOFyiOBdaHlkaD24WCMiQE2AGOB/wb2AS3W2m53lsNA0SDvvQ+4D6CkpCSYYUqYO3LkiNchRB1jjNchiLzDzTffTEtLC6dPn+ZrX/sa+fn5APzkJz/hqaee4vTp08ycOZNPfvKTHkcqIiIikUrlkbMFNeFgre0BZhhjMoE/ABMu4L2PA4+Dc0chOBFKJOhto6SLYJHo1ttOsr/Pf/7zYXkHQURERMKPyiNnC2YfDmdYa1uAJcDlQKYxpjfRUQzUhCIGEREREREREQmdYI5SkevWbMAYkwS8G9iJk3i43Z3tY4C6u5eAUA0HERER8Yo6sQ6tpqYmbXORMBDMGg4FwBJjzFZgHfCqtfZF4MvAF4wxe4Fs4IkgxiAiIiIiEjK6ARJ8tbW1fO973xvyEIYi4p2g9eFgrd0KzBxg+n5gXrDWKyLBpzsKIiIi4pXeofwqKys1lLDIMBeSPhxEQkF3FIJP21jE8fLLLzN+/HjGjh3Lww8/7HU4IiIiEoXCoTwS1FEqREREgumXP/spJ9vaA7a85LRUPnLv351znp6eHj772c/y6quvUlxczNy5c7n11luZNGlSwOIQERGR8BGo8oi1fqy1pGZkREx5RAkHEREJWyfb2vnQpHEBW95v39pz3nnWrl3L2LFjKS8vB+BDH/oQzz333LA7wYuIiEhoqDwyODWpEBERuQA1NTWMGjXqzPPi4mJqajTCs4iIiIROuJRHlHAQERERERERkYBTwkFEROQCFBUVcejQoTPPDx8+TFFRkYcRiYiISLQJl/KIEg4iIiIXYO7cuVRWVlJVVcXp06f57W9/y6233up1WCIiIhJFwqU8ok4jRURELkBsbCyPPvooN954Iz09PSxatIjJkyd7HZaIiIhEkXApjyjhICIiYSs5LXVIPTlfyPKG4qabbuKmm24K2HpFREQkfAWqPNLd0w1AembmkOYPh/KIEg4SMYwxXocgIiF2vjGqRYwxo4CfA3mABR631j5ijHkQ+ATQ4M76L9baP3kTpYiIhLNAlUd6R5kYjn0xXCwlHERkyKy1XocgInKhuoEvWms3GmPSgA3GmFfd135grf2uh7GJiIhENCUcROSCqTaJiIQLa20dUOf+32aM2QlEzq0jkSikGyAi4UOjVIiIiEhUMMaUAjOBNe6k+40xW40xTxpjRgzynvuMMeuNMesbGhoGmkVEPKIbICLDnxIOEvZ6s9w66YiIyGCMManAs8A/WmuPA48BY4AZODUgvjfQ+6y1j1tr51hr5+Tm5oYsXhERkUighINEDCUcRERkIMaYOJxkw6+stb8HsNYetdb2WGv9wE+AeV7GKCIiEomUcBAREbkAixYtYuTIkUyZMsXrUGQIjJONfgLYaa39fp/pBX1m+yCwPdSxiYiIXKxwKY+o00gREQlbP33q57S3nwjY8lJTU/i7j330nPPce++93H///Xz0o+eeT4aNK4F7gG3GmM3utH8B7jLGzMAZKrMa+KQ34YmISLgLVHmku6cHgMyM9IgpjyjhIBFDTSpEok97+wnGz78xYMvbvfov551nwYIFVFdXB2ydElzW2hXAQCeIP4U6FhERiUwqjwxOTSokYijhICIiIl7RUI0iIu+khINEDCUcRERExGsqj4iIvE0JBwl7GhYzdHT3RkREREREhkoJB4kYPp9251BRckdERES8ohsgIuFDV2gSMXQRLCKhcNddd3H55Zeze/duiouLeeKJJ7wOSUQkKqnsJ9EsXMojGqVCwp6aVIhEr9TUlCH15Hwhyzuf3/zmNwFbn4iIiIS/QJRHLNDTZ1jM8wmX8ogSDhIx1KRCJPqcb4xqERERkWALRHmkq6uL+vp6AIqKii55ecOFrtAk7PXWcFDCQURERCTyqQ8HiUTd3d1ehxAUukKTiKGEg4iIiHhFF8Ghp+a0Ekl6m1NEGl2hSdhTHw4iw5cK4IGh7SgSPlQeERl+wuE82lvDYTgfQy5mOyrhICIiQZGYmEhjY2NYnOSHM2stjY2NJCYmeh2KiIhEoePHj9PZ2el1GBctXMojXV1dXodwThdbHlGnkRL2hvvBQyRaFRcXc/jwYRoaGrwOJewlJiZSXFzsdRgiIsOCyn6h9cMf/pDRo0fzsY99zOtQLkq4lEdaW1vP7Nutra0eRzOwiymPKOEgYU8nHZHhKS4ujrKyMq/DEBGRCDWcq55Hks7OTvbs2eN1GBctHMojnZ2dPPTQQwAkxMfz9W98w+OIAkdNKiTs+f1+r0MQERGRKKcbICJysZqamgBIS4j3OJLAU8JBRERERETChpI7Emmam5sByIjA/pqUcBARERERkbChEcok0rS1tQGQlpDgcSSBp4SDiIiIiIiEDSUcJNKcOnUKgMS4yOtiUQkHEREREREREY90dHQQ6/MR44u8y/PI+0QiIiIiIhKxVMNBIk13dzexMTFehxEUSjhIWOvs7PQ6BBERERERkYvW09ODL0ITaEFLOBhjRhljlhhj3jLG7DDGfM6d/qAxpsYYs9l93BSsGCTyHT9+3OsQRERERCSEVMNBIo21lkjdnYPZK0U38EVr7UZjTBqwwRjzqvvaD6y13w3iuiVKtLS0eB1CVNEwVCIiIgPTOVJELpYxBn+EHkOClnCw1tYBde7/bcaYnUBRsNYn0UkJB2/ojoKIiIh4RTUcJNLExMREbNIyJH04GGNKgZnAGnfS/caYrcaYJ40xIwZ5z33GmPXGmPUNDQ2hCFPCUFNTk9chiIiIiEgIKeEgkSYuLo7T3T1ehxEUQU84GGNSgWeBf7TWHgceA8YAM3BqQHxvoPdZax+31s6x1s7Jzc0NdpgSppSMEhERERGRcJaYmIjfWrr9fq9DCbigJhyMMXE4yYZfWWt/D2CtPWqt7bHW+oGfAPOCGYNEtmNKOIRUpFb1EhERkfChGg4SaRITEwHo7Or2OJLAC+YoFQZ4Athprf1+n+kFfWb7ILA9WDFIZOvq6qKxsZFYn0Z3DTWd4EVERMRrKo9IpOhNOHR0R17CIZijVFwJ3ANsM8Zsdqf9C3CXMWYGYIFq4JNBjEEiWH19PX5rSYiNicjqR8ORajiISLgxxowCfg7k4ZQ9HrfWPmKMyQIWA6U45ZE7rbXNXsUpIiLRKykpCVDC4YJYa1cAA6Ud/xSsdUp0qaurAyA+JoZTEVj9aDjyu4kdn2qViEj4GGyY7nuBv1prHzbGfAX4CvBlD+MUEZEodSbhEIHXNLpqkLB16NAhEuNi1aQihHprOCjhICLhwlpbZ63d6P7fBvQO0/1+4Cl3tqeAD3gToYiIRLvk5GQATnV1eRxJ4OmqQcLW4UOHyE9LY+CKNBIMPT3OcD1KOIhIOOo3THeetbbOfekITpOLgd6jYbplSNSfgIhcrJSUFCAym1ToqkHCUkdHB0fr6ylMT/M6lKjSm3CIjQ1m9y8iIoE3wDDdZ1in+taAndRomG4REQm2hISEiL2hF5mfSiLeoUOHsNZSnJnhdShRpcut5qWEg4iEk4GG6QaO9o6c5f6t9yo+ERGJbsYYkt1+HCKNEg4SlqqqqjDGUJSe7nUoUaWzsxNwsrAiIuFgsGG6geeBj7n/fwx4LtSxiYiI9OptVhFplHCQsFRVVUV+WirxsTFehxJVOjo6gLfHChYRCQO9w3Rfb4zZ7D5uAh4G3m2MqQRucJ+LiIh4IjUtMpuKq160hJ3Ozk4OHz7M3OIir0OJOidPngTeHrpHRNyRn0kAACAASURBVGS4O8cw3QDvCmUsIiIig0lNTfU6hKBQDQcJOwcPHsTv91MyQv03hNrx404/a+lqyiIiIiIe6x2uWyQSqEmFyDCxd+9eYnw+RqnDyJBrbW0FID4+3uNIREREJFr1DkGqhINEkt4myz1+v8eRBJYSDhJ29lZWUpSRTlyM+m8ItWPHjnkdgoiIiIhIxElOTgagu7vb40gCSwkHCSvt7e0cOXqU0hGZXocSlY4ePep1CCIiIhLlVMNBIlGkdsquhIOElb179wJQmjXC40iiT3t7+5lRKkREREREJHCUcBAZBvbt20diXBx5aZHZi+twVlVV5XUIIiIiIqrhIBEpISHB6xCCQgkHCRvWWvbt3cvozAx8ZrARziRY9u/fr+0eYi0tLTQ2NnodhoiIDIHROTJklHCQSBSpnbIr4SBho7Gxkdbjxxmdpf4bQs1ay97KSo0MEmJPPPEE//M//+N1GCIiIiISZKrhIOKx/fv3AzBaHUaGXG1tLU3NzYzLzfE6lKjS1NTEyZMnvQ5DRERERIIsNjbW6xCCQgkHCRv79u0jLSGBEUlJXocSdbZt24bPGMblZnsdioiIiEQ5NakIHW3j0ImLi/M6hKBQwkHCgrWWqqr9lIzIUBvJEOvu7mbTxo2UZ48gMUIPhCIiIpdKF2YSibRfh45qOIh4qL6+nhMnTlKSqeYUobZjxw7aT5xgZlGh16GIiIgMW7owCx3VcAgdbePQiYmJ8TqEoFDCQcJC75CMJSPUaWEoWWtZtWoVI5KTKMsa4XU4IiIiw151dTWHDh3C7/d7HYrIJdN+HDo+X2RemkdmvQ2JONXV1aQlJpCRmOh1KFFl//79HDp0iBvGjXHuJijLLSIick5PPvkkABXjxnHnHXeQnJzscUQiF08Jh9CJ1IRDZH4qiTgHDxygOD1d/TeE2Ouvv05qQgLTCwq8DkVERCQsTL3iRkonzWbv3r088sgjbNq0SRdtEra074ZOpF7nKOEQJI2NjRw8eNDrMCLCiRMnaD1+nPz0NK9DiSqVlZVUV1dzWUkxsTE6VIiIiAxF5shCRk+Ywaxrb8XEJfHMM8/ww0ceYd26dZw+fdrr8CJCpF6YDUfqw0Eula4iguSpp57ixz/+sddhRITa2loA8tNSPY4kenR3d/PiCy8wIjmJGUWq3SAiInKhUjOzmXntLUyadz2d3ZY//vGPPPzwwzz33HMcOHBAd45FJCqoD4cgaWxs9DqEiNHQ0ABAToraQIbK6tWrOdbYyO3TJhMboe3JREREgs0YQ25xGTlFpbQ2HqGuajfrN2xk7dq1pKenM3nyZCZOnEhpaWnE9lAfTLr7LjL8KeEgw15TUxPxMTEkxcV5HUpUaGlp4a9//StjsrMYk5PtdTgiIiJhzxhDZk4BmTkFdHedprHuIA01VaxZu5ZVq1aRkJDA2LFjGTduHBUVFWRkaFSuc1GTitCJ1I4Mh6NITaAp4SDDXnNzM+lJiTq5hMiLL76Iv6ebG8aN8ToUERGRiBMbF09eyVjySsbS091Fc30tjXUH2bu/ih07dgCQm5tLRUUFY8eOpaysjPj4eI+jFpFg6+np8TqEoFDCQYa9U6dOkRSrXTUU3nrrLXbu3Mm1Y8rITEryOhwREZGIFhMbR07haHIKR2Ot5cTxZpqPHqa5vobVa9bw5ptv4vP5KCkpOZOAKCws1F1nCZnu7m6vQ4gakdqvi67iZNg73dlJsto1Bt3p06d58YUXyElNYc6oIq/DERERiSrGGFIzskjNyGLUuGn09HTTeuwoLQ01HKuvpfrVV3n11VdJSk6mYuxYJkyYQEVFBcnJ6uNKgidSL4KHo0gdxUYJBxn2urq7NSxjCCxZsoTW48e5e9Z0YnTnRERExFMxMbFk5RWRlefcBDjdcYrm+hqajh5m5+49bN26FWMMpaWlTJkyhSlTppCaqhG9JLBUmyZ0Ojs7vQ4hKJRwkGEvLjaWnu4ur8OIaMeOHWPFihVMyc+jOFMdVYmIiFyIrq7gl1PiE5PO9P1graWtuYHGuoMcrT1A1Qsv8OKLLzJ+/HjmzZtHRUWFLhQlINSHWuicOnXK6xCCQgkHGfZi4+Lo6uzwOoyI9sorrxBjDNeOLfM6FBERkbBz/PjxkK7PGEN61kjSs0ZSOmk2J443U39oH/urK9m1axd5eXncdNNNjB07NqRxSeRRwiF0IjXhoNSnDHvJycmcDMGdg2h18OBBduzYwbySYlLUC7aIiMgFa21t9WzdvX0/lE+Zy2UL/5YJcxZwvP0UP/3pT3nllVc8i0sigxIOoXPy5EmvQwgKJRxk2MvKyqK1o3PAsWlf27OP1g6n9sMzzzzDSy+9FOrwwt6rr75Kcnw880YVex2KiIhIWDpy5IjXIQDg88WQV1LBnBv+hoLS8Sxbtoxdu3Z5HZaEMSUcQifUNaVCRQkHGfaysrI43d09YC2H+vZ2ut3ec2tqaqirqwt1eGHt4MGD7N+/n3klRcTHaiQQEYk8xpgnjTH1xpjtfaY9aIypMcZsdh83eRmjhL/a2lqvQziLLyaG8qnzADh06JDH0Ug4G+iGnwRHb8IhPsJqHCvhIMNeXl4eAEfb2j2OJPIsXbKEpLg4ZhYWeh2KiEiw/AxYOMD0H1hrZ7iPP4U4JokwBw8c8DqEs3Sf7mTnuqUAjBs3zttgRGRImpubAYi0OiXqNFKGvaKiIgxQd7yN8uwsr8OJGPX19ezes4erykardoOIRCxr7XJjTKnXcUjkOnbsGI1NTRjA63vB/p4ejhzYw4Fdm+jq7ODWW29l9OjRHkclIkNxJEJraivhIMNeYmIiubm51LRGZrsmr6xYsYLYGB8zi1S7QUSi0v3GmI8C64EvWmubvQ5IwtNbb70FQKzPR5fbzDPUOk+doK56N3VVuzjdcYqSkhJuvvlmioqKPIlHRC5MR0cHzS0tXocRFEFLOBhjRgE/B/JwEr6PW2sfMcZkAYuBUqAauFMneTmfsvJyNq5fT4/fT4zGlb5kJ0+eZMvmzUzJzyM5Ps7rcEREQu0x4Js45ZNvAt8DFg00ozHmPuA+gJKSklDFJ2GitbWVZUuXMiozg/r2dghhvqGnu5vGIwc5eqCSpvoasJaxFRVcfdVVjBkzRp39SUBoPwqNA26zrJyUZNq6uj2OJrCCWcOhG+eOwUZjTBqwwRjzKnAv8Fdr7cPGmK8AXwG+HMQ4JAKMHTuWNWvWUNN6nJIRmV6HE/b27t1Ld08PU/LzvA5FRCTkrLVHe/83xvwEePEc8z4OPA4wZ84cr2vMyzBireUPf/gDPd3dvHfCOJ5avzHo6/T7/TTX19BweD+NtQfo7u4iPT2daxYsYPbs2WRnZwc9huFAHRmGjk83+kKisrKSWJ+P4swMdjY0eh1OQAUt4WCtrQPq3P/bjDE7gSLg/cC17mxPAUtRwkHOo7y8HJ/PR1VTsxIOAVBZWUliXBwF6WlehyIiEnLGmAK3nALwQWD7ueYX6a+zs5MXX3yRyspKbhg3hhHJSUFbl7WW1saj1B/ax7Haaro6O0hISGD69GlMnz6dsrKyqLso7E046O578GkbB5/f72fXrl2MyswgNgJ/yyHpw8HtrGkmsAbI63OSP4LT5GKg96gKo5yRmJhISUkJ+441cM2YMq/DCXsHDhxgVEY6Pp1ERCTCGWN+g3OjI8cYcxj4BnCtMWYGTpOKauCTngUoYefgwYP87umnaWpu5vLRo5gVpL6QTp1o4+iBSo4e2kvHiTZiY2OZOHEi06ZNY9y4ccTGqis2Cb5oS2Z5Yc+ePTQ3N3P15AnUHm/zOpyAC/qRyhiTCjwL/KO19njfLJm11hpjBqwTpSqM0t+ECRN4+eVqjnd0kJ6Y6HU4Yc3f00N8QmSN8SsiMhBr7V0DTH4i5IFI2Ovo6OCNN95g2bJlpCUk8OFZ0xmVmRHQdVhraT5aw+F9O2g+ehiAMWPGMHPmjUyaNImEhISAri9cqYZD6CjhEFzWWlasWEFaYgLjcnOUcLhQxpg4nGTDr6y1v3cnH+2tymiMKQDqgxmDRI5x48bx8ssvs7+xmRlFBV6HE950fhYRERmSI0eOsHr1arZs3szpri6m5I/khnFjSQhgDQNrLU1HDlG9cyPtLY2kpqZx/fXXM3v2bDIz1ZS0PyUcQkfbOLjWr19PVVUV148tj9iO8YM5SoXBuYOw01r7/T4vPQ98DHjY/ftcsGKQyDJy5Egy0tOpampSwuESJSen0NIeeRlUERGRQOjp6eGtt95i9erVVFdXE+vzMTEvl1lFheQHuP+jrs4Odm98g8a6g2RlZXHbbbcxbdo0NZk4ByUcJBIcPnyYF154gbKsEcweFblD2AbzSHYlcA+wzRiz2Z32LziJhqeNMX8PHADuDGIMEkGMMYwbP54tmzZpeMxLNG7cOJYuWcKpri6S4jQspoiIiLWWuro6tm3bxqZNm2hrayMjKYlrx5YxrSA/KOfLzlMn2PLGnzh96gQLFy7kiiuuICYmJuDriTR+vzP+qKr7S7jav38/Tz/9NClxcdwyeUJE96sWzFEqVjB4xe13BWu9EtkqKipYt24ddcfbKA5wu8loMmHCBJYsWcLeY41MLcj3OhwRERHP1NfXs3XrVrZu3UpjYyM+YyjLGsGN0yZTlp0VtAsBay271i+j53QHH//4x9VJ+gXo6ekBUC0QCTudnZ385S9/Yc2aNWQmJfHBaZMi/uaffqVBoLGBg6e8vBwDVDc1K+FwCQoLC8nNyWHtwRqm5OepSqKIiESVpqYmN8mwhaNHne7ERo/IZM74CsaPzAnJBcDJthZaGup43/vep2TDBTpx4gSAOtGUsOH3+6msrOSF55+nuaWF2cVFXDOmlLgoqNGkhEMQdHd3ex1CxEpKSqKwsJCDLa1ehxLWfD4f111/PU8//TS7G44xYWSu1yGJiIgEVU9PDzt37mTNmjXs378fgKKMdG6oGMP4kTmkhvji9VS7U5YZPXp0SNcbCfbt2wdAbq7KLzJ8+f1+Dh48yPbt29m+bRtt7e2MSE4Kygg3w5kSDkFw6tQpr0OIaKNLS1m7ejU9bvs9uThTp05lyeuvs6LqAONycvD5VMtBREQiT2trK+vXr2fd2rW0tbeTnpjI1eWlTM4bSUZSYIbZfm3PPk53O9X8t77xZ1Izsxk7ff453xMT6wxP3dHREZAYokVTUxM1NTWA+nCQ4eesJMP27bS1tRHr81GencW1o4upyMmOiloNfSnhEATt7e1ehxDRSkpKePPNNzmq7XxJfD4f77nxRn71q1+xubaOWcWFXockIiISMLW1tSxZsoRdO3fit5ayrBG8Z9pkyoPQL0N9ezu9DWpbG48MafjppNR0AI4dO8aYMWMCGk8k6u7u5o033mDp0qVehyJyltbWVqqrq6mqqmL3rl0cb2sjxudjTPYIri2ZwJjsrIAOoxtuoveTB1Fbm4YbDKbi4mIAjrYp4XCpJk6cSHl5OW9UHWBSXi6JEd5pjYiIRIcdO3bwu6efJtYY5o4qYkZRAZlJSV6HdZaExGTg7f4IZGDWWvbt28cLzz/PscZGxufmMK0wn99t2e51aBKFrLU0NTVRXV19JsnQ3NwMQEJsLCWZGVwzagJjcqI7ydCXtkIQtLS0ABCni7egyMzMJDExgfp2naAvlTGG973vfTz66KO8sf8A7x4/1uuQxKXOZ0VELpy1lpUrV/LnP/+ZgvQ0bps2mZT4eK/DGlDHSefGSXJysseRDC9+v58jR46cuaCrrq7ixImTZCYlccf0KZRnZ9GgMqCEiLWWhoYGqqqqnP2xqorj7s3lpLg4RmWmM7OinJLMTHJTUyJ6eMuLpYRDEDQ1NQGQmprqcSSRyRhDfn4BDc1N+lEHQH5+PvPnz2f16lVMLcwjPy3N65AEJRxERC7G8uXLeeWVVxiXm8PNk8YP67bSh/ZsxRjD+PHjvQ7FU93d3dTU1JxJMBw4cIDOzk4A0hMTKc1IZ9SoIibljRzW36dEDmsttbW1Zzp7bHJrMKQmJDAqI535BXkUZ2aQk5Kskd6GQAmHIGhoaADQDhhEubm5bK+pISdFdwUC4YYbbmDb1q28snsv98yeoX13GPCrU1QRkQvW1taGMYabJo4b1henh/dup656N1dffTUjRozwOpyQsdbS2tpKbW0tNTU1HDhwgEOHDp0Z4S07JZmJ2VkUZ6YzKjOD9MTAdOopcj7WWmpqas509tjc3IwxhtEjMpk7fiyjR4wgMylRZeSLoIRDENTX13sdQsTLzs7mVFcXft0FDojExEQWvve9PPPMM2w/cpSpBflehxT1lHAQkUCoqqoiLi7uTP9HkW7SpEmsWrWKqqbmYTnks9/vZ//2tdTs3cGkSZN497vf7XVIQWOtpaWlhZqaGmpra51HTQ0nTp4EnH4189LSmFGQx6jMDIoz0kkeps1fJHL19PTw+uuvs3nzZlpaWvC5SYb5E8ZRkZtNkprIXzIlHAKsq6uLFrfajQRPdnY2AN09uigLlOnTp7Nm9WqW7atmXG6OOrrxmBIOIhII//u//4vP5+Ob3/ym16GERGlpKakpKSzbV01aQgJFGekhWW9ndzfx8fHMnj2bDRs20N11+h3zdJxoY+e6pRxvqmf+/PncdNNNxAzjWhgXorcjvd7EQk1NDbU1NZxyh/z0GUNOSgrlGWnkFxeSl5bKyNSUYV0LRaLDjh07WLp0KaUjMrlcSYag0BVFgDU0NGAB1bYJroyMDAB6rC7KAsXn83HzLbfw2GOPsebAIRaMKfM6pKimhIOIBEo0HU98Ph8fvvtunl68mF9t2Mz80aO4smw0MT5fUNfb2d3N7NmzufnmmwHYuGXbmdestdQf2svezauIifFx5513Mn369KDGEwo9PT1UV1ezdetW3tqxg5OnTgFOciE3NYVxWZnkpaWSn5ZGbkoKsTHB/Q4kOCK9T6lt27aRmpDAnTOmqrlEkCjhEGC1tbUA5Kem0elxLJEsPd25Y9ETRYWoUCguLmbKlCls3LWLeSWjSIzTIcIrPT09XocgIhKWRo8ezT888AAvvfQSqzZuZH9TMzeOr6AgPXidIifExrJhwwYANmzYQFyS03F4d9dpKje/Sf2hfYwePZo77rgjrPts8Pv9HDx4kK1bt7J9+3ZOnDhBfEwMY3OyGVVSTH56KjkpKcQGOcEjoRPJ5ZGenh727NlDTlIip7q6SY5XzYZg0NVEgB0+fJiE2FgykxI52h25P1CvpaSkYIyhxx/ZWVcvXHPNNWzfvp2NNbVcUVridThRK5JP8CIDMcZcaa1deb5pIkORmJjIbbfdxsSJE/njH/7Az9dvYlxuNleVlZKbmhLw9SXExnK6/QSrVq0CICk9nlPtx9m+6lVOtbdyww03cM011+AL0wvxpqYmVq9ezbatWzne1kasz8eYnCwmlpVQnp2lphERrKury+sQgsbn8zFnzhzWrFnD46vXcVlJMXNGFWl/DjAlHALs4IEDFKanqUlFkPl8PpISE/HroizgCgsLqaioYOPBA8wvGYXPp53ZC709dotEkf8CZg1hmsiQTZo0ifLyclauXMnKFSvYs3YDk/JGclXZaEYkJwVtvT3d3Wxe9gIxPsOiRYsoLy8P2rpC4bXXXmPr1i2Myc7imlETGJOTpb6eosRJt5PPSGSM4ZZbbuGyyy7jL3/5C8t37WJTTR1XlpUwOT9PNXUCREeKAOru7qahoYHLRo+i1W3HJsGTlJTEqbY2r8OISPPmzeNXlZVUNTUxJifb63CiUjS1uZboZoy5HLgCyDXGfKHPS+mAbjPJJUtMTORd73oXl19+OcuXL2f1qlXsrG9gan4eV5aVBGXoxROtTaSmpvLxj/89OTk5AV9+qKWlpeEzPv5m6mS1c48yvQmHpKTgJei8NnLkSO655x6qqqp4+c9/5uVdlSzbV83k/JFMLywgJyXZ6xDDmhIOAdTc3IzfWrKTk5RwCIGExEROHD/udRgRady4cSQnJ7O17qgSDh5RkwqJIvFAKk6ZpG8j++PA7Z5EJBEpOTmZhQsXcuWVV7Js2TLWrl3L9iNHmV6Yz+WlJaQlJARwbZZFi/4uIpIN4IwO1uP3s6X2CFML8oLeCacMHydOnACc5syRrqysjE99+tPs3buXdevWsXHnTtYfqqEoI53phflMGJmr5hYXYUgJB2PMC0D/xvKtwHrgx9bajkAHFo463KF/EmPV4UgoxMfHR3zPuV6JjY1l6tSpbFy/nm6/X1XKPKB9W6KFtXYZsMwY8zNr7QEAY4wPSLXWKqt8CXQcGVhaWho333wzV111FUuWLGHjxo1srTvKzKIC5o8eRUp8/CWvIysri5EjRwYg2uFh4sSJrHrzTf6yu5I3qw8yq7iQGYX5JGr4wIjX3t4OREfCAZxmFhUVFVRUVNDe3s6mTZtYt3Ytf9q5h79W7mdy/khmFhWQEyXbIxCGWsNhP5AL/MZ9/rdAGzAO+AlwT+BDC2c6wYdCfHw8/Sudd3V1Ya1Vdb8AqKioYM2aNdS2HqdkRKbX4UQdXShIFPqOMeZTQA+wDkg3xjxirf1/HscVto4dO+Z1CMNaZmYmH/zgB7nmmmtYsmQJGzZtYlvdUa4qG82sosJL6sMoLS14I2J4IS0tjX944AEqKytZuXIly/bt483qg0wtyGNOcVFQ+8MQb7W5zZcjbZ8eitTUVK6++mquuuoqqqurWbt2LVt27GDj4VpKMjOYUVTIuNxs1fg5j6EmHK6w1s7t8/wFY8w6a+1cY8yOYAQWjk6fPg2gqjYhEhcX946LssOHD7N48WLuvPPOsO0JergoKyvD5/NR3dSshIMH1IeDRKFJ1trjxpi7gT8DXwE2AEo4XKRly5YBkJ+f73Ekw1tWVha33XYbCxYs4KUXX+SvlXvZVneU94wfS1FG+kUtMxJvfPh8PsaPH8/48eOpq6tj5cqVbNmyhY2HaynKSGdS3kgmjMzV0IIRpreGQ2wUdxJqjKGsrIyysjJOnDjBhg0bWLNmDc/v2ElKQjzTC/KZXlhAemIgm2VFjqHuOanGmBJr7UEAY0wJTntLgNNBiSwM9bZxSlL1spCIjY19R12S+MQktm3bhrWW2267jfgAVIuMVomJieTm5NDg7tcSWurDQaJQnDEmDvgA8Ki1tssYo6o+F8Hv9/P666+zadMmANLTL+6iOdrk5ubysXvvZceOHbz00kv8csNmphXkc31FuUZk6KegoIDbb7+d97znPWzevJnNmzbx6p69/LVyH2VZI5iUP5KKnGzdhIsAx9Vf2llSUlJYsGABV111FZWVlaxZs4Y3d+9m9YFDTC3I44rS4HREG86GevT8ArDCGLMPMEAZ8BljTArwVLCCCze9VRczk7SThcJANRySUjMorpjK9m1rqa+v54477qCwsNCjCMNf7siR1Ozf73UYUUnDYkoU+jFQDWwBlhtjRuN0HCkXoKGhgRdeeIF9+/YxJT9PSeMLZIxhypQpVFRUsGTJElasWEHN8eN8cMokstVT/Tukp6ezYMECFixYwJEjR9i8eTNbtmzhhR27iI+JYVxuNtMK8inOzIjIWh/RoKW52esQhqW+NX6amppYsWIF69etY/uReqcj2tGjSA1oR7Th67wJB7fjpjSgApjgTt7dp6PIHwYptrDT0NBARlKisrkhEjPIdh5VMZWU9BHs3rCcH/3oR8yfP5/rrrsuajq7CaScnBx2bN+O328vqS2rXLjeJlqD7ecikcZa+5/Af/aZdMAYc51X8YSbI0eO8MYbb7BlyxbiY2J4z/ixzCgs4BcbNnsdWlhKSEhg4cKFVFRU8Nvf/oZfbNjM+yaOoyJ34FEnRqamUtt6nB5rKS0tpaCgIMQRey8/P5+FCxfynve8h+rqarZs2cK2bdvYfqSe7JRkZhQWMCV/pDqaDCOnT5/myNGjXocx7GVlZXHrrbeyYMGCtzuirT3CzKJCriwrifoaUuf99NZavzHmn6y1T+PcdZBBtLS0kKFMVsjExsYO2rFeVl4xc264jaod61m1ejUbNmzg8ssv54orriA1NXXA98g7ZWRkYIH206fVLi3EehMOahYk0cIYkwf8G1BorX2vMWYScDnwhLeRDV8nT55k+/btbNq4kYOHDhEb42N2cWHARloQGDNmDJ/97P38+le/4vfb3uL6inLmjip+x3w3jBvD3mPHaO3oZNGiRVGdLPb5fJSXl1NeXs773vc+tm3bxtq1a/lr5T6W7atiwshcZhQVUJieploPw9zBgwfVp9QF6NsR7euvv866TZvY3XCM906ooDRrhNfheWao6ZbXjDFfAhYDZ+rmWWubghJVmGptaWFUsppThIrP5zvneCBx8QmMm3klRWMmcWDnJpYtW8bKlSuZPn068+fPV1OLIeht99ve2amEQ4h1dnYCSjhIVPkZ8FPgq+7zPTjlDiUc+mhsbGT37t3s2rWLqqoq/H4/WSnJXDe2nKkFeepHKggyMzP5xH338bvf/Y7Xd+wgMTaWqQXqiHMo4uPjmT17NrNnz6a2tpZ169axefNmth85SkF6GgvKSxk9IlOJh2HqrbfeIjbGR0JMdN+hv1BZWVncfvvtzJs3j2eeeYbFm7cxo7CAa8eWRWVth6F+4r91/362zzQLlAc2nPDl9/tpa28ndUSG16FIPynpI5h02fWcbGvh8N4dbNq8hQ0bNlBUVMScOXOYNm0aiercZUC9QyCdOK2+YUOtt4ZDghIOEj1yrLVPG2P+GcBa222MueTeU40xTwI3A/XW2inutCycZEYpTr8Rd1prh2VD5ZMnT7J//3727t3L3r17aXbbU2enJDO3uIiJebmMTE3RBVuQxcXFceedd/Lzn/+cP++qJCkujrE52V6HFVYKCwt5//vfz8KFC9m8eTPLli5l8eZtlGRmcPWYUoozVIYeTvx+P2+99RblWSOoLtv/1wAAIABJREFUbz/pdThhqaSkhH/4h3/gtddeY+XKlVQ1N3PHtClR1x/MkBIO1tqyYAcS7jo7O/H7/RoKKIQutHCVnJbJuJlXUjZ5DkcPVnKkeg/PPfccL730EhMnTmTGjBlUVFREdTXI/pKSnHG1O9SBYch1dXUBTiFXJEqcMMZk49zQwBgzH2gNwHJ/BjwK/LzPtK8Af7XWPmyM+Yr7/MsBWNclO3XqFNXV1VRVVbF/3z7qjhwBID42lpLMdGZXjKE8O4sRyUkeRxp9YmNjufvuu3nif/+X53fsYtG8WWQm6Xu4UAkJCVx22WXMnj2bdevWsWTJEn61YQtjsrO4dmwZOepza1iora2lra2NBcXjqW8/6HU4YSsuLo73vve9TJo0iV/98pf8etNW7pw+hby06GniPZROI0fi1GyY7E7aAfy3tbY+mIGFm94h7GKMz+NIokdPTw8GztmsYiBx8QkUj51C0ZjJtDUf4+jBSnbtrmTbtm0kJSUxdepUpk2bxujRo/H5ovv7TE52MrCnupRwCLXehIOJ8n1QosoXgOeBMcaYlUAucPulLtRau9wYU9pv8vuBa93/nwKW4lHCoW+CoWr/furq6rBAjM9HcUY6V5eXUjIig4K0NGJ0PPBcQkICH777bv7zkUf48849fGjmNNUuuUixsbFcfvnlzJ49m1WrVrF8+XJ+tm4T140tY1ZRobarx3bs2IEBxuRksbJaCYdLNfr/s3ff0XGdZ57nv2+hAmIhFXIgAkkwgJmSmUSKNq3clkWFtoIlWbIV2pZj75me2T1nZqe39/Ts2d4+O92z3aYsjaNkK1iWZFlUsiWKQSIJEgwgAkEkAiQBkiCRU6Hu/gGAliVSTFV1UVW/zzk8AApF1KMSWHXv7z7v886YwWOPP86zzzzD83v3c9fC+RSmxUZXz+cGDsaY1cBzTFwdmLoysAzYaYy537KsbaEtL3JMXYXUleDw8fv9GGMuODjyYowxeDOy8GZkUb7wC3R3dtB19AhVVXvYuXMnXq+XhQsXsnDhQvLzY/ONz+12Y4zR77UNpkJMkVhhWdYeY8w6oIKJLbjrLcsaC9HD5ViWdXzy8xNAzoXuaIx5DHgMJtpjr9bIyAhNTU3nOhhOnDhxLmDI96awqnQGxWmp5Hu9OOMUMExHaWlp3HzLLfzud7+j+thxlhRoJtTVcLvdrFu3jmXLlvHyyy/zbkMDzafPcMvc2SRqWaEtzpw5w47t25mV5dNcmCDy+XwTocOzz/LygUN845oleGNgWffFOhz+CfiqZVl7P3Hba8aYV5jYL/sLIasswng8Hnw+H8d7tWV4uPiDeBLscMThyyvGl1fMuH+M08fb6DraxLbt29m6dSuZmZksXryYxYsXk5GREbTHne4cDgcet5sRBQ4iEkKTSynu48/bb9cCx4CQD6e2LMsyxlwwubYsaxOwCWD58uVXlHB3dXVRW1tLQ0PDuanvcQ4HBd4UVk8GDHkKGCLK8uXL2bdvH1ub26jMzdGW6EGQnJzMgw8+yI4dO9i8eTPP7trD3Qtjq/V8OhgZGeG3v/0tWBZfmqVxfcGWlpbGQw89xP/413/ltZo67l2yMOq71y4WOHg/FTYAYFlWtTEmJUQ1RawZM2ZQs38/gSu84i6XZ6rDgSA/33FOF9lF5WQXlTM2OsKpjma62pt47733eO+99ygpKWHZsmVUVlbGxA4C8fHxjGhJhYiEiDFmLvBH4C1gLxPdDdcA/8kY80XLsupC8LCdxpg8y7KOG2PygKAvE+3t7aWqqorqvXs5dfo0ANkpyVxTWEBpRhoFqakKGCKYMYYNGzbw9NNPU33s+Hm3ypTLZ4xh1apVlJWV8fOf/YwX9h3kviULY27Inl06Ozt5/rnnOHX6FDdWzIqJq+92yMzM5Kt33MFvfvMbtjW3srY8usclXixwMMaY9E9Pbp6c7qx3yU8pLy+nqqqKrr5+u0uJCX6/n1AvcnC5PeSVziGvdA7Dg/10tjXS1XaYl19+mTfeeIOlS5eycuXKqO56cHs8jAXU3h9uGl4qMeTvge9ZlvXCJ280xtwJ/ANwZwge8zXgIeAfJz++GqwfPDQ0xLvvvsvOjz8mYFkUpaXy5dkzmZWVSYpH2wtHk5KSEkpLS9nZ1sGyggK7y4kqubm5PPLoo2zatInfVB/g/qWLSE3QyW+oDA0NsXv3bt57913cDgd/vWgBMzLS7S4rqi1cuJCGhgY+rq5mfm5OVIdqFwsc/hl42xjzt8CeyduWAf9t8nvyCQWTbzanBrR1TDhMDY0Ml/jEZGbMWUxxxSJ6Tp/geFMdO3Z8xI4dO1iwYAFf+tKX8Pl8YawoPNxuN6ODA3aXEXNifWCpxJQFlmV9ZjikZVkvG2P+z6v94caY55kYEOkzxrQD/5mJoOEFY8yjQCtwz9U+DkxsZ/vMT37CiRMnWFyQxzVFhdpNIsqtXLmS5557jtazZ+0uJer4fD4eeeQRfvL007x8oIaHli+J+tbzcOvq6mLHjh1U793L6NgYpRnp3DK3gmRP9HfwTgc33XQThw4d4o+NR7h70QK7ywmZzw0cLMvaZIw5xsTVh/lMbAhwCPg/LMt6PQz1RZSUlIlVJv2jozZXEkNsmONojCHNl0eaL4+yoQHaG2uoOXSIAwcOsGrVKjZs2BBVSy1cLhdjGmAYdrE4pFRi1uclmleddlqWde8FvvWlq/3Zn9bc3MzxEye4sWIWiwvygv3jZRqaPXs2brebus6TdpcSlXJzc7nzrrv45S9/yfaWNq4rK7G7pIjX399PQ0MD1dXVHDlyhDiHg7nZWSwryic3RSvmwyk5OZkvfvGLvPnmm7SdOUtxeprdJYXExXap+G+WZf0HY0zip1sd5bM8Hg/GGEY1YC8srnR3imDyJCRRvuBaimYtoOVQFdu2baPh8GEeevBB0tOjoxUtLi6OEbuLiGXT4PdcJMSyjTE/PM/thomtMSNGZmYmDoeD2q6TzM7K1IT9GOByuZgzZw6NdXW4HAqKQ2Hu3LksXryYHfv2MTvLpyGSl2l8fJz29nYaGhpoqK/n2PGJDXpS4j2sLSthUX6uXqts9IUvfIH3//Qn9nQci83AAbjFGPN3wN8BChwuQZzDoaGRYTI6Ooqxo8XhPNzxCcxeugZfQSm1O//IM888y3e+823io2DYjjFGv9M2OLcLizodJPo9DVzostpPwlnI1fL5fGzcuJHfvfIKT3+8my8UFbKkMB+P82KHWxLJysvL2b9/P4naPjBkbrvtNhrq69nW3MrGhfPtLmdaCwQCnDp1itbWVhobG2lsbGR4eBhjDAXeFNaWlVCWmUF2cpK6KacBl8vF0mXL2LF9O/0j0XmJ72LvgJuBM0CyMaaXiasN1tRHy7K8Ia4v4jgcDgI6NwuLwYEB4qbZC2VGTgGVq26g+oPfs2PHDtavX293SVctLi5OgYMNgrntq8g0lzjZTXm3ZVkv2l3M1VqyZAn5+fm8tXkzHzQ0sKP1KJW5OSwqyCU7WVdmo1Fp6cSE+WG9bodMQkICK1au5I9//KNmpX3KyMgI7e3ttLW1nfszPDwMQLLHw6yMNMoySylJTyfepfBzOrrmmmvYtm0b9SdP2V1KSFzst+5/syzrfzHGvGpZ1u1hqSjCGWOw0MlZqFmWRV9fH45p2L6YmplDYrKX45Mta5HO6XQSUIoWdqOaBSOxY6qb8j8CER84AOTk5PDgQw/R0dHB9u3b2X/gAHs6jpHnTWFhXg5zc7LV9RBFMjIySE5Kon9AA5ZDacWKFXy4ZQtVRztYWphvdzm2GR4epqGhgZaWFtra2jhx4sS5Zca+pCQqMtIoSPVSkOolPSFBXQwRICsri4yMDJpOnyEjCgcNX+zdbgewFOgNQy1RwaElFWFx6tQpBoeGSE9IYIAxu8v5C/093QwO9JGdnW13KUERFxeH3wrYXUbMUeAgMeTT3ZRTIr6bsqCggLvvvptbb72VvXv3UrV7N2/VN/Le4SZm+jKpzM2hNCN9WobncumMMeTk5tJ/5IjdpUS1pKQk5s6bR31dbcwNZR0YGKC2tpaamhqONDYyHgjgjosjz5vCyhlFFKR6yfd61cEQwSoqKtj18cekxkff9skX+610G2PuA1YZYzZ++puWZf32Qn/RGPMscBvQZVlW5eRt/wX4FjA1yvc/WZb1hyspfLqKi4vT1eAwODL5ph4/za4QDfadpWbHOyQlJrJy5Uq7ywkKt9vNqF+7VITbSJSu4xM5j6jvpkxMTGT16tWsWrWKjo4O9uzZw/79+6nrOkmyx8P8nGwW5ueQkRi9+7BHu5ycnHPHJhI68+fPZ//+/bT39NhdSsiNjIywZ88eampqaGlpwbIsUhMSWFqQz+zsTPK9XhzqXogapaWl7Nixg67+6OuUutjZ2hPA/UAa8Fef+p4FXDBwAH4K/Cvw80/d/s+WZf3fl1FjRHE6nYwFdHIWSpZlsXfvXtISEnDGTY/9mC3L4kRLA0cOfEy8x83DDz9MUlKS3WUFRXx8vHZesYE6HCSGxEw3pTGGwsJCCgsLueWWW6ivr2fPnj3sqq/n47ajFKalsjg/l9lZPlxxcXaXK5chIyPD7hJiwqxZs4hzOGg+fcbuUkJqdHSUn/30p7S2tZGZlMSKGUVUZPk06DGKFRYWAnC8tw9XlA2g/dzAwbKsrcBWY8xuy7KeuZwfbFnWFmNMyVXUFpHcbjdj4wGcao8Mmbq6Otrb27mxYhaHOrvsLoee0500HdhJb3cXpaWl3HXXXaSlRc+2NvHx8fgDAfwBLasIJwUOEkOuuJsykjmdTubPn8/8+fPp7e2lurqaXbt28ftD9cS7mliYl8PSgnxSEyJ/t6NYkJqaancJMcHj8ZCbm8vxkycvfucINT4+zq+ff57Wtja+Mn8Oc3OiY4mufD6v1xu1s2A+N3AwxnzRsqw/AmeCeBDwHWPMg8Bu4EeWZZ03ojTGPAY8BlBcXHwFD2MPt8fD6OAATsf0avWPFn6/n3ffeYe0hAQW5OXYGjj0dnfRWruX7s52kpOT2bhxI0uWLMHhmB5dF8GSONniOzQ2RkKUJa7T2cjIsN0liITL1XRTRgWv18vatWtZs2YNzc3NfPzxx+w+dIhdRzuYm+1jxYxispKjo2suWnm9ETtqJOIUFhXRceyY3WWETFNTE/UNDeSmJFOWqc6ZWGGMwZeVFXuBA7AW+CMTBwDntsP8xMfLPQj4N+DvJ//u3wP/BDxyvjtalrUJ2ASwfPnyiBmK4Ha7GenrBQ1tCTrLsnj11Vc50dnJHQvmEWfDib1lWXR3ttPecICzp46TkJDADTfcwMqVK3G73WGvJxwUONhjZFgzHCQ2XE03ZbRxOByUl5dTXl7O2bNn2b59O7t27eJQZxWzs3xcVzYDX5Qs14s2ydryNGxyc3PtLiGkysrKWLduHVu2bOHZnVXcNGc2pRnpdpclYeDz+WhpabG7jKC72FlxnzHmh8BB/hw0MPn5ZbMsq3Pqc2PM08Dvr+TnTGdOp5NB7VIRElu3bmXPnj2sLClmdpYvrI8dGPfTefQIHY0HGeg9S0qKl5tvvplrrrkGjyf6psl+0tQsisHRMTI0zyxsNDRSYkWIuikjXlpaGrfccgvXX38927dvZ/u2bRz+uIqF+bmsLSsl0a0AeDpJ1MDPsPH5wnsMGG5xcXHccMMNzJ07l5defJEXqg/gS0piTraPuTlZGi4bxTIzM4HoW1Z7scBhKq6tAK4BXmUidPgrYOflPpgxJs+yrOOTX97BRJARVbQtZmjs2rWLzZs3Myc7i+tKZ4TtccdGhuloquV4Uy2jI0Pk5uZy8w13sWDBApzTbIeMUPlzh4MGR4ZLIBBgTIM6w2p0dJT29nbKysrsLiUWBbubMqokJiayYcMGVq5cyfvvv89HO3bQcPI0G2aVMzcnSwPkpolo7XKcjqI9cJhSVFTEd556iqqqKvbv38/W5la2NreSk5LMnOws5mZnacZLlJmaBRNtZ5IXGxr5vwMYY7YASy3L6pv8+r8Ab3ze3zXGPA9cD/iMMe3AfwauN8YsZuJ5bAEev7ryp59AIECc3vyDJhAI8M4777BlyxZKM9K5Ze7ssBxcDQ/20374AMdbGgiM+5k9ezZr1qyhrKws5g7uEhISgIklFRIe0ZZsR4J3332Xbdu28bd/+7ekp6t1NcyC2k0ZrZKSkrj11ltZvnw5r/z2t7x+qI7Dp05z05xZeGIkABeB2Fq+4nK5WLFiBStWrKCnp4eDBw+yf/9+PjjSzAdHmsnzpjAnO4s52T688QofIl20Dp+91HeoHOCTR8Cjk7ddkGVZ957n5qhfm+n3+7UnbpCMjY3x8ssvc+DAARbl53LD7Fk4Qrz7x8jgAK11eznRdhgDLFq0iLVr15KdHbsTgqeWjIyNa7vXcFHgEH6NjY2AlrLYJKjdlNEuJyeHxx5/nA8//JB333mHzv5+7lpYSUZigt2liYRFtA3nvlSpqamsXr2a1atXc+bMGQ4cOMCB/fv5U2MTf2psoiDVy5zsLCqyfaRE+XLfaBWtS7MuNXD4ObDTGPPK5NdfBX4akooi3NjYGK4YfSEMpu7ubp5//nmOHTvG9eWlXFtceN7OghG/H7fbzbJly6iqqsI/dmUnauN+P231+2hvPACWxbXXXMPatWujanvLKzW1F/CYtsUMGwUOEkuuppsyVjkcDtatW8eMGTP41a9+yS/3VPO1xQvIjqErvxLbnE4n/hheepiens7atWtZu3Ytp06d4sCBAxw8cID3Dh/hvcNHKMlIZ1F+LrN8mbYMWZcrE9OBg2VZ/2CMeRO4bvKmb1iWtTd0ZUWusdFRPHFxdpcR0Wpra3nppZew/H42LpjHrM8ZEDni97Ns2TJuu+02APbsO3DZj3f21AkaqrYwNNDHwoULueGGG9RS/QkOhwOHw8G4AoewGR7WlpjhZmn2znRw2d2Usa6kpITHH3+CZ37yE35TfZAHli4iXZ0OEgMSEhLo6+uzu4xpwefzsX79etavX09XVxf79+9nT1UVrx6sJcHtojInh4X5ufiSovNkNprER+mymEte9GdZ1h5gTwhriQpjfj/OOCWJVyIQCPDuu+/ywQcfkJOSzFeXLCAt4fMPnDxOJ1VVVQBUVVXhSri8qzvtjQc5cmAn6Wnp3H/vNyktLb3i+qNVIBDQbJIwm2rrd+qqRNjF2oyWaUbdlFfA5/PxyKOPsunHP+al/TU8uHyxZjpI1FPgcH7Z2dls2LCBL37xizQ2NrJr1y6q6urYdbSdGRlprC0tIT/Va3eZcgHROpA+Ov+rbOQfG8PpSiBg6Wrw5RgeHuaFF16gvr6eRfm5bJg185KCG4/TyWj/ADt27AAgwXvpU6LbGvbTfHAX8+bN46677or67S2v1FTLolOdO2EzNDQEQHyUvvFMR+pwsJ+6Ka9cVlYW991/P8888wzvNjRy67w5dpckElI6Zvt8DoeD2bNnM3v2bPr7+6mqqmLb1q38oqqamb5M1paVkJWcZHeZEiN0NBtkfr+fOIchoPl6l6y7u5tf/uIXdJ08yQ2zZ7KkMD/kj9lz6gTNB3exYMEC7rnnnpgdQHQpzpw5A0CiS3u+h8vAwAAACXrOw2YqcFCHg73UTXnlSktLuf766/nTn/7EvNwcSjO0NFCilwKHS5ecnMy6detYsWIF27dvZ+uHH/LszioW5OXwxZllxOtYQ0JMZ1lB5h/343ToSvCl6uzs5N/+7f+j50w39yyqDEvYANBSuwev18vGjRsVNlzE4cOHASjJ0ADNcOnv7wcg0a2DgHCZChz0eiCRbN26dWRkZPDHw00E1LUjUcztnuhoVXfapfN4PKxfv54f/e3fsmbNGmpOdPHMzj0cPnna7tIkyunIKsj8/nGtu75Eg4OD/OIXv8CMj/P1ZYspCdPVGP/YKGdPHmfZsmXn3rDk/MbGxti/fz8ZSYna3zmM+vr6SHS7tcVuGE0tHYrT0iGJYC6XixtuuIFTAwM0nDxldzkiITO11n1cW3ZftsTERG6++WaeePJJktPS+O2BGt44VK/h4BIyOjMOovHxccbHx3FraORFjY+P8/zzz9Pb08MdlfPICOM2MCNDE+3q2dnZYXvMSDQ8PMxPf/pTOjo6WFFcaHc5MaW3t5dkj8KwcBocHASid2CTxI758+eTlpbG3vZjdpciEjLntuweG7O5kshVUFDAk3/zN1x//fUcPNHJKwcOMaYAR0JAZ8ZBNDo6sZuXS1fILurjjz+mqamJGytmUhDmablTa7QDSnIvqL+/n5/85Ce0tbbyV/PmsCAv1+6SYkpfby/JWk4RVlMHrZrhIJHO4XCwbNky2s720DOkLXYlOk11o6nD4eo4nU6+/OUvc/vtt3PkdDcv7a/Br+NjCTJdygkiBQ6XrqamhuzkZFtOZN3xE90Uvb29YX/s6cyyLNrb29m9ezf79+8nMO5n48L5lGdm2F1azOnr68OXkkzf5PaYElpTc0pEosXixYt57733qNeyColSU91oU8vh5Opce+21uFwuXnrpJd47fIQbK2bZXZJEEQUOQTR1hUwzHD7f8PAwba2tXGtTm77T5cbpctPT02PL4083Q0NDVFdXs3vXLk50duKMczAnK4triwu1ZZINxsfH6R8YIMmXocAhxAKBANXV1bz6u9/ZXYpIUGVkZJCbm0vjKQ2Dk+ikDofgW7JkCZ2dnXz44YcUeL1U5uXYXZJECQUOQTSVsjo1w+FznTp1ioBlkW3jyazL7WF4OHZbTUdHR2lubmbfvn3U1NTg9/vJTUnhhoqZzMvJxqN17Lbp7+/Hsiy8Hg8n6LO7nKg0Pj7OwYMH+eD99+ns6qIoLZUZ6WlsbW61uzSRoJk5cybbt20jMyl8M5JEwmUqcNDy2OD68pe/zNGjR3n38BGK09Pwxmv7Ubl6OqsIonOBgzocPldOTg7OuDiO9/YzNyf8gxsty8LvHzs3cCgWBAIBjh07xpEjRzh8+DBtra2MBwJ4nE4W5GSxKD+PnJRku8sUONd5k6I9xoPK7/fT2NhITU0NtYcOMTQ8TGZSIrfNq2Budjb7jh23u0SRoCouLmbr1q2c7B8gPHtAiYTP1BbGChyCKy4ujo0bN/Iv//2/83b9Ye5aVGl3SRIFFDgE0VRbV5yGjn0ul8tFYVERradOYVlW2Ie0DfX3MDYyTG5udA9CPHv2LI2NjTQ2NnKksZHBoSEAspOTWVaYT2lGOoWpqerImWbOnj0LoKsKV2kqZGtububIkSO0trQwOjaGx+lkpi+DOdnllGdmaEhkjDPGtAB9wDjgtyxrub0VBU9+fr7dJYiEjAKH0MnMzORLGzawefNm2s6cpTg9ze6SJMIpcAgidThcugULFvD6669Tc6Ir7GvEjjYcwOFwMH/+/LA+bqiNj4/T1tZGbW0tDfX1nDw1MSws2eOhLD2VktJiSjLSSXJru8XpbKrDQYHD5RkZGaGjo4PW1laOHj1Ka0sLw5MzMDKSEpmf7aPcl0lJehpxeo2Wv7Tesqyom66YmpqKy+lkTEP1JAopcAitFStWsG3rVrY1typwkKumwCGIzg2N1C4VF3Xttdeyf//+yTViqXjj48PyuN2d7ZxobWD16tWkpKSE5TFDaWRkhMOHD1NXV0d9XR2DQ0PEORwUpaVSObOM0ox0fEmJuoobQfr6+nDGOXDrdeSCLMuiu7ubo0eP0tbWRltbGydOnMCyLAAykxKpyEynOC2N4vRUkrU8RWKQw+EgPT2drpMn7S5FJOimjmumXvcluFwuF6vXrGHz5s10Dw7ZXY5EOAUOQaRdKi6dw+Hgrrvu4l/+5V94raaOv1684Iq2E81OTqajp5eAZZGc5iM5NfOC9+3v6aZ255/Izs5mw4YNV1O+rXp7e6mtraWuro4jR44wPj5OvMtFeWY6M8tLKM1I19DHCNbf30+S262Q6BP8fj8dHR20tLTQ1tbG0bY2BgYHAXA7neSlJLNyRhH5qV4KvCnEx9B8FrlqFvC2McYCfmxZ1ia7Cwqm9IwMBQ4SlRQ4hN78+fPZvHkzzae77S5FIpzOSoLo3AwHBQ6XJCMjgzvvvJPnn3+eVw4e4s4F8y/7udswu5z6rpP0j44yZ/lakrznH43V39PNga1vkhAfz4MPPog7ApcVDA8Ps3nzZnbt2gVAWkICS/NzmenLpDA1FYdDJ6jRYHBwkMQYP2EOBAJ0dHTQ1NTEkSNHaGtrOxfoZiQlUupNoaCogIJUL5lJiTgUzsiVW2NZVocxJht4xxhTZ1nWlk/ewRjzGPAYTAxijCRpaWqFluikwCH0MjIyyMzIoLn7jN2lSIRT4BBEf+5w0MHvpaqsrOSOO+7glVde4bWaOm6fPzfoJ849pzs5uP1tEuLj+eY3HyU9PfLmdR86dIjXXn2V/v5+lhcVsCg/l8xELZWIRgP9/STEYOAwPj5OS0sLNTU1HDp0iL6+iS1BfclJLMjJojgtjaK0VBLdsffcSOhYltUx+bHLGPMKcC2w5VP32QRsAli+fHlEnd1MLR30a46DRJmp4x/NcAit0rIyDu6rVuesXBX99gTRVOBwJUsDYtny5csZGRnhD3/4A78/VMdt8+YELXQ4fbyNQzv/RFpaKo984xsRFzYMDAzw6quvUlNTQ3ZyMhuXLyHXG/mzJ+TCent78cXQ/+PR0VF2797N1g8/pKe3F2ecg7KMDNYVTeykkhiB3UgSGYwxSYDDsqy+yc9vAP6rzWUFVWJiIjDROSUSTXTBJTxKSkrYvXs3w2MKLeXKKXAIoqHJbQc9cXpaL9fq1asZHx/nrbfeIoDFX82bc9VLUzrbGqmv2kJeXj4PP/wQSUlJQao2fHbu3ElNTQ2rSopZVVKs5TpRzu/3MzAwQErWhWeRRJPGxkZ+85vfMDg4SGFaKusr51KWmaHQVsIlB3hl8sRIc9I1AAAgAElEQVTFCTxnWdZme0sKrqn3vYGBAZsrEQkudTiEh7bXlWDQmXEQ9ff3E+9yaS39FVq7di0Oh4M333wTy6rjK/OvPHQ40dJA/Z4PKSsr44EHHsAToVPqS0tLAchMTFTYEAPOnj2LBaQmhGfXFjvV1dXxq1/9iszEBO5YuojCtFS7S5IYY1lWE7DI7jpCaarDYeqCiEi0UIdDePh8PuLi4s7NqRO5EjqDCaLe3l5SIvTEdrpYs2YNt9xyCw0nT/GH2vorGgZ0sqOZ+j0fMnPmTB588MGIDRtgYkBZSkoK+44dZ0wv9lGvt7cXIGzbxNpp69atpHo83K+wQSRk4idfSzTDQaKVhkaGVlxcHF6v1+4yJMIpcAiiM93deD1ab3y1Vq9ezQ033MChzpO8Xd94WW8m/WdPU7f7A4qKirj//vtxRfjwPYfDwZo1a2g728MzO6to0tZEUa2/vx+ApBgYjDg4MIDbGYdbyydEQiaSA3cRmR4UOMjVUuAQJGNjY5w8dYqs5MibEzAdrVu3jrVr11J97DgfNrVc0t8JBMap3fU+SYmJPPDAAxG59eX5rFmzhkcffRRXYhIv7jvIqwdr6RsZsbssCYGptud4Z/QHDqvXrKGzr5+3Gy4vVBSRSxfpobuI2G9qtxuRK6XAIUja29sJBALk6h9l0Nxwww0sW7aMHa1Hqes6edH7dzQeYrDvLBs3biQ5OTkMFYZPWVkZT333u2zYsIHG09385OMqthxpZnB0zO7SJIj+vNNN9L80L126dCJU7DjObw8cYkQt3yJBp8BBop1mOYReJA5dl+kl+o9qw+TAgQM44xyUZKTZXUrUMMbwla98haLCQv5Q28DJ/s+fst3ZdphZs2Yxe/bsMFUYXk6nk/Xr1/Pd732PWRUV7Gg9yr/v2MmfGpvoHxm1uzwJgtHRif+PLkf0LzMwxnDjjTdy6623cuR0N7+s2kfv8LDdZYlEFadTs8ElOqkzLnymLuJpcKRcKQUOQdDT00P13r3M8mXi0Zt7UDmdTu67/3488fG8WlOL/zzbH30y3F6zZk0Yq7NHZmYm9913H9/73veYV1nJrqMd/HjHTt5paNQJW4QbGRnBFRcXUzvdrFq1iocffph+v5+fV1XT1ddvd0kiUUOBg0SrqcDBoR28Qm5qScXUnCmRy6V/pVfJsixee+01xsf9XFdaYnc5Ucnr9bLxzjs5PTDIztajn/m+a3Lo3K233kp5eXm4y7NNdnY299xzDz/4wQ9YtGQJ1cdOsOmj3bxdf5jeYc14iERDQ0MxGVrOnDmTxx5/nDhPPM/t3U9r9xm7SxKJCmo3l2g1FTjodzz0ppZUDAx8fqexyIUocLgKgUCA119/nbq6Oq4rLSE9McHukqJWRUUFlZWVbG89SvfgX+4nbph4s5k5c2ZMvvFkZmayceNGfvjDH7J02TL2He9k00e7eKehUcMlI0x/fz+JMbBDxfnk5OTw+OOP401L4zf7DrC1uZVAQC2zIiLyWYHJjld1OISeAge5WvpXeoX8fj+vvPIKH3/8MdcWF3JNUYHdJUW9W2+9lTin85J3rYg16enpfPWrX+WHP/whiyc7Hn68YxfvNjQyNKbhkpGgv6+PpBge8paWlsaTf/M3LFq0mG3NrfyiqpoutXCKiMin+CcHDStwCL2EhIkLqlM7aYlcLv0rvQIdHR38j3/9V/bs2cOqkmKuLy+NySvr4eb1elmxYgV1XSfpHhy0u5xpKz09nY0bN/KDH/yAxUuWsKfjOJs+2k11xzECGrI0rfX19ZHsiY7tXK+Ux+Ph7rvv5t5776UvEODnu6vZ035MA8JEZFoKBALU1tbys5/9zO5SYkZfXx/vvvsuAKmpqTZXE/3i4+OBP3eViFyu2FssfBWGh4f58MMP2bJlC4kuF3ctrKTcl2F3WTFl9erVbN++nY9b27l5bnTuRhEsGRkZbNy4kVWrVvH666/zVn0j+451smF2OQWpXrvLk08JBAL09/eTrJ1uAKisrKS0tJSXXnqJdxoaGAuM84XiIrvLEhEBoLe3l6qqKnbt3ElPby/JHg8GUDQaWr29vTz7zDPnvo62bdCnI7c7ti+EyNVT4HAJRkdH+eijj9iyZQtDQ0PMy8lmw+xyEmK49dkuycnJLFmyhL17qvjirLKYHLB3uXJzc/nmN7/J/v37efPNN/llVTVLCvJYP7Ps3MBNsV9fXx8ByyIl3mN3KdNGUlISX//61/nFz3/ORy0tLCsowBmnxjyRS6Wp8sF19uxZWlpaOHToELW1tQQCAWZkpLG+ch4zfRl83NauZZ9Xwe/309/fT19fH729vZ/92NvLmbNnscbH+fLsmbzT0Gh3yTFBXdxytXS29jmGhobYvXs3W7dupb+/n7LMDK6bP4dcb4rdpcW0pUuXsmvXLuq7TrEwP9fuciKCMYZFixYxZ84c3nvvPbZt20ZHTx+3V84hIzHR7vIE6O7uBiB1snVRJjgcDpYuW0bD4cN09verO0cEGB8fZ2ho6IJ/Tp06RVtbG2fOaMeXK2VZFt3d3bS0tNDc3ExzczNnz54FIMHlYnlhPovy88jQwPCLGh8fPxcknD9M6KGvt4+B8yyXNcaQ7HGT7HbjdbvJz8pkQV4uTs1uCJs4XZySq6TA4TxOnz7N9u3b2VNVxejYGDPS07h96SIK07RObDooKioiMzOTgyc6FThcJo/Hwy233EJ5eTkvvfgiP9u1lxsrZjEvN9vu0mJeZ2cnAFnJSTZXMv3MmDGD+Ph4Xqup455FlWQmKSST6BIIBBgcHDx3Uvbpj319fQwNDp4LFEYvMgg42eMh35vC4pllvN/YxKzZWoJ4MZZlcerUqXPhQktzM719fQAkuF0UpaaybFY5RWmpZCUn4dBV378wPDzMgQMHPtOR0Nvby8DAwHmXmiR5PCS7XSS73eSkeUnOySLZ7SbF4ybZ4yHJ7SbR7Trvc32yXzsmhIsCB7laChwmjY+P09DQMHHlvL4ehzHMy8lmeVEBOSlaHzadGGNYsGABH7z/PsPafeGKVFRU8J2nnuLXv/41rx+qo3twkNWlM9Q2Z6P29nYSJg+85C95vV4effRR/uf/fJaf7d7Ll2eXsyBPYaNEh+HhYf7+7//+oveLcxgyEhLJTvWSnpiAN95DgtOFx+Ukwekk3uUk3uki3unE4fjza3ld18lQlh8Venp6+PG//zs9vb0AGGCmL5MVeTMpTEvFl5So98eLqK6u5vXXX7/g970eDwWpXvJTvRSkeslOTiJOXQoRQb/7crVCFjgYY54FbgO6LMuqnLwtA/gNUAK0APdYlmVrv193dzdVVVVUVVVNToj3sLKkmKUFeSR7tJZ6uqqoqOD999+nqVvtolcqNTWVb37zm/zud79j2549jAUC2nHFJpZlcaSxkeK0VD3/F5Cfn893vvMUL774In+obcAbH8+MdA3YlMjncrlYvnw5u3fvxhiDMQaHw4HDGPx+/7ndhcYDFicHBjg5MHFl1xhDgstFgstJvHPiT4LLRfxkADF1Mne8t4+U3Dzb/vsigcfjoXzmTJqbmiZmBABN3WcYGBuje2ho4kTZ68WrGTsX9IUvfIGSkhLOnDnDmTNnOHv2LGfPnp34/MwZeoeG6O06Se1kABbncJAS7yHV48Eb7yE1Pp7U+Phzn6d4PH8RnIlI5Aplh8NPgX8Ffv6J2/4OeM+yrH80xvzd5Nf/IYQ1nNfY2Bi1tbXs3r2bI0eOYICyzAy+vGAe5ZmZeoGLAIWFhSQmJNB8WoHD1YiLi+OOO+7A7Xbz0UcfMTY5iEknveHV2tpKb18fqwt0UvB5UlNTeeihh/jnf/5n/tjYxEPLl6itWSLe1OvwHXfc8ZnvBQIBRkZGGBgYOPdneHiYwcHBc3+GhobOfX5qcJCh4SFGR/+y+y89PT1c/zkRKT4+njvvvBOYGOB79OhR2traaGtro7qjg91HOwBIiZ9YqlLg9VKcnqYO2E8wxpCbm0tu7vm7z0ZGRujp6TlvGNF85gz9xzs/8/NSPB4yEuKZm5NNRbZPg8JFIlTI/uValrXFGFPyqZtvB66f/PxnwPuEMXA4fvw4VVVVVO/dy9DwMN74eFaXzmBhXg5eDWqLKA6Hg+IZMzjW1oZON66Ow+Hgtttuw+Vy8eGHH+KNj2fFDG0/GE47duwg3uVkTk6W3aVMey6Xi5tvvplf//rX7D92nMUF+XaXJBIyDoeDhIQEEhIS8Pl8l/z3/H4/gUDg3Ncu7ap1yVJSUpg3bx7z5s0DJp7LEydOnAsg2traqG9sAqAoLZXVpTPUnXYJPB4P2dnZZGeff2bU2NgYPT09fw4iJkOJo21tvFnXwLuHj1CR5WNhfi6FGh4sElHCHRXmWJZ1fPLzE0BOqB9wfHyc2tpatm3bRltbG3EOB7N8mSycM4uS9DS9QUSw4uJi6urqSE9QWHS1jDHceOONnDlzhi01B8lJSaY0Q1fEwqGuro6DBw+yYkYRbg1muiSVlZXk5uSwo7WdRfl5eh0X+RSnrgQHjdPppLCwkMLCQlatWgVAb28vBw4c4MMtW/j13v0UpnpZVToDrPONRpRL4XK58Pl8nwnWLMuira2NqqoqDhw4wMETnaQnJpCr7hKRiGHbO5JlWZYx5oKvzMaYx4DHYOLE8nINDw+za9cuduzYQU9PD2kJCXxxZhmVeTkkKOmPCoWFhQCMjo/bXEl0MMawceNGTnZ18VpNHd+4ZqnWq4bYmTNnePnll8hOTmZ1yQy7y4kYb775Jic6OynSzkEiYgOv18vq1au59tprqaqq4oMPPuCF6gN2lxWVjDHMmDGDGTNmcNttt3Hw4EGqqqqobWmxuzQRuUThHg/baYzJA5j82HWhO1qWtcmyrOWWZS3Pyrr0NmPLstizZw//zz/9E5s3bybVYdi4YB7fWrGca4oLFTZEkZyciQaZsfHARe4pl8rj8XDf/fczblm8d/iI3eVEtRMnTrDpxz9mfHSMr8yfgzNO07ovhWVZHDx4ELczjpvmzFZ3g4jYxuVysWLFCn70ox9x++23211O1HO73SxdupRvfetbVFRUAPzF0iERmZ7CfYT7GvDQ5OcPAa8G84d3dXXx9NNP8/LLL5PqcvLQ8iXcu2Qhs7J8GiwWhZKTk0lMSGBMHQ5B5fP5WHf99TScPEXT6W67y4lK9fX1bNq0CWt0lPuWLiQzKdHukiKGMYY777wTf8Di6Y928dNde9je0kb34KDdpYlIjHI6nVx77bV2lxFTUlMnOtwGJndtEZHpK5TbYj7PxIBInzGmHfjPwD8CLxhjHgVagXuC9Xi9vb08/fQmrDE/N8+ZzYK8HF35igG+rCza2trsLiPqXHfddezdu5f3DjdRkp6unVuCZHBwkDfeeIPq6mqykpO4a+F8Day9AuXl5fzgBz+gpqaGmpoaPmxq4cOmFiqyfawpnYEvKcnuEkVEJISSJl/nBxU2i0x7odyl4t4LfOtLwX6sQCDAiy++yNjIKA8uX4JPVwtjRnp6ugKHEHA6ndx4440899xz1HWdZF7u+adKy6UZHx+nurqatzZvZmhoiJUlxayaUaxlFFchIyOD6667juuuu46enh527tzJ9u3bqf+4ivuXLaIwVfMdRESiVdzkkOVxdbmKTHtRMca4v7+fpqYmVpYUK2yIMdpbPHTmzp1LdlYW21uPMjcnSx1DV8Dv97N3714+eP99zpw9S543hXuuWUJ2sqZrB1Nqairr1q1jdHSU7du3YwU0KV5EJJpN7cSiwEFk+ouKwCElJYWkxER6hobsLkXCLCUlxe4SopbD4WDd9dfz4osv0tx9hrLMDLtLihiDg4Ps3buXrVu30tvbS543hTsXzqc8M0PBTQgcPnyYF198kYGBAcp9GeRpj3YRkajmcEx0CGpopMj0FxWBgzGGufPmsXv3bgpTU1lSmG93SRImChxCq7Kyktdff52aE10KHC7CsixaW1vZuXMnNQcP4h8fpyDVy42LKinNSFfQEEJdXV0MDAyQmZjIsoJ8xgMBnA4tVxERiVZTgYM6HESmv6gIHABuu+02+vv6eLu+nsGxMVbOKNaguxiQrNb0kHI6nSxcuJA9VbsZ8fuJ00ncZ/T391NdXc3uXbs4eeoUHqeTBbnZLMrPIydFv5/hsHr1ahISEnj9tdd4Yd9BjDHkpCRTmOqlKC2V0ox0XJPrfUVEJPJNhfiWpSV0ItNd1AQOLpeLe++7j5dffpmt+/fTcOo0N1XMIs+rK+DRLEnT6ENu4cKF7Ny5k5bus5T71OUAE7MZ6uvr2bNnDw319QQsi/xULzfPmc2cnCzcOrkNu6VLlzJ//nyOHj1KS0sLzc3NVLe3s/toB/EuJ/Nyslmcn0dWsl4z5M+MMTcB/y8QB/zEsqx/tLkkiWDGGJ0Ah8lUh4Oeb5HpL2oCB5i4GnvPPfcwf/58fv/66/yiai9L8vNZXVpMotttd3kSAomJGhIaasXFxXjcbpq7u2M+cOjq6mLnzp3sq65mcGiIZI+Ha4oKqczL0cDaacDj8TBz5kxmzpwJTARDLS0tVFVVsa+mhj3tx/ireXO064oAYIyJA/4H8GWgHdhljHnNsqxD9lYmkUqBQ/hMdThohoPI9BdVgQNMvABVVlYyc+ZM3n77bXbt3MnBzi6+UFzINUUFaquNMvHx8XaXEPXi4uIoKy+nubnZ7lJsMT4+Tl1dHR999BFNTU3EORzM8mVSObuc0vR0Ld2axpxO57kAYnBwkJ///Oe8c/gIFdk+LQ8SgGuBRsuymgCMMb8GbgcUOMgVUZt/+Oi5FokcURc4TImPj+crX/kKK1eu5K233uLD2lr2dhxndUkxC/NydZIQJRw6aQiLsrIyamtr6R8ZsbuUsPH7/ezYsYMd27fT09uLN97DurISFubnqmMqAiUmJlJRUcHRo0fR8alMKgCOfuLrduALn76TMeYx4DGY6PgSERGRSxe1gcOUrKwsHnjgAVpbW9n85pu8VX+YXUc7uK5sBhVZPk2OF7kERUVFABzr6bO5kvA4cuQIr736KqdOn2ZGehpfXDCPmZmZCioj3NQSrNODgxroKZfMsqxNwCaA5cuXK64SmQbU4SASOWLm8vCMGTN47PHHeeCBB3AmJfHqwVp+UVVN65mzdpcmMu3l5eURFxfHsd7oDhzGxsZ44YUXePbZZ/EPDnL3okq+tmQhs7N8ChuiwPz584mPj+c3+w5wVK/9Ah1A0Se+Lpy8TeSq6CQ49HTBUCRyxEzgABMvTnPnzuWp736XO++8k0EMv967nxf3HeRk/4Dd5YlMW06nE19mJqcGovvfyUcffcS+fftYWVLMI9cupSwztodkRpvk5GSeeOIJklK8/GbfQQ6fPG13SWKvXcAsY0ypMcYNfA14zeaaJILpJFhE5LNiKnCY4nA4WLp0KT/44Q+56aabODYwyLM7q9hc18Dg6Kjd5YlMSzm5uZweHLK7jJAZGRnhgw8+oDQznbVlJRowG6WysrJ4/IknyMvL43cHD3F2KHp/p+XzWZblB74DvAXUAi9YllVjb1UicjnUTSIy/cVk4DDF5XJx3XXX8aMf/YhVq1Zx4EQXmz7aza62dsa1zY7IX/D5fFE9NHJgYICR4WHinVE/2ibmJSYmcv8DD+BwONjVpg76WGZZ1h8sy5ptWVa5ZVn/YHc9InJp1E0iEjliOnCYkpiYyK233spTTz1FcUkJf2xs4hdV1VpmIfIJGRnRvbwgIyOD69evp7bzJNUdx3XVJMp5vV5mzppF85kzdpciIiJXSO/VItOfAodPyM7O5qGHH+bee++lfzzAz3bv5aOWNgJ6MRMhPT3d7hJC7vrrr6ekpIS36g/z0v4atdtHubKyMs4MDtE7PGx3KSIichnU4SASORQ4fIoxhsrKSr73/e8zZ+5cPmhq4aX9BxkaG7O7NLkAvemER2pqqt0lhFxcXByPPPIIt9xyC+19/TzzcRXbmlsZ8fvtLk1CoLCwEIBTA4M2VyIiIiISnRQ4XEBSUhL33nsvt99+O21nevjZ7mq6+vvtLkvOQ4FDeKSkpNhdQljExcWxevVqvv/971Mxdy5bm1v59x272NbcyvCYgodoEh8fD8CIf9zmSkRERESikwKHz2GM4dprr+Wb3/oWAaeTX+3ZT6v2bpcY5XQ68bjddpcRNqmpqdx33308+eSTlJSXTwYPO/mwqYVhdTxFhamwMmBpSLCIiIhIKChwuATFxcU8+eSTpKWn8+K+g9R3nbK7JPkEdTiET1JSkt0lhF1hYSFf//rX+fa3v0357Nlsb2nj33fsYktTi5ZaRbjMzExcTicn+tS9JiISiTQ0UmT6U+BwiVJTU/nWY4+RX1DAazW1NJ/utrskmaTAIXwSYzBwmJKfn8/999/PU089xcyKCnZ8InjQjIfIFBcXR05uLl0KHEREIoqO/UQihwKHy5CYmMjDDz9Mdk4Orxys5Xhvn90lySco5Q69xMREu0uwXW5uLvfddx/f/e53mT1nzrng4eO2o4yNaxZApPF6vQwpMBIREREJCQUOlyk+Pp5vfOMbJKWk8LuDh9RSPQ1MpdwKHEJvasieQE5ODvfeey/f/va3KSop4f3GZp7+aDf7j53Q72IEGRoawhUXZ3cZIiIiIlFJgcMVSE5O5t5772VgzM8bh+p1cmGzqcBhXFeXQ87j8dhdwrSTn5/Pww8/zKOPPkpqVhZv1jXw893VtJ/tsbs0uYjh4WHajx4lLyXZ7lJEJIrouFBE5M8UOFyhwsJCbrrpJo6c7qbmRJfd5Qh6gw+HqcBBz/VnlZWV8cQTT3DPPfcwaAy/2rOP1w7W0j8yandpcgEtLS2M+f3MzvLZXYqIRAHNFRAR+SwFDldhxYoVFBcV8V5jk5ZWTAOBgLa2CzWXywWom+RCjDEsWrSIH/zgB6xfv57Dp7t5ZmcVB493KqSZhqZeM9xaUiEiIiISEgocroLD4eD2r36V4bExPm5tt7ucmKUZDuHjdrsBGB3VVfvP43a72bBhA9956imy8/J4o7ael/bXqNthmsnMzATg9OCgzZWIiIiIRCcFDlcpNzeXRYsWUdXRwYBOwmylDofQm+pwGFNHzyXJysriW9/6FrfddhtHe3r52e69mu0wjaSnpwPQNzxicyUiIiIi0UmBQxBcf/31+McDVHcct7uUmKQOh/BR4HD5HA4HK1eu5Iknn8SdmMjze/ezt/2Y3WUJE50oBhhVWCkiIiISEgocgiA7O5tZs2ax99hxAgGd9NpFgUPoTQUOfr/f5koiT25uLn/z7W8za/Zs3m5oZHtzq35nbRYIBLAAjXkTERERCQ0FDkFyzTXXMDAySvOZM3aXErN08hZ6cZPD9RQ4XJmEhATuv/9+Fi9ezIfNrWxparG7pJg2NfzU6dBboYiIiEgo6CgrSCoqKkiIj6e2U1tk2kWBQ+g5nU5AgcPViIuL48477+Saa67ho9aj7NHyCtu4XC7cLhf9mr8jIiIiEhIKHILE6XQyZ+5cjpw+o2UVNlHgEHpTgYO2xbw6DoeDr3zlK8ypqODdhkaaT3fbXVLMyi8o4Hhvn91liIjIFdCxn8j0p8AhiObMmcPw2BjHenvtLiWmaGhk+DgmW88VOFw9h8PBPX/912Tn5PBGbQODoxrEaYeioiK6+gcY1+uHiEjEmDr2E5HpT4FDEJWVlWGA1jNn7S5FJCSmZjhoC9Lg8Hg83H333Qz7/bzT0Gh3OTHJ5/MxHgjQP6KtMUVERESCTYFDECUmJpKbm8vRsz12lxKT1OEQelMdDgocgicvL4/r16+nruskrd0aOhtubrcbgDH9TouIiIgEnQKHICssKuJEXz8695VopMAhNK677jrSUlN5r7GJgF48wmp4eBgAz+R8EhEREREJHgUOQZafn8+I30/P5EGsSDTRDIfQcLlc3HDjjZzsH6C+65Td5cSUc4HD5HIhEREREQkeBQ5BlpOTA8DpwUGbKxEJvqnAQctXgm/BggVk+XzsaG3T8xtG57Z6VdeOiIiISNDZEjgYY1qMMQeMMdXGmN121BAqPp8PgBG/rgCHm07SQm9qKrSWVASfw+Fg7bp1nOwfoE1zYMKmr68PhzHEu7SkQkRERCTY7OxwWG9Z1mLLspbbWEPQJSYm4vG47S5DJCTU4RBaCxYsID4+nn0dx+0uJWYcP36czKREHGiLNREREZFg05KKIDPGkJ6eYXcZIiEx1eGgwCE0XC4XCxcupPF0N/5xdZGEw/DwMIkul91lSJgZY/6LMaZjstOy2hhzi901iYiIRCO7AgcLeNsYU2WMecymGkImLS3N7hJikk6CQ0+BQ+jNmTOHsfFx2s6etbuUmJCamkrPyIjdZYg9/nmy03KxZVl/sLsYERGRaGRX4LDGsqylwM3At40xaz99B2PMY8aY3caY3SdPngx/hVchNTXV7hJEQkqBQ+iUlZXhcDg4qjkOYVFYWMjZwSH6R0ftLkVEREQk6tgSOFiW1TH5sQt4Bbj2PPfZZFnWcsuylmdlZYW7xKuSkpICaOtAiT5THQ4SOi6Xi9zcXI719NpdSkwoKysD4MjpbpsrERt8xxiz3xjzrDEm/UJ3iuQLICIiInYLe+BgjEkyxqRMfQ7cABwMdx2hlJiYCMCQtsaUKKNdKsKjsLCQzv4B1EcSevn5+WT5fHT29dtdigSZMeZdY8zB8/y5Hfg3oBxYDBwH/ulCPyeSL4CIiIjYzY59wHKAVyZPXJzAc5ZlbbahjpCZChxGx8ZsriQ2aK5A+Oi5Do+cnBxG/H76R0aIT0yyu5yoZozhCytW8Pvf/97uUiTILMvacCn3M8Y8DegXQEREJATC3uFgWVaTZVmLJv/MtyzrH8JdQ6glJCTYXYKIRLCpq6jdg0M2VxIbFi5caHcJEmbGmLxPfHkHUdZpKSIiMl3Y0eEQ9RQ4SLRSh0N4ZNuahgkAACAASURBVGRMbK0b0PMcFklJ6iKJQf+XMWYxE7tmtQCP21uOiIhIdFLgEALx8fF2lyAiESw1NRVjjIIdkRCxLOvrdtcgIhIpCgsLKS4utrsMiVAKHELA4/HYXYJISKjDITwcDgfJSUn09WuQoYiIiNjr8ccf105lcsVs2RYz2rndbrtLEAkJvdmEjzc11e4SRERERHA4HDoGlCumwCEEnE41jkh0U4dD6Hm9XrtLEBERERG5KgocQsDh0NNqB50Eh57S7fBJSUmxuwQRERERkauiM2MRkWloqsNh3O+3uRIRERERkSujwEFEZBqa6nDQ4EgRERERiVQKHETksmn5SuglJSUBMDg4aHMlIiIiIiJXRoGDiFwyzXAIn8TERAACgYDNlYiIiIiIXBkFDiIi09BU4CDhoTBNREREJPgUOIjIZdOSitCLj4+3u4SYot2FRERERIJPR1gicsl0FTh8PB6P3SXEFAUOIiIiIsGnIywRkWnI5XLZXUJMUeAgIiIiEnw6whIRmYbUTRJeChxEREREgk9HWCIiEvMU8IiIiIgEnwIHEREREREREQk6BQ4iIhLTAoEAg4ODdpchIhFOw35FRD5LgYNEPG0fKCJXqru7m1/96ld2lyEiUWDjxo3MnDkTp9NpdykiItOGXhEl4uXl5dHd3W13GTHFsiy7SxC5KoFAgDfeeIOdO3fiADISE+geHLK7LBGJYBUVFVRUVNhdhojItKIOBxG5ZBqsJ9FicHCQXTt3Egc8dM1SlhcW2F2SiIhcJu0wJDL96V+piIjEnOTkZP76a19j3LJ4bu8+drd32F2SiIhcovLycvLy8lizZo3dpYjIRShwEBGRmDR//nyeePJJcgsKzy2niIuLs7kqERG5mKSkJL7zne+Qn59vdykichGa4RAiN910E8ePH7e7jJigNn8RuVIFBQU8+uijtLS0EAgESEhIsLskERERkaihwCFErrvuOrtLiBlr1qyhs7OTkpISu0uJelNrJefMmWNzJSLBY4yhtLTU7jJEREREoo4CB4l4RUVFfP/737e7jJjgdrt55JFH1MIYJjfeeCMjIyN2lyEiIiIiYXDvvfdy6NAhu8sIKgUOInJZysvL7S4hZqxdu9buEkREREQkTCorK6msrLS7jKDS0EgRERERERERCToFDiIiIiIiIiISdAocREREJCoZY+42xtQYYwLGmOWf+t5/NMY0GmPqjTE32lWjiIhINNMMBxEREYlWB4GNwI8/eaMxZh7wNWA+kA+8a4yZbVnWePhLFBERiV7qcBAREZGoZFlWrWVZ9ef51u3Ary3LGrEsqxloBK4Nb3UiIiLRT4GDiIiIxJoC4Ognvm6fvE1ERESCSEsqREREJGIZY94Fcs/zrf/VsqxXg/DzHwMeAyguLr7aHyciIhJTFDiIiIhIxLIsa8MV/LUOoOgTXxdO3na+n78J2ASwfPly6woeS0REJGZpSYWIiIjEmteArxljPMaYUmAWsNPmmkRERKKOAgcRERGJSsaYO4wx7cBK+P/Zu+/4uM4y7/+fS12yitVsy5ZtucU1bnF6IZCEhBQISyjZpQQDoSQ/AgQe2GVhAwsLy1KWXVrCAySUkOYU0njiJHaqiXEc23HiJK5xt2XJRbIk25Ku3x/nyBnLarZn5ow03/frNS/NnHqdW2fOuec697kPD5vZ/wNw91eAu4BXgb8C1+kJFSIiIvGnWypERERkQHL3+4D7uhn3XeC7yY1IREQkvZh76t+OaGa1wJtRx3EcKoBdUQeRJlTWyaOyTi6Vd/L0x7Ie7e6VUQeRLvppfaQ/7tf9mco7eVTWyaOyTq7+WN7d1kf6RcKhvzKzJe4+J+o40oHKOnlU1sml8k4elbUMRNqvk0vlnTwq6+RRWSfXQCtv9eEgIiIiIiIiInGnhIOIiIiIiIiIxJ0SDol1S9QBpBGVdfKorJNL5Z08KmsZiLRfJ5fKO3lU1smjsk6uAVXe6sNBREREREREROJOLRxEREREREREJO6UcBARERERERGRuFPCATCzNjNbZmYrzexuMysIh2eZWa2ZfT/8fJGZLTIzCz9nmtlLZnaWmd1kZm5m42OW+4Vw2Jzw8wYzezlc1zIz+59w+K1mtsXMcsPPFeG0J8dMW29m68P3jye7jI6XmZXHbMP2cDs7PntMuT9oZoM7zbvMzO7oNKzLsgrfZ5jZ/4TLe9nM/m5mY8Jxncv+LDOrMbPmmGHLzOyjnaZfYWZPmdnopBSYDFgxx5lXzGy5md1oZhnhuPPNbG+nffHCTvMtN7OlZnZWOLwm3Ncvjpmn0cxeD9//PsrtFZFjp/pI4qg+IhJQfUSSzt3T/gU0xrz/E/Cl8P27gOeAtbzV38UdwCfD918Afh2+vwlYAfxrzLKeA1YCc8LPG4CKLtZ/K7AR+Gz4uQLY0MU0V0VdVidYzjcBX+6m3G8Dvh7zeTLwMrAFGNSXsgKuBu4BMsLP1UBpd2UP1AAru4n18PTAtzr+z6n+AoaF++ha4EXgEeCkmP21BSiJmb4g3OdfDvfVZ4HCzv+f8PM1wM96Wf+1wGvhazFwTldlGn4+H3gI+DiwLHwdDGNZBnw/6vKM8/8mdn8fAjwOfCu2LPow38XAU93tv8DCjuNNOryi3N+JOZ6Fx6UtQG74+ahjuF569eWF6iPJKufD398uyl31kfiUseojKfpC9ZEBtb/TD+ojauFwtGeAjqsCVwM/JTihnBkO+yLwz2Y2Fbge+GrMvPcD7wEws3HAXmBXH9f738AXzSzrhKLvvxYBI2I+Xw38AXiMsExjdFdWVcA2d28HcPfN7r47AbGlpPBK133AQncf5+6nAP8MDA0nuRr4O/APMbPdAOxw95PdfRrwCeDQca7/cuDTBCf1ScBngNvNbFhP87n779x9prvPBLYCbw8/f+144ugP3H0nQWXo+o4rlH1UDMRjn+73ot7fu9AGzI3TskRA9ZGoqD5ygqI+Pqs+0neqj5y4qPf3LqRcfUQJhxjhCeNdwMtmlgdcCDwI/JlgZ8HdtxGcYBYB33H3+phF7AM2mdk04EPAnV2sZkFMc6MvxgzfSJDd+kicNyvlmVkmcAHwl5jBHyTIFB4u+xjdldVdwBVh2f7IzGZ1Gt9R9i/EDBvXqdnYuV2EeAlB5S3VvR045O6/6hjg7svd/ZmwwlkI/CtHlmcVQSa0Y/rX3f3Aca7/q8BX3H1XuKylBFeKrjvO5Q1o7r4OyCS4ugBwbqd9cVw4PD/8/Brwf4F/jyLeFBT1/t5Zuv9IkzhSfSQaqo/ETdTHZ9VHjoHqIycs6v29s5Srj6RMIBHLN7Nl4ftngN8A7wYWuHuzmc0DvmFmX3D3NuDnBM2rbu1iWXcQnNwvJjhpfbzT+Ld3HAC78D3gAeDhE9qa/qOj3EcAq4D5ABbcY7rL3Tea2Rbgt2ZW1qkydVRZuftmM5sIvCN8PWFm73f3J8JJuir7tWEmuysLzKwMaAS+cWKbmhTTCJpxdeVDBPvmM8BEMxvq7juA3wKPmdlVwBPAbe6+Opwn9nsBUMaRlbDOpnax/iXAx45tM9LWM+5+eRfDmzv2UTM7E/h9+CMi3UW9v3cW+8PjwWOYTySW6iPRUH0kvqI+Pqs+cmJUHzk2Ue/vnaVcfUQtHALNHU2o3P3/c/eDBFmoCy3oAOhFoJzgpEHYRM67WdZDBP/gje6+71iCCHe0ZcAHjm8z+p2OA9dowHgr83w1MCks+7UEzbbeFztjd2Xl7gfc/VF3/wrwH8CVJxDf28PYlhHcN9mfXQ3cEe6784D3A7j7MmAs8F8EB7S/m9nkcJ7Y78VM4JsnGENX35nuvkcDnpmNJWj2trOv87j7IoL78SoTFdcAEdX+/j3gK+jcKsdP9ZFoqD6SPKqPpBjVRxJK9RFSJIhUY2bFwLnAKHevcfcagpNP56Z0R3H3JoKmXN89ztV/F/jycc7bL4Vl9nngRjPLIThpnxxT9u+h67I/oqzMbLaZDQ/fZwDTgTdPMLZWgs5ePhpeXUhlrwCndB5oZicDE4D5YaXpQ8SUp7s3uvu97v454I/Apce5/le7WP8pYVwAdUBpzLgy+n5P8YBiZpXArwg6AepzJcfMJhE0e6xLVGz9SNT7+1HS8EeaJJjqI8ml+kjcRH18Vn2kj1QfiYuo9/ejpFp9RAmHrr0XeLLTvTQPENyPl9vbzO5+R3i/WFdi75k86jEx7v4K0N28A5a7v0TQq/Y/A1vcfWvM6KeBKWZW1WmezmU1BHjQzFaGy2oFftbLqjvfM/n5LmLbRnDvZqrf+/ckkGtm13YMMLPpwP8AN3VUmNx9ODDczEab2dlmVhpOmwNM4fgrRT8A/tPMysPlzSToWfcX4fiFhPe5hvfJfhhYcJzr6o867n18haBH6Mc48kpV53smr+o03zKC+7A/FjalTndR7+/dSbsfaZJQqo8kmeojcRH18Vn1kZ6pPhJfUe/v3UmZ+kjHo5VEZAAIr6j8N0GmtYXg0U+XApPd/bWY6X4M7AC2ERyMjCAB+TDwVXd3M2t098KYea4heMTR9T2s/7MEV2AcaABudPenw3ElwC8J7q004K/A18JmZh3zbwjXkZZXGuTYRLm/m9lNBI+u+qGZ3UrwGLF7wnH3ArPDK6IiImlH9RFJJ6qP9EwJBxERERERERGJO91SISIiIiIiIiJxp8diisgxMbOvE/ayG+Nudz/ejslEUpb2dxGR1KTjs6ST/ry/65YKEREREREREYk73VIhIiIiIiIiInGnhIOIiIiIiIiIxJ0SDiKCmQ02s89FHYeIiIikL9VHRAYeJRxEBGAwoBO8iIiIREn1EZEBRgkHEQH4PjDOzJaZ2d1mdmXHCDP7k5m9x8yuMbMHzGyhma02s3+LmebDZrY4nP9mM8uMZCtERESkP1N9RGSAUcJBRAC+Bqx195nAz4BrAMysBDgLeDic7jTgfcB04P1mNsfMJgMfBM4O528D/im54YuIiMgAoPqIyACTFXUAIpJa3P0pM/uFmVUSnMznuXurmQHMd/c6ADO7FzgHaAVOAf4eTpMP7IwkeBERERkQVB8RGRiUcBCRrvwe+DDwIeDjMcO903QOGHCbu/9zkmITERGR9KD6iEg/p1sqRASgASiK+Xwr8AUAd381ZvhFZlZmZvnAlcBzwBPAVWY2BCAcPzopUYuIiMhAovqIyACjFg4igrvXmdlzZrYSeNTdv2Jmq4D7O026GJgHVAN/dPclAGb2r8BjZpYBHAKuA95M3haIiIhIf6f6iMjAY+6dWySJSLozswLgZWC2u+8Nh10DzHH366OMTURERNKD6iMi/Z9uqRCRI5jZhcAq4H87Tu4iIiIiyaT6iMjAoBYOIiIiIiIiIhJ3auEgIiIiIiIiInGnhIOIiIiIiIiIxJ0SDiIiIiIiIiISd0o4iIiIiIiIiEjcKeEgIiIiIiIiInGnhIOIiIiIiIiIxJ0SDiIiIiIiIiISd0o4iIiIiIiIiEjcKeEgIiIiIiIiInGnhIOIiIiIiIiIxJ0SDtKvmVmjmY09wWXcambf6eO0NWbmZpYVfn7UzD52IuuPWfa5ZvZ6zOcNZnZhPJYdLu8VMzs/Xsvr4zrNzH5nZrvNbHEfpu+xfM3sO2a2y8y2h5/fa2abwv1gVuK25Kg4c83sVTOr6mb8NWb2bMzns81sdRjnlXFYv5vZ+BNdTiKZ2RVmdmfUcYiIJIrqIMe0fNVBEkx1k96pbhINJRwEM/tHM1sSHnC2hQfYc/o4b6QHF3cvdPd1Ea7/Xe5+W2/T9aWc3P0Zd58Yj7i6qsC4+1R3XxiP5R+Dc4CLgGp3P+1YZ44tXzMbBdwITHH3YeEkPwSuD/eDl8LprjGzl82sycy2m9kvzWxwfDbnsGuBp919Wx+n/zbwszDO+83sh+FJvsHMXjOzj8Y5PuDoylOidLUed38QmGpm0xO5bhHp31QHOaH1qw7Ss6TWQcIkTXN4bt9jZs+b2WfM7IR/b/UxAaS6SS/rUd0kGko4pDkz+xLw38B/AEOBUcAvgPdEGVdvEn2QSraBtj0xRgMb3H1/HJY1Cqhz952dlv9KxwczuxH4T+ArQAlwRjjNfDPL6Wqhx1n2nwH+cAzTHxEnsB+4IozxY8BPzeys44gj1f2ZoAIkInIU1UFSw0DbnhhJrYOErnD3onDc94GvAr+Jw/r7QnWTvlHdJNncXa80fREcUBqB9/cwzWnAImAPsA34GZATjnsacIIDVCPwwXD45cCycJ7ngekxy5sNvAQ0AHcDdwLfiRn/KWANUA/8BRgeM86B64DVwPqYYePD9/nAj4A3gb3As0B+OO5uYHs4/Glgasxyb42NodP2ZxJksHcB68L1O5AVjl8IfDJ8Px54KlzHLuDO7soJOB/YTHAi2k5wgjgf2Byz7g3APwOvAruB3wF54bhrgGc7xephDNcCh4CD4foejFneheH7XIJK3tbw9d9AbjiuI7YbgZ3h//3jPewjw8P/VX34v/tUOPwTQAvQFsbxreMtX+BCoBloD5f15/BvR7muBYrDYR/otI5CoBaYG36+CbgH+COwL1z+mPD/1AA8Dvwc+GM32zsqjCUrZlh5WAb7gMXAv3f8f8LY2sN5GjvKudMy/wLc2EMZfyX8P2wF5nLkfn8ZwXdqH7AJuClmvo3htI3h60xgHPAkUBeW+5+AwTHzfBXYEpbF68AF4fAM4Gvh9tQBdwFl3a0nHH424XdVL7300iv2heogHcu9FdVB+n0dpPM2dtqH24FpMdv+Q4Lz5g7gV7y1n1QADxHsu/XAMwTn3j9wZD3i/3SxLaqbqG6Ssq/IA9Arwn8+XAK0xh6cupjmFIKrxFlADbAK+ELM+MMHl/DzLIITxOkEB/KPhQfgXCCH4ER8A5AN/APBCek74bzvCA8ys8Pp/5egaVjsuuYDZTEH59iD288JTg4jwnWfxVsnsLlAEW+d5JbFLPdWuj/ZfwZ4DRgZrncB3Z/s/wx8PTz45QHn9FBO54dl/59hTPl0fbJfGbPu52LK6hq6Odl3t00cebL/NvA3YAhQSVAp+/dOsX07/D9dCjQBpd2U0dMEV6TygJkEP+7f0V2cJ1C+R5RPF9vc7f4M3Ab8OXx/E0Fl6Mrwf5VPUKH9IcE+eg7BCbK7hMNlwCudht1BcJIbBEwjOCk+GzP+cNl3sbx8ghP2JT18T3eEyx0E3N5pu88HTg63ZXo47ZXhuJrY8gyHjSdoYpob/u+fBv47HDeRoGIwPGb+ceH7G8J9pjqc9+aYMj1qPeHwsnB4cdTHO7300iu1Xj0ds2OmUR1EdZB+UQfpvI2dptsIfDZ8/xOCH/Jl4T7xIPC9cNz3CBIQ2eHrXMB6WnbMOlQ3Ud0kZV+6pSK9lQO73L21uwnc/UV3/5u7t7r7BoIv8tt6WOa1wM3u/oK7t3lw79sBggpDR6Xhf9z9kLvfS5Bx7fBPwG/dfam7HyDIrJ9pZjUx03zP3evdvTl2peH9cXOBG9x9S7ju58Pl4O6/dfeG8PNNwAwzK+mtgIAPEBzwNrl7PcHJoDuHCJqnDXf3Fnd/todpIcgs/5u7H+i8PTF+FrPu7wJX9yHmvvgn4NvuvtPda4FvAR+JGX8oHH/I3R8hyAwfdW+nmY0kyBR/NdzmZcD/Bfp639+xlG9vKuh+f94Wju+wyN3vd/d2ghPbqcA33f1g+H/7Sw/rGUyQYQfAzDKB94Xz73f3lQQJjr76FbAc+H/djP8A8Dt3X+lBs9CbYke6+0J3f9nd2919BUGls9vvqLuvcff54X5XC/w4Zvo2ghP2FDPLdvcN7r42HPcZ4Ovuvjnme3RVL01xO8op3n1oiEj/pzpI71QH6T91kJ5sBcrMzAj20S+G+1EDwe1EHwqnOwRUAaPDbX/GPfiF3Aeqm6hukrKUcEhvdUBFT19KMzvJzB4KO9/bR3BgrOhueoKT3Y1hZzl7zGwPQeZ4ePja0unguSnm/XCCqw8AuHtjGOOIbqaPVUGQ3V7beYSZZZrZ981sbbgNG2Lm6c3wTut8s7sJgf8DGLDYgt6Y5/ay7Fp3b+llms7rHt7L9H11RFl3sey6TpXAJoJbE7paTsdJM3ZZI7qYtrs4+lq+vdlF9/tzVTi+Q+f9rt7dm7oZ39lugqsSHSoJKrHHvB1m9l8EVwc+0EOloscyMrPTzWyBmdWa2V6Ck2+3+7aZDTWzO8xsS/h9+GPH9O6+BvgCwQl7Zzhdx34xGrgv5nu9iqASMLSHTewopz09TCMi6Ul1kN6pDhLoD3WQnowguEWiEigAXozZP/8aDgf4L4LbQh4zs3Vm9rVjWIfqJqqbpCwlHNLbIoLMf0+PwvklQXOzCe5eDPwLwQmtO5uA77r74JhXgbv/meAq84gww9thZMz7rQQHDgDMbBDBFZAtMdN0d+DbRXCv3rguxv0jQQdUFxLcM1rTsYoetqPDtk4xjupuQnff7u6fcvfhwKeBX/TSK3Rfstad1701fL+f4KQFgJkN40i9LfuIsu607GPRkbWPPcmN4sj/WU/6XL590LE//0PsQDMrBN4FPBEzOLZ8thFsQ0HMsNiYOlsBjImpJNcSNP88pu0ws2+Fcb3T3ff1MGlvZXQ7QYuMke5eQnBVomPf7mo/+I9w+Mnhd/rDMdPj7re7+zkE+4cTNLmF4Lv9rk7f7Tx339LNegAmE3TY1dP2iUh6Uh2kd6qD9L6cVKmDdMnMTiVIODxLsJ80E/Th0bF/lrh7IUDYCuZGdx8LvBv4kpldEC6qtzJV3UR1k5SlhEMac/e9wDeBn5vZlWZWYGbZZvYuM/tBOFkRwf3sjWY2Cfhsp8XsAGKfQf1r4DNhZtPMbJCZXRaeDBYRZB2vN7MsM3sPQWc6Hf4MfNzMZppZLsHB54WwGWVv29IO/Bb4sZkND68onBkup4igUlNHcIL8j76XEncBnzezajMrJeiYpktm9n4zqw4/7iY40LWHnzuXU19dF667jODezI5nBy8neKzPTDPLo1NTtj6s78/Av5pZpZlVEOwHfzzW4Nx9E8G9l98zszwLHjP0iWNYVp/Ltw+x7CVolvm/ZnZJuC/XhOvYTDc9N7v7m8AS4CYzyzGzMwl6ae5uPZsJrkCcFn5uA+4N5y8wsykE9w13y8z+maASeqG71/WyaXcB15jZlDAp8m+dxhcRXOFpMbPTwuV2qCXYB8d2mr4R2GtmIwg6feqIa6KZvSP83rTwVidZEFQWvmtmo8NpK8PvcHfrgaA55KO9bJ+IpCHVQfpEdZAepFIdpDMzKzazywn6Ufhjx+0FBPvoT8xsSDjdCDO7OHx/uZmND5Niewn21z79D1U3Ud0klSnhkObc/UfAl4B/JfhibgKuB+4PJ/kywUGigeAgeWenRdwE3GZBU6YPuPsSgl6ef0ZwwltD0GkP7n6Q4OrzJwiaMX2YoDfejnscHwe+AcwjyJyO46372vriy8DLwN8Jmq79J8E+/nuCpl5bCHpb/tsxLPPXBPevLQeWEhy8u3Mq8IKZNRJkdW/wt57PfRMx5XQM678deIyg9+S1wHcA3P0Ngg6VHifoMbvzvZq/IbjXbY+Z3c/RvkPwI3sFQZkt7Vj2cbia4IrNVuA+gntCH+/jvMdSvr1y9x8QXAH7IUEl9QWCffqC8N6+7vwTQS/JdQTlcCfhftmNmznyftPrCZp7bifoLOt3vYT6HwRXA9ZY8Oz5RjP7l2626VGCTsaeJPg+Pdlpks8B3zazBoJK210x8zYR3Hf7XLgvnEGQlJlNUJl5mCPLPJfgMV67wm0ZQnAfM8BPCfbrx8J1/Y2gY7bu1gPBvnFzL2UhImlKdZBeqQ7Su5Spg4QeDM+RmwiSND8GPh4z/qsE++XfLLh14HHe6p9iQvi5kSBB9gt3XxCO+x5BkmaPmX25m3WrbqK6SUrq6PlUJBJm9gLwK3fv7SAokjRmdifwmrt3zth3jM8leNzTBe6+LanB9RNmdgXwEXc/lsqtiEjSqA4iA4nqJr1T3SQaSjhIUpnZ2wien7uL4Kryr4CxOjBKlCy4x7IeWA+8k+Dq2pnu/lKkgYmISNyoDiIiknw9PTJEJBEm8tYzgdcBV+lELylgGEHzvXKC/h4+q2SDiMiAozqIiEiSqYWDiIiIiIiIiMSdOo0UERERERERkbjrF7dUVFRUeE1NTdRhiIiIpJQXX3xxl7tXRh1HulB9RERE5Gg91Uf6RcKhpqaGJUuWRB2GiIhISjGzN6OOIZ2oPiIiInK0nuojuqVCREREREREROJOCQcRERERERERiTslHEREREREREQk7vpFHw4iItL/HDp0iM2bN9PS0hJ1KP1eXl4e1dXVZGdnRx2KiIhIv6L6SPwcT31ECQcREUmIzZs3U1RURE1NDWYWdTj9lrtTV1fH5s2bGTNmTNThiIiI9Cuqj8TH8dZHdEuFiIgkREtLC+Xl5Tq5nyAzo7y8XFdmREREjoPqI/FxvPURJRxERCRhdHKPD5WjiIjI8dN5ND6OpxyVcBARERERERGRuFPCQUREkuass87qcXxNTQ0nn3wyM2fOZObMmTz//PNs2LCBadOmJSnCo51//vksWbKk2/gSuT4RERGJP9VHjm19J0KdRoqISNL05YS4YMECKioqDn/esGFDAiM6dp3ji9XW1kZmZmaSIxIREZFjofpI8qiFg4iIJE1hYSEA27Zt47zzzmPmzJlMmzaNZ555pk/zb9iwgXPPPZfZs2cze/bswxWG9vZ2Pve5zzFp0iQuuugiLr30Uu655x4AHnnkESZNmsQpp5zC5z//eS6//HIA9u/fz9y5cznttNOYNWsWDzzwAADNzc186EMfYvLkybz3ve+lubm512268cYbmTFjBosWLeLb3/42p556KtOmTePaa6/F3YEjrxTs2rWLmpqa41qfiIiInBjVR5JXH1ELB0kKd2f16tWMGTNGT70WlgAAIABJREFUz5EXEW6//XYuvvhivv71r9PW1kZTU9PhcW9/+9vJzMwkNzeXF1544Yj5hgwZwvz588nLy2P16tVcffXVLFmyhHvvvZcNGzbw6quvsnPnTiZPnszcuXNpaWnh05/+NE8//TRjxozh6quvPrys7373u7zjHe/gt7/9LXv27OG0007jwgsv5Oabb6agoIBVq1axYsUKZs+efUQMnePbv38/p59+Oj/60Y8AmDJlCt/85jcB+MhHPsJDDz3EFVdc0W1Z/PKXv+xxfSISP62traxdu5aTTjpJnciJiOojMRJVH1HCQZJi48aN3HbbbVx88cWcd955UYcjIhE79dRTmTt3LocOHeLKK69k5syZh8f11ETw0KFDXH/99SxbtozMzEzeeOMNAJ599lne//73k5GRwbBhw3j7298OwGuvvcbYsWMPPy/66quv5pZbbgHgscce4y9/+Qs//OEPgeCxWRs3buTpp5/m85//PADTp09n+vTpR8TQOb7MzEze9773HTH+Bz/4AU1NTdTX1zN16tQeT/C9rU9E4mfx4sU8/PDDfPzjH2f8+PFRhyMiEVN95C2Jqo8o4SBJsW/fPgC2bNkScSQiAvDcc89RVVXF2LFjI1n/eeedx9NPP83DDz/MNddcw5e+9CU++tGP9jrfT37yE4YOHcry5ctpb28nLy/vuGNwd+bNm8fEiROPexkAeXl5h++TbGlp4XOf+xxLlixh5MiR3HTTTYefV52VlUV7e/vh6UQk+bZt2wbAnj17Io5ERFKB6iOJr48krA8HM8szs8VmttzMXjGzb4XDx5jZC2a2xszuNLOcRMUgIiJde+SRR7j11lsjW/+bb77J0KFD+dSnPsUnP/lJli5d2qf59u7dS1VVFRkZGfzhD3+gra0NgLPPPpt58+bR3t7Ojh07WLhwIQATJ05k3bp1hzt6uvPOOw8v6+KLL+Z///d/D9/T+NJLLwFB5eP2228HYOXKlaxYsaLP29Vx4q6oqKCxsfHwfZsQ9Cj94osvAhwx/ETWJyIiIsdP9ZHE10cS2WnkAeAd7j4DmAlcYmZnAP8J/MTdxwO7gU8kMAZJER1fIN0vKZI6Ok6OUVi4cCEzZsxg1qxZ3Hnnndxwww19mu9zn/sct912GzNmzOC1115j0KBBALzvfe+jurqaKVOm8OEPf5jZs2dTUlJCfn4+v/jFL7jkkks45ZRTKCoqoqSkBIBvfOMbHDp0iOnTpzN16lS+8Y1vAPDZz36WxsZGJk+ezDe/+U1OOeWUPm/X4MGD+dSnPsW0adO4+OKLOfXUUw+P+/KXv8wvf/lLZs2axa5duw4PP5H1iYiIyPFTfSTx9RHr+CGYSGZWADwLfBZ4GBjm7q1mdiZwk7tf3NP8c+bMcT2TvH9bvnw5d911F9OnT+eDH/xg1OGIpL2vf/3rQNBRUaKsWrWKyZMnJ2z5nTU2NlJYWEhdXR2nnXYazz33HMOGDTs83N257rrrmDBhAl/84heTFle8dFWeZvaiu8+JKKS0o/pI/zdv3jyWLl3Ke9/7XubM0VdHJB2oPhJfx1ofSWgfDmaWCbwIjAd+DqwF9rh7azjJZmBEN/NeC1wLMGrUqESGKUnQ0TRILRxEJFEuv/xy9uzZw8GDB/nGN77BsGHDAPj1r3/NbbfdxsGDB5k1axaf/vSnI45UREREBirVR46U0ISDu7cBM81sMHAfMOkY5r0FuAWCKwqJiVCSZffu3YASDiKSOB33SXb2xS9+sV9eQRAREZH+R/WRIyWyD4fD3H0PsAA4ExhsZh2JjmpAjy1IIxkZSdnlREREREREJGKJfEpFZdiyATPLBy4CVhEkHq4KJ/sY8ECiYhAREREREZHUtHPnTg4dOhR1GJJAibzcXAUsMLMVwN+B+e7+EPBV4EtmtgYoB36TwBgkRXR0TqoWDiIiIhI13eIpEr2DBw/y05/+lMceeyzqUCSBEtaHg7uvAGZ1MXwdcFqi1isiIiIiIiKprbU1eI7ASy+9xGWXXRZxNJIoCe00UqQzXVEQkXj63e9vo3F/Y9yWVziokI9/9GO9TvfXv/6VG264gba2Nj75yU/yta99LW4xiIiISP+i+kj3lHAQEZF+q3F/IxPfeUbclvf6Y3/rdZq2tjauu+465s+fT3V1Naeeeirvfve7mTJlStziEBERkf5D9ZHu6YZ6SSq1cBCR/m7x4sWMHz+esWPHkpOTw4c+9CEeeED9H4uIiEjy9Jf6iBIOIiIix2DLli2MHDny8Ofq6mq2bNETnkVERCR5+kt9RAkHEREREREREYk7JRxERESOwYgRI9i0adPhz5s3b2bEiBERRiQiIiLppr/UR5RwEBEROQannnoqq1evZv369Rw8eJA77riDd7/73VGHJSIiImmkv9RH9JQKERHptwoHFfapJ+djWV5vsrKy+NnPfsbFF19MW1sbc+fOZerUqXGLQURERPoX1Ue6p4SDiIj0W315RnUiXHrppVx66aWRrFtEjp+7Rx2CiAxAqo90T7dUSFJ0nOD1WEwREUkmMxtpZgvM7FUze8XMbgiH32RmW8xsWfhK7RqbxJXqIyIiyaEWDiIiIjKQtQI3uvtSMysCXjSz+eG4n7j7DyOMTUREZEBTwkGSSlcUREQkmdx9G7AtfN9gZquA1OvGW0REZADSLRWSVEo4iIhIVMysBpgFvBAOut7MVpjZb82stJt5rjWzJWa2pLa2NkmRioiIDAxKOEhSKeEgIiJRMLNCYB7wBXffB/wSGAfMJGgB8aOu5nP3W9x9jrvPqaysTFq8IiIiA4ESDpIU6jRSRESiYmbZBMmGP7n7vQDuvsPd29y9Hfg1cFqUMYqIpBs9NSY9qA8HSaqMDOW4RCR+/njb72hqbIjb8goKi/jwxz7e4zRz587loYceYsiQIaxcuTJu65bEsCDT/Rtglbv/OGZ4Vdi/A8B7Af0zRUQiMBAuSKo+0j0lHCSpBsIBRURSR1NjA/949oS4Le/251b3Os0111zD9ddfz0c/+tG4rVcS6mzgI8DLZrYsHPYvwNVmNhNwYAPw6WjCk2TSFVWR1DGQvo+qj3RPCQdJio4Dilo4iEh/d95557Fhw4aow5A+cvdnga6y3Y8kOxZJHboAIpI69H08Pv2lPqJff5JUSjiIiIhIVAbSFVURkf5Av/4kKdTCQURERFKFrqiKiCSHfv1JUnQkHLKydBePiIiIiEi6U4uj9KCEgyRVZmZm1CGIiIiIiEiKUIujgU2XmyUpOjKYSjiISDwVFBb1qSfnY1leb66++moWLlzIrl27qK6u5lvf+haf+MQn4haDiIhIOhhILRxUH+meEg6SFLqlQkQSobdnVCfCn//856SvU0RERFKX6iPd0y0VkhRKOIiIiEjUBtIVVZH+ruP7qFsqBjYlHCQp9JQKERERSRX6gSMikhz69SdJ0d7eHnUIIhIBXU2MD5WjSHzouySSOpJ5QVLf/fg4nnJUwkGSQl9ykfSTl5dHXV2dvv8nyN2pq6sjLy8v6lBEBgy1cBCJXrLqB6qPxMfx1kd0Q70khVo4iKSf6upqNm/eTG1tbdSh9Ht5eXlUV1dHHYZIv6d7xkVST6K/j6qPxM/x1EeUcJCkUMJBJP1kZ2czZsyYqMMQETlMVzhFUkeyvo+qj0RLt1RIUrS1tUUdgoiIiAigFg4iqUAtjtKDEg4iIiIiIiKSVHqKXXrQf1cSrrW1NeoQRERERHRLhUgK0fcxPSjhIAlXX18fdQgiIiIiasItkkLUwiE96L8rCbdz586oQxARERERkRTS0am8EoADmxIOknDbt2+POgQRERERtXAQSSH6PqYHJRwk4d58882oQxARERERkRTS0cJBt1QMbPrvSkK1trayceNGMnOyow5FRERERERShFo4pIeEJRzMbKSZLTCzV83sFTO7IRx+k5ltMbNl4evSRMUg0du0aROtra1kKeEgIiIiIiIhtXBID1kJXHYrcKO7LzWzIuBFM5sfjvuJu/8wgeuWFLFq1SoyMjLIzsnmQNTBiIiIiIhIStBTKtJDwhIO7r4N2Ba+bzCzVcCIRK1PUo+7s2rVKkqqKjl0QOkGEREREREJ6CkV6SEp6SQzqwFmAS+Eg643sxVm9lszK+1mnmvNbImZLamtrU1GmBJnmzdvpr6+nsqa4VGHIiIiIqJ7xkVSiFo4pIeE/3fNrBCYB3zB3fcBvwTGATMJWkD8qKv53P0Wd5/j7nMqKysTHaYkwIsvvkhGViaVY6qjDkVERERERFKIWjikh4QmHMwsmyDZ8Cd3vxfA3Xe4e5u7twO/Bk5LZAwSjQMHDrBixQoqa0aow0gRERFJKfqBIxI9tThKD4l8SoUBvwFWufuPY4ZXxUz2XmBlomKQ6CxZsoQDBw4wfPLYqEMREREREZEUo4RDekjkUyrOBj4CvGxmy8Jh/wJcbWYzAQc2AJ9OYAwSgba2Np597llKhlVQXFkWdTgi0knHCV5EREQkKko4pIdEPqXiWaCrveeRRK1TUsPSpUvZt3cf0047M+pQRKQLra2tUYcgkjRmNhL4PTCU4GLHLe7+UzMrA+4EaggugHzA3XdHFaeIiMhApC5BJa4OHTrEE08+QXFlGWXVw6IOR0S60NbWFnUIIsnUCtzo7lOAM4DrzGwK8DXgCXefADwRfpY0oZZeIiLJoYSDxNWiRYto2NfAmDlT1TxKJEW1tLREHYJI0rj7NndfGr5vAFYBI4D3ALeFk90GXBlNhCIiIgOXEg4SN/v27WPBggWUjRzG4Co9ylQkVTU1NQGQn58fcSQiyWVmNcAs4AVgqLtvC0dtJ7jloqt5rjWzJWa2pLa2NilxSuLoYoiISHIp4SBx89e//pXW1lbGnz496lBEpAf79+8HYNCgQRFHIpI8ZlZI8KjuL7j7vthxHrSv77KNvbvf4u5z3H1OZaWS6SIiIsdCCQeJi/Xr17N8+XKqT55AfnFh1OGISA/q6uoAKC0tjTgSkeQws2yCZMOf3P3ecPCOjkd1h393RhWfJJ/6cBARSQ4lHOSEtba2ct/995NXOIhR0ydGHY6I9GLXrl0AFBcXRxyJSOJZ0Ib+N8Aqd/9xzKi/AB8L338MeCDZsYmIiAx0SjjICVu4cCF1u3Yx4ayZZGYn7EmrIhInW7ZsiToEkWQ6G/gI8A4zWxa+LgW+D1xkZquBC8PPIiIiEkf6dSgnZPv27Tz11FMMGTeSsuou+9sSkRTS0tLCpk2bog5DJGnc/Vmgu54CL0hmLBI9dRopkjoyMoJr37rFaWBTCwc5bm1tbdxzzz1k5WYz7jR1FCnSH6xdu1YndhEREYlcRwJQ9ZKBTQkHOW5PPfUU27ZtY8KZM8nJz406HBHpg7///e/k5OeRk6fvrIiIiERHCYf0oISDHJetW7eyYMEChoytpqJmRNThiEgf7Nixg9WrVzN88lgsU4d/EUlf+oEjEj0lHNKDapxyzFpbW8NbKXIYf8aMqMMRkT5auHAhGVmZDJ80JupQREREJM11JBza29sjjkQSSQkHOWYLFy5kx44dTDh7Jtlqli3SL6xZs4YVK1ZQPXW8vrciIiISOXUamR6UcJBj0vFUiqHjRlExanjU4YhIHxw8eJD777+fgpJCRs+YFHU4IiKR0Q8ckdShWyrSgxIO0mdtbW3Mu3ceWTnZjDv95KjDEZE+cHfuu+8+du/ezYSzZpGRlRl1SCIikdMPHJHo6ZaK9KCEg/TZokWL2LplK+POmKEm2SL9xJNPPsmKFSsYc8pUBldVRh2OiEikOn7giEj01MIhPSjhIH1SX1/P/PnzKRs5jMoxeiqFSH+wdOlSnnzySYZNGM3I6SdFHY6IiIjIYUo4pIesqAOQ1Ofu3H///bjBhDNn6uqASD+wZMkS7rvvPgZXVTLhrFn63oqIiEhKUcIhPSjhIL1asWIFa9euZfwZM8grLIg6HBHpxfPPP8/DDz9M2YihTLngdDIy1ZhNRATeule8o/NIEYmOEg7pQQkH6VFLSwsPP/wwRRWlDJ80NupwRKQH7e3tPPHEEyxcuJCK0cOZfP6pZGSqk0gRkQ5KOIikDj01Jj0o4SA9evzxx9m/fz+zLzgNy1CTbJFU1dLSwl133cXrr7/OsJNGc9JZszBVqEVEjtDa2goEj/nOyjqyGpybm0t1dbWSESIicaSEg3Rr9+7dvPDCC1RNHENRRWnU4YhIN2pra/nDH/9IfX0d48+cwfBJY9Vng4hIFxoaGoDggkpXiouKmDptGhMnTqSmpobs7OxkhieSltTCYWBTwkG6tXDhQgBGz5wUbSAi0iV3Z/HixTz66KNYZgbTLz5Hj74UEenBwYMHAXjnrLEMHVx4xLh9TQd4dVMtf1/8AosWLSIrK4uRI0dSU1PDyJEjqa6uZtCgQVGELTIg6eJIelDCQbq0d+9eli5dyrCJNeQOyo86HBHpZO/evdx7772sWbOG0uFDmHjubHIHqVNXEZGedNxSMaK8mGGlhUeNnzKqkkOtbWys3cv6HXvYtKuWhQvX03EBtqSkmOHDR1BVVcXQoUMZOnQoZWVlZKq/HJHjphYOA5sSDtKll19+mfb2dqqnjo86FBGJ4e4sW7aMBx98kNa2ViacOZOqSWN0lUBEpA8OHDjQ6zTZWZmMqypjXFUZAAdb29i+u5Gt9Q1s393Ijs1v8tprqw4nITIzMigrL6OycggVFRWUl5dTUVFBWVkZRUVFOj6LdEPfjfSghIN0aeXKlRSWDSa/+Ojsv4hEo76+ngceeIA1a9ZQPKScGeedou+oiMgx6Lil4ljkZGUyqrKEUZUlh4cdamujbl8ztXv3U9fQzK59TWzftIHXXltFe/tbV2uzs7IoLSultLSMsrIySktLKS0tZfDgwQwePJj8/Hz96JK0pxYOA5sSDnKUpqYmNm3axOhZk6MORUQIHuO2aNEi5s+fTzvO+DNmMHyyOoYUEYlKdmYmw0oLj7oto73d2dt0gN2NzexubGHP/uDv7u2bWb9uDQcPtR0xfW5ODiWDSxg8uJSSkhIGDx5MSUnJEa/OT9MQGShUj0kPOoLJUerq6gAoLB8ccSQismPHDubNm8eWLVsoGzmMCWfOJK9QfTWIiKSijAyjtDCP0sK8o8a5O80HW9nbdIB9+1vY23SAvftb2Nd8kH21W9n85gaaDhzdAmNQQQElJSUUd0pEFBcXH/6rp2mISKpSwkGOsnv3bgDyi/SjRiQqbW1tPPXUUyxYsIDMnGwmn38qlWOqdTVAROQ4NTc3R7p+M6MgN5uC3GyquuiwEuBQaxsNzQfZ13yAfU0HaGg6wL7mgzQ0tVC3bR8b1q2h5WDrUfMV5OdTXFJMScngw4mIzsmJnJycRG+iyHHRLRUDmxIOcpSmpiYAsvNyI45EJD1t3bqVefPmsX37dirHVDP+jBnk5Ov7KCJyIrZt2xZ1CL3KzsqkrCifsqLunxB2MExKNIRJicYwQdHQdJA9O7awccM6mg8cOmq+/Py88LaNI2/f6OhPoqioiIyMjERunsgRdBElPSjhIEcpKAhaNhxqOUhO/tFNAkUkMdrb23nuued47LHHyMrNYeoFZ1AxenjUYYmIDAj9IeHQFzlZmZQX5VPeQ1Kita39cEKiofkg+5oOhEmJA9Rv38SGdWtpOXhkUiIzI4PikhJKS0spKys76pWfr8ekS2KohcPApoSDHKWoqAiAg00tDCotjjgakfTQ2NjI3ffcw5rVq6moGc5JZ88mO1fNX0VE4uXNN9+MOoSkycrMoLQwn9LCnltK7Av7kdjbFCQn9uxvYe+eWl7dupmmliP7kxhUUEBFZSUVFRUMGTKEoUOHMmTIEIqLi3WlWk6I9p+BTQkHOUplZSUAe3fWUTpiSMTRiAx8a9eu5a677qKpuYkJZ82kauIYnXxFROKovb2ddevWRR1GSsnJyqSiuICK4q777DrY2saexhZ2h0/aqG9opr5hN6u2b+XFmFs2BhUUMHzECKqrqxk9ejSjR49WfxEicpgSDnKUwsJCampqqN2whZo4PBpzzd+W07SnAYAnnniCjRs3ctlll53wckX6O3fnb3/7Gw8//DD5xYXMuuLtFJaV9D6jiIgcky1bttDc3ExmdhZth47udFGOlpOVyZDBgxgyeNBR45oOHGLXviZ27t3Pjt372b5zK2vWrMYdMjMzGTt2LLNmzWLatGlkZmZGEL2IpAolHKRL06ZN46GHHqKxbs8JPx6zsX4v7a3Bc6dra2spLOy6Z2aRdNLa2sqDDz7IkiVLKB9VxaS3zSFLjzUTEUmI559/nszsLCUc4qQgN5tRlSWMqnwrSX7gUCtb6hpYv2M3r2/ZxF2rV/PUUwuZO/cTqvuJpDF1RStdmjFjBrm5uWx4aVXUoYgMOM3Nzfzud79jyZIljJx+ElMvOEPJBhGRBKmrq+Pll1/W7WoJlpudxdhhpVwwYyyffdcpvG3aaHbs2Mny5cujDk1EIqSEg3SpoKCA8847j7qN29i7oy7qcEQGjD179nDzzTezceNGJr1tDmPnTFMFWCSBzOy3ZrbTzFbGDLvJzLaY2bLwdWmUMUriuDuPPvoolmFUTxsfdTgD3sHWNt7YUsdfXniDZ17dSF5uLhMmTIg6LBGJkG6pkG6dddZZLFq0iLUvrGDW5W/D9GxmkROyfft2br3tVppbWjj54rMZXFUZdUgi6eBW4GfA7zsN/4m7/zD54UgyvfDCC6xatYqxp04jt0CPdYwnd6ex5SDb6hvZWt/Apl372FrfQHu7k5+Xx+mnn8G5555LSYn6JhJJZwlLOJjZSIKT+1DAgVvc/admVgbcCdQAG4APuPvuRMUhxy8nJ4fLL7+cO+64g00vr2bUjIlRhyTSb9XV1XHLLbdAZgYzLj1PnUOKJIm7P21mNVHHIcm3ceNGHnnkEcqqh1I9TVfZT8TB1jbq9jVRu6+JXXvDziL3Nh1+dGZmRgZVw6s455yZjB8/npqaGnUWKb1y96hDkCRIZAuHVuBGd19qZkXAi2Y2H7gGeMLdv29mXwO+Bnw1gXHICTj55JNZuXIlr770KuWjhjGoVD+SRI5VW1sbd999N23ezuzLzie/6Ogev0Uk6a43s48CSwjqK11e/DCza4FrAUaNGpXE8OR4NTc3M3/+fBYvXkxuQT4Tzz1Ft671gbuzr+kA9Y3N1Dc0U9fQ8beFfU0th6fLysxkyNChTJoyluHDhx9+ZasvIjlO+n4ObAlLOLj7NmBb+L7BzFYBI4D3AOeHk90GLEQJh5R2xRVXsG7dOlYtXMKsK84nM0sZa5Fj8cwzz7Bp0yYmve1UJRtEUsMvgX8naIH578CPgLldTejutwC3AMyZM0eX41JYe3s7y5Yt49FHH6WpuZkRk8dSM2syWbk5UYeWMtrdaWg6wO7GFnY3NrO7sYX68O/uxhba2tsPT5uTnU1FZSVjJoymoqKCoUOHMmTIEMrKytR6QeJCLRzSQ1L6cAibMs4CXgCGhskIgO0Et1x0NY+uKKSIwsJCrrrqKn7/+9+zbvHLTDhrZtQhifQrCxcupHxUFUPGVkcdiogA7r6j472Z/Rp4KMJw5AS4O9u2bWP16tW8vHIl27ZupXhIGadcdMYJP9a7v2rvaKnQ0HxEYmH3/hb2dEoqZGVmUlpWSuWI0UwqL6e8vJyKigoqKiooKirSlWdJqI6EQ4b6iRvQEp5wMLNCYB7wBXffF3vgcnc3sy5TW7qikFomTpzI2WefzXPPPUfpiCFUjB4edUgi/UZrayuDBher4iaSIsysKubix3uBlT1NL6mlubmZNWvW8MYbb/DGG2/Q2NgIQGH5YCaeM5uhE0YP+OOtu9N04BB1Mbc+1Dc0s3t/kGBob3+r6pydlUVZeRlDq2uYUl5OWVkZ5WFyobi4WD/2JDIdCYeB/n1NdwlNOJhZNkGy4U/ufm84eEfHid7MqoCdiYxB4ued73wn69av441nl1JUWarenkX6yMxwlDcViYKZ/ZngVs4KM9sM/BtwvpnNJLilYgPw6cgClF61tbWxffv2wwmGTZs24e5k5+YwePgQRsyaSNmIoeQU5EUdakI0thykdu9+avc2sWtf8KpraKblYOvhabIyMykvL2dodQ1TKyoOJxTKy8vVUkFSVnvY2kb758CWyKdUGPAbYJW7/zhm1F+AjwHfD/8+kKgYJL6ysrL44Ac+yM9//nNee2oJ0y85RwcIkT4oKy+ndu1mRp58Etm6l1gkqdz96i4G/ybpgUiv2tvb2bNnDzt27GDnzp3s2LGDHTt2UFtbS1tbGwBFFaWMnH4SZdXDKK4sHXCP7G5oPsDW+ga21zeybXcjO/c2sT98EgTAoIICKocMYfq4IVRWVlJZWUlFRQUlJSVqqSD9TkfCQfvuwJbIFg5nAx8BXjazZeGwfyFINNxlZp8A3gQ+kMAYJM4qKyu57LLLuP/++9ny6lqqp46POiSRlPf+q67i5ptv5vVnXmTqBWcoUSciac3d2bdv3xGJhe07dlC7cyeHDh06PF3eoAIKSouomjyWwvISSocPISd/YLViaDpwiHXbd7Nhxx427trH3v3B0yAyMjIYOmQIE6eMZdiwYQwbNoyhQ4dSWFgYccQi8dORcFAnpANbIp9S8SzQXa36gkStVxJvzpw5rFq1itUvvkJZ9TAKSnTyE+lJdXU1l1xyCY888gjrl7zCmDlTlXQQkQGvtbWVPXv2UFdXR11d3RGtFg4cOHB4utz8PPIHFzFkwigGlRZTUFrMoMHFZOUMzMcstra18+qmWl7esJNNu/biDgX5+dSMGcvZNTWMHDmSqqoqPWZSBrzW1uC2oKyspDzHQCKi/64cMzPjyiuv5Kc//SmvP/MiMy89D8vQjyeRnpx11lns3LmTJUuWsH/PPia/7dQBW5kWkfRx8OBBdu/eTV1dHfX19YeTC3X1dezds/cCvA58AAAgAElEQVSIx95l5+ZQUFpM+ZjhQVKhtIRBg4vIzsuNcAuS6/XNu5i/bD0NzQcoLy/j/PPfzuTJk6mqqlKzckk7zc3NAOTlDayWS3IkJRzkuBQXF3PZZZcxb948tq95k6qTaqIOSSSldSTqqqqqePjhh3npwYVMveAMCgYXRR2aiEiPWlpaDicUjkgq1NXR0NBwxLTZuTnkFQ0iv7SQkaOGkl9cGL4GkZ2Xm9atu15+cycPLX6D4VVVXPWhSxg3blxal4fInj17AMjPV0f0A5kSDnLcZs2axeLFi9nw4qtUjhlBlpr+ifTIzDjjjDMYOnQot99+O0v/soBxZ0xnWBo8wk1EUpu7s2fPHnbu3Hn4VVtbS11dHU1NTUdMm5OfR17RIAqGllI+oZr8okLyigeRX1yojnF7sOi1zVRXj+BTn7pWTcgl7TU1NfH4448DUF5eHnE0kkg62slxMzMuu+wyfvWrX7FpxRuMOWVq1CGJ9Atjxozh+uuv5+677+aNZ5dSv2k7J509K62aFYtINNydvXv3Hu6wsaNfhdraWg4efOtpCLn5eeSVFFJSPYShYQuF/KIgqZCZrerj8Whvd4qKipVskLTX1NTE/ffff7gvF3UaObDpiCcnZOTIkUybNo3XVr3OyJNP0j3pIn1UUlLC3Llzef7553nsscdYct8TnHTObMpHDos6NBEZAGITC7W1tUFnjTt3Urtz51GJhfzBRVSOH0nB4CIGDQ46bRyILRXW/G05B5uDHziPLllNdWUJF80cm7T1jx02mOVvvEFzc7OakEtaaWpqYv369axfv55169ezY/t2AEbNnMTGZa9FHJ0kmhIOcsLOPfdcVq5cybY3NjBy2oSowxHpNzIyMjjnnHMYN24cd951FyvnP8+wCaMZd9rJZA3Ayr6IJJa7s337dpYvX86KFSvYu3fv4XE5+XkUxCQWCgYXp12HjY31e/HwMXzb9+wnJzu5V1WnjqrkxTXbWLt2LdOmTUvqukWSaf/+/WzYsIH169ezdt06du7YAUBGVibFlWXUzJ7C4KpKCstLlHBIA0o4yAmrrq6mpqaGra+upXrKeD2xQuQYVVVVcf111/Hkk0/y9NNPs3vLTiacPZPykVVRhyYi/UB9fT3Lly9n+fLl1NbWYmaUjhjC+Ck1afkkiFR1qC1IdsQ+uUOkv3F3Dhw4QENDQ5evbdu2sXPnTgAyszIpHlJ+OMFQVFFKRuZbT2Npa22LajMkiZRwkLg4/fTTufPOO9mzYxelVZVRhyPS72RlZfHOd76TqVOncs8997By/iKGjh/F+NOnq7WDiBylra2NxYsXs2zZMjZv3gxAydAKJpw1k8qaEUowpJi12+p56O+rKSku5qSTToo6HJGjuDtNTU3dJhIaGhrY17CPxoZGDh06dNT8GZkZ5OTnk18yiDGnTKVkWMVRCQZJT0o4SFxMmjSJnJwcdq7ZqISDyAkYMWIE1113HQsWLOCpp55iz7ZaJp5zCqUjhkQdmoikkPXr1/PQQw+RnZfLmDnTGDK2mrzCgqjDkhjt7qzbvpvFb2zhzZ17qays4J/+6cPk5ioZJMnT1tbG/v37e0giNNCwbx+NjY20h7ccxcrKziKnIJ/s/FxySgYxpKqc3II8cvLzyIn5m5WTrSduSZeUcJC4yMnJYerUqbz8ykpOOnsWlqFspsjxysrK4qKLLmLy5MncdffdrPh/zzJiyjjGzJlKpno3FxFg7NixTJgwgTVr11IyrFzJhhTh7uzYs59Vm/5/9u48Os7rPvP899a+ogBUFaqwElzAnRJ3UdRqSZZtSYnXOIvbsR1n1I6T03OS7p5OT8853TOZ9KQnk8lMT5K25STtpeOknTg6tmNbtrWLEi2R4E6CJEgCJAiAKBD7vtWdPwqAuImLCNQLoJ7POXWq6q23qn6yWYX3fep3773MiZZO+ofHiEQifOQjH2HXrl1aoULuirWW8fFxhoaGGBoaYnh4+KrrK7cPDg0yNDTM2OjoDV/LG/DjC/rxBQMEy4qJLUtfFSDMhApalUbulv4FyZxZu3YtBw8epL+zh1hK6+mK3K2qqip+57d/m5/+9Kfs3buX3vZONjxxP8Fo2OnSRMRhLpeLX/7lX+bP/+LPOfHS2yzbspbSqrSCBwdMTmVpudzHmbZuGtt76BsaxWUMq+rqeGrrVtatW6egQW4om80yPDx8XWBwoxBhaHiY4aEhpqZuPO+BcbnwBfx4Az48fh/eohDxsuJcsBDwXxUm+IJ+XFqKUvJE334yZ1asyC0t1dOWUeAgMkd8Ph/PPPMMa9as4e/+7u84+INX2fD4Ln3GRIRgMMhn/9ln+dZ/+xaNbx0CIBSLUlKVorQyRSydwO3RScWMyfEJfD4f27Zto76+nrGJyff9Wn1Do5y91MO5Sz00Z/qYmJzC4/GwcuVKHlu/nvXr1xMKKfwpVOPj43R3d9PV1UVvb+97dh+Mjoy852t4fF68AX8uPAj4CKWKiQVSeP1+fAEfnukgwRvw4Q34cXs9GtIgC5ICB5kzoVCIdDpNf6bL6VJElpy6ujq+9KUv8c1vfZMjP36D1Q9uJbWqxumyROaMMeYBa+2bt9omV0ulUvzL3/uXdHZ20tjYSGNjI02nmmg9fgaX200sHae0Kk1pZYpgLFLQJyST4xNs27aNZ555BoATRw7c9nOnsllauwY4097N2fYeLvcPA1Aci7Fl6zZWr17NypUr8fk0yW+hGBkZoaurazZYmLl9uauLocHBq/Y1xuC9IhzwRgKUJmLv3p+59vvwBnPX6kCQpUKBg8ypdDpNQ+Mpp8sQWZKSySS/9aXf4tvf/jYnX9+PtZZ03TKnyxKZK/8fsPU2tsk1jDGUlZVRVlbGAw88wMTEBM3NzZw+fZrTp09z9u0jnAX8oSDRZAnRRMnstcfndbr8vPH4vNTX1wNQX19PSejmh8ETU1Ocu9TDqYtdnL3Uw+j4JG6Xi2W1tex4YA2rV68mmUwWdIizlFlrGRwcvCpUuDJcGL1mbgR/KEigKEy0PE6yqIZgNEKwKEwgGtaEilLQFDjInEqlUhw6dIiJsXG8WspPZM6FQiE+//nP881vfpNTew7g8XpI1FY6XZbI+2aMuR/YDSSNMb93xUNFgH7iex+8Xi91dXXU1dXx9NNP09PTQ2NjI83NzbRcbKHp/PHZfUPF0VwAMR1CREpjS/aXVY/Py9B4H3v37gXAHwtct4+1luZMH0ebO2hs62Z8copgMMC6DZtYu3YtdXV1WmViievu7uZv/+5v6cx0Xr38ozEEIyEC0TAlteWzgcJMqKBJnUVuTJ8MmVPxeG5c+ejgsAIHkXni8Xj4zGc+w1//1/9Kw6v72PQhH8VajlYWLx8QIXdMEr1iez/wKUcqWmJKSkrYuXMnO3fuBGB4eJjW1lYuXrzIxYsXaWlpoePMBSA3GWU4HiOaKKEoWUo0UVIQQzEmJqc41NRB/Zl2egZHCPj93LN5C5s2bWL58uW4l2gII9cbHR2lrbWNorI4y1ZUEYiGCRZFCERCuNxahU3kTt1W4GCM+QFgr9ncB+wHvmqtvfF6K1JwioqKABgbGiEaL3a4GpGly+/38/nPfY6vfOUrnHx9P9s/9jgehXyyCFlrXwNeM8Z83Vp7HsAY4wIi1tp+Z6tbmkKh0GwHBOR+1e/r67sqgGg900Jbwzkg1xkQiRdfNRTDHw4uiRDCWsuhpg5eP36B4dFxamqq+eBT97N+/Xq83sIZbiLvKi8vJ5FIMI6lYu0KjGvx/zsXcdLtdjicA5LA307f/2VgAFgNfA347NyXJovRTOAwPvzes+5OTk7S29s7e9/r9RIOa5k/kTsVDAb5pV/6Jb7yla9w5u0jrH14u9MlidyN/8MY8yVgCtgHFBlj/l9r7R87XNeSZ4yhuLiY4uJiNm7cCOSW6+vs7JwNIS5evEjr8TNks1kAfEE/kZmhGIkSipIleAOLa6jB+MQUf/PaUVo6+6mtXcaTT36IZcs0L06hM8awbds2fvKTn7Dnv32fcHER4dIiIqXFhEuKCJfG1MUrcgduN3DYba3dccX9Hxhj9llrdxhjjr/ns6TgRCIRAMZHxt5zn5aWFv74j68+fqyrW8X99++mrq4Ol0vtaiK3q6qqikcffZRXXnmF1MoaSirLnC5J5P1ab63tN8Z8Bvgx8PtAPaDAwQEul4tUKkUqlWLbtm0ATExMcOnSpVz40NpKy8WLnD/YMPucQCRMNFFMUSpOLJ0gUhJb0L8OX+odwuf18slPfpItW7YsiY4NmRsPPvgg0WiUtrY2Ll26RHtLO5dOn599PBAOESotIlISI1waI1xaRKgogtExrMh1bjdwiBhjaqy1FwCMMTXkxlsCjM9LZbIoud1uAsEgEzcJHEojAXatrZq93z88zsFzF/hm4xmKolE23XMPGzdupKqqSuGDyG149NFH2bdvH60NZxU4yGLmNcZ4gY8Bf2atnTDGXDucUxzk9Xqprq6murp6dtvo6ChtbW3vhhAtLZxtbgVyQzFiqTixdJLi8gSR0tiCOiFLJBL8+q//+uz8UyIzXC4XW7ZsYcuWLUBu6M3AwACXLl2avbS3t3PxWONs14/L7SZcEiVcEiNUHCVYFJm+hJfsRKwit+N2A4ffA/YYY84CBlgOfNkYEwa+MV/FyeIUDoeYGH3vwCES9HHv8vRV23avq6KxrZtj5zPs3fsWb775JuFwiLq61axatYoVK1YQi8Xmu3SRRcnj8bB161beeOMNxoZH8IeCTpck8n58FWgGDgOvG2OWkZs4UhawQCDAihUrWLFixey23t5empubaWpq4lzTOc7tOwqAx+uZ7X4oTieJJIod/WEhGo0qbJDbYoyhqKiIoqIiVq9ePbt9cnKSTCZDR0cH7e3tuSCitZ1Ljeeven4gEiJQFCFUFH43iIhFCETD+nFNlrxbBg7TEzdFgTpg7fTmU1dMFPn/zFNtskiFgiEGxt87cLgRt8vF2qoEa6sSjI5Pcu5SD41t3ZxqOM6hQ4eA3CzbtbW1s7+upFIpzRotMm379u28/vrrZM5epHpTndPliNwxa+1/Bv7zFZvOG2M+4FQ98v4VFxezefNmNm/eDEB/f/8VAUQTTftzo3HdHg/FFUkSyyqI15RrXLwsOh6Ph4qKCioqKma7IQBGRka4fPkyXV1ds5fLly9zuamNsbF3j5GNMQSiuWU1Q7F3OyJmVsVYSB1BIu/XLQMHa23WGPM/WWu/Q+5XB5GbCgaD9HYPvu/nB3we1tckWV+TxFpLR+8QFzr7aOns49SJYxw8eBDIfcmXl6epqKikoqKC8vJyysrKNKu0FKR4PE4ikaC3PaPAQRYlY0wK+I9AhbX2I8aY9cD9wF85W5ncraKiIu655x7uueceAAYHB2lububcuXM0nGzg1Bv1GJehOJ0kUVtBoqYCXygw53VESmMMdPaQnZqaPW4QmQ/BYPC64UeQG5oxPDx8wzCi80wL4+PvjlQ3LhfBaJhQcZRQcZRwcVFuqEYsitujH9xk8bjdIRUvGmP+FfDfgaGZjdba7nmpShY1n8/H1MTknLyWMYZ0SYR0SYSdqyux1tI7NEZbdz/t3YNc6unnYH07b789BYDLGOLxOKl0mlQqRVlZGWVlZcTjcXVDyJK3cuVK9tfXk53Kaq1wWYy+DvxX4N9N3z9N7rhDgcMSE4lE2LhxIxs3buQXfuEXaG1t5fjx4xw7fpzGtw7R+NYhYqk4iWUVJGorCURCc/K+q3bdS09bhuHeAT7+8Y9TUVExJ68rcruMMYTDYcLh8HUrolhrGRwcnA0gurq66OzsJJPJ0HKhHWvtzIvkgohYhNB0CBEuKSIUi+L23u6pnUj+3O6/yl+evv7tK7ZZYMUN9pUCN5+zPBtjKIkEKIkE2FCTmxwvF0KM0tE7REfvEJf7hmhtPsuxY8dmn+dyuYjHS0kmy0gkEiSTSRKJBIlEglBobg5kRJy2fPly3n77bYZ6+ogmSpwuR+ROJay13zHG/FsAa+2kMWbK6aJkfhljqKqqoqqqiieffJJMJsPx48c5fvw4Z985ytl9x0jUlFO1sY6islKtJCFLljGGaDRKNBqltrb2qscmJyfp6uoik8nMXjoymauWqoXcXBG5joh3g4hIaUyTVoqjbitwsNYun+9CZOkwxoDN38TiuRAiSEkkyNqqxOz28ckpugdGuNw/PHvpuNjMyYYGslfUFwoGiU+HD/F4/KpLIDD3LZ0i8yWdzk3GOtTTr8BBFqMhY0yc3A8aGGN2AX3OliT5ZIyZXYrzscceo6uri/r6et5++20OnX+NaKKEqg2rSCyv1ER7UlA8Hs/sZ+NKU1NTdHd3XxVEZDIZLp1sYnIy123scrkIx2NEEyUUJUuJJksIFkUU3kne3M6kkWXkOhs2TG86Dvy5tTYzn4XJ4jU2NobL43xLl8/jnh2OcaWpbJbeoVG6B0bpGRyhe2CEroF+zpzMcPCa5TzDoRCl14QQpaWlxONxgsGgvqxlQYnH43g8Hoa6dY4mi9LvAd8HVhpj3gSSwKfu9kWNMX8NPANkrLUbp7eVkhuuUUtuZYxPW2t77va9ZG7F43GefPJJHn30UQ4ePMieN9+k4bV9+Pcfo3LDKirXrdAvt1LQ3G43yWSSZDLJhg0bZrdns1l6enpyS3devEhLSwutZ1poazgH5JasjSZKiCZLiCZLKUqW4AvqRzaZHzc9KzTGPAB8m9y4ym9Ob94GvGOM+Yy19s35LU8Wo+HhYTyBhTvTtNvlIh4NEY9eP5RifHKK3sFRugdH6BnMBRI9g/2cPZXh0DVhRCDgnw4hErMhxMx1JKLkWPLP5XKRSCQY7htwuhSRO2atPWCMeQRYQ24J7lPW2ok5eOmvA3/Gu8cxAL8PvGSt/SNjzO9P3/83c/BeMg98Ph/33XcfO3bs4PTp0+zZs4dz7xzl0qlmVu3eTEl50ukSRRaU3FDi3A9lGzduBHLdEJ2dnVy8eHE2hGg5cnp2bohAJEQ0UUJJZRmlVWn8YS2xLXPjVj9D/wnwMWvtwSu2fd8Y8zy59bLvm7fKZNEaGh7GW7Q4v6R8HjdlxWHKisPXPTYxlQsjeodGp8OIUXoGh7lwrpGjR0ffncwH8Hm9lMZLSSSSsyHEzJwR4XBYYYTMm2QyyZnmc06XIXJHpodS/BrvLr/dALQBdz05tbX2dWNM7TWbPwo8On37G8CrKHBY8FwuF2vXrmXt2rWcOnWKH/zgBxz58RuUraxm5c5N+oVW5CbcbjfpdJp0Os327dsBGB8fp62tbTaEaD5/ntPNrQBESosprU5RWpWmKFmKcenYVd6fWwUORdeEDQBYaw8ZY6LzVJMsciMjI5Qki5wuY8553W6SsTDJ2PVhxFQ2S9/w2BVdEbnrtvPnOHH8+FVzRvj9PhLxBInpiStnWuESiQSeBTAURRa3RCLB0WPHtFKFLBrGmHXAy8BPgIPkuht2AP+zMeYxa+3JeXjblLW2ffr2JSD1XjsaY54FngWoqamZh1Lk/VizZg0rVqzgtdde4/XXX6e75RKrH9hKcnml06WJLBo+n4/a2trZSSqttXR0dHD69GlOnjzJhSOnuXD4FN6Aj5LKXPhQWpXC61+4ncyy8Nzq7MYYY0quHdc4PfZRR7JyHWstoyMjeArsi8jtclEaCVIaCQJXT9aXzVr6hkfpHhyle2CEnsERugaGaWo8xeHDh2f3M8ZQWlpKWVnZ7MRA6XRaS3rKHSkuLgZrGR8eIRC9PhwTWYD+APgfrbXfuXKjMeaTwB8Cn5zPN7fWWmPMe850bK19DngOYPv27fmbEVluyev18sQTT3DvvffyD9/9LideeZtlvetYtnmtOglF3gdjzGwXxMMPP8zIyAiNjY2cPn2aU6dOkTnbgjGG+LJyKtevIpaK67Mmt3SrwOFPgZ8aY/4VcGB62zbgP00/JnKViYkJrLVaB/gKLte7q2isTF8dRoxPTtEzOMLl/hG6plfSyLSe5+TJk7NDNDweD+l0ivLyCiorK6murqasrEwzdMsNFRXluovGhhQ4yKKxyVp73eSQ1trvGmP+4zy9Z4cxptxa226MKQc0EfYilkwm+R9+8zd5/vnnOXTwEMO9/ax5aBtudQ2K3JVgMMg999zDPffcQzabpbW1lePHj7N//34ON79OpDRGxfqVlK2oxu3Rj2NyYzf9JrbWPmeMaSP368MGcktVnQD+d2vtD/JQnywyMyfJxuhk+Hb4PG5SxRFSxVevpDE5laVrYJhM7xCZviE6egY5evgg+/btyz3P52PZsmUsX76clStXUlFRoQBCAIhGc6PdxkfHbrGnyIIx9D4fuxvfBz4H/NH09ffm6X0kTzweD5/61KdIp9O88MILTIyOs+lDD+hvo8gccblcVFdXU11dzWOPPcbhw4d5a+9eTu85QNO+Y6TX1FK1oQ5f0O90qbLA3GqViv9krf03xpjQta2OIjcyO3Giuqvuisftui6IsNbSPThKW/cArV39tHS00tjYCEAkHGb9hg1s3ryZmpoatbcVMK/XC0B2csrhSkRuW5kx5vdusN2QWxrzrhhj/pbcBJEJY8xF4N+TCxq+Y4z5InAe+PTdvo84zxjDQw89RDgc5rvf/S5nf36Yut1bnC5LZMnx+Xzs2LGD7du309TUxN69e2k42sClU83UbltP+erlmmRSZt2q1+ypK5aLUuAgt9Tb2wuAL6B0c64ZY4hHg8SjQTYtKwNgaHScpo5ezrR3c6B+P++88w7V1VU888wvUFVV5XDF4oSZwGFKgYMsHl8D3msi6r+82xe31v7qezz0+N2+tixMW7duJZPJ8MYbbxAqKaJy3UqnSxJZkowxrFixghUrVpDJZPj+979P41uHuHT6PHW7NxNNlNz6RWTJu1Xg8ALQA0SMMf3kfm2wM9fW2qW3FIHclba2NgAiiWKHKykM4YCPjcvK2LisjPHJKY6dz/BWw0W++tWv8sUvfnF21mEpHH5/Luybmph0uBKR2xaa7qb8JWvt3ztdjCwNTz75JB2ZDGfeOUppZYpgUeTWTxKR962srIwvfvGLHD58mB/96Ecc+P4rVG2sY8X2jep2KHC3Gtj2v1hri4EfWmuLrLXRK6/zUaAsLo2Njbg9HkJFWjU133weN1tXlvOFJ+4lm81y8OB1K9pKAfD5civETE5MOFyJyG17yuTGgf1bpwuRpcPlcvHxj30Mj9vDmZ8ffnfIp4jMG2MMmzdv5nd/93fZsWMHF481cuKVt9V1WeBuFTjsnb7un+9CZPE7e/YsR44coWLdCiWZDmnvGeT5vbkl61etWuVwNeIEl8uFPxBgcmzc6VJEbtdMN+U9xpj+Ky4D092VIu9LUVERTzzxBN0XO+hqaXe6HJGCEQwG+djHPsbTTz/N5fNtHPnxG0xoMuuCdavAwWeM+TVgtzHmE9debvZEY8xfG2MyxphjV2z7D8aYVmPMoenLU3PxHyHOGxsb4x+f/0dCRRGWbVnndDkFZXBknPozbXzj5cN8/cVDdA1N8MlPfpJNmzY5XZo4JBaLMTo47HQZIrfr2m7KInVTylzZtWsXJaUltBxpdLoUkYKze/dufu3Xfo3hnn6OvLBHwz0L1K3mcPgS8BmgGPiFax6zwD/e5LlfB/4M+OY12//UWvt/3UGNssB1d3fzN3/zN/T29HLvUw9rHd55NjE5xcWuAS5kejnX0culnkEAysqSPPXUQ2zbto1AIOBwleKkZCJB08ULTpchcrv2AltRN6XMA7fbze77d/PDH/6Qgcs9TpcjUnA2bNjAZz7zGb7xjW9w+s2DrH1ku1ZTKzA3DRystXuAPcaY/dbav7qTF7bWvm6Mqb2L2mQRaGho4O///u/JYtn4wfspTiecLmlJsdbSNzxGe/cArV25y6XeQbJZi8sYqmuq+eD2+1m3bh2pVMrpcmWBiMfjnGhoIDuVxeXWGvSy4F3VTXntg9bam/24IXJLW7du5Wc/+xltJ5ucLkWkIK1evZoPfvCD/OxnPyOaLKFqg4b9FpKbBg7GmMestS8DPXN4EPA7xphfB/YD/9Jae8O42RjzLPAsQE1Nzft4G5lPQ0NDvPrqq7z11ltEEyWs/8BOAtGw02UtajPhQkfvIJd6hrjUM8ilnkGGx3KT/3k8Hqqqqnhw4xaWL1/OsmXLZlckELlSeXk5NptluLefSFwrxsiCdzfdlCK3FAgEWL16NY3nzuL23aq5V0Tmw8MPP8yFCxdorD9O2fIqfCF14xaKW33rPgy8TO4AYHY5zCuu7/Qg4L8AfzD93D8A/gT4jRvtaK19DngOYPv27ZpaeIHo7e1lz5497Nu3j8nJScrXLmfVzntwaRjFHZmcynK5f5hM7xAdfUNkeofI9A0xOp4b2+YyhmRZkrUbNlFVVUVVVRWpVAqPRwdKcmsVFRUADHT1KnCQBe9uuilFblddXR3Hjh3D7wo5XYpIQXK5XDz99NP86Z/+KReOnGLVrnudLkny5FZnLwPGmN8DjvFu0MD07Ttmre2YuW2M+RrwT+/ndST/Ojs7ee211zh8+DAWS9nKamo2rSFUrOUvb8ZaS//wGJm+YTr7cqFCpm+Y7oGR2SW6vF4vqVSKezavoby8nPLyctLpNF6v1+HqZbEqLS3F5/Mx2NXrdCkitzRP3ZQiV5lZuclm9RuWiFPi8Tjbtm2j/sABqjfV4VGnbkG4VeAQmb5eA+wAvkcudPgF4J07fTNjTLm1dmZdoo+TCzJkgRodHeXEiRMcPnKEM42NuDxuytcup2pjHYGIfiG41vjkVC5U6M2FCpm+ITr7hhm7Ykbe4uIY6YoaNqXTpNNpysvLKS0txeXSOHuZOy6Xi/Lycnq6+pwuReR2zHU3pch1YrEY4XCYsQktGSzipEceeYT9+/dz6cwFqjbUOV2O5MGtJo38XwGMMa8DW/6w8w4AACAASURBVK21A9P3/wPww5s91xjzt8CjQMIYcxH498CjxpjN5A4gmoF/fnfly1ybmJjg1KlTHD58mFOnTjE1NUUgEqZm81oq163AF9R4K4Ch0XEu9QzS0TtEx/RwiO6BkdnH/X4f6XQ5m1fmJnNMp9OkUimtHiF5U15ezsX6/VhrNRu0LHRz2k0pciPGGCoqKmhs1PKYIk4qLS2ltraWzJkLmjyyQNzugPAUcGUkPD697T1Za3/1Bps1NnMBmpyc5OzZsxw5coQTJ04wPj6OLxggvaaW5IoqipKlBX3CMjw2QXvPIO3dA7R3D3Kpd4jBkbHZx0tKSiivXs6W6aEQ5eXlFBcXF/T/ZuK80tJSpiYmmRyfwOv3OV2OyM3MaTelyHtJpVIKHEQWgC1btvD8888zqE7MgnC7gcM3gXeMMc9P3/8Y8PV5qUjyYmRkhNOnT9PQ0MDp06cZGxvD4/OSqK2gbEU1xekkxjU3J8yT4xP4fL7cmK36+quGGCw01lq6B0do6ezn4uV+WrsHZjsXDJBIJFi5ei2VlZVUVFRQXl6urgVZkKLR3Pwq48OjChxkQbubbkqRO1FaWup0CSJCbplMgJ62jMOVSD7cVuBgrf1DY8yPgYemN33BWntw/sqS+dDT00NDQwMNDQ00NzeTzWbxBf2ULksTr6mgtLIMl3vuV5uYHJ9g27ZtPPPMMwCcOHJgzt/jbgyPTXDuUg9NHT00Z/oYHMk184RCQWpqlrG9pobq6moqKioULsiiEQrl5lmZGNN4ZVk07ribUuROxONxp0sQEaCoqIhEMklve6fTpUge3PYae9baA8DCOlOUm7LWcunSJY4fP86JEyfo6MgtEhIqjlK5cRXxmvK8DJfw+LzU19cDUF9fT0nI+aUdR8YnOHHhMicvXuZCZ66dKxQKsnLVGlasWMHy5ctJJBIaFiGL1thYbtiPx+v8503kNqmbUuZVUVGR0yWIyLTltbXUH9CpZSHQkegSY62lo6ODo0ePcvToUbq6usAYYmVxVuzYRLymnFAscusXmkMen5eh8T727t0LgD/mXJdAV/8we09e5ETLZaayWZLJBB/4wAdYu3YtFRUVWi1ClozBwUEAvEEtOSWLg7opZb5FIvk9/hGR91ZWVkZ2asrpMiQPFDgsAe8VMhSnE9Tt3kxiWUXBry4xOZXl1aPN7D/TjsfjYfuOHWzfvp2KigqnSxOZc9lslkOHDuH1+/AFFDjI4qFuSplPwWDQ6RJEZFpZWZnTJUieKHBYxMbHx9m/fz8/f/ttui5fVsjwHqayWb6z5zjnM33s3LmTJ554gnA47HRZIvNmz549XLhwgbWPbMeoa0dEBEDDJEUWEE3iWjgUOCxCY2NjvPPOO7zxxhsMDQ1RVBZXyHAT9WfaOZ/p45Of/CRbt251uhyRedPb28vhw4d58cUXSdRWUrai2umSRERERK6jOVUKhwKHRWR0dJSf//zn7Nmzh5GREUoqylj1yDaK0wmnS1vQzrZ3U15errBBlqSxsTFOnDjBgQMHOHfuHADF5UlW796sX/NERERkQfJ4PAQCAUZHR50uReaZAodFor+/nz//iz9ncGCQ0qoUazbvJFam5Z1uhzEGrHW6DJE5k81maWpq4uDBgxw7doyJiQmC0TDLtqwjtbKaYJEmRhMREZGFLRKJKHAoAAocFoFsNss//MM/MDwywuanHyGWUtBwJyrjUd480cLg4KBmqJZFZWJigsuXL9PZ2Tl7yXR20nX5MpOTk3h8XhLLK0mvqqEoFVdHg4jILbhcLrLZrNNliAi5wOHy5ctOlyHzTIHDIvDmm29y9uxZVj+wRWHD+7CqIs6eEy2cOXOGzZs3O12OyFWstQwPD+fChExmNmDIdGbo7em9at9gNEywOEp67XKiiRLiNeW4PW6HKhcRWXw8Hg/j4+NOlyEioEncC4QCh0XgrbfeIlgUJr261ulSFqVUcRhjIJPJOF2KFKhsNsvw8DB9fX309fXR1dV1VcfC6MjI7L4uj5tQUZRQSYRlteWEYlFCxVFCRRFcChdERO6K263vUZGFIhDQZPeFQIHDIrB7925eeOEFus63kaitdLqcRad/eAxrIRQKOV2KLEFTU1MMDg7S399PX1/fdde9fX0M9Pdf18LrC/oJxqKU1KQIFRcRikUIxaL4IyENjRARmScejw59RRaKmcDBaq61JU3fuovA7t27OXjwIGfePkK4tJhgkdqPbtfE1BQ/3n8Gj9vNxo0bnS5HFpnJyUkGBgZuGCTMXAYHB6/7Q+lyuwmEg3hDAfzxKEXVZfjDwdlLIBLCG/A79F8lIlK41OEgsnD4fD4gd7wlS5cCh0XA7Xbz8Y9/nL/6q79i//M/o2pDHTX3rsHt1f99N5PpG+Kf3mkk0zvIJz/1KYqLi50uSRaQmbkTenp66O3tpbe3l56enqvChKGhoeue5/Z48IeD+MJBQukSSkIV74YJody1x+9Tl4KIyAKkwEFk4ZgJHCYmJhyuROaTzlgXierqan73d3+Xn/zkJxw+fJiOMxdYvmMDZSuqdWJzjb7hUfY2XORQUwfBYIB/9tnPsnbtWqfLkjzLZrMMDg7OBglXhgo9vT309fZd9wfO4/Xgj4TwhYJEK5MkwjVXBQn+cBC316PPnIjIIqXAQWTh8Hq9AJrIdYlT4LCIxGIxPv3pT3PffffxT//0T5x8bT+tx89SuWElydoqXG6X0yXeUKQ0xkBnD9mpKeLRIGXFc780pbWWtu5B6s+00dByGWMM9913H48//rjmbliipqam6O/vvypIuPK6r6+Pqampq57j9fvwR0IEIiHKypYRmL4diITwR0J4fF6FCSIFxBjTDAwAU8CktXa7sxXJfNN3vMjCMRMAXnu8JkuLAodFaNmyZfzWb/0WBw8e5NXXXuPka/s5984x0mtqqVi7HH8o6HSJV1m161562jIM9w7w0IYa1lUn5+y1R8YnOH6hkyNNGTp6B/H7fOy6/34eeOABDaFYYkZHR2lqauLcuXOcPXuWTCZz3dwJvmAgFyjEglRUJvBHgtOBQphAJKRhSCJyIx+w1moh+ALhcuV+nNEkdSLOm5nEVYHD0qaj70XK5XKxbds2tmzZwtmzZ9m7dy+nDp2k5cgpEssqqFi3klgqviST/KlslnOXejjanOFMew9T2Szl5eX84sOPsXnzZvx+Tca3FIyPj3P+/PnZgKGtrQ1rLS63m6KyUqo21RGMhmc7FvzhEG4tGykiIjcxEzhcu3KQiOSfOhwKgwKHRc7lclFXV0ddXR1dXV28/fbb1NfXc7iplXBxEek1taRWVi/6GfFnhkwcO5+h4eJlRsYmCIdC3LdrF1u2bKGiosLpEmUOtLS0cPr0ac6ePUtLSwvZbBbjMhQlS6m5dw3F5UmKkqW4FCyIyNywwE+NMRb4qrX2uWt3MMY8CzwLUFNTk+fyZK7pBEdk4dDnsTAocFhC4vE4Tz31FE888QSHDx9m3/59nH37CE37j5FYVkn5mlpi6cSi6noYGBnjaHOGo+czdA+M4PF4WLduHVu2bGHVqlWa/GkJGRoa4itf+QoA0UQJlRtWUVyeJJaKayiEiMyXB621rcaYMuBnxpiT1trXr9xhOoR4DmD79u3qw1/k1OEgsnDMHMdrWcylTUfxS5DP52PHjh3s2LGD9vZ29u3bx6FDh8icayFYFKF8TS3pumULtuvBWsvZSz0cPNvO2Us9WJubt+KRJ7ayceNGAoGA0yXKPAiFQmzdupUDBw5QtWEVZSurnS5JRJY4a23r9HXGGPM8sBN4/ebPksVMcziILBwKAAuDAoclrry8nF/8xV/kwx/+MMeOHWPfvn2c23eM5gMNlK2oomL9SqLxhTG54sTUFEeaOtjX2E7P4AiRcJiHH36Ebdu2EY/HnS5P5pkxho9+9KN0dXVxak89kxMTlFSmCERCi6orR0QWB2NMGHBZawembz8J/G8OlyXzbObviQIHEedpSEVhUOBQIHw+H1u3bmXr1q10dHSwd+9eDh06xKXG8xSVxalcv4LEskpHltacymY5cPYSb528yPDoONXVVTz59AOsX79+dvZaKQwej4fPfOYzfO1rX6PxrUMABMIhYukEsfIExekEgWhYAYSIzIUU8Pz094kH+La19gVnS5J8UeAg4jx1HBUGnc0VoFQqxcc+9jE+9KEPceDAAfb+fC8Nr+7DFzxK5fqVVK5fmbcx880dvbxw8Cw9AyMsX76cxx9/nNraWp1QFrBwOMy/+Bf/gs7OztllMJuamug4ewEAfzhILJ0LH2LpJMEiBRAicuesteeAe52uQ/JLfy9EFg51HBUGBQ4FLBgM8sADD3D//ffT2NjIW2+9xZn641w8foaqjXVUrlsxb8HDVDbLK0ea2dfYRjxeyq//+i+xZs2aeXkvWXxcLhepVIpUKsWuXbuw1l4XQGTOtgDgDwWJJkuIxGNE4sVE4sX4Q0GH/wtERERE5HYocFjaFDgILpeLNWvWsGbNGi5cuMBLL7/Mmf3HaD3WSNWm1VSsW457joY2WAsTk1P8496TnLvUw65du/jwhz+M1+udk9eXpckYQ1lZGWVlZdx3331Ya7l8+TJNTU00NTXR2tpK8/mG2f39wQDhKwKIaLwYv+aCEBGRaTrBEVk4dHy2tClwkKvU1NTwhc9/ngsXLvDiiy9ydt9RWo83smrXvSRqK+/ilXNfJN97+9Tslo9+9KPs3LnzLiuWQmSMIZlMkkwmZ/8NjY2N0d7eTltbG21tbbS2tdFy5PTsQaXX7yNcGpsNICKJYoJFEf2RExEpQPruF3HezDGaPo9LmwIHuaGamhp+4zd+g+bmZn7wgx9w/OW3SdRWULdrM77QnS9LOTMZ5caNG0mlUlRVVbF69eq5LlsKmN/vp7a2ltra2tltExMTdHR0zIYQbW1ttDec4+L0bMger4fwdAARTZQohBARWeLU2SCycOjzWBgUOMhN1dbW8uUvf5k9e/bw0ksvsa/tZ6zYuYl03bI7Oymb3nXjxo1s2rRpfooVuYbX66WqqoqqqqrZbVNTU3R2dua6IFpbudjayqVTzVw8fgYAj8/77lCMRAnRRLFWxhARWSKy2Szw7nJ8IuIcfR4LgwIHuSW3280jjzzC+vXref755zm95wB9HZdZvXsLLn1ByCLjdrtJp9Ok02m2bt0KvBtCXLx4kdbWVlqv7YSYDiGiiRKiyRJiqTi+4J13+oiIiLOmpr/XdYIj4ryZz+PM8piyNClwkNuWTCb5zd/8TV555RVefvllRvuH2fD4fXgDfqdLE7krV4YQ27dvB2BycpJMJpMLIGYux8/MpvGhWJSiVJxYKk4snSCgSSlFRBa8/v5+QIGDyEIwOTkJgGeOJqeXhUn/78odcblcPP744yQSCb773e9y8AevsvHJ3YRiUadLE5lTHo+HiooKKioq2LFjB5D7w9jW1kZzczPNzc2cP3+eS6ebAfCHgxSVxYml48RSCcIlRQogREQWkGw2S29vL6BfVEUWgvHxcQB8Pp/Dlch8UuAg78u9995LSUkJ3/rWtzj6wpvc+/TDBCIhp8sSmVcej4eamhpqamp4+OGHyWazZDKZ2QCiubmZzqaLQG5VjKJUnNKqFPHqcvzhoMPVi4gUnvHxcTo7O8lkMrS0tDhdjoiQ+wGnoaGBPXv2ALmJv2XpUuAg71tNTQ1f+MIX+Mu//EuO/uRNNj/9sIZXSEFxuVyzQzF27dqFtZaenp7Z8OHs2bM0vnWIRg4RiRcTr04TryknEi9W94OIyBwaGxubDRZmLh2ZDnp7emf3MepqEHFUd3c3+/fvZ//+/QwNDc2eN8RiMYcrk/mkwEHuSkVFBZ/97Gf5+te/ztGfvsXmpx/WRJJSsIwxlJaWUlpaytatW7HWkslkOHXqFA0NDVw4fIrzh07iDwUoqUoTr0lTUlGGW2MXRURuy+jo6PXBQkcHfX19s/u4XC6CsSih4ii1y8sJFRcRLi4iUBSmu+USx1/6uYP/BSJLSzabZWJigomJCcbHx294PTo6yvHjx2lsbARjiFenWb77XorTCfZ86/v6EWaJ01Gu3LXly5fz6U9/mm9/+9uc23eMVbvudbokkQXBGEMqlSKVSvHwww8zNDTE6dOnOXnyJI2NjVw63YzL4ya5rJL06mXE0gn90RWRgjQ5OcnAwMDspb+//7r7/QMDjAwPzz7H5XYRikUJxYuoXVlJuDhKqKSIYDSsbgaRaZOTkzcMAW4WENzsenxinPHx3PMnxsdnV5q4FX84yLLNa0mvrp0dhj01eXvPlcVNgYPMiQ0bNrBr1y5+/vOfU1JRRrym3OmSRBaccDjMli1b2LJlC5OTk5w/f55jx45x+PBhOs5eIBgNk6pbRrquBn9Yc6KIyOI3MTHxnkHCzO3+gQFGR0aue64xBl8ogC8YwBcKUFycIh0JESqOEi4pIhAJY1wKaWXxstbe8uT/drZdGwhMjE8wPjHB5MTE7Opat80YPB43bq8Hl9uN2+PBeNy4PW5cHjfuQJCwJ5K77fFMX195+91tM/fdHjf+cFBBYIGat8DBGPPXwDNAxlq7cXpbKfDfgVqgGfi0tbZnvmqQ/Prwhz9Mc3Mzp/ccYMennsTj8zpdksiC5fF4WLlyJStXruQjH/kIJ06coL6+nnMHTtB84AQllSnKV9eSWFauP9AismANDg7S1NT0nkHC2Ojodc8xLoM/GMQb9OMLBSgtTeeChVAQXzCAP5QLGLx+vwIFWRJ+9KMf0dTczMQV4cDM5U65XK5cGDBzcu92v3uC73fjDUcIXHGy77oyPPC6cbvduLyea8ICN67pYMC4XOq2lDk1nx0OXwf+DPjmFdt+H3jJWvtHxpjfn77/b+axBskjr9fLJz7xCf7iL/6CC4dPsWLHRqdLElkUfD4fmzdvZvPmzXR3d3PgwAHq6+s58crbhGIRajavpWx5tQ68RWTB+d73vseJEyduuo8/HCRSGiMSLyYSLyZUHMUXDOD2enRiIwXB7XbjdrmYnP73nrX2tocizDAugy8QwOPz5gIDby4g8Hi9udvXXjy5a891j3lxuRUqSP7MW+BgrX3dGFN7zeaPAo9O3/4G8CoKHJaUyspKNm/ezJGjR6hYu5xANOx0SSKLSmlpKU888QSPPfYYDQ0NvPjSS5x8bT8XDp1i2Za1JGurFDyIyILxiU98gp07dzI4OMjAwACDg4Ozl4HBQQYHBhgeGmZsaISulktXPdflceMPBvAE/PiCfnwBP95QAF/Ajy8YyHVABP0KJ2TR+9CHPnTD7ZOTk4yPjzM2NsbY2NgNb197feXjo2NjjPb3Mz59/3ZDDGPMFWGEd7YL4trLzONujzt3fc1j/nAIl1tdmHJz+Z7DIWWtbZ++fQlIvdeOxphngWcht/yiLB5PPvkkR48epeVoI3W7Nztdjsii5HK52LBhA+vWrePEiRO8+NJLNLy6jwvFp1ixcyOlVWmnSxQRIRgMUldXd9N9pqamGB4eviqMuCqYGBhgYHCQvq5OhoeGbvgaLrcbX9CPdyacCAUIFRcRTZQQice02o8sSh6PB4/HQyg0N/M2XRlg3E5gcaPtY/0DjI2PMzE+zuTk5E3fzxhDMBomGIvMrgwTKooQKo7OLnkp4ti3s7XWGmPsTR5/DngOYPv27e+5nyw8sViMe+65hyPHjrJ8+wbN5SByF1wuFxs3bmT9+vUcO3aMF196iaM/fYtUXQ0rd96D1+9zukQRkZtyu91Eo1Gi0egt981mswwPD1/XLXFlODE4OEjv5Q7aTzXnnmQM4eLo7JCNaKKESGkMt1chhBSWuQ4wpqambhhMjI+PMzo6SldXF5cvXybT2Ul7w7mrOiy8AT/BojChWHQ6jIgQikUJRMO4NDdVQcn3N3GHMabcWttujCkHMnl+f8mTXbt2cfDgQTrOXKBy/UqnyxFZ9FwuF/fccw/r16/nlVde4bXXXqO3NcOq3ZtJ1FQ4XZ6IyJxwuVxEIhEikchN97PW0t/fT1tbG62trbOXjjMXcjsYQzgWJZK4OoQQkdvndrsJBoMEg8Fb7pvNZunt7aWzs5PLly/T2dmZu7R1cqnx/Ox+xmUIRiMEYxENvS4Q+Q4cvg98Dvij6evv5fn9JU+qqqpIp9Nkzl1U4CAyhzweDx/84AdZv3493/3udzn+4s9Jr15G3f2bcbndTpcnIpIXxhhisRixWIx169YB14cQbW1tXLwmhPD61XUpMh9cLhelpaWUlpayZs2aqx4bGRmZDSGuDCNaL5xxqFrJp/lcFvNvyU0QmTDGXAT+Pbmg4TvGmC8C54FPz9f7i/M2bNjASy+9xPjw9Utiicjdqays5Mtf/jIvv/wyr732GiN9g6x/7D58wYDTpYmIOOJGIQRAf38/ra2tHDp0iGPHjjlYoUhhCgaDVFdXU11dfdX2P/mTP6G7u9uhqiRf5m0AjbX2V6215dZar7W2ylr7V9baLmvt49baOmvtE9Za/QtbwtavXw9AV0v7LfYUkffD4/Hw5JNP8iu/8isMdfVx8AevMtjV63RZIiILSlFREevWreOpp55yuhQRucLy5csB7niJUFlcNGOHzJtUKkUkEqG3vdPpUkSWtE2bNvHss8/idbk5/OM3FDqIiNxALKY5HEQWkmQyCeSGXMjSpcBB5o0xhtraWvo6upwuRWTJq6ys5Ev//EuEgkGO/vQtRvoHnS5JRGTBKSoqcroEEZkWCOSGgSpwWNoUOMi8qq2tZWxoBDuVdboUkSWvuLiYL3z+C7gxHP3Jm4yPaP4UEZErzcy2b61WXBdx2szncXRUxytLmQIHmVeVlZUATE1MOlyJSGEoKyvjc5/7HBMjY5x6vV4H1SIiV5j5RXViYsLhSkTE682tGqPP49KmwEHmVSqVAmBqUoGDSL5UV1fzkY98hO7WDtpPNjldjojIguHx5BZom9RxiYjjZgIHfR6XNgUOMq/8fj+lpaVMTWj2WZF8uu+++1hVV8e5fUc1n4PIezDGfNgYc8oYc8YY8/tO1yPzbyZw0C+qIs5zu92AAoelToGDzLtkMklWy92I5JUxhk98/OO4jIum/cedLkdkwTHGuIE/Bz4CrAd+1Riz3tmqZL7NnOBks5pbSsRpM59HLYu5tClwkHmXSCScLkGkIMViMR566CE6m1vpz3Q7XY7IQrMTOGOtPWetHQf+DviowzXJPHO5coe+ChxEnKfAoTAocJB5V1pa6nQJIgXrwQcfJBwO01SvLgeRa1QCLVfcvzi9TZYwBQ4iC8fM51ETXC9tChxk3sViMadLEClYfr+fBx98kN72ToZ6+50uR2TRMcY8a4zZb4zZ39nZ6XQ5cpeMMYBOcEQWAn0eC4MCB5l3ChxEnLVt2zbcbrdWrBC5WitQfcX9qultV7HWPmet3W6t3Z5MJvNWnMwvneCIOG8mcFDH0dKmwEHmXSQScboEkYIWDofZuHEjHWcu6I+6yLv2AXXGmOXGGB/wK8D3Ha5J5tnMCY6IOE9DKgqDAgeZd+Fw2OkSRArepk2bmByfoLddLeEiANbaSeB3gJ8ADcB3rLWa7EREJE80pKIweJwuQJa+mRloRcQ5q1atwuv1crm5jdLKlNPliCwI1tofAT9yug4RkUKkwKEwqMNBRKQAeL1e6urq6GnNOF2KiIjjdIIj4jwFDoVBgYOISIFYsWIFo4NDjAwMOV2KiIgjNIeDyMKhSSMLgwIHyYuZSWFExDkrVqwAoE/zOIiIiIhIHugsUPJCgYOI85LJJH6/n4HLvU6XIiIiIgVOHUeFQWeBkhcKHESc53K5KC8vZ7BLgYOIiIgsDJrDYWnTWaDkhRJMkYWhvLycoZ4+0N92ESlgOsEREckPLYspeaEOB5GFIZFIMDU5xdTkiNOliIjknX4AEVk4Zs4PFAAubToLlLzQsjciC0M8Hne6BBEREREpEAocJC+07I3IwlBSUuJ0CSIiIiJSIBQ4SF7MBA4TExMOVyJS2GKxmNMliIiIiMxSB/TSpsBB8mImcJicnHS4EpHC5vV6CYZCTpchIuIoneCIOE9zqhQGBQ6SFwocRBaOomjU6RJEREREpAAocJC8UOAgsnBEIhGnSxARERGRAqDAQfJCk0aKLBwKHEREREQkHxQ4SF5NTU05XYJIwQtpDgcRERFZIDSnytKmwEHyQh0OIgtHMBgE9AdeREREnKNJIwuDAgfJKwUOIs4LBAIAjI2NOVyJiIiIiCxlChwkL2YSTP2iKuI8v98PKHAQERERkfmlwEHySoGDiPNmAofx8XGHKxERERGRpUyBg+SVhlSIOM/r9QLqcBARERGR+aXAQfJCQypEFo6ZwGFyctLhSkRERERkKVPgIHmlwEHEeTOBg4ZUiIiIiMh8UuAgeaXAQcR5Ho8HUIeDiIiIiMwvBQ6SVwocRJw3EzhMTU05XImIiIiILGUeJ97UGNMMDABTwKS1drsTdYiIFCJ1OIiIiIhIPjgSOEz7gLX2soPvL3mkSSNFFg6XK9fcplVjRERERGQ+aUiF5JUCBxHnzXQ4iIiIiIjMJ6cCBwv81BhTb4x59kY7GGOeNcbsN8bs7+zszHN5IiJL10yHg4iIiIjIfHLqqPNBa+1W4CPAbxtjHr52B2vtc9ba7dba7clkMv8VyrxQh4OI8xQ4SKEzxvwHY0yrMebQ9OUpp2sSERFZihw56rTWtk5fZ4DngZ1O1CH5p8BBxHkKHEQA+FNr7ebpy4+cLkZERGQpyvtRpzEmbIyJztwGngSO5bsOya+ZSSNFxHkKHEREREQkH5w46kwBe4wxh4F3gB9aa19woA5xgDocRJynAFAEgN8xxhwxxvy1MabE6WJERESWorxPVW6tPQfcm+/3FRGRHHU4SCEwxrwIpG/w0L8D/gvwB+Qmsf4D4E+A33iP13kWeBagpqZmXmoVERFZqrQ2N++a+AAACtRJREFUmohIgVGHgxQCa+0Tt7OfMeZrwD/d5HWeA54D2L59u9r0RERE7oB+5pK80pAKERFxmjGm/Iq7H0dzSYmIiMwLdTiIiIhIofk/jTGbyQ2paAb+ubPliIiILE0KHERERKSgWGs/63QNIiIihUBDKkRERERERERkzilwEBEREREREZE5p8BBREREREREROacAgfJK61SISIiIiIiUhgUOIiIiIiIiIjInFPgICIiIiIiIiJzToGDiIiIiIiIiMw5BQ4iIiIiIiIiMucUOIiIiIiIiIjInFPgICIiIiIiIiJzToGDiIiIiIiIiMw5BQ4iIiIiIiIiMucUOIiIiIiIiIjInFPgIHlRUlLidAkiIiIiIiKSRwocJC+2bt0KgLXW4UpEREREREQkHxQ4SF4YY5wuQURERERERPJIgYOIiIiIFIRoNAqA3+93uBIRkcLgcboAEREREZF8eOihh/B4PKxfv97pUkRECoICBxEREREpCMFgkMcee8zpMkRECoaGVEheJJNJANasWeNwJSIyw+XSnwARERFxxswcb4lEwuFKZD6pw0HyIpVK8a//9b8mFos5XYqIAE8//TTpdNrpMkRERKRABQIBHn30Ue69916nS5F5pMBB8qa4uNjpEkRk2u7du50uQURERAqYMYYPfvCDTpch80z9tCIiIiIiIiIy5xQ4iIiIiIiIiMicU+AgIiIiIiIiInNOgYOIiIiIiIiIzDkFDiIiIiIiIiIy5xQ4iIiIiIiIiMicU+AgIiIiS5Ix5peMMceNMVljzPZrHvu3xpgzxphTxpgPOVWjiIjIUuZxugARERGReXIM+ATw1Ss3GmPWA78CbAAqgBeNMauttVP5L1FERGTpUoeDiIiILEnW2gZr7akbPPRR4O+stWPW2ibgDLAzv9WJiIgsfQocREREpNBUAi1X3L84vU1ERETmkIZUiIiIyKJljHkRSN/goX9nrf3eHLz+s8CzADU1NXf7ciIiIgVlUQQO9fX1l40x552uQ+5aArjsdBEiAujzuFQsc7oAp1lrn3gfT2sFqq+4XzW97Uav/xzwHIAxplPHI0uCvv9EFg59HpeG9zweWRSBg7U26XQNcveMMfuttdtvvaeIzDd9HqXAfR/4tjHm/yY3aWQd8M6tnqTjkaVB338iC4c+j0uf5nAQERGRJckY83FjzEXgfuCHxpifAFhrjwPfAU4ALwC/rRUqRERE5t6i6HAQERERuVPW2ueB59/jsT8E/jC/FYmIiBQWdThIPj3ndAEiMkufRxEpVPr+E1k49Hlc4v7/9u4/1O66juP487WFMrPUaZo/wNUy03ItHUI6wZVFSKFmy42WXIMMNMwoMclI+2lREDasCNSVkiSzqZngWHOpSZs/rjqdZYMpLi1cEa3MnL7743wPfXe6ul9n98e5zwcc7v1+fnw/n3O5n/N98zmfz/ebqhrrPkiSJEmSpAHjCgdJkiRJktR3TjhIkiRJkqS+c8JBJNk/yXDzejbJxtZxNT/XJrk1yb49dYeT3NCTdm1zjj2b4wOSbGh+n5LkyuZ8jyRZk+RNTd6GJq3b9glJZiR5vpU2nOTsnvIPJ1mVZNI/j16DIckbk9yQZH2S+5P8Kslbm7wLk/w7yT6t8nslub4ZD2uT3J1k7yZvc8+5h5Is3kb75yZ5vHmtTjK3lbchyQGt45OT/DLJOa0x+p/WWL6iX38XSYPNeEQaX4xH1A8+pUJU1SZgNkCSy4DNVfWd5nhzVXXzlgDn09zVO8lRwFTgpCSvrap/tk77EvAJ4Ac9zZ1F55nns6rq5SSHAe1686rque5BkhnA+m4fRjCvqp5LcjlwKfDJHXz70riSJHTuqr+kqhY0ae8EDgL+ACwE1gAfBq5pqn0G+HNVHdOUPxJ4cSfb/yDwKWBuM7aOBZYlOb6qnn2lelV1Tbc/TUC/1ViWpG0xHpHGD+MR9YsrHLQj7gUObR0vBH4K3AGc1lP2e8Bnk/ROah0MPFNVLwNU1dNV9bfd0DdpopoHvFhVP+wmVNVDVXVXkpnA3nSC2YWtOgcDG1vlf19VL+xk+xcDF3UvzlX1ANAN7iVpPDAekXY/4xH1hRMO2i5JpgLvBW5pJZ8F3AD8jK0/bACeAu4GPt6T/nPgQ83Spu8meVdP/som73ettJk9SxhPGqGLHwCW7eDbksajdwD3v0LeAjpj7i7gyCQHNelXAxcnuTfJ15Ic0aozrT1+gK9so/23j9D+fU26JI0p4xFp1BiPqC/cUqFtmdZ8KBwKrAOWAySZAzxXVU8l2QhcnWR6Vf21VfebwM3Abd2Eqnq6WV71nua1Isn8qlrRFBlp2dOrLWFcmWQ6sBn40q69VWncWwic0Sz/XQrMBxZX1XCSNwPvB04B1iR5d1WtA55vj58kQ8CcXejDSM9S9vnKknY34xFp/DAe0XZzhYO2pfvhcDgQ/reMaSHwtmZv1Hrg9cCZ7YpV9QQwDHy0J/2Fqrq9qi4CvgGcvgv9m9f0bRi4fBfOI40XjwLH9SYmOQY4AljejLsFtL7Jq6rNVXVTVZ0HXAecupPtPzZC+8c1/QLYBOzXypsOuDdS0u5mPCKNLuMR9YUTDtouVfUv4ALgc0n2oHPRPqaqZlTVDDp7JnuXMULnhk6f7x4kOTbJIc3vU4BZwJO72LctwIXA2c23C9JE9mtgzyTndhOSzAKuBC7rjrmqOgQ4JMnhSU5Msl9Tdg/gaHZ+XH0b+FaS/ZvzzQaGgKua/DtpliY3S5sXASt3si1J2iHGI9KoMR5RXzjhoO1WVQ8CDwOXABur6k+t7N8ARyc5uKfOo8ADraQDgVuTrG3OtQV41Ufi8P97Ji8YoW/P0Nm76Y1kNKFVVQFnAKek8xiqR+ksBz6Zzt2i235B55uFmcCqJI8AD9LZ47h0J9u/hc4ezN8meRz4MbCoGWMAXwXekuShpq0/0vkGQ5JGhfGItPsZj6hf0vlfkiRJkiRJ6h9XOEiSJEmSpL7zKRWSNAkl+SKdu0q33VhVXx+L/kiSpMnHeGTwuaVCkiRJkiT1nVsqJEmSJElS3znhIEmSJEmS+s4JB0kk2TfJeWPdD0mSNHkZj0iDxwkHSQD7Al7gJUnSWDIekQaMEw6SAK4AZiYZTnJjktO7GUmuT3JakqEkNye5M8kTSb7cKrMoyeqm/o+STB2TdyFJkiYy4xFpwDjhIAngC8D6qpoNLAaGAJLsA5wA3NaUOx44E5gFzE8yJ8lRwFnAiU39l4CPjW73JUnSADAekQbMa8a6A5LGl6paleSqJG+gczFfWlVbkgAsr6pNAEluAuYCW4DjgDVNmWnAX8ak85IkaSAYj0iDwQkHSSP5CbAIWACc00qvnnIFBFhSVZeMUt8kSdLkYDwiTXBuqZAE8A/gda3ja4ELAarqsVb6+5JMTzINOB24B1gBfCTJgQBN/uGj0mtJkjRIjEekAeMKB0lU1aYk9yRZC9xeVRclWQcs6ym6GlgKHAZcV1X3ASS5FLgjyRTgReB84MnReweSJGmiMx6RBk+qelckSZrskuwFPAIcW1V/b9KGgDlV9emx7JskSZocjEekic8tFZK2kuQUYB3w/e7FXZIkaTQZj0iDwRUOkiRJkiSp71zhIEmSJEmS+s4JB0mSJEmS1HdOOEiSJEmSpL5zwkGSJEmSJPWdEw6SJEmSJKnvnHCQJEmSJEl9919y92pXnZrlkgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = (18, 12)\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.violinplot(x = 'type', y = 'diffOrg', data=df, hue='isFlaggedFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of diffOrg (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "sns.violinplot(x = 'type', y = 'diffDest', data=df, hue='isFlaggedFraud', split=True, palette='Pastel1')\n",
        "plt.title('Categorical distribution of diffDest (df dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.violinplot(x = 'type', y = 'diffOrg', data=df2, hue='isFlaggedFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of diffOrg (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.violinplot(x = 'type', y = 'diffDest', data=df2, hue='isFlaggedFraud', split=True, palette='Pastel2')\n",
        "plt.title('Categorical distribution of diffDest (df2 dataset)', fontsize = 12)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17a7f975",
      "metadata": {
        "id": "17a7f975"
      },
      "source": [
        "Seems like the new features has some interesting relationships with fraud and flaggedfraud transactions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03305c81",
      "metadata": {
        "id": "03305c81"
      },
      "source": [
        "# SMOTE oversampling for imbalanced data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82dfe47b",
      "metadata": {
        "id": "82dfe47b"
      },
      "source": [
        "We had noted that the dataset is highly imbalanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "ff9dc565",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff9dc565",
        "outputId": "44c045de-8cf2-4131-8825-d0712b245a76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fraud ratio \n",
            "0    0.998709\n",
            "1    0.001291\n",
            "Name: isFraud, dtype: float64 \n",
            "\n",
            "Flagged fraud ratio \n",
            "0    0.999997\n",
            "1    0.000003\n",
            "Name: isFlaggedFraud, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(f'Fraud ratio \\n{fraud_ratio} \\n\\nFlagged fraud ratio \\n{flaggedFraud_ratio}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1dc33c8f",
      "metadata": {
        "id": "1dc33c8f"
      },
      "source": [
        "So to give better results, we can use the SMOTE technique."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "4addbd37",
      "metadata": {
        "id": "4addbd37"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c239006b",
      "metadata": {
        "id": "c239006b"
      },
      "source": [
        "Preparing datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "64671dad",
      "metadata": {
        "scrolled": true,
        "id": "64671dad"
      },
      "outputs": [],
      "source": [
        "X = df\n",
        "Y = df['isFraud']          # target label\n",
        "X = pd.concat([X, pd.get_dummies(X['type'])], axis = 'columns')       # one-hot columns for transaction type\n",
        "X = X.drop(['isFraud', 'type'], axis=1)\n",
        "\n",
        "X2 = df2\n",
        "Y2 = df2['isFraud']\n",
        "X2 = pd.concat([X2, pd.get_dummies(X2['type'])], axis = 'columns')\n",
        "X2 = X2.drop(['isFraud', 'type'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "947684db",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "947684db",
        "outputId": "d466c1cb-7d92-402b-dc4f-6307c5f736ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6362620, 2770409)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "len(X), len(X2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SMOTE resampling"
      ],
      "metadata": {
        "id": "REXwOupND8lP"
      },
      "id": "REXwOupND8lP"
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "c9a3ce21",
      "metadata": {
        "id": "c9a3ce21"
      },
      "outputs": [],
      "source": [
        "X, Y = SMOTE().fit_resample(X, Y)\n",
        "X2, Y2 = SMOTE().fit_resample(X2, Y2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "539a92bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "539a92bf",
        "outputId": "4405c949-965f-4720-925d-d94daebd7a18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12708814, 5524392)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "len(X), len(X2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(Y).count(1), list(Y2).count(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih1sATb7SsSG",
        "outputId": "57f23b86-4543-419a-8376-49ba99d5a458"
      },
      "id": "Ih1sATb7SsSG",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6354407, 2762196)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(Y).count(0), list(Y2).count(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfab7vrrS6vF",
        "outputId": "a5e87c3b-2055-48e5-cacf-17b46fe939eb"
      },
      "id": "tfab7vrrS6vF",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6354407, 2762196)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With smote resampling, now our dataset size has almost doubled and has balanced classes."
      ],
      "metadata": {
        "id": "U3inplStS-v0"
      },
      "id": "U3inplStS-v0"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "zRVY5c3lvmrm"
      },
      "id": "zRVY5c3lvmrm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train, validation and test Split"
      ],
      "metadata": {
        "id": "wQv8t_dTvyl7"
      },
      "id": "wQv8t_dTvyl7"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "kqg87dTytErI"
      },
      "id": "kqg87dTytErI",
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set random state for reproducibility"
      ],
      "metadata": {
        "id": "6waB4toR-JFH"
      },
      "id": "6waB4toR-JFH"
    },
    {
      "cell_type": "code",
      "source": [
        "random_state = 55"
      ],
      "metadata": {
        "id": "pr-h4LAgtRjM"
      },
      "id": "pr-h4LAgtRjM",
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get list of random permutations"
      ],
      "metadata": {
        "id": "HsBFCNOY-GpV"
      },
      "id": "HsBFCNOY-GpV"
    },
    {
      "cell_type": "code",
      "source": [
        "p = np.random.RandomState(seed=random_state).permutation(len(X))\n",
        "p2 = np.random.RandomState(seed=random_state).permutation(len(X2))"
      ],
      "metadata": {
        "id": "phXi6xB7-Wxy"
      },
      "id": "phXi6xB7-Wxy",
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random shuffle dataset before splitting"
      ],
      "metadata": {
        "id": "vVgKGftRBNBx"
      },
      "id": "vVgKGftRBNBx"
    },
    {
      "cell_type": "code",
      "source": [
        "X, Y = X.iloc[p], Y.iloc[p]\n",
        "X2, Y2 = X2.iloc[p2], Y2.iloc[p2]"
      ],
      "metadata": {
        "id": "Xe5ovKWw_5kG"
      },
      "id": "Xe5ovKWw_5kG",
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train, validation, test split\n",
        "\n",
        "Train: 80%, validation:10%, test:10%"
      ],
      "metadata": {
        "id": "vxVNTBzPBTIY"
      },
      "id": "vxVNTBzPBTIY"
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = random_state)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size = 0.5, random_state = random_state)\n",
        "\n",
        "x2_train, x2_test, y2_train, y2_test = train_test_split(X2, Y2, test_size = 0.2, random_state = random_state)\n",
        "x2_val, x2_test, y2_val, y2_test = train_test_split(x2_test, y2_test, test_size = 0.5, random_state = random_state)"
      ],
      "metadata": {
        "id": "fOW8LlX8wE6r"
      },
      "id": "fOW8LlX8wE6r",
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before training, let's free up some RAM space."
      ],
      "metadata": {
        "id": "SiVKps8icvGE"
      },
      "id": "SiVKps8icvGE"
    },
    {
      "cell_type": "code",
      "source": [
        "import gc"
      ],
      "metadata": {
        "id": "Dc4YetQocukM"
      },
      "id": "Dc4YetQocukM",
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del df, df2, df_missing, df_outliers, dff_outliers, X, Y, X2, Y2, cols"
      ],
      "metadata": {
        "id": "E15Orgl8fnkF"
      },
      "id": "E15Orgl8fnkF",
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBopjaHxc5i4",
        "outputId": "44055dc2-9747-46d5-d370-9e7c33f9d9bb"
      },
      "id": "rBopjaHxc5i4",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23812"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the classifier\n"
      ],
      "metadata": {
        "id": "RVRFpWT00sbO"
      },
      "id": "RVRFpWT00sbO"
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "tQZefgyB006a"
      },
      "id": "tQZefgyB006a",
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBClassifier(tree_method = \"gpu_hist\", random_state=random_state)\n",
        "model2 = XGBClassifier(tree_method = \"gpu_hist\", random_state=random_state)"
      ],
      "metadata": {
        "id": "E4z1KLstxGwY"
      },
      "id": "E4z1KLstxGwY",
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)\n",
        "model2.fit(x2_train, y2_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlhxwtSP1e8h",
        "outputId": "691ba461-996c-43cd-cf72-5c51a97706e6"
      },
      "id": "xlhxwtSP1e8h",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(random_state=55, tree_method='gpu_hist')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z7RAnGkeIyv",
        "outputId": "a0bcdc44-2364-45b2-d871-d29d0c25f56b"
      },
      "id": "2Z7RAnGkeIyv",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "d_Dp55szIX_G"
      },
      "id": "d_Dp55szIX_G"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's define a function that returns all evaluation metrics at once."
      ],
      "metadata": {
        "id": "5TdNN5y7i2fE"
      },
      "id": "5TdNN5y7i2fE"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, roc_auc_score, accuracy_score, f1_score\n",
        "\n",
        "def evaluate(trainedmodel, x, y):\n",
        "  '''\n",
        "    To evaluate model performance\n",
        "  '''\n",
        "  y_pred   = trainedmodel.predict(x)\n",
        "  f1       = f1_score(y, y_pred)\n",
        "  report   = classification_report(y, y_pred)\n",
        "  acc      = accuracy_score(y, y_pred)\n",
        "  roc_auc  = roc_auc_score(y, y_pred)\n",
        "\n",
        "  eval = {'y_pred': y_pred, \n",
        "          'f1_score': f1,\n",
        "          'accuracy':acc,\n",
        "          'roc_auc_score': roc_auc\n",
        "          }\n",
        "\n",
        "  return eval, report\n"
      ],
      "metadata": {
        "id": "IuVmxG7XKQPo"
      },
      "id": "IuVmxG7XKQPo",
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_train, report_train = evaluate(model, x_train, y_train)\n",
        "eval_val, report_val = evaluate(model, x_val, y_val)\n",
        "\n",
        "eval2_train, report2_train = evaluate(model2, x2_train, y2_train)\n",
        "eval2_val, report2_val = evaluate(model2, x2_val, y2_val)"
      ],
      "metadata": {
        "id": "4F6dneUbTj-7"
      },
      "id": "4F6dneUbTj-7",
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pprint\n",
        "pprint._sorted = lambda x:x"
      ],
      "metadata": {
        "id": "i1ZNFD7sm_4o"
      },
      "id": "i1ZNFD7sm_4o",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Evaluation metrics on training data (for df): \\n')\n",
        "pprint.pprint(eval_train)\n",
        "print('\\n',report_train)\n",
        "\n",
        "print('\\n\\nEvaluation metrics on val data (for df): \\n')\n",
        "pprint.pprint(eval_val)\n",
        "print('\\n', report_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VJkwQ_gXIhi",
        "outputId": "977408a0-8474-4617-9a63-87a790e8349d"
      },
      "id": "7VJkwQ_gXIhi",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation metrics on training data (for df): \n",
            "\n",
            "{'accuracy': 0.9888765188647131,\n",
            " 'f1_score': 0.9889569339660917,\n",
            " 'roc_auc_score': 0.9888763453855397,\n",
            " 'y_pred': array([1, 1, 1, ..., 0, 0, 1])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99   5083404\n",
            "           1       0.98      1.00      0.99   5083647\n",
            "\n",
            "    accuracy                           0.99  10167051\n",
            "   macro avg       0.99      0.99      0.99  10167051\n",
            "weighted avg       0.99      0.99      0.99  10167051\n",
            "\n",
            "\n",
            "\n",
            "Evaluation metrics on val data (for df): \n",
            "\n",
            "{'accuracy': 0.9889108421638217,\n",
            " 'f1_score': 0.9889934466764395,\n",
            " 'roc_auc_score': 0.9889087137490279,\n",
            " 'y_pred': array([0, 0, 1, ..., 0, 1, 0])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99    635253\n",
            "           1       0.98      1.00      0.99    635628\n",
            "\n",
            "    accuracy                           0.99   1270881\n",
            "   macro avg       0.99      0.99      0.99   1270881\n",
            "weighted avg       0.99      0.99      0.99   1270881\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Evaluation metrics on training data (for df2): \\n')\n",
        "pprint.pprint(eval2_train)\n",
        "print('\\n', report2_train)\n",
        "\n",
        "print('\\n\\nEvaluation metrics on val data (for df2): \\n')\n",
        "pprint.pprint(eval2_val)\n",
        "print('\\n', report2_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTTSN80DmK3F",
        "outputId": "1e6866db-4630-4db3-e722-8e0ff9693fce"
      },
      "id": "tTTSN80DmK3F",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation metrics on training data (for df2): \n",
            "\n",
            "{'accuracy': 0.9864335731108835,\n",
            " 'f1_score': 0.9865613877876889,\n",
            " 'roc_auc_score': 0.9864326760500436,\n",
            " 'y_pred': array([0, 1, 1, ..., 0, 1, 0])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99   2209546\n",
            "           1       0.98      1.00      0.99   2209967\n",
            "\n",
            "    accuracy                           0.99   4419513\n",
            "   macro avg       0.99      0.99      0.99   4419513\n",
            "weighted avg       0.99      0.99      0.99   4419513\n",
            "\n",
            "\n",
            "\n",
            "Evaluation metrics on val data (for df2): \n",
            "\n",
            "{'accuracy': 0.9866229574667973,\n",
            " 'f1_score': 0.9867605431939517,\n",
            " 'roc_auc_score': 0.9866136044295387,\n",
            " 'y_pred': array([0, 0, 1, ..., 1, 1, 1])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99    275945\n",
            "           1       0.98      1.00      0.99    276494\n",
            "\n",
            "    accuracy                           0.99    552439\n",
            "   macro avg       0.99      0.99      0.99    552439\n",
            "weighted avg       0.99      0.99      0.99    552439\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have got very impressive metrics on both training and validation sets. However, these results are interesting, because model trained with df has slightly better performance than df2. (Comparing accuracy and f1-scores.)\n",
        "\n",
        "Also, we have very low bias and variance. So, hopefully, we need not implement furthur hyperparameter tuning. <br>\n",
        "\n",
        "$\\implies$ Let's see test performance of the models."
      ],
      "metadata": {
        "id": "aLUGdAWsP5IK"
      },
      "id": "aLUGdAWsP5IK"
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5W7WN5fZoW7P",
        "outputId": "72e4ea18-f831-44b3-b35f-1850380f22a1"
      },
      "id": "5W7WN5fZoW7P",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "204"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_test, report_test = evaluate(model, x_test, y_test)\n",
        "eval2_test, report2_test = evaluate(model2, x2_test, y2_test)"
      ],
      "metadata": {
        "id": "2xdia7NDWOw0"
      },
      "id": "2xdia7NDWOw0",
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Evaluation metrics on test data (for df): \\n')\n",
        "pprint.pprint(eval_test)\n",
        "print('\\n', report_test)\n",
        "\n",
        "print('\\n\\nEvaluation metrics on test data (for df2): \\n')\n",
        "pprint.pprint(eval2_test)\n",
        "print('\\n', report2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RU1e3uS1o1or",
        "outputId": "488c778c-b23b-445f-e071-13c9104e5421"
      },
      "id": "RU1e3uS1o1or",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation metrics on test data (for df): \n",
            "\n",
            "{'accuracy': 0.9889376039632318,\n",
            " 'f1_score': 0.9890120024666136,\n",
            " 'roc_auc_score': 0.9889411303365628,\n",
            " 'y_pred': array([1, 1, 0, ..., 1, 1, 1])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99    635750\n",
            "           1       0.98      1.00      0.99    635132\n",
            "\n",
            "    accuracy                           0.99   1270882\n",
            "   macro avg       0.99      0.99      0.99   1270882\n",
            "weighted avg       0.99      0.99      0.99   1270882\n",
            "\n",
            "\n",
            "\n",
            "Evaluation metrics on test data (for df2): \n",
            "\n",
            "{'accuracy': 0.9866809065237854,\n",
            " 'f1_score': 0.9867795026591921,\n",
            " 'roc_auc_score': 0.9866970433041412,\n",
            " 'y_pred': array([1, 0, 0, ..., 0, 1, 0])}\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99    276705\n",
            "           1       0.98      1.00      0.99    275735\n",
            "\n",
            "    accuracy                           0.99    552440\n",
            "   macro avg       0.99      0.99      0.99    552440\n",
            "weighted avg       0.99      0.99      0.99    552440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although very comparable, model has slightly better test performance than model2. (Possibly because of more data.)\n",
        "Both results are impressive and we have got **99% accuracy and f1-score during test** in both cases.\n",
        "\n",
        "However, we can infer that 'model' is the best classifier."
      ],
      "metadata": {
        "id": "FQygiRVWpigV"
      },
      "id": "FQygiRVWpigV"
    },
    {
      "cell_type": "code",
      "source": [
        "best = model"
      ],
      "metadata": {
        "id": "kEmAFC2mpS7v"
      },
      "id": "kEmAFC2mpS7v",
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature importances"
      ],
      "metadata": {
        "id": "Ov1NQpUhvbCr"
      },
      "id": "Ov1NQpUhvbCr"
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance"
      ],
      "metadata": {
        "id": "0XQdPiuhv4iK"
      },
      "id": "0XQdPiuhv4iK",
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize=(16,5))\n",
        "\n",
        "plot_importance(booster=best, importance_type='weight', title='Weight', show_values=False, height=0.5, ax = ax[0])\n",
        "\n",
        "# plot importances with split mean gain\n",
        "plot_importance(booster=best, importance_type='gain', title='Gain', show_values=False, height=0.5, ax = ax[1])\n",
        "\n",
        "# plot importances with sample coverage\n",
        "plot_importance( booster=best, importance_type='cover', title='Coverage', show_values=False, height=0.5, ax = ax[2])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "93ETPo-3u6LV",
        "outputId": "85cc591b-a25e-448b-8bbb-1f89dde3d23e"
      },
      "id": "93ETPo-3u6LV",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 3 Axes>"
            ],
            "image/png": 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u8JiZmZmZmZmZNTgXeMzMzMzMzMzMGtyqtQ7Aum/RkmUM/epNtQ6jrHHDlzLG8XWb4+uZWsc3f8Komp3bmku95/ru8O+HmdnbNUuur/X9V29xP/qO7wl6l0fwmJmZmZmZmZk1uLot8EhaWGb9REmHptfzJQ3qwjHHSDqvt2Ks4HwHS3pY0mOSZks6uK/ObWbWCJzrzcyan3O9mVnf8CNaVSJpe+DHwD4RMU/S+4DbJD0VEQ8XtV01IpbWJFAzM+s253ozs+bnXG9mjaIuRvBI+pKkR9LXyUXbJOk8SXMl3Q68q2j3r6Qq+oOShqV9DpD0gKQZkm6XNKTEOUu2kTRe0q8ltUt6StLY3D5Hp8r9LEm/S+sGS7pG0tT09d+p+ZeBH0TEPID0/QzglLRfu6RzJD0E/K+kD6Rjz5R0pqRHeuFHa2ZWN5zrnevNrPk51zvXm1nt1HwEj6QdgWOADwICHpB0Z67JIcCWwNbAEOBR4Ne57S9HxHBJRwPnAPsDdwO7RERI+izwFWBc0ak7arMVMBJYC5gr6QJgC+CbwG4RsUDSeqntucDZEXG3pE2AW4D3A9uQVfrzHgJOyC2/IyJ2Sj+HR4BjI+I+SRM6+HkdBxwHMGjQYE4bXr9vEAzpn03sVa8cX884vo61t7d3uH3hwoWdtmkmzvXNm+u7o6P/+632u9Fq/YXW63Mr9de53rm+1vdfvcX96DuV5MdmyaN90Y+aF3iA3YFrI+I1AEl/BPbIbd8TuDwilgHPS7qjaP/Lc9/PTq/fA1wpaQPgHcC8EuftqM1NEbEYWCzpRbIL0F7AVRGxACAiXkpt9wa2llTYd21JAyvs+5Wpz+sCa0XEfWn9ZWQXtJVExIXAhQCbbDoszppdD/+EpY0bvhTH132Or2dqHd/80W0dbm9vb6etreM2Tca5vklzfXd09PvRar8brdZfaL0+t1h/netbPNfX+v6rt7gffaeze2ZonjzaF/2oi0e0eihKvP4ZcF5EDAeOB9YosV9HbRbnXi+j40LYKmTvGIxIXxtFxEKydyR2LGq7IzAnt/xaB8c1M7MVnOvNzJqfc72ZWQ/UQ4FnCnCwpDUlDSAbujklt/0u4AhJ/VJVfmTR/kfkvhcq5esAz6XXnypz3kra5N0BHCZpfYDcUM5bgZMKjSSNSC9/DHxN0tC0fijwdeCs4gNHxH+AVyV9MK06soJ4zMwaiXO9c72ZNT/neud6M6uhmo/XiojpkiYCD6ZVF0fEjNzQyGvJhlE+CjzDimRf8E5JD5NV549K68YDV0n6N1kCf1+JU1fSJh/nHEmnA3dKWgbMAMYAY4GfpxhWJbtwfS4iZko6FbhB0mrAEuArETGzzCk+A1wkaTlwJ/ByR/GYmTUS5/q3ONebWdNyrn+Lc72Z1UTNCzwAEfET4CdF6wam7wGcWGa/oenlqUXrrweuL9F+IjCxkzbji5a3zb2+BLikaPsCVrzbUHysPwJ/LLOtrWjVnIjYDkDSV8kmbjMzaxrO9YBzvZk1Oed6wLnezGqkLgo8BsAoSV8j+zd5muxdhA71X60fcyeMqnZc3dbe3l7RpFm14vh6xvGZdUvT5XozM1tJy+b6Zrn/cj+sUbnAUyci4krS7PtmZtacnOvNzJqfc72Z1Uo9TLJsZmZmZmZmZmY94AKPmZmZmZmZmVmDc4HHzMzMzMzMzKzBucBjZmZmZmZmZtbgXOAxMzMzMzMzM2twLvCYmZmZmZmZmTU4F3jMzMzMzMzMzBqcCzxmZmZmZmZmZg3OBR4zMzMzMzMzswa3aq0DsO5btGQZQ796U63DKGvc8KWMcXzd5vh6pqfxzZ8wqhejMeu+esz1/v0wM+td9Zjru6Pe7w8r5X6U5ut//fMIHjMzMzMzMzOzBucCT4UkjZf0ZUnflbR3WreHpDmSZkrqL+nMtHxm2n6cpMfT14OSdq9tL8zMrB5IOljS1rWOw8ysFgr31V3cZ2G14ilxrmXp/n6OpFmSxknq1t9Nkr7e2/GZmZXjR7S6KCJOyy2OBs6IiN9DVtAB1ouIZZL2B44Hdo+IBZJ2AK6TtHNE/D1/TEn9ImJZX/XBzMxq7mDgRuDRWgdiZmYrWRQRIwAkvQu4DFgb+HY3jvV14Ae9GJuZWVkewdMBSd+Q9FdJdwNbpnUTJR0q6bPA4cD3JF0qaRIwEJgm6QjgVOCUiFgAEBHTgUuAE9Jx5kv6oaTpwGGSPpJG+kyT9FNJN/Z9j83Mmp+k61KunZMK80hamBuFebuknSW1S3pK0oGpzRqSfiNptqQZkkam9WMknZc7/o2S2nLHPT29A3y/pCGSdgMOBM5M7xBv1uc/BDOzLpA0VNJjki5KefLWNHp9M0k3p5w6RdJWkvpJmqfMumk0zJ7pOHdJ2jwddntJ90l6QtKxaftASX+RND3l2oNKxFKyTbkY07ZhKbfPSvttltafImmqpIclfadU3yPiReA44MTUp37pelHY7/h0rA1S/2ZKekTZSP8JQP+07tLe/VcxM1uZCzxlSNoROBIYAXwE+EB+e0RcDEwiK+KMjogDSdX+iLgS2AaYVnTYh9L6gn9FxA7AdcAvgf0iYkdgcDX6ZGZmAHw65dqdgLGS1gcGAHdExDbAq8D3gX2AQ4Dvpv1OACIihgNHAZdIWqOTcw0A7o+I7YG7gGMj4l5WXD9GRMSTvdw/M7Nq2Bz4ecqT/wE+BlwInJRy6peB89Oo9LnA1sDuwHRgD0mrAxtHxBPpeNsBewG7AqdJ2hB4Azgk3R+PBM6SpKI4OmpTKkaAS9P67YHdgBck7Zva70x2v79joRBVLCKeAvoB7wI+A7wcER8g+/vgWEnvAz4O3JJG/mwPzIyIr7Li74PRlf6gzcy6y49olbcHcG1EvA6QRuj0tivT962ApyJiXlq+nOydgpWkd5uPAxg0aDCnDV9ahbB6x5D+2czt9crx9Uyzx9fe3t57wZSwcOHCqp/Dyhor6ZD0emOyG/w3gZvTutnA4ohYImk2MDSt3x34GUBEPC7paWCLTs71JtmjWJAV/fepJMB6z/XV/L/bar8brdZfaL0+N1F/50XEzPR6Gllu3A24KleDWT19nwLsCbwPOAM4FrgTmJo73vURsQhYJGkyWaHlJuAHqdCyHNgIGALkpzdQmTYlY5S0FrBRRFwLEBFvAKQCz77AjNR+INn14K5Ofg77AttJOjQtr5P2mwr8WtJqwHW5OMqq91zfHfV+f1gp96O0WuWyZsmjfdEPF3iq51FgR+CO3LodgTm55de6etCIuJDs3RI22XRYnDW7fv8Jxw1fiuPrPsfXMz2Nb/7ott4LpoT29nba2qp7DltZenRqb2DXiHhdUjuwBrAkIiI1Ww4sBoiI5ZI6+4+0lLePiM2P6skfdxkVXnfrPddX8/ej1X43Wq2/0Hp9bqL+Ls69XkZWVPlPYa6aIncBnwc2BE4DTgHayAo/BVG0T5DNbzkY2DEV2efz9pxKJ22KY+zfQX9ENpfmLztokzWUNk3HezHtd1JE3FKi3Z7AKGCipJ9ExG87Om695/ruqPf7w0q5H6VV+/64nGbJo33RDz+iVd5dwMHp+eK1gAO6uP+PgB+mof9IGgGMAc4v0XYusKmkoWn5iO4EbGZmnVoH+Hcq7mwF7NKFfaeQ/WGBpC2ATcjy93xghKRVJG1M9i50Z14F1upK4GZmdeYVYJ6kwwDS/DTbp20Pko3uWZ5GzMwk+/CR/OiYg5TNbbY+WfFnKlmOfjEVbkYC7y1x3kravCUiXgX+JungFOfqktYEbgE+LWlgWr+RsgmV30bSYOAXwHmpYH8L8Pk0UgdJW0gaIOm9wD8i4iLgYmCHdIglhbZmZtXW+GXJKomI6ZKuBGaRVeundrJL8f6TJG0E3CspyG7mPxERL5Rou0jSF4CbJb3W1XOZmVnFbgY+J+kxsuLM/V3Y93zggvTY1lJgTEQslnQPMI9s5OZjZPNNdOYK4CJJY4FDPQ+PmTWo0WR58ZvAamS5bVbKjc+yIsdOIZu7bHZu34eBycAg4HsR8XyaiPiGlGcfAh4vcc5K2hT7JPBLSd8FlgCHRcStkt4P3JceMVsIfILsvr+/pJmpT0uB3wE/Sce6mOzxtOlp7p9/kn0yYhtwiqQl6VhHp/YXAg9Lmu55eMys2lzg6UBEnA6c3sH2MUXLA4uWLwAuKLPv0KJVkyNiq3Sh+DnZBcvMzHpRRCwG9iuxaWCuzfiifQam728Ax5Q4ZuGxglLnyx/3auDq9PoesglIzczqXkTMB7bNLf84t/nDZfbZI/f6MrKPGi8sjy+zzwKySZdLbRvYWZtyMaaJnfcqccxzgXNLrO9X5vhExHKyjz7/etGmS9JXcftTyT5d18ys6lzgqR/HSvoU8A6yyd46fR64/2r9mDthVNUD66729vaaPadZCcfXM47PrG/Ue643M7Oea5Zc3yz3X+6HNSoXeOpERJwNnF3rOMzMzMzMzMys8XiSZTMzMzMzMzOzBucCj5mZmZmZmZlZg3OBx8zMzMzMzMyswbnAY2ZmZmZmZmbW4FzgMTMzMzMzMzNrcC7wmJmZmZmZmZk1OBd4zMzMzMzMzMwanAs8ZmZmZmZmZmYNzgUeMzMzMzMzM7MGt2qtA7DuW7RkGUO/elOtwyhr3PCljHF83VYv8c2fMKrWIZi1tHrJ9c4FZmbVUy+5vqfq5f61p9yPFXz9bywewWNmZmZmZmZm1uBc4DEzMzMzMzMza3Au8FSZpJMlrVnrOMzMzMzMzMysebnAU30nAy7wmJn1IknvlnSFpCclTZP0J0lbpG0nS3pD0jq59mtKulTSbEmPSLpb0sC0bWHRscdIOq+T8x8n6fH09aCk3XPb5ksalFtuk3SjpGMkzUxfb6ZYZkqa0Fs/FzOzZuJcb2bWNZ5kuRdJGgD8AXgP0A+4CtgQmCxpQUSMlLQv8B1gdeBJ4JiIWChpftp3P2AR8PGI+L8adMPMrK5JEnAtcElEHJnWbQ8MAf4KHAVMBT4K/Cbt9r/APyJieGq/JbCkm+ffHzge2D0iFkjaAbhO0s4R8fdy+0XEbwrxpJw/MiIWdCcGM7Nm51xvZtZ1LvD0rg8Dz0fEKID0jsIxpMSeqvzfBPaOiNcknQp8Cfhu2v/liBgu6WjgHGD/4hNIOg44DmDQoMGcNnxp1TvVXUP6ZzO31yvHV5n29vaS6xcuXFh2Wz1wfE1tJLAkIn5RWBERswAkbQYMBL4AfIMVN/0bAE/n2s/twflPBU4p3LBHxHRJlwAnAN/qwXHfUo+5vq/+v7ba70ar9Rdar8+t1t9e5FzfgOrl/rWn3I8V6iF/NUse7Yt+uMDTu2YDZ0n6IXBjREzJ3nx4yy7A1sA9af07gPty2y/PfT+71Aki4kLgQoBNNh0WZ82u33/CccOX4vi6r17imz+6reT69vZ22tpKb6sHjq+pbQtMK7PtSOAKYAqwpaQhEfEP4NfArZIOBf5C9o7wE2mf/pJm5o6xHjCpg/NvU+L8DwGf6lo3yqvHXF8uF/S2VvvdaLX+Quv1udX624uc6xtQvdy/9pT7sUJfXf870ix5tC/64Tl4elFE/BXYgazQ831JpxU1EXBbRIxIX1tHxGfyhyjz2szMKnMUcEVELAeuAQ4DiIiZwKbAmWQ39VMlvT/tsyiXl0cAxbm7q0rlb+d0M7Pe41xvZlaCCzy9SNKGwOsR8XuyC8sOwKvAWqnJ/cB/SxqW2g8oTBSXHJH7nh/ZY2ZmK8wBdixeKWk4sDlwW5r34EiyPwIAiIiFEfHHiPgC8HvgI908/6Mlzr9jigvgX8A7c9vWAzz/gplZ1zjXm5l1kQs8vWs48GAa/vlt4Ptkwy5vljQ5Iv4JjAEul/QwWRFnq9z+70zr/xf4Yp9GbmbWOO4AVk9zFwAgaTvgp8D4iBiavjYENpT0Xkn/Lemdqe07yB6XfbrUwSvwI+CHktZPxxtBltvPT9vbgU+mbf2ATwCTu3kuM7NW5VxvZtZFjf9gYR2JiFuAW4pWPwT8LNfmDuADZQ5xZkScWqXwzMyaQkSEpEOAc9Jk9W8A84E24PNFza8le3f3BeCC9KksqwA3kQ3r7875J0naCLhXUpCN1PxERLyQmnwvnWsW2aO5N73TkjsAACAASURBVJO9i2xmZhVyrjcz6zoXeBpY/9X6MXfCqFqHUVZ7e3tdTMpVjuMza1wR8TxweAXtvpRb/G2ZNgOLlicCEzs57gXABWW2vQx8vJP9h3a0Pa/ec72ZWbU41zeeZrl/dT+sUbnAUye6cgEwMzMzMzMzM8tzgcfMzKwESd8gfTJLzlURcXot4jEzs97nXG9mzcQFHjMzsxLSzb1v8M3MmphzvZk1E3+KlpmZmZmZmZlZg3OBx8zMzMzMzMyswbnAY2ZmZmZmZmbW4FzgMTMzMzMzMzNrcC7wmJmZmZmZmZk1OBd4zMzMzMzMzMwanAs8ZmZmZmZmZmYNbtVaB2Ddt2jJMoZ+9aZah1HWuOFLGdNE8c2fMKqK0ZiZlVYPud75z8ysuuoh1/eGer//r1Sj9cPXaSvwCB4zMzMzMzMzswbXUgUeScskzZT0iKSrJK2Z1q8q6Z+SJqTlfSTdJ0lpuZ+kGZJ2kzReUkgaljvuyWndTml5vqTZ6VwzJf00rZ8o6TlJq6flQant8FzblyTNS69v7+ufkZlZLaUc++Uu7rOwWvGUOFfhOjJH0ixJ4yR161oq6eu9HZ+ZWa2Vy8npPvjQ9Hq+pEFdOOYYSef1VowVnOuf6d7/CUm3SNqtm8caIekjvR2jmVk5LVXgARZFxIiI2BZ4E/hcWr8P8FfgMEmKiNuAp4HPpO0nAQ9FxL1peTZwZO64hwFzis41Mp1rRESMza1fBnw63zAiZhfaApOAU9Ly3j3rrpmZ9bLCdWQbsmvHfsC3u3ksF3jMzOrTlRHxXxGxOTAB+KOk93fjOCMAF3jMrM+0WoEnbwpQGIVzFHAu8Aywa1r3ReBrkrYBTgROze17HXAQgKTNgJeBBRWe9xzgi5I8/5GZNTxJQyU9JumiNKrlVkn9JW0m6WZJ0yRNkbRVGg05T5l102iYPdNx7pK0eTrs9mkU5ROSjk3bB0r6i6TpaYTkQSViKdmmXIxp2zBJt6fRONNTTkfSKZKmSnpY0ndK9T0iXgSOA05Mfeon6czcfsenY22Q+lcYQbpHGjHaP627tHf/VczM+oakL6W89oikk4u2SdJ5kuamUenvKtr9KylXP6g0Ml7SAZIeSKNnbpc0pMQ5S7ZJI0B/Lald0lOSxub2OTrl5VmSfpfWDZZ0TcrZUyX9d6k+RsRk4EKyfE+p61taf1j6OcxKOf8dwHeBI1KuP6J7P2Uzs8q1ZJEhFVf2A26WtAawN3A8sC5ZsefeiHhB0jnAfcDYiHgpd4hXgGclbUtW6LkSOKboNJMlLUuvL4mIs9PrZ4C7gU8CN3Qj9uNIF5hBgwZz2vClXT1EnxnSP5ugrF51Nb729vbqBVPCwoUL+/ycXeH4eqbe4+uizYGjIuJYSX8APkaWEz8XEU9I+iBwfkTsJWkusDXwPmA6sIekB4CNU1uA7YBdgAHADEk3AS8Ch0TEK8qG9d8vaVJERC6ON0q16SDG3wOXAhMi4tp0PVhF0r6p/c6AgEmS9oyIu4o7HhFPSepH9ofLQcDLEfEBZY/i3iPpVuCjwC0RcXpqu2ZETJF0Yhq5uZJ6y/V9+X+1yX43OtVq/YXW63Oz9lfSjmS5/oNkufIBSXfmmhwCbEmW84cAjwK/zm1/OSKGSzqa7A3Q/cnukXeJiJD0WeArwLiiU3fUZitgJLAWMFfSBcAWwDeB3SJigaT1UttzgbMj4m5JmwC3AOVG6Uwn+1sBsmLP265vwF7AacCHIuI5SetGxJuSTgN2iogTy/wM6yrX94Z6v/+vVKP1o1yOaZb8435UrtUKPP0lzUyvpwC/Ag4EJkfEIknXAN+SdHJELAN+TnbjP7HEsa4ge0zrQ8D/Y+UCz8iIKDeq5wzgeqDLU7NHxIVkFxY22XRYnDW7fv8Jxw1fSjPFN390W/WCKaG9vZ22tr49Z1c4vp6p9/i6aF5EFHLrNGAosBtwVSrYAKyevk8B9iQr8JwBHAvcCUzNHe/6iFgELJI0mazQchPwA2UjfpYDG5H9wfD33H4q06ZkjJLWAjaKiGsBIuINgFTg2ReYkdoPJCv4rFTgKbIvsJ3SHBPAOmm/qcCvJa0GXJeLo6x6y/V9mf+a7HejU63WX2i9Pjdxf3cHro2I1wAk/RHYI7d9T+DydE/9vKQ7iva/PPe98Eboe4ArJW0AvAOYV+K8HbW5KSIWA4slvUh2DdgLuKpwX55703ZvYOvcdWptSQPL9LUwL+dAyl/f7gEmpjcR/ljmOG9Tb7m+N9T7/X+lGq0f5a7TzZJ/3I/KNc7/2t6xqPjdUklHAbtLmp9WrU92IbgtIpZLCkq7ETiTbG6eV3JJvlOp4j8TOLyrHTAzq0OLc6+Xkd1Q/6fM6JS7gM8DG5K923kK0EZW+CkozrsBjAYGAztGxJKUs9coatdRm+IY+3fQHwFnRMQvO2iTNZQ2Tcd7Me13UkTcUqLdnsAospv/n0TEbzs7tplZk4sSr38G/CQiJklqA8aX2K+jNsW5vqO/dVYhGwn0Rn5lmXv6/wIeS/uUvL5FxOfSiJ5RwLQ0wsnMrE+18hw8SFqb7J2GTSJiaEQMBU4ge0yrQxHxOtm8PKd38/SnA136pBgzswbxCjBP0mHw1jwM26dtD5K9+7k83VTPJBv2nh8dc5CkNSStT1b8mUo2GubFVLgZCby3xHkrafOWiHgV+Jukg1Ocqyv7dMVbgE8X3smVtJGk4rkjkDQY+AVwXnpU7Bbg82mkDpK2kDRA0nuBf0TERcDFwA7pEEsKbc3MGtAU4GBJa0oaQPZIVr5YfxfZ/DP90mibkUX7H5H7fl96vQ7wXHr9qTLnraRN3h1kH6SyPkDuEa1byT5IhbS+3COz/0P2GNVFEVH2+iZps4h4ICJOA/4JbAy8Sva4mJlZn2jpAg/ZheiONJSz4HrggDR/Qoci4oqImF5m82St+Ojzld6pjYg5ZM/zmpk1o9HAZyTNIvuUwYMAUr59Frg/tZtCdvM7O7fvw8Dk1OZ7EfE82Vw5O0maDRwNPF7inJW0KfZJYKykh4F7gXdHxK3AZcB96VhXs+IGvTAx8hzgdrI/EAqTMF9MNsfEdEmPAL8ke/e4DZglaQbZHzLnpvYXAg/LkyybWQNK98ATyQr3DwAXR8SMXJNrgSfI8uJvWVHEKXhnyr3/S/bhJpCNxrlK0jTKf4BJJW3ycc4he2P1znRN+knaNJbsmvGwpEdZ8em6sGJi5L+SfeLhxyLisbSt5PUNOFPZpNGPkF1PZpFdy7aWJ1k2sz7SUo9oRcTAouVLgEuK1r1ENsS/3D7jyxy7Lfd6aJk2Y4qWP9pZGzOzehYR84Ftc8s/zm3+cJl99si9voysmFJYHl9mnwWs+JTD4m0DO2tTLsaIeILssdziY57LikJMfn2/MscnIpaT/SFQ/PHnK11rUvtTefsnNJqZNZSI+AkrCiaFdYWcHGSfRFtqv6Hp5alF668ne7O1uP1EsmJSR23GFy3n836pe/4FrBhFVPJcZWKfR4nrW6n7euAl4APljmVm1ttaqsDTbPqv1o+5E0bVOoyy2tvb+3xi4q6o9/jMzKD+c72ZmfVcs+T6Zrm/bpZ+WOtp9Ue0zMzMzMzMzMwangs8ZmZmZmZmZmYNzgUeMzMzMzMzM7MG5wKPmZmZmZmZmVmDc4HHzMzMzMzMzKzBucBjZmZmZmZmZtbgXOAxMzMzMzMzM2twLvCYmZmZmZmZmTW4igo8kjaTtHp63SZprKR1qxuamZn1Jed6M7Pm51xvZta8Kh3Bcw2wTNIw4EJgY+CyqkVlZma14FxvZtb8nOvNzJrUqhW2Wx4RSyUdAvwsIn4maUY1A7POLVqyjKFfvanWYZQ1bvhSxjRRfPMnjKpiNGZ1wbm+DvVWrncOM7PEub4O1ft9faXq/f6/Uj3th6+5ViuVjuBZIuko4FPAjWndatUJyczMasS53sys+TnXm5k1qUoLPMcAuwKnR8Q8Se8Dfle9sMzMrAac683Mmp9zvZlZk6qowBMRjwKnAtPT8ryI+GE1AysmabykL3dxn4XViqfEuZZJmilpjqRZksZJ6tanlEn6em/HZ2bWmXrI9b0hl48fkXSVpDXT+lUl/VPShLS8j6T7JCkt95M0Q9Ju6ZoTaY6KwnFPTut2SsvzJc1O55op6adp/URJz+UmMR2U2g7PtX1J0rz0+va+/hmZWetyrneuN7PmVemnaB0AzARuTssjJE2qZmANaFFEjIiIbYB9gP2Ab3fzWC7wmFmfa6JcX8jH2wJvAp9L6/cB/gocJkkRcRvwNPCZtP0k4KGIuDctzwaOzB33MGBO0blGpnONiIixufXLgE/nG0bE7EJbYBJwSlreu2fdNTOrnHO9c72ZNa9KR5iMB3YG/gMQETOBTTvaQdJQSY9JuiiNarlVUv/00Yw3S5omaYqkrVIlfZ4y66aK/J7pOHdJ2jwddvtUgX9C0rFp+0BJf5E0PVXXDyoRS8k25WJM24ZJul3ZaJzpkjZL60+RNFXSw5K+U6rvEfEicBxwYupTP0ln5vY7Ph1rg9S/wrsPe6R3G/qndZdW+O9jZtYbxtPFXN8ApgCFd2aPAs4FniF7PAHgi8DXJG0DnEj2rnbBdUDherEZ8DKwoMLzngN8UVKlH2ZgZtZXxuNc71xvZk2p0mS0JCJeTiMbC5ZXsN/mwFERcaykPwAfI3vu93MR8YSkDwLnR8RekuYCWwPvIxsyuoekB4CNU1uA7YBdgAHADEk3AS8Ch0TEK5IGAfdLmhQRkYvjjVJtOojx98ClwISIuFbSGsAqkvZN7XcGBEyStGdE3FXc8Yh4SlI/4F1kF42XI+IDaRjnPZJuBT4K3BIRp6e2a0bEFEknpqr/SiQdR1Y8YtCgwZw2fGkF/wy1MaR/NgN9vepqfO3t7dULpoSFCxf2+Tm7wvH1TJ3G191cX5fSDfd+wM0pj+8NHA+sS/YHwL0R8YKkc4D7gLER8VLuEK8Az0raliyPX0l2DcubLGlZen1JRJydXj8D3A18ErihG7H3eq6vw/9vJdXp70bVtFp/ofX6XIf9da5v4lxfa/V+/1+pnvajXn7n6zD/dIv7UblKCzxzJH0c6JdG04wF7u1kH4B56V0BgGnAUGA34KrcRWX19H0KsCdZgecM4FjgTmBq7njXR8QiYJGkyWSFlpuAH6QRP8uBjYAhwN9z+6lMm5IxSloL2CgirgWIiDcAUoFnX6DwUZIDyQo+KxV4iuwLbCfp0LS8TtpvKvBrSasB1+XiKCsiLgQuBNhk02Fx1uz6fcNg3PClNFN880e3VS+YEtrb22lr69tzdoXj65k6ja+7ub7e9JdUyKdTgF8BBwKTI2KRpGuAb0k6OSKWAT8nK+hPLHGsK8iG7n8I+H+sfNM/MiLKvdN7BnA92XWqS6qR6/s6h3VXnf5uVE2r9Rdar8912F/n+pU1Ta6vtXq//69UT/tRL9fcOsw/3eJ+VK7SR7ROArYBFgOXkQ1dPLmC/RbnXi8D1gP+k3uGdUREvD9tvwvYg6xo8yeyqnsbWcIuyI/KKSyPBgYDO6ZRL/8A1ihq11Gb4hg7+k0WcEYu9mER8auSDaVN0/FeTPudlNvvfRFxaxr5syfwHDBR0tEdnNvMrNq6m+vrzaJcvj0pIt4kexd3b0nzyYr56wN7AUTEcla+vhTcSPbO7DMR8UpXgoiIJ8jmuTi8e90wM6sK5/qVOdebWVPotCyZHh26KSJGAt/o4fleAeZJOiwirlI2jGe7iJgFPEj2EY1PRcQbqSJ/PLB/bv+DJJ1B9ohWG/BVsonQXoyIJZJGAu8tcd51Kmjzloh4VdLfJB0cEdelx6r6AbcA35N0aUQslLQR2TDXF/P7SxoM/AI4LyJC0i3A5yXdkWLYgqyoMwj4W0RclM6xA/BbYImk1SJiSYU/VzOzHunlXF9XJK1N9gbCxhGxOK07huwPgds62jciXpd0KtmEnd1xOt14V9fMrBqc60tzrjezZtHpCJ40pHG5pHV66Zyjgc9ImkU2Q/1B6TyLgWeB+1O7KcBaZDPbFzwMTE5tvhcRz5PNlbOTpNnA0cDjJc5ZSZtinwTGSnqYbNjquyPiVrJ3Ou5Lx7o6xQgrJkaeA9wO3AoUJmG+GHgUmC7pEeCXZMW1NmCWpBnAEWQTwkE2VPNhT7JsZn2lCrm+nhwC3FG44U+uBw5IxfUORcQVETG9zObJWvFxuL8tse8c0kcRm5nVmnN9ec71ZtYMKn2wcCEwW9JtwGuFlUUfE/g2ETEf2Da3/OPc5g+X2WeP3OvLyIopheXxZfZZwIoZ8ou3DeysTbkY03DLvUoc81xWFGLy6/uVOX5hSOjXWfnjzy9JX8XtT+Xts/uX1H+1fsydMKqzZjXT3t5eN8+fllLv8ZnVQJdzfT0q5P7c8kq5Nk2wObiDfcaXOXZb7vXQMm3GFC1/tLM2Han3XG9mDce5fsXy+DLHbsu9HlqmzZiiZed6muf+uln6Ya2n0gLPH9OXmZk1L+d6M7Pm51xvZtakKirwpIq4mZk1Med6M7Pm51xvZta8KirwSJpHiVnnI2LTXo/IzMxqwrnezKz5OdebmTWvSh/R2in3eg2yT65ar/fDMTOzGnKuNzNrfs71ZmZNqtNP0QKIiH/lvp6LiHOAxp8FzMzM3uJcb2bW/JzrzcyaV6WPaO2QW1yFrPJf6egfMzNrAM71ZmbNz7nezKx5VZrMz8q9XgrMAw7v/XDMzKyGnOvNzJqfc72ZWZOqtMDzmYh4Kr9C0vuqEI+ZmdWOc72ZWfNzrjcza1IVzcEDXF3hOjMza1zO9WZmzc+53sysSXU4gkfSVsA2wDqSPprbtDbZrPtmZtbgnOvNzJqfc72ZWfPr7BGtLYH9gXWBA3LrXwWOrVZQZmbWp5zrzcyan3O9mVmT67DAExHXA9dL2jUi7uujmKxCi5YsY+hXb6p1GGWNG76UMQ0c3/wJ/sRQaw3O9fWtu7neOczM8pzr61u939dXqt7v/ys1bvhS2modhFk3VDrJ8gxJJ5AN63xrCGdEfLoqUZmZWS0415uZNT/nejOzJlXpJMu/A94NfAi4E3gP2XDOuiNpfUkz09ffJT2XW470/RFJN0hat2jfmZKuKFo3MR1j9bQ8SNL89HoVST9Nx5staWrhUwgkzU/rCufeTdJQSYty62ZKOrqo/cOS7pT03j75gZmZreBc71xvZs2vYXJ9o5N0sKStax2HmbWOSgs8wyLiW8BrEXEJMAr4YPXC6r6I+FdEjIiIEcAvgLNzy6+l19sCLwEnFPaT9H6gH7CHpAFFh10GlHpX4whgQ2C7iBgOHAL8J7d9ZOHcEXFvWvdkbt2IiPhtUfvtgHbgm939GZiZdZNzvXO9mTW/hsn1TeBgwAUeM+szlRZ4lqTv/5G0LbAO8K7qhNRn7gM2yi0fRfaOxq3AQUVtzwG+KKn4kbYNgBciYjlARPwtIv5dhdjMzPqCc71zvZk1v2bM9SuRdJ2kaZLmSDourVso6cy07nZJO0tql/SUpANTmzUk/SaNtpwhaWRaP0bSebnj3yipLXfc0yXNknS/pCGSdgMOBM5MIzk36/Mfgpm1nErn4LlQ0juBbwGTgIHAaVWLqsok9QP+H/Cr3OojgH2ArYCTgMty254B7gY+CdyQW/8H4G5JewB/AX4fETNy2ydLWgYsjojCOyObSZqZa3NSREwpCvHDwHVlYj8OOA5g0KDBnDZ8aWfdrZkh/bMJyupVZ/G1t7f3XTAlLFy4sOYxdMTx9Uydxudc30S5vg7/f1WkTn83qqbV+gut1+c67G9T5foOfDoiXpLUH5gq6RpgAHBHRJwi6Vrg+2TXhK2BS8h+HicAERHDlX20/K2StujkXAOA+yPiG5J+BBwbEd+XNAm4MSKuLrVTI93XV6re7/8rNaR/415H8+ow/3SL+1G5igo8EXFxenknsGn1wqm6/umGeyPgMeA2AEk7AQsi4hlJzwG/lrReRLyU2/cM4HrgrWnhI+JvkrYE9kpff5F0WET8JTUZGRELimJ4Mj1CUMpkSesBC8kuuiuJiAuBCwE22XRYnDW70hpd3xs3fCmNHN/80W19F0wJ7e3ttLXVNoaOOL6eqcf4nOuBJsr1tc5h3VWPvxvV1Gr9hdbrc731t4lyfWfGSjokvd4Y2Bx4E7g5rZtNVphfImk2MDSt3x34GUBEPC7paaCzAs+bwI3p9TSyolGnGum+vlL1fv9fqXHDl3J4Hf3edle95Z/ucj8qV9EjWmmY4a8k/Tktby3pM1WNrDoWpRvu9wJixbwMRwFbpQk1nwTWBj6W3zEingBmAocXrV8cEX+OiFOAH5A9a9tdI1NsM4Hv9OA4ZmZd5lzvXG9mza+Jcn1Z6dGpvYFdI2J7YAbZJ4YtiYhIzZYDiwHSI7idVSWW8va/ndbIvc4fd1kFxzIzq4pK5+CZCNxCNskkwF+Bk6sRUF+IiNeBscA4Se8gu5EfHhFDI2Io2bwMR5XY9XTgy4UFSTtI2jC9XgXYDni6h7EtJfvZHp3e4TUz6ysTca4H53oza24TaaJcX8Y6wL8j4vX0mNUuXdh3CjAaID2atQkwF5gPjFD2yYobAztXcKxXgbW6EriZWU9UWuAZFBF/IKt0F25Ml1Utqj6Q5k94GPga8FxEPJ/bfBewtaQNivaZA0zPrXoXcIOkR9KxlgLn0bHNij46d2yJ2F4ALif3yS9mZn3AuR7nejNrek2X60u4GVhV0mPABOD+Lux7PrBKemzrSmBMRCwG7gHmAY8CP+Xt14lyrgBOSZM1e5JlM6u6SocPviZpfSAAJO0CvFy1qHpJRIwvWh5YtHxAevmdovXLgHenxTFF2z6ae30zK57jLT730BLr5gP9K2kfESeVamdmVkXO9Su2OdebWbNqyFzfFakgs1+JTQNzbcYX7TMwfX8DOKbEMYM0sqfEtvxxrwauTq/vwR+TbmZ9qNICz5fIZpXfTNI9wGDg0KpFZRXpv1o/5k4YVeswympvb6/rST7rPT6zGnCur0P1nuvNrOE419ehZsn1zXJ/3Qyf2GStqcMCj6RNIuKZiJgu6X+ALckmrJwbEUv6JEIzM6sq53ozs+bnXG9m1vw6m4PnutzrKyNiTkQ84ouAmVlTca43M2t+zvVmZk2uswKPcq83rWYgZmZWM871ZmbNz7nezKzJdVbgiTKvzcyseTjXm5k1P+d6M7Mm19kky9tLeoWs4t8/vSYtR0SsXdXozMysLzjXm5k1P+d6M7Mm12GBJyL69VUgZmZWG871ZmbNz7nezKz5dfaIlpmZmZmZmZmZ1TkXeMzMzMzMzMzMGpwLPGZmZmZmZmZmDc4FHjMzM/v/7d17mBxVnf/x94ckQEK4mohcDTeBSDAQfgoY2IF1WRRdQEDAKMQLiLgiigguLr+oy4KLKCIqgotRQUCQQAQ13DIkKEJMMrmBIbCZRQEhEREGEwjhu3/UaSia7slcu7u6P6/n6Weqq05VnVPT9a3Tp06dNjMzM7OCW9evaFkDW7VmLWPOvrXe2ajqjHEvMXkA8td5wWEDkBszs2LqS6x33DQzK5ZGr9f31EDV/+vB105rBu7BY2ZmZmZmZmZWcG7gMTMzMzMzMzMruMI18EiaIunzvVyna7DyU2Ff20q6WdIySY9I+pak9auk3VrSDbXKm5lZPVWLxZKmSjo6TXdKGtWLbU6WdOlA5bEH+1ohaX6K8TMk7d/HbY2X9J6BzqOZWZG4Xm9mNrAK18DTyCQJuBG4KSJ2Ad4CjATOq5B2aEQ8HhFH1zibZmbWd9dFxF4pxl8A3Chp9z5sZzzgBh4zswbler2ZFdGgN/BIGiPpQUlXSFoi6TZJwyXtJOnXkuZKmi1pN0lDJC1XZjNJayUdmLYzS9IuabNvk3Rvak0/KS0fKelOSfMkLZJ0eIW8VExTLY9p2c6S7pC0IK23U5p/pqQ5khZK+nLaxcHA6oj4IUBErAU+C3xU0oh093e6pLuAO9N+F6ftjZD0M0kPSJom6T5J+wzW/8XMbDBJ+pykxel1etkySbpU0lJJdwBvLFv9CylG3y9p57TO+1JcnJ9i8pYV9lkxTbpDfKWkdkn/I+m03DonpDi+QNJP0rzRkn6eYvwcSe+sVMaImAlcDpyc1nvddS3NPyYdhwXpWrY+8BXgWEkdko7t21E2M6st1+tdrzezxlarX9HaBTg+Ik6S9DPgKOAjwCkRsUzSO4DvRsTBkpYCY4EdgHnAAZLuA7ZLaQH2BPYFNgLmS7oVeAo4MiKeVda9/3eSpkdE5PKxulKabvJ4FXA1cEFETJO0IbCepENS+rcDAqanC9Zbgbn5gqd9PQrsnGbtDewZEU9LGpNLeirw14gYK2kPoKPSgZR0MunLxKhRozl33EvrPPj1suXwbCT9/mpvb+9/Ziro6uoatG0PBOevf5y/+pE0gSzGv4MsRt4n6e5ckiOBXcli/ZbAA8CVueV/i4hxkk4ALgbeC9wD7BsRIenjwBeAM8p23V2a3YCDgI2BpZK+R3Y39kvA/hGxUtIWKe23gG9GxD2StgdmANV66cwDPpGmL6fsukb2BeFc4J8j4jFJm0XEi5LOBfaJiH+tcgz7FeuL/Nlq5nOjklYrL7RemZuwvK7Xt2C9vqcGqv5fD/nztFnOW5ejsdSiHLVq4FkeEaXANhcYA+wPXJ8CO8AG6e9s4ECyC8H5wEnA3cCc3PZujohVwCpJM8kC8q3Af6aA/DKwDdkXhz/n1lOVNBXzKGljYJuImAYQEasB0oXgEGB+Sj+S7MLQE7dHxNMV5k8k+1JBRCyWtLDSyhFxOdmXCLbfcee4aFHj/tL9GeNeYiDy1zmprf+ZqaC9vZ22tsHZ9kBw/vrH+auricC0iHgeQNKNwAG55QcC16S7oY+nu5951+T+fjNNbwtcJ2krYH1geYX9dpfm1oh4AXhB0lNk0N3YTwAAIABJREFUsf9g4PqIWAmQi83vAsbmrk+bSBpZpaxKZRxJ9evab4Cp6UvGjVW28xr9jfWDFTdrocnPjddptfJC65W5Ccvrev2rWqZe31MDVf+vh/y1s1nOW5ejsdSiHLU6+17ITa8lC77PRMT4CmlnAZ8Etia763km0EZ2gSiJsnUCmASMBiZExBpJncCGZem6S1Oex+HdlEfA+RHx/dfMlN4FHF02bxNge+Bhslb+57vZrpmZvTbGl6a/DXwjIqZLagOmVFivuzTlMb676996ZD2BVudn5r645O0FPJjWqXhdi4hT0h3tw4C5qYeTmVlRuV7ver2ZNah6DbL8LLBc0jHwyngMb0vL7ie7C/Byqlx3kHV/n5Vb/3BJG0p6A9lFYg6wKfBUCvAHAW+usN+epHlFRDwH/EnSESmfG0gaQdZd/6OlO7qStpH0RuBOYER6rABJQ4CLgKkR8fd1HJPfAB9I640Fxq0jvZlZo5oNHJHGINiI7JGsfGV+Ftn4M0NSb5uDytY/Nvf33jS9KfBYmj6xyn57kibvLuCYdC0h94jWbcCnS4kkVfrSgqR/IOtaf0VEVL2uSdopIu6LiHOBFcB2wHNkj4uZmRWd6/Wv53q9mdVFPX9FaxLwMUkLgCXA4QCpC/0fgd+ldLPJKsGLcusuBGamNF+NiMfJnqndR9Ii4ATgDxX22ZM05T4MnJa6Vv4WeFNE3Ab8FLg3besGYOP0XPCRZF8YlgEPkT0f/G892M93gdGSHgD+g+yY/K0H65mZNZSImAdMJavY3wf8ICLm55JMA5aRjb3zY15txCnZPMXcz5ANaAlZb5zrJc0FVlbZdU/S5PO5hOzXUO5O16JvpEWnkV0rFqaYfEputdLAyA+RxfajIuLBtKzidQ24UNkAoIvJriMLyK5hY+VBls2sObhe/1qu15tZXei1Y5VZvaS7AsMiYrWyEf3vAHaNiBerrbPrrrvG0qVLa5bH3mr0ZyWdv/5x/vpnsPMnaW5E+Bc7mkCjx/qB1ujn7kBrtfJC65V5MMvrWN+YmrFe31PNcn67HI2l1cvRm1hfzBGwmtMIYKakYWTPAp/a3UXAzMzMzMwakuv1ZlYXbuBpEOm5YN+BMTMzMzMrMNfrzaxe6jkGj5mZmZmZmZmZDQA38JiZmZmZmZmZFZwbeMzMzMzMzMzMCs4NPGZmZmZmZmZmBecGHjMzMzMzMzOzgnMDj5mZmZmZmZlZwbmBx8zMzMzMzMys4NzAY2ZmZmZmZmZWcG7gMTMzMzMzMzMruKH1zoD13ao1axlz9q31zkZVZ4x7icl9yF/nBYcNQm7MzIqpt7HeMdTMrHgavV7fU32t/9eDr5fWjNyDx8zMzMzMzMys4BqigUdSV5X5UyUdnaY7JY3qxTYnS7p0oPLYg32tkDRf0jJJMyTt38dtjZf0noHOo5mZDR5Jp0saUe98mJnZ4HGsN7NG1xANPE3iuojYKyJ2AS4AbpS0ex+2Mx5wA4+ZWbGcDrjSb2bW3Bzrzayh1byBR9LnJC1Or9PLlknSpZKWSroDeGPZ6l+QtEjS/ZJ2Tuu8T9J9qffMHZK2rLDPimkkTZF0paR2Sf8j6bTcOidIWihpgaSfpHmjJf1c0pz0emelMkbETOBy4OS03k6Sfi1prqTZknZL849Jx2GBpFmS1ge+AhwrqUPSsX07ymZmNlgkbSTp1hS7F0v6/8DWwExJM1OaQyTdK2mepOsljUzzOyX9V/m1zMzMGotjvZkVkSKidjuTJgBTgX0BAfcBHwJmR8RISe8HPgkcCmwJPAB8PCJukNQJXBER50k6AfhARLxX0ubAMxERkj4O7B4RZ0iaDOwTEf/aTZopwCHAQcDGwFLgTcBbgGnA/hGxUtIWEfG0pJ8C342IeyRtD8yIiN3z+8qV9QjgExHxbkl3AqdExDJJ7wDOj4iDJS0CDo2IxyRtFhHPVNpW2TE8mdRwNGrU6AnnXnxFP/8rg2fL4fDkqt6vN26bTQc+MxV0dXUxcuTImuyrL5y//mn1/B100EFzI2KfQdtBC5N0FFnsPim93xRYQBa7V6bHiW8E3h0Rz0s6C9ggIr5S7VpWYR99jvW1iqGDpdHP3YHWauWF1ivzYJbXsX7wNHqsb1R9rf/XQ3fXy2aJUy5HY+lrOXoT62v9K1oTgWkR8TyApBuBA3LLDwSuiYi1wOOS7ipb/5rc32+m6W2B6yRtBawPLK+w3+7S3BoRLwAvSHqKrGHpYOD6iFgJEBFPp7TvAsZKKq27SamlvgKlMo4E9geuz623Qfr7G2CqpJ+RXSDWKSIuJ+sdxPY77hwXLWrcH0I7Y9xL9CV/nZPaBj4zFbS3t9PWVpt99YXz1z/Onw2iRcBFkr4G3BIRs3PxHbKbGGOB36T56wP35pZXupa9Rn9ifa1i6GBptXOj1coLrVfmVitvE2noWN+o+lr/r4furpfNct66HI2lFuUoxtn3qqgw/W3gGxExXVIbMKXCet2leSE3vZbuj8l6wL4RsTo/syzYl+wFPJjWeSYixpcniIhTUo+ew4C5qYeTmZk1sIh4SNLeZOOl/UfqpZkn4PaIOL7aJqpMm5lZg3CsN7MiqvUYPLOBIySNkLQRcGSaVzKLbPyZIam3zUFl6x+b+1tqId8UeCxNn1hlvz1Jk3cXcIykNwBI2iLNvw34dCmRpNc12qT5/0DW3fKKiHgWWC7pmLRMkt6WpneKiPsi4lxgBbAd8BzZ42JmZtaAJG0N/D0irgIuBPbmtbH7d8A79epYcRtJektuE5WuZWZm1kAc682siGragyci5kmaCtyfZv0gIubnesBMI3s86gHgUV4fDDeXtJCs102ptXwK2eNPfyVrmNmhwq57kiafzyWSzgPulrQWmA9MBk4DvpPyMJSsQeqUtNqxkiaSjay/HDgqIh5MyyYB35P0JWAYcC3ZM7wXStqF7A7AnWneo8DZkjrIxuq5rru8mplZzY0ji98vA2vIxo7bD/i1pMcj4qA0nto1kkqP5H4JeChNV7qWmZlZY3GsN7PCqfkjWhHxDeAbZfNGpr8BVBxcOCLGpMmzyubfDNxcIf1UsgGdu0szpez9HrnpHwE/Klu+kldb4yvuq0rel5MNHF0+//0Vkj8N/L9q2zIzs/qKiBnAjLLZvyd7HLiU5i6qx/ILI+KsKsvMzKwBONabWREVbQweyxk+bAhLLzis3tmoqr29vfCDfZqZ1Vujx3ozM+u/Zon1rv+b1ZcbeMzMzGok1xvVzMyalGO9mdVLrQdZNjMzMzMzMzOzAeYGHjMzMzMzMzOzgnMDj5mZmZmZmZlZwbmBx8zMzMzMzMys4NzAY2ZmZmZmZmZWcG7gMTMzMzMzMzMrODfwmJmZmZmZmZkVnBt4zMzMzMzMzMwKzg08ZmZmZmZmZmYFN7TeGbC+W7VmLWPOvrXe2ajqjHEvMbmb/HVecFgNc2NmVkw9ifWOp2Zmxdbo9fqeWlf9v1H4umnNyj14zMzMzMzMzMwKzj14zMzMzMysKUmaAnQBmwCzIuIOSQcAlwFrgP2ArwDvAX4JPA+cBKwANgIWAV+KiAf6sO824MWI+G3/S2Jmtm5N14NH0pskXSvpEUlzJf1S0lvSstMlrZa0aS79CElXS1okabGkeySNTMu6yrY9WdKl69j/yZL+kF73S5qYW9YpaVTufZukWyR9RFJHer2Y8tIh6YKBOi5mZq2qntcFSVMkfT5NT5X0mKQN0vtRkjoHochmZlYmIs6NiDvS20nA+RExPiJWAScDe0bEmWn5N9OyXYDrgLskje7DbtuA/fubdzOznmqqBh5JAqYB7RGxU0RMAL4IbJmSHA/MAd6fW+0zwJMRMS4i9gA+Rtaa35f9vxf4BDAxInYDTgF+KulN3a0XET9MF5HxwOPAQen92X3Jh5mZZep9XahgLfDRAdqWmZlVIOkcSQ9JugfYNc2bKuloSR8HPgB8NTXmTwdGAnMlHVu+rYi4DrgN+GDazgRJd6cbBjMkbZXmnybpAUkL002FMWTfBT6bbtweUIOim1mLa7ZHtA4C1kTEZaUZEbEAQNJOZMH7VOAc4IcpyVbA/+bSL+3H/s8CzoyIlWlb8yT9CPgU8O/92K6ZmfVNva8L5S4mq+xfMYDbNDOzRNIE4DhgPNl3nXnA3NLyiPhB6mF/S0TckNbpSjdaS490lZsH7CZpGPBt4PCIWJEahM4ja7g/G9ghIl6QtFlEPCPpMqArIr4+WOU1M8trtgaePcgF8DLHAdcCs4FdJW0ZEU8CVwK3SToauBP4UUQsS+sMl9SR28YWwPRu9v/WCvv/PXBi74pRnaSTybqRMmrUaM4d99JAbXrAbTk8G0m/mvb29tplpoKurq6656E7zl//OH+W1Pu6UO5R4B7gw8AvqiXqbaxvps9Sq50brVZeaL0yt1p5G8ABwLSI+DtA6qHTX0p/dyW7rtyedRBlCPBEWrYQuFrSTcBNPdpoger1PbWu+n+jWNc52SznrcvRWGpRjmZr4OnO8cCREfGypJ8DxwCXRkSHpB2BQ4B3AXMk7RcRDwKrSq35kI21AOzTjzxED+dV30DE5cDlANvvuHNctKhx/4VnjHuJ7vLXOamtdpmpoL29nba2+uahO85f/zh/1gP1ui6cD9wMVP0d2d7G+nrH04HUaudGq5UXWq/MrVbeJrUX2U1bAUsiYr8KaQ4DDgTeB5wjady6Nlqken1Prav+3yjWdd1slvPW5WgstShHU43BAywBJpTPTAF2F7LW9k6yu7bHl5ZHRFdE3BgRpwJXkY2i3xcPVNj/hJQvgL8Am+eWbQGs7OO+zMxs3ep9XXid1Buog2wMCDMzG1izgCMkDZe0MVmDS59JOoqswf8aYCkwWtJ+adkwSW+VtB6wXUTMJBuyYVOyR4CfAzbuz/7NzHqj2Rp47gI2SN0dAZC0J3AJMCUixqTX1sDWkt4s6Z2SNk9p1wfGkht7oZf+C/iapDek7Y0HJgPfTcvbybrlI2kI8CFgZh/3ZWZm61bv60I15wGfH+Btmpm1vIiYR/bLVwuAX5ENpN9bpYGRl5HV1w+OiBUR8SJwNFl9fwFZY/3+ZI9qXSVpETAfuCQiniF7FPdID7JsZrXS+P3neiEiQtKRwMWSzgJWA51kP1H4ybLk08ju2D4BfC/90sp6ZF3mf97H/U+XtA3wW0lB1mr/oYgoPZv71bSvBWRdPH9NdmfYzMwGQb2vC93ka4mkecDeA7ldMzODiDiPrCG92vLJZe9H5qanAFO6WbeD7FGschMrpH0I2HNd+TUzGyhN1cADEBGP04Nu7xHxudzbH1dJM7Ls/VRg6jq2+z3ge1WW/Y30E4vdrD+mu+V5w4cNYekFh/U0ec21t7c31bgQZlZM9bwupC8KpenJZcveX56+kkaP9WZm1n/NEutd/zerr2Z7RMvMzMzMzMzMrOU0XQ+eWpB0DtmvreRdn7qDmplZi/F1wczMzMzqzQ08fbCu53rNzKy1+LpgZmZmZvXmR7TMzMzMzMzMzArODTxmZmZmZmZmZgXnBh4zMzMzMzMzs4JzA4+ZmZmZmZmZWcG5gcfMzMzMzMzMrODcwGNmZmZmZmZmVnBu4DEzMzMzMzMzK7ih9c6A9d2qNWsZc/at9c7GKzovOKzeWTAzazo9ifWOv2ZmxdZo9fre8nXIrDG4B4+ZmZmZmZmZWcG5gcfMzMzMzMzMrODcwFOBpCmSPi/pK5LeleYdIGmJpA5JwyVdmN5fmNI/lpYtk3SjpLF93HebpP0HtkRmZsVSisO9XKdrsPJTYV/bSro5xfxHJH1L0vpV0m4t6YZa5c3MrFlIekOqX3dI+nOuvt0hKdLfxZJ+IWmzsnU7JF1bNm9q2sYG6f0oSZ1pej1Jl6TtLZI0R9IOaVlnmlfa9/6SxkhalZvXIemEsvQLJd0t6c01OWBm1vLcwNONiDg3Iu5IbycB50fE+IhYBZwM7BkRZ6bl30zLdgGuA+6SNLoPu20D3MBjZtagJAm4Ebgpxfy3ACOB8yqkHRoRj0fE0TXOpplZ4UXEX1L9ejxwGa/Wt8cDz6fpPYCngU+V1pO0OzAEOEDSRmWbXQt8tMLujgW2JqvfjwOOBJ7JLT+otO+I+G2a90hu3viI+HFZ+j2BduBLfT0GZma94QaeRNI5kh6SdA+wa5o3VdLRkj4OfAD4qqSrJU0nq8zPlXRs+bYi4jrgNuCDaTsTUuv9XEkzJG2V5p8m6YHUun+tpDHAKcBn012AA2pQdDOzfkt3Mh+UdEXq3Xhb6u24k6Rfp/g3W9JukoZIWq7MZpLWSjowbWeWpF3SZt8m6d7US+aktHykpDslzUt3Rw+vkJeKaarlMS3bWdIdkhak9XZK889Md3EXSvpy2sXBwOqI+CFARKwFPgt8VNIISZMlTZd0F3Bn2u/itL0Rkn6WYv80SfdJ2mew/i9mZi3iXmCb3PvjgZ+Q1cfLrxMXk9W1y39sZivgiYh4GSAi/hQRfx2EvJmZDRr/ihZZAwxwHDCe7JjMA+aWlkfEDyRNBG6JiBvSOl3p7gGSplTY7DxgN0nDgG8Dh0fEitQgdB7ZnYOzgR0i4gVJm0XEM5IuA7oi4utV8noyWe8hRo0azbnjXhqAIzAw2tvbX/O+q6vrdfMaifPXP85f/zR6/vpoF+D4iDhJ0s+Ao4CPAKdExDJJ7wC+GxEHS1oKjAV2IIuXB0i6D9gupQXYE9gX2AiYL+lW4CngyIh4VtIo4HeSpkdE5PKxulKabvJ4FXA1cEFETJO0IbCepENS+rcDAqanhqi3krtGAKR9PQrsnGbtTXYX+OnUeF9yKvDXiBgraQ+go9KB7G2sb6bPUpOeG1W1Wnmh9crcauWtNUlDgH8E/js3+1jgn4DdgE8DP80texS4B/gw8Ivc/J8B96QbrHcCV0XE/NzymZLWAi9ExDvSvJ0k5eP4pyNidlkWDwVuqpL3hq3X91bpM94sn3eXo7G4HD3nBp7MAcC0iPg7QO6LQH8o/d0V2AO4PX1hGQI8kZYtBK6WdBNVAn+5iLgcuBxg+x13josWNc6/sHNS22vet7e309bWVjFtI3D++sf5659Gz18fLY+IUkV3LjCG7JHT61P8A9gg/Z0NHEjWwHM+cBJwNzAnt72b0yOxqyTNJGtouRX4z9TQ8jLZXdEtgT/n1lOVNBXzKGljYJuImAYQEasBUgPPIUCpgj+SrMGnJ26PiKcrzJ8IfCvtZ7GkhZVW7m2sL4+/Rdak50ZVrVZeaL0yt1p5a2h4alzZBngQuB0g9YpcGRGPSnoMuFLSFmUx+XzgZrJrCpD12JG0K1kvzYPJemAeExF3piQHRcTKsjw8UrrhW8FMSVsAXcC/V0rQyPX63ipdh5rl8+5yNBaXo+f8iNbg2YvsYiNgSe7Z3HERcUhKcxjwHbI7vXMqdBU1MyuSF3LTa4EtgGfKxifYPS2fRda4/nbgl8BmZGOQ5e985nvllN5PAkYDE1Kl+klgw7J03aUpz2N3cVe8Ovba+IjYOSL+G3gAmPCahNImwPbAw2nW891s18zM+m9VivFvJovXpTF4jifrRd8JPAJsQtZb8xURsYysB+UHyua/EBG/SmNs/idwRD/yd1DKWwfw5XWkNTMbEG7gycwCjkjjRWwMvK8/G5N0FNld32uApcBoSfulZcMkvVXSemSPIswEzgI2Jbs7/BywcX/2b2bWIJ4Flks6BrLBiSW9LS27n6x3z8upx0wH8AmyeFxyuKQNJb2BrPFnDlmsfCoi1kgqVZ7L9STNKyLiOeBPko5I+dxA0ghgBtm4OiPT/G0kvZGs6/4IvfprKUOAi4CppZ6g3fgN6QuFsl9bHLeO9GZm1o0Ud08DzlD2a4YfAMZFxJiIGEM2Bs/xFVY9D3jl1xol7S1p6zS9Htljwv/bz7y9BJwOnJB685iZDSo38AARMY/sl68WAL/itY8I9FRpYORlwIeAgyNiRUS8CBwNfE3SArIvMfuTPap1laRFZN3/L4mIZ8ieBT5SHmTZzJrDJOBjKf4tIQ12GREvAH8EfpfSzSZr3F6UW3chMDOl+WpEPE42Vs4+KXaeAPyhwj57kqbch4HT0iNTvwXeFBG3kY3bcG/a1g3Axmm8nyOBY1LMf4hs3J9/68F+vkvW6P8A8B9kx+RvPVjPzMyqSGPlLAS+CDyWrhcls4CxSj9ykltnCdkYcCVvBH6RBsVfCLwEXLqOXe+k1/5M+mkV8vYE2U3fT71+dTOzgeVHgpKIOI8KP3GbWz657P3I3PQUYEo363aQjTVRbmKFtA+R3TEwMyuMiOgkG2+s9D4/UPyhVdY5IDf9U3KDYKa4WmmdlcB+VZaNXFeaanlM3fUPrrDNb5HGzCmb/0eq9PaMiKnA1Nz7ztx+VwMfiojV6Ze67qCfd4jNzFpB+XUhXxdP70sx+ctl89cCb0pvJ5cte39u+tfAr6vse0yFeZ3A8J6kj4hPV0pnZjbQ3MBTYMOHDWHpBYfVOxtmZtZzI8gG3hxGNmbEqamnZ1WO9WZmzc+x3swGght4zMzMaiSN97NPvfNhZmZmZs3HY/CYmZmZmZmZmRWcG3jMzMzMzMzMzArODTxmZmZmZmZmZgXnBh4zMzMzMzMzs4JzA4+ZmZmZmZmZWcG5gcfMzMzMzMzMrODcwGNmZmZmZmZmVnBu4DEzMzMzMzMzKzg38JiZmZmZmZmZFdzQemfA+m7VmrWMOfvWmu2v84LDarYvMzPL5GO947CZWXOqdb2+P3wtMmtc7sFjZmZmZmZmZlZw7sFjZmYtSdIUoAvYBJgVEXdIOgC4DFgD7Ad8BXgP8EvgeeAkYAWwEbAI+FJEPNCHfbcBL0bEb/tfEjMzMzOzFu/BI+lNkq6V9IikuZJ+KektadnpklZL2jSXfoSkqyUtkrRY0j2SRqZlXWXbnizp0m72PUXS59P0VEmPSdogvR8lqXMQimxmZmUi4tyIuCO9nQScHxHjI2IVcDKwZ0ScmZZ/My3bBbgOuEvS6D7stg3Yv795NzMz1+nNzEpatoFHkoBpQHtE7BQRE4AvAlumJMcDc4D351b7DPBkRIyLiD2Aj5Hd5R0Ia4GPDtC2zMysAknnSHpI0j3ArmneVElHS/o48AHgq6niPx0YCcyVdGz5tiLiOuA24INpOxMk3Z2+XMyQtFWaf5qkByQtTF9AxgCnAJ+V1JF6DZmZWR+4Tm9m9qpWfkTrIGBNRFxWmhERCwAk7URWqT8VOAf4YUqyFfC/ufRLBzA/F5NV9q/oLpGkk8nuKDNq1GjOHffSAGahe+3t7b1K39XV1et1asn56x/nr38aPX/NSNIE4DhgPNn1bx4wt7Q8In4gaSJwS0TckNbpiojxaXpKhc3OA3aTNAz4NnB4RKxIDULnkVXyzwZ2iIgXJG0WEc9IugzoioivV8lrxVjfCp+ZVjs3Wq280HplbrXy1kEh6/Qpf3Wr1/dHd5/nZvm8uxyNxeXouVZu4NmDXMW+zHHAtcBsYFdJW0bEk8CVwG2SjgbuBH4UEcvSOsMldeS2sQUwvRf5eRS4B/gw8ItqiSLicuBygO133DkuWlS7f2HnpLZepW9vb6etrXfr1JLz1z/OX/80ev6a1AHAtIj4O0DqodNfSn93Jbuu3J7dTGYI8ERathC4WtJNwE092Wi1WN/bOFxErXZutFp5ofXK3GrlrYNC1umhvvX6/ujuWtQsn3eXo7G4HD3Xso9orcPxwLUR8TLwc+AYgIjoAHYELiQL9nMk7Z7WWZXGZRif7vae24f9ng+cif8vZmZFsRfwIFlDz5LcdWBcRByS0hwGfAfYm+y6UYwavJlZ8blOb2YtpZWDzhJgQvlMSeOAXcjuwnaStfwfX1oeEV0RcWNEnApcRfbrKgMi3TnoIBsDwszMBtYs4AhJwyVtDLyvPxuTdBRwCHANsBQYLWm/tGyYpLdKWg/YLiJmAmcBm5I9LvAcsHF/9m9mZoDr9GZmr2jlBp67gA3Ss68ASNoTuASYEhFj0mtrYGtJb5b0Tkmbp7TrA2PJPb87QM4DPj/A2zQza3kRMY/sl68WAL8iG3Szt0oDIy8DPgQcHBErIuJF4Gjga5IWkFXs9yd7VOsqSYuA+cAlEfEMWbf9Iz3IsplZv7lOb2aWtGw38YgISUcCF0s6C1gNdJL9dO0ny5JPI2v1fwL4Xhqtfz3gVrLungOZryWS5pF15TczswEUEeeRVbqrLZ9c9n5kbnoKMKWbdTuAAyssmlgh7UPAnuvKr5mZdc91ejOzV7VsAw9ARDxOD7pORsTncm9/XCXNyLL3U4Gp3WxzSm56ctmy95enr2T4sCEsveCwniQ1M7OCcqw3M+te0ev04FhvZgOjlR/RMjMzMzMzMzNrCi3dg6cWJJ1DGrE/5/r0mICZmZmZmTU41+nNrAjcwDPI1jXeg5mZmZmZNTbX6c2sCPyIlpmZmZmZmZlZwbmBx8zMzMzMzMys4NzAY2ZmZmZmZmZWcG7gMTMzMzMzMzMrODfwmJmZmZmZmZkVnBt4zMzMzMzMzMwKzg08ZmZmZmZmZmYF5wYeMzMzMzMzM7OCcwOPmZmZmZmZmVnBuYHHzMzMzMzMzKzg3MBjZmZmZmZmZlZwbuAxMzMzMzMzMys4N/CYmZmZmZmZmRWcIqLeebA+kvQcsLTe+ejGKGBlvTPRDeevf5y//hns/L05IkYP4vatRgoQ6wdao5+7A63VygutV+bBLK9jfZNooljfLOe3y9FYWr0cPY71Q/uwcWscSyNin3pnohpJv3f++s756x/nz5pIQ8f6gdZq50arlRdar8ytVl7rs6aI9c3yeXc5GovL0XN+RMvMzMzMzMzMrODcwGNmZmZmZmZmVnBu4Cm2y+udgXVw/vrH+esf58+aRat9Vlze5tdqZW618lrfNMvnxOVoLC5HYxn0cniQZTMzMzMzMzOzgnNtI28/AAAJcUlEQVQPHjMzMzMzMzOzgnMDj5mZmZmZmZlZwbmBp6AkHSppqaSHJZ3dAPnZTtJMSQ9IWiLpM2n+FpJul7Qs/d28jnkcImm+pFvS+x0k3ZeO4XWS1q9X3lJ+NpN0g6Q/SHpQ0n4Ndvw+m/63iyVdI2nDeh5DSVdKekrS4ty8isdLmUtSPhdK2rtO+bsw/X8XSpomabPcsi+m/C2V9M+DnT8rhkaL9esyUOelpBNT+mWSTszNnyBpUVrnEknqbh+DXNZeXfeKXt603w0l3S9pQSrzl9P8itcCSRuk9w+n5WNy26oY86p95ut1vVEP6w7NUFarj0aJ880Sv5slNjdTvG2WOCqpM/3fOyT9Ps1rvM9VRPhVsBcwBHgE2BFYH1gAjK1znrYC9k7TGwMPAWOB/wLOTvPPBr5Wxzx+DvgpcEt6/zPguDR9GfDJOh/DHwEfT9PrA5s1yvEDtgGWA8Nzx25yPY8hcCCwN7A4N6/i8QLeA/wKELAvcF+d8ncIMDRNfy2Xv7HpPN4A2CGd30Pq+Xn0q/6vRoz1Pchzv89LYAvgf9LfzdP05mnZ/Smt0rrv7m4fg1zWXl33il7etC8BI9P0MOC+lL+K1wLgVOCyNH0ccF2arhjzuvvMV9tHDcrco7pDM5TVr9q/uvsc1CEvTRG/aZLYTBPFW5okjgKdwKiyeQ33uap7YPOrTx+u/YAZufdfBL5Y73yV5fFm4J+ApcBWad5WwNI65Wdb4E7gYOCWdOKs5NUv2685pnXI36ZkDSgqm98ox28b4I8pGA1Nx/Cf630MgTG8tiJS8XgB3weOr5SulvkrW3YkcHWafs05DMwA9qvH/9qvxnkVIdZXyXe/zkvgeOD7ufnfT/O2Av6Qm/9KukaIleu67jVheUcA84B3VLsW5GNZunasJLv+Vox51T7z1OmaTS/qDkUvq1/1eVX7HNQxP2NosvhNE8RmChxvaaI4SuUGnob7XPkRrWIqfdku+VOa1xBSd7q9yFqat4yIJ9KiPwNb1ilbFwNfAF5O798APBMRL6X39T6GOwArgB+mLow/kLQRDXL8IuIx4OvAo8ATwN+AuTTWMYTqx6sRz5mPkrXOQ2Pmz+qvWT4XvT0vu5v/pwrzu9tHTfTwutcU5U1d7TuAp4Dbye6eVrsWvFK2tPxvZNff3h6Lel2ze1N3KHpZrT4aPc4XOp4VPTY3SbxtpjgawG2S5ko6Oc1ruM+VG3hsQEkaCfwcOD0ins0vi6zZMeqQp/cCT0XE3FrvuxeGknWL/V5E7AU8T9YF7xX1On4A6VnPw8kaorYGNgIOrUdeeqqex2tdJJ0DvARcXe+8mNVSLc7LWp/79b7u1bq8EbE2IsaT3ZV9O7BbrfZdSwWpO5jVTNHiWTPE5qLH2yaMoxMjYm/g3cCnJB2YX9gonys38BTTY8B2uffbpnl1JWkYWSC9OiJuTLOflLRVWr4VWQt0rb0T+BdJncC1ZF0EvwVsJmloSlPvY/gn4E8RcV96fwNZg08jHD+AdwHLI2JFRKwBbiQ7ro10DKH68WqYc0bSZOC9wKQUpKGB8mcNpVk+F709L7ubv22F+d3tY1D18rpX+PLmRcQzwEyyru/VrgWvlC0t3xT4C70/Fn/pZh+Dpbd1hyKX1eqn0eN8IeNZs8XmAsfbpoqj6YkGIuIpYBpZo1vDfa7cwFNMc4Bd0ujg65MNQjW9nhlKo3z/N/BgRHwjt2g6cGKaPpHsOdiaiogvRsS2ETGG7FjdFRGTyALl0fXMW0lE/Bn4o6Rd06x/BB6gAY5f8iiwr6QR6X9dyl/DHMOk2vGaDpyQRrTfF/hbrqtjzUg6lKyb6r9ExN9zi6YDxyn79YAdgF3IBlqz1tZwsb6PentezgAOkbR56j14CNmz808Az0raN8WhE8q2VdNY2YfrXqHLCyBptNKv/0kaTjauxYNUvxbk83k02fU3qB7zKn7m0zo1vd70oe5Q2LJaXTV6nC9cPGuW2NwM8baZ4qikjSRtXJom+zwsphE/V5UG5vGr8V9kI3M/RPYs5jkNkJ+JZN3FFgId6fUesmcg7wSWAXcAW9Q5n228OoL7jmTB4WHgemCDOudtPPD7dAxvIhtZvWGOH/Bl4A8pmP2EbCT7uh1D4Bqy8YDWkPWA+li140U22Np30vmyCNinTvl7mOy529I5clku/Tkpf0tJo+b75Vejxfoe5HdAzkuyMaoeTq+P5Obvk2LQI8ClpIHp6xEre3vdK3p50373BOanMi8Gzk3zK14LgA3T+4fT8h1z26oY86p95qvto0blbmMddYdmKatftX9V+xzUIR9NEb9pkthMk8VbCh5H0/YWpNeS0r4a8XNVWsnMzMzMzMzMzArKj2iZmZmZmZmZmRWcG3jMzMzMzMzMzArODTxmZmZmZmZmZgXnBh4zMzMzMzMzs4JzA4+ZmZmZmZmZWcENrXcGzAwkrSX7Cb2SIyKis07ZMTOzQeBYb2bW/BzrrZ78M+lmDUBSV0SMrOH+hkbES7Xan5mZOdabmbUCx3qrJz+iZVYAkraSNEtSh6TFkg5I8w+VNE/SAkl3pnlbSLpJ0kJJv5O0Z5o/RdJPJP0G+Imk0ZJ+LmlOer2zjkU0M2t5jvVmZs3Psd4Gkx/RMmsMwyV1pOnlEXFk2fIPAjMi4jxJQ4ARkkYDVwAHRsRySVuktF8G5kfEEZIOBn4MjE/LxgITI2KVpJ8C34yIeyRtD8wAdh/EMpqZtTrHejOz5udYb3XjBh6zxrAqIsZ3s3wOcKWkYcBNEdEhqQ2YFRHLASLi6ZR2InBUmneXpDdI2iQtmx4Rq9L0u4Cxkkr72ETSyIjoGrhimZlZjmO9mVnzc6y3unEDj1kBRMQsSQcChwFTJX0D+GsfNvV8bno9YN+IWD0QeTQzs/5xrDcza36O9TaYPAaPWQFIejPwZERcAfwA2Bv4HXCgpB1SmlJXztnApDSvDVgZEc9W2OxtwKdz++juToOZmQ0yx3ozs+bnWG+DyT14zIqhDThT0hqgCzghIlZIOhm4UdJ6wFPAPwFTyLp9LgT+DpxYZZunAd9J6YYCs4BTBrUUZmbWnTYc683Mml0bjvU2SPwz6WZmZmZmZmZmBedHtMzMzMzMzMzMCs4NPGZmZmZmZmZmBecGHjMzMzMzMzOzgnMDj5mZmZmZmZlZwbmBx8zMzMzMzMys4NzAY2ZmZmZmZmZWcG7gMTMzMzMzMzMruP8DaNFkD/UGlJ4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot, we can infer that \n",
        "- diffOrg has the most gain, i.e. diffOrg contributed most towards model prediction. oldbalanceOrg, amount, and newbalanceDest follows. <br>\n",
        "$\\implies$ The most important features are diffOrg, oldbalanceOrg, amount and newbalanceDest.\n",
        "- As we look further down the gain plot, we find that PAYMENT contributed more than TRANSFER towards model prediction. This can be attributed towards much greater number of PAYMENT samples in the distribution (than TRANSFER). Also, TRANSFER and PAYMENT has very similar weights. <br>\n",
        "This result doesn't make sense because all PAYMENTs were non-fraud in the original dataset and did not contribute towards target label.\n",
        "- The gain of CASH_IN over diffDest is also not very intuitive, because diffDest has more sample cover than CASH_IN and also has more weight. And CASH_IN is another feature that didn't contribute towards target label.\n",
        "- Even with comparatively high weight for step, it has very less gain. This may be due to the fact that fraud transactions per category has an almost uniform distribution on step - yet, the result is quite unexpected.\n",
        "- The feature isFlaggedFraud, although present in the dataset, seems to be completely ommitted. XGBoost algorithm might have ignored isFlaggedFraud because it had only very less number of positive examples.\n",
        "- It seems justified that CASH_OUT has a low weight, because it has a comparatively high sample coverage, but requires only moderate gain for model prediction."
      ],
      "metadata": {
        "id": "wCgsP0ZPzfsS"
      },
      "id": "wCgsP0ZPzfsS"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fraud prevention"
      ],
      "metadata": {
        "id": "kyD87dA6CERt"
      },
      "id": "kyD87dA6CERt"
    },
    {
      "cell_type": "markdown",
      "source": [
        "- When updating the infrastructure for preventing fraud transactions, the company should lookout for anomalies in diffOrg.\n",
        "$$\\text{diffOrg = oldbalanceOrg  - newbalanceOrig + amount}$$\n",
        "- Detecting anomalies in a feature that combines oldbalanceOrg, amount and newbalanceDest, will also be useful for fraud prevention. E.g.\n",
        "$$\\text{suspectRatio} = \\frac{\\text{oldbalanceOrg + amount}}{\\text{newbalanceDest}}$$\n",
        "- Using a different criteria for flagging fraudulent transactions will also be useful."
      ],
      "metadata": {
        "id": "0c4W2p-0Ccwg"
      },
      "id": "0c4W2p-0Ccwg"
    },
    {
      "cell_type": "markdown",
      "source": [
        "After updating the infrastructure, we can retrain the model using new data and compare the model performances to determine whether the updates will be successful towards proactive fraud detection."
      ],
      "metadata": {
        "id": "XoXtnVPXF2cm"
      },
      "id": "XoXtnVPXF2cm"
    }
  ],
  "metadata": {
    "kernelspec": {
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      "language": "python",
      "name": "python3"
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        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "INSAID Internship Screening Task - Jincy P Janardhanan.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
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  "nbformat_minor": 5
}