FinML Loan Classification.ipynb

finml-loan-classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.3"
    },
    "nav_menu": {},
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    },
    "colab": {
      "name": "FinML Loan Classification.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/firmai/0937fda931e3d01010f2b1860bf36228/finml-loan-classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGyWLCHj9GQP"
      },
      "source": [
        "# Loan Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqV1JHLO9GQR"
      },
      "source": [
        "# To support both python 2 and python 3\n",
        "from __future__ import division, print_function, unicode_literals\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \"../..\"\n",
        "CHAPTER_ID = \"classification\"\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True):\n",
        "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
        "    print(\"Saving figure\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    try:\n",
        "        plt.savefig(path, format='png', dpi=300)\n",
        "    except:\n",
        "        plt.savefig(fig_id + \".png\", format='png', dpi=300)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1h26_qjv9GQb"
      },
      "source": [
        "# Loans - Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1sblmQN9GQe"
      },
      "source": [
        "from pathlib import Path\n",
        "\n",
        "df = pd.read_csv('https://storage.googleapis.com/public-quant/course//content/loans.csv')\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfUpiT-59GQs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "081aa0d2-552e-4a26-adf8-c5d6fdf7b608"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
              "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
              "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
              "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
              "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
              "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
              "\n",
              "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
              "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
              "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
              "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
              "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
              "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
              "\n",
              "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
              "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
              "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
              "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
              "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
              "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
              "\n",
              "  total_cu_tl inq_last_12m  \n",
              "0         NaN          NaN  \n",
              "1         NaN          NaN  \n",
              "2         NaN          NaN  \n",
              "3         NaN          NaN  \n",
              "4         NaN          NaN  \n",
              "\n",
              "[5 rows x 74 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9512ed87-72ed-4cb1-9f1d-836bea6778bc\" 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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>...</th>\n",
              "      <th>total_bal_il</th>\n",
              "      <th>il_util</th>\n",
              "      <th>open_rv_12m</th>\n",
              "      <th>open_rv_24m</th>\n",
              "      <th>max_bal_bc</th>\n",
              "      <th>all_util</th>\n",
              "      <th>total_rev_hi_lim</th>\n",
              "      <th>inq_fi</th>\n",
              "      <th>total_cu_tl</th>\n",
              "      <th>inq_last_12m</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1075358</td>\n",
              "      <td>1311748</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 74 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9512ed87-72ed-4cb1-9f1d-836bea6778bc')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-9512ed87-72ed-4cb1-9f1d-836bea6778bc 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-9512ed87-72ed-4cb1-9f1d-836bea6778bc');\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",
              "\n",
              "\n",
              "<div id=\"df-04d43928-4472-4222-ac33-fec12aac1151\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04d43928-4472-4222-ac33-fec12aac1151')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-04d43928-4472-4222-ac33-fec12aac1151 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "By-xDyta9GQ0"
      },
      "source": [
        "# Replace the name of some columns\n",
        "df = df.rename(columns={\"loan_amnt\": \"loan_amount\", \"funded_amnt\": \"funded_amount\", \"funded_amnt_inv\": \"investor_funds\",\n",
        "                       \"int_rate\": \"interest_rate\", \"annual_inc\": \"annual_income\"})\n",
        "\n",
        "# Drop irrelevant columns\n",
        "df.drop(['id', 'member_id', 'emp_title', 'url', 'desc', 'zip_code', 'title'], axis=1, inplace=True)\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCZdVlGm9GQ5"
      },
      "source": [
        "## Similar Distributions:\n",
        "<a id=\"similar_distributions\"></a>\n",
        "We will start by exploring the distribution of the loan amounts and see when did the loan amount issued increased significantly. <br>\n",
        "\n",
        "<h4> What we need to know: </h4> <br>\n",
        "<ul>\n",
        "<li> Understand what amount was <b>mostly issued</b> to borrowers. </li>\n",
        "<li> Which <b>year</b> issued the most loans. </li>\n",
        "<li> The distribution of loan amounts is a <b>multinomial distribution </b>.</li>\n",
        "</ul>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Hg0gBbk9GQ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "0d069737-7726-446f-d69a-eb2c7685c0fa"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 3, figsize=(16,5))\n",
        "\n",
        "loan_amount = df[\"loan_amount\"].values\n",
        "funded_amount = df[\"funded_amount\"].values\n",
        "investor_funds = df[\"investor_funds\"].values\n",
        "\n",
        "sns.histplot(loan_amount, ax=ax[0], color=\"#F7522F\", kde=True)\n",
        "ax[0].set_title(\"Loan Applied by the Borrower\", fontsize=14)\n",
        "sns.histplot(funded_amount, ax=ax[1], color=\"#2F8FF7\", kde=True)\n",
        "ax[1].set_title(\"Amount Funded by the Lender\", fontsize=14)\n",
        "sns.histplot(investor_funds, ax=ax[2], color=\"#2EAD46\", kde=True)\n",
        "ax[2].set_title(\"Total committed by Investors\", fontsize=14)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdiM3UVt9GRE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "300b883b-55f7-4eee-b2cd-bcb2c0863152"
      },
      "source": [
        "df['issue_d'].head()\n",
        "dt_series = pd.to_datetime(df['issue_d'], errors = 'coerce')\n",
        "df['year'] = dt_series.dt.year"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-c9cbe11d0309>:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  dt_series = pd.to_datetime(df['issue_d'], errors = 'coerce')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xzaHX169GRJ"
      },
      "source": [
        "Loan Status and Issuance Amount: </h2>\n",
        "<a id=\"types_of_loans\"></a>\n",
        "In this section, we will see what is the amount of bad loans declared so far, of course we have to understand that there are still loans that are at a risk of defaulting in the future.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDsrYEqg9GRK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "07076402-736f-4fd0-9762-7748f98ffe43"
      },
      "source": [
        "df[\"loan_status\"].value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_status\n",
              "Fully Paid                                             54314\n",
              "Current                                                32417\n",
              "Charged Off                                            11237\n",
              "Does not meet the credit policy. Status:Fully Paid      1988\n",
              "Late (31-120 days)                                       969\n",
              "Does not meet the credit policy. Status:Charged Off      761\n",
              "In Grace Period                                          457\n",
              "Late (16-30 days)                                        153\n",
              "Default                                                   92\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>54314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current</th>\n",
              "      <td>32417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>11237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Does not meet the credit policy. Status:Fully Paid</th>\n",
              "      <td>1988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (31-120 days)</th>\n",
              "      <td>969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Does not meet the credit policy. Status:Charged Off</th>\n",
              "      <td>761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In Grace Period</th>\n",
              "      <td>457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Default</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZ6WqyTj9GRU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "a3859782-aa2d-463b-e6dd-c282b3e61b73"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.barplot(x='year', y='loan_amount', data=df, hue='year', palette='spring', legend=False)\n",
        "plt.title('Issuance of Loans', fontsize=16)\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Average loan amount issued', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvz0-t8_9GRd"
      },
      "source": [
        "## The Importance of Credit Scores:\n",
        "<a id=\"credit_scores\"></a>\n",
        "Credit scores are important metrics for assesing the overall level of risk. In this section we will analyze the level of risk as a whole and how many loans were bad loans by the type of grade received in the credit score of the customer.\n",
        "\n",
        "<h4> What we need to know: </h4>\n",
        "<ul>\n",
        "<li> The lower the grade of the credit score, the higher the risk for investors. </li>\n",
        "<li> There are different factors that influence on the level of risk of the loan.</li>\n",
        "</ul>\n",
        "\n",
        "<h4> Summary: </h4>\n",
        "<ul>\n",
        "<li> The scores that has a lower grade received a larger amounts of loans (which might had contributed to a higher level of risk). </li>\n",
        "<li> Logically, the <b>lower the grade the higher the interest</b> the customer had to pay back to investors.</li>\n",
        "<ul>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8eRhcOS9GRe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "d61a5bc0-5294-4d5a-8f02-c68f456b8916"
      },
      "source": [
        "# Let's visualize how many loans were issued by creditscore\n",
        "f, ((ax1, ax2)) = plt.subplots(1, 2)\n",
        "cmap = plt.cm.coolwarm\n",
        "\n",
        "by_credit_score = df.groupby(['year', 'grade']).loan_amount.mean()\n",
        "by_credit_score.unstack().plot(legend=False, ax=ax1, figsize=(14, 4), colormap=cmap)\n",
        "ax1.set_title('Loans issued by Credit Score', fontsize=14)\n",
        "\n",
        "\n",
        "by_inc = df.groupby(['year', 'grade']).interest_rate.mean()\n",
        "by_inc.unstack().plot(ax=ax2, figsize=(14, 4), colormap=cmap)\n",
        "ax2.set_title('Interest Rates by Credit Score', fontsize=14)\n",
        "\n",
        "ax2.legend(bbox_to_anchor=(-1.0, -0.3, 1.7, 0.1), loc=5, prop={'size':12},\n",
        "           ncol=7, mode=\"expand\", borderaxespad=0.)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a2c9231b8b0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nwAKjcr9GRm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970
        },
        "outputId": "44c62f0a-3db9-4a91-ea3d-f1abea4089a6"
      },
      "source": [
        "# Determining the loans that are bad from loan_status column\n",
        "%matplotlib inline\n",
        "\n",
        "bad_loan = [\"Charged Off\", \"Default\", \"Does not meet the credit policy. Status:Charged Off\", \"In Grace Period\",\n",
        "            \"Late (16-30 days)\", \"Late (31-120 days)\"]\n",
        "\n",
        "\n",
        "df['loan_condition'] = np.nan\n",
        "\n",
        "def loan_condition(status):\n",
        "    if status in bad_loan:\n",
        "        return 'Bad Loan'\n",
        "    else:\n",
        "        return 'Good Loan'\n",
        "\n",
        "\n",
        "df['loan_condition'] = df['loan_status'].apply(loan_condition)\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(16,12))\n",
        "\n",
        "ax1 = fig.add_subplot(221)\n",
        "ax2 = fig.add_subplot(222)\n",
        "ax3 = fig.add_subplot(212)\n",
        "\n",
        "cmap = plt.cm.coolwarm_r\n",
        "\n",
        "loans_by_region = df.groupby(['grade', 'loan_condition']).size()\n",
        "loans_by_region.unstack().plot(kind='bar', stacked=True, colormap=cmap, ax=ax1, grid=False)\n",
        "ax1.set_title('Type of Loans by Grade', fontsize=14)\n",
        "\n",
        "\n",
        "loans_by_grade = df.groupby(['sub_grade', 'loan_condition']).size()\n",
        "loans_by_grade.unstack().plot(kind='bar', stacked=True, colormap=cmap, ax=ax2, grid=False)\n",
        "ax2.set_title('Type of Loans by Sub-Grade', fontsize=14)\n",
        "\n",
        "by_interest = df.groupby(['year', 'loan_condition']).interest_rate.mean()\n",
        "by_interest.unstack().plot(ax=ax3, colormap=cmap)\n",
        "ax3.set_title('Average Interest rate by Loan Condition', fontsize=14)\n",
        "ax3.set_ylabel('Interest Rate (%)', fontsize=12)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Interest Rate (%)')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6T8TYBry9GRt"
      },
      "source": [
        "## Data Cleaning and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lw8IABFR9GRw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "56aa4763-1aa8-4818-ec09-5c489df72238"
      },
      "source": [
        "target_list = [1 if i=='Bad Loan' else 0 for i in df['loan_condition']]\n",
        "\n",
        "df['loan_condition'] = target_list\n",
        "df['loan_condition'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_condition\n",
              "0    88719\n",
              "1    13669\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_condition</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>88719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ka0dLSF9GR3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "67a18690-c7c7-4023-da12-76fcd2ffb106"
      },
      "source": [
        "# Number of columns for each data type\n",
        "df.dtypes.value_counts().sort_values().plot(kind='barh')\n",
        "plt.title('Number of columns distributed by Data Types',fontsize=20)\n",
        "plt.xlabel('Number of columns',fontsize=15)\n",
        "plt.ylabel('Data type',fontsize=15)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Data type')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmJVGtZv9GSA"
      },
      "source": [
        "We would want to label encode the columns that have 2 categories and one-hot encode columns with more than 2 categories. Also, columns like emp_title, url, desc, etc. should be dropped because there aren't any large number of unique data for any of the categories they contain. Principal Component Analysis can also be carried out for the one-hot encoded columns to bring the feature dimensions down."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LX3kJBJy9GSC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "12ae18a1-aa84-4bd5-9ac7-bd02a95932ad"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   loan_amount  funded_amount  investor_funds        term  interest_rate  \\\n",
              "0         5000           5000          4975.0   36 months          10.65   \n",
              "1         2500           2500          2500.0   60 months          15.27   \n",
              "2         2400           2400          2400.0   36 months          15.96   \n",
              "3        10000          10000         10000.0   36 months          13.49   \n",
              "4         3000           3000          3000.0   60 months          12.69   \n",
              "\n",
              "   installment grade sub_grade emp_length home_ownership  ...  open_rv_12m  \\\n",
              "0       162.87     B        B2  10+ years           RENT  ...          NaN   \n",
              "1        59.83     C        C4   < 1 year           RENT  ...          NaN   \n",
              "2        84.33     C        C5  10+ years           RENT  ...          NaN   \n",
              "3       339.31     C        C1  10+ years           RENT  ...          NaN   \n",
              "4        67.79     B        B5     1 year           RENT  ...          NaN   \n",
              "\n",
              "  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi total_cu_tl  \\\n",
              "0         NaN        NaN      NaN              NaN    NaN         NaN   \n",
              "1         NaN        NaN      NaN              NaN    NaN         NaN   \n",
              "2         NaN        NaN      NaN              NaN    NaN         NaN   \n",
              "3         NaN        NaN      NaN              NaN    NaN         NaN   \n",
              "4         NaN        NaN      NaN              NaN    NaN         NaN   \n",
              "\n",
              "   inq_last_12m  year loan_condition  \n",
              "0           NaN  2011              0  \n",
              "1           NaN  2011              1  \n",
              "2           NaN  2011              0  \n",
              "3           NaN  2011              0  \n",
              "4           NaN  2011              0  \n",
              "\n",
              "[5 rows x 69 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-019369dc-ec55-4384-b974-1249d00079d1\" 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>loan_amount</th>\n",
              "      <th>funded_amount</th>\n",
              "      <th>investor_funds</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>...</th>\n",
              "      <th>open_rv_12m</th>\n",
              "      <th>open_rv_24m</th>\n",
              "      <th>max_bal_bc</th>\n",
              "      <th>all_util</th>\n",
              "      <th>total_rev_hi_lim</th>\n",
              "      <th>inq_fi</th>\n",
              "      <th>total_cu_tl</th>\n",
              "      <th>inq_last_12m</th>\n",
              "      <th>year</th>\n",
              "      <th>loan_condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>B2</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 69 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-019369dc-ec55-4384-b974-1249d00079d1')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-019369dc-ec55-4384-b974-1249d00079d1 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-019369dc-ec55-4384-b974-1249d00079d1');\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",
              "\n",
              "\n",
              "<div id=\"df-24a91032-058d-4ce9-99d0-4da3e105bb94\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24a91032-058d-4ce9-99d0-4da3e105bb94')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-24a91032-058d-4ce9-99d0-4da3e105bb94 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPQVb3y19GSO"
      },
      "source": [
        "\n",
        "\n",
        "First, I'll be converting the date object columns into integer number of years or months just because I do not want to blow up the number of feature columns by performing one-hot encoding on them. For filling the null values I have taken the dates with the highest number of counts.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfTZmIeH9GSP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cdfbdf1-d844-42a7-bc3c-6c180136c0b5"
      },
      "source": [
        "df['issue_d']= pd.to_datetime(df['issue_d']).apply(lambda x: int(x.strftime('%Y')))\n",
        "df['last_pymnt_d']= pd.to_datetime(df['last_pymnt_d'] .fillna(str(df['last_pymnt_d'].mode().values[0])),format='%d/%m/%y').dt.month\n",
        "df['last_credit_pull_d']= pd.to_datetime(df['last_credit_pull_d'] .fillna(str(df['last_credit_pull_d'].mode().values[0])),format='%d/%m/%y').dt.month\n",
        "df['earliest_cr_line']= pd.to_datetime(df['earliest_cr_line'] .fillna(str(df['earliest_cr_line'].mode().values[0])),format='%d/%m/%y').dt.month\n",
        "df['next_pymnt_d'] = pd.to_datetime(df['next_pymnt_d'] .fillna(str(df['next_pymnt_d'].mode().values[0])),format='%d/%m/%y').dt.year"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-f246a2a4a70a>:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  df['issue_d']= pd.to_datetime(df['issue_d']).apply(lambda x: int(x.strftime('%Y')))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_kgBZ809GSW"
      },
      "source": [
        "Some more cleanup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3GrkseC9GSa"
      },
      "source": [
        "df['mths_since_last_delinq'] = df['mths_since_last_delinq'].fillna(df['mths_since_last_delinq'].median())\n",
        "df['emp_length'].fillna(value=0,inplace=True)\n",
        "df['emp_length'].replace(to_replace='[^0-9]+', value='', inplace=True, regex=True)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG0jXx6T9GSi"
      },
      "source": [
        "Let's see how we can handle our categorical data. Two of the methods we can use are Label Encoding and One Hot Encoding. The problem with label encoding is that it gives the categories an arbitrary ordering. The value assigned to each of the categories is random and does not reflect any inherent aspect of the category. So, If we only have two unique values for a categorical variable (such as Yes/No), then label encoding is fine, but for more than 2 unique categories, one-hot encoding is the better option. However, due to the large number of columns originated after One-Hot Encoding, we may have to conduct Principle Component Analysis (PCA) for dimensionality reduction.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cWa-1uF9GSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c91309c6-c0aa-4fcc-9221-8b019d7780b2"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "count = 0\n",
        "\n",
        "for col in df:\n",
        "    if df[col].dtype == 'object':\n",
        "        if len(list(df[col].unique())) <= 2:\n",
        "            le = preprocessing.LabelEncoder()\n",
        "            df[col] = le.fit_transform(df[col])\n",
        "            count += 1\n",
        "            print (col)\n",
        "\n",
        "print('%d columns were label encoded.' % count)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "term\n",
            "pymnt_plan\n",
            "initial_list_status\n",
            "application_type\n",
            "4 columns were label encoded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pf7MkvAt9GSr"
      },
      "source": [
        "\n",
        "And one-hot encoding the rest of the categorical columns,\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hS2STR39GSt"
      },
      "source": [
        "cols_drop = [\"loan_status\",\"loan_condition\"]\n",
        "extra_cols = df[cols_drop]\n",
        "df = pd.get_dummies(df.drop(cols_drop, axis=1))\n",
        "df = pd.concat((extra_cols, df),axis=1)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgzF6xSR9GSx"
      },
      "source": [
        "For the 'mths_since_last_delinq' column, I'll be filling in the missing value with the median of the columns as the data in the column is continuous."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAQPurHK9GS0"
      },
      "source": [
        "Let me remove all the columns with more than 70% missing data as they won't be helping for modelling and exploration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LNgm1b79GS2"
      },
      "source": [
        "temp = [i for i in df.count()<len(df) *0.30]\n",
        "df.drop(df.columns[temp],axis=1,inplace=True)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oycnXwg9GTC"
      },
      "source": [
        "Drop the rows records with null values; in practice you should find a way to fill these values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eH3TSE839GTE"
      },
      "source": [
        "df.dropna(inplace=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhzSB9429GTL"
      },
      "source": [
        "Now we have about 60,000 records left"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CVYvSqK9GTM"
      },
      "source": [
        "# Binary classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BnN39Z39GTN"
      },
      "source": [
        "X = df.drop([\"loan_condition\",\"loan_status\"],axis=1).values\n",
        "y = df[\"loan_condition\"].values"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcONW_qO9GTS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b58aba1-61fb-4dbb-c41e-9a5f778f1cf5"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59815"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZA_ro5S9GTg"
      },
      "source": [
        "X_train, X_test, y_train, y_test = X[:40000], X[40000:], y[:40000], y[40000:]"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oa7Xw8Y9GTl"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "shuffle_index = np.random.permutation(40000)\n",
        "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]\n",
        "y_train_c = y_train.copy()"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_SO3rlC9GTr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "49907b9b-153f-44d7-e1cc-c3bdf6f0e8f2"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      loan_status  loan_condition  loan_amount  funded_amount  investor_funds  \\\n",
              "42535     Current               0        27050          27050         27050.0   \n",
              "42536     Current               0         9750           9750          9750.0   \n",
              "42537     Current               0        12000          12000         12000.0   \n",
              "42538  Fully Paid               0        12000          12000         12000.0   \n",
              "42539     Current               0        15000          15000         15000.0   \n",
              "\n",
              "       term  interest_rate  installment  annual_income  issue_d  ...  \\\n",
              "42535     0          10.99       885.46        55000.0     2013  ...   \n",
              "42536     0          13.98       333.14        26000.0     2013  ...   \n",
              "42537     0           6.62       368.45       105000.0     2013  ...   \n",
              "42538     0          13.53       407.40        40000.0     2013  ...   \n",
              "42539     0           8.90       476.30        63000.0     2013  ...   \n",
              "\n",
              "       addr_state_SD  addr_state_TN  addr_state_TX  addr_state_UT  \\\n",
              "42535          False          False          False          False   \n",
              "42536          False          False          False          False   \n",
              "42537          False          False          False          False   \n",
              "42538          False          False          False          False   \n",
              "42539          False          False          False          False   \n",
              "\n",
              "       addr_state_VA  addr_state_VT  addr_state_WA  addr_state_WI  \\\n",
              "42535          False          False          False          False   \n",
              "42536          False          False          False          False   \n",
              "42537          False          False          False          False   \n",
              "42538          False          False          False          False   \n",
              "42539          False          False          False          False   \n",
              "\n",
              "       addr_state_WV  addr_state_WY  \n",
              "42535          False          False  \n",
              "42536          False          False  \n",
              "42537          False          False  \n",
              "42538          False          False  \n",
              "42539          False          False  \n",
              "\n",
              "[5 rows x 168 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e66e763-8b8c-4ac4-9bc6-3f831d8f6c67\" 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>loan_status</th>\n",
              "      <th>loan_condition</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>funded_amount</th>\n",
              "      <th>investor_funds</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>...</th>\n",
              "      <th>addr_state_SD</th>\n",
              "      <th>addr_state_TN</th>\n",
              "      <th>addr_state_TX</th>\n",
              "      <th>addr_state_UT</th>\n",
              "      <th>addr_state_VA</th>\n",
              "      <th>addr_state_VT</th>\n",
              "      <th>addr_state_WA</th>\n",
              "      <th>addr_state_WI</th>\n",
              "      <th>addr_state_WV</th>\n",
              "      <th>addr_state_WY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42535</th>\n",
              "      <td>Current</td>\n",
              "      <td>0</td>\n",
              "      <td>27050</td>\n",
              "      <td>27050</td>\n",
              "      <td>27050.0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.99</td>\n",
              "      <td>885.46</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42536</th>\n",
              "      <td>Current</td>\n",
              "      <td>0</td>\n",
              "      <td>9750</td>\n",
              "      <td>9750</td>\n",
              "      <td>9750.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.98</td>\n",
              "      <td>333.14</td>\n",
              "      <td>26000.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42537</th>\n",
              "      <td>Current</td>\n",
              "      <td>0</td>\n",
              "      <td>12000</td>\n",
              "      <td>12000</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.62</td>\n",
              "      <td>368.45</td>\n",
              "      <td>105000.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42538</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>0</td>\n",
              "      <td>12000</td>\n",
              "      <td>12000</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.53</td>\n",
              "      <td>407.40</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42539</th>\n",
              "      <td>Current</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>15000</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.90</td>\n",
              "      <td>476.30</td>\n",
              "      <td>63000.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 168 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e66e763-8b8c-4ac4-9bc6-3f831d8f6c67')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5e66e763-8b8c-4ac4-9bc6-3f831d8f6c67 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-5e66e763-8b8c-4ac4-9bc6-3f831d8f6c67');\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",
              "\n",
              "\n",
              "<div id=\"df-5b477842-0b14-4a4f-b12e-35d8c5ca0091\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b477842-0b14-4a4f-b12e-35d8c5ca0091')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5b477842-0b14-4a4f-b12e-35d8c5ca0091 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whC8gcMK9GT3"
      },
      "source": [
        "**Note**: a few hyperparameters will have a different default value in future versions of Scikit-Learn, so a warning is issued if you do not set them explicitly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGfc7XR69GT5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab960392-0381-4046-b926-2a477798a9e1"
      },
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier(loss='hinge', alpha=0.0001, max_iter=1000, tol=1e-3, random_state=42)\n",
        "\n",
        "print(sgd_clf.get_params())"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'alpha': 0.0001, 'average': False, 'class_weight': None, 'early_stopping': False, 'epsilon': 0.1, 'eta0': 0.0, 'fit_intercept': True, 'l1_ratio': 0.15, 'learning_rate': 'optimal', 'loss': 'hinge', 'max_iter': 1000, 'n_iter_no_change': 5, 'n_jobs': None, 'penalty': 'l2', 'power_t': 0.5, 'random_state': 42, 'shuffle': True, 'tol': 0.001, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sgd_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 94
        },
        "id": "cQRxMF-biYXD",
        "outputId": "a7ebcbcc-213b-4454-cf29-84e0fc661514"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-bwy5K_9GUC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5df1b69c-18eb-4c8f-82fe-a38715d5e321"
      },
      "source": [
        "sgd_clf.predict([X_test[0]]) # Predicts Good Loan"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRcnV4K99GUK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec69e991-545d-4538-eb44-7785c167c060"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9520774 , 0.96467412, 0.96849921])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23AzQKdn9GUT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3168d5a-3c5c-4771-dbbd-c33ffe8a4ba7"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.base import clone\n",
        "\n",
        "skfolds = StratifiedKFold(n_splits=3)\n",
        "\n",
        "for train_index, test_index in skfolds.split(X_train, y_train):\n",
        "    clone_clf = clone(sgd_clf)\n",
        "    X_train_folds = X_train[train_index]\n",
        "    y_train_folds = (y_train[train_index])\n",
        "    X_test_fold = X_train[test_index]\n",
        "    y_test_fold = (y_train[test_index])\n",
        "\n",
        "    clone_clf.fit(X_train_folds, y_train_folds)\n",
        "    y_pred = clone_clf.predict(X_test_fold)\n",
        "    n_correct = sum(y_pred == y_test_fold)\n",
        "    print(n_correct / len(y_pred))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9520773961301935\n",
            "0.9646741168529214\n",
            "0.968499212480312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAK-nY6Q9GUd"
      },
      "source": [
        "from sklearn.base import BaseEstimator\n",
        "class NeverClassifier(BaseEstimator):\n",
        "    def fit(self, X, y=None):\n",
        "        pass\n",
        "    def predict(self, X):\n",
        "        return np.zeros((len(X), 1), dtype=bool)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmBc_Agq9GUi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22d8f387-357e-4d35-f67c-0fc283b4cd96"
      },
      "source": [
        "never_clf = NeverClassifier()\n",
        "cross_val_score(never_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.87978101, 0.88314708, 0.88157204])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OvXUoG39GUu"
      },
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train, cv=3)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvZSmECZ9GU1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aec332b7-9afc-482b-b047-1f0e4f6aaca7"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_train, y_train_pred)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[34955,   305],\n",
              "       [ 1225,  3515]])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oorjpBZE9GU7"
      },
      "source": [
        "y_train_perfect_predictions = y_train"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psFKhfRT9GVF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd52ad7c-1737-445d-d880-9e5f26a72dd6"
      },
      "source": [
        "confusion_matrix(y_train, y_train_perfect_predictions)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[35260,     0],\n",
              "       [    0,  4740]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9ql2bLl9GVM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a18078b-69d6-4833-fe6b-ed928dda9f94"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "precision_score(y_train, y_train_pred)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9201570680628273"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1of1X7R9GVW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccc1f7d0-b41c-473d-c405-ecca0e818eae"
      },
      "source": [
        "3590 / (3590 + 494)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8790401567091087"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBPQTx5q9GVg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffa00442-de03-40fc-ad2c-da0f4cc11545"
      },
      "source": [
        "recall_score(y_train, y_train_pred)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7415611814345991"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ci9D-j_9GVr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "541483ef-2bf6-4a4e-f865-986c90e83edb"
      },
      "source": [
        "3590 / (3590 + 1150)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7573839662447257"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_DEIiBa9GV0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "127deaf2-b0a6-41a4-ca3f-262071503d42"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(y_train, y_train_pred)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8212616822429908"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QB62--V99GV5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc6f4192-6bcf-4141-abe7-8e9d8072a29d"
      },
      "source": [
        "3590 / (3590 + (1150 + 494)/2)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8136899365367181"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhFttAhn9GWA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dba55071-ea2f-4362-bc18-1ba46d22a5bc"
      },
      "source": [
        "y_scores = sgd_clf.decision_function([X_test[0]])\n",
        "y_scores"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-798410345.438097], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7Z0Wa8n9GWM"
      },
      "source": [
        "top = y_scores[0]*1.1"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVtoCwuJ9GWU"
      },
      "source": [
        "threshold = 0\n",
        "y_some_digit_pred = (y_scores > threshold)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLdZ7G6H9GWZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed6b7bce-23fa-4869-eeb4-8c97e7a6d2c3"
      },
      "source": [
        "y_some_digit_pred"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe3J6kEc9GWl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08641eba-2a47-42ac-e49c-a407245813a9"
      },
      "source": [
        "threshold =  top\n",
        "y_some_digit_pred = (y_scores > threshold)\n",
        "y_some_digit_pred"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0ddZLD79GW-"
      },
      "source": [
        "y_scores = cross_val_predict(sgd_clf, X_train, y_train, cv=3,\n",
        "                             method=\"decision_function\")"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItA9QFL99GXC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fac694c3-e8d1-4be5-f21f-a19d1ed8a548"
      },
      "source": [
        "y_scores"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-8.07040340e+08, -2.32331392e+08, -7.08761106e+08, ...,\n",
              "        1.22895439e+09, -9.24834588e+08, -1.30474314e+08])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxDKMTD49GXG"
      },
      "source": [
        "Note: there was an [issue](https://github.com/scikit-learn/scikit-learn/issues/9589) in Scikit-Learn 0.19.0 (fixed in 0.19.1) where the result of `cross_val_predict()` was incorrect in the binary classification case when using `method=\"decision_function\"`, as in the code above. The resulting array had an extra first dimension full of 0s. Just in case you are using 0.19.0, we need to add this small hack to work around this issue:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43jNL15q9GXJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e55acaa-89bc-4c32-ebfc-ba2a51d71e79"
      },
      "source": [
        "y_scores.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000,)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5UWMsfQ9GXR"
      },
      "source": [
        "# hack to work around issue #9589 in Scikit-Learn 0.19.0\n",
        "if y_scores.ndim == 2:\n",
        "    y_scores = y_scores[:, 1]"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcTXnbhI9GXV"
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQ4pnWmL9GXY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "30ae8447-0ebc-4444-b3b8-fa40d98d8e1e"
      },
      "source": [
        "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
        "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
        "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
        "    plt.xlabel(\"Threshold\", fontsize=16)\n",
        "    plt.legend(loc=\"upper left\", fontsize=16)\n",
        "    plt.ylim([0, 1])\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
        "plt.xlim([top, -top])\n",
        "save_fig(\"precision_recall_vs_threshold_plot\")\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving figure precision_recall_vs_threshold_plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAGGCAYAAADmRxfNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3OUlEQVR4nO3dZ3hU1dqH8XvSE9IIEJJAEkroHQy9K8UCItIUFRTEckAsKAcVFRV5FY4ePahYKXZEEEVBQJoU6UgJTSAQktACpEAyafv9MGbCkAQypEwC/5/XXO5Ze609z4wj7GdWMxmGYSAiIiIiIlIETo4OQEREREREyj8lFiIiIiIiUmRKLEREREREpMiUWIiIiIiISJEpsRARERERkSJTYiEiIiIiIkWmxEJERERERIpMiYWIiIiIiBSZEgsRERERESkyJRYiIiIiIlJkdicWKSkpvPzyy/Tu3ZuAgABMJhOzZs0qdPvz588zatQoqlSpQoUKFejWrRvbtm2zNwwRERERESlD7E4szpw5w6uvvsrevXtp1qyZXW2zs7O5/fbb+frrrxk9ejRvvfUWp06domvXrhw8eNDeUEREREREpIxwsbdBcHAw8fHxBAUFsWXLFiIjIwvddt68eaxfv57vv/+eAQMGADBo0CDq1q3Lyy+/zNdff21vOCIiIiIiUgbY3WPh7u5OUFDQNb3YvHnzqFq1Kv3797eWValShUGDBrFw4ULMZvM1XVdERERERByrVCdvb9++nZYtW+LkZPuyrVu35uLFixw4cKA0wxERERERkWJi91CoooiPj6dz5855yoODgwGIi4ujSZMmec6bzWab3ozs7GzOnj1LpUqVMJlMJRewiIiIiMgNzDAMkpOTCQkJydM5cLlSTSxSU1Nxd3fPU+7h4WE9n58pU6YwadKkEo1NRERERETyFxMTQ/Xq1a9Yp1QTC09Pz3znUaSlpVnP52fChAk8/fTT1ueJiYmEhYURExODr69vyQQrIiLlzrFj8NFHsH077NwJycn513N2hsOHwd8/t+yxx2DNGnBxAScnqFgRKlSw1HV2tpQ5O8Ntt8EDD+S2u3ABCvvb15gxEBqa+3z7dvj226u38/KCl1+2LfvuO0v7q2neHIYMsS17442CP5tLDRgArVrlPo+Lg/ffv3o7gPHj4dK/otesgaVLr94uKAhGj7Yt+/xzOHLk6m07dYKePXOfZ2UV/r/NsGFQu3bu8/37oTBryjg55f1v8+uvsHnz1dvWqQP33mtbNn06nD179ba9e0Pr1rnPz54t/H+bxx+HSpVyn2/ZAkuWXL2dn5/lO3ypuXPh0oU9/fzAxydv24YN4fL1fmbPLly8t9wC1arlPo+NheXLC9d22DDb55s3Q1TU1duFhECPHrZlP/4IiYlXb3vTTdCoUe7zCxdg3ryrtwPo29fyZ0+Ogwdh/fqrt/P0hEGDbMtWrYKjR6/eNiICOnSwLfv6a8jIsBynpiYxfnwoPvn9h72MyTAM4+ovmb+cVaFmzpzJ8OHDr1q/Tp061KlTh19//dWm/LPPPmPkyJHs3Lkz36FQl0tKSsLPz4/ExEQlFiIiYnXqFISFwZXWAnFygjvvhKlTbW8kRUQkL3vuu0t18nbz5s3Ztm0b2dnZNuUbN27Ey8uLunXrlmY4IiJSjiUkwCuvWH6VzhEYCC1aWI4rVbL8cnj33fDpp3D6NBiGpf78+UoqRESKW4klFvHx8ezbt4+MnH4UYMCAAZw8eZL58+dby86cOcP3339Pnz598p1/ISIicqnsbJg5EypXtgxzuXxYwxtvwO7dcOaMZdjDvHkwYoSlvoiIlJxrmmMxffp0zp8/T1xcHAA///wzx48fB2DMmDH4+fkxYcIEZs+ezZEjR6hRowZgSSzatm3Lgw8+SFRUFJUrV+aDDz4gKytLk7NFROSqdu+GUaNgw4bcsj//hEtH0XbrVvpxiYjINSYW06ZN4+gls0Hmz59v7YW477778PPzy7eds7Mzv/76K88++yzvvfceqampREZGMmvWLOrVq3ctoYiIyA1i/nwYPtx20nFkpGUytYiIOF6RJm87iiZvi4jcGAwDJk+GDz6A+Pjc8vBweO89ywoqIiJScuy57y7V5WaL2/d7vqdmUE2qeFXBzdkNfw9/fN19cXdxx8lUqvPSRUSkmF24APfdZ1ni8VKVKsGOHbZLxYqIiOOV68Ri5E8jwSNvuQkTfh5+OJuccXZytv7bzdmNwAqBhPiE4OniiZerF9V8qlHBrQIuTi64OLng5+6Hm7MbTianfB8uTi74uvvi5eplTWbcnN1sXsfZZHkt7QouInLtPDzg8n1Tp0yB556zLBkrIiJlS7lOLApiYHA+7Xy+5w6fO0xl98pUcKlQojG4OLng4eKBs8kZJycnS9JhcsbPw48KrhXw9fDFzcmSfDg7OVsSF5zwdPUkxDsEb3dv/D388XDxIMAjgKreVanoUREXJxfr9ZS43BicnJxwdXXVf2+54Tg7WzZp6tEDJk607D2h/w1ERMqucp1Y/N8t/0d8RjwX0i9wIeMC59LOkZaZRpI5iWRzMllGFlnZWWQZWbiaXOlcpTPtqrSjmnc1y405ZfhvqCzgguUwKTGJpJNJBVbNSTRcnFwwYcLJ5JTnJtT6Xk25z00mEzn/5JxzdXK1tP+nzGQy2TzHRNn+3K5Trq6u+Pj4ULlyZZydnR0djkiJSUy07NybIyDAsiuwEgoRkbKvXCcWj0U+VqjJ21lZWcTExGA2m/Hx9cHD0wMnFydL0pGdhZHzj2GQlW3ZacnAsPl3jmwjm2wjG8MwyDay89S3qYNBTrGBYVteRmX988+VmDDh6uyKq5OrNZGx9rqYnHKHhDk542Jysfay5CQzmv9SeIZhkJWVRUpKCufPnyc1NZXQ0FAlF3JdiomB9u1h+nRL70QOJRUiIuVDuU4sCuvMmTOYzWbCwsLw9PR0dDjWhMSavBi5/07PTicrO4vM7EyyjCxSM1PBwNr7cnmbnPLM7MxSS1gMDNJJJ91IzymA7Cs2sWEymXDCKbfHxGTCxcnFOjfF1dnVplfk0uc5PTEmctu4OLtYk5nL610vvL298fPz49ixY5w5c4aqVas6OiSRYnXhgiWZOH4cHnsM2rYFfc1FRMqX6z6xMAyD5ORk/Pz8ykRSAVh+2XfO/1d7j/xmoxdCTrKSbWSTmZ1pLTdyu0xsemGsPTWGbZk5y0y2kW1zzZx6OXUyszPJyM7gWhmGYekVuSQPSs9Kv+brFcTZ5Iy7izterl54uHhYe0pyko+cCfguTi7lIhHx9PTE19eX5ORkAgMDy0XMIoVx4QJ06AB//WV5npICaWmOjUlEROx33ScWGRkZZGRk4O3t7ehQStSlyYo77iX+epcOCcvpNclJai6d25KRlUFGdoZN3ZxjsCQ0mdmZ1mSmOGUZWVzMuMjFjItXrZszhMvN2c3aa+Lm7GazSlhZuJH38fHh/PnzZGRk4Obm5uhwRIosKwuGDMlNKgDefdeyT4WIiJQv131ikZ1tuWHVmPTilTOfAsAV1yJfLyfpMGearUlLRnYGJkx5h4yRO78lMzvTppfl0jrpWek2vTdXculrXsi4YC0/znHrsbPJ2brkcE4vSH7Dr5xNztbExNWpeFdzyvke53yvRcq7SZNg0SLLsacn/P47tGvn2JhEROTaXPeJRY6y8GuzFMxkMlkmersV31fSMCy9ISnpKbmJB4Y1ecnMziQ9K93ai5KZnXnFRCTL+KcXJjvDMvelkHLmg+Q8nJ2ccXVyxd3FHQ8XD9yc3fB08SzUd1TfY7meHDoEr72W+/z775VUiIiUZzdMYiE3HpPJsnpVRc+KhW6TM88kJyG5kH7BmnRc2qthDwODjOyMK7ZzMjnh5eqVu9yvyWQdluXj5mNZWUuracl1ZsqU3OPOneH22x0Xi4iIFJ0SC5FLODs54+XkBYCve/5LGWdlW+ZupGel2wzTypnkntPzYc4yW+eeZGRnXLE3JNvIJiU95YqxmTDhariSkJLA/PXzCfIPIiIgAn8Pf/w8/PB08SSwgiZ1S/mwZw/MmmU59vaG+fMdGo6IiBQDJRYCQI0aNTh69KhNmZubG1WrVqVdu3aMHj2aTp06OSi6gq1atYpu3brRpUsXVq1adc3XmTVrFg8++CDDhg1jVs7dTgGcnZzxcfex+zVy9j7JGYKVlpmGOctMakYqFzIuXHU+SM68EXOmmU+2fcLRC0fz1KlaoSpdanShcZXGhPuHU8WrCs2DmhPkHaSEQ8qU4GDLkrLr1sG4cVCpkqMjEhGRolJiITY6dOhAREQEAOfPn2fLli3MnTuX77//nmnTpvH00087OMLyy2QyWffbcHdxt0lOciavZ2ZnWns9ksxJ1p6OnGQkPfPKy/KevHCSuXvmMpe5NuWeLp74efjx+E2PExEQQYhPCIEVAgn3D8fL1atE3q/IlQQEwLJl8NxzloeIiJR/JiNnGZ1yJCkpCT8/PxITE6+683ZaWhpHjhyhZs2aeHhc2x4RN4KcHouZM2cyfPhwa3laWhqPPPIIc+bMwdnZmaioKOrWreu4QC9z8eJFjh07hpeXF2FhYdd8ncTEROLj4/Hz8yM4OLgYIyxeKRdSOHTkENvTthOdEs35tPOcSztHWmYax5OOsz5mvV3XczY5UzugNuF+4YT6htIyuCVNqzalbqW6VPXW7mQiIiI3Onvuu9VjIVfk4eHB+++/zw8//MCFCxeYP38+//73vx0dlpWXlxf169cv8nX8/Pzw8/MrhohKlouzC27ObgxpPCTfRDnJnMTWuK3EJcexNX4ryeZktsRvYceJHfleL8vI4kDCAQ4kHADg8x2fW88FeQfRPKg5TQOb0jGsI3Uq1aGSZyWqVKhSIu9Nrn//+Y9lz4pq1RwdiYiIlAQtMyNX5e3tTb169QCIjo4GLMN6csbsz5w5k3bt2uHn54fJZLLWAYiLi+Ppp5+mQYMGeHl54ePjQ2RkJNOnTyczs+A5BStWrGDgwIFUr14dd3d3qlSpQmRkJC+//DIJCQnWeqtWrcJkMtG1a9c819i6dSuDBw+mevXquLm54evrS61atbj77rtZuHChTd1Zs2ZhMplsemsutWnTJgYNGkRISAhubm4EBgbSp08fli1blm/94cOHYzKZmDVrFkeOHOH+++8nKCgId3d3ateuzYsvvojZbC7w/V8rX3dfutXsxtCmQ3m719t80vcTtj+yndinY1kydAlf9f+K17q9xhOtn6B/g/7Ur1wfF6f8f184kXKCJX8v4a31b9H32740eL8BgdMCqTK1Cl1ndeXhnx7m022fcjDhIOWw41NK2aJFlrkU9evDp586OhoRESkJ6rGQQklKSgLA3d12V+8xY8bwwQcf0L59e26//XYOHz5sTTjWrFlDv379OHfuHDVq1KBHjx6YzWY2bdrEmDFj+Pnnn1m0aBGurrYb7D3xxBP873//A6B58+Z06tSJxMRE9u/fz6uvvkq3bt3yTSQu9fvvv3PrrbeSkZFBs2bNaNeuHVlZWcTGxvLLL7+QlZXFnXfeWaj3/sknn/Doo4+SnZ1NixYt6Nq1K0ePHmXRokUsWrSIV155hZdffjnftjt27GDs2LFUrFiRLl26cPbsWdatW8fkyZPZs2cPCxYsKFQMRRXiE0KIT0i+5zKzM0m4mMDG2I3sPrWbqNNR7Dq1i50nd+Zb/8zFM6w+uprVR1fz6XbLHaK7szuR1SLpUasHd9W/i/qV6+PqXPSNE+X6kJYGY8ZYjlNSoEIFx8YjIiIlQ4mFXNXOnTs5fPgwYLnRv9ScOXNYt24dbdu2tSk/ceIE/fv35/z583zwwQc88sgjODlZOsgSEhIYNGgQS5cuZcqUKbz00kvWdv/73//43//+R6VKlfj+++/p1q2bzXU3bdpUqDkQkydPJiMjgy+//JKhQ4fanEtMTGTv3r2Feu+7du3i8ccfxzAM5syZw/333289t3jxYvr168crr7xC+/bt6dGjR5727777Li+88AKTJk2y7pq9e/du2rZty48//siGDRto5+AdwVycXKjqXZW+9frSt15fa7lhGESdjmLtsbXsPbOX+JR49pzaw99n/8acZdvbYs4ys/bYWtYeW8vLq17GxcmFmv41aRbUjNYhrWkZ3JLIapEFLuEr17dPP4Wcjszu3S3DoURE5PqjxOIfb79teVxNy5bw00+2ZX37wrZtV2/79NOWR47kZGjQoHDxLVwIrVrlPl+0CA4csL1ecUtMTGTdunU88cQTZGdnExISwqBBg2zqjBs3Lk9SAfDf//6XhIQERo8ezWOPPWZzrlKlSsyZM4eaNWsyffp0Jk6ciMlkIjMzk9f+2Yb3448/zpNUALRu3bpQsZ88eRKA2267Lc85Pz+/fGPOz7vvvktmZib9+/e3SSoAbr31VkaNGsX06dOZOnVqvolFq1ateO2112yWem3cuDH3338/M2bMYPny5Q5PLApiMploFNiIRoGNbMoNw+B40nE2xm5kW/w2NsVuYlPsJpLTk611MrMzOXj2IAfPHmRe1DxreU3/mvRv0J8+dfvQMrjlNS3bK+WLYcD77+c+f/ZZ0MrHIiLXJyUW/0hKgtjYq9cLDc1bdvp04dr+M5rIyjAK1w4g/bJVRlNT816vODz44IM8+OCDecpr167NDz/8QIXLxjAMGDAg3+v88ssvAAwePDjf89WqVaNOnTpERUVx8OBB6taty9atWzl9+jSVK1fmrrvuKtL7aN26NVFRUQwdOpTnn3+etm3b4uJi/9c9Z2+MguZejBgxgunTp/PHH3+QlZVl7ZXIcccdd+S7f0SDfzLK2MJ+AcoQk8lEqF8ooX6hDGiY+99/c+xmfjn4C9tPbCfqdBQxiTF5ejaOnD/Cfzb8h/9s+A8ALYJa0Kt2L1oGt6RKBcueG/4e/qX5dqSEbdgA+/ZZjtu2hV69HBuPiIiUHCUW//D1LdxKJVXyWRCnSpXCtb18hS6TqfCro7i52T739Mx7veJw6T4WOZOU27ZtS+/evfO9Ma9Ro0a+18kZOlWYTfVOnz5N3bp1rRv01atXr8ibuU2ZMoWdO3eyePFiFi9ejKenJy1btqRr164MHTrUemN/NTk3/jVr1sz3fO3atQHLsrwJCQkEBgbanC9oCdyc5drS0tIKFUd5EFktkshqkdbnGVkZbIrdxK5Tu1h+eDmbYjcRkxRj02b7ie1sP7HdpqxepXp0Ce9CREAEPu4+NKrSiFYhrbTfRjn11Ve5x489pt4KEZHrmRKLf1w+TMkelw+NKiwfHzh+/Nra3nHHtbW7mpEjRxb463x+PD098y3Pzs4GLD0al/dyXK5SCWy5GxQUxJYtW1i9ejXLly9n3bp1bNy4kXXr1vHGG28wZcoUxo8fX+yve7mceSU3IldnVzqEdaBDWAcevelRAE5fOM03u79hY+xG1h1bx9HEvLuH70/Yz/6E/bbXcnKldbXWRARE0DK4Jf3q9yPUN1S7iZdxWVnwww+WYw8PKGJHpIiIlHFKLKREhIaGcvDgQcaPH89NN91UqDY5v+4fOHAAwzCKfNOYswxtzgpSaWlpzJo1i3/96188//zzDBgwwNrjUJBq1apx6NAhDh8+TOPGjfOcz+mZ8fDwICAgoEjx3giqVKjCE22eACxzNdbHrGffmX2cunCK6PPRLD+ynMPnDudpl5GdwbqYdayLWcfsv2YzdslYa7IxvPlw7m1yr3o0yqBNm+Cf6U706mX5MUVERK5fSiykRNx6660cPHiQuXPnFjqxuOmmm6hcuTKnT5/mxx9/LPI8i8t5eHjw6KOP8tFHH7Fjxw527tx51cSia9euHDp0iFmzZtG3b9885z//3LKhXKdOna5pDseNzGQyWXs0LnX6wmm2n9hOwsUEDp87zF8n/+KXg79wMeOiTb1Lk42Hf36YUN9QqnpXJbBCIJ3COtG6Wms6hHbA3cV2iWQpPUuX5h736eO4OEREpHToTkhKxLPPPsucOXN4++23qVq1KmPGjMHtsokiR44cYd26ddx3330AuLi48MILL/DUU08xatQoKlWqROfOnW3abN68meDgYKpXr37F1582bRqDBg3KM8dh3759HDx4EIDw8PCrvo+xY8cye/ZsfvzxR7788ktrrABLly7lo48+AiyrY0nxqFKhCj1r97Qpy8zO5ETKCdbHrOfP43+y+9Ru1h5bS2pmqrVOTFKMdQ7Hrwd/tZbfGnErt9W5jW41uhHsE4yPm4/22Cgljz8OderAkiXQs+fV64uISPmmxEJKRPXq1Vm4cCF3330348aN46233qJx48YEBwdb95E4dOgQbdq0sblZHzt2LPv372fGjBl06dKFFi1aUK9ePZKSkti3bx+HDx9m5cqVV00sXn/9dZ599lnq169PgwYN8PT0JC4ujrVr15KZmckDDzxAy5Ytr/o+mjRpwvvvv89jjz3G/fffzzvvvEP9+vU5evQo69evxzAMXnnlFXrqrqlEuTi5UN23OoMaDWJQI8uSx1nZWSzYt4Af9/3ItvhtHE86brPkbY7Ffy9m8d+Lbcp61OrBhI4T6Fqjq+ZplKAqVeDeey0PERG5/imxkBLTuXNn9uzZw/Tp0/nll1/YvHkzZrOZwMBAwsLCuO+++7j77rtt2phMJj788EPuvPNOZsyYwZ9//snu3bvx9/enZs2aDBs2jKZNm171td9//31+//13Nm/ezOrVq7lw4QJBQUH06NGDUaNGFXrXbYBRo0bRrFkzpk2bxtq1a9m5cyd+fn7cdtttjB07Nt/9K6TkOTs5M6DhAJslb7ONbLbFb2N9zHqW/L0kT0KRY9nhZSw7vAx/D3/uaXwPdze4m45hHTVsSkREpAhMhmEYjg7CXklJSfj5+ZGYmGhdtrMgaWlpHDlyhJo1a+Lh4VFKEYqUDH2f7ZORlcGiA4vYGr+V/Qn7+Wn/T6RnpRdY/5Zat9CuejsiAiK4rc5tVPaqXIrRXn/MZnBXriYiUq7Zc9+tHgsRuW65OrtyV4O7uKtB7kIAKekpLNy3kJk7ZvL7kd9t6i8/vJzlh5dbn3cJ78LIliO5pdYtBHkHlVrc14Nbb4Vu3WDQIChguxsREbnOqMdCpBzR97l4xSbFMi9qHquPrmZV9CrOpZ0rsG7dSnW5s96d3NvkXpoHNS+9IMuh1FTLBp6ZmRAUBPHxjo5IRESulT333UosRMoRfZ9LTraRzfb47Rw+d5ilh5by+Y7PyTay860b4hNCtxrdqFWxFi2CWnBn/TtxMt24myFe7o8/IGdBtzZt4M8/HRuPiIhcOw2FEhGxk5PJiVYhrWgV0oqBjQYy/bbp/LD3B/44+gdrjq0h6nSUtW5cchxf7frKpn2TwCa0rd6WbjW6cXOtmwmsEFjab6HMWL8+9/jhhx0Xh4iIlC71WIiUI/o+O86+M/v4cPOHrI1Zy44TOwrszchRr1I9hjYZSqfwTnQK64Szk3MpRep4np6QlmY53rcP6tVzbDwiInLtNBTqEroRk+uJvs9lw4X0C+w4sYNlh5exLX4bG2M3curCqQLrB1YIZGiToYxrP44Qn5BSjLT0paSAj0/u8/L3N4yIiFxKQ6FEREpQBbcKdAjrQIewDgAYhkFMUgwbj2/kt0O/8duh3ziedNxa/9SFU7zz5zu88+c7tKvejm41utEqpBU9a/fE283bUW+jRGzdmntcp47j4hARkdKnxEJEpIhMJhNhfmGE+YUxsNFADMNgx4kdrIxeyTe7v2FL3BZr3Q3HN7Dh+AYAnE3OdA7vTL/6/ehTtw81K9Z01FsoNitW5B5PmOC4OEREpPRpKJRIOaLvc/m0+9RuXln1CksPLSU5PTnfOh4uHoyOHM34juPL9cZ8ISG5y8vGxED16o6NR0REisae+26tjygiUsIaBzZm3qB5JP47ka2jtvLh7R8yoOEAXJxyO43TMtOYtmEaYe+E8c2ubyiHv/mQnp6bVISFKakQEbnRKLEQESklJpOJlsEtefSmR/l+4PekvpDKmuFrGNFihHUfjNTMVO6dfy9h/w1j5ZGVV119qizJyoKXX7Yce19fU0dERKQQlFiIiDiIi5MLncI78WnfT4l6PIp21dtZzx1POk73Od0JeyeMF1e8yNnUs2W+F8PTE557DrZsgZ07HR2NiIiUNiUWIiJlQL3K9fjjwT94o/sbuDm7Wctjk2OZ/MdkKr1VidB3Qpm2fhoxiTEOjPTKvLygVStwvnG27RARkX8osRARKSOcnZyZ0GkCZ549w+Tuk6lfub7N+djkWJ5d9iw1363Jnd/eyZ5TexwUqYiISF5KLASAGjVqYDKZbB7u7u5Ur16dO++8k0WLFjk6RLvlvI/Lde3aFZPJxKpVq0o/KJFC8HH34flOz7P3X3v59u5vGdxosE0vRpaRxU/7f6Lxh43p/11/9p/Z78BoLdLTYc0aSEx0dCQiIuIoSizERocOHRg2bBjDhg3jtttuw8XFhZ9++ok+ffrw9NNPOzo8kRvO4MaD+XbAt6S9kMa2UdsY2WKkdaI3wIJ9C6j/fn0avt+Q2Ttmk2zOfznbkhYVBV26gL8/PPmkQ0IQEREHU2IhNkaOHMmsWbOYNWsWCxYs4O+//2b06NEAvPPOO2zevNnBEYrcmEwmEy2CW/BJ3084Oe4kQ5sMtTm/98xehi8cTo13a/DJ1k9KfTWpHTtyj0NDS/WlRUSkjFBiIVfk4uLC1KlTrRui/Pzzzw6OSEQqe1Xmy/5fEvd0HKNajrI5dzb1LKMWjSJoWhCvr3mdixkXSyWmSxOL5s1L5SVFRKSMUWIhV+Xh4UGdOnUAOHnyZJ7zv//+O/379yc4OBg3NzcCAwO566672LBhQ4HXvHjxIv/973/p2LEjFStWxN3dnfDwcPr06cPXX39tU/fo0aO8+eabdO/enbCwMNzd3fH396djx4589NFHZGeXn3X+RYpTsE8wH/X5iKR/J/F2z7cJ8Aywnjt98TQTV06kwhsV+Ncv/+L0hdMlGsuliUWzZiX6UiIiUkbZnViYzWbGjx9PSEgInp6etGnThmXLlhWq7fLly+nWrRuVK1fG39+f1q1b88UXX9gdtJS+pKQkAKpWrWpTPm7cOG655RYWLlxIWFgY/fr1o1atWixcuJBOnToxc+bMPNeKiYkhMjKSp556iu3btxMZGUn//v0JDw/njz/+4Pnnn7ep/8UXX/Dvf/+b6Oho6tatS//+/WnevDmbN2/m0UcfZeDAgWV+fX+RkuTj7sNT7Z7ixDMn+OiOj2gS2MTm/AdbPiDoP0EMmTeEUxdOFfvrG0ZuYlG9OlSuXOwvISIi5YFhpyFDhhguLi7GuHHjjI8++sho166d4eLiYvzxxx9XbLdw4ULDZDIZ7du3N/73v/8Z06dPNzp37mwAxttvv21XDImJiQZgJCYmXrVuamqqERUVZaSmptr1Gjea8PBwAzBmzpyZ51xUVJTh7OxsAMbmzZut5R9//LEBGBEREcZff/1l02b16tWGj4+P4ebmZhw4cMBanpWVZdx0000GYPTs2dM4deqUTbvU1FTjl19+sSnbtGmTsWvXrjxxxcbGGs2aNTMAY+7cuXnOA0Z+X/EuXboYgLFy5cp8P4uyTN9nKazV0auNLjO7GLyCzcPtNTdjzK9jjK1xW4vttQ4fNgxLemEYd9xRbJcVEZEywJ77bpNhFP6n3k2bNtGmTRumTp3KuHHjAEhLS6Nx48YEBgayfv36Atv27NmTPXv2cPjwYdzd3QHIzMykfv36VKhQgb/++qvQyVBSUhJ+fn4kJiZax/4XJC0tjSNHjlCzZk08PDzyrXPTxzdxIuVEoV+/LAnyDmLLqC1Fvk6NGjU4evQoM2fOZPjw4QAkJiayceNGxo4dy759+3jxxRd57bXXAMjOziY0NJS4uDi2bNlCq1at8lxz6tSpPPfcczzzzDNMmzYNgIULF9KvXz+Cg4M5cOAA3t7eRYp76dKl9OrVi4EDBzJ37lybczlLzV7+Fe/atSurV69m5cqVdO3atUivX9oK830WudS+M/t4btlz/Hwg7/yoZ9o9w7Se04r8GgsWQP/+luMXX4R//pgQEZHrgD333S72XHjevHk4OzszalTuZEEPDw9GjBjB888/T0xMDKEFLAeSlJRkHUtvfXEXFyqXgT7zEykniE2OdXQYZcKDDz7Igw8+aFPm7OzMl19+ydChuavQbN++nbi4OGrXrp1vUgFYb9ovTTiXLFkCwL333mtXUmE2m1m6dCmbN2/m1KlTmM1mDMMgOdmytOb+/Y5fx1+kLKpfuT4/3fMTa4+t5eGfH2bfmX3Wc//Z8B+WHV7G/EHzqR1Q+5pfQxO3RUQE7Ewstm/fTt26dfNkK61btwZgx44dBSYWXbt25c0332TixIkMGzYMk8nE119/zZYtW/L80lzagryDHPr6RVHcsXfo0IGIiAgATp8+zR9//EFycjKPPfYYderUsf63Pnz4MACHDh3KdxO6S50+nTtp9OjRowDUr1+/oOp5/PnnnwwePJhjx44VWCdnDoiI5K9jWEeiHo/iz+N/MvLnkUSdjgJg58mdNPmwCW/1eIvRrUdf07WVWIiICNiZWMTHxxMcHJynPKcsLi6uwLYTJ07kyJEjTJ48mddffx0ALy8vfvjhB+68884rvq7ZbMZsNlufF/dNZHEMJbpejBw50joUCizDoe666y5WrlzJoEGDiIqKwsvLy7oSU1BQEL169briNYvSK3Xx4kX69evHyZMnefDBB3nssceIiIjA19cXZ2dnDhw4QL169TR5W6QQTCYT7ULbsefxPXzx1xe8uPJFjiUeIzUzlTGLx/DT/p/4bsB3VPSsaNd1L1wAkwm8vaFmzRIKXkREyjy7EovU1FSboUw5csZ6p6amFtjW3d2dunXrMmDAAPr3709WVhYff/wx9913H8uWLaNt27YFtp0yZQqTJk2yJ1QpJn5+fnz33XfUr1+fo0eP8vbbb/Piiy9ae6YqVarErFmzCn29sLAwAPbt23eVmhZr1qzh5MmTtGzZks8//zzP+YMHDxb6tUUk1/3N7ue2Orcx7Mdh/HLwFwCWHV5GsxnN+Obub+gQ1qHQ11q+3JJcREeDkxYxFxG5Ydn1V4Cnp6dNz0GOtLQ06/mCjB49mp9//plvv/2WIUOGMHToUJYvX05wcDBjx4694utOmDCBxMRE6yMmJsaesKWIqlSpwosvvgjAtGnTOH/+PJGRkVSuXJmoqCj27NlT6Gv17t0bgG+++YYLFy5ctf7Zs2eB3ITkcl9++WWhX1tEbFXyqsTP9/zMM+2esZbFJMXQZVYX5u6xb4hqhQrQqFFxRygiIuWJXYlFcHAw8fHxecpzykJCQvJtl56ezmeffcbtt9+O0yU/Z7m6unLrrbeyZcsW0tPTC3xdd3d3fH19bR5Suh5//HHCwsJITEzkP//5D66urrz88ssYhsFdd93F2rVr87TJyspixYoV/Pnnn9ayvn370qJFC+Li4hg4cCAJCQk2bdLS0li8eLH1eYMGDQDLJnxRUVE2dT/++GO+++674nybIjcck8nEtJ7T2P3YbpoHNQcgy8hi8LzBfLrtU8cGJyIi5YpdiUXz5s05cOBAnjkOGzdutJ7PT0JCApmZmWRlZeU5l5GRQXZ2dr7npOxwd3fnlVdeAeDdd9/l7NmzjB49mmeffZaDBw/SqVMnGjduTL9+/bjnnnusGyHefPPN7LhkZqeTkxMLFiygXr16LF68mLCwMHr16sW9995Lly5dCAoK4rHHHrPWb9GiBXfeeSfJycm0aNGCXr16cc8999CgQQMeffTRPJvpici1aRTYiPUPrad3RG9r2cM/P8wnWz9xYFQiIlKe2JVYDBgwwDo3IofZbGbmzJm0adPGOu7+2LFjNmPoAwMD8ff3Z8GCBTY9EykpKfz888/Ur1//isOopGx44IEHaNiwIcnJyUydOhWAt956i3Xr1jF06FBSUlJYsmQJv/zyC3FxcXTt2pVPP/2UwYMH21wnPDycLVu28Oabb9KoUSM2bNjA/PnzOXr0KF26dOHNN9+0qf/9998zdepU6tWrx9q1a1m6dClhYWH89ttvjBw5stTev8j1ztPVk0X3LGJAwwHWslGLRvHGH28U2MbJyTJx+7nnSiNCEREpy+zaIA9g0KBBLFiwgKeeeoqIiAhmz57Npk2b+P333+ncuTOQuwHZpZeePHkyL774Ii1atOCBBx4gKyuLzz77jL179+bZI+FqinuDPJHyQt9nKQ1Z2VkM+WEI86LmWcvGtB7De7e+Z1MvMxNcXS3HYWHwz2rSIiJyHbHnvtvu9TvmzJnDk08+yRdffMETTzxBRkYGixYtsiYVBXnhhRf46quvcHV1ZdKkSUycOBFfX1/mzZtnV1IhIiIly9nJmbkD5jK0Se6fzf/b9D/GLR1nU+/QodzjK2wzIyIiNwi7eyzKAvVYyI1K32cpTYZh8MKKF5iydoq1bEzrMfy3939xMjmxYAH0728pf/ll+GcaloiIXEdKtMdCRERuDCaTiTdufoOHWz5sLfvfpv/h/KozMYkxXLrStJaaFRERJRYiInJFH/f5mHHtbIdBNfygIZPWvgCVLQt1KLEQERElFiIiclVTe07l2fbP4mSy/LWRkp5CZrs34PFG0OojIiIcHKCIiDicEgsRESmUt3q8RcxTMdxS65bcQqds6PMoQxcO5EL6BccFJyIiDqfEQkRECi3EJ4Sl9y1lYsAeiLrbWj4vah43fXITh88ddmB0IiLiSEosRETELiaTCeezDWHuPFj2f9byfWf2Ufu92szdM5dyuOCgiIgU0Q2TWOgvObke6HssZcXYsTBxItzsMZ7pXb+1zr0AGDxvMCN+GqHvq4jIDea6TyxcXFwAMJvNDo5EpOhyvsc532sRR/H3h1dfheXL4V9dBrPvX/toHtTcen7mjpm0+rgVM7fP1NwLEZEbxHW/QR7AsWPHyMzMJDw8HGdn51KIUKT4ZWVlcfToUVxcXAgLC3N0OCJ5ZGZn8sqqV5j8x+Q857qEd6FFUAsCKwTSr34/6leuj8lkckCUIiJiD3vuu2+IxOLixYvExMTg7OyMn58fnp6eODs76y81KfMMwyArK4vU1FQSExPJzs4mNDQUT09PR4cmUqDX17zOpNWTyMzOLLBORY+K9IroxS01b6FbzW6E+ITg4aLd5EVEyholFvlIT0/n1KlTXLx4kaysrBKOUKR4OTs74+XlRWBgIG5ubo4OR25we/bAunXQsCE0bQr5/TF8If0CH2z+gI2xG1kVvYqE1IQrXtPZ5EyrkFaE+ITg6eJJ06pNaVq1KW2rtyXAM6CE3omIiFyNEosrMAyDjIwMsrOzSyg6keLl5OSEq6uretikzHjzTfj3vy3Hc+bA/fdfuX62kc3BhIOsObqGY4nH2HB8A1vitpBoTizU61XyrIS3mze+7r60q96Oip4VGdpkKI0DG+v/CxGREmbPffcNNwPUZDLpF18RkSLYtSv3uGnTq9d3MjlRr3I96lWuZy3LyMpg4f6FLDu0jBMXTrA+Zj1nLp7Jt31CaoK1x2PXKcuLv7nuTYK8gziRcoJhzYYR5B3EbXVuo3FgYyq4VsDdxf3a36CIiFyTG67HQkREiqZ5c/jrL3B2hgsXwL2Y7uEvZlwkNimWv8/+TfT5aHaf2s2uU7s4nnScI+eP2HUtbzdvwvzCaF2tNVW8quDp4klV76rUrVSXDqEd8HTVPCURkcLQUCgRESkRGRng7Q3p6dCgAURFlc7rpmWmse/MPv4++zeHzh5i9l+z2Xtm7zVdy9PFkx61e1A3oC631rmVxoGNqexV2WYvDhERsVBiISIiJWLXrtzhT0OGwDffOC6WbCObUxdOsS1+G1vitrD71G7Op53nbOpZElITiD4fXehrebh40LZ6WxpWbkg132oEeQfRIqgFDas01LAqEbmhaY6FiIiUiEsTiZo1HRcHWOZu5MytuK3ObXnOn009y8GEg5izzCRcTODvs3+z69Quvtj5RZ66aZlprIpexaroVXleI9wvnJoVaxLmF0bTwKa0D21PZLVI9XCIiFxGiYWIiBTa0aO5xxERjoujMAI8A2hTvU2e8ln9ZvHn8T/ZHr+d/Qn7OZBwgH1n9nE08WieutlGNkfOH8kzx8PN2Y3aFWtTO6A2tfxrEe4fTq2KtahXqZ42/xORG5YSCxERKbT09NzjLl0cF0dROJmcaB/anvah7a1lhmEQlxzH8aTjxCXHsTV+KwcSDnDk/BEOJhzMszRuelY6e8/szXeeRw3/GrSt3pbqPtVpWrUprau1JiIgAmcn5xJ/byIijqQ5FiIiUmgtW8L27eDkBKmpcKOs3p2Ylsihc4dYe2wtq6JXse/MPg6fO4w5y1yo9p4unrQLbUfzqs0J9gkmyDuI+pXr06hKI61QJSJlmiZvi4hIiRg5EjZvBsOAnTsdHY1jZRvZxCXHEX0+mqPnj3Lo3CHrPA0D+/5qHdliJLUq1qJ2QG1uqXWLdhsXkTJDiYWIiJQowwBNI8jfhfQLxCXHcfjcYTbHbWbHiR1sit1ETFJModr7uPkwuvVomlVtRpB3EM2DmuPn4VfCUYuI5E+JhYiISBliGAaxybEcOnuIkxdOEnU6ig82f4CnqyfHEo9dtX2tirVoH9qettXackfdOwj3Dy+FqEVElFiIiIiUG4lpiRw8e5DD5w7z68Ffmf3X7Ku2aRzYmC7hXegY1pHuNbtTxauKVqISkRKhxEJERIqdhj+Vjj2n9rD9xHZOppxk75m9bIvfxr4z+0jNTC2wjaeLJ22rt+Wu+ndxR907qOFfQ4mGiBQLJRYiIlLsOneGkyehQQP44Qdw1uqppcacaWbHiR0sPbSUBfsW8NfJv8g2sq/YpoZ/DUJ9Qwn3DyfcL5wWQS3oFdELbzfvUopaRK4HSixERKRYGQb4+UFyMtSoAUeOXLWJlKAkcxKLDizip/0/cebiGQ6ePViouRpuzm5EhkQS5B1EZa/KBFYIpH7l+txc82aqelcthchFpLxRYiEiIsUqJgbCwizHt98OixY5Nh6xZRgGu0/tZsaWGexL2Ed8cjzxKfGcTztf6Gs0DmxMk8AmBHsHW3cSbxLYhDC/MA2rErmB2XPfrZ23RUTkqvZessF0gwaOi0PyZzKZaFK1Ce/f/r5N+fm088Qlx7Hn1B5WRq/kl4O/FNizsfvUbnaf2p2nvJJnJZoHNadp1aY0q9qMZkHNaFSlEa7OriXyXkSk/FJiISIiV6XEonzy9/DH38OfhlUaMrDRQABS0lNIuJjAmYtn2HB8AwcSDrApdhObYjflu7FfQmoCvx/5nd+P/G4tq1qhKgMaDqBjWEc6hHYg1C+01N6TiJRdGgolIiJX9eij8NFHluP166FdO8fGI8UvZ2O/uOQ4jpw/wq6Tu9h1ahd/nfyLUxdOXbFtNZ9qdArvRJ+6fRjUaBAuTvrdUuR6oTkWIiJSrDp0sCQUAOfOgb+/Q8ORUnYi5QQ7T+7krxN/seTQElYeWZlv7wZABdcKPNn2SYY2GUqDKureEinvlFiIiEixyc6GihUhKQlCQ+HY1RcfkutckjmJP4//ydpja1kXs45NsZtISU/JUy/ML4ybQm6iXfV2PNzyYfw8/BwQrYgUhRILEREpNgcPQt26luN+/WDBAoeGI2VQRlYGC/Yt4L2N77E+Zn2+vRmBFQK5v+n9DGw4kNbVWmulKZFywp77bqdSiklERMqppUtzj0NCHBeHlF2uzq4MajSItQ+t5fjTx5naYyqtq7XG08XTWufUhVP8Z8N/aPtZWyq+WZGB3w/ku93fXXWjPxEpP9RjISIiVxQdDX36wO7dlv0rbr/d0RFJeZGVncWWuC28tf4t5u+dn28dZ5MzNfxr0DK4JbUr1ub2urfTtnpbTQAXKSM0FEpERIrV8uXg5gadOzs6EimvTqac5JNtn/D+5vc5kXLiinUDPAO4q/5d3NvkXjqFddKeGSIOpMRCREREyqxTF04xe8ds1sas5ci5I+w6tavAut5u3jSr2oz6letbH+2qt6OSV6VSjFjkxqXEQkRERMqNJHMSG2I2cOT8EX479BtL/l5CWmZagfXdnN24pdYtDG82nDbV21DNpxrOTs6lGLHIjUOJhYiIFItly+DPP6F1a2jfHnx8HB2R3AguZlxkwd4F/HLwF9YcXUNscuwV6zuZnKjmU42etXtyR907qOlfk0aBjTRPQ6QYKLEQEZFi8cgj8PHHluPVqzXHQhwj2ZzMgYQDRJ2OYvXR1czfO59zaeeu2MbTxZM21dvQJbwLD7V4iDC/sFKKVuT6osRCRESKRYsWsGMHODlBYiJ4ezs6IhFLorH478Vsi9/G3jN7iU2KZc/pPQUOnzJhonN4Z0a0GEGviF4EVggs5YhFyi8lFiIiUmQXLoCfH2RlQZMmsHOnoyMSKVhqRipL/l7Clrgt7EvYx9a4rRxNPJpv3RZBLehbry/tQ9vTvWZ3DZkSuQJ77rv1f5KIiORr0yZLUgHQrp1jYxG5Gk9XT+5qcBd3NbjLWhaTGMNn2z/j8+2fE5MUYy3ffmI7209sB6CSZyXqVa5H4yqNGdBwAJ3CO+Hh4lHq8YtcD9RjISIi+XrzTfj3vy3Hs2bBsGEODUfkmmVmZ7L00FKWH17OmqNr2Bq/tcC6TiYnHmn1CMOaDaNJ1SZ4uXqVYqQiZY+GQomISJHdfTfM/2ez5KgoaNDAsfGIFJfD5w6z8shK5u+bz44TO4hLjiuwbufwzjzS6hHuaXwPJpOpFKMUKRuUWIiISJGFhsLx45YlZs+ft0zgFrkeJZuTWbh/IV/u/JKV0StJz0rPU6d2xdr0q9+Pm2vezC21btFu4HLDUGIhIiJFEh8PISGW427dYMUKx8YjUloS0xL5ZNsnbD+xna93fZ1vHSeTE0+3fZrHIh+jVsVapRyhSOmy577b7t+fzGYz48ePJyQkBE9PT9q0acOyZcsK3f67776jXbt2VKhQAX9/f9q3b88K/Y0lIlKmbNmSexwZ6bg4REqbn4cf49qP46v+X2F+0cysO2dxc82bMZE7DCrbyGbahmnUfq823WZ3Y+q6qaw9tpb45HgHRi7ieHavCjV8+HDmzZvHk08+SZ06dZg1axa33XYbK1eupGPHjlds+8orr/Dqq68yYMAAhg8fTkZGBrt37yY29so7aoqISOmqWhVGjIA9e6BtW0dHI+IYbs5uDGs+jGHNh5FwMYFFBxbx4ZYP2RS7CQPLgI9V0atYFb3K2ibML4zIkEjqVapH/cr1tW+G3FDsGgq1adMm2rRpw9SpUxk3bhwAaWlpNG7cmMDAQNavX19g2z///JP27dvzn//8h6eeeqpIQWsolIiIiDhKfHI87/z5Dj/s/YHD5w5ftX64Xzi31LqFyJBIWldrTbOgZjiZNGlJyocSm2Px3HPP8fbbb3P27FmbC0+ZMoXnn3+eY8eOERoamm/bIUOGsGbNGo4fP47JZOLChQt4X+MWrkosRERExNEMw+Dg2YMsPbSUgwkH2XlqJ5tiN3Ex4+IV20UERDCk0RDubng3Tas2VZIhZVqJJRY9evQgNjaWqKgom/Lff/+dW265hZ9++ok+ffrk27ZKlSq0b9+ebt268frrr5OQkEBQUBAvvPACo0ePLmwIgBILERERKZsyszM5cu4If538i8UHF7M5bjN7Tu8h28jOt37VClVpH9qeES1G0DuiN85OzqUcsciVldjO2/Hx8QQHB+cpzymLi8t/Hehz585x5swZ1q1bx4oVK3j55ZcJCwtj5syZjBkzBldXVx555JECX9dsNmM2m63Pk5KS7AlbRETskJIChmFZZlZE7OPi5EKdSnWoU6kOAxoOAOBC+gW2n9jO5tjN/Lj/R9YcXWOtf/LCSRbsW8CCfQuoVbEWN9e8mec6PEetirXUkyHljl09FrVr16ZevXr8+uuvNuWHDx+mdu3avPPOOzz55JN52sXExBAWFgbAt99+y+DBgwHIzs6mSZMmJCUlERMTU+DrvvLKK0yaNClPuXosRESK3/TpMGYMBAXBjBlw552Ojkjk+nL0/FEWHVjEnJ1z2Bq3lSwjK0+dAM8A2lVvR/vQ9txR9w6aVm3qgEhFSnC5WU9PT5uegxxpaWnW8wW1A3B1dWXAgAG5L+7kxODBgzl+/DjHjh0r8HUnTJhAYmKi9XGlJERERIpm82bLv0+cgHw6qUWkiML9w/lX63+xceRGzjx3hi/u+iJPnbOpZ/nl4C+8sOIFms1oRouPWvDs0mc5kXLCARGLFI5diUVwcDDx8XnXaM4pC8nZTekyAQEBeHh4UKlSJZydbccOBgZalmA7d+5cga/r7u6Or6+vzUNERErGmn9GaXh4QLNmjo1F5Hrn7+HPfU3vI/ulbFYNW8XLXV7mjrp3UNmrsk29HSd2MG3DNEL+E0KHzzvwxV9fcCH9goOiFsmfXXMsmjdvzsqVK0lKSrK5ud+4caP1fH6cnJxo3rw5mzdvJj09HTc3N+u5nHkZVapUsTd2EREpZqdPQ3S05TgyEtzdHRqOyA3DZDLRpUYXutToAuSuODUvah5z/prD/oT9lnIM1sesZ33Meh766SE6hXWiW41uPNjiQar7VnfkWxCxr8diwIABZGVl8fHHH1vLzGYzM2fOpE2bNtalZo8dO8a+ffts2g4ePJisrCxmz55tLUtLS+Orr76iYcOGBfZ2iIhI6fnrr9zjFi0cF4fIjc5kMlG3Ul2e7/Q8+0bv49iTx3i2/bM0qNzAWiczO5OV0St5adVLhL0TRrvP2jF13VT+OvFXgatQiZQkuyZvAwwaNIgFCxbw1FNPERERwezZs9m0aRO///47nTt3BqBr166sXr2aSy+dmppKZGQkBw4cYOzYsYSFhfHFF1+wbds2fv75Z2699dZCx6DlZkVESsakSfDKK5bjOXPg/vsdGo6IXMYwDNbFrOPLnV/yy8FfOJ50PN96Pm4+RFaLZGDDgQxoOCDP0CqRwiqxfSzA0sswceJEvvzyS86dO0fTpk157bXX6NWrl7VOfokFwKlTp3juuef4+eefuXDhAs2bN2fSpEk2bQtDiYWISMno1QuWLrUcHzkCNWo4NBwRuQLDMIg+H83MHTP5etfXHDp3qMC6Nfxr0KZaG55p9wyR1SJLMUop70o0sSgLlFiIiBS/7GwICIDERMtSs3FxYDI5OioRKYxLdwFffng52+K3EZOU/yqa3m7efHDbB9zV4C683bxLOVIpb5RYiIiI3fbsgcaNLcf9+sGCBQ4NR0SKwDAM1h5by/y981l/fD17Tu3hQobtKlJerl50Du/M6MjR3FbnNkz6JUHyUWI7b4uIyPUrKgq8vODiRejUydHRiEhRmEwmOoV3olO45X9mc6aZV1e/yje7v+HI+SMAXMy4yJK/l7Dk7yUEewfzRJsneKDZA4T4aEEduTbqsRAREav0dMsGebVrW4ZDicj1xTAsy9V+tPUjvtiZd2M+J5MTvWr3oluNbvSO6E2DKg1wcdLv0DcyDYUSERERkSu6kH6BOX/NYd7eeaw4siLfOtV8qvFQi4f4V+S/qOpdtZQjlLJAiYWIiIiIFNqRc0eY/ddspm+aTkJqQr51+jfoz78i/0WnsE64OruWcoTiKEosRETELoahFaBEBLKNbH7e/zPbT2znz+N/8tuh3/LUqeJVhSGNh9A4sDGNqjSiXWg7nEx27bks5YgSCxERscvgwZCQAF27wrhx4OHh6IhEpCzYd2Yfk/+YzJc7v7xiveZBzZnQcQI9avWgomfFUopOSoMSCxERKTSzGQIDISkJqlSBkyfVeyEits5cPMNP+39i0YFFLNhX8FrULk4u9Krdi1e7vUrL4JalGKGUFCUWIiJSaKtXW3oqAIYOhS+v/MOkiNzgjicdJ+p0FIsPLubzHZ+TZE7Kt16nsE40D2pOREAETQKb0LVGV+2VUQ5pHwsRESm0Zctyj2++2XFxiEj5UN23OtV9q9Ozdk/e6f0OaZlprDm6hl8P/srcPXOJT4kH4I9jf/DHsT+s7WpXrM3IliMZ32G8EozrlHosRERucM2bw19/WYY/xcVp/woRuXbmTDPvb36f6ZumWzfiy88jrR6hVXAr7m1yLxXcKpRihGIvDYUSEZFCSUiAypUtxy1bwtatjo1HRK4fpy+cJup0FNtPbOep357Kt46HiwfDmw2nQ1gH6gTUoWVwSy1lW8YosRARkUJZsAD697ccP/MMTJvm2HhE5PqUmZ3J+5veZ8G+Baw5ugaDgm8/BzQcwIudXqRZULNSjFAKosRCREQKZdQo+OQTy/FPP0GfPo6NR0Suf8nmZJYfXs5Xu77il4O/kJaZlm+9ZlWbcWe9O7kp5CZ61O6Bh4vWwXYEJRYiIlIoderA33+DmxucPQsVNNRZREpRsjmZldErWRW9iuWHl7Pr1K4C695e53Z61u7J3Q3upppvtVKM8samxEJERK7qxAkIDrYcd+gAa9c6Nh4RkfNp55nz1xxm7ZjF9hPb863j4eLB0CZD6RDagX71+2lDvhKmxEJERArl5En4/Xfw8oJ+/RwdjYhIrsPnDjN/73x2ndrF0kNLOZFyIk8dN2c3etbuyX1N7uPuhnfj4qSdFIqbEgsRERERuW4YhsGuU7v4cPOHzNg6I986vu6+DGgwgNe6v0aIT0gpR3j9UmIhIiIiItel40nH2XNqDwv3L+TDLR/mW6drja50q9GNdtXbcUutW7QhXxEosRARERGR697FjIt8u/tbftj7A78e/DXfOrUq1qJjWEc6hXViaJOheLp6lnKU5ZsSCxERuaLXX4djx6BvX+jZ07IqlIhIebbr5C4+2fYJvxz8hcPnDudbp1GVRjzd7mluqXULYX5hpRxh+aTEQkRECmQYULs2HDkCLi5w+jT4+zs6KhGR4mEYBn+f/ZsNxzfw+fbP2XB8A+lZ6XnqNajcgPah7RnUaBA317wZZydnB0Rb9imxEBGRAu3aBU2bWo5vuQWWLXNsPCIiJcmcaeb7qO+ZtHoSf5/9O986Qd5B9Kzdk+c7Pk+9yvVKOcKyTYmFiIgUaPJkePFFy/F778GYMY6NR0SkNGRkZfDHsT/49eCv/H7kd3ac2JFvvVoVa9G9Rne61+xOnUp1aB7U/IZexlaJhYiIFKhNG9i0yXIcHQ3h4Q4NR0TEIZLNyfy0/yfGLRuX7x4ZObxcveheszs9a/VkSOMhVKlQpRSjdDwlFiIikq+4OKhWzXLcrBns2OHQcEREyoS9p/eycP9Cfjv0G+tj1uc7JyNHnYA6tA9tT5tqbRjYaCCVvSqXYqSlT4mFiIjk6+OP4ZFHLMcvvQSTJjk2HhGRsiY1I5UVR1awOW4zUaej+D7q+wLr+rr7MrzZcLrU6MIdde/Azfn6W2JPiYWIiOSrd2/47TfL8ZYt0KqVY+MRESnr0rPSWXxwMWuOrmFl9Ep2ntxJlpGVp16QdxA/Dv6RNtXbOCDKkqPEQkRE8khIgKpVISvLMq/iyBHQZrQiIvZJzUhl16ldfLL1E2b/NZuM7Ayb82PbjGVAwwG0qdYGV2dXB0VZfOy573YqpZhERMTBFi+2JBUAAwYoqRARuRaerp60rtaaT/p+Qvwz8XzW9zMCPAOs59/d+C6dZnYi5O0Q+n7Tl1XRq8jIyrjCFa8f6rEQEblBZGfDxo0wbx4MHQotWzo6IhGR68OF9Av898//8vofr5OWmZbnvL+HP/3r9+e+pvfRrWY3B0R47TQUSkRERESklB1POs7P+3/mgy0fEHU6imwjO0+dmv416RjWkY5hHYkIiKB1tdZ4u3k7INrCUWIhIiIiIuJAZ1PPsvjgYn4+8DPf7fmuwHrOJmeaVm1KDf8aRARE0LRqU5oENqFJ1SY4mRw/a0GJhYiIWJnN4OamORUiIo5yMuUk3+7+lh/2/sCm2E2Ys8xXbePu7E79yvVpHNiYTmGdaFO9DbUq1sLXvXTvfZVYiIiI1QsvwMKF8PjjMGwYVKjg6IhERG5c5kwz62LWEXU6il0nd7EiegWHzx3Od9hUQQY3Gky76u0Y2GggIT4hJRitEgsREfmHYUCtWhAdDc7OEBMDwcGOjkpERC6VmZ3J8aTjbI7dzB/H/iAmKYa9p/fy99m/890z41J1Aurwn57/oX1oeyp5VSr22JRYiIgIAGvXQqdOluNbboFlyxwbj4iIFJ4508zG2I0sP7yc76O+52LGRU6knCA9Kz3f+vUq1aNdaDvaVW9HZEgkTao2wcXJpUgxKLEQEREA+vWzDIMC+PRTGDHCoeGIiEgRZWRlsC1+G1PWTuGPY39wNvVsgXUDPAPoUasHt9S6hV61exHqF2r36ymxEBER9uyBxo0tx8HBcPgweHg4NiYRESk+6Vnp/HLgF/449gfrY9azLX5bnp3ALzW40WBGthxJu+rtqOBWuAl3SixERISHH7b0UgC88w48+aRDwxERkRKWlpnG1ritbIrdxLqYdfx26DdS0lPy1HNxcqGGfw161OrBPY3v4aaQm/B09cz3mkosRERucMnJEBQEFy9aVoE6dgwCAhwdlYiIlKbM7Ey2xG1h+qbpzN0zt8DeDHdnd9qHtqdjWEciQyKJrBZJkHcQYN99d9Fmc4iISJn05ZeWpALg/vuVVIiI3IhcnFxoW70tbau35e1eb/P74d9ZcWQFn27/1KaeOcvMyuiVrIxeaS2r6FGRHrV7UMuzVqFfTz0WIiLXmawsaNgQDhywPN+2DVq0cGxMIiJSdhiGwaFzh1hzdA3LDy9n9dHVxCXH5V85Dfg/1GMhInIjMpuhTx94/31o21ZJhYiI2DKZTEQERBAREMFDLR7CMAyOJR5jc9xm5kXNY/nh5ZxLO2fXpn2gHgsRketWfDycO2fpvRAREbGHOdPM4XOH2X50O0Mjh6rHQkTkRhYcrF22RUTk2ri7uNOgSgOquVcrdBunEoxHRERERERuEEosRESuEy+9BP/+NyQlOToSERG5EdmdWJjNZsaPH09ISAienp60adOGZcuW2f3CPXr0wGQyMXr0aLvbioiIrbg4eO01ePNNy27bZrOjIxIRkRuN3YnF8OHDefvttxk6dCjvvvsuzs7O3Hbbbaxdu7bQ15g/fz4bNmyw96VFRKQANWvmHt95J7i7Oy4WERG5MdmVWGzatIlvv/2WKVOmMHXqVEaNGsWKFSsIDw/nueeeK9Q10tLSeOaZZxg/fvw1BSwiIrb++APS03OfP/GE42IREZEbl12Jxbx583B2dmbUqFHWMg8PD0aMGMGGDRuIiYm56jXeeustsrOzGTdunP3RioiIjYwMeOSR3Oe9e0OdOo6LR0REblx2JRbbt2+nbt26edawbd26NQA7duy4Yvtjx47xf//3f7z55pt4enraF6mIiOTx6quwd6/lODISFi1ybDwiInLjsmsfi/j4eILzWRQ9pywuroCtwP/xzDPP0KJFC4YMGWLPy2I2mzFfMhMxSUueiIhw5Ai8/rrl2MUFPvwQnJ0dG5OIiNy47EosUlNTcc9nRqCHh4f1fEFWrlzJDz/8wMaNG+0MEaZMmcKkSZPsbicicr3KzoaRI3OfP/YYtGrluHhERETsGgrl6elp03OQIy0tzXo+P5mZmTzxxBPcf//9REZG2h3khAkTSExMtD4KM5dDROR6tmYNrFiR+3ziRMfFIiIiAnb2WAQHBxMbG5unPD4+HoCQkJB8282ZM4f9+/fz0UcfER0dbXMuOTmZ6OhoAgMD8fLyyre9u7t7vj0lIiI3qq5dYelSGDECZs6EKlUcHZGIiNzo7OqxaN68OQcOHMgzxyFneFPz5s3zbXfs2DEyMjLo0KEDNWvWtD7AknTUrFmTpUuXXkP4IiI3rh494MABuPlmR0ciIiICJsMwjMJW3rhxI23btmXq1KnW5WLNZjONGzemUqVK/Pnnn4Alkbh48SL169cHYN++fezbty/P9e666y5uu+02Hn74Ydq0aZPvxPD8JCUl4efnR2JiYp4VqkRErlexsRAUpAnaIiJSeuy577ZrKFSbNm0YOHAgEyZM4NSpU0RERDB79myio6P57LPPrPUeeOABVq9eTU7OUr9+fWuScbmaNWvSr18/e8IQEbnhJCRAp06WIVCffgpOdvU3i4iIlDy7EguwDF2aOHEiX3zxBefOnaNp06YsWrSIzp07l0R8IiI3vJQUuP12y/KyR45AzZqarC0iImWPXUOhygoNhRKRG0VWliWp+O03y/PAQNi4EWrUcGhYIiJygyixoVAiIlJ6srMtQ5/WrrU89/OzrASlpEJERMoiJRYiImWQYUCDBpZVn8Cys/b8+dCsmWPjEhERKYim/4mIlDFms2V/ipykAuDzz6F7d8fFJCIicjXqsRARKUPOnoU77oANG3LLZsyA++93XEwiIiKFoR4LEZEyxMnJMuwpx+TJ8MgjjotHRESksNRjISJShvj7W1aAeuIJeOwxaNnS0RGJiIgUjhILEREHS0qCS1fw8/SETz5xXDwiIiLXQkOhREQc6O23ITwctm51dCQiIiJFo8RCRMQBDANeeAGeeQbOn4eePeHoUUdHJSIicu00FEpEpJQlJ8Ojj8LXX+eWPfAAhIY6LiYREZGiUmIhIlKKtm2DwYPh779zy159FSZOdFxMIiIixUFDoURESoFhwLvvQtu2uUmFry98/72SChERuT6ox0JEpIQlJMCDD8LPP+eWRUbCt99CrVqOi0tERKQ4qcdCRKSEnT0LK1fmPh83DtauVVIhIiLXFyUWIiIlrE4d+PBDqFwZfv0Vpk4FNzdHRyUiIlK8lFiIiBSzXbtg/nxIScktu+8+OHgQbr3VcXGJiIiUJCUWIiLFJDUVXnwRWraEefPA29v2vL+/Q8ISEREpFUosRESKwfLl0KQJTJ4MmZnwzTewYIGjoxIRESk9SixERIogLg7uuQd69IBDh3LLe/aE7t0dF5eIiEhpU2IhInINzGZ46CGoVs2ybGyOjh1hzx747Tfw83NcfCIiIqVN+1iIiNgpKgruvhv27cstq1QJ/u//LMmGk36yERGRG5ASCxERO128aJtUDB8O06ZZkgsREZEblX5XExG5CrPZ9vlNN0Hv3hASYpm0PXOmkgoRERElFiIiBdi7Fx54AKpXh4wM23MffwyHD8PNNzsmNhERkbJGiYWIyGX+/BN69YKGDeGLL+DMGfj0U9s6oaHg7u6Y+ERERMoizbEQEQEMA1auhOnTtf+EiIjItVBiISI3tKQkmDMHPvjAMvTpUuHhMGYMjBihXbNFRESuRomFiNzQ3nsPJk60LQsKggkT4LHHwNXVMXGJiIiUN5pjISI3jLQ0OHfOtmzkyNzkoVMny2Z3R4/CE08oqRAREbGHEgsRue7t2wfPPGPZJfv1123PBQXBhx/Czp2wZg0MHgxubo6JU0REpDwzGYZhODoIeyUlJeHn50diYiK+vr6ODkdEyqCYGFiyBL75xjIpO0dAAMTGgoeH42ITEREpL+y579YcCxG5bpjN8Mcflh6IH3+E7Gzb8+7ucNttkJioxEJERKS4KbEQketCdLRlR+yEhLznIiLg0Udh+HDtkC0iIlJSlFiISLmUnQ1Ol8wSCwuDChVyE4vgYEsi0a8fREaCyeSIKEVERG4cSixEpNw4dQp++w3mzoXz5y3DnnI4OcFDD8GePXD33XDXXZqELSIiUpqUWIhImZaUBPPnw5NPWuZGXCo6GmrUyH3+8sulGJiIiIjYUGIhImVOVBQsXmxJKDZtgszMvHVCQuDwYdvEQkRERBxHiYWIlDm9e1uWi71cw4bQujU8+CB07Gg7x0JEREQcS4mFiJS6pCTL3hKTJ1uWfV2zxvZ8kya5iUXdutCzJ9x7L7Rtq0nYIiIiZZUSCxEpcampsHEjrFgBy5dbhjdlZeWez8gAV9fc50OGwM03W/acqF+/9OMVERER+ymxEJEScfw4/Oc/sGULbN5s2bwuPyYTHDpkm0Dcf3/pxCgiIiLFR4mFiBTZmTOWXonQ0NwyJyf473/zr1+vHnTtavn38OFQsWIpBCkiIiIlSomFiNglLQ127rT0RKxda9lTIivLkiDMnJlbLyQEGjWy7CsRHg7du1uSiZtvhmrVHBW9iIiIlBQlFiJyRX//bZkXsWULbN0Ku3fnv/zrxo15y777DoKCoFKlko9TREREHEuJhYgAls3nduyAyEjw8sot/+kneOaZK7ft1AnatMlb3qhRsYYoIiIiZZgSC5EbUGwsbN9uSSS2b7c8jhyxnFu7Fjp0yK3bqlXusZOTZS+JVq3gppssSUjLlrYrOomIiMiNSYmFyA3AMOCll2DbNksyERdXcN3t220Ti5Yt4b33LMlEs2ZQoUKJhysiIiLlkBILketAdjYcPgx//WWZA+HvD2PH5p43mSzzHQ4ezL+9l5claWjeHJo2tT3n4wNjxpRU5CIiInK9sDuxMJvNvPTSS3zxxRecO3eOpk2b8vrrr9OjR48rtps/fz7fffcdmzdv5sSJE4SGhnLHHXcwceJE/P39rzV+kRtKejpER1uSh6go2LfPkixERUFKSm69Jk1sEwuwJA0HD1qWdm3e3DKMqUULy3GdOuDsXHrvQ0RERK4/JsMwDHsa3HPPPcybN48nn3ySOnXqMGvWLDZv3szKlSvp2LFjge0qV65MSEgI/fr1IywsjF27djFjxgxq1arFtm3b8PT0LHQMSUlJ+Pn5kZiYiK+vrz3hi5R5hgEJCZbVmJo0sR169OGH8PjjV7+Gi4sl0XB3zy07eNDyPDTU0oMhIiIicjX23HfblVhs2rSJNm3aMHXqVMaNGwdAWloajRs3JjAwkPXr1xfYdtWqVXTt2tWmbM6cOQwbNoxPPvmEkSNHFjYMJRZyXUhLs0yY3r/fstfDli2WlZl27bJsOAewZo1lxaUcy5fD5Z2DJhPUqGEZytSsGTRubBnOFBFhmWwtIiIicq3sue+2ayjUvHnzcHZ2ZtSoUdYyDw8PRowYwfPPP09MTAyhl269e4nLkwqAu+66i2HDhrF37157whApF9LTISbGMv+hTp3ccsOA2rVzV2G6kqgo28SiYUMYNAjq1rUkEfXrW65lR4efiIiISImwK7HYvn07devWzZOttG7dGoAdO3YUmFjk58SJE4BlmJRIeZOVZVld6ciR3Ed0dO5xbKwlqbjzTvjxx9x2JhN4eBR83aAgS69D7dqWxOFSISGWSdgiIiIiZY1diUV8fDzBwcF5ynPK4q60hmU+3nzzTZydnRkwYMAV65nNZsxms/V5UlKSXa8jYi/DgAsX4NQpS7IQHQ3Dh9sOLXruOXj77atfK7+eidatLSs3hYdbejNcXS1DnOrU0S7VIiIiUj7ZlVikpqbifuls0H94/PPza2pqaqGv9fXXX/PZZ5/x3HPPUefScSL5mDJlCpMmTbInVJFCSUiAzz+39DysX2957uQEx4/D5V/n3r0tPQY5atQo+LoBAVCzpuXRuHHe87NmFUf0IiIiImWHXYmFp6enTc9BjrS0NOv5wvjjjz8YMWIEvXr1YvLkyVetP2HCBJ5++mnr86SkJLuGXMn1L2clpfh4y+P4cThxAk6etDxyjp9+Gh5+OLfdhQuWnofCOHrUNrFo3hz69rUkDzVq5CYSNWqA1hQQERGRG41diUVwcDCxsbF5yuPj4wEIufSuqwB//fUXffv2pXHjxsybNw8Xl6uH4O7unm9PiVz/0tMtCUFOwnDihGWi8gMP2NZr0wY2b7769S4flhQcbJnzcPnaaA0aWM4FBFgShRo1ICzMtk6nTrYTq0VERERuZHYlFs2bN2flypUkJSXZTODeuHGj9fyVHDp0iN69exMYGMivv/6Kt7e3/RFLuZczd6F6dcvcghzLlsGcObaJREJC3vaNGuVNLKpUufrrVqgAmZm2Za6u8MMPlvbVqlkmTmuFJRERERH72ZVYDBgwgGnTpvHxxx9b97Ewm83MnDmTNm3aWIcnHTt2jIsXL1L/kiVtTpw4Qc+ePXFycuK3336jSmHuBKXMMgxITrbMR7g0P0xKgg8+gLNnLY9z53KPcx4XL1rq7tsH9erltj18GL788uqv/U8HmY02bSwrMAUHW5KDatVyj6tWtTwKymPvuqvw71tERERE8mdXYtGmTRsGDhzIhAkTOHXqFBEREcyePZvo6Gg+++wza70HHniA1atXc+nee7179+bw4cM899xzrF27lrVr11rPVa1alR6X7/olpSIjw3Kj7+dnW/7VV5admgtKEM6dsyy3OmUK/Pvfue3S02HChMK99tmzts+DgnKP3d0tz4ODcx+XPr/cSy8V7jVFREREpGTYlViAZbfsiRMn8sUXX3Du3DmaNm3KokWL6Ny58xXb/fXXXwC89dZbec516dJFiUURGAakpFiG+ly6HOrmzbBiRf5JQc5xSgq0bQsbNthe84MPLKskXc25c7bP/f3zr+fmZpmvEBAAgYGWoUeX9yB06wZ791p6F/z9LXMfRERERKR8sDux8PDwYOrUqUydOrXAOqtWrcpTZlw+O1byyMy0DOdxc8stS0iAr7++cs/B2bOWtidOWG7Kc6xaZdubUJDLew7AkgDkx88PKlbMTRJq1bI97+ICCxda6gUE5Nb19Lx6ouDrq9WURERERMoruxMLyV9GhmV+QVKSpdcgPNz2/EcfQUxMbp2kJEhMtE0UkpNhxgx45JHcdmfPwhNPFC6Gs2dtE4uCkgNXV9ub/po189Z5/nl4/PHcBKJiRUsvQiEW8aJv38LFKyIiIiLXjxs6scjIsAwFSk7O+0hJgfvvt/2VfeZMmD/fcj4pKfffSUnwz1YeAPTsCb/9Zvta06fD7t1Xj+nyoUUFJQcAPj62N/6XDoMCuPlmy4pHlyYRAQHg5XX13oN27a4eq4iIiIhIjnKdWGzcmDu/ICcZuDxR+Ne/oFWr3DYbNlh+UU9Ohnz2+rMxYIDlJjzHwYOwaNHV40pKyltW0BAfFxfbG//AQNvz/v7wxRe2CURAgKX80qVa85Oz/4KIiIiISEkr14lFz55Xr9Ojh21i4eoKZ84U7vrJybaJReXKuccuLpYeAz+/3LkBOY9Ll1DN8eabll6NS+v5+FgmMF+p98DZGe67r3DxioiIiIg4SrlOLAojOdn2ub+/ZU6Bj4/tw9s7b9mlSQXAyJGWm3xfX8tyqPasWtSxY5HfioiIiIhImVWuE4vHHrP0Inh75yYGlx77+Fh2d75URIRlI7ZroVWLRERERETyV64Ti//7P93oi4iIiIiUBU5XryIiIiIiInJlSixERERERKTIlFiIiIiIiEiRKbEQEREREZEiU2IhIiIiIiJFpsRCRERERESKTImFiIiIiIgUmRILEREREREpMiUWIiIiIiJSZEosRERERESkyJRYiIiIiIhIkSmxEBERERGRIlNiISIiIiIiRabEQkREREREikyJhYiIiIiIFJkSCxERERERKTIlFiIiIiIiUmRKLEREREREpMiUWIiIiIiISJEpsRARERERkSJTYiEiIiIiIkWmxEJERERERIpMiYWIiIiIiBSZEgsRERERESkyJRYiIiIiIlJkSixERERERKTIlFiIiIiIiEiRKbEQEREREZEiU2IhIiIiIiJFpsRCRERERESKTImFiIiIiIgUmRILEREREREpMiUWIiIiIiJSZEosRERERESkyJRYiIiIiIhIkSmxEBERERGRIlNiISIiIiIiRabEQkREREREikyJhYiIiIiIFJkSCxERERERKTIlFiIiIiIiUmR2JxZms5nx48cTEhKCp6cnbdq0YdmyZYVqGxsby6BBg/D398fX15c777yTw4cP2x20iIiIiIiULXYnFsOHD+ftt99m6NChvPvuuzg7O3Pbbbexdu3aK7ZLSUmhW7durF69mueff55Jkyaxfft2unTpQkJCwjW/ARERERERcTyTYRhGYStv2rSJNm3aMHXqVMaNGwdAWloajRs3JjAwkPXr1xfY9q233mL8+PFs2rSJyMhIAPbt20fjxo157rnneOONNwoddFJSEn5+fiQmJuLr61vodiIiIiIiUnj23Hfb1WMxb948nJ2dGTVqlLXMw8ODESNGsGHDBmJiYq7YNjIy0ppUANSvX5+bb76ZuXPn2hOGiIiIiIiUMXYlFtu3b6du3bp5spXWrVsDsGPHjnzbZWdns3PnTm666aY851q3bs2hQ4dITk62JxQRERERESlDXOypHB8fT3BwcJ7ynLK4uLh82509exaz2XzVtvXq1cu3vdlsxmw2W58nJiYClq4ZEREREREpGTn324WZPWFXYpGamoq7u3uecg8PD+v5gtoB19QWYMqUKUyaNClPeWho6NWDFhERERGRIklOTsbPz++KdexKLDw9PW16DnKkpaVZzxfUDrimtgATJkzg6aeftj7Pzs7m7NmzVKpUCZPJVPg3UIySkpIIDQ0lJiZGE8iLSJ9l8dLnWXz0WRYvfZ7FS59n8dFnWbz0eRafsvBZGoZBcnIyISEhV61rV2IRHBxMbGxsnvL4+HiAAl8wICAAd3d3az172oKlp+Py3g5/f//Chl2ifH199T9NMdFnWbz0eRYffZbFS59n8dLnWXz0WRYvfZ7Fx9Gf5dV6KnLYNXm7efPmHDhwIM/cho0bN1rP5/siTk40adKELVu25Dm3ceNGatWqhY+Pjz2hiIiIiIhIGWJXYjFgwACysrL4+OOPrWVms5mZM2fSpk0b65yHY8eOsW/fvjxtN2/ebJNc7N+/nxUrVjBw4MCivAcREREREXEwu4ZCtWnThoEDBzJhwgROnTpFREQEs2fPJjo6ms8++8xa74EHHmD16tU2s8cff/xxPvnkE26//XbGjRuHq6srb7/9NlWrVuWZZ54pvndUStzd3Xn55ZfznZAu9tFnWbz0eRYffZbFS59n8dLnWXz0WRYvfZ7Fp7x9lnbtvA2WydYTJ07kyy+/5Ny5czRt2pTXXnuNXr16Wet07do1T2IBcPz4cZ566imWLl1KdnY2Xbt25Z133iEiIqJ43o2IiIiIiDiE3YmFiIiIiIjI5eyaYyEiIiIiIpIfJRYiIiIiIlJkSiyKydatW7njjjsICgrC29ubpk2b8t5775GVleXo0Mqt5cuX0717d/z8/PDx8aFVq1Z89913jg6rXHv44YcxmUzccccdjg6lXPr999956KGHqFu3Ll5eXtSqVYuRI0fmu0eP5DKbzYwfP56QkBA8PT1p06YNy5Ytc3RY5dLmzZsZPXo0jRo1okKFCoSFhTFo0CAOHDjg6NCuC5MnT8ZkMtG4cWNHh1Jubdu2jb59+xIQEICXlxeNGzfmvffec3RY5c7BgwcZMmQI1atXx8vLi/r16/Pqq69y8eJFR4d2RZpjUQy2bt1K+/btqVOnDiNGjMDLy4vFixezcOFCnnjiCd59911Hh1juzJw5kxEjRtCjRw/69u2Ls7Mz+/fvp1q1aowbN87R4ZVLW7ZsoV27dri4uHDzzTezaNEiR4dU7tx0002cPXuWgQMHUqdOHQ4fPsz06dPx8vJix44dBAUFOTrEMumee+5h3rx5PPnkk9SpU4dZs2axefNmVq5cSceOHR0dXrkyYMAA1q1bx8CBA2natCknTpxg+vTppKSk8Oeff+qGuAiOHz9OvXr1MJlM1KhRg927dzs6pHJn6dKl9OnThxYtWjB48GC8vb05dOgQ2dnZvPXWW44Or9yIiYmhadOm+Pn58eijjxIQEMCGDRuYNWsWffv2ZeHChY4OsWCGFNnDDz9suLm5GQkJCTblnTt3Nnx9fR0UVfl15MgRw9PT03jiiSccHcp1Izs722jXrp3x0EMPGeHh4cbtt9/u6JDKpdWrVxtZWVl5ygDjhRdecFBUZdvGjRsNwJg6daq1LDU11ahdu7bRrl07B0ZWPq1bt84wm802ZQcOHDDc3d2NoUOHOiiq68PgwYON7t27G126dDEaNWrk6HDKncTERKNq1arGXXfdlefPSbHP5MmTDcDYvXu3TfkDDzxgAMbZs2cdFNnVaShUMUhKSsLDwwN/f3+b8uDgYDw9PR0TVDk2Y8YMsrKyePXVVwFISUnJs3Sx2OeLL75g9+7dTJ482dGhlGudO3fGyckpT1lAQAB79+51UFRl27x583B2dmbUqFHWMg8PD0aMGMGGDRuIiYlxYHTlT/v27XFzc7Mpq1OnDo0aNdJ3sAjWrFnDvHnz+O9//+voUMqtr7/+mpMnTzJ58mScnJy4cOEC2dnZjg6rXEpKSgKgatWqNuXBwcE4OTnl+TOgLFFiUQy6du1KUlISjzzyCHv37uXo0aPMmDGD+fPnM2HCBEeHV+4sX76c+vXr8+uvv1K9enV8fHyoVKkSEydO1B9S1yA5OZnx48fz/PPPa6hOCUhJSSElJYXKlSs7OpQyafv27dStWxdfX1+b8tatWwOwY8cOB0R1fTEMg5MnT+o7eI2ysrIYM2YMI0eOpEmTJo4Op9xavnw5vr6+xMbGUq9ePby9vfH19eWxxx4jLS3N0eGVK127dgVgxIgR7Nixg5iYGL777js+/PBDnnjiCSpUqODYAK/Arp23JX8PP/wwe/bs4aOPPuLTTz8FwNnZmenTp/Poo486OLry5+DBgzg7O/Pggw/y3HPP0axZM+bPn8/rr79OZmYmU6ZMcXSI5cqrr76Kp6cnTz31lKNDuS7997//JT09ncGDBzs6lDIpPj6e4ODgPOU5ZXFxcaUd0nXnq6++IjY21trLK/aZMWMGR48eZfny5Y4OpVw7ePAgmZmZ3HnnnYwYMYIpU6awatUq/ve//3H+/Hm++eYbR4dYbvTu3ZvXXnuNN954g59++sla/sILL/D66687MLKrU2JxmezsbNLT0wtV193dHZPJhLOzM7Vr16ZXr14MHDgQDw8PvvnmG8aMGUNQUBD9+vUr2aDLsGv5PFNSUsjOzub//u//GD9+PAB33303Z8+e5d133+X555/Hx8enJMMuk67lszxw4ADvvvsu33zzDe7u7iUcYflyLZ/n5dasWcOkSZMYNGgQ3bt3L+4Qrwupqan5fvc8PDys5+Xa7du3j3/961+0a9eOYcOGOTqccichIYGXXnqJiRMnUqVKFUeHU66lpKRw8eJFHn30UesqUP379yc9PZ2PPvqIV199lTp16jg4yvKjRo0adO7cmbvvvptKlSrxyy+/8MYbbxAUFMTo0aMdHV7BHD3Jo6xZuXKlARTqsXfvXsMwDGPKlClGUFCQkZycbHOtrl27GiEhIUZGRoYj3kqZcC2fZ4UKFQzAOHr0qM21Zs+ebQDG6tWrHfFWHO5aPsvevXsbXbp0sbmOJm9bXMvneam9e/caAQEBRvPmzY2kpCQHvIPyoVGjRkb37t3zlO/Zs8cAjBkzZjggqutDfHy8UatWLSM0NNSIjY11dDjl0qOPPmpERETYTIjX5O1r06hRo3z/js5Z4GL27NkOiqz8+eabbwxPT08jJibGpnz48OGGl5eXcebMGQdFdnXqsbhM/fr1mTlzZqHq5nTlf/DBB3Tv3h1vb2+b83379uXpp58mOjqaiIiIYo+1PLiWzzMkJISDBw/mmbQUGBgIwLlz54o3yHLC3s9yxYoVLFmyhPnz5xMdHW09l5mZSWpqKtHR0QQEBOQZ+36juJbvZo6YmBh69uyJn58fv/766w3Zg1ZYwcHBxMbG5inP2fsjJCSktEO6LiQmJnLrrbdy/vx5/vjjD32O1+DgwYN8/PHH/Pe//7UZkpeWlkZGRgbR0dH4+voSEBDgwCjLj5CQEPbs2aO/u4vBBx98QIsWLahevbpNed++fZk1axbbt2/nlltucVB0V6bE4jJBQUEMHz7crjYnT57MdyO8jIwMwHIjd6O6ls+zVatWHDx4kNjYWGrVqmUtz/mD/0btrrb3szx27Bhg6Yq+XGxsLDVr1uSdd97hySefLKYIy5dr+W6CZehEz549MZvN/P777/nOH5BczZs3Z+XKlSQlJdkksRs3brSeF/ukpaXRp08fDhw4wPLly2nYsKGjQyqXYmNjyc7O5oknnuCJJ57Ic75mzZqMHTtWK0UVUqtWrVi2bJl18naOG/3v7mtx8uRJKlasmKe8PNxXalWoYlC3bl2WLVtGQkKCtSwrK4u5c+fi4+ND7dq1HRhd+ZMzCfazzz6zlmVnZzNz5kwCAgJo1aqVo0IrV7p3786CBQvyPKpUqcJNN93EggUL6NOnj6PDLFcuXLjAbbfdRmxsLL/++qvGCxfCgAEDyMrK4uOPP7aWmc1mZs6cSZs2bQgNDXVgdOVPVlYWgwcPZsOGDXz//fe0a9fO0SGVW40bN873z8hGjRoRFhbGggULGDFihKPDLDcGDRoE2P7dDfDpp5/i4uJiXelIrq5u3bps376dAwcO2JR/8803ODk50bRpUwdFdnXaebsYfPXVV9x3333Url2bUaNG4enpyTfffMOGDRt4/fXXeeGFFxwdYrliGAY9evRgxYoVPPzwwzRr1owff/yRZcuW8dFHH9mshy/2q1GjBo0bN9bO29egX79+LFy4kIceeohu3brZnPP29r6hF2q4kkGDBrFgwQKeeuopIiIimD17Nps2beL333+nc+fOjg6vXHnyySd599136dOnj/VG7lL33XefA6K6vnTt2pUzZ85o5+1rMGLECD7//HMGDRpEly5dWLVqFd9//z0TJkzgjTfecHR45caaNWvo3r07lSpVYvTo0VSqVIlFixaxePFiRo4cySeffOLoEAvm4Dke140lS5YYXbp0MSpXrmy4ubkZTZo00aTEIkhOTjbGjh1rBAUFWT/PL7/80tFhXRc0efvahYeHFzjBOzw83NHhlVmpqanGuHHjjKCgIMPd3d2IjIw0lixZ4uiwyqUuXbpccaEBKTpN3r526enpxiuvvGKEh4cbrq6uRkREhPHOO+84OqxyaePGjcatt95qBAUFGa6urkbdunWNyZMnl/kFgdRjISIiIiIiRaY5FiIiIiIiUmRKLEREREREpMiUWIiIiIiISJEpsRARERERkSJTYiEiIiIiIkWmxEJERERERIpMiYWIiIiIiBSZEgsRERERESkyJRYiIiIiIlJkSixERMoYk8lk96Nr164ArFq1yuZ5eRMdHY3JZKJGjRql8npF+bxyPnsRkdKSkpLCyy+/TO/evQkICMBkMjFr1qwiXXPr1q3ccccdBAUF4e3tTdOmTXnvvffIysqy+1ouRYpERESK3bBhw/KUnThxgt9++63A8/Xr1y/xuERExLHOnDnDq6++SlhYGM2aNWPVqlVFut7WrVtp3749derUYfz48Xh5ebF48WLGjh3LoUOHePfdd+26nhILEZEyJr9fn1atWmVNLIr665SIiJRPwcHBxMfHExQUxJYtW4iMjCzS9T766CMA1qxZQ0BAAACPPPIIXbp0YdasWXYnFhoKJSIiIiJSDri7uxMUFFSouosXL6ZTp05UqFABHx8fbr/9dvbs2WNTJykpCQ8PD/z9/W3Kg4OD8fT0tDs+JRYiItepjIwM3nzzTRo1aoSnpyeVKlWif//+7N27N0/dS+c2ZGVl8fbbb9OiRQu8vb3zzCM4cOAAjzzyCLVr18bDwwM/Pz86d+7Ml19+mW8ciYmJvPjiizRp0oQKFSrg7u5OSEgIHTp04KWXXiIjIyPfdoZh8PHHH9OqVSsqVKiAn58fPXv2ZMOGDQW+5+PHjzNmzBjq1Kljja1Dhw589NFH1zReeMOGDdx66634+/vj7e3NTTfdxOeff273dUREStMXX3zB7bffjre3N2+++SYTJ04kKiqKjh07Eh0dba3XtWtXkpKSeOSRR9i7dy9Hjx5lxowZzJ8/nwkTJtj/woaIiJR5K1euNADjan9s59Rr3769ccsttxheXl5G7969jbvvvtsIDQ01AMPf3984cuSITbsjR44YgBEWFmb07dvXcHNzM26++WbjnnvuMZo2bWqtN3fuXMPDw8MAjPr16xt33XWX0b17d6NChQoGYDz44IM2171w4YLRuHFjAzCqVKli9OnTxxgyZIjRtWtXIygoyACMc+fO5YkjPDzcGDZsmOHq6mp0797dGDRokFG3bl0DMNzd3Y0///wzz3vftGmTERAQYH0fgwcPNnr37m2Nt1evXobZbM738+rSpUue682dO9dwdnY2AKNx48bGPffcY3Ts2NEwmUzG008/Xaj/HiIiJWXz5s0GYMycOdOmPDk52fD39zcefvhhm/ITJ04Yfn5+NuWZmZnG6NGjDVdXV+ufac7OzsaHH354TTHpT0QRkXLA3sQCMFq0aGHEx8dbz6Wmphq9evUyAGPUqFE27XJu6AGjevXqxv79+/Nce+fOnYa7u7vh4eFh/PDDDzbnoqOjjSZNmhiAMXv2bGv57NmzDcC49dZbjfT0dJs2WVlZxqpVq2xu9i+NIzw83CaOzMxM46GHHjIAo2fPnjbXSktLM8LDww3AePTRR21e69ChQ0aNGjUMwHj++efz/bwuTyzi4+MNHx8fAzDefvttm3PLly+3JitKLETEUQpKLObPn28AxooVK4zTp0/bPHr27GlERETY1H/nnXeMO+64w5g9e7bx3XffGf369TNcXFyMBQsW2B2T/kQUESkH7E0sTCaTsWPHjjzn//zzTwMwatWqZVN+6Q39nDlz8r324MGDDcCYNm1avuc3bdpkAEarVq2sZW+99Va+N+cFuTSOn376Kc/5+Ph4a6/FpcnDF198YQBGSEiIkZaWlqfdvHnzDMDw8fExUlNTreUFJRavv/66ARht27bNN86xY8cqsRARhyoosXjzzTetfz7l9/D19bXWnTJlihEUFGQkJyfbXKNr165GSEiIkZGRYVdMWhVKROQ6lLMU4eUaNGgAQGxsbIFt77777jxl2dnZLF68GIDBgwfn2+6mm27C29ub7du3k5aWhoeHh3XFkrfeeotKlSpxxx13WFceuRIXFxd69+6dpzwoKIiKFSty7tw5EhISrJMYc5ZcHDJkCO7u7nna9e/f39pu69atdOjQ4Yqvn3O9oUOH5nt+2LBhdq+WIiJSGrKzswHLPIv8Jnq7uOTe/n/wwQd0794db29vmzp9+/bl6aefJjo6moiIiEK/thILEZHrUFhYWL7lvr6+AJjN5nzPBwYG4uXllac8ISGBpKQkAEJDQ6/6+gkJCVSrVo2uXbsyfvx4pk6dyrBhwzCZTNSpU4cOHTpw55130qdPH5yc8q4jEhwcjKura4Hv4dy5c6SlpVnLchKlmjVr5tvGZDJRs2ZNzp07d8WkKsfx48eveL2CykVEHK127dqA5c/zW2655Yp1T548me/CFjmLamRmZtr12kosRESuQ/ndrBdGQcsL5vwCBvlv0He5S3sN/u///o9HH32Un3/+mbVr17Ju3TpmzpzJzJkziYyMZOXKlVSoUKFY4hcRudH16tULX19f3njjDbp165bnR5rTp09TpUoVAOrWrcuyZctISEigUqVKAGRlZTF37lx8fHysSUphKbEQEZGrqly5Mp6enqSmpjJt2jQqV65sV/saNWowZswYxowZA8DmzZu577772Lx5M2+99RaTJk0qUnzVqlUD4PDhwwXWOXLkiE3dq11v3759NssyXqqgchGRkjZ9+nTOnz9PXFwcAD///LO1l3XMmDH4+fnx4Ycfcv/999OyZUuGDBlClSpVOHbsGL/88gsdOnRg+vTpAPz73//mvvvuo02bNowaNQpPT0+++eYbtm7dyuuvv15gz3FB9JOQiIhclbOzMz169ABg7ty5Rb5eZGQkjz/+OAA7duwo8vW6du0KwHfffWczRCrHggULOHfuHD4+PrRq1eqq1+vSpQsAX331Vb7n58yZc+3BiogUwbRp05g4cSIffvghAPPnz2fixIlMnDiRc+fOAXDvvffy+++/U61aNaZOncrYsWP59ttvad68OQ8++KD1WkOHDmXJkiVUr16dqVOnMm7cOFJSUpgxYwYvvPCC3bEpsRARkUJ5+eWXcXNz49lnn2X27Nk2w6Ny7N69m/nz51ufL1iwgDVr1uSpm5GRwZIlSwAIDw8vcmwDBw4kLCyMuLg4nn76aZtxwUeOHOGZZ54BLL/meXh4XPV6I0aMwNvbmw0bNvDee+/ZnFu1ahUzZswocswiItciOjoaw7Kya55HjRo1rPW6du3KkiVLOH/+PKmpqfz999/MnDkzz48rvXr1YtWqVZw+fRqz2czOnTt55JFHrik2JRYiIlIoLVu2tO6uPXz4cMLDw+nVqxf33Xcft912G6GhoTRp0sSmR2P16tV06dKFqlWr0rNnT+677z7uvPNOqlevzpIlS6hWrRrPPfdckWNzd3dn3rx5BAQE8OGHHxIREcGQIUO4/fbbadiwIUeOHKFXr168/PLLhbpeSEgIn3zyCc7OzowdO5amTZty77330qVLF7p3786jjz5a5JhFRK43mmMhIiKFNnDgQCIjI3nvvfdYtmwZ69atIysri6pVqxIREcHo0aMZMGCAtf7w4cPx9PRk7dq1REVFsXr1avz8/AgLC+PJJ59k1KhR1gmDRRUZGcmOHTt48803Wbx4MQsWLMDd3Z0WLVrwwAMPMHLkSJtlFq9myJAhVK9enddff50NGzZw6NAh6tWrx4wZMxg1ahTvvPNOscQtInK9MBmGYTg6CBERERERKd80FEpERERERIpMiYWIiIiIiBSZEgsRERERESkyJRYiIiIiIlJkSixERERERKTIlFiIiIiIiEiRKbEQEREREZEiU2IhIiIiIiJFpsRCRERERESKTImFiIiIiIgUmRILEREREREpMiUWIiIiIiJSZEosRERERESkyP4fr5Qa/a1veegAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnPL4eqx9GXd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9be92a6c-ad25-41ab-9d26-700927e9c360"
      },
      "source": [
        "(y_train_pred == (y_scores > 0)).all()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXAcSDP29GXj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "2d5c3bae-fbd4-4c19-a2ff-183dae95f406"
      },
      "source": [
        "def plot_precision_vs_recall(precisions, recalls):\n",
        "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
        "    plt.xlabel(\"Recall\", fontsize=16)\n",
        "    plt.ylabel(\"Precision\", fontsize=16)\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_precision_vs_recall(precisions, recalls)\n",
        "save_fig(\"precision_vs_recall_plot\")\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving figure precision_vs_recall_plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ClUXdCQ9GXo"
      },
      "source": [
        "# ROC curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdrzpQs49GXp"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_train, y_scores)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkZJVNvm9GXs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "4616c4ff-9256-4ea3-e8ba-17c32465a591"
      },
      "source": [
        "def plot_roc_curve(fpr, tpr, label=None):\n",
        "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate', fontsize=16)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_roc_curve(fpr, tpr)\n",
        "save_fig(\"roc_curve_plot\")\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving figure roc_curve_plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACQqUlEQVR4nOzdd1hTZ8MG8DsJGxkiIEOGC8WJE/e2zrrqwIVbW1u11lVtrbP1tVarra3WioobtVpntbbiqlsBrVsBQUQQhLADSc73hzVfraAhJJwA9++6vN5yck5y0zfF3DzPcx6JIAgCiIiIiIiIikAqdgAiIiIiIir5WCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIWCyIiIiIiKjIjLJYZGRkYN68eejatSscHBwgkUiwadMmra9PTU3F+PHj4eTkBGtra7Rv3x7Xrl0zXGAiIiIiojLOKItFUlISFi5ciNu3b6N+/fqFulatVqNHjx7Yvn07PvroI3z99ddITExEu3btcP/+fQMlJiIiIiIq20zEDpAfV1dXxMfHw8XFBVeuXEGTJk20vnbPnj04d+4cdu/ejf79+wMABg4cCB8fH8ybNw/bt283VGwiIiIiojLLKEcszM3N4eLiotO1e/bsQcWKFdGvXz/NMScnJwwcOBD79++HQqHQV0wiIiIiIvqHURaLoggLC0PDhg0hlb76rTVt2hRZWVm4d++eSMmIiIiIiEovo5wKVRTx8fFo06bNa8ddXV0BAE+ePEHdunXzvVahULwyoqFWq/H8+XNUqFABEonEMIGJiIiIiIqBIAhIT0+Hm5vba7+E14dSVyyys7Nhbm7+2nELCwvN4wVZsmQJFixYYLBsRERERERii42NRaVKlfT+vKWuWFhaWua7jiInJ0fzeEFmz56NTz75RPO1XC6Hp6cnYmNjYWtrq/+wRERERFQscvJUiH2eBYVShSyFGqcfPMNvN+JhY2ECQQDyVGrkqQTEy3PEjlpoVmZSZOWq4WxjhhouNoh5no3hzb3wLDYKTq6VYGJqBgBIehqHWUO7wMbGxiA5Sl2xeHlHqf96eczNza3Aa83NzfMd7bC1tWWxICIiIipmeSo1Lkc9R1qOEiq1AJUgQKVWQ6UGVGo17jxNh0wigUQC5KkEpOXk4cSdRNSoaINclRoPEjKQrlDCRCqBUi3k8woyPFO8PC4BIIHU3Eov2T0drGAilUD2nz/XH8vRt4E7LEylyFMJkEkkqOVmCxOZBKYyKVRqAa52FihnbgKpVAKZRAKpRAKpFJBKXjyHVCKBVAJUKGcOWwuT16bsC4KAn3/+GYunTMH48eOxatUqAEBamgdmAQab4l/qioWfnx/OnDkDtVr9ytyxixcvwsrKCj4+PiKmIyIiIqI8lRpZuSokpuXgeWYulGpBM2IQlZSBhDQFgs5G6fz8F6Oev/J1/qXiVfZWpjCVSWEmk+JpWg7KW5mhgac9TKQSPEnNRtsazjCVSmAik8JUJoFCqYavqw1MpFKY/nPMztIUVZzKQSYVb23uy42id+/eDQC4f/8+8vLyYGpqavDXLtHFIj4+HnK5HFWrVtX8y+rfvz/27NmDvXv3avaxSEpKwu7du/Huu+/mOyJBRERERNoTBAHRyVnIylUiV6lGYroCyRm5AAClWo1cpRpn7ifBvbwllCo1lCoBeWoB5x4kITkzt9hymsokMJNJkZmrQhPv8niWrkD1ijao7GgNM5kU1uYm6O3nBjf7gqfKlyQXLlzA4MGDER0dDRMTEyxZsgSffPKJQRZq58doi8Xq1auRmpqKJ0+eAAAOHjyIx48fAwAmTZoEOzs7zJ49G8HBwYiKioK3tzeAF8WiWbNmGDVqFG7dugVHR0f8+OOPUKlUXJhNRERE9A9BEKBQqpGpUOJJag7y/ikE6TlKRCVlwFQmhUKpxvmHybCzNMXZB0kwkb6YrhOXWvDNcAyhaWUHdPat+MqUIhOpBFKpBLlKNao5l4OFqUxTJFzsLGBt9mIqUVmgVquxbNkyfP7551AqlahSpQp27NiBpk2bFmsOiSAIbx8bEoG3tzcePXqU72Mvi8TIkSNfKxYAkJKSghkzZuDXX39FdnY2mjRpgm+++QaNGzcuVIa0tDTY2dlBLpdzjQUREREZjTyVGk/lOVCqBSj/mUKUmJ6D9H/WIrycVnT9cSosTGVQKFX4NewJarraICwmVez4AAAbcxO0qFYBkc8y8U7tijCRSmFmIoWJVII8lRr1KtmjoVd5lDM32t+DG424uDjUrl0bcrkcgwYNwk8//QQ7O7vXzjP0Z1ujLRbGgMWCiIiIxJKpUOJCZDJCLsciPUeJ85HJcLA2w/NinEpUkErlLZGpUKKKUznUdbeDmYkUqVm5qOFiCwdrU826A3NTKdztLTUjHSYyCazMTGBnafj5/mXN3r17kZKSgtGjRxe4ONvQn21ZAYmIiIgMKD0nD4+Ss/AsXaEZYVCqBajUAlKzcnE/MQMSCfDn7UTEy3NgY26CdIUy3+fSV6kwlUmQpxLgX9kB5qYyRCdlomllB1QoZwYz2Yu7FZW3MoVXBSuYmUhhYSJDZSdr2FqYwpojCKLLy8vD559/jvbt26Nr164AgH79+omcisWCiIiIKF+5SjWycpWITMqEPDsPt56kQSqR4Pitp/BwsIJKLUAtCP/8L6DW3A5VwN9xcqRk5en0ugWVipccy5nB1c4SN+Lk6FnPFZamMs2dihLSctDA88X0IbN/RggEAajhYgNLMxnc7CxhaSbTKRcZh6ioKAwePBgXL17Exo0b8eDBA6OZWcNiQURERGWWUqXGrfg0nLiTiNSsPDyV5+Dozadvve6agdcp2FqYoIaLDeLlOWhVzRF9G7ijgWd5mJkUz919yDjt2rUL48aNQ1paGuzt7bF27VqjKRUAiwURERGVYtFJmbjyKAUXI5NhIpNCIgHCYlKR8M/+CcXBw8ES2blqVHG0RrOqFWBtJtPc1chEJoUAwKmcOSqVt4S3ozUXK9NrsrKy8PHHH+Pnn38GALRo0QLbt2+Hl5eXyMlexXcuERERlWhKlRo34uS4FpMKCQCFUo2TdxNf2yRNF218nJCdq0QFa3PUdLWBlZkMNVxsYWthAsdy5ppbn77cCVn2zy1QZf/skGxhymlHVDRpaWlo3rw5bt26BYlEgjlz5mD+/PkwMTG+j/HGl4iIiIjKPKVKjQyFEg8SM5Cdp8KT1Gwcuh4PWwtTKJRq5KrUOH3vGRzLmSEpQ/eRB3OTF3s1tPFxQutqjmhetQLsLE3hZm8p6u7JRC/Z2tqiadOmeP78ObZt24YOHTqIHalALBZEREQkqtjnWfjrQRL+uJ2Iv+PkeJqWo/W12pQKx3Jm6FXfHY29y8PTwQoSCWBuIkNVJ+sCb8tJJKbU1FQolUo4OjoCAL7//ntkZWXB2dlZ5GRvxmJBREREBqVSv9i87UlqNsJj5bgYmQyFUo3T959BX7tpudtbws/DHpUdrVHH3Q7mJlLYW5nCz8Oe5YFKlAsXLiAgIAC1atXCoUOHIJVKUa5cOZQrV07saG/FYkFERESFlpCWg5w81T/7MrzY6flJajay81S4FZ+Gg+FP8ESu/chDQVpVc4SlmQx+HvaQSSWoUdEGdf7ZkM3cRAozmRRSTlmiUkCtVmPZsmX47LPPoFKpIJPJ8PTpU7i5uYkdTWssFkRERGWQWi0gO0+FPNWL9Qp5KgGPn2fhcvSLBc8K5YsF0VkKFazNZchVqfHXg2RYmcmQlavSaxYnG3MIAjD9HR94VrBCbVc72FlxZ2YqO54+fYrAwEAcP34cABAQEICffvrJqG4lqw0WCyIiolJKEAQo1QJylWo8Ss5CxONUnLr7TKt9Ggqia6loXd0RMc+z0Ku+G2RSCfwrV0D1iuVQwdqMU5WoTPv9998xfPhwJCYmwtLSEqtXr8aoUaNK5H8XLBZEREQlgDw7D5kKJVTqFzs7v9zhWfVPcTh0/QmSM3Lx551EyLPzYCaTIlelNlgeB2szuNtbonrFcjCVvtjh2UQqwbMMBVpUdYSdpSlaVnOEg7WZwTIQlXR5eXn46KOPkJiYiLp16yIkJAS+vr5ix9IZiwUREZHIEtJycORGPLJyVVAo1cjOVeLPO4mo4miNP24n6vSc2paKtj5OMPtnrYKpTIL0HCUaezugjrstzE1kMDORwtPB6sV6BhMpTKSSEvmbVCJjZGpqiu3btyM4OBhff/01LC0txY5UJBJB0Nf9GEqftLQ02NnZQS6Xl7g5bkREZDyUKjWeZ+bi6M2nOBjxBBamMpy5nwQ7S1Ok5+RBbaC/ib0rWMHZxgKmJhJk5apQwdoMPhVt0NG3Imq72XLzNiIRhISEIC0tDePGjSv21zb0Z1uOWBAREelIpRZw52kaImLlyMpVIjo5EzHPs2FvaYpcpRqXo58jObPgfRbk2XmFej07S1O08XGCTALIpFLIpNDs/Gwqk8LazAS9/NxQ2dEapjJpUb89ItKjrKwsTJkyBevXr4eZmRlatmyJWrVqiR1Lr1gsiIiozHsqz/n/OyQp1YiX52B/eBySMhRISFPA3soUStWLhdAqtRqKfxZD64OJVII67naQSSXo6OuMOm52MP1nWpKTjTmcbSxgYSrl9COiEuzGjRsICAjArVu3IJFIMGPGDPj4+IgdS+9YLIiIqExQqQVkKJTIU6mRp1Ljz9uJ+PzXv4s1Q7+G7rA2M0Fgcy94c1SBqNQTBAHr1q3Dxx9/jJycHLi4uGDbtm3o0KGD2NEMgsWCiIhKFUEQ8CAxAxejnuNBYgYep2TpvAD63zRTjv75XxOZFM8zc1HNuRzMZFIENPWAm50lZDIJvBysYGVmAjMTKSxMpbAy41+3RGWNIAgYOnQoduzYAQDo1q0bNm3aBGdnZ5GTGQ5/0hERUYl39O94bPgrGpeinuv8HHXd7eDravPPNCQpEtJy0Kq6I9pUd0Kl8pacikREhSKRSFC3bl3s2bMHS5YswdSpUyGVlu5RSt4V6g14VygiIuMhCAKO3HiK6ORM/H4rAY7WZshTCzh971mhnqd9DSdYmL64jaqthSk+6lANFW0tDJSaiMoStVqNZ8+eoWLFipqvb9++jdq1a4uc7AXeFYqIiEq1nDwVUrPyEBaTgptP0hB6NxEKpRrCPxvA5akEKNVqJKQptHo+U5kEeSoBzao4wM+jPJp4l0dNV1tUtDGHCdc0EJGBPH36FMOHD0d8fDwuXboEKysrSKVSoykVxYHFgoiIikV0UiZO3XuGYzefokI5c5x/mISkjIJvxVoYL8vE+dkd4GpXsjeYIqKS59ixYwgMDERiYiIsLS1x7do1tGrVSuxYxY7FgoiIDOLqo+eY++tNmJlIER6bqtNz2FuZwkT64tarMqkENhamyMpV4qu+deFgbQY3e0uYm0hhbsLbsRJR8cvLy8Pnn3+Or7/+GgBQt25dhISEwNfXV+Rk4mCxICIinWUqlDhxJxEb/4qCvZUZsnNVOB+ZrNNz9WvoDkWeGn0auKO6czl4O1rrOS0Rkf5ERUVh8ODBuHjxIgBg4sSJ+Oabb2BpWXZHTVksiIjorZQqNaKTs7D897v4/VYCPMpbIrqQG8RJJYBjOXNMbFcVdSvZw6uCFazNTGBpJjNQaiIiw5kyZQouXrwIe3t7BAUFoV+/fmJHEh2LBRERAXhx16XL0Sl4kpqNtJw8XIp6DgHA4evxr537tlLxcs2Dm50Fvh/SEI28yhsoNRGROH788UcIgoDVq1fDy8tL7DhGgcWCiKiMylWqcfOJHJHPMrH897t4Is8p9HO42VlAoVSjc62KGNnSG14O1jAzkUIm5XoHIipdbty4gaNHj2LGjBkAgEqVKuHgwYMipzIuLBZERGXEk9RsBJ2Nwql7z/AgMaPQ17vYWsDV3gLNqlTAlI7VYWHKKUxEVPoJgoB169bh448/Rk5ODmrWrIl3331X7FhGicWCiKiUyVOp8fBZBp6kZuNi5HNcfywHAK0XVVdzLoexrSrD0kwGWwtTeFWwgqeDFfeAIKIyJzU1FWPHjsUvv/wCAOjWrRv8/f1FTmW8WCyIiEqw7FwVTt1LxKl7z/A4JRtn7icV+jna+Dihk68zarvZcS0EEdE/zp8/j8GDB+PRo0cwNTXFkiVLMHXqVEil/CVLQVgsiIhKkJTMXBy9+RQ/n4lE5LNMnZ7j8x6+6OXnBmcbCz2nIyIqHb777jt88sknUKlUqFKlCnbu3IkmTZqIHcvosVgQERkxlVrAybuJWB36AGExqVpfZ2Umg6+rLdrXcIJjOXM09nZARVtzWJuZQMqF1UREb+Tu7g6VSoXBgwdj7dq1sLW1FTtSicBiQURkJPJUalyKeo7IpEx8e/we1IKA1Ky8t17X2Ks8vB2t0bGmM+q426FSeUvuQk1EVEipqamwt7cHALz33nv466+/0Lx5c/48LQQWCyIikcQkZ+HcwyT8dDoSEgkKNbVpSsfq6ORbEbXdbDkCQURUBLm5ufj8888RHByM8PBwuLq6AgBatGghcrKSh8WCiKgYxaVmY8jPF/CoELtW16hoA1d7C0xoUxXNqjjwt2dERHoSGRmJwYMH49KlSwCAffv2YeLEiSKnKrlYLIiIDEQQBDx8lomdl2JwPU6OS1HP33qNpakMjb3Lo7efO5p4l4dXBetiSEpEVPaEhIRg/PjxSEtLg729PTZs2IC+ffuKHatEY7EgItITtVrA7qux+P7EAzxOydbqGjc7C4xs6Y32NZxRvaKNgRMSEVFWVhamTJmC9evXA3gx5Wn79u3w8vISOVnJx2JBRFQEj1OysOL3e/jt76fIzlNpdY1MKsG64Y3Q0beigdMREdF/LVmyBOvXr4dEIsGcOXMwf/58mJjwI7E+8N8iEVEhpOXk4dTdZ9h28REuRL59apOTjTnqutuhfiV79GvoDg8Hq2JISUREBfn0009x7tw5fPbZZ+jQoYPYcUoVFgsiordIyczFzF+u4/itBK3O7+Pnhqmdfbg+gojICKSkpGDt2rWYNWsWpFIprK2t8eeff4odq1RisSAi+pd4eTaCzz3CqXvPEPs8CxkK5VuvsbM0xdL36qJrHddiSEhERNo6f/48Bg8ejEePHsHU1BTTp08XO1KpxmJBRGWSUqXG/vAnePQ8C/KsXOy9Fod0LUoEAFibyfBeo0oY1MQDtd3sDJyUiIgKS61WY+nSpZg7dy5UKhWqVKmCtm3bih2r1GOxIKJSL0+lRuzzLGTlqhB6JxHLj98r9HNUdbLGUH8vjGjhDRk3pCMiMlpPnz7F8OHD8ccffwAABg8ejLVr18LW1lbkZKUfiwURlTqCICBenoPQu4mYt/8mlGqhUNc38S6P1tWdMKyZF8pbmXJDOiKiEuLkyZMYNGgQEhMTYWVlhdWrV2PkyJH8OV5MWCyIqFR4npmL1SceYMNfUYW6rkvtihjVsjLKW5nBvbwlypnzxyIRUUllY2ODlJQU1KtXDzt37oSvr6/YkcoU/g1KRCWGIAhQKNXIyVPhSWoOUrJycTn6OVb+cV+r67vVcYG7vSXMTKQY1bIynGzMDZyYiIgMLSsrC1ZWL27l3ahRIxw9ehQtWrSAhYWFyMnKHhYLIjJqcanZ2Hv1MS5EJeNC5HOotJzW5FjOHBWszdCjniu613VBNWfuak1EVNqEhIRg0qRJOHbsGBo0aAAA3JtCRCwWRGR0rsWkYETQJeQoVchTFW59xOc9fDGsmRcsTGUGSkdERGLLysrClClTsH79egDAypUrERwcLHIqYrEgItE8SEzHs/RcZOUqERGbinMPk3HlUcpbr2tWxQF2lqaIl+egS20XmJtI0bOeG1zsOOxNRFTa3bhxA4MGDcLt27chkUjw2WefYd68eWLHIrBYEFExy85VYf6Bmwi5Eqv1NU29HdC1jguaVnaAr6stb/dKRFQGCYKAtWvXYurUqVAoFHB1dcXWrVs59cmIsFgQkcElZSiw81IMvvldu/0jZFIJVGoBv37YEn4e9oYNR0REJcK+ffswceJEAEC3bt0QHBwMJycnkVPRv7FYEJFBxCRnYe3ph9hxKQbCG5ZJWJhKMaFNVZibSmFlKkOXOi5wtbMsvqBERFQi9OnTBz169ECHDh3w8ccfQyqVih2J/oPFgoiKTBAEpOUoseNSDJb/flerBdczutTAxHZVuWkRERHlS6VSYd26dRgxYgSsrKwglUpx8OBB/r1hxFgsiKjQlCo17jxNx/ozkfg1/InW183oUgMBTTxQoRz3jyAiooLFx8dj+PDh+PPPPxEWFoZ169YBAEuFkWOxICKtqdQCPv/1b+y4FKPV+U29HdC2hhOGNfOCnaWpgdMREVFpcPToUQQGBuLZs2ewsrJC8+bNxY5EWmKxIKICZSqUWHbsLv64nYDHKdlvPb9L7YqwtTDF7O6+cLA2K4aERERUWuTm5uKzzz7DN998AwCoV68eQkJCULNmTZGTkbZYLIjoFckZCmz8KxqrQx+89dx367uhc62K6F7HBSYyLqIjIiLdPHr0CAMHDsSlS5cAAB9++CG++eYbWFhwf6KShMWCqIxKy8lDpkIJRZ4au6/G4vpjOc7cT3rrdXaWpuhe1wULe9eBKcsEERHpgYmJCR4+fIjy5csjKCgIffv2FTsS6YDFgqiMyMpV4taTNPz1IBnf/qHdfhIvzehSA2NbV4a5icxA6YiIqKzJy8uDqemL9Xfu7u7Yu3cvvL294enpKXIy0hWLBVEpdfVRCnZfiYVEItF6sfVLjuXMENjcG+NaV4GlGcsEERHp140bNzBo0CB8+eWXmtGJNm3aiJyKiorFgqiUuP44FdN3R0CpFhD5LFPr6/r4ucHCVAYLUxmmdvKBnRXv3kRERIYhCALWrl2LqVOnQqFQYO7cuejduzc3uyslWCyISqC7T9NxMSoZm85FI0uhwtO0HK2vHdOqMiramuO9hpW4nwQRERWblJQUjB07Fnv37gUAdO/eHZs2bWKpKEVYLIhKiJTMXASdjdLqbk0AUM25HKzNZHi/bVX4utrCxc4CFqac1kRERMXv3LlzGDx4MGJiYmBqaoqlS5diypQpLBWlDIsFkRHLyVPhrwdJGBN8Ravz61eyw8LedVDfw96wwYiIiLR0//59tGnTBiqVClWrVsXOnTvRuHFjsWORAbBYEBkJQRAQHpuKG3FynH+YjN/+fvrG8+2tTPFZd1/UdrNDLTfbYkpJRERUONWrV8eECROQmpqKNWvWwNaWf2eVViwWRCJRKFUIi0nFxcjn2H7pERLSFFpdN6lDNUxoWxXlzPmfLxERGadjx46hbt26cHNzAwCsWrUKMpkMEolE5GRkSPxkQlSMQi7H4OTdZ28djfivPn5u+KhDNVRztjFQMiIioqLLzc3FZ599hm+++Qbt27fH8ePHIZPJYGLCj5xlAf9fJjKwnDwVpu+OwKHr8VqdX9fdDsOaecKrgjX8POy54JqIiEqEyMhIBAQE4PLlywCA2rVrQ6VSQSbj32NlBYsFkYEolCp0+OYU4lKzCzzH2kyG1tWd0MHXGe1qOMHZxqIYExIREelHSEgIxo8fj7S0NJQvXx4bNmxAnz59xI5FxYzFgsgAlv9+F9+fyP+2sFUcrbF+RGN4V7CGVMq5pkREVHJlZWVhypQpWL9+PQCgVatW2LZtGzw9PUVORmJgsSDSkx9CH+CXq48RmZT/rtefdquJ8a2rsEwQEVGpIQgCzp49C4lEgs8//xxffPEF11OUYfx/nqgI0nLysO9aHOYduFngOR93qo4pHavzThhERFQqCIIAQRAglUphbW2NkJAQJCcno3379mJHI5GxWBAV0vPMXCz//S62XYwp8JzKjtZQqQUcnNQKdpamxZiOiIjIcFJSUjB27Fj4+/tj5syZAIB69eqJnIqMhUQQBEHsEMYqLS0NdnZ2kMvl3MyljBIEAWcfJGHftTiYyqQIuRL71mvuf9kNpjJpMaQjIiIqPn/99ReGDBmCmJgYWFlZ4dGjR3B0dBQ7FhWCoT/bcsSC6D9Ss3Lxy7U4LP/9LrJyVVpdM6yZJz5sXw2udpYGTkdERFS8VCoVli5dii+++AIqlQpVq1ZFSEgISwW9hsWCCIBSpcaK4/fw48mHWl8zs2sNjG1VBWYmHJ0gIqLSKT4+HsOGDcOJEycAAEOGDMGaNWs4k4PyxWJBZdrDZxn4ZFcEImJT33heA097TGxXDTUq2qC8tSlsLLhugoiISrfs7Gw0bdoUjx8/hpWVFX744QeMGDGCNyOhArFYUJmSk6dC7PMs/BD6AL+GPynwPJ+K5dC+hjNGtPCGmz2nNxERUdljaWmJ6dOnY+PGjdi5cydq1qwpdiQycly8/QZcvF06xKVmY/eVWKz84/5bz53coRo+7uTDvSaIiKhMioyMRGZmJurWrQvgxU1McnNzYW5uLnIy0gcu3iYqpPDYVFyJfo71Z6LwNC3nrec38iqPsa0qo2sdFw7vEhFRmbVz506MHz8eLi4uuHr1KmxsbCCRSFgqSGssFlQqXIp6jsWHb+H6Y7lW5/f2c0O9SvYIbO7FW8MSEVGZlpmZiSlTpiAoKAgAUL9+fWRmZsLGxkbkZFTSsFhQiZSalYvlv9/DnadpuByd8tbzrc1k+LRbTbSr4QwPB6tiSEhERGT8bty4gUGDBuH27duQSCT4/PPP8cUXX8DEhB8RqfD4rqESQalSY/P5R9gXFofnmbmIS81+4/k1KtpgUscXd3GqXpG/cSEiIvo3QRCwdu1aTJ06FQqFAq6urti2bRvat28vdjQqwVgsyCjFPs/CiTuJuBiVjCM3nmp1TTlzE/wc2BjNqjhwrQQREdEbCIKAX3/9FQqFAt27d8emTZvg5OQkdiwq4VgsyCgoVWqceZCElX/cf+ueEi+ZyiQY2cIbw5p5wauCtWEDEhERlQKCIEAikUAqlWLz5s3YvXs3Jk6cCKmU6w2p6Hi72Tfg7WYNRxAE3IiT47N9f+NGnHYLrgHg3fpumN2tJveWICIiKgSVSoX//e9/ePz4MdasWSN2HBIJbzdLpcqvYXGYs+8GsnJVbz3X3soUH7arhvoe9mjoaQ8T3r2JiIio0J48eYLhw4fjxIkTAIDhw4ejRYsWIqei0ojFggxCrRYQl5qNQ9fjkZaThyM34vEoOeuN15ibSNHR1xljWlVBI6/yxZSUiIio9Prtt98QGBiIpKQkWFlZ4YcffkDz5s3FjkWllNEWC4VCgS+++AJbtmxBSkoK6tWrh8WLF6Nz585vvfaPP/7Al19+iRs3bkCpVMLHxweTJk3C8OHDiyF52bbt4iN89+d9JKQp3nquRAK0ru6EEc290KGmMxdcExER6Ulubi7mzJmD5cuXA3ixN0VISAhq1KghcjIqzYy2WIwcORJ79uzBxx9/jOrVq2PTpk3o3r07QkND0apVqwKvO3DgAPr06YPmzZtj/vz5kEgk2LVrl6atT506tRi/i7LhqTwHG89F4adTkVqdb2kqw/FP2qBSee4nQUREZAh9+vTBb7/9BgCYNGkSvv76a1hYWIiciko7o1y8fenSJfj7+2PZsmWYPn06ACAnJwd16tSBs7Mzzp07V+C177zzDm7evInIyEjNFvRKpRI1a9aEtbU1IiIitM7BxdsFu/M0DeM2X0Hs8zfvJ9GtjgsqO1rjndoucCxnxjJBRERUDA4fPozAwEBs2LABvXv3FjsOGYkyuXh7z549kMlkGD9+vOaYhYUFxowZgzlz5iA2NhYeHh75XpuWloby5ctrSgUAmJiYwNHR0eC5Szt5Vh4+3Xsdv/399n0lLs3pCGdb/maEiIioOGRmZuL27dto3LgxAKBHjx6IioriL0apWBllsQgLC4OPj89r/zE0bdoUABAeHl5gsWjXrh2WLl2KuXPnYsSIEZBIJNi+fTuuXLmCXbt2GTx7aXM/IR0fbr+GewkZbzzPztIUH7aviqH+XrA2N8q3FRERUal048YNDBo0CPHx8YiIiICnpycAsFRQsTPKT4Dx8fFwdXV97fjLY0+ePCnw2rlz5yIqKgpffvklFi9eDACwsrLCL7/88tahQIVCAYXi/xcdp6Wl6RK/xJNn5eHcwyR8tCMMKnXBM+UqWJthzbBGaFrZoRjTEREREfBiT6i1a9di6tSpUCgUcHNzw9OnTzXFgqi4GWWxyM7OfmUq00svFx1lZxc8r9/c3Bw+Pj7o378/+vXrB5VKhXXr1mHYsGE4fvw4mjVrVuC1S5YswYIFC4r+DZRAuUo1fvs7HlN2hr/xvPY1nNDY2wEftK0KqZR3cSIiIhJDSkoKxo4di7179wIAunfvjk2bNsHJyUnkZFSWGWWxsLS0fGXk4KWcnBzN4wX56KOPcOHCBVy7dk2zPf3AgQNRu3ZtTJkyBRcvXizw2tmzZ+OTTz7RfJ2WllbglKvS4kJkMgLWXXjrefsmtkADT+4tQUREJLZz585h8ODBiImJgampKZYuXYqPP/6Yt20n0RllsXB1dUVcXNxrx+Pj4wEAbm5u+V6Xm5uLoKAgzJw5U1MqAMDU1BTdunXD6tWrkZubCzMzs3yvNzc3z3ekpDTa+FcUFhy89cZzZnWtiVputmhT3ZE/rIiIiIzEjh07EBMTg6pVqyIkJASNGjUSOxIRACMtFn5+fggNDUVaWtorC49ejjb4+fnle11ycjKUSiVUKtVrj+Xl5UGtVuf7WFkhCAL2hcXhk10F33J3QKNKmNyxOjwceFtYIiIiY7Rs2TLY2tpi1qxZXKBNRsUo97G4ePEimjVr9so+FgqFAnXq1EGFChVw4cKLqTsxMTHIyspCzZo1AQAqlQqOjo5wdnbGjRs3NCMTGRkZ8PX1Rbly5XD79m2tc5SWfSzk2XkYG3wZl6NTCjzn5PR28Ha0LsZUREREpI3ffvsNGzduxI4dOyCTycSOQyVYmdzHwt/fHwMGDMDs2bORmJiIatWqITg4GNHR0QgKCtKcFxgYiFOnTuFlN5LJZJg+fTo+//xzNGvWDIGBgVCpVAgKCsLjx4+xdetWsb4lUaRk5mLVn/ex6Vx0vo97V7DCsaltYG7CH1JERETGJjc3F3PmzMHy5csBvLil/sSJE0VORVQwoywWALB582bMnTsXW7ZsQUpKCurVq4dDhw6hTZs2b7zus88+Q+XKlbFq1SosWLAACoUC9erVw549e/Dee+8VU3rxCIKAsNhU9Pux4N3JO9eqiMV96qAiN7AjIiIySg8fPkRAQACuXLkCAJg0aRJGjx4tciqiNzPKqVDGoqRNhdr0VxTmv2FB9pzuNTGudRUuxCYiIjJiO3bswIQJE5Ceno7y5ctj48aNb92Li0gbZXIqFBVO0NkoLDpUcKH4vIcvhvp7wdKMU56IiIiM2cKFCzFv3jwAQOvWrbFt27ZSf+t7Kj1YLEqwT3+5jp2XY/N9zMbCBDvGNUMdd7tiTkVERES66tu3L5YuXYrp06dj7ty5MDHhRzUqOfhuLYEyFUrUnncs38c8HCxxaFJr2FmaFnMqIiIiKixBEBAREaG5lX7dunURFRUFZ2dncYMR6YDFogRRqtSYs+8Gdl15/NpjtVxt8csHLTjdiYiIqIRISUnBmDFjcPDgQZw9exb+/v4AwFJBJRaLRQnxIDEDnVaceu24s405Ls7pyAXZREREJchff/2FIUOGICYmBqamprh9+7amWBCVVCwWRkwQBPwQ+gDf/H4v38c/7lQdH3fyKeZUREREpCuVSoUlS5Zg/vz5UKlUqFatGnbu3IlGjRqJHY2oyFgsjIxaLeD0/Wf47cZThFzJf2G2VwUrnJzejqMUREREJciTJ08wbNgwhIaGAgCGDh2KNWvWwMbGRuRkRPrBYmEkwmJSsOzYXZx7mFzgOdZmMmwb1wx+HvbFF4yIiIj04tdff0VoaCisrKzw448/IjAwkL8kpFKFxUIkd5+m48z9Z7gQ+Rx/3E5447ldalfEqoAGsDDlwmwiIqKS6oMPPkBUVBTGjh2LGjVqiB2HSO+48/YbGGJ3wpw8FXqtPot7CRlvPK+tjxOG+nuijY8TCwUREVEJ9ODBA3z22WdYv349pzuRUeDO26WESi1g3OYrOHEn8Y3nHfioJeq623FolIiIqATbsWMHJkyYgPT0dDg4OGDNmjViRyIyOBaLYhAvz0bzJSfyfWz5gPqo6WqDqk7lODJBRERUwmVmZmLSpEnYuHEjAKB169aYM2eOyKmIigeLhQGp1AL6/PAXbsTJX3vs8x6+GNu6igipiIiIyBAiIiIQEBCAO3fuQCKRYO7cuZg7dy5MTPhxi8oGvtMNZNUf9/HtH6/vP9HJ1xnrRzQRIREREREZyqFDh9C/f38oFAq4ublh27ZtaNeundixiIoVi4WeqdUCqsw5ku9j28f5o0VVx2JORERERIbWpEkT2Nvbo3Hjxti0aRMcHfn3PZU9LBZ69NuNeHyw7dprx/s1dMfX79WDiUwqQioiIiIyhPv376N69eoAgIoVK+LixYvw9PTkDViozOInXT05dvNpvqXi5oIuWDHQj6WCiIiolFCpVFi8eDF8fX2xc+dOzXEvLy+WCirT+GlXD6KSMjFhy9VXjr1TqyKi/9cD1uYcFCIiIiotnjx5gs6dO2Pu3LlQqVQ4ffq02JGIjAY/9RZBhkKJURsv4XJ0yivHw7/oDHsrM5FSERERkSEcOXIEI0aMQFJSEqytrfHDDz8gMDBQ7FhERoPFQgcKpQrvrTmHv+PSXnts3ru1WCqIiIhKkdzcXMyePRsrVqwAAPj5+WHnzp2oUaOGyMmIjAuLRSFtOR+Nuftv5vvY5tFN0cbHqZgTERERkSGdO3dOUyomTZqEr7/+GhYWFiKnIjI+LBaFcOres3xLxeiWlfFZD1/IpFywRUREVNq0a9cOCxYsQP369dG7d2+x4xAZLYkgCILYIYxVWloa7OzsIJfLkaSQosPyU688Pv/dWhjZsrJI6YiIiMgQMjMzMWvWLMyYMQNeXl5ixyHSm39/trW1tdX783PEQgu7rsRi8e/Rrxy7MLsjXOw4DEpERFSaREREICAgAHfu3EFERAROnz7NW8gSaYm3m9XCwoO3Xvl6+js+LBVERESliCAI+PHHH+Hv7487d+7Azc0NixYtYqkgKgSOWBTSj0MbontdV7FjEBERkZ48f/4cY8eOxb59+wAAPXv2xMaNG+Ho6ChyMqKShcWiEE5Ma4sqTuXEjkFERER6cu/ePXTq1AmxsbEwNTXFsmXLMHnyZI5UEOmAxUJLAU08WCqIiIhKGU9PTzg4OMDc3Bw7d+5Eo0aNxI5EVGKxWGhpdjdfsSMQERGRHiQkJMDR0REymQwWFhbYv38/HBwcYGNjI3Y0ohKNi7e10MirPOysTMWOQUREREV0+PBh1KlTB19++aXmmJeXF0sFkR6wWGihpit/2BAREZVkubm5mDZtGnr27ImkpCQcOHAAeXl5YsciKlVYLLTQzsdZ7AhERESkowcPHqBFixZYsWIFAGDSpEk4e/YsTE05G4FIn7jGQgsO1vzBQ0REVBJt374dEyZMQEZGBhwcHLBhwwb07t1b7FhEpRKLhRYszWRiRyAiIqJCiouLw5gxY5CTk4PWrVtj27Zt8PDwEDsWUanFYkFERESlkru7O7777jvExcXh888/h4kJP/YQGRL/CyMiIqJSQRAE/Pjjj2jYsCGaN28OABg3bpzIqYjKDhYLIiIiKvGeP3+OMWPG4Ndff4WXlxeuX78OW1tbsWMRlSksFkRERFSinT17FkOGDEFsbCxMTU0xdepU7ktBJALebpaIiIhKJJVKhcWLF6Nt27aIjY1FtWrVcP78eUyZMgUSiUTseERlDkcsiIiIqMRJT09H7969ERoaCgAYNmwYfvzxR45UEImIxYKIiIhKnHLlysHGxgbW1tb48ccfERgYKHYkojKPxYKIiIhKhNzcXOTl5cHa2hoSiQQbNmxAUlISatSoIXY0IgLXWBAREVEJ8ODBA7Ro0QLjx4+HIAgAgAoVKrBUEBkRFgsiIiIyatu3b0eDBg1w9epVHD16FHFxcWJHIqJ8sFgQERGRUcrMzMTo0aMxdOhQZGRkoE2bNoiIiEClSpXEjkZE+WCxICIiIqMTHh6ORo0aYePGjZBKpZg/fz5OnDjBUkFkxLh4m4iIiIxKXl4e+vbti+joaLi7u2Pbtm1o27at2LGI6C2KXCzkcjkuX76MZ8+ewcvLCy1atNBHLqMiATfZISIiKi6mpqZYv349vvvuOwQFBcHR0VHsSESkBZ2nQqWnp2Ps2LFwdnZGly5dMGzYMKxfv17z+Pr16+Hm5oaLFy/qJSgRERGVXmfPnsWvv/6q+bpjx47Yv38/SwVRCaJTscjOzka7du2wYcMGlC9fHt26ddPc+u2lnj17IiEh4ZUfEkRERET/plKpsHjxYrRt2xaBgYF4+PCh2JGISEc6TYVasWIFwsLCMHjwYKxbtw7W1taQSl/tKC4uLvD19UVoaKheghIREVHp8uTJEwwbNkzzWaFPnz5wdnYWORUR6UqnEYuQkBC4uLggKCgI1tbWBZ7n4+ODx48f6xyOiIiISqcjR46gfv36CA0NhbW1NYKDg7F582bY2NiIHY2IdKRTsXj48CGaNm0KCwuLN55nZWWFpKQknYIRERFR6SMIAqZPn44ePXogKSkJfn5+uHbtGgIDA8WORkRFpFOxkMlkyMvLe+t5jx8/fuOIBhEREZUtEsn/32lx8uTJuHDhAnx8fERMRET6otMai6pVqyIiIgJKpRImJvk/RUZGBq5fv45atWoVKSARERGVfFlZWbCysgIAfPXVV+jWrRs6duwocioi0iedRix69eqF+Ph4LF68uMBzFi9eDLlcjr59++ocjoiIiEq2jIwMjBo1Cp06ddLMdjAzM2OpICqFdBqxmDp1KjZu3IhFixYhPDwcAwcOBAAkJCRg79692LVrF3bv3g1vb2+8//77eg1MREREJUN4eDgCAgJw9+5dSKVSnD59moWCqBSTCP/dgEJLt27dQq9evRAZGfnKfEngxcIsLy8vHD58uERPhUpLS4OdnR3+jopHbW8XseMQERGVCIIg4IcffsD06dOhUCjg7u6Obdu2oW3btmJHIyrTXn62lcvlsLW11fvz6zRiAQC1atXC33//jU2bNuHIkSOIjIyEWq2Gh4cHunXrhvHjx2vmUhIREVHZ8Pz5c4wePRr79+8H8GLD3I0bN3IHbaIyQOcRi7KAIxZERESF06tXLxw8eBBmZmZYtmwZJk2a9NrMBiISh6FHLHRavL1582acO3furedduHABmzdv1uUliIiIqARatmwZGjRogPPnz2Py5MksFURliE7FYuTIkVi/fv1bzwsKCsKoUaN0eQkiIiIqAZ48eYKtW7dqvq5RowauXr2Khg0bipiKiMSgU7HQVmmZZcXftRAREb3u8OHDqF+/PkaMGIHTp09rjnOUgqhsMmixSExM5AJuIiKiUiY3NxfTpk1Dz549kZSUhPr168PFhWsRico6re8K9e/fRADA06dPXzv2klKpxM2bN/H777+jbt26RUtIRERERuPBgwcICAjA1atXAQBTpkzB0qVLYW5uLnIyIhKb1sWiXbt2rwxtHjt2DMeOHXvjNYIg4IMPPtA9HRERERmNkJAQjB07FhkZGXBwcMDGjRvRq1cvsWMRkZHQuli0adNGUyxOnToFZ2dn1KxZM99zzczMUKlSJbz33nvo3r27fpISERGRqORyOTIyMtCmTRts27YNlSpVEjsSERkRrYvFyZMnNf8slUrRrVs3bNiwwRCZiIiIyEjk5eXB1NQUADBu3DjY29vjvffeg0wmEzkZERkbnRZvh4aGYtasWfrOQkREREZCEASsXr0adevWRUpKCoAXd3saOHAgSwUR5UunYtG2bVvUqFFD31mIiIjICDx//hx9+/bFpEmTcPfuXa32riIi0noq1JvI5XKkpaUVuG+Fp6enPl6GiIiIDOzMmTMYOnQoYmNjYWZmhmXLlmHSpElixyKiEkDnYpGSkoIvvvgCu3fvxrNnzwo8TyKRQKlU6voyREREVAxUKhW++uorzJ8/H2q1GtWrV0dISAgaNGggdjQiKiF0KhZyuRzNmjXDgwcPIJPJYGlpiaysLLi6uuLp06cQBAESiYQjFURERCXEokWLsGDBAgBAYGAgVq9eDRsbG5FTEVFJotMai2XLluH+/fsIDAyEXC5H//79IZFIEBcXh/T0dKxZswb29vZo27YtoqKi9J2ZiIiI9Gzy5Mnw9fVFcHAwgoODWSqIqNB0GrE4cOAAHB0dsWbNGlhYWLyycZ6VlRUmTJiA+vXro1WrVmjRogXGjx+vt8BERERUdAqFArt378bQoUMhkUjg4OCAGzdu8I5PRKQznUYsIiMj0ahRI1hYWACAplioVCrNOc2aNUPz5s0RFBSkh5hERESkLw8ePEDLli0xfPhwBAcHa46zVBBRUehULACgfPnymn+2srICAM19rl/y9PTEnTt3dH0JIiIi0rNt27ahQYMGuHr1KhwcHODo6Ch2JCIqJXQqFm5uboiLi9N8/XKR9vXr1185LzIyEiYmermjLRERERVBRkYGRo4ciWHDhiEjIwNt2rRBREQEevbsKXY0IioldCoWdevWxd27dzVft27dGoIgYN68eUhPTwcAbN26FRcvXkStWrX0k5SIiIh0EhERgUaNGiE4OBhSqRTz58/HiRMnUKlSJbGjEVEpotNwQteuXbFv3z6Ehoaiffv2aN68OVq2bIm//voLDg4OsLW1RWpqKiQSCWbOnKnvzERERFQIycnJuH//Ptzd3bFt2za0bdtW7EhEVArpNGIxePBgnDlzBj4+Pppje/fu1QynpqSkwN7eHitWrMC7776rn6RERESkNbVarfnnDh06YOvWrQgPD2epICKDkQiCIOjzCbOysiCXy1GxYkVIpTqvDTcKaWlpsLOzw82oeNTydhE7DhERkVbOnDmD999/H/v370e1atXEjkNERuLlZ1u5XA5bW1u9P7/eP/lbWVnB1dVVUyr+vRaDiIiIDEelUmHRokVo164dbt26hc8++0zsSERUhhhsSOHBgwcYNmwY6tata6iXICIion/ExcWhU6dO+OKLL6BWqxEYGIj169eLHYuIypBCL96Ojo5GQkICKlasCG9v73wfX7BgAbZt2walUvnKrtxERESkf4cPH8aIESOQnJwMa2trrFmzBsOHDxc7FhGVMVqPWFy8eBF169ZF1apV0aJFC1StWhUNGjTAtWvXAABKpRKffvopfH19sXnzZiiVSrRp0wZ//fWXwcITERGVdQcPHkTPnj2RnJys+XuZpYKIxKDV4u1Hjx6hbt26yMzMxH9Pr1ChAiIiIjB48GCcPXsWgiDAz88PX331Fbp27Wqw4MWBi7eJiMjY5ebmonXr1mjRogX+97//wdzcXOxIRGSkDL14W6upUCtWrEBGRgaqV6+O+fPno169ekhLS8OhQ4ewfPlytGvXDg8ePICNjQ2+/fZbjB49Wu9BiYiI6IXDhw/jnXfegampKczMzHDq1ClYWFiIHYuIyjitpkL9+eefsLa2xokTJzB48GDUrl0bzZs3x5dffonFixfjwYMHkEgkOHr0qN5KhUKhwKxZs+Dm5gZLS0v4+/vj+PHjWl8fEhKC5s2bw9raGvb29mjRogVOnDihl2xERERiyMjIwKhRo9CzZ0/MmzdPc5ylgoiMgVbF4tGjR2jWrBnc3d1feywgIAAA4O/vj+bNm+st2MiRI7FixQoMHToUq1atgkwmQ/fu3XH27Nm3Xjt//nwMHjwYHh4eWLFiBRYvXox69eohLi5Ob/mIiIiKU3h4OBo3boxNmzZBKpXC0tJS7EhERK/QaipUZmYmKlWqlO9jL49XrVpVb6EuXbqEnTt3YtmyZZg+fToAIDAwEHXq1MHMmTNx7ty5Aq+9cOECFi5ciOXLl2Pq1Kl6y0RERCQGQRDwww8/YNq0acjNzYW7uzu2b9+ONm3aiB2NiOgVWt8V6m23jTU1NS1ymJf27NkDmUyG8ePHa45ZWFhgzJgxOH/+PGJjYwu8duXKlXBxccGUKVMgCAIyMjKKnId3zCUiIjEkJyejb9++mDRpEnJzc/Huu+8iIiKCpYKIjJLBNsgrirCwMPj4+Ly2Wr1p06YAXgwHF+TPP/9EkyZN8N1338HJyQk2NjZwdXXF6tWrDRmZiIhI75KTk/HHH3/AzMwM3333Hfbv348KFSqIHYuIKF9ab5AXHByM4ODgfB+TSCQFPi6RSKBUKgsVKj4+Hq6urq8df3nsyZMn+V6XkpKCpKQk/PXXXzhx4gTmzZsHT09PbNy4EZMmTYKpqSkmTJhQ4OsqFAooFArN12lpaYXKTUREVFSCIGhmCfj4+GDr1q3w8vJCgwYNRE5GRPRmWo9YCIKg85/Cys7Ozvc+3C/vepGdnZ3vdS+nPSUnJ2P9+vWYPn06Bg4ciMOHD6NWrVpYvHjxG193yZIlsLOz0/zx8PAodHYiIiJdxcXFoXPnzjh16pTmWJ8+fVgqiKhE0GrEIjQ01NA5XmFpafnKyMFLOTk5mscLug54sd6jf//+muNSqRSDBg3CvHnzEBMTA09Pz3yvnz17Nj755BPN12lpaSwXRERULA4dOoSRI0ciOTkZjx8/xs2bNyGTycSORUSkNa2KRdu2bQ2d4xWurq753ho2Pj4eAODm5pbvdQ4ODrCwsIC9vf1rP4ydnZ0BvJguVVCxMDc3546lRERUrBQKBT799FOsXLkSANCgQQPs3LmTpYKIShyjXLzt5+eHe/fuvbbG4eLFi5rH8yOVSuHn54dnz54hNzf3lcderstwcnLSf2AiIiId3L9/Hy1atNCUio8//hjnz5+Hj4+PuMGIiHRglMWif//+UKlUWLduneaYQqHAxo0b4e/vr5meFBMTgzt37rxy7aBBg6BSqV5ZSJ6Tk4Nt27ahVq1aBY52EBERFaeHDx+iYcOGuHbtGipUqICDBw/i22+/5cg5EZVYWt8Vqjj5+/tjwIABmD17NhITE1GtWjUEBwcjOjoaQUFBmvMCAwNx6tSpVxaIT5gwAevXr8eHH36Ie/fuwdPTE1u2bMGjR49w8OBBMb4dIiKi11SpUgXdu3dHQkICtm3bBnd3d7EjEREViVEWCwDYvHkz5s6diy1btiAlJQX16tXDoUOH3ropkKWlJU6cOIGZM2diw4YNyMzMhJ+fHw4fPowuXboUU3oiIqLXhYeHw8vLC+XLl4dEIsGGDRtgYWHB9RREVCpIBF3uB1tGpKWlwc7ODrei4+Hr5SJ2HCIiKqEEQcDq1asxffp09OzZE3v27NHsVUFEVFxefraVy+WvbUStD0Y7YkFERFQaJCcnY/To0Thw4AAAIC8vDzk5OQXeOp2IqKQyysXbREREpcHp06fh5+eHAwcOwMzMDN999x3279/PUkFEpRKLBRERkZ6pVCosWLAA7du3x+PHj+Hj44MLFy5g0qRJnAJFRKVWkadCyeVyXL58Gc+ePYOXlxdatGihj1xEREQlVlpaGoKCgqBWqzFixAisXr0a5cqVEzsWEZFB6TxikZ6ejrFjx8LZ2RldunTBsGHDsH79es3j69evh5ubm2ZTu5KMv10iIqLCKF++PLZv344tW7Zg06ZNLBVEVCboVCyys7PRrl07bNiwAeXLl0e3bt3w35tL9ezZEwkJCfj111/1kZOIiMhoKRQKTJ06FRs2bNAca9WqFYYNGyZiKiKi4qXTVKgVK1YgLCwMgwcPxrp162BtbQ2p9NWO4uLiAl9fX4SGhuolKBERkTG6f/8+Bg0ahLCwMFhZWaFnz55wdnYWOxYRUbHTacQiJCQELi4uCAoKgrW1dYHn+fj44PHjxzqHIyIiMmZbt25Fw4YNERYWhgoVKiAkJISlgojKLJ2KxcOHD9G0aVNYWFi88TwrKyskJSXpFIyIiMhYZWRkYMSIERg+fDgyMjLQtm1bREREoGfPnmJHIyISjU5ToWQyGfLy8t563uPHj984okFERFTS5OTkoGnTprh9+zakUinmzZuHzz77DDKZTOxoRESi0mnEomrVqoiIiIBSqSzwnIyMDFy/fh2+vr46hyMiIjI2FhYWeO+991CpUiWEhobiiy++YKkgIoKOxaJXr16Ij4/H4sWLCzxn8eLFkMvl6Nu3r87hiIiIjEFycjKio6M1X8+bNw8RERFo06aNeKGIiIyMTsVi6tSpcHd3x6JFi9CnTx9s374dAJCQkIC9e/ciICAAy5Ytg7e3N95//329BiYiIipOp0+fRv369fHee+9BoVAAAExMTODg4CByMiIi4yIR/rsBhZZu3bqFXr16ITIy8rUN5ARBgJeXFw4fPoxatWrpJagY0tLSYGdnh9uPnqKmZ0Wx4xARUTFSqVRYvHgxFi5cCLVaDR8fHxw7dgze3t5iRyMi0snLz7ZyuRy2trZ6f36dFm8DQK1atfD3339j06ZNOHLkCCIjI6FWq+Hh4YFu3bph/PjxsLKy0mdWIiKiYvH48WMMGzYMp06dAgCMGDECq1ev5g7aRERvoPOIRVnAEQsiorLn0KFDGDlyJJKTk1GuXDmsWbOGO2gTUalg6BELndZYJCcn6zsHERGR6NRqNRYvXozk5GQ0bNgQ165dY6kgItKSTsXC3d0dAwcOxG+//Qa1Wq3vTERERKKQSqXYvn07Zs6ciXPnzqF69epiRyIiKjF0mgplbm6OvLw8SCQSuLi4YMSIERg5ciR8fHwMkVE0nApFRFT6bd26FZGRkfjiiy/EjkJEZFBGORUqPj4eq1atQv369REfH4+lS5fC19cXrVq1woYNG5CRkaHvnKKSvP0UIiIqYTIyMjBixAgMHz4c8+bNw7lz58SORERUoulULBwcHDBp0iRcu3YN4eHhmDRpEipUqIBz585h3LhxcHFxwciRIzV30yAiIjImYWFhaNSoETZv3gypVIoFCxbA399f7FhERCWa3u4KpVQqceDAAWzatAlHjx6FUqmERCJB5cqV8eDBA328RLF7OVx059FT1OBUKCKiEk8QBKxevRrTp09Hbm4uKlWqhG3btnEHbSIqE4xyKlR+TExM0K9fPxw4cACPHz/GpEmTIAgCoqKi9PUSRERERTJ8+HBMnjwZubm56NWrF8LDw1kqiIj0RG/FAgAUCgV27NiB4cOH48cff9TnUxMRERVZly5dYGZmhu+//x6//vorKlSoIHYkIqJSQ+edt//t4sWL2LhxI3bt2gW5XA5BEGBvb4+AgACMHj1aHy9BRERUaCqVCtHR0ahatSqAFyMWbdq0gZeXl8jJiIhKH52LRXx8PLZs2YJNmzbh7t27EAQBEokEHTp0wOjRo9GvXz+Ym5vrMysREZHWHj9+jGHDhuH+/fsIDw+Hk5MTALBUEBEZiE7Fonv37jh+/DjUajUEQYC3tzdGjhyJkSNHwtPTU98ZiYiICuXgwYMYNWoUkpOTUa5cOdy4cQMdOnQQOxYRUammU7E4evQoLC0t0a9fP4wePRrt27fXdy4iIqJCUygUmDVrFlatWgUAaNiwIXbu3MkdtImIioFOxWLt2rUICAgwyG2qiIiIdHHv3j0EBAQgLCwMADB16lQsWbKE03KJiIqJTsVi/Pjx+s5BRERUJEuWLEFYWBgqVKiA4OBg9OjRQ+xIRERlil7uCkVERCS2lStXQq1W46uvvoK7u7vYcYiIyhytisXo0aMhkUjw1VdfoWLFioW6haxEIkFQUJDOAYmIiPITFhaGLVu2YPny5ZBIJLCzs0NwcLDYsYiIyiyJIAjC206SSqWQSCS4ffs2fHx8IJVqv6+eRCKBSqUqUkixvNz2/M6jp6jhWVHsOEREBEAQBHz//feYMWMGcnNz8fPPP2Ps2LFixyIiMnovP9vK5XKDrJXWasRi48aNAABXV9dXviYiIipOycnJGDVqFA4ePAgA6NWrF/r27StyKiIiArQcsSirXra6uzFP4ePBEQsiIjGdPn0aQ4YMQVxcHMzMzLB8+XJ8+OGHkEgkYkcjIioRjGLEgoiISEyrV6/GlClToFar4ePjg5CQEPj5+Ykdi4iI/kX7xRL/IpPJMGbMmLeeN27cOJiYsLsQEVHRNGjQABKJBCNGjMDVq1dZKoiIjJBOn/oFQYC2M6g404qIiHQRFxenuW1sy5YtERERgdq1a4ucioiICqLTiIW2srKyYGpqasiXICKiUkahUODjjz+Gj48Pbt26pTnOUkFEZNwMVixSU1Nx9uxZzZ2kiIiI3ubevXto3rw5Vq1ahaysLBw7dkzsSEREpCWtp0JVqVLlla/37NmDkydP5nuuUqnE06dPoVKpMGHChCIFJCKismHLli344IMPkJmZiQoVKiA4OBg9evQQOxYREWlJ62IRHR2t+WeJRIKMjAxkZGQUeL6ZmRn69OmDr776qkgBiYiodEtPT8eHH36ILVu2AADatWuHrVu3atZXEBFRyaB1sYiKigLwYjF2lSpV0L9/fyxbtizfc83MzODk5MQ7QhER0Vv9/PPP2LJlC6RSKebPn485c+ZAJpOJHYuIiApJ60/+Xl5emn8eMWIEWrdu/coxIiIiXUyePBmXL1/GxIkT0bp1a7HjEBGRjrjz9htw520iIv1LTk7G//73PyxevBjm5uZixyEiKjO48zYREZUap06dwtChQxEXFwe1Wo3ly5eLHYmIiPREq2IxevRoSCQSfPXVV6hYsSJGjx6t9QtIJBIEBQXpHJCIiEo+lUqFRYsWYdGiRVCr1ahRowaGDx8udiwiItIjraZCSaVSSCQS3L59Gz4+PpBKtd/+QiKRQKVSFSmkWDgVioio6B4/foyhQ4fi9OnTAICRI0fi+++/R7ly5URORkRUthjFVKiNGzcCgGazu5dflxUSSMSOQERUIp06dQr9+vXD8+fPUa5cOaxduxZDhw4VOxYRERkAF2+/wctWdy8mAdU9nMWOQ0RU4kRFRcHPzw/VqlXDzp07Ub16dbEjERGVWUYxYkFERKSt58+fw8HBAQBQuXJlhIaGonbt2rwDFBFRKaf9YgktRUVFYf/+/QgPD9f3UxMRkZHbsmULvL29cezYMc2xhg0bslQQEZUBOhWLAwcOoF+/frh06dIrx5ctWwYfHx/069cPjRo1KtTdo4iIqORKT09HYGAgAgMDkZ6ejg0bNogdiYiIiplOxWLz5s04evQofH19Ncfu3LmDTz/9FIIgoH79+rCyskJwcDAOHjyot7BERGR8rl27hkaNGmHLli2QSqVYtGgRtm/fLnYsIiIqZjoVi7CwMNSvXx82NjaaY9u2bQMA/Pjjj7h27RouX74MmUyGdevW6ScpEREZFUEQsGrVKjRv3hz3799HpUqVcPLkSXz++eeQyWRixyMiomKmU7FISkqCu7v7K8dOnjwJS0tLjBw5EgBQs2ZNtGrVCjdv3ixySCIiMj4nTpzAxx9/jNzcXPTu3RsRERFo3bq12LGIiEgkOt0VKicn55XfRqlUKly7dg3NmjWDmZmZ5ribmxsuXLhQ9JRERGR0OnbsiAkTJqBOnTr48MMPIZFwzx8iorJMp2Lh7OyM+/fva76+cOECsrOz0bJly1fOy87OhrW1ddESEhGRUVCpVFi+fDlGjRoFJycnAMDatWtFTkVERMZCp6lQLVq0QEREBHbu3Am5XI6vvvoKEokEnTp1euW827dvw83NTS9BiYhIPI8fP0aHDh0wa9YsjBw5EtxblYiI/kunYjFr1iyYmJhg6NChcHBwwG+//YaGDRuiTZs2mnNiY2Nx584dNGnSRG9hiYio+B04cAD169fH6dOnUa5cOQwZMoTTnoiI6DU6FYuGDRviyJEjaNu2LXx9fTFy5EgcOnTolXN27doFOzs7dOzYUS9BiYioeCkUCkyZMgW9e/fG8+fP0ahRI4SFhWHo0KFiRyMiIiMkETieXaC0tDTY2dnhXkwCqns4ix2HiKjYxMTEoE+fPggLCwMAfPLJJ1iyZMkrN+ggIqKS5eVnW7lcDltbW70/v06Lt4mIqHSzt7eHXC6Ho6MjgoOD0b17d7EjERGRkStysbhw4QJCQ0MRFxcHAHB3d0f79u3RrFmzIoczFpxKTERlQVZWFiwtLSGRSGBra4tff/0VFSpU4E04iIhIKzoXi5iYGAwdOhTnzp0DAM0dQl4u6GvZsiW2bt0KT09PPcQkIiJDunbtGgICAjBlyhR8+OGHAIC6deuKnIqIiEoSndZYpKamolGjRoiKioKFhQW6dOmCqlWrAgAiIyNx9OhR5OTkoGrVqrhy5Qrs7Oz0Hrw4vJyHdj82AdUqcY0FEZU+giDgu+++w8yZM5Gbm4tq1arh5s2bXEtBRFQKGeUai+XLlyMqKgrdu3fHunXrXhsmf/r0KcaNG4cjR45g+fLlWLhwoV7CEhGR/iQlJWH06NE4ePAgAKBPnz4ICgpiqSAiIp3oNGJRp04dPHv2DFFRUbCyssr3nKysLFSuXBlOTk74+++/ixxUDByxIKLS6tSpUxg6dCji4uJgbm6O5cuXY+LEidyfgoioFDP0iIVO+1hERUWhbdu2BZYKALCyskLbtm0RFRWlczgiItK/J0+e4J133kFcXBxq1KiBixcv4sMPP2SpICKiItFpKpRMJkNeXt5bz1MqlZBKdeouRERkIG5ubvjiiy/w8OFDfP/997C2thY7EhERlQI6FYvq1avj5MmTSE1Nhb29fb7nPH/+HKGhofDx8SlKPiIi0oMDBw6gatWqqF27NgBgzpw5HKEgIiK90mk4YcCAAZDL5ejRowdu3rz52uM3btxAz549kZaWhkGDBhU5JBER6SYnJweTJ09G7969ERAQgOzsbABgqSAiIr3TacRiypQpCAkJwfnz51G/fn00aNAAlStXBvDidrPh4eFQq9Xw8/PD5MmT9RqYiIi0c/fuXQQEBCA8PBwA8M4770Amk4kbioiISi2d7goFACkpKfjggw+wZ88eqNXqVx6TSqUYMGAAfvjhBzg4OOglqBh4VygiKqk2b96MiRMnIjMzE46OjggODkb37t3FjkVERCIyyn0sAKB8+fLYuXMnYmNjcfr0acTFxQEA3N3d0aZNG3h4eOgtJBERaScrKwvvv/8+tmzZAgBo3749tm7d+tp+Q0RERPpW6GKRkJCAR48ewdzcHDVr1oSHhweGDh1qiGxERFRIZmZmePToEaRSKRYsWIDZs2dz+hMRERULrYvFvXv3MH78eJw5c0ZzzMLCAhMmTMDSpUthampqkIBERPRmgiBAqVTC1NQUJiYm2LZtG6Kjo9GqVSuxoxERURmiVbF49uwZ2rZti8TERPx7SUZ2djZWrVqFpKQkbN682WAhiYgof0lJSRg1ahSqVq2KlStXAgAqVaqESpUqiRuMiIjKHK1uN7tixQokJCTA3d0dGzduxPXr13H27FlMmzYNMpkM27Ztw61btwydVTS8KSMRGaNTp06hfv36OHToENauXYuYmBixIxERURmm1YjFb7/9BgsLC5w4cQLVqlXTHG/RogXs7e0xd+5cHD16FLVq1TJYUCIiekGpVGLx4sVYtGgR1Go1atSogZCQEHh6eoodjYiIyjCtRiwiIyPRrFmzV0rFS4GBgQCAqKgo/SYjIqLXPH78GB07dsSCBQugVqsxatQoXL16FfXr1xc7GhERlXFajVhkZGTAy8sr38de3lY2KytLf6mIiOg1SqUSbdu2RWRkJGxsbLB27VoMGTJE7FhEREQAtByxAACJ5M0rDXTcZ4+IiLRkYmKC//3vf2jcuDGuXbvGUkFEREZF62JBRETF7+7duzh9+rTm6wEDBuDChQv5Tk0lIiISk0TQYqhBKpW+dcSiwBeQSKBUKnW6Vmwvtz1/EJuAqpWcxY5DRGXM5s2bMXHiRFhZWSEiIgKurq5iRyIiohLs5WdbuVwOW1tbvT+/1hvkcaoTEVHxSE9Px8SJE7F161YAgL+/v86/3CEiIiouWhWL0NBQQ+cgIiIA165dw6BBg/DgwQPIZDIsWLAAn376KWQymdjRiIiI3kirYtG2bVtD5yAiKtMEQcB3332HGTNmIC8vDx4eHtixYwdatmwpdjQiIiKtGO3ibYVCgVmzZsHNzQ2Wlpbw9/fH8ePHC/08nTt3hkQiwUcffWSAlERE+iGRSBAREYG8vDz07dsX4eHhLBVERFSiGG2xGDlyJFasWIGhQ4di1apVkMlk6N69O86ePav1c+zduxfnz583YEoioqJRq9Waf/7++++xadMm/PLLL3BwcBAxFRERUeEZZbG4dOkSdu7ciSVLlmDZsmUYP348Tpw4AS8vL8ycOVOr58jJycG0adMwa9YsA6clIio8pVKJefPmoXfv3ppyYW1tjREjRnChNhERlUhGWSz27NkDmUyG8ePHa45ZWFhgzJgxOH/+PGJjY9/6HF9//TXUajWmT59uyKhERIUWGxuLDh06YOHChTh06BB+//13sSMREREVmVEWi7CwMPj4+Lx2f92mTZsCAMLDw994fUxMDP73v/9h6dKlsLS0LHog/vKQiPTkwIED8PPzw5kzZ2BjY4Nt27aha9euYsciIiIqMq33sShO8fHx+W4E9fLYkydP3nj9tGnT0KBBAwQEBBTqdRUKBRQKhebrtLS0Ql1PRFSQnJwczJw5E99//z0AoFGjRti5cyd30CYiolLDKEcssrOzYW5u/tpxCwsLzeMFCQ0NxS+//IKVK1cW+nWXLFkCOzs7zR8PD49CPwcRUX6GDRumKRXTpk3DuXPnWCqIiKhUMcpiYWlp+crIwUs5OTmax/OjVCoxefJkDB8+HE2aNCn0686ePRtyuVzzR5u1HERE2nh5++zDhw/jm2++gZmZmdiRiIiI9KrIU6HkcjkuX76MZ8+ewcvLCy1atChyKFdXV8TFxb12PD4+HgDg5uaW73WbN2/G3bt38dNPPyE6OvqVx9LT0xEdHQ1nZ2dYWVnle725uXm+IyVERIWVnp6OCxcuoHPnzgCAJk2aIDIykj9jiIio1NJ5xCI9PR1jx46Fs7MzunTpgmHDhmH9+vWax9evXw83NzdcvHix0M/t5+eHe/fuvbbG4eVz+fn55XtdTEwM8vLy0LJlS1SuXFnzB3hROipXrsy7rxCRwV29ehUNGzZEz549X7nZBEsFERGVZjoVi+zsbLRr1w4bNmxA+fLl0a1bNwiC8Mo5PXv2REJCAn799ddCP3///v2hUqmwbt06zTGFQoGNGzfC399fs/YhJiYGd+7c0ZwTEBCAffv2vfYHALp37459+/bB399fh++YiOjtBEHAypUr0bx5czx48AAuLi75TuskIiIqjXSaCrVixQqEhYVh8ODBWLduHaytrSGVvtpRXFxc4Ovri9DQ0EI/v7+/PwYMGIDZs2cjMTER1apVQ3BwMKKjoxEUFKQ5LzAwEKdOndKUmpo1a6JmzZr5PmflypXRp0+fQmchItLGs2fPMGrUKBw+fBgA0LdvXwQFBaF8+fIiJyMiIioeOhWLkJAQuLi4ICgoSHOnpvz4+Pjg0qVLOgXbvHkz5s6diy1btiAlJQX16tXDoUOH0KZNG52ej4jIUE6ePImhQ4fiyZMnMDc3x7fffov333+fO2gTEVGZolOxePjwITp37vzGUgEAVlZWSEpK0imYhYUFli1bhmXLlhV4zsmTJ7V6rv9O0yIi0qfz58/jyZMnqFmzJkJCQlCvXj2xIxERERU7nYqFTCZDXl7eW897/PgxrK2tdXkJIiKjJgiCZkRi1qxZMDMzw/vvv8+feUREVGbptHi7atWqiIiIgFKpLPCcjIwMXL9+Hb6+vjqHIyIyRgcOHED79u01m3VKpVJMmzaNpYKIiMo0nYpFr169EB8fj8WLFxd4zuLFiyGXy9G3b1+dwxERGZOcnBxMnjwZvXv3xqlTp/Dtt9+KHYmIiMho6DQVaurUqdi4cSMWLVqE8PBwDBw4EACQkJCAvXv3YteuXdi9eze8vb3x/vvv6zUwEZEY7t69i4CAAM2+FNOmTcP06dPFDUVERGREJIKOK5tv3bqFXr16ITIy8rU7nwiCAC8vLxw+fBi1atXSS1AxpKWlwc7ODg8eJ6Cqu7PYcYhIJJs3b8bEiRORmZkJR0dHBAcHo3v37mLHIiIiKpSXn23lcjlsbW31/vw6jVgAQK1atfD3339j06ZNOHLkCCIjI6FWq+Hh4YFu3bph/PjxsLKy0mdW0UjAW0YSlVVff/01Zs2aBQDo0KEDtmzZAjc3N5FTERERGR+dRyzKgpet7uHjRFRxdxI7DhGJ4PHjx2jUqBEmT56MTz/9FDKZTOxIREREOjHaEQsiotJIEAScPHkS7du3BwBUqlQJDx48gI2NjcjJiIiIjJtOd4UiIiqNnj17hnfffRcdOnTA/v37NcdZKoiIiN5OpxGLKlWqaH2uRCLBw4cPdXkZIqJic/LkSQwdOhRPnjyBubk5nj9/LnYkIiKiEkWnYhEdHf3WcyQSySs70xIRGSOlUomFCxdi8eLFEAQBNWvWREhICOrVqyd2NCIiohJFp2IRFRWV73G1Wo1Hjx7h0KFD+P777zF79myMGjWqSAGJiAwlNjYWQ4YMwdmzZwEAo0ePxnfffccdtImIiHSgU7Hw8vIq8LHKlSujXbt28Pf3x+DBg9G2bds3nk9EJJbLly/j7NmzsLGxwU8//YTBgweLHYmIiKjEMujtZuvWrQtXV1f8/vvvhnoJg+LtZolKv+XLl6NPnz6oWrWq2FGIiIgMytC3mzXoXaF8fX1x+fJlQ74EEZHW7t69i3feeQfx8fGaY9OmTWOpICIi0gODFou4uDjk5uYa8iWIiN5KEAQEBwejUaNGOH78OKZMmSJ2JCIiolLHYMVi69atOH/+PGrVqmWolyAieqv09HQEBgZi5MiRyMzMRIcOHbBy5UqxYxEREZU6Oi3eHj16dIGPpaen486dO7h16xYkEgl/M0hEorl69SoCAgLw4MEDyGQyLFiwAJ9++ilkMpnY0YiIiEodnYrFpk2b3nqOra0tFixYgGHDhunyEkRERXL8+HH06NEDeXl58PDwwI4dO9CyZUuxYxEREZVaOhWLjRs3FviYmZkZ3N3d0bRpU1hYWOgcjIioKFq0aIEqVaqgVq1aWL9+PRwcHMSOREREVKoZ9HazJd3LW3JFxiWishtvN0tk7K5duwY/Pz9IpS+Wjz179gyOjo6QSCQiJyMiIhKfUd5udvTo0Zg5c6a+sxAR6USpVOKLL75A48aNsWLFCs1xJycnlgoiIqJiolOx2Lp1K6KiovSdhYio0GJjY9G+fXssWrQIgiDg4cOHYkciIiIqk3QqFi4uLvwtIBGJbv/+/ahfvz7Onj0LGxsb7NixA2vWrBE7FhERUZmkU7Ho3Lkz/vrrL+Tl5ek7DxHRW+Xk5GDSpEno06cPUlJS0KRJE4SFhSEgIEDsaERERGWWTsVi/vz5UCgUGDduHNLT0/WdiYjoje7evYuffvoJADB9+nScPXsWVatWFTkVERFR2abz7Wa7du2KzZs34/Dhw+jUqRO8vb1haWn52rkSiQRz584tclAiopfq16+P1atXw8PDA926dRM7DhEREUHL28126NABXbt21dwJSiqVQiKR4E2XvnxcIpFApVLpL3Ex4u1miYxDeno6Jk+ejMmTJ6NBgwZixyEiIiqRDH27Wa1GLE6ePAlvb2/N11988QUXbxNRsbh69SoCAgLw4MEDXLp0CdevX4dMJhM7FhEREf2HTlOh5s+fr+cYRESvEgQBK1euxKxZs5CXlwcPDw+sW7eOpYKIiMhI6VQsiIgM6dmzZxg5ciSOHDkCAOjbty/Wr18PBwcHkZMRERFRQVgsiMioREdHo0WLFoiPj4e5uTm+/fZbvP/++5x+SUREZORYLIjIqHh6eqJOnTqws7NDSEgI6tWrJ3YkIiIi0oJWd4V6eRconV5AIoFSqdTpWrHxrlBExSM2NhYVKlSAlZUVACApKQmWlpawtrYWORkREVHpYei7Qmm9QZ4gCDr/ISIqyP79+1G/fn18/PHHmmOOjo4sFURERCWM1lOhunbtilmzZhkyi9HizG4i/cvJycGMGTOwevVqAEB4eDiysrI0oxZERERUsmhdLFxcXNC2bVtDZiGiMuLOnTsICAhAREQEAGD69On48ssvYWZmJnIyIiIi0hUXbxNRsREEAcHBwfjwww+RlZUFJycnBAcHo1u3bmJHIyIioiJisSCiYpOSkoLp06cjKysLHTp0wNatW+Hq6ip2LCIiItIDFgsiKjYODg7YtGkTrl+/jlmzZnEXbSIiolKExYKIDEatVmPlypWoWrUqevfuDQDo2bMnevbsKXIyIiIi0jetioVarTZ0DiIqZZ49e4aRI0fiyJEjKF++PO7cuQNnZ2exYxEREZGBcMSCiPQuNDQUQ4cORXx8PMzNzfHll1/CyYmbTBIREZVmWm+QR0T0NkqlEl988QU6duyI+Ph4+Pr64tKlS/jggw8gkXBHGCIiotKMIxZEpBcKhQKdOnXC2bNnAQBjxozBqlWruIM2ERFRGcERCyLSC3Nzc9StWxc2NjbYsWMH1q9fz1JBRERUhkgEQRDEDmGs0tLSYGdnh6i4RHi7cX440X/l5OQgIyMDjo6OAIDs7Gw8ffoUlStXFjkZERER/dfLz7ZyuRy2trZ6f36OWBCRTu7cuYNmzZph4MCBUKlUAABLS0uWCiIiojKKxYKICkUQBGzatAmNGjVCREQE/v77b0RGRoodi4iIiETGYqEN3s2GCMCLIdRhw4Zh1KhRyMrKQseOHREREYHq1auLHY2IiIhExmJBRFq5cuUKGjZsiO3bt0Mmk+Grr77CsWPH4OrqKnY0IiIiMgK83SwRvZVarcbYsWPx8OFDeHp6YseOHWjRooXYsYiIiMiIcMSCiN5KKpVi8+bNGDx4MMLDw1kqiIiI6DUsFkSUr9DQUPz000+ar+vVq4ft27ejfPnyIqYiIiIiY8ViQUSvUCqVmDt3Ljp27IiPPvoIV69eFTsSERERlQBcY0FEGjExMRgyZAj++usvAMCoUaPg6+srcioiIiIqCVgsiAgAsG/fPowZMwYpKSmwtbXFunXrMGjQILFjERERUQnBqVBEhGnTpqFfv35ISUlB06ZNERYWxlJBREREhcJiQUTw8PAAAMyYMQNnzpxBlSpVRE5EREREJQ2nQhGVQYIgICUlBQ4ODgCAKVOmoEWLFmjatKnIyYiIiKik4ogFURmTlpaGYcOGoVmzZkhPTwcASCQSlgoiIiIqEhYLojLkypUraNiwIbZv347IyEicOnVK7EhERERUSrBYEJUBarUaK1asQIsWLfDw4UN4enri9OnT6Nmzp9jRiIiIqJTgGguiUu7Zs2cYMWIEfvvtNwDAe++9h59//pk7aBMREZFeccRCCxKxAxAVwSeffILffvsN5ubmWLNmDXbv3s1SQURERHrHEQuiUu6bb75BXFwcVq1ahbp164odh4iIiEopjlgQlTIxMTFYsWKF5uuKFSvixIkTLBVERERkUByxICpF9u3bhzFjxiAlJQXu7u7cPZuIiIiKDUcsiEqBnJwcfPTRR+jXrx9SUlLQtGlTNGnSROxYREREVIawWBCVcHfu3IG/vz9++OEHAMCMGTNw5swZVKlSReRkREREVJZwKhRRCbZjxw6MHTsWWVlZcHJywpYtW9ClSxexYxEREVEZxBELohLM1tYWWVlZ6NixIyIiIlgqiIiISDQcsSAqYTIzM2FtbQ0A6NGjB44fP44OHTpAKuXvCYiIiEg8/CRCVEKo1WqsWLECVatWRUxMjOZ4p06dWCqIiIhIdPw0QlQCPHv2DD179sS0adOQkJCAjRs3ih2JiIiI6BWcCkVk5E6cOIFhw4YhPj4e5ubmWLlyJSZMmCB2LCIiIqJXcMSCyEgplUrMnTsXnTp1Qnx8PHx9fXH58mW8//77kEgkYscjIiIiegWLBZGRWrVqFRYvXgxBEDB27FhcvnwZdevWFTsWERERUb5YLIiM1MSJE9GqVSvs3LkTP//8s+ZOUERERETGiMVCC5x1QsUhJycHK1euhEqlAgBYWlri9OnTGDRokMjJiIiIiN6Oi7eJjMDt27cREBCA69evIz09HXPnzgUArqUgIiKiEoMjFkQiEgQBGzduROPGjXH9+nU4OzujadOmYsciIiIiKjSOWBCJJC0tDe+//z527NgB4MVGd1u2bIGLi4vIyYiIiIgKjyMWRCIICwtDw4YNsWPHDshkMixZsgTHjh1jqSAiIqISiyMWRCIwMTFBXFwcvLy8sGPHDjRv3lzsSERERERFwmJBVExyc3NhZmYGAKhbty7279+PJk2aoHz58iInIyIiIio6ToUiKgYnTpxA9erVcfHiRc2xd955h6WCiIiISg0WCyIDUiqV+Pzzz9GpUyfExMRg4cKFYkciIiIiMgijLRYKhQKzZs2Cm5sbLC0t4e/vj+PHj7/1ur1792LQoEGoUqUKrKysUKNGDUybNg2pqamGD030LzExMWjXrh2+/PJLCIKAsWPHYteuXWLHIiIiIjIIiSAIgtgh8jN48GDs2bMHH3/8MapXr45Nmzbh8uXLCA0NRatWrQq8ztHREW5ubujTpw88PT1x48YNrF27FlWqVMG1a9dgaWmpdYa0tDTY2dnhUfwzeLo46uPbojJi3759GD16NFJTU2Fra4t169ZxB20iIiIS1cvPtnK5HLa2tnp/fqMsFpcuXYK/vz+WLVuG6dOnAwBycnJQp04dODs749y5cwVee/LkSbRr1+6VY5s3b8aIESPw888/Y+zYsVrnYLEgXYSGhqJDhw4AgKZNm2LHjh2oUqWKyKmIiIiorDN0sTDKqVB79uyBTCbD+PHjNccsLCwwZswYnD9/HrGxsQVe+99SAQB9+/YFANy+fVvvWYn+q127dnj33XcxY8YMnDlzhqWCiIiIygSjvN1sWFgYfHx8XmtSTZs2BQCEh4fDw8ND6+d7+vQpgBfTpIj0TRAE7Ny5Ez179oSNjQ0kEgn27dsHmUwmdjQiIiKiYmOUIxbx8fFwdXV97fjLY0+ePCnU8y1duhQymQz9+/d/43kKhQJpaWmv/AEACSSFej0qO9LS0jB06FAMGTIEEydO1BxnqSAiIqKyxiiLRXZ2NszNzV87bmFhoXlcW9u3b0dQUBCmTZuG6tWrv/HcJUuWwM7OTvOnMKMiVPZcvnwZDRo0wI4dOyCTyVC7dm0Y4ZIlIiIiomJhlMXC0tISCoXiteM5OTmax7Vx5swZjBkzBl26dMGXX3751vNnz54NuVyu+fOmtRxUdqnVaixfvhwtWrRAZGQkvLy8cObMGXz66aeQSDi6RURERGWTUa6xcHV1RVxc3GvH4+PjAQBubm5vfY6IiAj06tULderUwZ49e2Bi8vZv1dzcPN+REqKXnj17hhEjRuC3334DALz33ntYv3497O3txQ1GREREJDKjHLHw8/PDvXv3NGscXrp48aLm8Td5+PAhunbtCmdnZxw5cgTlypUzVFQqY9RqNa5duwYLCwusXbsWu3fvZqkgIiIigpEWi/79+0OlUmHdunWaYwqFAhs3boS/v79m7UNMTAzu3LnzyrVPnz7FO++8A6lUimPHjsHJyalYs1Ppo1arNf9csWJF7N69G5cuXcKECRM49YmIiIjoH0Y5Fcrf3x8DBgzA7NmzkZiYiGrVqiE4OBjR0dEICgrSnBcYGIhTp069smC2a9euiIyMxMyZM3H27FmcPXtW81jFihXRuXPnYv1eqGSLiYnBkCFD8NFHHyEgIAAA0Lp1a5FTERERERkfoywWwIvdsufOnYstW7YgJSUF9erVw6FDh9CmTZs3XhcREQEA+Prrr197rG3btiwWpLW9e/dizJgxSE1NRUxMDPr16wczMzOxYxEREREZJYnA+2MW6OW25zHxSfBwqSB2HCom2dnZmDZtGtasWQPgxcaMO3bs4A7aREREVKK9/Gwrl8tf24haH4xyjQWRWG7fvg1/f39NqZgxYwbOnDnDUkFERET0FkY7FYqouMXHx6NJkybIzMyEs7MzNm/ejC5duogdi4iIiKhEYLEg+oerqyvGjx+PGzduYMuWLXBxcRE7EhEREVGJwWJBZdrly5fh7OwMLy8vAMDSpUshk8kglXKWIBEREVFh8NMTlUlqtRrLly9HixYtMGTIECiVSgCAqakpSwURERGRDjhiQWVOYmIiRowYgaNHjwIA3NzckJOTwx3aiYiIiIqAv5rVBjdXLjX+/PNP1K9fH0ePHoWFhQV++ukn7Nq1i6WCiIiIqIhYLKhMUCqV+Oyzz9C5c2c8ffoUtWrVwuXLlzF+/HhIJGyOREREREXFYkFlglKpxKFDhyAIAsaNG4fLly+jTp06YsciIiIiKjW4xoJKNUEQIJFIYGFhgZCQEFy/fh0DBw4UOxYRERFRqcNiQaVSdnY2pk2bBhcXF3zxxRcAgJo1a6JmzZoiJyMiIiIqnVgsqNS5ffs2Bg0ahBs3bsDExAQjRozQ7FNBRERERIbBNRZUagiCgKCgIDRq1Ag3btyAs7MzDh06xFJBREREVAw4YkGlglwux/vvv4+dO3cCADp16oQtW7bAxcVF5GREREREZQOLBZV4SqUSLVu2xM2bNyGTyfDll19ixowZ3EGbiIiIqBjxkxeVeCYmJpg0aRK8vb1x9uxZzJo1i6WCiIiIqJhJBEEQxA5hrNLS0mBnZ4eYp0nwqFhB7Dj0L4mJiXj69Cnq1asH4MX6iszMTO6gTURERFSAl59t5XI5bG1t9f78/LUulTh//vkn6tevj169eiE1NRUAIJFIWCqIiIiIRMRiQSWGUqnEZ599hs6dO+Pp06ewtrZGcnKy2LGIiIiICFy8TSXEo0ePMGTIEJw7dw4AMG7cOKxcuRJWVlYiJyMiIiIigMWCSoC9e/dizJgxSE1Nha2tLX7++WcMHDhQ7FhERERE9C8sFlqQSMROUHYJgoDg4GCkpqaiadOm2LlzJypXrix2LCIiIiL6D66xIKMmkUiwYcMGLFy4EGfPnmWpICIiIjJSLBZkVARBwPr16zF+/Hi8vBNyhQoVMHfuXJiamoqcjoiIiIgKwqlQZDTkcjkmTJiAkJAQAECfPn3QvXt3kVMRERERkTZYLMgoXLp0CQEBAYiKioJMJsPixYvRtWtXsWMRERERkZZYLEhUarUay5cvx5w5c6BUKuHl5YUdO3agefPmYkcjIiIiokJgsSBRjRkzBps2bQIADBgwAOvWrYO9vb2omYiIiIio8Lh4m0Q1dOhQWFtb46effkJISAhLBREREVEJxRELKlZKpRJ///03/Pz8AACdOnVCdHQ0HB0dxQ1GREREREXCEQsqNo8ePULbtm3Rpk0bPHz4UHOcpYKIiIio5GOxoGLxyy+/wM/PD+fOnYNEInmlWBARERFRycdiQQaVnZ2NiRMnon///khNTYW/vz/Cw8PxzjvviB2NiIiIiPSIxYIM5vbt2/D398eaNWsAALNmzcKZM2dQuXJlkZMRERERkb5x8TYZTHBwMG7cuAFnZ2ds2bKFoxREREREpRiLBRnMwoULoVAoMGvWLLi4uIgdh4iIiIgMiFOhtCARO0AJcenSJQwdOhR5eXkAADMzM3z77bcsFURERERlAIsFFZlarcayZcvQsmVLbN++HStWrBA7EhEREREVM06FoiJJTExEYGAgjh07BgAYMGAAJkyYIHIqIiIiIipuHLEgnf3xxx+oX78+jh07BgsLC6xbtw4hISGwt7cXOxoRERERFTOOWJBO1q5di4kTJ0IQBNSuXRshISGoXbu22LGIiIiISCQcsSCdtG/fHlZWVpgwYQIuXbrEUkFERERUxnHEgrR29+5d1KhRAwBQo0YN3L59Gx4eHiKnIiIiIiJjwBELeqvs7GxMnDgRtWrVwqlTpzTHWSqIiIiI6CUWC3qj27dvw9/fH2vWrIFarcalS5fEjkRERERERohToShfgiAgKCgIkydPRnZ2NpydnbFlyxa88847YkcjIiIiIiPEYkGvkcvlmDBhAkJCQgAAnTt3xubNm7mDNhEREREViFOh6DX79+9HSEgITExMsHTpUhw9epSlgoiIiIjeiCMW9Jrhw4cjPDwcAwcORLNmzcSOQ0REREQlAEcsCAkJCRgzZgxSU1MBABKJBCtWrGCpICIiIiKtccRCCxKJROwIBvPHH39g2LBhSEhIgEKhwNatW8WOREREREQlEEcsyqi8vDzMmTMH77zzDhISElC7dm3Mnj1b7FhEREREVEJxxKIMio6OxpAhQ3D+/HkAwIQJE7BixQpYWVmJnIyIiIiISioWizLm7NmzePfdd5Gamgo7OzusX78e/fv3FzsWEREREZVwLBZljK+vL8qVK4eaNWtix44d8Pb2FjsSEREREZUCLBZlwOPHj+Hu7g6JRIIKFSogNDQUXl5eMDU1FTsaEREREZUSXLxdigmCgPXr18PHxwfBwcGa49WqVWOpICIiIiK9YrEopeRyOQYPHoxx48YhOzsbBw4cgCAIYsciIiIiolKKxaIUunTpEho0aICQkBCYmJhg6dKl2LNnT6nej4OIiIiIxMU1FqWIWq3G8uXLMWfOHCiVSnh7e2PHjh3cQZuIiIiIDI4jFqXItWvXMGvWLCiVSgwYMABhYWEsFURERERULDhiUYo0btwYCxYsgIuLC8aOHcupT0RERERUbFgsSrC8vDwsWrQIgYGBqFatGgBg7ty5IqciIiIiorKIU6FKqOjoaLRt2xaLFi1CQEAAVCqV2JGIiIiIqAxjsSiBfvnlF/j5+eH8+fOws7PDp59+CplMJnYsIiIiIirDWCy0YCwrFbKzs/HBBx+gf//+kMvlaNasGcLDw9G/f3+xoxERERFRGcc1FiVEXFwcunbtir///hsSiQSzZs3CwoULuYM2ERERERkFFosSwsnJCZaWlqhYsSK2bNmCzp07ix2JiIiIiEiDxcKIyeVyWFlZwdTUFGZmZti9ezcsLCxQsWJFsaMREREREb2CayyM1MWLF9GgQQPMmzdPc8zLy4ulgoiIiIiMEouFkVGr1fj666/RqlUrREVFISQkBJmZmWLHIiIiIiJ6IxYLI5KQkIBu3bph1qxZUCqVGDBgAK5evQpra2uxoxERERERvRGLhZE4fvw46tevj99//x0WFhZYt24dQkJCYG9vL3Y0IiIiIqK34uJtI5CSkoL33nsP6enpqF27NkJCQlC7dm2xYxERERERaY3FwgiUL18e33//Pc6fP48VK1bAyspK7EhERERERIUiEQRBEDuEsUpLS4OdnR2eJCbD1clBr8+9Z88eODs7o02bNnp9XiIiIiKi/Lz8bCuXy2Fra6v35+cai2KWnZ2N999/HwMGDMCQIUOQnJwsdiQiIiIioiLjVKhidPPmTQQEBODvv/+GRCJBYGCgQdoiEREREVFxY7EoBoIgYP369ZgyZQqys7NRsWJFbN26FZ06dRI7GhERERGRXrBYGJhCoUBgYCB27doFAOjSpQuCg4O5gzYRERERlSpcY6EFiUT3a83MzAAAJiYm+Prrr3HkyBGWCiIiIiIqdXhXqDd4uXI+/lkyXBy1vyuUWq1GTk6O5raxcrkcd+/eRdOmTQ0VlYiIiIjojXhXqBImISEB3bt3R2BgIF52Njs7O5YKIiIiIirVuMZCj44fP47hw4cjISEBFhYWuHPnDnx9fcWORURERERkcByx0IO8vDzMnj0bXbp0QUJCAmrXro0rV66wVBARERFRmcERiyKKjo7G4MGDceHCBQDA+++/jxUrVsDS0lLkZERERERExYfFoggEQUCvXr1w48YN2NnZISgoCO+9957YsYiIiIiIih2nQhWBRCLBjz/+iNatWyM8PJylgoiIiIjKLKMtFgqFArNmzYKbmxssLS3h7++P48ePa3VtXFwcBg4cCHt7e9ja2qJ3796IjIzUS66bN29i3759mq9btWqFU6dOwdvbWy/PT0RERERUEhltsRg5ciRWrFiBoUOHYtWqVZDJZOjevTvOnj37xusyMjLQvn17nDp1CnPmzMGCBQsQFhaGtm3bIjk5Wec8giDg559/RpMmTTBs2DDcvn1b85ikKDvoERERERGVAka5xuLSpUvYuXMnli1bhunTpwMAAgMDUadOHcycORPnzp0r8Noff/wR9+/fx6VLl9CkSRMAQLdu3VCnTh0sX74cX331VaHzpKWlYcqHH2DXrl0AgC5dusDBQfsN84iIiIiISjujHLHYs2cPZDIZxo8frzlmYWGBMWPG4Pz584iNjX3jtU2aNNGUCgCoWbMmOnbsqCkGhdWpXVvs2rULJiYm+Prrr3HkyBFUrFhRp+ciIiIiIiqNjLJYhIWFwcfH57Wtxl/uXh0eHp7vdWq1GtevX0fjxo1fe6xp06Z4+PAh0tPTC50nNjYG3t7eOHv2LGbMmAGp1Cj/tRERERERicYop0LFx8fD1dX1teMvjz158iTf654/fw6FQvHWa2vUqJHv9QqFAgqFQvO1XC4HAHTr3gM/rV0DOzs7pKWlFe6bISIiIiIyAi8/xwqCYJDnN8pikZ2dDXNz89eOW1hYaB4v6DoAOl0LAEuWLMGCBQteO/7bkcPw9PR8e3AiIiIiIiOXnJwMOzs7vT+vURYLS0vLV0YOXsrJydE8XtB1AHS6FgBmz56NTz75RPN1amoqvLy8EBMTY5B/+VR6pKWlwcPDA7Gxsa9N4SP6N75XSFt8r5C2+F4hbcnlcnh6ehrsJkRGWSxcXV0RFxf32vH4+HgAgJubW77XOTg4wNzcXHNeYa4FXox05DfaYWdnx/9QSSu2trZ8r5BW+F4hbfG9Qtrie4W0Zaj1wka5CtnPzw/37t17bT3DxYsXNY/nRyqVom7durhy5cprj128eBFVqlSBjY2N3vMSEREREZV1Rlks+vfvD5VKhXXr1mmOKRQKbNy4Ef7+/vDw8AAAxMTE4M6dO69de/ny5VfKxd27d3HixAkMGDCgeL4BIiIiIqIyxiinQvn7+2PAgAGYPXs2EhMTUa1aNQQHByM6OhpBQUGa8wIDA3Hq1KlXVrZPnDgRP//8M3r06IHp06fD1NQUK1asQMWKFTFt2rRC5TA3N8e8efPynR5F9G98r5C2+F4hbfG9Qtrie4W0Zej3ikQw1P2miignJwdz587F1q1bkZKSgnr16mHRokXo0qWL5px27dq9ViwA4PHjx5g6dSp+//13qNVqtGvXDt9++y2qVatW3N8GEREREVGZYLTFgoiIiIiISg6jXGNBREREREQlC4sFEREREREVWZksFgqFArNmzYKbmxssLS3h7++P48ePa3VtXFwcBg4cCHt7e9ja2qJ3796IjIw0cGISi67vlb1792LQoEGoUqUKrKysUKNGDUybNg2pqamGD02iKMrPlX/r3LkzJBIJPvroIwOkJGNQ1PdKSEgImjdvDmtra9jb26NFixY4ceKEAROTWIryXvnjjz/Qvn17ODo6wt7eHk2bNsWWLVsMnJjEkpGRgXnz5qFr165wcHCARCLBpk2btL4+NTUV48ePh5OTE6ytrdG+fXtcu3at0DnKZLEYOXIkVqxYgaFDh2LVqlWQyWTo3r07zp49+8brMjIy0L59e5w6dQpz5szBggULEBYWhrZt2yI5ObmY0lNx0vW9Mn78eNy+fRvDhg3Dd999h65du2L16tVo3rw5srOziyk9FSdd3yv/tnfvXpw/f96AKckYFOW9Mn/+fAwePBgeHh5YsWIFFi9ejHr16uW7qSyVfLq+Vw4cOIB33nkHubm5mD9/Pr788ktYWloiMDAQ3377bTGlp+KUlJSEhQsX4vbt26hfv36hrlWr1ejRowe2b9+Ojz76CF9//TUSExPRrl073L9/v3BBhDLm4sWLAgBh2bJlmmPZ2dlC1apVhebNm7/x2qVLlwoAhEuXLmmO3b59W5DJZMLs2bMNlpnEUZT3Smho6GvHgoODBQDCzz//rO+oJLKivFf+fb63t7ewcOFCAYDw4YcfGiouiago75Xz588LEolEWLFihaFjkhEoynulc+fOgpubm5CTk6M5lpeXJ1StWlWoV6+ewTKTeHJycoT4+HhBEATh8uXLAgBh48aNWl0bEhIiABB2796tOZaYmCjY29sLgwcPLlSOMjdisWfPHshkMowfP15zzMLCAmPGjMH58+cRGxv7xmubNGmCJk2aaI7VrFkTHTt2xK5duwyam4pfUd4r7dq1e+1Y3759AQC3b9/We1YSV1HeKy99/fXXUKvVmD59uiGjksiK8l5ZuXIlXFxcMGXKFAiCgIyMjOKITCIpynslLS0N5cuXf2WvAhMTEzg6OsLS0tKguUkc5ubmcHFx0enaPXv2oGLFiujXr5/mmJOTEwYOHIj9+/dDoVBo/VxlrliEhYXBx8cHtra2rxxv2rQpACA8PDzf69RqNa5fv47GjRu/9ljTpk3x8OFDpKen6z0viUfX90pBnj59CgBwdHTUSz4yHkV9r8TExOB///sfli5dyr/0S7mivFf+/PNPNGnSBN999x2cnJxgY2MDV1dXrF692pCRSSRFea+0a9cON2/exNy5c/HgwQM8fPgQixYtwpUrVzBz5kxDxqYSKCwsDA0bNoRU+motaNq0KbKysnDv3j2tn8sod942pPj4eLi6ur52/OWxJ0+e5Hvd8+fPoVAo3nptjRo19JiWxKTre6UgS5cuhUwmQ//+/fWSj4xHUd8r06ZNQ4MGDRAQEGCQfGQ8dH2vpKSkICkpCX/99RdOnDiBefPmwdPTExs3bsSkSZNgamqKCRMmGDQ7Fa+i/FyZO3cuoqKi8OWXX2Lx4sUAACsrK/zyyy/o3bu3YQJTiRUfH482bdq8dvzf77W6detq9VxlrlhkZ2fnu425hYWF5vGCrgOg07VUMun6XsnP9u3bERQUhJkzZ6J69ep6y0jGoSjvldDQUPzyyy+4ePGiwfKR8dD1vfJy2lNycjJ27tyJQYMGAQD69++PunXrYvHixSwWpUxRfq6Ym5vDx8cH/fv3R79+/aBSqbBu3ToMGzYMx48fR7NmzQyWm0oefX7eKXPFwtLSMt+5Yjk5OZrHC7oOgE7XUsmk63vlv86cOYMxY8agS5cu+PLLL/WakYyDru8VpVKJyZMnY/jw4a+s3aLSq6h/B5mamr4y6imVSjFo0CDMmzcPMTEx8PT0NEBqEkNR/g766KOPcOHCBVy7dk0zvWXgwIGoXbs2pkyZwl9k0Cv09XkHKINrLFxdXREfH//a8ZfH3Nzc8r3OwcEB5ubmOl1LJZOu75V/i4iIQK9evVCnTh3s2bMHJiZlrsuXCbq+VzZv3oy7d+9iwoQJiI6O1vwBgPT0dERHRyMrK8tguan4FeXvIAsLC1SoUAEymeyVx5ydnQG8mC71f+3daUyU19sG8GtgmGEAFUUQwbWIIlSLGkTBBpXaokWMgqBgmSraqtEYl1pjaF27oUWt2lar4r6AQgIuqBW0VqOBVOuKS13buqKAK7Lc7wfD/B1nUGBg6CvXL5kvZ3vOOTlhnps5z3nozVHVtfLs2TOsXLkSH374od6eeSsrK/Tt2xfZ2dl49uxZzXSa/l+qjvudMnUusPD29sb58+dRUFCgl14WvXt7exutZ2FhgQ4dOiA7O9sg7+jRo3jrrbdQr169au8v1Z6qrpUyf/31F4KCguDk5ISdO3fCzs6uprpKtayqa+XatWsoKiqCv78/WrdurfsAz4OO1q1bY8+ePTXadzIvU76DvL29cefOHYObwrK99o6OjtXfYao1VV0rubm5KC4uRklJiUFeUVERSktLjeZR3eXt7Y0//vgDpaWleulHjx6FjY0N2rZtW+G26lxgERYWpttrWKawsBAJCQnw9fVF8+bNATz/ws/JyTGom5WVpRdcnDt3DhkZGRg8eLB5BkBmY8pauXnzJt5//31YWFhg9+7d/MJ/w1V1rQwZMgQpKSkGHwDo168fUlJS4Ovra97BUI0y5e9KREQESkpKsGbNGl3a06dPsWHDBnh6evJX8zdMVdeKk5MT7O3tkZKSoheEPnz4EGlpafDw8ODW7Trsxo0byMnJQVFRkS4tLCwMt27dQnJysi7t7t27SEpKQv/+/Y0+f1GuSr314g0xePBgUSqV8tlnn8myZcvEz89PlEqlHDhwQFcmICBAXp6egoICcXNzEycnJ4mLi5MFCxZI8+bNxcXFRW7fvm3uYZAZVHWtvPPOOwJApk6dKuvWrdP77Nmzx9zDIDOo6loxBnxB3hutqmvl8ePH4uXlJVZWVjJlyhT54YcfxMfHRywtLWXnzp3mHgaZQVXXyty5cwWAdOrUSRYsWCDz58+X9u3bCwBZv369uYdBZrJ48WKZM2eOjBkzRgDIoEGDZM6cOTJnzhzJy8sTERGtVisA5PLly7p6xcXF0q1bN7Gzs5NZs2bJ0qVLxcvLS+rVqyc5OTmV6kOdDCyePHkiU6ZMEWdnZ1Gr1eLj4yPp6el6Zcq7Abh+/bqEhYVJ/fr1xc7OToKDg+XChQvm6jqZWVXXCoByPwEBAWYcAZmLKX9XXsbA4s1mylq5deuWaLVaadSokajVavH19TWoS28OU9bKhg0bpGvXrmJvby8ajUZ8fX1l69at5uo61YKWLVuWe+9RFkgYCyxERO7duycxMTHi4OAgNjY2EhAQIFlZWZXug0JEpFK/oRAREREREb2kzj1jQURERERE1Y+BBRERERERmYyBBRERERERmYyBBRERERERmYyBBRERERERmYyBBRERERERmYyBBRERERERmYyBBRERERERmYyBBRERERERmYyBBRHVOa1atYJCoXjlZ+HChVVuf+bMmVAoFJg5c2a19bm6GBu7Wq1GixYtEBERgYMHD9ZKv8r6Yu665lDenDdr1gwDBgzA9u3ba7uLRETVQlnbHSAiqi3+/v5o06aN0TxPT08z98a8Xhx7Xl4esrOzkZiYiKSkJMyfPx+TJk2q5R4+17NnTxw4cACZmZno2bNnbXfHJC/OeX5+Po4dO4bU1FSkpqZi4sSJiI+Pr5brrF69GsOHD4dWq8Xq1aurpU0ioopgYEFEddbIkSPx8ccf13Y3asXLY3/69Ck+/fRTrF27FlOnTkVwcDDatm1rtv6cPXu2Vuqa08tzXlxcjIkTJ2LJkiVYsGABhg4dCh8fn9rrIBGRibgVioiIYG1tjaVLl8LW1hYlJSVITk426/U9PDzg4eFh9rq1SalUYt68eahfvz4AIC0trZZ7RERkGgYWRESvkJycjJEjR+Ltt99Gw4YNYW1tjdatW2PEiBE4d+5cpdtLSkrCe++9BwcHB1hZWcHBwQGenp4YNWoUTpw4YbTO1q1bERQUBEdHR6hUKri6umLYsGE4c+aMqcPTY2dnh3bt2gEArly5ope3e/duBAcHw8nJCSqVCi4uLoiIiEB2drbRtvLz8xEbG4sOHTrA1tYWarUaLi4u8Pf3x5dffomioiK98i8/J7F//34oFAocOHAAANCrVy+9ZxRe3OLzct28vDxoNBpYWlrin3/+KXe8YWFhUCgUWLRokUGeuebc2toa7u7uAIBbt24Z5P/6668YP348vL290bhxY92zGREREcjKyjIo36pVKwwfPhwAsGbNGr05M7aVzFzjJKK6gVuhiIheITw8HGq1Gp6enujduzeKi4tx6tQpJCQkIDExEXv27IGfn1+F2po9ezZmzJgBpVIJPz8/uLq6Ij8/H9euXcPKlSvh5eWFjh076soXFxcjKioKiYmJUKvV6NKlC1xdXXH+/Hls2LABycnJSE5ORlBQULWNt6CgAACgVqt1aV988QXmzp0LhUIBPz8/tGjRAmfPnkViYiK2bduG5cuXY8SIEbryjx8/Ro8ePXDq1Ck4OjoiMDAQtra2uHnzJnJycnD48GFMmjQJ9vb25fbD2dkZWq0W6enpuHXrFj744AM4Ozvr8st7NgYA7O3tMXDgQGzatAnr1q3DtGnTDMrk5uYiLS0NKpUKw4YN06XX5pw3adLEIG/06NG4fv06vLy84O/vD6VSiZycHCQmJiI5ORmbN29GaGiornxYWBiOHDmCQ4cOwc3NDT169NDlvfirTm2Mk4jqACEiqmNatmwpACQhIeG1ZTdv3iwPHz7USystLZWlS5cKAPHy8pLS0lK9/BkzZggAmTFjhi7t6dOnotFoxM7OTnJycgyuc+XKFTl79qxe2vTp0wWA+Pr6yqVLl/TykpKSxNLSUho2bCj3799/7TjKvGrsf/75p1hYWAgAWbVqlYiI7Nq1SwCItbW17NmzR6/8ihUrBIBYWVnJqVOndOlr1qwRANK3b1959uyZXp2SkhLZv3+/FBYW6qUDEGNfSQEBAQJAMjMzyx2Tsbp79+4VAOLh4WG0zqJFiwSAhIaG6qWbe87PnDkjlpaWAkCysrIM8lNSUuTevXtG05VKpTg4OMjjx4/18hISEgSAaLXacvtUE+MkImJgQUR1TtmNXnmfgICACrXTvXt3ASCnT5/WSzcWWNy+fVsASMeOHSvUdm5urmg0GrG2tpa///7baJmxY8cKAFm8eHGF2hQxfpObl5cnO3bsEDc3NwEgLi4uumAqMDBQAMikSZOMthccHCwAZNSoUbq0uLg4ASDx8fEV7ld1BxalpaW6sR4+fNigjre3twCQ7du369LMPee7d+8WDw8PASCxsbEVbq/M0KFDBYDs2LFDL/11gUVNjZOIiFuhiKjOKu+42ZcfBL548SLS09Nx8eJFPHjwACUlJQD+tyf+3Llzrz2e1tHREa1atcKJEycwefJkxMTEvLJOZmYmnjx5gsDAQLi6uhot07NnT/z44484fPgwxo0b98rrv2z48OG6vfgvcnNzw7Zt22Bra4vi4mIcOnQIAMo9PSsmJgbbt29HZmamLq3sZKO4uDg4ODggODgYjRo1qlT/TKVQKKDVajF79mysXr0a3bt31+UdP34cx48fR9OmTfW2+tTGnFtaWmL9+vWIiooqt96///6LHTt2ICcnB/n5+SguLgYAnD59GsDz9devX78K96Omx0lEdRcDCyKqs1533GxJSQnGjRuHZcuWQUTKLVe2R/511q5di7CwMMTHxyM+Ph6NGjWCr68v+vTpg48++giNGzfWlb106RIAYN++fa99+dudO3cqdP0XvRhUqVQqODk5oVu3bggKCoJS+fyrITc3F0+fPgUAtG7d2mg7bm5uAKD3kHTPnj3x+eefY968edBqtVAoFHB3d4e/vz8GDBiA/v37w8Ki5s8OGT58OObMmYMtW7Zg4cKF0Gg0AICEhAQAQHR0NCwtLXXlzTnnd+7cwcGDB/HgwQOMGTMG7u7u6Nq1q0GdWbNm4auvvjJ42P1FFV1/ZWp6nERUdzGwICIqx6JFi/Dzzz/D2dkZ8fHx8PPzQ5MmTWBtbQ0AiIyMxKZNm14ZdLzo3XffxZUrV7Bjxw4cOHAAhw8fxu7du7Fr1y7MmDEDKSkpCAwMBACUlpYCeP6Qsr+//yvbrcpRqzX9Do9vv/0Wo0ePRlpaGn7//XccOnQICQkJSEhIgI+PDzIzM2Fra1tj1ween5DUq1cvZGRkICUlBZGRkSgqKsLGjRsBwODXA3PPeX5+PgYOHIjMzEyEh4fjzJkzsLGx0eUnJydj5syZsLOzw5IlS9C7d2+4uLhAo9FAoVBg+vTp+Oabbyq8/srU9DiJqO5iYEFEVI7ExEQAwLJlyxASEmKQf+HChUq3qdFoEBYWhrCwMADP/yMcGxurO1np6tWrAIDmzZsDANq1a1drb092cHCAWq1GYWEhLl26pHdiVZmy/34b21LTqlUrjB8/HuPHjwcAZGVlYdiwYcjKykJcXBxmzZpVswPA8+AhIyMDCQkJiIyMRFpaGu7evQs/Pz/d0bplzD3nDRo0wJYtW+Dh4YGrV68iPj4esbGxuvyy9ffVV1/hk08+MahflfUH/DfWFhG9mfgeCyKicty7dw8A0LJlS4O806dP4/jx4yZfw9HREXFxcQCAa9eu4f79+wCAwMBAqFQq7N+/H7dv3zb5OlWhVCp1x5WWdwO6atUqAM/fM/E6Pj4+GDt2LABUeO5UKhUA6J4rqKzQ0FA0aNAAGRkZuH79um4blLHnS2pjzh0dHXXBxPz585GXl6fLe9X6u337Nvbu3Wu0zdfN2X9hbRHRm4mBBRFROdq3bw8AWLp0qW77CADcuHED0dHRlbrZvXr1KlasWGF0P3zZG5cbNmyoewtzkyZNMH78eDx69Aj9+/fHyZMnDeoVFhYiNTUVOTk5lRpXZUyePBkA8NNPP2Hfvn16eatXr0ZqaiqsrKwwYcIEXXpKSgp+++03vTkDgKKiIqSnpwMwfrNsTLNmzQD870HlytJoNBgyZAhKS0vx3XffIT09HTY2NoiIiDAoW1tzPnbsWLRo0QL5+fn4/vvvdell62/58uV49uyZLj0/Px9arRb5+flG2yubs/JecvdfWVtE9Aaq5VOpiIjMrqLvsThy5IioVCoBIG3atJHw8HAJCgoSjUYjXl5eMnDgQKPtGDtu9tixY7p3Pvj4+Eh4eLiEh4dLp06dBIAoFApZsWKFXjtFRUUSGRkpAMTCwkI6deokoaGhEhERIf7+/mJraysAZNeuXdU+9hfFxsbq+tijRw+JjIyUzp07CwCxtLSUlStX6pWfMGGCAJDGjRtLnz59JCoqSkJCQsTJyUkAiKurq1y/fl2vDso5bnb79u0CQFQqlQQHB8uIESMkJiZGDh069Nq6ZY4cOaJ3nHB0dHS5ZWtrzletWiUApF69epKbmysiIpcuXRJ7e3vdnIWGhkpISIg0aNBAmjZtKiNGjDBYZyIihYWF4uLiIgCkU6dOEh0dLTExMRIXF1ej4yQiYmBBRHVOZW6uT5w4ISEhIdK0aVOxtrYWd3d3mTp1qhQUFIhWq61wYFFQUCALFy6UgQMHiru7u9jZ2Ymtra20bdtWoqOjJTs7u9w+7Ny5UwYNGiSurq5iZWUl9vb20r59exkyZIhs3LhRHj16VCNjf9GuXbukX79+4uDgIEqlUpydnWXw4MFy9OhRg7LHjh2TadOmSY8ePcTV1VVUKpU4OjpKly5d5Ouvv5a7d+8a1HlVcPDLL79I586dxcbGRlfuxf6/LrAQEfHy8tKVe9U7McqYe86Li4vF09NTAMi0adN06ZcvX5aoqChp0aKFqNVqadmypYwePVpu3rxpdJ2VOXnypISEhIijo6PupYfG3s9SneMkIlKIVPI4CSIiIiIiopfwGQsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjIZAwsiIiIiIjLZ/wEiCPQT96u1tQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Pex5KBU9GXy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbe9c2a4-4ea1-40ec-f963-61549ade9215"
      },
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(y_train, y_scores)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91560899622096"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLQmdD_M9GX7"
      },
      "source": [
        "**Note**: we set `n_estimators=10` to avoid a warning about the fact that its default value will be set to 100 in Scikit-Learn 0.22."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iD1EmpAW9GX8"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
        "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train, cv=3,\n",
        "                                    method=\"predict_proba\")"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uHUINxK9GYA"
      },
      "source": [
        "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
        "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train,y_scores_forest)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T31yrafH9GYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "d624206e-0f9a-4280-f05f-456b6f73f9ef"
      },
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
        "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
        "plt.legend(loc=\"lower right\", fontsize=16)\n",
        "save_fig(\"roc_curve_comparison_plot\")\n",
        "plt.show()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving figure roc_curve_comparison_plot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfF3x1aY9GYc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8af5359-a8f7-41ce-f5ef-663dae704191"
      },
      "source": [
        "roc_auc_score(y_train, y_scores_forest)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9690842469802384"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKkjzFZT9GYh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f6fea6b-0feb-4fa9-cccd-74224275ea2f"
      },
      "source": [
        "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train, cv=3)\n",
        "precision_score(y_train, y_train_pred_forest)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9978802331743508"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUexrMw49GYp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6c78dc8-3e93-4047-f8d5-262a1c521551"
      },
      "source": [
        "recall_score(y_train, y_train_pred_forest)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7945147679324894"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJH82Z5K9GYu"
      },
      "source": [
        "# Multiclass classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHcQhXmq9GYw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c30aaeb3-7444-4354-cc06-08e6a5e312da"
      },
      "source": [
        "df[\"loan_status\"].unique()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Current', 'Fully Paid', 'Late (31-120 days)', 'Late (16-30 days)',\n",
              "       'Charged Off', 'In Grace Period', 'Default'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFYR3ffm9GY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "996b94b5-34c1-4668-c863-ff26a3bc6dae"
      },
      "source": [
        "df[\"loan_status\"].value_counts()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_status\n",
              "Current               30438\n",
              "Fully Paid            22147\n",
              "Charged Off            5661\n",
              "Late (31-120 days)      917\n",
              "In Grace Period         429\n",
              "Late (16-30 days)       140\n",
              "Default                  83\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Current</th>\n",
              "      <td>30438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>22147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>5661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (31-120 days)</th>\n",
              "      <td>917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In Grace Period</th>\n",
              "      <td>429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Default</th>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYUa97WB9GY7"
      },
      "source": [
        "class_names = ['Current', 'Fully Paid', 'Late (31-120 days)', 'Late (16-30 days)',\n",
        "       'Charged Off', 'In Grace Period', 'Default']"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmUw4T049GY-"
      },
      "source": [
        "df[\"loan_status\"] = df[\"loan_status\"].replace({'Current':0, 'Fully Paid':1, 'Late (31-120 days)':2, 'Late (16-30 days)':3,\n",
        "       'Charged Off':4, 'In Grace Period':5, 'Default':6})"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZnbArAB9GZB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afb54fa7-d055-45ef-d59f-d64250da6678"
      },
      "source": [
        "df[\"loan_status\"].unique()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiM73Oxx9GZJ"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "X = df.drop([\"loan_condition\",\"loan_status\"],axis=1).values\n",
        "y = df[\"loan_status\"].values\n",
        "\n",
        "X_train, X_test, y_train, y_test = X[:40000], X[40000:], y[:40000], y[40000:]\n",
        "shuffle_index = np.random.permutation(40000)\n",
        "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOXZAq4R9GZS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7faf7e00-2ecb-4302-cda6-b57cf1e51145"
      },
      "source": [
        "sgd_clf.fit(X_train, y_train)\n",
        "sgd_clf.predict([X_test[0]])"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viStGo7O9GZb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c33c233f-823f-4f9d-ef34-393f2179fc32"
      },
      "source": [
        "some_scores = sgd_clf.decision_function([X_test[0]])\n",
        "some_scores"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-217250355.32371166, 723140625.2624027, -128539881.82149376,\n",
              "        -120907265.95959485, -592268479.5415019, -84795661.47282602,\n",
              "        -135480700.8681991]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k-ES6EZ9GZi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4d96a3f-ed55-407f-b2ab-864e1aa15cdf"
      },
      "source": [
        "np.argmax(some_scores)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9eRAdL89GZz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11d10d4b-54ad-42d8-dd93-e763a04bb672"
      },
      "source": [
        "sgd_clf.classes_"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7u2UQ9o9GZ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "939d05e9-b6e4-4b6d-d54c-e17f08fdc8e1"
      },
      "source": [
        "sgd_clf.classes_[5]"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ta2EkdAn9GZ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47d20fef-f938-4880-91ec-340f96459ca4"
      },
      "source": [
        "from sklearn.multiclass import OneVsOneClassifier\n",
        "ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, tol=None, random_state=42))\n",
        "ovo_clf.fit(X_train, y_train)\n",
        "ovo_clf.predict([X_test[0]])"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl5y3eWq9GaE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf37988f-f7da-4812-f2d3-3c1c30a612cc"
      },
      "source": [
        "len(ovo_clf.estimators_)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqIAeLmr9GaI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cfc0837-d1be-4d02-8234-2b030c33f4f1"
      },
      "source": [
        "forest_clf.fit(X_train, y_train)\n",
        "forest_clf.predict([X_test[0]])"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6BkWcPW9GaQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bed1d330-9413-40f1-a1af-3d64bc4d4d57"
      },
      "source": [
        "forest_clf.predict_proba([X_test[0]])"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eV83YJSc9GaY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71a27049-9a4e-47d6-8c2c-d3123accf8ee"
      },
      "source": [
        "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96325184, 0.95912398, 0.96204905])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Dj_3UJC9Gab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4ec5da5-ff96-4d2c-dfd9-51aa3c6d602f"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
        "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96640168, 0.9681992 , 0.96639916])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PA2KD2td9Gaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a600ad1b-9b48-47da-f28e-3b725399d9fb"
      },
      "source": [
        "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
        "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
        "conf_mx"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20574,    31,    69,     4,     7,    74,     0],\n",
              "       [   82, 14405,     0,     0,    12,     2,     0],\n",
              "       [  520,     0,   113,     0,     1,    14,     0],\n",
              "       [   90,     0,     5,     0,     0,     4,     0],\n",
              "       [   41,    16,     0,     1,  3585,     0,     0],\n",
              "       [  274,     2,    13,     0,     0,     3,     0],\n",
              "       [   38,     0,    19,     0,     0,     1,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndItIoim9Gaj"
      },
      "source": [
        "def plot_confusion_matrix(matrix, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Normalized confusion matrix'\n",
        "        else:\n",
        "            title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = matrix\n",
        "    # Only use the labels that appear in the data\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(12,12))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='True label',\n",
        "           xlabel='Predicted label')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    fig.tight_layout()\n",
        "    return ax"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_OmAqnT9Gal",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f504617-e847-4b1f-b156-ffff2bbf2a8d"
      },
      "source": [
        "\n",
        "plot_confusion_matrix(conf_mx\n",
        "                      , classes=class_names\n",
        "                      , title='Confusion matrix')"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[20574    31    69     4     7    74     0]\n",
            " [   82 14405     0     0    12     2     0]\n",
            " [  520     0   113     0     1    14     0]\n",
            " [   90     0     5     0     0     4     0]\n",
            " [   41    16     0     1  3585     0     0]\n",
            " [  274     2    13     0     0     3     0]\n",
            " [   38     0    19     0     0     1     0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABIgAAARsCAYAAAD1+a1XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1xV9R/H8fcBZYiCooA4UJxpSpp7b9FclSP3KM2VOUpNK1fmtjS3ZdIvbThKTUtzZo7cplmpuQe4ARERBH5/EMeuoCCKwL2vZ4/zyPs93/O9n+893ot87vd8jhEbGxsrAAAAAAAA2Cy7tA4AAAAAAAAAaYsEEQAAAAAAgI0jQQQAAAAAAGDjSBABAAAAAADYOBJEAAAAAAAANo4EEQAAAAAAgI0jQQQAAAAAAGDjMqV1AAAAAAAAIH2IiIhQZGRkWoeRKhwcHOTk5JTWYaRbJIgAAAAAAIAiIiLknC2ndDc8rUNJFblz59apU6dIEj0ACSIAAAAAABC3cuhuuBxLdpHsHdI6nCcrOlJBf36hyMhIEkQPQIIIAAAAAADcY+8gw8oSRLFpHUAGQJFqAAAAAAAAG0eCCAAAAAAAwMZxiRkAAAAAALjHsIvbrIm1zScV8AoBAAAAAADYOBJEAAAAAAAANo4EEQAAAAAAgI2jBhEAAAAAALjHkGQYaR3Fk2Vl00kNrCACAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHDSIAAAAAAHCPYRe3WRNrm08q4BUCAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGUaQaAAAAAADcYxhxmzWxtvmkAlYQAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNowYRAAAAAAC4x7CL26yJtc0nFfAKAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNI0EEAAAAAADuMQzr3B7Bnj179MYbb+jZZ5+Vi4uLfHx81KZNGx07dixB37/++kuNGjVS1qxZ5e7urk6dOunKlSsJ+sXExGjSpEny9fWVk5OT/Pz89PXXXyf6/KkxZlIoUg0AAAAAAPAfEydO1Pbt29W6dWv5+fkpKChIM2fO1PPPP6/ffvtNpUqVkiSdP39eNWvWlJubm8aNG6ewsDBNmTJFhw8f1u7du+Xg4GCO+e6772rChAnq0aOHKlSooJUrV6p9+/YyDENt27Y1+6XGmMlhxMbGxj7m6wYAAAAAADK40NBQubm5ybFcPxn2jmkdzhMVG31Hd/bNUEhIiFxdXZPsv2PHDpUvX94iGXP8+HGVLl1arVq10qJFiyRJffr0UUBAgP7++2/5+PhIkjZs2KAGDRpo3rx5ev311yVJFy5ckK+vr15//XXNnDkzLqbYWNWqVUunTp3S6dOnZW9vn2pjJgeXmAEAAAAAAPxH1apVLZJDklS0aFE9++yz+uuvv8y25cuXq2nTpmYiR5Lq16+vYsWKacmSJWbbypUrFRUVpT59+phthmGod+/eOn/+vHbu3JmqYyYHCSIAAAAAAGATQkNDLbY7d+4k+9jY2FhdunRJuXLlkhS3gufy5csqX758gr4VK1bUgQMHzMcHDhyQi4uLSpQokaBf/P7UGjO5SBABAAAAAID/sJMMK9v+TX/kz59fbm5u5jZ+/PhkvyqLFy/WhQsX9Morr0iSAgMDJUne3t4J+np7e+v69etmAiowMFBeXl4y7iuWHX/sxYsXU23M5KJINQAAAAAAsAnnzp2zqEHk6Ji8Wkt///23+vbtqypVqqhLly6SpNu3bz9wDCcnJ7OPo6Oj+f+H9UutMZOLBBEAAAAAALAJrq6uySpS/V9BQUFq0qSJ3NzctGzZMrPws7OzsyQleplaRESERR9nZ+dk93vSYyYXl5gBAAAAAAAkIiQkRI0bN1ZwcLDWrl2rPHnymPviL+WKvyzsvwIDA+Xu7m6u8PH29lZQUJDuv5F8/LHx46bGmMlFgggAAAAAANxjGNa5PaKIiAg1a9ZMx44d0+rVq1WyZEmL/Xnz5pWHh4f27t2b4Njdu3erTJky5uMyZcooPDzc4g5okrRr1y5zf2qNmVwkiAAAAAAAAP4jOjpar7zyinbu3KmlS5eqSpUqifZr2bKlVq9erXPnzpltGzdu1LFjx9S6dWuzrUWLFsqcObNmz55ttsXGxmru3LnKmzevqlatmqpjJgc1iAAAAAAAAP7jrbfe0qpVq9SsWTNdv35dixYtstjfsWNHSdLw4cO1dOlS1alTR/3791dYWJgmT56s0qVLq1u3bmb/fPnyacCAAZo8ebKioqJUoUIFrVixQr/++qsWL15s1jVKrTGTw4i9/2I1AAAAAABgc0JDQ+Xm5ibH8gNkZEre3b0yiti7d3Rn7zSFhIQkq0h17dq19csvvzx4vP+kUo4cOaJBgwZp27ZtcnBwUJMmTTR16lR5eXlZHBMTE6OJEydq3rx5CgwMVNGiRTVs2DB16NAhwfipMWZSSBABAAAAAIB7CaIKg6wzQbTno2QniGwRNYgAAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMZxFzMAAAAAAHCPYcRt1sTa5pMKWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNo0g1AAAAAAC4x7CL26yJtc0nFfAKAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNowYRAAAAAAC4xzDiNmtibfNJBawgAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbRw0iAAAAAABwj2EXt1kTa5tPKuAVAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxlGkGgAAAAAA3GMY1lfU2TDSOoJ0z8rOOAAAAAAAAB4VCSIAAAAAAAAbR4IIAAAAAADAxlGDCAAAAAAA3GNnxG3WxNrmkwpYQQQAAAAAAGDjSBABAAAAAADYOBJEAAAAAAAANo4aRAAAAAAA4B7DLm6zJtY2n1TAKwQAAAAAAGDjSBABAAAAAADYOBJEAAAAAAAANo4EEQAAAAAAgI2jSDUAAAAAALjHMOI2a2Jt80kFrCACAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHDSIAAAAAAHCPYRe3WRNrm08q4BUCAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHDSIAAAAAAHCPYcRt1sTa5pMKWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNo0g1AAAAAAC4x7CL26yJtc0nFfAKAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNowYRAAAAAAC4xzDiNmtibfNJBawgAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbRw0iAAAAAABwj2EXt1kTa5tPKuAVAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbRw0iAAAAAABwj2HEbdbE2uaTClhBBAAAAAAAYONIEAEAAAAAANg4EkQAAAAAAAA2jgQRAAAAAACAjaNINQAAAAAA+A87ybC29STWNp8nj1cIAAAAAADAxpEgAgAAAAAAsHEkiAAAAAAAAGwcNYgAAAAAAMA9hhG3WRNrm08qYAURAAAAAACAjSNBBAAAAAAAYONIEAEAAAAAANg4ahABAAAAAIB7DEMyrGw9CTWIkmRlZxwAAAAAAACPigQRAAAAAACAjSNBBAAAAAAAYONIEAEAAAAAANg4ilQDAAAAAIB7DDsrLFJtZfNJBbxCAAAAAAAANo4EEQAAAAAAgI0jQQQAAAAAAGDjqEEEAAAAAADuMYy4zZpY23xSASuIAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGUYMIAAAAAADcY9jFbdbE2uaTCniFAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGkSACAAAAAACwcRSpBgAAAAAA9xhG3GZNrG0+qYAVRAAAAAAAADaOBBEAAAAAAICNI0EEAAAAAABg46hBBAAAAAAA7jHs4jZrYm3zSQW8QgAAAAAAADaOBBEAAAAAAICNI0EEAAAAAABg40gQAQCAdO/48eNq2LCh3NzcZBiGVqxY8UTHP336tAzDUEBAwBMd1xoULFhQXbt2TeswAABPk2FY54aHIkEEAACS5cSJE+rZs6cKFSokJycnubq6qlq1apo+fbpu376dqs/dpUsXHT58WB9++KG+/PJLlS9fPlWfzxr9+eefGjVqlE6fPp3WoQAAgHSIu5gBAIAkrVmzRq1bt5ajo6M6d+6sUqVKKTIyUtu2bdPgwYN15MgRzZ8/P1We+/bt29q5c6feffddvfHGG6nyHAUKFNDt27eVOXPmVBk/Pfjzzz81evRo1a5dWwULFkz2cUePHpWdHd8pAgBg7UgQAQCAhzp16pTatm2rAgUKaNOmTfL29jb39e3bV//884/WrFmTas9/5coVSVL27NlT7TkMw5CTk1OqjZ/RxMbGKiIiQs7OznJ0dEzrcAAAwFPA10EAAOChJk2apLCwMC1YsMAiORSvSJEi6t+/v/n47t27+uCDD1S4cGE5OjqqYMGCGj58uO7cuWNxXMGCBdW0aVNt27ZNFStWlJOTkwoVKqT//e9/Zp9Ro0apQIECkqTBgwfLMAxz9UvXrl0TXQkzatQoGffVGVi/fr2qV6+u7NmzK2vWrCpevLiGDx9u7n9QDaJNmzapRo0acnFxUfbs2dWiRQv99ddfiT7fP//8o65duyp79uxyc3NTt27dFB4e/uAX9l+1a9dWqVKldOjQIdWqVUtZsmRRkSJFtGzZMknSL7/8okqVKsnZ2VnFixfXhg0bLI4/c+aM+vTpo+LFi8vZ2Vk5c+ZU69atLS4lCwgIUOvWrSVJderUkWEYMgxDW7ZskXTvXKxbt07ly5eXs7Oz5s2bZ+6Lr0EUGxurOnXqyMPDQ5cvXzbHj4yMVOnSpVW4cGHdunUryTkDANK3+J8T1rbh4UgQAQCAh/rhhx9UqFAhVa1aNVn9u3fvrhEjRuj555/Xxx9/rFq1amn8+PFq27Ztgr7//POPWrVqpQYNGmjq1KnKkSOHunbtqiNHjkiSXn75ZX388ceSpHbt2unLL7/UtGnTHin+I0eOqGnTprpz547GjBmjqVOnqnnz5tq+fftDj9uwYYP8/f11+fJljRo1SoMGDdKOHTtUrVq1ROv4tGnTRjdv3tT48ePVpk0bBQQEaPTo0cmK8caNG2ratKkqVaqkSZMmydHRUW3bttW3336rtm3b6oUXXtCECRN069YttWrVSjdv3jSP3bNnj3bs2KG2bdvqk08+Ua9evbRx40bVrl3bTFDVrFlTb775piRp+PDh+vLLL/Xll1+qRIkS5jhHjx5Vu3bt1KBBA02fPl1lypRJEKdhGPr8888VERGhXr16me0jR47UkSNHtHDhQrm4uCRrzgAAIH3hEjMAAPBAoaGhunDhglq0aJGs/r///ru++OILde/eXZ9++qkkqU+fPvL09NSUKVO0efNm1alTx+x/9OhRbd26VTVq1JAUl2TJnz+/Fi5cqClTpsjPz0+urq4aOHCgnn/+eXXs2PGR57B+/XpFRkbqp59+Uq5cuZJ93ODBg+Xu7q6dO3fK3d1dkvTiiy+qbNmyGjlypL744guL/mXLltWCBQvMx9euXdOCBQs0ceLEJJ/r4sWL+uqrr9SuXTtJUoMGDfTMM8+offv22rFjhypVqiRJKlGihPz9/bV8+XJzVU+TJk3UqlUri/GaNWumKlWqaPny5erUqZMKFSqkGjVq6JNPPlGDBg1Uu3btBDH8888/Wrt2rfz9/R8aq6+vr6ZOnaqePXtq8eLFKlKkiCZPnqz+/furZs2aSc4VAACkT6wgAgAADxQaGipJypYtW7L6//jjj5KkQYMGWbS/9dZbkpSgVlHJkiXN5JAkeXh4qHjx4jp58mSKY75ffO2ilStXKiYmJlnHBAYG6uDBg+ratauZHJIkPz8/NWjQwJznf/13RY0k1ahRQ9euXTNfw4fJmjWrxQqr4sWLK3v27CpRooSZHJJk/vm/r4+zs7P556ioKF27dk1FihRR9uzZtX///mTMNo6vr2+SyaF4r7/+uvz9/dWvXz916tRJhQsX1rhx45L9XAAAIP0hQQQAAB7I1dVVkiwuaXqYM2fOyM7OTkWKFLFoz507t7Jnz64zZ85YtPv4+CQYI0eOHLpx40YKI07olVdeUbVq1dS9e3d5eXmpbdu2WrJkyUOTRfFxFi9ePMG+EiVK6OrVqwlq7dw/lxw5ckhSsuaSL1++BLUR3NzclD9//gRt9495+/ZtjRgxQvnz55ejo6Ny5colDw8PBQcHKyQkJMnnjufr65vsvpK0YMEChYeH6/jx4woICLBIVAEAgIyHS8wAAMADubq6Kk+ePPrjjz8e6bjkFoK0t7dPtD02NjbFzxEdHW3x2NnZWVu3btXmzZu1Zs0arV27Vt9++63q1q2rn3/++YExPKrHmcuDjk3OmP369dPChQs1YMAAValSRW5ubjIMQ23btk32iilJj5zg2bJli1l4/PDhw6pSpcojHQ8ASL+ssqiztc0nFbCCCAAAPFTTpk114sQJ7dy5M8m+BQoUUExMjI4fP27RfunSJQUHB5t3JHsScuTIoeDg4ATt969SkiQ7OzvVq1dPH330kf788099+OGH2rRpkzZv3pzo2PFxHj16NMG+v//+W7ly5Uo3xZiXLVumLl26aOrUqWbB7+rVqyd4bZ7kP/QDAwPVr18/NWzYUE2bNtXbb7+d6OsOAAAyDhJEAADgoYYMGSIXFxd1795dly5dSrD/xIkTmj59uiTphRdekKQEdxr76KOPJMUVVH5SChcurJCQEB06dMhsCwwM1Pfff2/R7/r16wmOjb9DV/wKmPt5e3urTJky+uKLLywSLX/88Yd+/vlnc57pgb29fYJVSjNmzEiwkio+oZVYUu1R9ejRQzExMVqwYIHmz5+vTJky6bXXXkvWaikAAJA+cYkZAAB4qMKFC+urr77SK6+8ohIlSqhz584qVaqUIiMjtWPHDi1dutS8o9Zzzz2nLl26aP78+QoODlatWrW0e/duffHFF3rxxRct7mD2uNq2bauhQ4fqpZde0ptvvqnw8HDNmTNHxYoVsyjOPGbMGG3dulVNmjRRgQIFdPnyZc2ePVv58uVT9erVHzj+5MmT1bhxY1WpUkWvvfaabt++rRkzZsjNzU2jRo16YvN4XE2bNtWXX34pNzc3lSxZUjt37tSGDRuUM2dOi35lypSRvb29Jk6cqJCQEDk6Oqpu3bry9PR8pOdbuHCh1qxZo4CAAOXLl09SXEKqY8eOmjNnjvr06fPE5gYAAJ4eEkQAACBJzZs316FDhzR58mStXLlSc+bMkaOjo/z8/DR16lT16NHD7PvZZ5+pUKFCCggI0Pfff6/cuXNr2LBhGjly5BONKWfOnPr+++81aNAgDRkyRL6+vho/fryOHz9ukSBq3ry5Tp8+rc8//1xXr15Vrly5VKtWLY0ePdos+pyY+vXra+3atRo5cqRGjBihzJkzq1atWpo4ceIjF3ROTdOnT5e9vb0WL16siIgIVatWTRs2bEhwR7LcuXNr7ty5Gj9+vF577TVFR0dr8+bNj5QgOn/+vAYOHKhmzZqpS5cuZnuHDh20fPlyDRkyRI0bN05Xrw8AIAWMfzdrYm3zSQVGLGuBAQAAAACweaGhoXJzc5Nzi1kyMlvX3Sljo27r9sq+CgkJMe/SCkvUIAIAAAAAALBxJIgAAAAAAABsHDWIAAAAAACAyTAMGYaVFe2xtvmkAlYQAQAAAAAA2DgSRAAAAAAAADaOBBEAAAAAAICNowYRkISYmBhdvHhR2bJls77rcAEAAAA8EbGxsbp586by5MkjOzvWYiDjIUEEJOHixYvKnz9/WocBAAAAIAM4d+6c8uXLl9ZhPBaKVMcJCwvT5MmTtWvXLu3evVs3btzQwoUL1bVr1/uGfvDY9evX1/r16yVJp0+flq+vb6L9vv76a7Vt29ai7a+//tLAgQO1bds2OTg4qEmTJvroo4/k4eFh0S8mJkZTpkzRnDlzFBgYqGLFimnYsGFq167dI82XBBGQhGzZskmSHEp2kWHvkMbRICXObpmS1iEAAADAyt0MDVUR3/zm7w/I+K5evaoxY8bIx8dHzz33nLZs2ZJovy+//DJB2969ezV9+nQ1bNgwwb527drphRdesGirUqWKxePz58+rZs2acnNz07hx4xQWFqYpU6bo8OHD2r17txwc7v1u+u6772rChAnq0aOHKlSooJUrV6p9+/YyDCNB0ulhSBABSYjPBhv2DiSIMihXV9e0DgEAAAA2wupW3tgwb29vBQYGKnfu3Nq7d68qVKiQaL+OHTsmaNuyZYsMw0h0Fc/zzz+f6DH/NW7cON26dUv79u2Tj4+PJKlixYpq0KCBAgIC9Prrr0uSLly4oKlTp6pv376aOXOmJKl79+6qVauWBg8erNatW8ve3j5Z8+XCSAAAAAAAgPs4Ojoqd+7cj3zcnTt3tHz5ctWqVeuBlxveunVLkZGRDxxj+fLlatq0qZkckuIuVytWrJiWLFlitq1cuVJRUVHq06eP2WYYhnr37q3z589r586dyY6bBBEAAAAAADDF1yCytk2SQkNDLbY7d+488dfvxx9/VHBwsDp06JDo/tGjRytr1qxycnJShQoV9PPPP1vsv3Dhgi5fvqzy5csnOLZixYo6cOCA+fjAgQNycXFRiRIlEvSL359cJIgAAAAAAIBNyJ8/v9zc3Mxt/PjxT/w5Fi9eLEdHR7Vq1cqi3c7OTg0bNtTkyZO1atUqffzxx7p8+bIaN26sNWvWmP0CAwMlxV3idj9vb29dv37dTGwFBgbKy8srwaWN8cdevHgx2XFTgwgAAAAAANiEc+fOWdQodXR0fKLjh4aGas2aNXrhhReUPXt2i30+Pj5at26dRVunTp1UsmRJvfXWW2rSpIkk6fbt2w+MzcnJyezj6Oho/v9h/ZKLFUQAAAAAAMAmuLq6WmxPOkG0fPlyRUREPPDysvu5u7urW7duOnr0qM6fPy9JcnZ2lqREL3+LiIiw6OPs7JysfslBgggAAAAAAJjSulZQatYgSm2LFy+Wm5ubmjZtmuxj8ufPL0m6fv26pHuXh8VfavZfgYGBcnd3NxNb3t7eCgoKUmxsbIJ+kpQnT55kx0GCCAAAAAAA4DEFBgZq8+bNatmy5SOtTDp58qQkycPDQ5KUN29eeXh4aO/evQn67t69W2XKlDEflylTRuHh4frrr78s+u3atcvcn1wkiAAAAAAAAB7TN998o5iYmAdeXnblypUEbRcuXNDnn38uPz8/i6LULVu21OrVq3Xu3DmzbePGjTp27Jhat25ttrVo0UKZM2fW7NmzzbbY2FjNnTtXefPmVdWqVZMdP0WqAQAAAAAAEjFz5kwFBwebdwP74YcfzFpB/fr1k5ubm9l38eLFypMnj2rXrp3oWEOGDNGJEydUr1495cmTR6dPn9a8efN069YtTZ8+3aLv8OHDtXTpUtWpU0f9+/dXWFiYJk+erNKlS6tbt25mv3z58mnAgAGaPHmyoqKiVKFCBa1YsUK//vqrFi9eLHt7+2TPlQQRAAAAAABAIqZMmaIzZ86Yj7/77jt99913kqSOHTuaCaKjR49q3759GjRokOzsEr9Yq2HDhpo7d65mzZqlGzduKHv27KpZs6bee+89Pf/88xZ98+fPr19++UWDBg3SO++8IwcHBzVp0kRTp05NcPnahAkTlCNHDs2bN08BAQEqWrSoFi1apPbt2z/SXI3Y+ysZAbAQGhoqNzc3OZbuIcPeIa3DQQrc2DMzrUMAAACAlQsNDZVXTjeFhIRY3EY9I4n/3Sdbq3kyMif/7lcZQWzUbd1c1jNDn5/URg0iAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBxFKkGAAAAAAAmwzBkGEZah/FkWdt8UgEriAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxlGDCAAAAAAAmAxDVliDKK0DSP9YQQQAAAAAAGDjSBABAAAAAADYOBJEAAAAAAAANo4aRAAAAAAAwGTIsL4aRBQhShIriAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxpEgAgAAAAAAsHEUqQYAAAAAACbDsMIi1dY2n1TACiIAAAAAAAAbR4IIAAAAAADAxpEgAgAAAAAAsHHUIAIAAAAAAPcY/27WxNrmkwpYQQQAAAAAAGDjSBABAAAAAADYOBJEAAAAAAAANo4aRAAAAAAA4B7DkGFYV9GeWCubT2pgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOItUAAAAAAMBkWGGRamubT2pgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DhqEAEAAAAAABM1iGwTK4gAAAAAAABsHAkiAAAAAAAAG0eCCIk6ceKEevbsqUKFCsnJyUmurq6qVq2apk+frtu3b6d1eCny448/atSoUWkdxmN7+9WG2rZosC5vm6IzG8dryUc9VLSAp0UfR4dM+vidNjq/eaKubJ+qr6d0l6d7Nos+tw/MTLC19i9n7p8/umOiffYtezfxuLo10O0DMzX57ZZPftI2bv7cOapQ1k+e7q7ydHdVrepVtG7tT+b+BZ/OV8N6teXp7irnzIaCg4PTLlgkcOHCBXXr3FF5vXIqRzZnlS9TWvv27jX3X7p0ST1e7Spfnzxyd82i5k0a6Z/jx9MwYiTX5EkT5JzZ0NuDBqR1KEim4kUKyjmzkWAb0K9vWoeG+yT3XMXGxqpF08Zyzmxo1coVaRMskm3u7FkqXqSgsmd1Uo2qlbRn9+60DgnAf1CDCAmsWbNGrVu3lqOjozp37qxSpUopMjJS27Zt0+DBg3XkyBHNnz8/rcN8ZD/++KNmzZqV4ZNENZ4vornfbtW+I2eUKZO9Rr/RTKvnvKGyL49VeESkJGnS2y3VuPqz6jBkgULDbuvjd9rom6ndVbfbxxZj9Rjxpdbv+NN8HHzzXvLv7cnL9P4nK83HmezttevbYfpu/YEEMZUr6aPXWlbToWPnn/R0ISlvvnz6YNwEFSlSVLGxsVr05Rdq/XIL/bbngEo++6zCw8PVwL+RGvg30oh3h6V1uPiPGzduqG6taqpVq45W/PCTPDw89M8/x5UjRw5Jcb/YtGn5ojJnzqyly1fK1dVVn0z7SC80qq8Dh/6Ui4tLGs8AD7J3zx4t+HSeSpf2S+tQ8Ai27dyj6Oho8/GfR/5Qk0YN9HKr1mkYFRKT3HM1Y/o06opkEEuXfKuhgwdpxqy5qlCxkmZ+Mk3Nm/jr9yNH5enpmfQAeLqMfzdrYm3zSQUkiGDh1KlTatu2rQoUKKBNmzbJ29vb3Ne3b1/9888/WrNmzWM/z61btxL9xSc2NlYRERFydnZ+7OewVi3emG3x+PWRi3Ru0wSVLZlf2/efkGtWJ3V9sYq6Dg/QL3uOmX1+//59VSxdULsPnzaPDbl5W5eu3Uz0eULDIhQaFmE+blbbTzlcnfXlqp0W/VycHbRwXFf1+eBrvdO90ROaJf6rSdNmFo9Hf/ChPp03R7t3/aaSzz6rfv0HSJK2/rLl6QeHh5o6eaLy5cuv+QsWmm0FfX3NP/9z/Lh27/pN+w7+oZLPPitJ+mTWHBXMl1tLvvla3V7r/tRjRtLCwsLUrUsHzZ77qSaMG5vW4eAReHh4WDyeMmmCChUurBo1a6VRRHiQ5Jyr3w8e1PRpU7X9t73yze99/xBIZz6Z9pG6vdZDnbt2kyTNmD1XP/20Rl8EfK7BQ95J4+gASFxihvtMmjRJYWFhWrBggUVyKF6RIkXUv39/nT59WoZhKCAgIEEfwzAsVumMGjVKhmHozz//VPv27ZUjRw5Vr15dklSwYEE1bdpU69atU/ny5eXs7Kx58+ZJkoKDgzVgwADlz59fjo6OKlKkiCZOnKiYmBhz7Pg4pkyZovnz56tw4cJydHRUhQoVtGfPHrNf165dNWvWLDM+a6rK75rVSZJ0IyRcklS2hI8cMmfSpt+Omn2Onb6ks4HXVcnP1+LYacPa6NymCfr1y7fVuUXlhz5PlxeraNOuozobeOO+MV7R2l//0OZdRx9wJJ6k6OhoLfn2G926dUuVKldJ63CQhDWrV+n5cuXVvm1r+eTxVOXyZfX5Z5+a++/cuSNJcnJyMtvs7Ozk4OioHdu3PfV4kTwD+vVVo8ZNVLde/bQOBY8hMjJS33y1SF26vmo1/yawVomdq/DwcHXt3F7TPpml3Llzp3GESEpkZKQO7N9n8blpZ2enunXra/dvOx9yJICniRVEsPDDDz+oUKFCqlq16hMfu3Xr1ipatKjGjRun2NhYs/3o0aNq166devbsqR49eqh48eIKDw9XrVq1dOHCBfXs2VM+Pj7asWOHhg0bpsDAQE2bNs1i7K+++ko3b95Uz549ZRiGJk2apJdfflknT55U5syZ1bNnT128eFHr16/Xl19++cTnllYMw9Dkt1tpx4ET+vNEoCQpd05X3YmMUkiYZa2oy9dC5ZXT1Xw8evZq/bL7mMIjIlW/yjOaPuwVZc3iqNlf/5Lgebw93ORfraS6Dg+waG/tX05lnsmv6h0nPfnJwcIfhw+rdo0qioiIUNasWfXtsu9VomTJtA4LSTh18qQ+nTdHbw4YpCFDh2vf3j16a+CbcnBwUMfOXVT8mWeU38dH7783TDNnz5OLi4s+mf6xLpw/r6CgwLQOH4lY8u03Onhgv7b9tifpzkjXVq1coeDgYHXs3DWtQ0ESEjtXQ94aqMqVq6pZ8xZpFxiS7erVq4qOjpanp5dFu6eXl44e/TuNogJwPxJEMIWGhurChQtq0SJ1ftA+99xz+uqrrxK0//PPP1q7dq38/f3NtrFjx+rEiRM6cOCAihYtKknq2bOn8uTJo8mTJ+utt95S/vz5zf5nz57V8eP36noUL15cLVq00Lp169S0aVNVqVJFxYoV0/r169WxY8eHxnnnzh3zW30p7nVJr6YNa6Nni3ir3n21hZJjwqdrzT//fvS8sjg7amDn+okmiDo0q6Tgm7e1avMhsy2fV3ZNHtxSTXvP1J3IuymbAJKtWPHi2rX3oEJCQvT9d8vU49Uu+nnjLySJ0rmYmBg9X668xowdJ0kqU7asjhz5Q5/On6uOnbsoc+bM+mbJd+r9+mvK4+kue3t71a1XX/6NGlsk0pE+nDt3ToMH9dfqn9ZbrPpCxvTFwgXyb9RYefLkSetQkIT7z9XqH1Zpy5ZN+m1PwrqIAICU4xIzmOITIdmyZUuiZ8r06tUr0XZfX1+L5JAkLV26VDVq1FCOHDl09epVc6tfv76io6O1detWi/6vvPKKmRySpBo1akiSTp48+chxjh8/Xm5ubub230RUevLx0NZ6oUYp+ff4RBcuB5vtQddC5eiQWW5ZLes4eeZ01aVrD0527Tl8Wvly55BD5oR54y4tKuvrNbsVdfdesciyJXzkldNVO78aqpt7puvmnumqWb6o+rSrpZt7psvOjuX6T5KDg4MKFymi58uV0wcfjldpv+c0a8b0tA4LScjt7a0SJSyTeM88U0Lnzp01Hz9frpx27TuooKvBOnUuUKvWrNW1a9fk61voaYeLJBzYv0+XL19WlYrPK6tTJmV1yqRft/6i2TM/UVanTBYFdZG+nTlzRps2blDXV6nzld4ldq62bN6kkydOKHeu7OZ7UZLatWmphvVqp1GkeJhcuXLJ3t5ely9fsmi/fOkSlwimU/8ty2FNGx6OFUQwubrGXX5082biRYsfl6+vb7Lbjx8/rkOHDiUoUBjv8uXLFo99fHwsHscni27csKyXkxzDhg3ToEGDzMehoaHpLkn08dDWal73OTXsMV1nLl6z2Hfgr7OKjLqrOpWKa8XGg5KkogU85ePtrl2HTj1wTL/i+XQ95JYioyxXA9UoV1RFfDwVsMLy+vDNu4+qXKsPLdrmj+6oo6cuaWrAesXEsPohNcXExFisdEP6VKVqNR07Zlmf6/jxY/LxKZCgr5ubm6S4wtX79+3VyNEfPJUYkXx16tbT3gOHLdpe795NxYs/o7cGD5W9vX0aRYZH9eUXC+Xp6anGLzRJ61CQhMTO1dtD3lG3+5J75cuW1qQpHye4sQPSBwcHB5V9vpw2b9qo5i1elBT3b5nNmzeqV5830jY4ACYSRDC5uroqT548+uOPP5Ls+6Ds68O+PX3QnckSa4+JiVGDBg00ZMiQRI8pVqyYxeMH/aM8JZdoODo6ytHR8ZGPe1qmDWujVxqXV+uB8xV2K0JeOeNWfIWERSjiTpRCwyIUsGKnJr71sq6H3NLNWxH6aGhr/fb7SfMOZi/ULCXPnNm0+9BpRURGqV7lZzTktYaa9r+NCZ6v64tVtPvQKbPGUbyw8DsJ2m7djtT1kFsJ2vF43n93mPwbNVb+/D66efOmvv3mK239ZYt++HGdJCkoKEiXgoJ04p9/JEl//HFY2bJmU34fH7m7u6dl6Dav35sDVadmVU2aME4tW7XRnj279fln8zVzznyzz/JlS+Xh4aH8+X30xx+H9fag/mrW4kXVb9AwDSNHYrJly6ZnS5WyaHNxcZF7zpwJ2pF+xcTE6H9fLFSHTl2UKRP/FE7PHnSucufOneiqk/w+PhZ3ikT68uaAQerxaheVK1de5StU1MxPpin81i117tItrUMD8C9+KsJC06ZNNX/+fO3cuVNVqjz4DknxK3SCg4Mt2s+cOfNE4ihcuLDCwsJUv/6Tu0OMtSwp7NmmpiRp/WcDLNp7jPhSi37YJUkaMmW5YmJi9fWU7nJ0yKQNO/5S//Hfmn2j7karZ5uamvRWSxmGoRPnrmjo1O/0+Xc7LMZ0zeqkF+uV0duTl6XupPBQVy5f1mvdOisoMFBubm4qVdpPP/y4TvXqN5AkfTZ/rj78YLTZv0GduL8j8z9bqE5duqZFyPhX+QoV9O2y7zXi3WEaN3aMCvr6avLUaWrXvoPZJygwUEMHD4pbZu/trQ4dO2vYu++nYdSAddu0cYPOnT2rLl1fTetQkATOlXVp3eYVXb1yRWNGj9CloCD5PVdGK1evlZeXV9IHA3gqSBDBwpAhQ7R48WJ1795dmzZtSvCBfeLECa1evVr9+/dXrly5tHXrVg0YMMDcP3v27CcSR5s2bTRq1CitW7cuQX2i4OBgZc2a9ZG/9XNxcTGPz549+xOJMy04l016Ge6dyLsaOGGJBk5Ykuj+9Tv+0vodfyU5TmhYhHJWHZRkv3j+PaiJkxrmfrrgofvfGzFK740Y9XSCwSN7oUlTvdCk6QP39+33pvr2e/MpRoQn6eeNW9I6BDyi+g0a6nYUl0FnBI9yrjinGUPvvm+od18uKcsIrLFmj7XNJzWQIIKFwoUL66uvvtIrr7yiEiVKqHPnzipVqpQiIyO1Y8cOLV26VF27dpUkde/eXRMmTFD37t1Vvnx5bd26VceOHXsicQwePFirVq1S06ZN1bVrV5UrV063bt3S4cOHtWzZMp0+fVq5cuV6pDHLlSsnSXrzzTfl7+8ve3t7tW3b9onECwAAAABARkaCCAk0b95chw4d0uTJk7Vy5UrNmTNHjo6O8vPz09SpU9WjRw9J0ogRI3TlyhUtW7ZMS5YsUePGjfXTTz/J09PzsWPIkiWLfvnlF40bN05Lly7V//73P7m6uqpYsWIaPXq0Wcz1Ubz88svq16+fvvnmGy1atEixsbEkiAAAAAAAkGTEpqSKL2BDQkND5ebmJsfSPWTYO6R1OEiBG3tmpnUIAAAAsHKhoaHyyummkJAQ8w7RGU387z4enb+QnUOWtA7niYqJDNeV/3XJ0OcntbGCCAAAAAAAmKhBZJvs0joAAAAAAAAApC0SRAAAAAAAADaOBBEAAAAAAICNI0EEAAAAAABg4yhSDQAAAAAATBSptk2sIAIAAAAAALBxJIgAAAAAAABsHAkiAAAAAAAAG0cNIgAAAAAAcI/x72ZNrG0+qYAVRAAAAAAAADaOBBEAAAAAAICNI0EEAAAAAABg46hBBAAAAAAATIZhyDCsq2iPtc0nNbCCCAAAAAAAwMaRIAIAAAAAALBxJIgAAAAAAABsHDWIAAAAAACAiRpEtokVRAAAAAAAADaOBBEAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DiKVAMAAAAAABNFqm0TK4gAAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMZRgwgAAAAAANxj/LtZE2ubTypgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DhqEAEAAAAAAJNhGDIM6yraY23zSQ2sIAIAAAAAALBxJIgAAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMZRpBoAAAAAAJgoUm2bWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOGkQAAAAAAMBkyAprEMm65pMaWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOGkQAAAAAAMBkGFZYg8jK5pMaWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOGkRAMp3ZPFmurq5pHQZSoOKYDWkdAh7D7hH10zoEAAAAwOqRIAIAAAAAAPcY/27WxNrmkwq4xAwAAAAAAMDGkSACAAAAAACwcSSIAAAAAAAAbBw1iAAAAAAAgMkwDBmGdRXtsbb5pAZWEAEAAAAAANg4EkQAAAAAAAA2jgQRAAAAAACAjaMGEQAAAAAAMFGDyDaxgggAAAAAAMDGkSACAAAAAACwcSSIAAAAAAAAbBw1iAAAAAAAgMkw4jZrYm3zSQ2sIAIAAAAAALBxJIgAAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMZRpBoAAAAAAJjiilRbV1VnK5tOqmAFEQAAAAAAgI0jQQQAAAAAAGDjSBABAAAAAADYOBJEAAAAAADgHiO+DpH1bEpBDaKwsDCNHDlSjRo1kru7uwzDUEBAQIJ+Xbt2lWEYCbZnnnkmQd+YmBhNmjRJvr6+cnJykp+fn77++utEn/+vv/5So0aNlDVrVrm7u6tTp066cuXKY435MBSpBgAAAAAAuM/Vq1c1ZswY+fj46LnnntOWLVse2NfR0VGfffaZRZubm1uCfu+++64mTJigHj16qEKFClq5cqXat28vwzDUtm1bs9/58+dVs2ZNubm5ady4cQoLC9OUKVN0+PBh7d69Ww4ODo88ZlJIEAEAAAAAANzH29tbgYGByp07t/bu3asKFSo8sG+mTJnUsWPHh4534cIFTZ06VX379tXMmTMlSd27d1etWrU0ePBgtW7dWvb29pKkcePG6datW9q3b598fHwkSRUrVlSDBg0UEBCg119//ZHHTAqXmAEAAAAAANzH0dFRuXPnTnb/6OhohYaGPnD/ypUrFRUVpT59+phthmGod+/eOn/+vHbu3Gm2L1++XE2bNjWTQ5JUv359FStWTEuWLEnRmEkhQQQAAAAAAEyJ1dOxhi01hYeHy9XVVW5ubnJ3d1ffvn0VFhZm0efAgQNycXFRiRIlLNorVqxo7pfiVgVdvnxZ5cuXT/A8FStWNPs9ypjJwSVmAAAAAADAJty/wsfR0VGOjo6PNaa3t7eGDBmi559/XjExMVq7dq1mz56t33//XVu2bFGmTHGpl8DAQHl5eSVIVnl7e0uSLl68aPb7b/v9fa9fv647d+7I0dEx2WMmBwkiAAAAAABgE/Lnz2/xeOTIkRo1atRjjTl+/HiLx23btlWxYsX07rvvatmyZWah6Nu3byeajHJycjL3//f/SfV1dHRM9pjJwSVmAAAAAADAJpw7d04hISHmNmzYsFR5noEDB8rOzk4bNmww25ydnXXnzp0EfSMiIsz9//1/cvsmp19ykCACAAAAAAA2wdXV1WJ73MvLHsTZ2Vk5c+bU9evXzTZvb28FBQUpNjbWom/8JWV58uQx+/23/f6+7u7uZtzJHTM5SBABAAAAAACTYVjn9jTdvHlTV69elYeHh9lWpkwZhYeH66+//rLou2vXLnO/JOXNm1ceHh7au3dvgnF3795t9nuUMZODBBEAAAAAAEAKRERE6ObNmwnaP/jgA8XGxqpRo0ZmW4sWLZQ5c2bNnj3bbIuNjdXcuXOVN29eVa1a1Wxv2bKlVq9erXPnzpltGzdu1LFjx9S6desUjZkUilQDAAAAAAAkYubMmQoODjbvBvbDDz/o/PnzkqR+/frpxo0bKlu2rNq1a6dnnnlGkrRu3Tr9+OOPatSokVq0aGGOlS9fPg0YMECTJ09WVFSUKlSooBUrVujXX3/V4sWLZW9vb/YdPny4li5dqjp16qh///4KCwvT5MmTVbp0aXXr1i1FYybFiL3/QjUAFkJDQ+Xm5qagq8FydXVN63CQApU+2JjWIeAx7B5RP61DAAAASFJoaKi8cropJCQkw/7eEP+7T5EBy2Xv6JLW4TxR0Xdu6Z9pLR/5/BQsWFBnzpxJdN+pU6eUPXt29evXT7/99psuXryo6OhoFSlSRB06dNDbb7+tzJkzWxwTExOjiRMnat68eQoMDFTRokU1bNgwdejQIcH4R44c0aBBg7Rt2zY5ODioSZMmmjp1qry8vFI85sOQIAKSQIIo4yNBlLGRIAIAABmBNSWIig36zioTRMc+ejlDn5/URg0iAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBx3MUMAAAAAACYDCNusybWNp/UwAoiAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBxJIgAAAAAAABsHEWqAQAAAACAyTAMGVZW1dna5pMaWEEEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOGkQAAAAAAMBkGHGbNbG2+aQGVhABAAAAAADYOBJESNSWLVtkGIa2bNlitnXt2lUFCxZMs5geJiAgQIZh6PTp00n2LViwoLp27ZrqMQEAAAAAkFGQILIi8UmSxLZ33nknrcNLoGDBghYxenp6qkaNGvr+++/TOjSrEx0drdEj31eJYoXk7ppFzz5TROM//ECxsbGSpKioKL03bKgqlPVTruxZVahAXnXv1kUXL15M48itT7kC2TWjw3Pa8HYNHRpTX3We8Xhg3/eaPaNDY+qrY5X8ie7PbG9oSe9KOjSmvornzmqxr6hXVgW8Vk573q+jn9+qrm7VC1jsb17GW4fG1LfY9rxf5/EniGSbO3uWihcpqOxZnVSjaiXt2b07rUPCI+D8ZQzbft2qli82k69PHjlnNrRq5QpzX1RUlN4dNlTly5RWTjcX+frk0WtdO/OzL52aPHG8qlWuII8c2eSTx1OtW76oY0ePpnVYeER8dgLpGzWIrNCYMWPk6+tr0VaqVKk0iubhypQpo7feekuSdPHiRc2bN08vv/yy5syZo169eiV7nE6dOqlt27ZydHRMrVAztKmTJ+qz+XM1f0GASpZ8Vvv37VXPHq/Kzc1Nfd54U+Hh4Tp48IDeGf6eSvs9p+DgG3p70AC1frmFtv+2J63DtyrODvY6GhSm7/df1LR2zz2wX90SHvLL56ZLoREP7DOoYVFduXlHz3hns2h3cbTXvM5l9dvJ6/pg1d8q6pVVo18sqdDbd7V83wWz382Iu2r+yQ7z8b/5QjwFS5d8q6GDB2nGrLmqULGSZn4yTc2b+Ov3I0fl6emZ1uEhCZy/jOPWrVsq7fecOnd9VW1bv2yxLzw8XAcP7Nc7774vP7/ndOPGDb09qL9av9Rc23ftTaOI8SC/bv1FvXr3VbnyFXT37l2NfH+4mr7QUAcO/SkXF5e0Dg/JwGdnxhL/Jb41sbb5pAYSRFaocePGKl++fFqHkSx58+ZVx44dzcedO3dWkSJF9PHHHz9Sgsje3l729vapEaJV+O23nWrSrLkav9BEklSgYEEt+fYb7d0Tl/xxc3PT6p9+tjjmo+kzVLNqJZ07e1b5fXyeeszWatvxa9p2/NpD+3hmc9SwF4qr1/8OaGbHMon2qV40p6oUyalB3xxSjWK5LPY18cutzPZ2GrHiT92NjtWJK7dU3DubOlf1sUgQxcbG6lpY5GPPCY/uk2kfqdtrPdS5azdJ0ozZc/XTT2v0RcDnGjwk/a34hCXOX8bh36ix/Bs1TnSfm5ub1qxdb9H28fSZqlG1os6ePSsffvalK6vWrLV4PH9BgHzyeOrA/n2qXqNmGkWFR8FnJ5D+cYmZjTEMQ6NGjUrQ/qh1eWJjY1WwYEG1aNEiwb6IiAi5ubmpZ8+ejxxf7ty5VaJECZ06dUqSdOjQIXXt2lWFChWSk5OTcufOrVdffVXXrln+gp1YDaLY2FiNHTtW+fLlU5YsWVSnTh0dOXLkkWOyBpUrV9GWzZt0/NgxSdKh33/Xzh3b1NC/0QOPCQ0JkWEYcsue/SlFCSnu7grjWj6rgO1ndOLKrUT7uLs4aGTzEhq+/A9FREUn2P9c/uzad+aG7kbfWxK0459r8vVwUTane98LZHGw19pB1fTzW9U1vd1zKuzBN7BPQ2RkpA7s36e69eqbbXZ2dqpbt752/7YzDSNDcnD+rFtoaNzPvuz87Ev3QkNCJEk5crincSRIDj47gYyBFURWKCQkRFevXrVoy5Ur1wN6p4xhGOrYsaMmTZqk69evy9393g/nH374QaGhoRYrg5IrKipK586dU86cOSVJ69ev18mTJ9WtWzflzp1bR44c0fz583XkyBH99ttvD10mOGLECI0dO1YvvPCCXnjhBe3fv18NGzZUZKTtrZh4e8g7uhkaqjKlS8je3l7R0dEaNWas2rbvkGj/iIgIvTf8HbV5pZ1cXV2fcrS27dXqBXU3JlaLfzv3wD5jXy6pJXsv6M+LN5Unu1OC/TmzOujCjdsWbfErhXJlddDNiLs6fS1cI1f8pWOXbiqrUyZ1rVZA/+tRQS/P3KlLoXee7KRg4erVq4qOjpanp5dFu6eXl44e/TuNokJycf6sV0REhN4bNpSffRlATEyMBr81QFWqVtOz6bSMAizx2QlkDCSIrFD9+vUTtMWmQnGRzp0768MPP9SSJUssLgdbtGiRChYsqOrVqyc5RlRUlJnMunjxosaPH69Lly6pX79+kqQ+ffqYNYriVa5cWe3atdO2bdtUo0aNRMe9cuWKJk2apCZNmuiHH34wE0nvvvuuxo0b99CY7ty5ozt37v2CHBoamuQ80rvlS5fom2++UsD/FqtEyWd16PeDGvL2QHl751HHzl0s+kZFRalju1cUGxur6TNnp1HEtqmEdzZ1qJxfr8zd9cA+7SvlVxaHTFqw9dRjPdehcyE6dC7EfPz72RCt6FdFrcrn1axNJx9rbADIaOJ+9rVRbGysPpk1J63DQRIG9OurI0f+0MYt29I6FMBqUYPINpEgskKzZs1SsWLFUv15ihUrpkqVKmnx4sVmguj69ev66aefNGTIkGS9AX/++Wd5eNy7i5O9vb06deqkiRMnSpKcnZ3NfREREQoLC1PlypUlSfv3739ggmjDhg2KjIxUv379LOIYMGBAkgmi8ePHa/To0UnGnpEMHzZEbw0eqtavtJUklSpdWmfPntGUSRMsEkTxyaFzZ8/ox5838g3qU1auYHa5uzho3aB7ydVM9nZ6y7+YOlT2UeOPt6tioRx6Lr+b9o6oa3Hs1z0r6sdDQXrv+z91LSxSObM6WOyPf3z1ATWH7sbE6u/Am/Jxz/KEZ4X75cqVS/b29rp8+ZJF++VLl5Q7d+40igrJxfmzPlFRUerQro3Onjmjn9Zv4mdfOjfgzTf044+rtWHTVuXLly+tw0Ey8dkJZAwkiKxQxYoVn1qR6s6dO+uNN97QmTNnVKBAAS1dulRRUVHq1KlTso6vVKmSxo4dK8MwlCVLFpUoUcLiuv/r169r9OjR+uabb3T58mWLY0NCQvQgZ86ckSQVLVrUot3Dw0M5cuR4aEzDhg3ToEGDzMehoaHKnz/x24xnFLfDw2VnZ1lyzN7eXjExMebj+OTQiX+O66f1m8zL/PD0/HAwSL+duG7RNqdzWa3+PUgr98fddnnCj0c1c+MJc79HNkfN6/K8hiz9Q4fPx70nfj8XrH71iiiTnaG7MXGrB6sUdtepK7d0M+Juos9tZ0hFvbLq1+NXE92PJ8fBwUFlny+nzZs2qnmLFyXFXS6xefNG9erzRtoGhyRx/qxLfHLoxD/HtXb9Zn72pWOxsbEa2L+fVq38Xj9v2KKC992xF+kbn51AxkCCCJKk6OiEhW6To23btho4cKAWL16s4cOHa9GiRSpfvryKFy+erONz5cqV6CVx8dq0aaMdO3Zo8ODBKlOmjLJmzaqYmBg1atTIIrnxJDk6OsrR0TFVxk4rLzRppkkTxil/fh+VLPmsDh48oBnTP1bnLnF3kYiKilL7V1rr4MH9Wv79D4qOjlZQUJAkyd3dXQ4ODg8bHo/A2cFePu73VsblzeGs4rmzKuR2lIJC7ijkdpRF/7vRsboWdkenr4VLkoJC7ki6dwlkeGTce/fc9XCzdtCPh4LUq3YhjX6xpD7/9bSKeGVVh8o+mvTTMfO4nrV9dehciM5evy3Xf2sQeWd30nf7LqbW1PEfbw4YpB6vdlG5cuVVvkJFzfxkmsJv3TLfk0jfOH8ZR1hYmE7884/5+PSpU/r94EHlcHeXt7e32r/SSgcO7Nd3K1bzsy+dG9Cvr7795ist/W6lsmbLZp4rNzc3ixXnSL/47ATSPxJENiZHjhwKDg62aIuMjFRgYGCKxnN3d1eTJk20ePFidejQQdu3b9e0adMeP1BJN27c0MaNGzV69GiNGDHCbD9+/HiSxxYoUMDsW6hQIbP9ypUrunHjxhOJLyOZOu0TjRn1vga82VdXLl+Wd548erX76xr+XtzrevHCBa1ZvUqSVLlCWYtj167fpJq1aj/tkK3Ws3lc9fmr5czHQxrHXQ668sBFvf/9n0/kOcLuRKvn/w7o3abF9U2vigoOj9LcLSctbnHv6pRZI1uUUK6sjgq9HaU/A2+q86d7dfIBd07Dk9W6zSu6euWKxoweoUtBQfJ7roxWrl4rLy+vpA9GmuP8ZRz79+2Vf/065uOhg+NWCHfs1EXvjRil1T/E/eyrVL6MxXHrNmzmZ186M39eXG2ohvVqW7Z/tlCdunR9+gHhkfHZCaR/JIhsTOHChbV161aLtvnz56d4BZEkderUSS+//LIGDx4se3t7tW3b9nHDlBR3CZSUsMB2chJQ9evXV+bMmTVjxgw1bNjQrEP0pJJXGU22bNk0eeo0TZ46LdH9BQoWVHhk6qzIgqW9p2/Ib8SGZPdv/PH2h+6/GByR6HjHL4Wp64J9Dzxu8tpjmrz22AP3I/X17vuGevdlWX1GxfnLGGrWqq3bUQ++UcfD9iF94VxZBz47Mw7DiNusibXNJzWQILIx3bt3V69evdSyZUs1aNBAv//+u9atW6dcuXKleMwmTZooZ86cWrp0qRo3bixPT88nEqurq6tq1qypSZMmKSoqSnnz5tXPP/+sU6eSvnuTh4eH3n77bY0fP15NmzbVCy+8oAMHDuinn356rLkCAAAAAGCN7JLuAmvSo0cPDR06VFu3btVbb72lU6dOaf369XJxcUnxmA4ODnrllVckKdnFqZPrq6++kr+/v2bNmqVhw4Ypc+bM+umnn5J17NixYzV69GgdOHBAgwcP1okTJ/Tzzz8/1lwBAAAAALBGRuz91+8AKTBw4EAtWLBAQUFBypLFum6THRoaKjc3NwVdDebWtxlUpQ82pnUIeAy7Rzy4kD0AAEB6ERoaKq+cbgoJCcmwvzfE/+5T6p2Vsne0ri/Wo+/c0h8TWmTo85PauMQMjy0iIkKLFi1Sy5YtrS45BAAAAAC2xpBh1nG1Foasaz6pgQQRUuzy5cvasGGDli1bpmvXrql///5pHRIAAAAAAEgBEkRIsT///FMdOnSQp6enPvnkE5UpUyatQwIAAAAAAClAgggpVrt27QS3oAcAAAAAABkPCSIAAAAAAGAyjLjNmljbfFIDt7kHAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGUaQaAAAAAACYDMOQYWVVna1tPqmBFUQAAAAAAAA2jgQRAAAAAACAjSNBBAAAAAAAYOOoQQQAAAAAAEyGEbdZE2ubT2pgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DhqEAEAAAAAAJNhGDKsrGiPtc0nNbCCCAAAAAAAwMaRIAIAAAAAALBxJIgAAAAAAABsHAkiAAAAAAAAG0eRagAAAAAAYDKMuM2aWNt8UgMriAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxlGDCAAAAAAAmAzDkGFlRXusbT6pgRVEAAAAAAAANo4EEQAAAAAAgI0jQQQAAAAAAGDjqEEEAAAAAADuMSSrK9ljbfNJBawgAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxlGkGgAAAAAAmAzDkGFlVaqtbT6pgRVEAAAAAAAANo4EEQAAAAAAgI0jQQQAAAAAAGDjqEEEAAAAAABMhhG3WRNrm09qYAURAAAAAACAjSNBBAAAAAAAYONIEAEAAAAAANg4ahABAAAAAACTYRgyrKxoj7XNJzWwgggAAAAAAMDGkSACAAAAAACwcVxiBiTT3ehY3Y2OTeswkAK7R9RP6xDwGGJjed9lZCznBgAAyBhIEAEAAAAAAJNhxG3WxNrmkxq4xAwAAAAAAMDGkSACAAAAAACwcSSIAAAAAAAAbBwJIgAAAAAAABtHkWoAAAAAAGAyDMPq7kRqbfNJDawgAgAAAAAAsHEkiAAAAAAAAGwcCSIAAAAAAAAbRw0iAAAAAABgogaRbWIFEQAAAAAAgI0jQQQAAAAAAGDjSBABAAAAAADYOGoQAQAAAAAAk2HEbdbE2uaTGlhBBAAAAAAAYONIEAEAAAAAANg4EkQAAAAAAAA2jgQRAAAAAACAjaNINQAAAAAAMBmGIcPKqjpb23xSAyuIAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGUYMIAAAAAACYDCNusybWNp/UwAoiAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBx1CACAAAAAAAmwzBkWFnRHmubT2pgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DgSRAAAAAAAADaOItUAAAAAAMBkSLK2ms5WNp1UwQoiAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBx1CACAAAAAAAmO8OQnZUVIbK2+aQGVhABAAAAAADYOBJEAAAAAAAANo4EEQAAAAAAgI2jBhEAAAAAADAZRtxmTaxtPqmBFUQAAAAAAAA2jgQRAAAAAACAjSNBBAAAAAAAYOOoQQQAAAAAAEyGYciwsqI91jaf1MAKIgAAAAAAgPuEhYVp5MiRatSokdzd3WUYhgICAiz6xMTEKCAgQM2bN1f+/Pnl4uKiUqVKaezYsYqIiEgwZnzy7f5twoQJCfpeuHBBbdq0Ufbs2eXq6qoWLVro5MmTica6YMEClShRQk5OTipatKhmzJjxyPNlBREAAAAAAMB9rl69qjFjxsjHx0fPPfectmzZkqBPeHi4unXrpsqVK6tXr17y9PTUzp07NXLkSG3cuFGbNm1KsHqpQYMG6ty5s0Vb2bJlLR6HhYWpTp06CgkJ0fDhw5U5c2Z9/PHHqlWrlg4ePKicOXOafefNm6devXqpZcuWGjRokH799Ve9+eabCg8P19ChQ5M9XxJEAAAAAAAA9/H29lZgYKBy586tvXv3qkKFCgn6ODg4aPv27apatarZ1qNHDxUsWNBMEtWvX9/imGLFiqljx44Pfe7Zs2fr+PHj2r17t/m8jRs3VqlSpTR16lSNGzdOknT79m29++67atKkiZYtW2Y+f0xMjD744AO9/vrrypEjR7LmyyVmAAAAAAAA93F0dFTu3Lkf2sfBwcEiORTvpZdekiT99ddfiR53+/btRC9Bi7ds2TJVqFDBIin1zDPPqF69elqyZInZtnnzZl27dk19+vSxOL5v3766deuW1qxZ89D4/4sEEQAAAAAAMNkZ1rk9TUFBQZKkXLlyJdgXEBAgFxcXOTs7q2TJkvrqq68s9sfExOjQoUMqX758gmMrVqyoEydO6ObNm5KkAwcOSFKCvuXKlZOdnZ25Pzm4xAwAAAAAANiE0NBQi8eOjo5ydHR84s8zadIkubq6qnHjxhbtVatWVZs2beTr66uLFy9q1qxZ6tChg0JCQtS7d29J0vXr13Xnzh15e3snGDe+7eLFiypevLgCAwNlb28vT09Pi34ODg7KmTOnLl68mOyYWUH0GM6dOycnJydt3749rUNJ97p27aqCBQumaQxr165V1qxZdeXKlTSNAwAAAACQNvLnzy83NzdzGz9+/BN/jnHjxmnDhg2aMGGCsmfPbrFv+/bt6t+/v5o3b65evXpp3759KlWqlIYPH67bt29Lkvn/xBJXTk5OFn1u374tBweHRONwcnIy+yVHuksQBQQEyDAM7d2797HHCg8P16hRoxKtNP4kjBkzRpUqVVK1atXMtu+//17+/v7KkyePHB0dlS9fPrVq1Up//PFHguO//fZbdezYUUWLFpVhGKpdu/YjPX9gYKDeeecd1alTR9myZZNhGA+sqj5r1iw1bNhQ3t7eypYtm8qWLas5c+YoOjo6Qf+YmBhNmjRJvr6+cnJykp+fn77++utHii09atSokYoUKZIqHwDpzbixo+XqbG+xlXuupKS4bPTbA9/U834l5JnDRSWLFtTgQf0VEhJiMca5s2fV6qWm8nLPqkI+ufXesCG6e/duWkwHDzB39iwVL1JQ2bM6qUbVStqze3dahwRJ237dqpYvNlehAnmVxcFOq1ausNi/4vvv1OwFf+XLnUtZHOz0+8GDCcZ4o09PPftMEbm7ZpFPHk+1fvlFHf3776czASQL77+MKe792Uy+PnnknNlI8P5E+vEo56pfn15yzmxoxvRpTy0+pAyfnUgPzp07p5CQEHMbNmzYEx3/22+/1XvvvafXXnvNXBH0MA4ODnrjjTcUHBysffv2SZKcnZ0lSXfu3EnQP75uUXwfZ2dnRUZGJjp2RESE2S850l2C6EkKDw/X6NGjUyVBdOXKFX3xxRfq1auXRfvhw4eVI0cO9e/fX7Nnz1bv3r114MABVaxYUb///rtF3zlz5mjlypXKnz9/squK/9fRo0c1ceJEXbhwQaVLl35gv5MnT6pfv36KjY3VoEGDNGXKFPn6+qpPnz569dVXE/R/9913NXToUDVo0EAzZsyQj4+P2rdvr2+++eaRY0xvevbsqXnz5pnXa1qzEiWf1fFTF8zt541bJUlBgRcVFHhRH46fpN/2HdKcTz/XhvXr9Eav7uax0dHRav1yM0VFRmr95m2a++lCLV70hT4cMzKtpoP7LF3yrYYOHqR33xupnbv3y8/vOTVv4q/Lly+ndWg279atWyrt56ePp89MdH/4rVuqUrWaPhg34YFjlH2+nOZ9+rkOHPpTK9esVWxsrJo18U80qY+nj/dfxhX3/nxO0z6ZldahIAnJPVcrV3yv3bt+k3eePE8pMqQUn50ZjCEZhmFVm/6tQeTq6mqxPcnLy9avX6/OnTurSZMmmjt3brKPy58/v6S4L/Mlyd3dXY6OjgoMDEzQN74tz7+fe97e3oqOjk7wXoqMjNS1a9fMfslBDaIUWrRokTJlyqRmzZpZtI8YMSJB3+7duytfvnyaM2eOxV+SL7/8Unnz5pWdnZ1KlSr1yDGUK1dO165dk7u7u5YtW6bWrVsn2i937tw6fPiwnn32WbOtZ8+eevXVV7Vw4UK9//77KlKkiCTpwoULmjp1qvr27auZM2ea8deqVUuDBw9W69atZW9v/8ixphctW7ZUv379tHTp0kSTY9YkU6ZM8kqk4n7JZ0tp0TfLzMeFChXWiFEfqMernXX37l1lypRJGzf8rL//+lOr1vwsTy8v+T1XRu+NGK2R7w3TsPdGPnAJI56eT6Z9pG6v9VDnrt0kSTNmz9VPP63RFwGfa/CQd9I4Otvm36ix/Bs1fuD+9h07SZLOnD79wD6vdX/d/HOBggU1cvQHqlS+jM6cPq1ChQs/sViRMrz/Mq6k3p9IP5Jzri5cuKBBA/rphzXr9FKLJk8pMqQUn52wdrt27dJLL72k8uXLa8mSJcqUKfnplpMnT0qSPDw8JEl2dnYqXbp0oldW7dq1S4UKFVK2bNkkSWXKlJEk7d27Vy+88ILZb+/evYqJiTH3J0eGXEEUGRmpESNGqFy5cnJzc5OLi4tq1KihzZs3m31Onz5tvrijR482s4ajRo0y+/z9999q1aqV3N3d5eTkpPLly2vVqlXJimHFihWqVKmSsmbNmmRfT09PZcmSRcHBwRbt+fPnl51dyk9BtmzZ5O7unmS/XLlyWSSH4iV2272VK1cqKirK4hZ5hmGod+/eOn/+vHbu3Jnk861YsUKlSpWSk5OTSpUqpe+//z7RflOmTFHVqlWVM2dOOTs7q1y5clq2bJlFn1q1aum5555L9PjixYvL39/ffPzNN9+oXLlyypYtm1xdXVW6dGlNnz7d4hhPT0/5+flp5cqVSc4jozvxz3EV880nvxJF9FrXjjp39uwD+4aGhiibq6v5IbZ71296tlRpeXp5mX3qNfBXaGio/vrzSKrHjoeLjIzUgf37VLdefbPNzs5OdevW1+7fkn6PImO5deuWvvzfQhX09VW+f79dQtrh/QekDzExMXqtaycNHDRYJRP5dy7SFz47Ye3++usvNWnSRAULFtTq1asfeFlXYvVwb968qWnTpilXrlwqV66c2d6qVSvt2bPHIkl09OhRbdq0yWJxSN26deXu7q45c+ZYjDtnzhxlyZJFTZokP4GeIVcQhYaG6rPPPlO7du3Uo0cP3bx5UwsWLJC/v792796tMmXKyMPDQ3PmzFHv3r310ksv6eWXX5Yk+fn5SZKOHDmiatWqKW/evHrnnXfk4uKiJUuW6MUXX9Ty5cvN5ElioqKitGfPnodeTxgcHKyoqCgFBQVp2rRpCg0NVb169Z7sC/GYErvt3oEDB+Ti4qISJUpY9K1YsaK5v3r16g8c8+eff1bLli1VsmRJjR8/XteuXVO3bt2UL1++BH2nT5+u5s2bq0OHDoqMjNQ333yj1q1ba/Xq1eZf4k6dOqlHjx76448/LFZZ7dmzR8eOHdN7770nKW4pX7t27VSvXj1NnDhRUtybNL4A2H+VK1dOK1aseOAc7ty5Y3Gt5/1V7jOC8hUqas78z1W0WHEFBQVqwocfqFH9Wvpt3yEz0xzv2tWrmjT+Q3V7tYfZdvlSkDzuq4Lv6RmXLLp0KSj1J4CHunr1qqKjo81zEs/Ty0tHj1KnxlrMmztb7w0bqlu3bqlYseJa/ePPrN5LB3j/AenD1MkTlSlTJvXt92Zah4Jk4LMTGdnMmTMVHBxs3g3shx9+0Pnz5yVJ/fr1k52dnfz9/XXjxg0NHjxYa9assTi+cOHCqlKliiRp1qxZWrFihZo1ayYfHx8FBgbq888/19mzZ/Xll19a/FuvT58++vTTT9WkSRO9/fbbypw5sz766CN5eXnprbfeMvs5Ozvrgw8+UN++fdW6dWv5+/vr119/1aJFi/Thhx8ma1FJvAyZIMqRI4dOnz5t8eL16NFDzzzzjGbMmKEFCxbIxcVFrVq1Uu/eveXn56eOHTtajNG/f3/5+Phoz5495jWHffr0UfXq1TV06NCHJojOnj2r27dvy9fX94F9KleurKNHj0qSsmbNahapSi8iIyM1bdo0+fr6qkKFCmZ7YGCgvLy84q7R/I//3krvYYYOHSovLy9t27ZNbm5ukuJWATVs2FAFChSw6Hvs2DGLzOobb7yh559/Xh999JGZIGrdurX69eunRYsWacKEe/U6Fi1aJBcXFzPxt2bNGrm6umrdunVJXgJXqFAhXb16VZcvX05wK0BJGj9+vEaPHv3QMdK7hv73lmSXKu2n8hUqqVRxX32/fIk6d7339zA0NFStXmqm4iVKaNh71BcC0pO27TqoXr0GCgoK1LSPpqpj+1e06Zdt5p0rAMBW7d+3T7NmTNeO3fsT/JsVwJNhGHGbNUnpfKZMmaIzZ86Yj7/77jt99913kmTmGc6dOydJeuedhJdLdunSxUwQVatWTTt27NBnn32ma9euycXFRRUrVtTnn3+uunXrWhyXLVs2bdmyRQMHDtTYsWMVExOj2rVr6+OPPzavlorXp08fZc6cWVOnTtWqVauUP39+ffzxxwkWSyQlQyaI7O3tzSRATEyMgoODFRMTo/Lly2v//v1JHn/9+nVt2rRJY8aM0c2bNy0KFvv7+2vkyJG6cOGC8ubNm+jx165dk6SHFpZeuHChQkNDdfLkSS1cuFC3b99WdHT0Y11S9iS98cYb+vPPP7VmzRqLayNv376drFvpJSYwMFAHDx7UO++8YyaHJKlBgwYqWbKkbt26ZdH/v8mhGzduKDo6WjVq1LC4Y5qbm5tatGihr7/+WuPHj5dhGIqOjta3336rF198US4uLpKk7Nmz69atW1q/fr0aNWr00LnHn7erV68mmiAaNmyYBg0aZD4ODQ01i4ZlVNmzZ1fhIsV08sQJs+3mzZt6ufkLypYtm7769jtlzpzZ3OfplVv79u6xGOPy5UuSJC+vhHWN8HTlypVL9vb25jmJd/nSJeVOpO4UMqb4W68WKVpUFStVVh5Pd61a8b3atG2X1qHZNN5/QNrbvu1XXb58WcUK+Zht0dHRemfIW5o5Y5qO/nM67YJDovjsREZ2+iF1I+PFxsYma6wGDRqoQYMGyX7ufPnyaenSpcnq26NHD/Xo0SPpjg+RPrIVKfDFF1/Iz89PTk5Oypkzpzw8PLRmzZoEt+pOzD///KPY2Fi9//778vDwsNhGjoxbRZGcavoP+0tQpUoV+fv7q3fv3lq3bp0WLVqUotvnRUZGKigoyGJ73LvYTJ48WZ9++qk++OADiyJWUlzSJjm30ktMfFa1aNGiCfYVL148Qdvq1atVuXJlOTk5yd3d3bws8P5z2LlzZ509e1a//vqrJGnDhg26dOmSOnXqZPbp06ePihUrpsaNGytfvnx69dVXtXbt2kTjjD9vD/rGydHRMUFl+4wuLCxMp06dkFfuuJVgoaGherFpIzk4OOibZSsSrEioWKmyjvxxWFf+8z7YvHG9XF1d9UyJkk81diTk4OCgss+X0+ZNG822mJgYbd68URUrV0nDyJBaYmNjFRsbqzuRCT+f8XTx/gPSXvuOnbRn/yHt2nvQ3Lzz5NHAtwbrhzXr0jo8JILPTiBjyJAriBYtWqSuXbvqxRdf1ODBg+Xp6Sl7e3uNHz9eJ/6zQuJBYmJiJElvv/22RZHj/4q/q1dicubMKSlu1Uty5MiRQ3Xr1tXixYs1ZcqUZB0Tb8eOHapTp45F26lTp1SwYMFHGideQECAhg4dql69epn1e/7L29tbmzdvVmxsrEUC5f5b6T2uX3/9Vc2bN1fNmjU1e/ZseXt7K3PmzFq4cKG++uori77+/v7y8vLSokWLVLNmTS1atEi5c+dW/fr3itx5enrq4MGDWrdunX766Sf99NNPWrhwoTp37qwvvvjCYrz48/bf2kvW5t13Bqtxk6bK71NAQRcvatzYUbK3t1frNm3N5NDt2+H6dOH/dDM0VDf/rbOUy8ND9vb2qle/oZ4pUVI9XuusDz6cqEuXgvTB6BHq0bPPE70NJFLuzQGD1OPVLipXrrzKV6iomZ9MU/itW+rcpVtah2bzwsLCdOKff8zHZ06f0u8HD8rd3V35fXx0/fp1nTt7VoGBcZfsHj8WdzmyV+7cyp07t06dPKllS79VvQYN5ZHLQxcunNeUSRPl7Ows/0YvJPqceLp4/2Vc978/T5+Ke3/mcHeXj4/PQ47E05bUuYr/93i8zJkzy8srt4ol8qUk0gc+O4H0L0MmiJYtW6ZChQrpu+++s0hixK/+ifegFSKFChWSFPeD5L9JhuTy8fGRs7OzTp06lexjbt++nazVTfd77rnntH79eou2lC7DXLlypbp3766XX35Zs2bNSrRPmTJl9Nlnn+mvv/5SyZL3Vors2rXL3P8g8TWGjh8/nmBffD2meMuXL5eTk5PWrVtnkXBYuHBhgmPt7e3Vvn17BQQEaOLEiVqxYoV69OiRoNaQg4ODmjVrpmbNmikmJkZ9+vTRvHnz9P7771sk/E6dOqVcuXIluG7Tmly4cF6vdu6g69evKVcuD1WuWk0bf9mhXB4e+nXrFu3d8+/5fLaYxXGH/z6hAgUKyt7eXkuWr9LA/n1Uv3Y1ZXFxUfsOnfXuiIxdm8matG7ziq5euaIxo0foUlCQ/J4ro5Wr18rLyyvpg5Gq9u/bq0YN7l1DPnRwXBHBjp26aP6ChVqzepV6dn/V3N+5Y9wlY8PfG6H3RoySo5OTtm/fplkzpuvGjRvy9PJS9eo1temX7YleFounj/dfxrV/317517/3xdvQwXGXlHfs1EWffh6QRlEhMZwr68NnJ5D+ZcgEUXxi4L+rXHbt2qWdO3dafPuTJUsWSUpwe3lPT0/Vrl1b8+bNU79+/cwCzPGuXLny0ORB5syZVb58eYvbzcVLrPDx6dOntXHjRpUvXz75k/xXjhw5UpTEut/WrVvVtm1b1axZU4sXL35gLaQWLVpo4MCBmj17tmbOnCkp7nWeO3eu8ubNq6pVqz7wOby9vVWmTBl98cUXFnWI1q9frz///NOiSLW9vb1ZTyje6dOnH3h3sU6dOunjjz9Wz549FRYWlqDo+LVr1yy+SbKzszPvWHf/JXP79u0zi4RZq4Avv37gvho1ayv0dtKXKfoUKKDlK9Yk2Q9pp3ffN9S77xtpHQbuU7NWbYVHxjxwf6fOXdWpc9cH7s+TJ49WrOK9l97x/suYataqrdtRyasTgbT1qOeKukMZA5+dGYfx73/WxNrmkxrSbYLo888/T7SGTP/+/dW0aVN99913eumll9SkSROdOnVKc+fOVcmSJRUWFmb2dXZ2VsmSJfXtt9+qWLFicnd3V6lSpVSqVCnNmjVL1atXV+nSpdWjRw8VKlRIly5d0s6dO3X+/Hn9/vvvD42vRYsWevfddxUaGmpRo6Z06dKqV6+eypQpoxw5cuj48eNasGCBoqKiLO7CJcUlbbZu3SopLil169YtjR07VpJUs2ZN1axZM8nXKb7/kSNHJElffvmltm3bJknmJWRnzpxR8+bNZRiGWrVqlaDIlZ+fn5lMyZcvnwYMGKDJkycrKipKFSpU0IoVK/Trr79q8eLFSd4hbPz48WrSpImqV6+uV199VdevX9eMGTP07LPPWpybJk2a6KOPPlKjRo3Uvn17Xb58WbNmzVKRIkV06NChBOOWLVtWpUqV0tKlS1WiRAk9//zzFvu7d++u69evq27dusqXL5/OnDmjGTNmqEyZMipRooTZ7/Llyzp06JD69u2b5GsLAAAAAICtMGKTW277KQkICFC3bg++DvXcuXPKmzevJkyYoHnz5ikoKEglS5bUBx98oKVLl2rLli0WVcZ37typfv366fDhw4qMjNTIkSM1atQoSdLJkyc1evRo/fzzz7p27Zo8PT1VtmxZde3aVS1btnxonJcvX1bevHm1cOFCi9Uso0aN0po1a3TixAndvHlTnp6eqlmzpoYPH67SpUtbjDFq1KgH3k79v3E+zMNu7Rl/ards2ZKgjtHDnismJkYTJ07UvHnzFBgYqKJFi2rYsGHq0KFDkvFIcbf9e++993Ty5EkVLlxYY8eO1cqVKxOcm88//1wTJkzQ2bNn5evrq6FDh+r06dMaPXp0ogXAJ0+erCFDhmjcuHEJCn4vX75c8+fP18GDBxUcHKzcuXOrcePGGjVqlMUleXPnztVbb72loKAgZcuWLVnzCQ0NlZubm85fumEVBattUeZMGbYeP5T8u0IgfeIW1AAAWxEaGiqvnG4KCQnJsL83xP/u0/DjTcrsnDWtw3miom6H6eeBdTP0+Ult6S5BlJG89tprOnbsmHl3LaSu6dOna+DAgTp9+nSKC0mWLVtWtWvX1scff5zsY0gQZXwkiDI2fkxlbCSIAAC2ggRR+kaCKGnp9hKzjGDkyJEqVqyYtm/frmrVqqV1OFYtNjZWCxYsUK1atVKcHFq7dq2OHz+udeu4/SkAAAAAPIidEbdZE2ubT2ogQfQYfHx8FBERkdZhWLVbt25p1apV2rx5sw4fPqyVK1emeKxGjRpZ1EECAAAAAABxSBAhXbty5Yrat2+v7Nmza/jw4WrevHlahwQAAAAAgNUhQYR0rWDBgtQfAQAAAAAglZEgAgAAAAAAJsMwrO5GE9Y2n9TArX0AAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBxFKkGAAAAAAAmw4jbrIm1zSc1sIIIAAAAAADAxpEgAgAAAAAAsHEkiAAAAAAAAGwcNYgAAAAAAIDJzjBkZ2VFe6xtPqmBFUQAAAAAAAA2jgQRAAAAAACAjSNBBAAAAAAAYOOoQQQAAAAAAEyGEbdZE2ubT2pgBREAAAAAAICNI0EEAAAAAABg40gQAQAAAAAA2DhqEAEAAAAAAJNhGDKsrGiPtc0nNbCCCAAAAAAAwMaRIAIAAAAAALBxJIgAAAAAAABsHAkiAAAAAAAAG0eRagAAAAAAYDKMuM2aWNt8UgMriAAAAAAAAGwcCSIAAAAAAAAbR4IIAAAAAADAxlGDCAAAAAAAmOwMQ3ZWVrTH2uaTGlhBBAAAAAAAYONIEAEAAAAAANg4EkQAAAAAAAA2jhpEAAAAAADAZPy7WRNrm09qYAURAAAAAACAjSNBBAAAAAAAYONIEAEAAAAAANg4EkQAAAAAAAA2jiLVAAAAAADAZBiGDMO6yjpb23xSAyuIAAAAAAAAbBwJIgAAAAAAABtHgggAAAAAAMDGUYMIAAAAAACY7Iy4zZpY23xSAyuIAAAAAAAAbBwJIgAAAAAAABvHJWZAMmWyN5TJnnWJwNPGLUkBAACA1EeCCAAAAAAAmAzDsLov6axtPqmBS8wAAAAAAABsHAkiAAAAAAAAG0eCCAAAAAAAwMaRIAIAAAAAALBxFKkGAAAAAAAWqOlse1hBBAAAAAAAYONIEAEAAAAAANg4EkQAAAAAAAA2jhpEAAAAAADAZBiGDCsrQmRt80kNrCACAAAAAACwcclaQXT27NkUP4GPj0+KjwUAAAAAAEDqS1aCqGDBgilajmUYhu7evfvIxwEAAAAAAODpSVaCqHPnzlyvBwAAAACADbAz4jZrYm3zSQ3JShAFBASkchgAAAAAAABIKxSpBgAAAAAAsHGPdZv7yMhIbdiwQX///bdu3bql999/X5IUERGh0NBQ5cqVS3Z25KAAAAAAAADSsxRnb1atWiUfHx81a9ZMb7/9tkaNGmXuO3TokLy9vfXNN988iRgBAAAAAACQilKUINq+fbtatWolR0dHTZ8+Xe3bt7fYX7FiRRUpUkTLly9/IkECAAAAAICnwzAMq9zwcCm6xOyDDz5Q9uzZtW/fPuXKlUvXrl1L0Kd8+fLatWvXYwcIAAAAAACA1JWiFUS7du1SixYtlCtXrgf2yZ8/v4KCglIcGAAAAAAAAJ6OFCWI7ty5I1dX14f2CQ4OpkA1AAAAAABABpCiS8wKFSqkPXv2PLTPzp079cwzz6QoKAAAAAAAkDaMfzdrYm3zSQ0pWuLTsmVLbd++XQsXLkx0/5QpU/THH3/olVdeeazgAAAAAAAAkPpStIJo8ODBWr58ubp3766vvvpKd+7ckSQNGTJEO3fu1I4dO1SmTBm98cYbTzRYAAAAAAAAPHkpShBlzZpVv/76q9544w0tWbJE0dHRkuJWDhmGoTZt2mj27NlydHR8osECAAAAAADgyUtRgkiScuTIocWLF+uTTz7Rnj17dP36dbm6uqpChQry8vJ6kjECAAAAAICnxM4wZGdYV9Uea5tPakhxgihezpw51ahRoycRCwAAAAAAANLAYyeIDhw4oAMHDigkJERubm4qW7asypYt+yRiAwAAAAAAwFOQ4gTRnj171KNHDx0+fFiSFBsbK+PfJVulS5fWZ599pvLlyz+ZKAEAAAAAAJBqUpQg2r9/v+rUqaPw8HDVq1dPNWrUkJeXly5duqStW7dq06ZNqlOnjn799VeVKVPmCYcMAAAAAABSi2HEbdbE2uaTGlKUIBo2bJiioqK0bt06NWjQIMH+devWqVmzZnrnnXe0du3axw4SAAAAAAAAqccuJQft2LFDrVu3TjQ5JEn+/v5q1aqVduzY8VjBAQAAAAAAIPWlKEGUKVMmFShQ4KF9fH19ZW9vn6KgAAAAAAAA8PSkKEFUtWpV7dq166F9fvvtN1WvXj1FQQEAAAAAAODpSVGCaPLkydq3b5/ee+89hYeHW+wLDw/X8OHDdeDAAU2aNOmJBAkAAAAAAJ4OwzCscsPDJatI9auvvpqgzc/PT+PHj9ecOXNUtmxZ8y5mBw4cUHBwsGrUqKEpU6ZowYIFTzxoAAAAAAAAPDlGbGxsbFKd7OxStNBIhmEoOjo6RccC6UVoaKjc3NwUdDVYrq6uaR0OUoBvCwAAAJDaQkND5ZXTTSEhIRn294b43326BPwmhyxZ0zqcJyoyPExfdK2coc9PakvWCqJTp06ldhwAAAAAAABII8lKECV1xzIAAAAAAGAdDCNusybWNp/UkLJrxwAAAAAAAGA1krWC6EEiIiK0Z88eXbx4UXfu3Em0T+fOnR/nKQAAAAAAAJDKUpwgmjVrlt5//32FhIQkuj82NlaGYZAgAgAAAAAASOdSdInZd999p379+il//vyaMmWKYmNj1aJFC40bN06NGjVSbGysWrZsqc8///xJxwsAAAAAAFKRnWFY5YaHS1GCaNq0afL09NTOnTs1cOBASVKZMmU0dOhQrVmzRosWLdKKFSsobg0AAAAAAJABpChBdOjQITVv3lxZsmQx26Kjo80/t2/fXnXr1tWYMWMeP0IAAAAAAACkqhQliKKiouTh4WE+dnZ2VnBwsEWf5557Tvv373+s4AAAAAAAAJD6UpQgypMnjwIDA83HBQoU0IEDByz6nDlzRpkyPdZN0gAAAAAAAPAUpChBVKFCBYvVQY0aNdL27ds1fvx4HTlyRPPmzdN3332nChUqPLFAAQAAAABA6jMM69zwcClKELVu3Vp37tzR6dOnJUnDhg1Tvnz59N5778nPz0+9e/dW1qxZNWnSpCcZKwAAAAAAAFJBihJEL730kv766y8VLFhQkuTh4aGDBw9qwoQJev311zVu3Dj98ccfKl269JOMNcM6d+6cnJyctH379rQOJU0EBATIMAwzoZgWrl27JhcXF/34449pFgMAAAAAAOlVihJEicmRI4cGDx6sOXPm6J133lHevHmf1NCJik867N2797HHCg8P16hRo7Rly5bHDywRY8aMUaVKlVStWjWz7ejRoxo4cKCqVq0qJyenJBMoN2/e1JAhQ+Tr6ytHR0flzZtXrVq1Unh4eJLP//3338vf31958uSRo6Oj8uXLp1atWumPP/5ItP+qVav0/PPPy8nJST4+Pho5cqTu3r37yPNOT3LmzKnu3bvr/fffT+tQ0szNmzc1+K0BKl6koNxds6hOzWrau3ePuT82NlZjRo2Qr08eubtmUZNGDfTP8eNpGDGSMnf2LBUvUlDZszqpRtVK2rN7d1qHhGQYO2aUnDMbFttzpZ5J67DwiHj/ZVycO+swedIEOWc29PagAWkdCh4B7z8gfXtiCaKMLDw8XKNHj06VBNGVK1f0xRdfqFevXhbtO3fu1CeffKKbN2+qRIkSDx0jJCRENWrU0Oeff6527dppzpw5evPNNxUREaE7d+4kGcPhw4eVI0cO9e/fX7Nnz1bv3r114MABVaxYUb///rtF359++kkvvviismfPrhkzZujFF1/U2LFj1a9fv0effDrTq1cv7d+/X5s2bUrrUNJEn549tGnDBi1Y+D/t2X9I9eo3UNNGDXThwgVJ0kdTJmnOrBn6ZOYc/bLtN2XJ4qLmTRspIiIijSNHYpYu+VZDBw/Su++N1M7d++Xn95yaN/HX5cuX0zo0JEPJZ5/VqXOB5rZxy7a0DgmPgPdfxsW5sw579+zRgk/nqXRpv7QOBY+A91/GYhiGVW54uGTdZux///tfip+gc+fOKT7WGixatEiZMmVSs2bNLNqbN2+u4OBgZcuWTVOmTNHBgwcfOMawYcN05swZ7d+/X76+vmb70KFDkxXDiBEjErR1795d+fLl05w5czR37lyz/e2335afn59+/vln8y50rq6uGjdunPr3769nnsm433KXKFFCpUqVUkBAgOrWrZvW4TxVt2/f1orvl2vJ8hWqXqOmJOm9EaP045rV+nTeHI0c/YFmzpiuocPeVbPmLSRJny38QgXz5dYPK1eo9Stt0zJ8JOKTaR+p22s91LlrN0nSjNlz9dNPa/RFwOcaPOSdNI4OSclkn0m5c+dO6zCQQrz/Mi7OXcYXFhambl06aPbcTzVh3Ni0DgePgPcfkP4lawVR165d1a1bt0fa4o9JS5GRkRoxYoTKlSsnNzc3ubi4qEaNGtq8ebPZ5/Tp0/Lw8JAkjR492swsjho1yuzz999/q1WrVnJ3d5eTk5PKly+vVatWJSuGFStWqFKlSsqaNatFu7u7u7Jly5bk8cHBwVq4cKFef/11+fr6KjIyMlmrhpLi6empLFmyKDg42Gz7888/9eeff+r11183k0OS1KdPH8XGxmrZsmVJjnvkyBHVrVtXzs7Oypcvn8aOHauYmJgE/VauXKkmTZqYl70VLlxYH3zwgaKjo80+I0eOVObMmXXlypUEx7/++uvKnj27ubpl79698vf3V65cueTs7CxfX1+9+uqrCY5r0KCBfvjhB8XGxiY5F2ty9+5dRUdHy8nJyaLd2dlZO3ds1+lTp3QpKEh16tY397m5ualCxUratWvn0w4XSYiMjNSB/ftUt96982VnZ6e6detr92+cr4zgn3+Oy9cnj0oUK6SunTro7NmzaR0Skon3X8bFubMOA/r1VaPGTSzOI9I/3n9AxpCsFUQLFy5M7ThSRWhoqD777DO1a9dOPXr00M2bN7VgwQL5+/tr9+7dKlOmjDw8PDRnzhz17t1bL730kl5++WVJkp9f3JLVI0eOqFq1asqbN6/eeecdubi4aMmSJXrxxRe1fPlyvfTSSw98/qioKO3Zs0e9e/dO8Ry2bdumiIgIFSlSRK1atdKKFSsUExOjKlWqaNasWSpTpkyyxwoODlZUVJSCgoI0bdo0hYaGql69eub+AwcOSJLKly9vcVyePHmUL18+c/+DBAUFqU6dOrp79675Ws2fP1/Ozs4J+gYEBChr1qwaNGiQsmbNqk2bNmnEiBEKDQ3V5MmTJUmdOnXSmDFj9O233+qNN94wj42MjNSyZcvUsmVLOTk56fLly2rYsKE8PDz0zjvvKHv27Dp9+rS+++67BM9brlw5ffzxxzpy5IhKlSqV6Dzu3LljkYQLDQ196LwzgmzZsqlS5SqaMG6sij9TQl5eXlryzdfa9dtOFS5cRJcuBUmSPL28LI7z9PTSpaBLaREyHuLq1auKjo6Wp+d958vLS0eP/p1GUSG5KlSspPkLAlSsWHEFBQXqww9Gq36dGtp38I9kfXGAtMX7L+Pi3GV8S779RgcP7Ne23/Yk3RnpCu8/IGNIVoKoS5cuqR1HqsiRI4dOnz4tBwcHs61Hjx565plnNGPGDC1YsEAuLi5q1aqVevfuLT8/P3Xs2NFijP79+8vHx0d79uyRo6OjpLgVNdWrV9fQoUMfmiA6e/asbt++bXFZ2KM6/m+R4GHDhqlw4cL63//+p5CQEI0ePVp169bVkSNH5O3tnayxKleurKNHj0qSsmbNqvfee0+vvfaauT8wMFCSEh3P29tbFy9efOj4EydO1JUrV7Rr1y5VrFhRUtzfnaJFiybo+9VXX1kkjnr16qVevXpp9uzZGjt2rBwdHVWkSBFVqVJFixYtskgQrVmzRjdu3FCnTp0kSTt27NCNGzf0888/WyS3xo5NuOy4UKFCkuJWSz0oQTR+/HiNHj36oXPNiBYs/J96vf6aihTMJ3t7e5Up+7zavNJOB/bvS+vQAJvi36ix+efSfn6qULGSihcuoOVLl6jrq6895EgAsF3nzp3T4EH9tfqn9QlWRAN48uxkfQWLrW0+qcGqXyN7e3szORQTE6Pr16/r7t27Kl++vPbv35/k8devX9emTZvUpk0b3bx5U1evXtXVq1d17do1+fv76/jx42aB38Rcu3ZNUlyiKqXCwsIkxRUJ27hxo9q3b6/evXtrxYoVunHjhmbNmpXssRYuXKi1a9dq9uzZKlGihG7fvm1xSdft27clyUyE/ZeTk5O5/0F+/PFHVa5c2UwOSZKHh4c6dOiQoO9/k0Pxr22NGjUUHh6uv/++9y1C586dtWvXLp04ccJsW7x4sfLnz69atWpJkrJnzy5JWr16taKioh4aY/y5uHr16gP7DBs2TCEhIeZ27ty5h46ZURQqXFg/b9yiKzdu6tjJs/p1xy5FRUWpYKFC8vKKq4Vy+ZLlaqHLly/JK7dXYsMhDeXKlUv29va6fPm+83XpEnVtMqDs2bOrSNFiOnHin7QOBcnA+y/j4txlbAf279Ply5dVpeLzyuqUSVmdMunXrb9o9sxPlNUpk8W/aZH+8P4DMgarThBJ0hdffCE/Pz85OTkpZ86c8vDw0Jo1axQSEpLksf/8849iY2P1/vvvy8PDw2IbOXKkJCWr6v7j1LuJT6Q0a9bMoo5R5cqV5evrqx07dkiKS+4EBQVZbPerUqWK/P391bt3b61bt06LFi3SsGHDEjxXYjWOIiIiEr1U7L/OnDmT6Gqh4sWLJ2g7cuSIXnrpJbm5ucnV1VUeHh7m6q3/nptXXnlFjo6OWrx4sblv9erV6tChg1mFvlatWmrZsqVGjx6tXLlyqUWLFlq4cGGi84g/Fw+rYO/o6ChXV1eLzZq4uLjI29tbN27c0Ib169S0WXMV9PWVV+7c2rJ5o9kvNDRUe3bvUqVKVdIwWiTGwcFBZZ8vp82b7p2vmJgYbd68URUrc74ymrCwMJ06eUK5cydvNSjSFu+/jItzl7HVqVtPew8c1q69B83t+XLl1bZdB+3ae1D29vZpHSIegvcfkDEk6xKzjGrRokXq2rWrXnzxRQ0ePFienp6yt7fX+PHjLVakPEh8ceW3335b/v7+ifYpUqTIA4/PmTOnJOnGjRspiD5Onjx5JEleXglXcXh6eppjf/vttwmKgj8sMZUjRw7VrVtXixcv1pQpUyTdu7QsMDBQ+fPnt+gfGBhosTLocQQHB6tWrVpydXXVmDFjVLhwYTk5OWn//v0aOnTo/9m77/ia7j+O4++bIQkZiBipTY2araAUtfeqUXtVKVFKraqarVWjdo0qNUqN2mqP2qNGtbRq1hZKlgiS8/tDc+pKEH7iJve+nnmch+ac7z33872n5+aez/1+P8eqqHWqVKlUs2ZNzZs3T/3799fixYsVGRlpNRXQYrFo8eLF2rNnj1auXKl169bpvffe0+jRo7Vnzx6rxFrM65UmTZoX0pekZMP6dTIMQ7ly5dapUyf16Se9lCt3HrVs1UYWi0Ufdv5II4YNUY6crypr1mwaPLC/Mvj7q1adurYOHXHo0vVjtXuvlYoUCVBA0WKaOH6sboeHq2Ur294cAE/3Sa8eqlGzljJnzqJLly7pi8ED5OzsrHcbN7F1aIgnzr+ki2OXdHl5eSnfI+UBUqRIodS+vrHWI3Hi/AMSP7tOEC1evFjZs2fXjz/+aDViJGb0T4zHjSaJqVfj6uqqihWf/U4JmTNnloeHh86cOfPMj41RpEgRSYpzKtulS5fM285XqVJFGzZseKZ9R0REWI3WiSl4feDAAatk0KVLl3ThwgW1b9/+ifvLkiWLWTPpYTF1j2Js3bpVN27c0I8//qgyZcqY6x/3OrVs2VJ16tTR/v37NW/ePL3++uvKly9frHZvvvmm3nzzTQ0ZMkTff/+9mjVrpgULFuj999+P9Rx58+Z9Yl/sUUhwsPr3+1QXL1xQqtSpVfedeho4eIhcXV0lSR/36KXw8HB9GPiBgm/dUsm3Smn5yp+Y559INXy3ka4HBWnwoP66euWKChYqrOWr1saZTEbicvHiBbVs3kT/3LihNH5+KvlWKW3bsce8oyYSP86/pItjB9gO5x+Q+Nl1gihmqKlhGGYSaO/evdq9e7cyZ85stkuePLkkWd3yXXowQqds2bKaOnWqOnfuHKt4c1BQ0BM/0Lu6uiogIEAHDhx47j7kzp1bhQoV0vLly3X9+nVz5Mv69et1/vx5de7cWdKD0T+PK1Z97do1pU2b1mrd2bNntWnTJquizvny5VOePHk0bdo0ffDBB+br9/XXX8tisahBgwZPjLV69eoaO3as9u3bZyaYgoKCzOlhMR4+LjHu3r2ryZMnx7nfatWqKU2aNBoxYoS2bdtm3uUsxs2bN5UyZUqrRF9MsuvRaWa//PKLfHx84kww2bv6Dd9V/YbvPna7xWJR/4GD1X/g4JcYFf4fHTt9qI6dPnx6QyQqc+YtsHUIeAE4/5Iujp39WL9pq61DwDPi/Es6LBbLE8tyJEX21p+EkOQTRN9++63Wrl0ba/1HH32kmjVr6scff9Q777yjGjVq6MyZM5oyZYpee+01s/iz9KD2zmuvvaYffvhBuXLlUurUqZU/f37lz59fkyZNUqlSpVSgQAG1a9dO2bNn19WrV7V7925duHBBR44ceWJ8derUUd++fRUSEmJVyyY4OFgTJkyQJO3cuVOSNHHiRKVMmVIpU6a0umvXV199pUqVKqlUqVL64IMPFBwcrDFjxihXrlzq2LHjU1+jAgUKqEKFCipcuLBSpUqlv/76SzNmzNC9e/c0fPhwq7YjR45U7dq1VblyZTVu3Fi//fabJk6cqPfff/+po2569eqlOXPmqGrVqvroo4/M29xnyZJFv/76q9muZMmSSpUqlVq1aqUuXbrIYrFozpw5j50S5+rqqsaNG2vixIlydnZWkybW0zC+++47TZ48We+8845y5Mih0NBQTZ8+Xd7e3qpevbpV2w0bNqhWrVq8OQAAAAAA8BCL8f9UULahWbNmxaq587Dz58/rlVde0fDhwzV16lRduXJFr732mj7//HMtWrRIW7du1dmzZ832u3fvVufOnXX06FHdvXtXAwYM0MCBAyVJp0+f1qBBg7R+/XrduHFDadOm1euvv67WrVurfv36T4zz2rVreuWVVzRz5kyrujlnz55VtmzZ4nxMlixZrGKTpI0bN6pfv346fPiwkidPrho1aujLL7+MV9X/gQMHavXq1Tp16pRCQ0OVNm1alSlTRp9++qkKFCgQq/2yZcs0aNAgHT9+XH5+fmrdurX69+9vTkV6kqNHj6pz587au3evfH191aFDB/n7+6tt27Y6c+aMsmbNKunBrem7d++uI0eOKFWqVGrevLkqVKigKlWqaMuWLSpbtqzVfvfv369ixYqpcuXKWrdundW2Q4cOaeTIkdq5c6euXr0qHx8fFStWTAMHDjSn6EnSH3/8obx582rjxo2qUKHCU/sSIyQkRD4+Prpy/ZbdFax2FCQEAQAAkNBCQkKUztdHwcHBSfa6Ieba54N5+5UsuefTH5CE3L0dpqnNiibp45PQ/q8E0d27d7Vx40b98ccfCg8PV79+/SQ9uONVSEiI0qRJIycnu79R2lO1bdtWJ06c0Pbt220dSpJ15MgRFS5cWLNnz1aLFi2eax9du3bVzz//rF9++eWZEgYkiJI+EkQAAABIaCSIEjcSRE/33FPMVqxYofbt2ysoKMis8ROTIPr1119VokQJzZkzR02bNn1hwSZVAwYMUK5cubRz50699dZbtg4nSZo+fbo8PT1Vr16953r8jRs39M0332jhwoUkCwAAAADgCSwWycnOLpu4DHy65xres3PnTjVo0EBubm4aN25crCRQsWLFlDNnTi1ZsuSFBJnUZc6cWXfu3CE59BxWrlypESNGaNq0aWrXrp1SpEjxXPvx9fVVWFhYrJpEAAAAAADEJSwsTAMGDFDVqlWVOnVqWSwWzZo1K862x48fV9WqVeXp6anUqVOrRYsWCgoKitUuOjpaX375pbJlyyZ3d3cVLFhQ8+fPf2n7fJLnGkH0+eefK2XKlPrll1+UJk0a3bhxI1abgIAA7d2793l2D5g6d+6sq1evqnr16ho0aJCtwwEAAAAAOIjr169r8ODBypw5swoVKqStW7fG2e7ChQsqU6aMfHx8NHToUIWFhWnUqFE6evSo9u3bp2TJkplt+/btq+HDh6tdu3YqWrSoli9frqZNm8pisahx48YJus+nea4E0d69e9WgQQPzlutxyZQpk5YvX/48uwdMjxbrBgAAAADgZciQIYMuX76s9OnT68CBAypatGic7YYOHarw8HD98ssvypw5s6QHM6sqVaqkWbNmqX379pKkixcvavTo0erUqZMmTpwoSXr//ff19ttvq2fPnmrYsKGcnZ0TbJ9P81xTzCIjI59a1OnWrVsUqAYAAAAAIIlxstjn8qzc3NzidefwJUuWqGbNmmYiR5IqVqyoXLlyaeHChea65cuX6969ewoMDDTXWSwWdezYURcuXNDu3bsTdJ9P81wZnOzZs2v//v1PbLN7927lyZPneXYPAAAAAACQ6F28eFHXrl1TQEBArG3FihXToUOHzN8PHTqkFClSKG/evLHaxWxPqH3Gx3MliOrXr6+dO3dq5syZcW4fNWqUfvvtNzVq1Oh5dg8AAAAAAPDChYSEWC2RkZH/1/4uX74s6cF0tEdlyJBB//zzj/kcly9fVrp06WLdWTvmsZcuXUqwfcbHcyWIevbsqbx58+r9999XpUqVtGnTJklSr169VLp0afXu3VuFCxfWhx9++Dy7BwAAAAAAeOEyZcokHx8fcxk2bNj/tb+IiAhJD6ajPcrd3d2qTURERLzbveh9xsdzFan29PTU9u3b9eGHH2rhwoWKioqS9GDkkMVi0bvvvqvJkyfHGSQAAAAAAEi8LBZLrBEpSV1Mf86fP29VU/n/zVt4eHhIUpwjke7cuWPVxsPDI97tXvQ+4+O5EkSSlCpVKs2bN0/jx4/X/v379c8//8jb21tFixZVunTpnne3AAAAAAAACcLb2/upN916FjFTuWKmhT3s8uXLSp06tZmEypAhg7Zs2SLDMKwScDGP9ff3T7B9xsf/fZsxX19fVa1aVU2bNlXNmjVJDgEAAAAAAIfwyiuvyM/PTwcOHIi1bd++fSpcuLD5e+HChXX79m0dP37cqt3evXvN7Qm1z/jgPvQAAAAAAADPqX79+lq1apXOnz9vrtu0aZNOnDihhg0bmuvq1KkjV1dXTZ482VxnGIamTJmiV155RSVLlkzQfT7Nc00xK1++fLzaWSwWs4A1AAAAAABAUjJx4kTdunXLvBvYypUrdeHCBUlS586d5ePjo08//VSLFi1SuXLl9NFHHyksLEwjR45UgQIF1KZNG3NfGTNmVNeuXTVy5Ejdu3dPRYsW1bJly7R9+3bNmzdPzs7OZtuE2OfTWAzDMJ71BXJyevLAI4vFYs5/iylgDSRVISEh8vHx0ZXrt17oXFW8PPZWYA8AAACJT0hIiNL5+ig4ODjJXjfEXPt0/uGA3JJ72jqcFyrydpgmNAp45uOTNWtWnTt3Ls5tZ86cUdasWSVJv//+uz7++GPt2LFDyZIlU40aNTR69OhYZXiio6M1YsQITZ06VZcvX9arr76qPn36qFmzZrH2nxD7fJLnShA9TkhIiA4ePKhPP/1UGTNm1Pz5858pWwUkRiSIkj4SRAAAAEhoJIgSt+dNEDmSF1qDyNvbW2XLltW6deu0b98+DRky5EXuHgAAAAAAAAkgQYpUe3l5qVq1apo5c2ZC7B4AAAAAAAAv0HMVqY4PJycnXb58OaF2DwAAAAAAEoDF8mCxJ/bWn4SQICOITp8+rUWLFpnFmgAAAAAAAJB4PdcIovfeey/O9ffv39fFixe1Y8cO3bt3T4MHD/6/ggMAAAAAAEDCe64E0axZs564PXfu3Orevbvef//959k9AAAAAAAAXqLnShCdOXMmzvVOTk5KmTKlvLy8/q+gAAAAAACAbThZLHKys6I99tafhPBcCSKLxaJkyZIpffr0LzoeAAAAAAAAvGTPVaQ6W7Zs+vTTT190LAAAAAAAALCB50oQpUqVSr6+vi86FgAAAAAAANjAcyWISpcurb17977oWAAAAAAAAGADz5UgGjZsmH799VcNHjxY9+/ff9ExAQAAAAAAG3Gy0wVP9lxFqr/88ksVKFBAgwYN0tSpU1WoUCGlS5dOlkeqglssFs2YMeOFBAoAAAAAAICEEe8EkbOzswYOHKh+/fpp1qxZ5vrLly/r8uXLcT6GBBEAAAAAAEDiF+8EkWEYMgxDknTmzJkECwgAAAAAAAAv13NNMcuSJcuLjgMAAAAAACQCFsuDxZ7YW38SAnWaAAAAAAAAHNwzJYgeLUINAAAAAACApO+ZEkQDBw6Us7NzvBcXl+eawQYAAAAAAICX6JkyON7e3kqZMmUChQIAAAAAAGzNSRY52dkMIifZV38SwjMliLp166b+/fsnVCwAAAAAAACwAYpUAwAAAAAAODgSRAAAAAAAAA6OBBEAAAAAAICD4zZjAAAAAADAZLE8WOyJvfUnIcQ7QRQdHZ2QcQAAAAAAAMBGmGIGAAAAAADg4EgQAQAAAAAAODhqEAEAAAAAAJOT5cFiT+ytPwmBEUQAAAAAAAAOjgQRAAAAAACAg2OKGRBPFotFFu6NCABwEKeuhtk6BPwfcqTztHUIAIAkhgQRAAAAAAAwWSySk519OW5n3UkQTDEDAAAAAABwcCSIAAAAAAAAHBwJIgAAAAAAAAdHDSIAAAAAAGCyWOyvZo+99SchMIIIAAAAAADAwZEgAgAAAAAAcHAkiAAAAAAAABwcCSIAAAAAAAAHR5FqAAAAAABgcrI8WOyJvfUnITCCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHDWIAAAAAACAyfLvjz2xt/4kBEYQAQAAAAAAODgSRAAAAAAAAA6OBBEAAAAAAICDowYRAAAAAAAwOVkeLPbE3vqTEBhBBAAAAAAA4OBIEAEAAAAAADg4EkQAAAAAAAAOjgQRAAAAAACAg6NINQAAAAAAMFGk2jExgggAAAAAAMDBkSACAAAAAABwcCSIAAAAAAAAHBw1iAAAAAAAgMlischisa+iPfbWn4TACCIAAAAAAAAHR4IIAAAAAADAwZEgAgAAAAAAcHDUIAIAAAAAACYny4PFnthbfxICI4gAAAAAAAAcHAkiAAAAAAAAB0eCCAAAAAAAwMGRIAIAAAAAAHBwFKkGAAAAAAAmi+XBYk/srT8JgRFEAAAAAAAADo4EEQAAAAAAgIMjQQQAAAAAAODgqEEEAAAAAABMThaLnOysaI+99SchMIIIAAAAAADAwZEgAgAAAAAAcHAkiAAAAAAAABwcNYgAAAAAAIDJyfJgsSf21p+EwAgiAAAAAAAAB0eCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHEWqAQAAAADAfyySxd6KOttbfxIAI4gAAAAAAAAcHAmiJMBisejDDz+0dRgvnMVi0cCBAxNk31evXlWDBg3k6+sri8WisWPHSpL++usvVa5cWT4+PrJYLFq2bFmCPD8AAAAAAEkJCSIbOnXqlD744ANlz55d7u7u8vb21ltvvaVx48YpIiLC1uElKjdu3FDPnj2VO3duubu7K3Xq1KpSpYpWrVoVZ/tu3bpp3bp16tOnj+bMmaOqVatKklq1aqWjR49qyJAhmjNnjgICAl5mNxKNkV8Ol4erRT0+7mqumzF9mipXKKu0qb3l4WrRrVu3bBYfrO3Y/rPq162lbJn95eFq0Yrly2K1+eP4cTV4p7bS+frI1yeF3nqzqP7++++XHyzibcrkScqdM6tSerqrdMni2r9vn61DQjzE53xEwlsw+xu9U/FNFc/jr+J5/NWsdnlt37ze3N66QTXlz+hltQz65COrfRw9/IvaNqqpEq9lVMl8mdS+WV39ceyouf3i+XOx9pE/o5eO/MK5aku8dyZtHD8gcaMGkY2sXr1aDRs2lJubm1q2bKn8+fPr7t272rFjh3r27Knff/9d06ZNs3WYicKff/6pChUqKCgoSG3atFFAQIBu3bqlefPmqVatWurRo4dGjhxp9ZjNmzerTp066tGjh7kuIiJCu3fvVt++fe1yRFZ8Hdi/XzOmT1WBAgWt1t++fVuVqlRVpSpV1b9vHxtFh7iEh4erQMFCatn6PTVuWC/W9tOnTqlC2VJq1aatPus/SN7e3jp27He5u7vbIFrEx6KFP6h3z481YdIUFS1WXBPHj1XtGlV05Pc/lTZtWluHhyd42vmIlyN9Bn916zNIWbLlkCFDyxd9r85tG2vx2p3KmTuvJKlB09b6sMdn5mPcPTzM/74dHqYOzd9RucrV9dnQMYq6H6VJo4fog2Z1tXHfH3J1dTXbfrNgpXLmymv+7pMq9UvoIeLCe2fSxvFLWpxkkZOdFe2xt/4kBBJENnDmzBk1btxYWbJk0ebNm5UhQwZzW6dOnXTy5EmtXr36pcYUHh6uFClSvNTnjI979+6pQYMGunnzpn7++WcVL17c3NatWzc1a9ZMo0aNUkBAgBo1amRuu3btmlKmTGm1r6CgIEmKtd6RhIWFqU2rZpo8ZbqGD/3Calvnj7pKkn7etvXlB4YnqlK1mqpUrfbY7QP691WVqtU1dPiX5rrsOXK8jNDwnMaPHaM2bdupZes2kqQJk6fop59W67tZ36pnr09sHB2e5GnnI16OspWqW/3+Ue8B+mH2DB05uM9MELl7JFeatOnifPzpkycUfOumOvX4TBn8M0qSOnbro3qV3tTlC38rc7b/3kNTpkr92P3g5eK9M2nj+AGJH1PMbODLL79UWFiYZsyYYZUcipEzZ0599NFHsdYvW7ZM+fPnl5ubm/Lly6e1a9dabT937pwCAwOVO3dueXh4yNfXVw0bNtTZs2et2s2aNUsWi0Xbtm1TYGCg0qZNq4wZM5rbJ02apOzZs8vDw0PFihXT9u3bVbZsWZUtW9ZqP5GRkRowYIBy5swpNzc3ZcqUSb169VJkZGSsdt26dZOfn5+8vLxUu3ZtXbhwIV6v1ZIlS/Tbb7/pk08+sUoOSZKzs7OmTp2qlClTmrWMYvpmGIYmTZoki8Vi1jrKkiWLJKlnz56yWCzKmjVrvGKwJ107d1LVajVUvkJFW4eCFyQ6Olpr16zWq7lyqVb1Ksrsn1alSxZn2ksidvfuXR06+IvVeejk5KTy5Stq357dNowMSJqioqK0ZvliRUSEq3CR/z4rrF76g0oVyKK6FYrpq2EDFBFx29yWLcerSpkqtX6cP1v37t7VnYgI/bhgtrK/mlv+mbJY7f/DNo1UplA2tXinkrasf7lf4OE/vHcmbRw/IGlgBJENrFy5UtmzZ1fJkiXj/ZgdO3boxx9/VGBgoLy8vDR+/HjVr19ff//9t3x9fSVJ+/fv165du9S4cWNlzJhRZ8+e1ddff62yZcvq2LFjSp48udU+AwMD5efnp/79+ys8PFyS9PXXX+vDDz9U6dKl1a1bN509e1Z169ZVqlSprJJI0dHRql27tnbs2KH27dsrb968Onr0qL766iudOHHCqvjz+++/r7lz56pp06YqWbKkNm/erBo1asT7tZKkli1bxrndx8dHderU0XfffaeTJ0+qTJkymjNnjlq0aKFKlSqZjytYsKBSpkypbt26qUmTJqpevbo8PT3j3GdkZKRVkiskJCResSZ2C39YoMOHDmrHnv22DgUv0LVr1xQWFqZRXw7XgEFf6IuhI7R+/Vo1blhP6zZuUekyb9s6RDzi+vXrioqKUtpHRiSkTZdOf/75h42iApKeE8d/V7M6FXQ38o6Sp/DUuOnfK0euPJKkGnUbyj9jZvmly6ATx3/TV0P76+ypvzTum+8lSSk8vTRz0U/q0raJpo4bIUnKki2Hps5bJheXBx+Pk6dIoZ79h+r1gDdlcXLSxjXL1aVtE42fMV/lKsfvcwxeHN47kzaOH5A0kCB6yUJCQnTx4kXVqVPnmR53/PhxHTt2TDn+nTZSrlw5FSpUSPPnzzfr6dSoUUMNGjSwelytWrVUokQJLVmyRC1atLDaljp1am3atEnOzs6SHmT2+/Xrp6JFi2rz5s3mB6SCBQuqdevWVgmi77//Xhs3btS2bdtUqlQpc33+/PnVoUMH7dq1SyVLltSRI0c0d+5cBQYGatKkSZIeTKNr1qyZfv3116f2+9ixY/Lx8TFH/8SlUKFC5mtUq1YtZc+eXS1atFCuXLnUvHlzs523t7e6deumN954w2r9o4YNG6ZBgwY9Nbak5Pz58+r58Uda9dMG6tLYmejoaElSzdp11KVrN0lSocKFtXf3Lk2fNoUEEQC7lS3Hq1qybqdCQ0O0fvUy9e32gWYtXqscufKoYfP3zHa58uaTX7r0atuopv4+e1qZs2bXnYgI9e/RSa8XLa4vJ32r6KgozZo6XoGtGmjBqm1y9/BQqtRp1Kp9Z3M/BQoX0bWrVzRzyjgSRADsnsXyYLEn9tafhMAUs5csZjSKl5fXMz2uYsWKZnJIepC08fb21unTp811Hg8VX7x3755u3LihnDlzKmXKlDp48GCsfbZr185MDknSgQMHdOPGDbVr185MDklSs2bNlCpVKqvHLlq0SHnz5lWePHl0/fp1cylfvrwkacuWLZKkNWvWSJK6dOli9fiuXbvGq9+hoaFPfa1itr+okT59+vRRcHCwuZw/f/6F7NeWDh38RdeuXVOJYm/I091Fnu4u2v7zNk2eOF6e7i6KioqydYh4TmnSpJGLi4vy5n3Nan3uPHl1nruYJUpp0qSRs7Ozrl27arX+2tWrSp8+vY2iApIe12TJlDlbDuUr+Lq69Rmk3K8V0NwZk+NsW+D1B3ctPX/2weem1csW6uKFc/pizBQVKFxEhYoU05cTv9XFv89p8xOmkRV8PUB/nz392O1IOLx3Jm0cPyBpIEH0knl7e0t6kPh4FpkzZ461LlWqVLp586b5e0REhPr3769MmTLJzc1NadKkkZ+fn27duqXg4OBYj8+WLZvV7+fOnZP0oAbSw1xcXGLV6/nrr7/0+++/y8/Pz2rJlSuXpAfTXmL26eTkZJXckqTcuXPHq99eXl5Pfa1itj9r0u1x3Nzc5O3tbbUkdeXKV9CBQ0e198Bhc3mjSIAaN2mmvQcOWyUKkbQkS5ZMRQKK6sSff1qt/+uvE8r8hJF3sJ1kyZLp9TeKaMvmTea66OhobdmyScXeLGHDyICkLTo6WnfvRsa57Y/fH4xaTpP2wYXonYgIOTk5yfLQ18kWJyfJYpHx78jMx+3HLy0Xs7bAe2fSxvEDkgammL1k3t7e8vf312+//fZMj3vcBbxhGOZ/d+7cWTNnzlTXrl1VokQJ+fj4yGKxqHHjxuY0lIc9POLoWUVHR6tAgQIaM2ZMnNszZcr03Pt+WN68eXX48GH9/fffcSbJJJlT1V577bU4t+NB8ixf/vxW61KkSKHUvr7m+itXrujqlSs6dfKkJOm3347Ky9NLmTJnVurU3NLXlsLCwszjIklnz5zRkcOHlSp1amXOnFnduvdUi6aNVKp0Gb1dtpzWr1urNatWat3GrbYLGk/UpevHavdeKxUpEqCAosU0cfxY3Q4PV8tWbWwdGp7iaecjXo6vhg1Q6XKVlOGVTAoPC9PqZQu1f/d2TZ23TH+fPa01yxapdPnKSpkqtU4c/00jBvVRQPG3lPu1B3/zSpQpp9FDPtMXfT9W0zYfyIg29M2kMXJxcVGxkmUkScsXzZOrazLlyV9QkrTxpxVa+sMcDRo50Wb9dnS8dyZtHD8g8SNBZAM1a9bUtGnTtHv3bpUo8eIy5osXL1arVq00evRoc92dO3d069ateD0+ps7PyZMnVa5cOXP9/fv3dfbsWRUsWNBclyNHDh05ckQVKlSw+vYtrn1GR0fr1KlTVqOG/nxktMPj1KxZU/Pnz9fs2bP12WefxdoeEhKi5cuXK0+ePLFGPuHZfDNtioZ8/l/tpUrlHnxAnvbNTLVo1dpGUUGSDv5yQFUq/ndO9u75sSSpeYtWmv7tLNWp+44mTJqikV8OU/duXZQrV27NX7hEbz1UHwyJS8N3G+l6UJAGD+qvq1euqGChwlq+aq3SpeNW2ond085HvBz/XA/Sp10/UNC1K/Ly8lauvPk1dd4ylSxTXpcvXdCe7Vs055tJioi4rfQZMqpStdr64KNe5uOz58ytiTMX6uuvhql5nYqyWJyUN39BTZnzo/zS/TdCaMq4Ebp84bycXVyULUcujZr8nSrXrGuDHkPivTOp4/glLU6WB4s9sbf+JAQSRDbQq1cvzZs3T++//742b94c603x1KlTWrVqVZy3un8SZ2dnqxFFkjRhwoR415cJCAiQr6+vpk+frjZt2ph1iObNm2c1lU2S3n33Xa1Zs0bTp09X+/btrbZFREQoOjpaKVKkULVq1fTpp59q/PjxZpFqSRo7dmy8YmrQoIGGDBmi4cOHq2rVqgoICDC3RUdHq2PHjrp586YmT4675gAeb/2mrVa/f9Z/oD7rP9AmseDJyrxdVhH3jCe2adXmPbVq894T2yBx6djpQ3Xs9KGtw8Azis/5iIT3+ejH/93P4J9Rs5asfeo+SpYpr5Jlyj92e52GzVSnYbPnig8Jh/fOpI3jByRuJIhsIEeOHPr+++/VqFEj5c2bVy1btlT+/Pl19+5d7dq1S4sWLVLr1q2feb81a9bUnDlz5OPjo9dee027d+/Wxo0b5evrG6/HJ0uWTAMHDlTnzp1Vvnx5vfvuuzp79qxmzZqlHDlyWI0UatGihRYuXKgOHTpoy5YteuuttxQVFaU//vhDCxcu1Lp16xQQEKDChQurSZMmmjx5soKDg1WyZElt2rRJJx8anv+0mBYvXqwKFSqoVKlSatOmjQICAnTr1i19//33OnjwoLp3767GjRs/8+sFAAAAAAAeIEFkI7Vr19avv/6qkSNHavny5fr666/l5uamggULavTo0WrXrt0z73PcuHFydnbWvHnzdOfOHb311lvauHGjqlSpEu99fPjhhzIMQ6NHj1aPHj1UqFAhrVixQl26dLG6PbqTk5OWLVumr776SrNnz9bSpUuVPHlyZc+eXR999JFZrFqSvv32W/n5+WnevHlatmyZypcvr9WrV8e7TlHevHl15MgRDR8+XCtWrNDMmTPl4eGhgIAArVixQrVq1Yr/iwQAAAAAAGKxGI/OSQIeER0dLT8/P9WrV0/Tp0+3dTgvXUhIiHx8fHT1RrBd3NEMAID4OHU1zNYh4P+QI52nrUMAHE5ISIjS+fooODjpXjfEXPuM2fCrPFK8mLtEJxYR4aH6uFLBJH18EhojiGDlzp07cnNzs5pONnv2bP3zzz8qW7as7QIDAAAAALwUThaLnJ5wM6KkyN76kxBIEMHKnj171K1bNzVs2FC+vr46ePCgZsyYofz586thw4a2Dg8AAAAAACQAEkSwkjVrVmXKlEnjx4/XP//8o9SpU6tly5YaPny4kiVLZuvwAAAAAABAAiBBBCtZs2bVihUrbB0GAAAAAAB4iUgQAQAAAAAAk8XyYLEn9tafhOBk6wAAAAAAAABgWySIAAAAAAAAHBwJIgAAAAAAAAdHDSIAAAAAAGBykkVOdla0x0n21Z+EwAgiAAAAAAAAB0eCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHEWqAQAAAACAyWJ5sNgTe+tPQmAEEQAAAAAAgIMjQQQAAAAAAODgSBABAAAAAAA4OGoQAQAAAAAAk5PsbzSJvfUnIfAaAQAAAAAAODgSRAAAAAAAAA6OBBEAAAAAAICDowYRAAAAAAAwWSwWWSwWW4fxQtlbfxICI4gAAAAAAAAcHAkiAAAAAACAh7Ru3docSRXXcvHiRUlS2bJl49xetWrVWPuMjIxU79695e/vLw8PDxUvXlwbNmyI8/l37dqlUqVKKXny5EqfPr26dOmisLCwBO0zU8wAAAAAAAAe8sEHH6hixYpW6wzDUIcOHZQ1a1a98sor5vqMGTNq2LBhVm39/f1j7bN169ZavHixunbtqldffVWzZs1S9erVtWXLFpUqVcpsd/jwYVWoUEF58+bVmDFjdOHCBY0aNUp//fWXfvrppxfc0/+QIAIAAAAAAHhIiRIlVKJECat1O3bs0O3bt9WsWTOr9T4+PmrevPkT97dv3z4tWLBAI0eOVI8ePSRJLVu2VP78+dWrVy/t2rXLbPvpp58qVapU2rp1q7y9vSVJWbNmVbt27bR+/XpVrlz5RXQxFqaYAQAAAAAAk8VOl//X999/L4vFoqZNm8badv/+/SdOAVu8eLGcnZ3Vvn17c527u7vatm2r3bt36/z585KkkJAQbdiwQc2bNzeTQ9KDZJKnp6cWLlz4AnoSNxJEAAAAAADAIYSEhFgtkZGR8XrcvXv3tHDhQpUsWVJZs2a12nbixAmlSJFCXl5eSp8+vfr166d79+5ZtTl06JBy5cpllfSRpGLFikl6MK1Mko4ePar79+8rICDAql2yZMlUuHBhHTp06Bl6+2xIEAEAAAAAAIeQKVMm+fj4mMujtYMeZ926dbpx40as6WU5cuRQ3759NX/+fM2ePVvFixfXF198EWvK2eXLl5UhQ4ZY+41Zd+nSJbPdw+sfbRvTLiFQgwgAAAAAADiE8+fPW43icXNzi9fjvv/+e7m6uurdd9+1Wj9jxgyr31u0aKH27dtr+vTp6tatm958801JUkRERJzP5e7ubm5/+N/HtY3ZnhAYQQQAAAAAAExOFotdLpLk7e1ttcQnQRQWFqbly5erSpUq8vX1fWr77t27S5I2btxorvPw8IhzOtudO3fM7Q//+7i2MdsTAgkiAAAAAACAx1i2bFmcdy97nEyZMkmS/vnnH3NdhgwZzOljD4tZ5+/vb7Z7eP2jbWPaJQQSRAAAAAAAAI8xb948eXp6qnbt2vFqf/r0aUmSn5+fua5w4cI6ceKEQkJCrNru3bvX3C5J+fPnl4uLiw4cOGDV7u7duzp8+LDZLiGQIAIAAAAAAIhDUFCQNm7cqHfeeUfJkye32hbXXdAMw9AXX3whSapSpYq5vkGDBoqKitK0adPMdZGRkZo5c6aKFy9ujjry8fFRxYoVNXfuXIWGhppt58yZo7CwMDVs2PCF9zEGRaoBAAAAAIAVi60DSCR++OEH3b9/P87pZQcPHlSTJk3UpEkT5cyZUxEREVq6dKl27typ9u3b64033jDbFi9eXA0bNlSfPn107do15cyZU999953Onj0bq9D1kCFDVLJkSb399ttq3769Lly4oNGjR6ty5cqqWrVqgvWVBBEAAAAAAEAc5s2bp7Rp06pixYqxtmXJkkWlS5fW0qVLdeXKFTk5OSlv3ryaMmWK2rdvH6v97Nmz1a9fP82ZM0c3b95UwYIFtWrVKpUpU8aq3RtvvKGNGzeqd+/e6tatm7y8vNS2bVsNGzYswfopSRbDMIwEfQYgiQsJCZGPj4+u3gi2uh0iAAD27NTVMFuHgP9DjnSetg4BcDghISFK5+uj4OCke90Qc+0zbesxJff0snU4L9TtsFC1L/takj4+CY0aRAAAAAAAAA6OKWYAAAAAAMBksTxY7Im99SchMIIIAAAAAADAwZEgAgAAAAAAcHAkiAAAAAAAABwcCSIAAAAAAAAHR5FqAAAAAABgslgssthZVWd7609CYAQRAAAAAACAgyNBBAAAAAAA4OCYYgbEU1S0oahow9Zh4Dk4OzGcFACeVY50nrYOAQAAvEQkiAAAAAAAgMlJ9jfdyN76kxB4jQAAAAAAABwcCSIAAAAAAAAHR4IIAAAAAADAwVGDCAAAAAAAmCwWiywW+7rRi731JyEwgggAAAAAAMDBkSACAAAAAABwcCSIAAAAAAAAHBwJIgAAAAAAAAdHkWoAAAAAAGCy/LvYE3vrT0JgBBEAAAAAAICDI0EEAAAAAADg4EgQAQAAAAAAODhqEAEAAAAAAJPFYpHFYl9Ve+ytPwmBEUQAAAAAAAAOjgQRAAAAAACAgyNBBAAAAAAA4OCoQQQAAAAAAExOsr/RJPbWn4TAawQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4EEQAAAAAAgIOjSDUAAAAAADBZLBZZLBZbh/FC2Vt/EgIjiAAAAAAAABwcCSIAAAAAAAAHR4IIAAAAAADAwVGDCAAAAAAAmCz/LvbE3vqTEBhBBAAAAAAA4OBIEAEAAAAAADg4EkQAAAAAAAAOjhpEAAAAAADAZLE8WOyJvfUnITCCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHDWIAAAAAACAyUkWOcm+ivbYW38SAiOIAAAAAAAAHBwJIgAAAAAAAAdHgggAAAAAAMDBkSACAAAAAABwcBSpBgAAAAAAJovlwWJP7K0/CYERRAAAAAAAAA6OBBEAAAAAAICDI0EEAAAAAADg4KhBBAAAAAAATJZ/f+yJvfUnITCCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHDWIAAAAAACAyWJ5sNgTe+tPQmAEEQAAAAAAgIMjQYRErXXr1sqaNesL3afFYtHAgQNf6D4BAAAAAEjKSBD9a9asWbJYLDpw4MAL3/fKlStVq1YtpUuXTsmSJVPq1KlVpkwZjR49WiEhIS/8+RLa1q1bZbFYzMXV1VXZs2dXy5Ytdfr0aVuHB0mjvhymMiWLKb2vt7JmTKfGDd7RiT//NLefO3tWnm5OcS4/LlkUa383btxQruyZ5OnmpFu3br3EniAuI0cM01tvFpVfKi9l9k+rhvXrWh1fJC47tv+s+nVrKVtmf3m4WrRi+TKr7V8MHqhC+fPI1yeFMvilUvUqFbVv716bxIr4mzJ5knLnzKqUnu4qXbK49u/bZ+uQEE8cu6Rp2pSvVfT1gkqb2ltpU3vr7VIltG7tT7YOC8+I8w9I3EgQJaDo6Gi1adNGtWvX1rlz5xQYGKgpU6ZowIAB8vf312effaZ33nnH1mE+ty5dumjOnDmaNm2aatSooR9++EFFixbVpUuXXthzTJ8+XX9y4fvMdvz8s9p3CNTm7bu1cs163bt3T3VqVlF4eLgkKWOmTDp17pLV0rf/QHl6eqpylWqx9tfpg/eVP3/Bl90NPMb2n7epQ8dO2rZjj1b9tEH3791TzeqVzeOLxCU8PFwFChbS2PGT4tye89Vc+mrcRB04dFSbtu5QlixZVat6ZQUFBb3kSBFfixb+oN49P1bfzwZo976DKliwkGrXqKJr167ZOjQ8Bccu6XolY0Z9PnS4du39RTv3HFDZcuXVsF4dHfv9d1uHhnji/AMSP4thGIatg0gMZs2apTZt2mj//v0KCAh4IfscPny4+vTpo27dumn06NGyPFIV6/Lly5o9e7Z69+792H1ER0fr7t27cnd3fyExvQhbt25VuXLltGjRIjVo0MBcP2HCBHXp0kVDhw5Vnz59/q/nCA8PV4oUKf7fUONksVg0YMCAeE8zCwkJkY+Pjy4F3ZK3t3eCxJTQgoKClC1jOq3duFWlSpeJs03JYm+o8Ouva/LUGVbrp0/9WksWL9Qnn/ZTzaoVdeHqP0qZMuVLiPrFcXay74p0QUFByuyfVhs2b3vs8UXi4OFq0Q+Ll6p2nbqPbRMSEqJ0vj5as26jypWv8PKCQ7yVLllcRQKKauz4iZIe/K3OmS2TOnbqrJ69PrFxdHgSjp198U+bWkOHj1Tr99raOhTEgyOcfzF/w4ODg5PsdUPMtc/iPaeUwtPL1uG8UOFhoWrwZo4kfXwSGiOInqB169by9PTUxYsXVbduXXl6esrPz089evRQVFTUEx97+/ZtjRgxQvny5dPIkSNjJYckKUOGDLGSQxaLRR9++KHmzZunfPnyyc3NTWvXrpUkjRo1SiVLlpSvr688PDxUpEgRLV68OM7nnzt3rooVK6bkyZMrVapUKlOmjNavX2/V5qefflLp0qWVIkUKeXl5qUaNGvr9//gWpnz58pKkM2fOPNNzxLzOp06dUvXq1eXl5aVmzZqZ2x6tQRQeHq7u3bsrU6ZMcnNzU+7cuTVq1Cg9muuMjIxUt27d5OfnJy8vL9WuXVsXLlx47v4lZSHBwZKkVKlTx7n90MFf9OuRw2rZ2voD1vHjxzR86OeaPuM7OTnxdpFYmcc3VdzHF0nH3bt3NeObafLx8VGBgoVsHQ7icPfuXR06+IvKV6hornNyclL58hW1b89uG0aGp+HY2Y+oqCgt/GGBwsPDVfzNErYOB/HA+QckDVzxPUVUVJSqVKkiX19fjRo1Sm+//bZGjx6tadOmPfFxO3bs0K1bt9SkSRM5Ozs/03Nu3rxZ3bp1U6NGjTRu3DgzQTJu3Di9/vrrGjx4sIYOHSoXFxc1bNhQq1evtnr8oEGD1KJFC7m6umrw4MEaNGiQMmXKpM2bN5tt5syZoxo1asjT01MjRoxQv379dOzYMZUqVUpnz559pnhjnDp1SpLk6+v7zM9x//59ValSRWnTptWoUaNUv379OJ/DMAzVrl1bX331lapWraoxY8Yod+7c6tmzpz7++GOrtu+//77Gjh2rypUra/jw4XJ1dVWNGjWeq29JWXR0tHr36KYSJd9Svnz542zz3cwZyp0nr94sUdJcFxkZqTYtmmrIsC+VKXPmlxUunlF0dLR6du/64Pjmj/v4IvFbs3qV0qT0VEpPd00Y95VW/bRBadKksXVYiMP169cVFRWltGnTWa1Pmy6drly5YqOoEB8cu6Tvt6NHlSalp3xSuKlLpw76YfFS5X3tNVuHhXjg/AOSBhdbB5DY3blzR40aNVK/fv0kSR06dNAbb7yhGTNmqGPHjo993B9//CFJyv/IBVtUVJRu3rxptc7X19dqhNGff/6po0eP6rVH/uCdOHFCHh4e5u8ffvih3njjDY0ZM8ZMfJw8eVKDBw/WO++8o8WLF1uN+ogZYRMWFqYuXbro/ffft0p0tWrVSrlz59bQoUOfmgCTpNDQUF2/fl337t3ToUOH9NFHH8lisah+/frP/ByRkZFq2LChhg0b9sTnXLFihTZv3qwvvvhCffv2lSR16tRJDRs21Lhx4/Thhx8qR44cOnLkiObOnavAwEBNmjTJbNesWTP9+uuvT3yOyMhIRUZGmr8nxULiD+vWpZOOHftNGzZvj3N7RESEFv0wX737fGa1fsBnfZQ7T141btr8ZYSJ59S1cyf9/vtv2rR1h61Dwf/h7bLltPfAYV2/fl0zZ0xX86bv6uede5U2bVpbhwYAiUau3Lm198BhBQcHa+mPi9XuvVZav2kbSSIAeEEYQRQPHTp0sPq9dOnST71bV0xSwdPT02r90aNH5efnZ7XcuHHDqs3bb78dKzkkySo5dPPmTQUHB6t06dI6ePCguX7ZsmWKjo5W//79Y00JiklCbdiwwRzddP36dXNxdnZW8eLFtWXLlif2LcZ7770nPz8/+fv7q0aNGgoPD9d3332ngICA53qOJyXcYqxZs0bOzs7q0qWL1fru3bvLMAz99NNPZjtJsdp17dr1qc8xbNgw+fj4mEumTJme+pjE6uOPPtTan1ZrzbrNeiVjxjjbLPtxsW7fvq0mzVtard+2dYuWLlkkn+Su8knuqppVHwwJzuLvpy8GD0jw2PF0Xbt8qDVrVmndhi3K+Jjji6QhRYoUypEzp4q/+aamTJ8hFxcXfTdzxtMfiJcuTZo0cnZ21rVrV63WX7t6VenTp7dRVIgPjl3SlyxZMuXImVNvFCmiz4cMU4GChTRpwjhbh4V44PxLeiwW+1zwZIwgegp3d3f5+flZrUuVKlWsUUCP8vJ6UNArLCzMan3OnDm1YcMGSdLs2bM1Z86cWI/Nli1bnPtctWqVvvjiCx0+fNhqhMvDo49OnTolJyenOBNMMf766y9J/9UMelR8C3b1799fpUuXlrOzs9KkSaO8efPKxcXluZ7DxcUlXhe4586dk7+/v/n6xsibN6+5PeZfJycn5ciRw6pd7ty5n/ocffr0sZquFhISkuSSRIZhqHvXzlq5Ypl+Wr9FWR/z/5QkfTfrW1WvWTvW/+fzFixWxJ0I8/eDB/arY/u2Wr/5Z2XLnuPR3eAlMgxD3T7qrBXLl2r9xq1PPL5ImqKjo63e55F4JEuWTK+/UURbNm8yi41HR0dry5ZN6hD4oW2DwxNx7OwP75VJB+cfkDSQIHqKZ60fFCNPnjySpN9++0116tQx13t6eqpixQcjMXbsiHtKyMMjhWJs375dtWvXVpkyZTR58mRlyJBBrq6umjlzpr7//vtnii06OlrSgxpBcWXsY5I8T1OgQAGzL//vc7i5uSWaIshubm5yc3OzdRj/l25dOmnRD/O1YPEyeXl56eq/c7u9fXys/v86dfKkdm7/WT8uXx1rH9kfSa7duH5dkpQ7T94kdxcze9O1cyf9sOB7LfpxuTy9vMy5+z6PHF8kDmFhYTp18qT5+9kzZ3Tk8GGlSp1avr6+GjFsiGrUrK30GTLoxvXrmvr1JF26eFH16je0YdR4ki5dP1a791qpSJEABRQtponjx+p2eLhatmpj69DwFBy7pKtf3z6qUrWaMmXKrNDQUP2w4Hv9vG2rVq5ZZ+vQEE+cf0DiR4IogZQuXVo+Pj5asGCB+vTp838nP5YsWSJ3d3etW7fOKnkxc+ZMq3Y5cuRQdHS0jh07psKFC8e5r5hRNWnTpn1sguf/lVDPkSVLFm3cuFGhoaFWo4hiaj5lyZLF/Dc6OlqnTp2yGjX0559/vrBYErNvpk2RJFWrVM5q/ZTp36p5y9bm73O++1avZMyoCpUqv8zw8H+aNvVrSVLlCmWt138zUy1atX75AeGJDv5yQFUq/ncu9u75YIRi8xatNGHyFP355x+aO+c73bh+Xal9fRUQUFQbt2zXa/ny2SpkPEXDdxvpelCQBg/qr6tXrqhgocJavmqt0qVL9/QHw6Y4dklX0LVratumpa5cviwfHx/lL1BQK9esU4WKlWwdGuKJ8w9I/BLHkA07lDx5cvXq1Uu//fabPvnkk1i3YJcU57rHcXZ2lsViUVRUlLnu7NmzWrZsmVW7unXrysnJSYMHDzZH8Tz6fFWqVJG3t7eGDh2qe/fuxXquoKCgeMf1OAn1HNWrV1dUVJQmTpxotf6rr76SxWJRtWrVJMn8d/z48Vbtxo4d+1zPm9SERUbHuTycHJKkgZ8P1R8nz8UrgVnm7bIKi4xm9FAiEHHPiHMhOZQ4lXm7bJzHa/q3s+Tu7q4fFv2o0+cuKjg8Umf+vqRFPy5XQNGitg4bT9Gx04c6ceqcgsMjtX3XXhUrXtzWISGeOHZJ05TpM/TnybMKDo/U35euac26jSSHkiDOv6TD1rWCqEFkG4wgSkCffPKJjh8/rpEjR2r9+vWqX7++MmbMqJs3b+rgwYNatGiR0qZNK3d396fuq0aNGhozZoyqVq2qpk2b6tq1a5o0aZJy5sxpdVeunDlzqm/fvvr8889VunRp1atXT25ubtq/f7/8/f01bNgweXt76+uvv1aLFi30xhtvqHHjxvLz89Pff/+t1atX66233oqVgHlWCfUctWrVUrly5dS3b1+dPXtWhQoV0vr167V8+XJ17drVHLlUuHBhNWnSRJMnT1ZwcLBKliypTZs26eRD0zwAAAAAAMADJIgSkJOTk+bMmaP69etr+vTpmjBhgm7evClPT0/lz59fQ4YMUbt27WLd6Swu5cuX14wZMzR8+HB17dpV2bJl04gRI3T27NlYt20fPHiwsmXLpgkTJqhv375Knjy5ChYsqBYtWphtmjZtKn9/fw0fPlwjR45UZGSkXnnlFZUuXVpt2ryYecAJ8RxOTk5asWKF+vfvrx9++EEzZ85U1qxZNXLkSHXv3t2q7bfffis/Pz/NmzdPy5YtU/ny5bV69eokV3AaAAAAAICEZjGeZZ4T4IBCQkLk4+OjS0G34n2HNyQuzk6MJwUAAEDCCgkJUTpfHwUHByfZ64aYa58f951SCk+vpz8gCQkPC1W9YjmS9PFJaNQgAgAAAAAAcHBMMQMAAAAAACbLvz/2xN76kxAYQQQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4aRAAAAAAAwORkebDYE3vrT0JgBBEAAAAAAICDI0EEAAAAAADg4EgQAQAAAAAAODhqEAEAAAAAAJPl3x97Ym/9SQiMIAIAAAAAAHBwJIgAAAAAAAAcHAkiAAAAAAAAB0cNIgAAAAAAYLJYHiz2xN76kxAYQQQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4EEQAAAAAAgIOjSDUAAAAAADBZJFlkX1Wd7as3CYMRRAAAAAAAAA6OBBEAAAAAAICDI0EEAAAAAADg4KhBBAAAAAAATE6WB4s9sbf+JARGEAEAAAAAADg4EkQAAAAAAAAOjgQRAAAAAACAg6MGEQAAAAAAMFn+/bEn9tafhMAIIgAAAAAAAAdHgggAAAAAAMDBkSACAAAAAABwcCSIAAAAAAAAHBxFqgEAAAAAgMliebDYE3vrT0JgBBEAAAAAAICDI0EEAAAAAADg4EgQAQAAAAAAODhqEAEAAAAAAJPl38We2Ft/EgIjiAAAAAAAABwcCSIAAAAAAAAHR4IIAAAAAADAwVGDCAAAAAAAmJxkkZPFvqr2OFGF6KkYQQQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4EEQAAAAAAgIOjSDUAAAAAADBZ/l3sib31JyEwgggAAAAAAMDBkSACAAAAAAB4yNatW2WxWOJc9uzZY9V2165dKlWqlJInT6706dOrS5cuCgsLi7XPyMhI9e7dW/7+/vLw8FDx4sW1YcOGOJ8/vvt8kZhiBsSTs5NFzk4MTAQAAAAAR9GlSxcVLVrUal3OnDnN/z58+LAqVKigvHnzasyYMbpw4YJGjRqlv/76Sz/99JPV41q3bq3Fixera9euevXVVzVr1ixVr15dW7ZsUalSpZ5rny8SCSIAAAAAAPAfihCZSpcurQYNGjx2+6effqpUqVJp69at8vb2liRlzZpV7dq10/r161W5cmVJ0r59+7RgwQKNHDlSPXr0kCS1bNlS+fPnV69evbRr165n3ueLxhQzAAAAAACAxwgNDdX9+/djrQ8JCdGGDRvUvHlzM5EjPUj8eHp6auHChea6xYsXy9nZWe3btzfXubu7q23bttq9e7fOnz//zPt80UgQAQAAAAAAhxASEmK1REZGPrF9mzZt5O3tLXd3d5UrV04HDhwwtx09elT3799XQECA1WOSJUumwoUL69ChQ+a6Q4cOKVeuXFZJH0kqVqyYpAfTyp51ny8aCSIAAAAAAOAQMmXKJB8fH3MZNmxYnO2SJUum+vXra9y4cVq+fLm++OILHT16VKVLlzaTNJcvX5YkZciQIdbjM2TIoEuXLpm/X758+bHtJJltn2WfLxo1iAAAAAAAgMny7489ienP+fPnrUbxuLm5xdm+ZMmSKlmypPl77dq11aBBAxUsWFB9+vTR2rVrFRER8dh9uLu7m9slKSIi4rHtYrY//G989vmikSACAAAAAAAOwdvbO9Y0r/jKmTOn6tSpox9//FFRUVHy8PCQpDinqd25c8fcLkkeHh6PbRez/eF/47PPF40pZgAAAAAAAPGQKVMm3b17V+Hh4eY0sJhpYQ+7fPmy/P39zd8zZMjw2HaSzLbPss8XjQQRAAAAAABAPJw+fVru7u7y9PRU/vz55eLiYlW4WpLu3r2rw4cPq3Dhwua6woUL68SJEwoJCbFqu3fvXnO7pGfa54tGgggAAAAAAOAhQUFBsdYdOXJEK1asUOXKleXk5CQfHx9VrFhRc+fOVWhoqNluzpw5CgsLU8OGDc11DRo0UFRUlKZNm2aui4yM1MyZM1W8eHFlypRJkp5pny8aNYgAAAAAAMB/LJLFvmpU61lrbjdq1EgeHh4qWbKk0qZNq2PHjmnatGlKnjy5hg8fbrYbMmSISpYsqbffflvt27fXhQsXNHr0aFWuXFlVq1Y12xUvXlwNGzZUnz59dO3aNeXMmVPfffedzp49qxkzZlg9d3z3+aIxgggAAAAAAOAhdevW1fXr1zVmzBgFBgbqhx9+UL169XTgwAHlzZvXbPfGG29o48aN8vDwULdu3TRt2jS1bdtWixcvjrXP2bNnq2vXrpozZ466dOmie/fuadWqVSpTpoxVu2fZ54tkMQzDSNBnAJK4kJAQ+fj46OqN4Oeudg8AAADAvoWEhCidr4+Cg5PudUPMtc+mw3/L0ytp9uFxwkJDVKFw5iR9fBIaI4gAAAAAAAAcHDWIAAAAAACAyaJnLtmT6NlbfxICI4gAAAAAAAAcHAkiAAAAAAAAB0eCCAAAAAAAwMFRgwgAAAAAAPyHIkQOiRFEAAAAAAAADo4EEQAAAAAAgIMjQQQAAAAAAODgqEEEAAAAAABMln9/7Im99SchMIIIAAAAAADAwZEgAgAAAAAAcHAkiAAAAAAAABwcCSIAAAAAAAAHR5FqAAAAAABgslgeLPbE3vqTEBhBBAAAAAAA4OBIEAEAAAAAADg4EkQAAAAAAAAOjhpEAAAAAADAZPl3sSf21p+EwAgiAAAAAAAAB0eCCAAAAAAAwMGRIAIAAAAAAHBw1CACAAAAAAD/oQiRQ2IEEQAAAAAAgIMjQQQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4i1QAAAAAAwGT598ee2Ft/EgIjiAAAAAAAABwcCSIAAAAAAAAHR4IIAAAAAADAwVGDCAAAAAAAmCyWB4s9sbf+JARGEAEAAAAAADg4EkQAAAAAAAAOjgQRAAAAAACAg6MGEQAAAAAAMFn+XeyJvfUnITCCCAAAAAAAwMGRIMJL9ddff6ly5cry8fGRxWLRsmXLEuR5ypYtq7JlyybIvgEAAAAAsDckiBDLrFmzZLFYzMXd3V3+/v6qUqWKxo8fr9DQ0Ofed6tWrXT06FENGTJEc+bMUUBAwAuM/PEuXbqkgQMH6vDhwy/l+RKbaVO+VtHXCyptam+lTe2tt0uV0Lq1P5nbr1y5ovdatVDWjOnl65NCJYq+oaU/LrFhxIiPKZMnKXfOrErp6a7SJYtr/759tg4Jcdix/WfVr1tL2TL7y8PVohXLl1ltv3r1qtq911rZMvsrtXdy1a5RVSf/+ss2wSLeOP+SLo5d0vW091Mkfpx/QOJGggiPNXjwYM2ZM0dff/21OnfuLEnq2rWrChQooF9//fWZ9xcREaHdu3erbdu2+vDDD9W8eXNlzJjxRYcdp0uXLmnQoEEOmyB6JWNGfT50uHbt/UU79xxQ2XLl1bBeHR37/XdJ0vttWurEiT+16McVOnDoqOq8U0/Nm7yrw4cO2ThyPM6ihT+od8+P1fezAdq976AKFiyk2jWq6Nq1a7YODY8IDw9XgYKFNHb8pFjbDMPQu/Xr6syZ01q0ZLn27D+kzJmzqHrVigoPD7dBtIgPzr+ki2OXtD3p/RSJH+cfkPiRIMJjVatWTc2bN1ebNm3Up08frVu3Ths3btS1a9dUu3ZtRUREPNP+goKCJEkpU6ZMgGjxJDVq1lLVatWV89VX9WquXBr0+RB5enpq3949kqQ9u3cpsFNnFS1WTNmyZ9cnn36mlClT6tDBX2wcOR5n/NgxatO2nVq2bqO8r72mCZOnyCN5cn0361tbh4ZHVKlaTQMHf6E6dd+Jte3kX39p3949Gj/xawUULapcuXNr/KSvdSciQgsXzLdBtIgPzr+ki2OXtD3p/RSJH+dfEmOx0wVPRIIIz6R8+fLq16+fzp07p7lz55rr//jjDzVo0ECpU6eWu7u7AgICtGLFCnP7wIEDlSVLFklSz549ZbFYlDVrVknSuXPnFBgYqNy5c8vDw0O+vr5q2LChzp49a/XcAwcOlMUS+6yOmRL3aPsYW7duVdGiRSVJbdq0MafOzZo16/lfiCQsKipKC39YoPDwcBV/s4Qk6c0SJbV40Q/6559/FB0drYU/LNCdO3dU5u2ytg0Wcbp7964OHfxF5StUNNc5OTmpfPmK2rdntw0jw7OKjIyUJLm7u5vrnJyclMzNTbt27rBVWHgCzr+ki2MH2A7nH5A0kCDCM2vRooUkaf369ZKk33//XW+++aaOHz+uTz75RKNHj1aKFClUt25dLV26VJJUr149ffXVV5KkJk2aaM6cORo7dqwkaf/+/dq1a5caN26s8ePHq0OHDtq0aZPKli2r27dv/9/x5s2bV4MHD5YktW/fXnPmzNGcOXNUpkyZ/3vfSclvR48qTUpP+aRwU5dOHfTD4qXK+9prkqS58xfq3r17eiWdr3xSuKlz4Af6YfFS5ciZ08ZRIy7Xr19XVFSU0qZNZ7U+bbp0unLlio2iwvPInSePMmXOrH6f9dHNmzd19+5djRo5QhcvXNCVK5dtHR7iwPmXdHHsANvh/AOSBhdbB4CkJ2PGjPLx8dGpU6ckSR999JEyZ86s/fv3y83NTZIUGBioUqVKqXfv3nrnnXdUsGBBeXt7q1u3bnrjjTfUvHlzc381atRQgwYNrJ6jVq1aKlGihJYsWWImpJ5XunTpVK1aNfXv318lSpSweu64REZGmt/qS1JISMj/9fyJRa7cubX3wGEFBwdr6Y+L1e69Vlq/aZvyvvaaBg3op1u3bmnNuo3y9U2jlSuWqXmTd7Vxy3blL1DA1qEDdsvV1VULFv6oju3byj9tajk7O6t8hYqqUrWaDMOwdXgAAABwIIwgwnPx9PRUaGio/vnnH23evFnvvvuuQkNDdf36dV2/fl03btxQlSpV9Ndff+nixYtP3JeHh4f53/fu3dONGzeUM2dOpUyZUgcPHkzorsQybNgw+fj4mEumTJleegwJIVmyZMqRM6feKFJEnw8ZpgIFC2nShHE6feqUpkyeqKnTv1W58hVUsFAh9e03QG8UCdDUrykCmRilSZNGzs7OunbtqtX6a1evKn369DaKCs/rjSJFtPeXw7py/ZbOnL+sFavX6saNG8qWLbutQ0McOP+SLo4dYDucf0mPxU5/8GQkiPBcwsLC5OXlpZMnT8owDPXr109+fn5Wy4ABAyTpqXcmiIiIUP/+/ZUpUya5ubkpTZo08vPz061btxQcHPwyumOlT58+Cg4ONpfz58+/9BhehujoaEVGRprT+JycrN8OnJ2dFR0dbYvQ8BTJkiXT628U0ZbNm8x10dHR2rJlk4r9W1cKSY+Pj4/8/Px08q+/dPCXA6pZu46tQ0IcOP+SLo4dYDucf0DSwBQzPLMLFy4oODhYOXPmNBMIPXr0UJUqVeJsn/MpdWw6d+6smTNnqmvXripRooR8fHxksVjUuHFjqwRFXAWqpQdFl18kNzc3c6qcvejXt4+qVK2mTJkyKzQ0VD8s+F4/b9uqlWvWKXeePMqRM6c+DPxAw0aMkq+vr1asWKZNGzfox+WrbB06HqNL14/V7r1WKlIkQAFFi2ni+LG6HR6ulq3a2Do0PCIsLEynTp40fz975oyOHD6sVKlTK3PmzFqyeJH8/PyUKVNm/fbbUfX4+CPVqlNXFStVtmHUeBLOv6SLY5e0Pe39FIkb5x+Q+JEgwjObM2eOJKlKlSrKnv3BFAhXV1dVrFjxSQ97rMWLF6tVq1YaPXq0ue7OnTu6deuWVbtUqVJJkm7duqWUKVOa68+dO/fU53hccslRBF27prZtWurK5cvy8fFR/gIFtXLNOlWoWEmStGzFGn3W9xM1eKeWwsLClCNHTn3z7XeqWq26jSPH4zR8t5GuBwVp8KD+unrligoWKqzlq9YqXbp0T38wXqqDvxxQlYrlzN979/xYktS8RStN/3aWrly+rN49P34wzD5DBjVr3lJ9+vazVbiIB86/pItjl7Q97f0UiRvnH5D4kSDCM9m8ebM+//xzZcuWTc2aNZO7u7vKli2rqVOnqnPnzsqQIYNV+6CgIPn5+T1xn87OzrGKsU6YMCHWyKAcOXJIkn7++WfVrl1bkhQeHq7vvvvuqXGnSJFCkmIlnRzFlOkznrg956uvasHCJS8pGrwoHTt9qI6dPrR1GHiKMm+XVcS9xxec7tS5izp17vISI8KLwPmXdHHskq6nvZ8i8eP8SzoslgeLPbG3/iQEEkR4rJ9++kl//PGH7t+/r6tXr2rz5s3asGGDsmTJohUrVsjd3V2SNGnSJJUqVUoFChRQu3btlD17dl29elW7d+/WhQsXdOTIkSc+T82aNTVnzhz5+Pjotdde0+7du7Vx40b5+vpatatcubIyZ86stm3bqmfPnnJ2dta3334rPz8//f333098jhw5cihlypSaMmWKvLy8lCJFChUvXlzZsmX7/14kAAAAAADsAAkiPFb//v0lPSgqlzp1ahUoUEBjx45VmzZt5OXlZbZ77bXXdODAAQ0aNEizZs3SjRs3lDZtWr3++uvmPp5k3LhxcnZ21rx583Tnzh299dZb2rhxY6yaRq6urlq6dKkCAwPVr18/pU+fXl27dlWqVKnUps2T5y67urrqu+++U58+fdShQwfdv39fM2fOJEEEAAAAAIAki/Ho3B4AVkJCQuTj46OrN4Ll7e1t63AAAAAAJEIhISFK5+uj4OCke90Qc+2z69hFeXolzT48TlhoiEq+9kqSPj4JjRFEAAAAAADAZPl3sSf21p+E4GTrAAAAAAAAAGBbJIgAAAAAAAAcHAkiAAAAAAAAB0eCCAAAAAAAwMFRpBoAAAAAAPyHKtUOiRFEAAAAAAAADo4EEQAAAAAAgIMjQQQAAAAAAODgqEEEAAAAAABMln9/7Im99SchMIIIAAAAAADAwZEgAgAAAAAAcHAkiAAAAAAAABwcNYgAAAAAAIDJYnmw2BN7609CYAQRAAAAAACAgyNBBAAAAAAA4OBIEAEAAAAAADg4EkQAAAAAAAAOjiLVAAAAAADAZPl3sSf21p+EwAgiAAAAAAAAB0eCCAAAAAAAwMGRIAIAAAAAAHBw1CACAAAAAAD/oQiRQ2IEEQAAAAAAgIMjQQQAAAAAAODgSBABAAAAAAA4OGoQAQAAAAAAk+XfH3tib/1JCIwgAgAAAAAAcHAkiAAAAAAAABwcCSIAAAAAAAAHR4IIAAAAAADAwVGkGgAAAAAAmCyWB4s9sbf+JARGEAEAAAAAADg4EkQAAAAAAAAOjgQRAAAAAACAg6MGEQAAAAAAMFn+XeyJvfUnITCCCAAAAAAAwMGRIAIAAAAAAHBwJIgAAAAAAAAcHDWIAAAAAADAfyhC5JAYQQQAAAAAAODgSBABAAAAAAA4OBJEAAAAAAAADo4aRAAAAAAAwGT598ee2Ft/EgIjiAAAAAAAABwcCSIAAAAAAAAHxxQz4CkMw5AkhYaE2DgSAAAAAIlVzPVCzPUDkNSQIAKeIjQ0VJKUM1smG0cCAAAAILELDQ2Vj4+PrcMAnhkJIuAp/P39df78eXl5eclisb/CZiEhIcqUKZPOnz8vb29vW4eDZ8TxS7o4dkkbxy9p4/glbRy/pMvej51hGAoNDZW/v7+tQ/n/WSS7u/Sxt/4kABJEwFM4OTkpY8aMtg4jwXl7e9vlH2pHwfFLujh2SRvHL2nj+CVtHL+ky56PHSOHkJRRpBoAAAAAAMDBkSACAAAAAABwcEwxAxycm5ubBgwYIDc3N1uHgufA8Uu6OHZJG8cvaeP4JW0cv6SLY5d0WGR/JXvsrT8JwWJwDz4AAAAAABxeSEiIfHx8dOjkFXl52VedqNDQEL2eM72Cg4PttgbW/4spZgAAAAAAAA6OBBEAAAAAAICDowYRAAAAAAD4D0WIHBIjiAAAAOBQ/vjjD4WGhto6DAAAEhUSRAAAAHAYI0eOVIUKFXT69Glbh4Ln8Pvvv9s6BACwWySIACAJWr9+vXbt2mXrMPAcHr55KDcSTbqio6NtHQKew7p16zR06FC1atVK6dKlM9dzLiYNjRo1Ut26dbVt2zZbhwIAdokEEQAkIYZh6M8//1TNmjU1YMAA7d+/39Yh4RmdOXPG/G+LhcnwScnChQu1fPlynTlzRk5O/32EIrmQdJw4cULJkiVTzZo1lT59eklSREQE52IS0bFjR128eFF9+/bV1q1bbR0OANgdEkSAg4uMjLT6nQudxM1isSh37twaO3asjh49qoEDB2rfvn22DgvxtG/fPuXMmVM1a9ZUr169dOvWLd29e1cSI1ISu6ioKE2fPl0DBgxQoUKF1L17d61Zs0YSib6k4Ny5c5IkJycnBQUF6fTp0zIMQ+XKlVPbtm0VFRVl4wjxNFFRUSpbtqw2bdqkAwcOqE+fPiSJgARksdMfPBkJIsABnT59WqtXr5Ykubm56cCBA2rQoIEkLnQSu5gkQmBgoAYNGqS9e/dq0KBB2rNnj40jQ3y89tpr2rZtm3LlyqX58+crX7586tu3r06dOiUnJycStImYs7OzNmzYoNWrV2vYsGFauXKlWrVqpQ8++EBBQUEcu0Rs9OjRatu2rS5evKjAwECVKVNGXbt2Vf78+XXs2DE1b96cBFES4OzsrLt376pEiRLavn27Dh8+rM8//1wbN260dWiIp/v370v678vI8PBwvhwBEhkSRICDuX//vvbt26d3331X/fv31+XLl1WpUiWdO3dOf//9t63Dw1M4OTmZI04++OADjRkzRgcPHtTw4cMZSZQEpEiRQqVLl9aIESP0+++/q1q1alq4cKHKli2rzZs3k6BNxGIuYl555RV16tRJy5YtU2BgoObPn6/atWtr48aN5sUPEo+lS5eqb9++Kl68uJydnWWxWLRixQqFh4fr5MmTevfdd/Xmm28qWbJkXKgmclFRUUqWLJlOnz4tV1dXvfPOO9qyZYtGjRqlLVu22Do8PMXZs2e1YMECHTt2TBaLRb/99puaNGmio0eP2jo0AA8hQQQ4GBcXFxUpUkSNGzfWF198oRw5cqhs2bL6/vvvlTFjRluHh6eI+YAsScOGDdOFCxcUGhqqdevWacCAAfrll19sHCGeJCYB5OLiIm9vb33zzTcaMWKEMmbMqGrVqmnOnDk2jhCP83DNIenBaLBevXpp0aJFunz5sjp16qS1a9cyEiWR2bx5s3LkyKEPPvjArDk0f/58pUqVSpkyZdL333+vFStWKCwsLNYxRuJhGIacnZ11/Phxvf3222rVqpUiIiIUEBCgTZs2qV+/fkw3S8SioqJ0+fJlde/eXZ9//rlWrFihN998U6GhofLx8bF1eAAewl9CwAG9+uqratq0qZydnXXnzh2lSpVKr776qpycnHTv3j1bh4cncHZ2liTVrl1b48eP14ULF9S9e3fVrVtX69at02effUbh6kTq4SlIFovFPNcaN26scePGqXLlynr//fe1dOlSSdQkSkweTfrE/J4iRQpVqVJFK1as0P379/Xpp5+at+Bmylni4OrqqqtXr5ojZEuXLq39+/dry5Yt2rx5s3Lnzq3u3btrwYIFCg8Pt3G0eByLxaJbt27pvffeU9q0afX1119r6dKlWr9+vRYuXKj9+/dTuDoRiqnT5uzsrMKFC2vMmDFasmSJGjVqpICAAE2ePFlZs2a1bZB4LIvFPhc8GQkiwMHEXLRcunRJzZo1U61atTRr1iz17t1b0oMP03wDnjiEhobGuX7BggXasGGDPvvsM3311VcaNGiQ5s+fr7Fjx2rbtm3q378/I4kSmaioKHP0UMyFqqurq5kEKlasmAYPHqxSpUqpZcuW+vXXX6lJlEjcv3/fTMwuWrRIZ8+eNX+PUbBgQc2bN09Xr15V//79JVHPLbGoX7++7t+/r+7duysgIEDHjx9XrVq1lDVrVmXOnFlLlizRq6++qk8++UTz588nSZSIRURE6MyZMypVqpRKliwpSfL09NQ777yjVatW6cCBAxo0aJA2bdpk40ghSQMGDFCDBg00fvx4SZKHh4eKFSum+/fvKzIyUh4eHrxPAokQCSLAQcRciMb8MW7RooWmTZumkSNHqmXLlho5cqR69uwpSbEufh5+PF6OpUuXKnv27Prjjz9ibbtx44bu37+vChUqyM3Nzax70qVLFw0dOlTr1q1Tv379qEmUSERFRZnn1EcffaS2bdtqxowZkmSVBHr99df1ySefKGPGjOrZs6eCgoL48GxjUVFRcnFxkSQ1adJEgYGBatWqle7evRsreVesWDENGTJEq1ev1pgxY2wRLh4RFRWlt956Sz/++KP279+v3377TV26dFGdOnXM984MGTJo2bJlypkzZ6wkEQnaxCU4OFgRERHm++K9e/fk4uIiwzBUqVIl9enTR9u2bdP48eO1du1aG0eL2rVrq0SJEho/frzGjRsn6cGNUfr3768+ffpo48aNGjhwoI4dO2Y+hs+agO2RIAIcQFRUlJycnHTlyhXt2LHDHIKdLFky5cqVS5988olatGih0aNHm0kiSfrjjz80aNAgSbHrbyBhJUuWTPfu3bMaLv9wki8qKkp//vmnDMOQi4uLua1r166qUqWKeXeznTt32iJ8/Cumbob04MPy4sWLFRAQoHLlypltYo6nJFWqVEnNmzfXwYMHzVFgjOiznZhj984772jbtm0aMWKEvvvuOyVLlkwWi0WGYZhJBGdnZ1WvXl3Vq1fX6tWrFRQUZMvQHU5cyZyYRMLKlSvNAv8rVqwwCxq7uLgoKipK6dOnN5NEn332mebNm6ewsDAStDYWc0xj/s2TJ4+KFy+u5cuXKzQ0VK6urlaF4YsXLy5vb2+tXLlSY8aMUUREhE3ixoNjVqRIEX311VfKnDmzxo0bp4kTJypz5swaOHCgBgwYoEmTJmnJkiUaNGiQjh8/Lum/z5oREREkaJEo7N+/Xx9++KHy5cunFClSKHPmzHr33Xd14sQJq3atW7eWxWKJteTJkyfWPqOjo/Xll18qW7Zscnd3V8GCBTV//vw4n//48eOqWrWqPD09lTp1arVo0SLBP19YDM4+wK4ZhiGLxWLeMenSpUuKjo5WtWrV1LNnT5UpU0ZOTk76888/NWzYMM2ePVudOnVShQoVNGPGDK1evVonT55U9uzZbd0Vh3L//n2dP39e2bJlkyRduXLFLLB66dIlFShQQOXLl9e8efOULFky84OUxWJR8+bNtXHjRt24cUOLFy9WnTp1bNYPPNC5c2ctXrxYEyZMUKVKleIsyhkdHW1+OC5RooTc3d25M08iMHXqVH322WcaP368atWqJU9PT/NYhYSEyNvb26r92rVrVaNGDS1btky1atWyUdSOJeZ43Lt3T6GhoTp58qRSpkyplClTKm3atNq0aZMyZMigP//8U/Xr11eRIkU0dOhQVapUSdJ/o/yuXLmiGjVq6NChQzp69Kjy5ctn4545ppjjGXNc7t27J1dXV0nS8uXL1bRpUxUtWlRbtmyxSuLNmDFD27dvV8eOHeXj4xPnhRlenpjPn7/++qu6du2qM2fOqFu3burSpYsk6c6dO5o5c6a6dOmievXq6fPPP1euXLl08uRJzZ49W+XLl1fZsmVt2wkHFRISIh8fH/16+qq8vLyf/oAkJDQ0RAWzp1NwcHCsv99xadCggXbu3KmGDRuqYMGCunLliiZOnKiwsDDt2bNH+fPnl/QgQbRgwQJ98803Vo/38fGJ9VmgT58+Gj58uNq1a6eiRYtq+fLlWr16tebPn6/GjRub7S5cuKDXX39dPj4+6tKli8LCwjRq1ChlzpxZ+/btM29a88IZAOxWdHS0YRiGcevWLaNw4cJGpUqVjAkTJhjffPONkS5dOiNPnjzGwoULjaioKMMwDOOPP/4wOnbsaLi6uhopUqQwMmXKZBw+fNiWXXBIMcctRmBgoOHj42OcPHnSXDdy5EjDYrEY7du3N27fvm2uv3jxolGnTh1j2bJlxl9//fXSYsbjXblyxShSpIjx/vvvG+Hh4YZhGMbNmzeNPXv2GIMHDzaGDx9u3LhxwzAMw7h3755hGIaxcuVK49VXXzX2799vs7gdVcz7YYyePXsaWbJkMS5fvmwYhmFcunTJmDRpklG6dGnD39/f6NGjh/Hnn38ahvHfudu6dWujSZMm5vFEwok5XkFBQUabNm2M1157zXBxcTGSJ09u5MqVy/jiiy+MmzdvGnfv3jUMwzBWrFhhWCwWIyAgwFi/fn2s/Vy8eNFYsGDBy+8IDMMwjPv37xuGYRinTp0yunfvbtSpU8eoVauW8cMPPxjXr1837t69awwcONCwWCxGyZIljZ07dxqXL182tmzZYlSsWNGoWLGiuQ/YzqPvo4cPHzbKlStnZMuWzRg7dqy5PjIy0vj6668NV1dXo3r16saQIUOMatWqGRaLxeozD16u4OBgQ5Lx6+mrxpmgCLtafj191ZBkBAcHx+u12LlzpxEZGWm17sSJE4abm5vRrFkzc12rVq2MFClSPHV/Fy5cMFxdXY1OnTqZ66Kjo43SpUsbGTNmtHr/6tixo+Hh4WGcO3fOXLdhwwZDkjF16tR4xf88XBIm7QTA1ox/v7m5efOmjhw5IhcXF/Xq1UsVK1aUJFWsWFGlS5dW7969ZbFYVK9ePeXOnVsDBgxQ06ZN9ddff6lChQrKnDmzjXvieB7+RjQ6OlpVq1bV5s2bVbNmTa1cuVI5c+ZUo0aNdPnyZY0bN07nz59XvXr1lC5dOq1du1YbNmxQmzZtlDNnTnMfTBF8eR6uOSQ9GDJ/6dIl5c+fX8mTJ9fx48fVr18/7dy5U7du3VJkZKRWr16t9evXy93dXdKDEUShoaHatWuXAgICbNUVh2MYhnmuxJw3Xl5eunjxorZu3SonJyd98cUX+ueff/TKK6+odOnSGj16tNKkSWO+l0pSrly5NG3aNN29e9esYYQXL+Z4Xb16VSVLlpS7u7tKlCih9u3b69q1a5o/f7769eun7du3q1+/fipWrJhq1aqllStXqlatWvr0008lPZjaGTNixd/fX40aNZLEe+fLZjx0K/uyZcvK3d1dnp6eCg4O1qpVq1SrVi198cUX6tOnjzw9PTVu3DiVKlVKqVKlUnR0tJydnbV169Y46yji5XJyctLly5eVOnVqubm5qVChQvrqq6/UrVs3sx7RRx99pGTJkqlt27ZKnjy53n//fW3fvl1+fn46dOiQcuTIYeNeADIL4j/s1VdfVb58+cypkQ+LiopSeHj4Y0cnLV++XPfu3VNgYKC5zmKxqGPHjmratKl2796tUqVKSZKWLFmimjVrWl2LVaxYUbly5dLChQvVvn37/7d7cUuw1BMAm7t69aqRNm1ao0KFCkaJEiXM9Xfu3DEMwzAuX75sZMqUyciWLZvVSCIkDjHH4/79+8b69euN3LlzG6+++qo5MujChQvGpEmTjPTp0xsWi8VwdnY2vL29jREjRtgybPyre/fuxv79+42oqCije/fuhouLi1GgQAHDw8PDKFiwoNG3b1/j/Pnzxpw5cwyLxWJ+GxTz7dHnn39urF692pZdcFhNmzY16tSpYxjGg/Ms5htti8ViVKlSxZg5c6bZtnz58sbrr79uREREmOvu3r1r7Nix4yVH7ZhCQkKM0qVLG0WLFjV27txpte348eNGv379DC8vL6NEiRLGvn37zFFeq1evNiwWi1G8eHFj7dq1tggdhhFrlN2NGzeMN9980yhVqpR5Dl28eNEYN26cYbFYjHLlyhl//PGHERUVZZw9e9b44osvjC5duhgDBgxgxEkicuvWLSNjxoxG1apVrd4bHzeSyDAM4/Tp08bOnTuNK1euvOxw8QhHGEF0/vx5Izg42Fxiro3iIzo62njllVeMypUrm+tatWplWCwWI3ny5IYkI1WqVEZgYKARGhpq9dj333/fSJEiRazZAidPnjQkGePHjzcM48FnD0lxfqZv3ry5kTp16mc5pM+Er7UAO5Y2bVrVrFlTM2fOlCT98ssvKlKkiNzc3HTv3j2lT59e+/btU7FixfTZZ5/p7t27atq0KYU5bSSukSfSg+K3ZcuW1fjx49WlSxdVq1ZNP/30k3LmzKnAwEA1btxYP//8s1xcXOTn56fixYtL4ttvW9q4caPGjBmjvHnzKiAgQG3btlW6dOm0adMmvfvuu6pbt645bz1dunTy9PRUmjRpJP1XGLlXr15mfSnOyZfn9u3bCg8P1/Hjx3X27FllzZpVs2fP1t69e+Xq6qpSpUopefLkkqRTp04pPDxchQsXNkd/GYYhV1dXvfXWW7bshsPYvXu3/vzzTw0dOtT8pvf+/ftycXFRnjx59NFHH8nX11effvqphg0bph9//FGSVL16da1Zs0bVq1dX586dtWnTJmXKlMmWXXEo/fv31wcffKBXXnnFan1QUJD+/PNP9ezZ0zyH0qVLpy5dusjLy0tt27bV1KlTNWbMGGXJkkV9+/a1Rfh4CicnJ3Xr1k2DBg1Sq1at9N1338nd3f2xI4kkKVu2bGbdRSChPfp+P2DAAA0cODBej503b54uXryowYMHm+syZMigXr166Y033lB0dLTWrl2ryZMn68iRI9q6das5mvjy5ctKly5drM91GTJkkPSgzmhMu4fXP9r2n3/+UWRkpNzc3OLX4WdAggiwMzEXkzHJgalTpypdunQaPny4ZsyYIT8/P2XOnFmurq5WSaIcOXJo1KhRql27try8vGzdDYfzcHJo6tSpOnfunG7fvq0OHTooS5Ys8vDwULly5cwkUczFTc6cOZU6dWrVrVvXan8kh2yrYsWKqlGjhoYPH64mTZoob968yps3r3r06GH1oeDSpUv6+eef5evrG+tCKab4IMmhlyt58uQaOHCgihcvrgULFuiTTz5RmjRpVKNGDat2f//9t77//nudOHFCXbt2NddzvF6uPXv26NatW2Yx/ujoaKtpfb6+vmrSpIlOnz6tCRMmaMqUKerQoYMMw1DVqlW1dOlSnT17luTQS7RmzRp98cUX2r17t3766Sc5OTmZf68uXLigW7duyc/PT5KsLoDatGmjnTt3avLkyerQoYNy5cpl7pNEuu09fAy8vLz0/vvvy8PDQ927d39skmjy5Mm6c+eOevfubePoESfLv4s9+bc/58+ft5oGFt9Eyx9//KFOnTqpRIkSatWqlbl+2LBhVu0aN26sXLlyqW/fvlq8eLFZfDoiIiLO54r5kinm7osx/z6tbUIkiLh6AOxEzK2w7927J0kKCwuT9OA2vkOHDlVgYKCmTJmiCRMm6Pz585JklSQ6ffq0Fi5cSHLIBmJqJ0hSnTp11KdPH82YMUPz5s3TW2+9pWnTpunq1atydXVVuXLlNGHCBDk7O6t27drmbTaNR25ISXLIdqKjoyVJzZs317Vr1zRr1ixz/cMXMLt27dLIkSM1YsQIffjhh+bIL7w8Me+b0n/nUFRUlPLly6dGjRppypQpcdYYWLBggbp166bhw4erd+/eVncdwcsVc75dv35dUtzvfWnTplXbtm3l5uamrVu3yjAMRUdHKzo6WnXq1DFHMDz6PoqEUbVqVU2cOFFffvmlXFxcrF73gIAAZc2aVUuWLJH04OIoKirKPM5ly5bV3bt3debMGat9khyynZhjc//+fat/vb291axZM40ePVorV65U69atdefOHUlSoUKFNHbsWKVIkUILFizQzZs3bRM8HJa3t7fVEp9ES8ydLn18fLR48eKn1jvr1q2bnJyctHHjRnOdh4eHIiMjY7WNOTc8PDys/o1P2xeNKwjADsSMPvnrr7/UsWNHlShRQlWqVNHo0aN18eJFSdLEiRMVGBio0aNHa/z48bGSROnSpdOrr75qy244rJgLmjZt2uiXX37R6NGjtWfPHgUFBSlv3rwaPXq0vvnmGwUFBcnV1VVly5bVhAkTFBkZqVKlSik4OJgPxzbycIIhRszxrFOnjtWFjpOTk3khNHv2bL333ntavHixvvzyS3Xv3l0SF6gvk/FvQVxJCg8PN88hZ2dnubq6qlatWrp48aL27dsn6b9j/fvvv2vevHk6f/68xo8fb37zHXORhJcrZcqUunfvnn7//XdJ/12cPiw6OloFCxZUqVKldPz4cYWHh8vZ2TlWMon30YQXFRUlJycnBQYG6vXXX9eJEydUs2ZNczqFi4uLmjZtqnXr1pnn1sPHKjQ0VClTplTKlClt1QU8wsnJSefOnVPu3Ln166+/ysXFJVaSaOTIkVq2bJk++OAD3b59W5JUsGBBzZ49W8uWLVOqVKls2QXgqYKDg1WtWjXdunVLa9eulb+//1Mf4+HhIV9fX/3zzz/mugwZMujKlSuxPu/FvAfG7DdmalnM+kfbxhSATwgkiIAkznjorh8lS5bU/v375evrq/Tp0+uTTz7Re++9p127dkl6kCTq1KmTRo8erUmTJuncuXOSHiSJkPAevYB8+FuBNWvWaMeOHfrkk09Uv359ZcuWTbt27dKRI0d07949DR48WNOmTdO1a9fk6uqqt99+WxMmTNCoUaPk4+PzsruCf8UkGDp16qRx48YpLCzM/KPv7u6ugQMH6ueffzZHEcVcgObLl0+dOnXS3LlzzelJj44wQsKKea1bt24tPz8/DR06VIcOHTK3N2zYUBUrVtTQoUMVGhpqHut8+fJp7NixWrhwodq2bSuJKZ22VLt2bWXMmFEjRowwp5c9mrh9uJ7bw//i5XN2drb6W7hr1y5t27ZNjRs31pUrV5QiRQq1bNlS1apV04QJExQYGKiwsDBFRkZqz549+uGHH5QlSxZlz57dhr3Ao06dOqX79++rQoUKOnbsWKwkUaNGjdSwYUPNmTNHH3/8sTl9Jn/+/MqSJYstQwee6s6dO6pVq5ZOnDihVatW6bXXXovX40JDQ3X9+nVzyqwkFS5cWLdv3441Onnv3r3mdkl65ZVX5OfnpwMHDsTa7759+8x2CYFPM0ASZ7FYFBwcrI4dO6pQoUKaPn26Vq1apaVLl6pChQrau3ev7t69a34gmzBhgrp06aIvv/xSM2bMiPPbVrx4Md+a3rx5UyNGjFCzZs303nvvaf78+ZIeXMDkyZNHNWrUkLe3tw4ePKhKlSqpfv36OnbsmN5++22NHj1aM2bM0JUrV+Tq6qpq1aqpZcuWkhi9YEt79uzRli1b1L17d5UrV04DBgwwvyEtWbKk8uXLpx9//FEhISEyDEOGYahIkSIKDAzU22+/Lcn69up4ee7du6c6deqoXr16+uqrr1SpUiX179/fTKp36NBBN2/e1OzZs2UYhpl4yJEjh7JmzSqJY2dr/v7+ql+/vg4cOKCmTZtKepCEiIqKsvqG9uTJkzp79qzKli0rDw8PRuvZSEwy9ejRo/r222/VunVrjRw5UufOnVO9evV0+fJl5cqVSyNGjFDt2rU1ZcoU5cmTRwULFlSTJk30+++/a/bs2VYXXLC98uXLa/r06fL391fJkiX1+++/m0mi6OhopUmTRi1btpS/v7+mTZumjh072jpkxIPFTn+eRVRUlBo1aqTdu3dr0aJFKlGiRKw2d+7cUWhoaKz1n3/+uVnvLkadOnXk6uqqyZMnm+sMw9CUKVP0yiuvmDdbkKT69etr1apV5qwPSdq0aZNOnDihhg0bPlM/noXF4C8kkOT9/fffKlasmPr27avOnTtLknr27KkJEyZowoQJatSokby9va0KWPfp00etWrWKdxYczy9mCuCVK1dUrVo1nT17Vq6urmbNjAkTJqhTp046f/68MmXKpIsXL6p06dLKnz+/xo4dq+zZs2vu3Llq06aNPD091alTJ3322WdmkTrYXlBQkE6dOqW+ffvq6NGj8vDwUMeOHdWqVSvt3btX9evX188//6y33nqL0SY29KTXfv369dq4caMmTJigNGnSqGrVqurYsaMaNWqkvHnzasWKFZIohvuyxdyRLK7XPWbdtWvX1LhxY23dulV16tTR3LlzlSJFCrPdlStXNGPGDI0aNUqzZs0yC1rDNs6ePauSJUsqICBAkydPlr+/v8aPH6+xY8fK399fS5YsUYYMGXTx4kX98ssvWrBggcLDw5U7d261b99eOXPmtHUXHNqT3kfXrFmjPn366Ny5c9q1a5fVZ8whQ4Zo+/btatGihQICApQ7d+6XFTKeUUhIiHx8fHT0zDV5eXk//QFJSGhoiApkS6vg4GCrItWP07VrV40bN061atXSu+++G2t78+bNdfbsWb3++utq0qSJ8uTJI0lat26d1qxZo6pVq2r16tVW50yvXr00cuRItW/fXkWLFtWyZcu0evVqzZs3z/yiQ3pQSPv1119XypQp9dFHHyksLEwjR45UxowZtX///gSbYkaCCEiCYhIOQUFB8vLy0pEjR1SiRAn99ddfypEjh3r06KFx48Zp8uTJatasmZInT66IiAitWbNG9evXt3X4DuXh5NAbb7yhrFmzqk+fPqpZs6bWrl2r9u3b6+LFi9q8ebPKli0rSVq2bJk6dOig2bNnq3LlypKkuXPnavbs2YqKilLNmjXVrVs3G/bKcT18t7mYP58PX7SGhYXp119/1eTJk7V27Vrdu3dPDRs21JIlS1SsWDEtWrQoXh9I8OLFJBok6cSJEzIMQx4eHsqcObNVu2PHjmnevHmaPXu2XFxc5OzsrNOnT2vOnDlq1qyZLUJ3WN99952uX7+u9u3by8vLK84kUczF6uXLl/Xee+9p3bp1yp07t5o0aaISJUro4sWL2rhxoxYvXqyBAwfqk08+sVFvHFvMe6dhGNq2bZt69eqliRMnqkiRIua0s7iSREhcYo7jxYsXtX37dp08eVIVKlRQ9uzZlS5dOknWSaKlS5eqaNGiOnnypD799FPly5dPQ4cOpbRBIkeC6D9ly5bVtm3bHrvdMAzdunVLnTt31p49e3Tp0iVFRUUpZ86catasmXr06BHr//fo6GiNGDFCU6dO1eXLl/Xqq6+qT58+cX7G+P333/Xxxx9rx44dSpYsmWrUqKHRo0eb51tCIEEEJFG//fabunTpoh49eiggIEBFixZVs2bNFB0drdGjR2vixIlq2bKlWeG+X79++vrrr3Xw4MFYF0RIWNeuXVOJEiWUKlUqzZ07Vzly5DD/WMycOVNt27bVN998o/fee0/SgxFF3bt316ZNm1S6dGldvXpVvXv3loeHh77++mtbdsWhPZwcGjVqlA4fPiwXFxcVK1ZMgYGBsdpv3bpVW7Zs0bhx4xQSEiI/Pz+tW7dOhQsXZhTRS/bwsWvbtq02b96s69evy8fHR0OGDFHjxo3l5uZmJiCioqIUGRmpQYMG6dChQ9q4caNq1aql77//XsmTJ2cE0UtSvHhxHThwQOPHj1erVq3k6ekZZ5Io5vjeuHFD06ZN05IlS3Tw4EFze5EiRdSuXTu1b99eEjWjbOXUqVOqW7eucuTIIU9PT82dO1fSf8nbR5NES5cuVbp06Z44igwJ6+H3zpjz5tixY6pVq5aCgoJ0584dGYahZs2aqWPHjubdODdt2qShQ4dq+/btypIli8LCwnTnzh3t2rVLefPmtWWXEA8kiBybi60DABB/MX+cIyMjFRgYKMMwlCFDBnl7e+utt97SV199JelBMeoPPvjAfNyuXbu0Y8cOVaxYUalTp7ZV+A5r8ODBOnPmjIoXLy5/f3+5uroqIiJCHh4e5p07wsLCzPalS5eWxWLRkCFDVKpUKV28eFE//PCDxo0bZ7bhg/LLFR0dbX5IrlWrlrZs2aLMmTMrMjJSK1as0Nq1a80pSHfu3JG7u7vKli2rsmXLqmnTplq8eLHGjRunoUOHauHChVycvkQP362sbt262rlzp5o2barMmTPr119/Vdu2bRUUFKR27dpZFXxPnjy5RowYoaCgIM2cOVN9+vTRkSNHrOoDIGHEvL/t3btXlStXVu/evRUdHW1Os330/S9mBIqvr6969uypHj16aOfOnbp3757Sp08vX19f884wJIdsZ+/evQoKCtLJkyf1zjvvSHpQB8zV1dU8Ll26dJEkTZo0SeXLl9fWrVvNekP8zXu5OnfurEaNGqlUqVKSHtRKPH36tKpWrarcuXNr5MiRKl++vL7//nv17NlT//zzj3r37q233npLFSpUULZs2bR8+XJt2bJFadOmVY8ePczpN0gaLJLs7bSzs+4kDANAknL+/HljzZo1xttvv238+OOP5voLFy4YOXPmNFxdXY3PP//cuH//vhEdHW2sX7/eqFy5spExY0bjzz//tGHkjuvOnTtGvXr1DC8vL6NLly7GP//8Y25r3LixkS5dOuPatWuGYRhGVFSUYRiGsXr1aiNt2rRG8uTJjVdeecUYM2aMTWJ3dPfv37f6/YMPPjAyZcpkzJ0714iIiDAMwzA6duxoWCwWo0qVKma7u3fvGoZhGNHR0YZhGEZISIjx+eefG+nTpzf27dv3kqJ3TOfPn49z/RdffGFky5bNmD9/vnH79m3DMAxj+fLlhsViMZycnIwBAwYYISEhZvuYczE6Otr4559/jAIFChgNGzY07ty5Yx5XJJx79+6Z/12uXDnD3d3dGDdunBEcHGwYhvFMxyCmLcfN9r755hsjV65choeHh7F3717DMP57n334nBs6dKhRsGBB48yZM7YK1aHVq1fPSJEihfHzzz+b627fvm106NDBKFmypLF7925z/ZAhQwyLxWJ4e3sblStXNvbs2WO1r6ioKPPYImkIDg42JBm/nblmnLtxx66W385cMySZf0sQG1PMgCTA+Pfb0rCwMOXLl08uLi66e/eu9u7dK39/f3PEwvnz51WzZk2dPHlS6dOnl5ubm8LCwuTk5KQVK1aoYMGCtu6Kw4kZGn/37l01bNhQmzdvVps2bTR69Gj16tVL06ZN0/z581W7dm2rGg0Wi0Xnz59XeHi4LBaLWcyRb78T3pw5c/TLL79o7Nixkv4bYr979261bdtWH3zwgVq3bi0fHx/t3r1blSpVUr58+XTw4EGVK1dO69evlxS7uO7vv/+uAgUKmHfuwYu3bt06VatWTStXrlSNGjXM9f/8848CAwPl5eWlYcOGKU2aNNq5c6cqV66s2rVrK3Xq1JoyZYqGDBmiDh06KGXKlLH2XbNmTQUFBZm3okXCerhmlCQVKlRI4eHh6ty5s957773H1iRC4vCkv1Xffvut+vfvrzt37mjr1q3Knz+/+T4b8zjj37oeMaNs8fJs2bJFDRo00MiRI9WyZUvzPIyOjtb777+v5MmTa+LEiZKkzz77TCNGjNDcuXN19epVde3aVVWqVFH//v3jvNsTkoaYKWa/nbkmLzubhhUaEqL8TDF7Iq4ygEQuKipKFotFx48flyT16dNHN27c0MWLF7Vjxw5Jkru7u+7du6dMmTJp06ZNGj16tEqVKqWCBQuqa9eu2rZtG8khG3FxcVFUVJSSJUumRYsWqXz58vruu+8UEBCgqVOn6vvvvzcLUcdMg7FYLDIMQ5kyZVKePHnM5JDB7bQTXGhoqFatWqWpU6eqb9++kv47LjHTHCpXrvxgbv7Ro6pUqZLq1aunuXPnKjAwUBs3bjSnTjxaMyMiIkLp06fX5cuXbdAzx+Dn56eyZcuqWbNmWrt2rbk+derUevPNN9W5c2elSZNGJ06cUM2aNVWzZk199dVX6ty5s9KnT68BAwZo/PjxCgkJMR9rGIbOnDmj0NBQubi4WE0HRcKIjo42L0rbtGmjxo0bKyoqSqdPn9aIESM0a9YshYWFme+VSFyioqLk5OSkv//+W9OmTdNHH32kmTNnmp9Z3nvvPQ0ePFgpUqRQ5cqV9dtvv8nZ2dl8XHR0tCwWC8khG4mKilJYWJjCw8Pl4uKi48eP69NPP5WTk5M+/vhjjR8/XtKDRN+IESM0evRo1a5dW126dNHbb7+tw4cPq3fv3tq/f7+NewLgeTCCCEgCTp48qaJFi6pBgwaaPn26pk6dqsDAQOXIkUPffvutOT/80W9ckXjEfDt69+5dNWrUSMuXL1flypU1b948+fr6MjIoETl16pSGDRumJUuWqEOHDho2bJi5LSwsTJ6enrp27ZoqVKigTJkyadKkScqWLZvOnDmjEiVK6Nq1aypcuLBVkdzIyEh16tRJ3377rXm3QSSMX3/9Vb169dKOHTv0v/buO77G8//j+OvkZIjIQGKLWdSmCEGVUhGxaY2oIFYVNRMJEiNIYpSqVmK3araKoFbFanTYYo+YQRKZJCfr/v2R37mbCOrbVo4mn6dHHuW+r3Ny3efuGff7XNfn2rRpE87Ozjn263Q6hgwZwsWLF/n222/VALZ79+6cP3+ea9eucezYsRzffv/44498/PHHHD16VML2PKRfun7kyJG0bNmSq1evEhQUxOXLl/Hz83thTSJhOPr3svDwcNq3b09KSgpGRkYkJiZSunRpRo8ezWeffQbA6tWr8fHxIS0tjX379lGrVi15L3wDxMTE0LBhQ4oXL86QIUPUukLffPMNtra2QNZ7Yb9+/dBoNKxatUoN8959912Sk5OJjY3l559/lkVR/qNkBFHBJq/AQryhMjIy1L9/+eWX1KlThwEDBgAwbNgwFi9eTGRkJF5eXoSFhQF/jljInvtKBvxm0H87ampqysaNG+nSpQvHjh1j+vTpJCYmqt+aCsOrUqUK3t7edOnSheDgYDw8PNR9pqamAERERPDw4UNcXV2pVKkSkLUUqVarxcvLi+HDh+e4TzMzM9zd3Tl79qyEQ6+J/rWubt26+Pv706FDBz788EP27duXq+358+epWLGiGg5duXKFa9euMWPGDM6cOZNrakTXrl25du2ahEN56MSJE+zfv5+BAwcyYcIE3nvvPYYMGcLhw4epX78+3t7erFq1isTERBlJ9AYxMjIiMjKSrl27Urt2bTZs2MCjR4/Yv38/8fHx+Pj4qCOi3dzcmD59OoULF6Zhw4ZcvHhRwiEDy8jIoHjx4hw/fpyIiAjGjh2rFqTWh0OQ9XkzIiKCjIwMNRw6duwY6enprF+/njNnzkg4lA9o8umPeDkZaiDEG0qr1XLp0iV27NhBSkoKbdu2VUcKAYwcOZKMjAymTJmCh4cH/v7+NGvWLNe3qPKtat562befz4ZEvXr1YvXq1SiKwuzZs7G0tJRvTw1Mv6JOpUqVGDt2LElJSSxbtgwLCwumTZumBkTJyclER0djYmICwP379wkLC6NZs2aMHTuW4sWLAzlXm2vatKlhDqqAyL7S3KNHj6hbty67du2ib9++rF+/nrZt2wJZYZ2VlRUXL14kMjKS6OhoQkJCiI+Pp0KFCtSpU0e9P31wa2RkRIkSJQx2bAVReno6cXFx2NvbY25ujqIoZGZmYm5uzs6dO2nRogUBAQEADBgwQL4JfoP88ccfpKamMnr0aNq0aQPA3r170el0LF68mPLly6tt3dzcePr0KV9//bX6/BWGoz8HiqKQmJgIZE29vnfvHrVr11b3GRsbU6VKFY4dO0ZgYCDFihVj06ZN3Lt3j0KFCmFhYWGwYxBC/DMSEAnxhkpLS2PZsmUsWrQIrVbL3LlzgayLFuX/l23WLwc7ZcoUvL29mT59Oi1btjRktwu07OFOZGQkpUuXztUme0i0efNmevXqxbp160hPT8ff318ucgwoMzNTDXwGDhxIbGwshw4d4smTJ/j6+pKens6MGTMAqFixIq1atWLQoEHs27ePBw8e8NNPPzFv3jw1HAIJaPOKkm0p+44dO3Ljxg1MTU1p3Lgxhw8fpnv37mzatAknJycg6zXT3d2dypUrY21tzePHj5kxY0aOkUP657IEtoZRpEgRTE1N1dEm+nOcnp6OpaUlPXv2xMfHh9mzZ5OcnMy4ceNkinUei4iI4PHjx8TExFCnTh1KlSoFZI2mjI+Pp127dhgZGTFx4kQ+//xzli5dyocffkiRIkWIj4/n6tWrNGrUiE8++YR+/fphbW1t4CMS+i81fv/9d8aPH0/t2rUZN24ckyZNIjMzkw4dOqDRaDA2NmbNmjW0b98eHx8fNBoN5cqVIyQkhHLlyhn6MIQQ/4B86hHiDZJ9ipGJiQmDBw9m9OjRmJiYcPDgQXQ6HUZGRupKHwCjR49mzpw5hIaGMmfOHFJSUgzV/QJPfyH54YcfqoFe9qmCes+GRE2bNmXZsmU5ataIvKc/f3369GHXrl28//77bNu2ja1bt9KoUSMWLFjA5MmTAahQoQIeHh507dqVrVu3cvfuXebPn6+GtjLdJW/pgzhvb28OHTrE9OnTOXToEKGhofzwww/Ur1+fnj17qoWr3333XX766SeGDBnC4MGD+e677/D09ASQqZ557HmvkQC1atXC1dWVr7/+mv3792NkZJSjzl7p0qVp3bo1JUqUwNzcXMKhPPbDDz/QtWtXGjVqRPv27enZsycbN24EoGTJkpiYmBATE4OHh4caDrm6ulKkSBEAPD09WbFihVr0XcIhw9K/7ulfS7t06cL06dNxdXVlx44d3L9/H09PzxzF/y0tLTlw4ADbtm1jx44dHDx4UB2BKYT475Ii1UK8YSIiIrhy5Yq6stXFixdZvHgxy5YtY9y4ccybN09tm33ESlBQEK1atVJragjD6dixI5cuXSI8PJxChQq9sICqvnC1Tqdj7969dOrUyQC9FdldvnyZli1b0rdvX2bPnk3hwoXV7b6+vmzbto3x48czc+ZMIGvo/ZMnTwDUb89lmqDh6GsFHT58mGLFiqnbf/31V8aNG8f58+f5/vvv1elmz5Jzl7eyBz779u0jJiYGjUaDk5MT1tbW/P7777i5uXHt2jW2b99O+/btAXjw4AEeHh4UK1aMhQsXGvIQCqRVq1YxdOhQunTpQv369dHpdMybN49KlSqxceNGSpYsSfXq1bGzs+PWrVssXbqUPn36qK+n+sLjPXr0YNq0aRLuGZj+s0hkZCSnTp3i/v37lC1blg4dOqhtfvnlF7p27Urp0qXx9/dXR2OK/ElfpPpCRP4sUl2zohSpfhl5RRbiDaEoCunp6bRt25bk5GSCg4Nxdnbm7bffZuzYsWRmZrJgwQJMTEzUVZWMjIzUN/ahQ4ca+AiE/uJy/Pjx9OrVi9mzZzNjxowXTjPST5cwMzNTwyG5QDWsp0+fEh0dTf369SlcuDDp6ekYGRlRvXp1fH19OXv2LH5+fmi1Wnx9fbG0tMTS0lK9vaIocv4M6PHjxxgbG6vhkD6AcHBw4JNPPqF///64urqyevXq517gyLnL14GqGAAAVAVJREFUO9mXsu/ZsydHjx7l8ePHQNboobFjx9K/f38CAwPx9vamY8eO9OjRg7JlyxIREUFISAhffPGFen+yklne+Oabbxg8eDATJkzg008/VQsRv/XWW7i5ubF9+3a8vb2ZNm0ac+fOpW7dunTs2FENh8LCwpg7dy5paWkMGjRIwiED09duCw8Pp1OnTsTHxxMbG4upqSnvvPMOK1eupHr16jg6OrJt2za6dOmCh4cHRkZGtGvXTp5zQuRD8klIiDeERqPBxMSEbdu2kZ6ejoeHBzt37gSgWrVqjB8/niFDhuDv769OhQCkqKMB6KdEPDsVRf9BqXHjxtSuXZvdu3fz9OlT4MVTjp79cCwXqIZVvHhxbG1t2bVrF3FxcRgbG6PRaMjIyKB69epMnjwZS0tL5s+fz6hRo3LdXj4sG4b++dWhQwcuXLjAmjVrgKznV2pqKgD9+vWjQYMGZGZm4uzsTEREhKG6K/jzta5fv34cPXoUHx8fjhw5wsmTJ7l//z7e3t4cO3YMZ2dnvv76a8aPH09oaCirVq0iPDycwMBAhg0bpt6fPPdev6NHjzJgwACcnJwYMWJEjlWq6tSpg5GREffv3weyQr8hQ4YQHh6Ok5MTn3zyCa6urgwePJiTJ0/y/fffU7FiRQMdidAzMjLi9u3bdOjQgWrVqrFy5UoiIyP56quvCAsLY9SoUcTGxgLQrFkztm3bRnR0NO7u7vz8888G7r0Q4nWQKxEhDOjZ0CAtLY1atWpx+PBh7t27h6en53NDosDAwOdenIrXKywsDMgK5dLS0tQLnJs3bwJ/XqBYWloya9YsTpw4oV6oysXLf4O9vT3t2rVj165d/PTTT6SkpKDRaNTzl5CQgIWFBW3btlVXdBGGpz8/Tk5O2NnZMX/+fHbv3g2grjx39uxZYmNjmTNnDj/99JNcnL4B/vjjD8LCwvDy8uLjjz/GwcEBnU5HQkICzs7O6jlycHDA39+f33//nfDwcPbt28eYMWMAqRmVl6ysrGjatCknT57k9OnT6HQ6dd/Tp0/JzMykatWqAJQvX57PPvuMjRs3UqhQIfbs2cPp06dp3rw5R48elVo1b5CdO3diZGSEp6cnnTp1omTJkpw7d47ChQvTo0cPzMzM1LbNmjVj/fr1mJmZUalSJQP2WgjxukgNIiEMLC4uDiMjI3UerH5KxKVLl3B0dKRs2bLMmTMHFxcXAK5evYqvry8hISFcvXpVll7OI8eOHaNVq1a4u7vz9ddfq9t79+5NaGgokyZNolu3buoHpkePHtGvXz+SkpLYsmULZcuWNVTXxSvST++LiYnhgw8+ICoqitmzZ+Ps7EyxYsWIjIzE398fRVHw8fHJUeNGvB7Zpw3pz89fTSUKCQmhS5cuVK9endGjRzN8+HC19tDGjRvZsmULNWvWzHGfwjBCQkLo1asXv/32G3Xq1OHgwYN07NiRbt26ERgYSJkyZQCIjo7G1tZWvZ3+/wGZVpb3Ll68yODBg7l+/TqLFy/mo48+4vHjxzRt2pSyZcvy888/5zo3GRkZxMXFYW5ujomJibpapMg7z3uu6LcNHDiQP/74g3PnzgGoq8599dVX9O7dmyJFipCYmJhjOnVKSgqFChXK02MQeefPGkRR+bQGkZ3UIHoJ+VQkRB66du0aISEhLF68mKioKOLj46lduzZBQUHEx8cDWVMi0tPTqVGjBseOHePu3bv4+Piwfft2IGue//Tp07l8+bKEQ3moXLlyDBgwgK1bt6orVQE4OjrSrl07Jk6ciIuLC6NHj+bx48eUKFGCkSNH8uuvvxIeHg7IN92G8KLH/HkrJ+mDguLFi7NixQpKlizJ4MGD6dGjBx4eHgwaNIgvv/ySGjVqqOGQfMfy+mRmZqLRaEhLSyMzM5OEhASAHIHRsxRFwcXFhR07dpCamsonn3yCnZ0drVq1YsaMGXz88cdqOAQypTMvveg5l5GRgY2NDadPn6Zjx450796defPmqeHQ4sWLGT9+vDpVEP78f0DCobz39ttvs3z5cqpWrcpnn33GihUraN68OVZWVnz11Ve5wiF9jZvixYtTuHBhCYcMJCUlhYcPH3LkyBEOHTpEamoq6enpAJQtW5b09HSSkpLw9PRUV53r16+fuurcoEGD8PLyUu9PwiEh8i8ZQSREHtm8eTP+/v5cvHiR1q1bM3DgQDp16kT37t05fPgwc+bMwdXVVV3qVT+S6Ntvv8XNzY3WrVszbNgwevbsaeAjKVji4uKwsrLCyMiIO3fu4Ofnx6ZNm+jTpw9ffvklkDU18MyZM8yfP5+ff/6ZzMxM+vbty8CBA5kwYQKJiYns27dPvqnIY/oC7nFxcezcuZN79+5RqlQp2rdvT8mSJf/y9omJiUycOJHDhw9z9+5dKlSowJAhQ3IEhOL10I/sefToERMmTODixYukpaXRpk0b+vTpQ6NGjdTaUNnrsOk/0mg0Gi5evMgff/zBnj17KFu2LI0aNaJXr15qOwkX8k72x/vUqVM0aNAAgDt37uDi4oK5uTnnzp2jV69ezJgxQ61tc+PGDT777DM0Gg1r1qzBxsbGUIcgnnHhwgWGDh3KL7/8QqVKldi8eTMNGzY0dLfEcxw6dIg1a9awZcsWkpKSAKhbty7dunVj9OjRhIWF0alTJ9q0acORI0f46quv+Oijj9TC4tu3b2fKlCkMGTKEkSNHSrBeAMgIooJNAiIh8sC3337LoEGD6NevH927d6dTp07qBVBaWhpubm788MMPBAYG0r9/f6ytrdX9Z86coXPnzty5cwcHBwf279+PhYWFoQ+pQFi/fj2zZ89m/fr11KxZ86UhEfy5AtasWbM4dOgQd+7cUYdkBwcH07lzZ5nSkkf0F6QPHz6kVatWxMfHExcXR2ZmJpaWlvj6+tK1a1fKlSv30tsDxMbGkpSUhJmZmTpqT87j6xcdHY2DgwOWlpbUqFEDrVbLDz/8gJ2dHe7u7nh7e6PVanOFRH9Fzl3eyv54jx8/nk2bNhEcHKyuIufl5cW8efMoX74827dvp1atWgDcvXuXZcuWsWzZMhYvXkzv3r0Ndgzi+c6fP8+ECRM4fvw4wcHBdO3aVUYIvWG+++47Jk6cSLly5WjdujUNGzbk3Llz/Pjjj4SHh+Ps7My3337LzJkzWbhwIb179yYgIEB9bzxx4gTe3t5ERkYSEhJC+fLlDXxEIi9IQFSwydqSQrxmR44cYfz48bi7u+Pp6Zlj1Q9FUTAxMWH16tVA1rxvjUZD7969KV68OJD1Aez999/H29ubjIwMCYfykK2tLZGRkYwYMYJly5ZRo0YNypcvj7e3N5AVIBkZGalLLZuZmWFvb09QUBCnTp3i8OHDBAQEEBkZyTfffEPnzp3lwjSPaDQaUlJS6N+/P5aWlixevJgWLVpw+PBh1qxZw+jRozl16hTjxo1TL0ifvb3+wrZo0aIULVpU3SdL2eeNpUuXkpaWxpIlS2jRogWQVRC+f//+LFq0iNu3b/P1119jbGz8P4U+cu7yjn4kLMCtW7eIioqiUKFCeHp6otVqadeuHbNnz+b+/fts2rQJV1dXxo0bR3R0NMePH2fr1q34+fmp4ZCM/Hqz1K5dmwULFjBkyBA+/fRTADp16iTTj94Qq1evxt3dnWHDhjF48GB1hNeHH37I5MmTGTBgAN9//z3dunXDz8+Px48fs2bNGjIzM3FxceH8+fMcOnSIy5cvc/jwYQmHCiCNJusnP8lvx/M6SEAkxGui/yC7a9cuLCwscHNzU8OhZy8wTUxMWLVqFQDjxo3j3r179OnTh7i4OL799lvS09OpUKFCriXRxevVtm1bvv/+e/r168fgwYNZsWJFrpBo3bp1AHzxxRdotVpSU1MxNTWlQYMGNGjQAGdnZ1avXs0XX3zBtm3b6NKliyEPqUCJiori8uXLDB06lA8++ACADz74ACcnJ6pVq8bMmTNJTExk+vTpvP3227lu/6IgQS5Q88bly5exsrJSw6HU1FQqVarEtm3bGDVqFN9//z0mJiYsWbIErVYr4cEbRlEU9T2rS5cu3Lp1i+TkZIoVK8bvv//OuHHjmDt3Lh07dmT16tVUrVqV7du3M3jwYLRaLQ0bNuSLL75Ql7KXkV9vppo1axIcHKyGRFqtlg4dOmBubm7orhVoa9euZdCgQYwdO5bx48erNb30tcAKFy7M5s2bcXV15bvvvmPVqlX4+flRvHhx1q9fz6ZNmyhZsiSNGzdm5cqVz32PFELkTzLFTIjXSKfT0ahRIypUqEBISMgr3Wb06NEsWbIEc3NzjI2NMTY25tChQ7KktoEoisKhQ4fo378/ZcuWZcWKFbz99tvqdLPZs2ezceNG+vbty5IlS4Cc35pD1qgHBwcH3NzcCAgIMNShFDhXr16ladOmeHp6MnHiRNLS0nJMf5g7dy5eXl6MGDECX19f7OzsDNhb8azx48ezceNGfvnlFzVc108ni42NZdiwYfz88894e3urdWrEm2fs2LEsX76cr7/+mvfff5+SJUsSFBTEzJkzsbW1VVcKhKyabzdu3MDGxobChQtTqlQpQMIhQ3uV8PXChQuMGDGCY8eO8cMPP9C5c+c86p14Vnh4OHXq1KFatWrs2rWLypUrA+RaWU4/Nffdd9/l/PnzHDhwgAYNGnD//n2SkpIoUaIEpqamai0iUXDop5hdvJU/p5i9XUGmmL2MvNsK8RqlpaWRnp6uLs2sXzHiReLj41m8eDH79+9n2rRpjBkzht9++03CIQPQr5Ck0Who2bIl33zzDffu3WPw4MFcvHiRzMxMypcvj5eXFx999BHfffedOsTe2Ng4x4o9lSpVwtHRkQMHDpCYmGiQ4ymIypUrR6lSpfjpp5+ArJF6mZmZ6rn19PTEw8ODoKAgfv75Z0BWmnuTNGzYkPv376vnD1BrDhUtWpSlS5dSrlw5goODefDgASCryr0Jsp+D2NhYfvvtNxo3bkyvXr0oVaoUGo2GYcOGERgYSGRkJJMmTWLfvn0A2NjY0LBhQypXrqwWkpcpnXlL/96Vnp6ufmZ52cqBejVr1mTJkiW0bduWGjVqvP6OihcqW7YsY8eOJSIigqCgIJKTk4Gco1+1Wq16fn19fYmLi+O7774DoHTp0lSrVk0NaoUQBYu84wrxGhUpUoSSJUty9epVdbj98z5g6bcdPXqUcePG0bJlSzw8PJgxYwZVqlTJ624XeNm/rdbpdGi1Wlq0aPGXIdHmzZsZPnw4QI6iudevXyc8PJySJUv+T8V0xf9Of3Ganp6Oubk5Hh4eHDx4kJkzZwJ/ThvTt/P29qZVq1Z4eXnx5MkTuRDNYy8LdPr160e3bt3w8vLit99+U9vrQyJbW1uCg4O5dOkS33//PSDT/wwtIyNDXeYcoGjRojx9+hQjIyNMTU0B1IvSPn36MHnyZC5cuMDEiRPZvXt3jvuSpezznn5UyZUrVxgwYACNGzemSZMmTJo0iVu3bmFkZPTSkKhOnTps376datWq5WGvxbNsbGzw8fFh1KhRBAQEMHXqVJ4+fZqrnX6kc6VKlbC0tCQ+Ph6Q55wQBZ18EhbiNdF/QP7ggw+4fPky06ZNA3juByz9RWlQUBCnT5+WVUAMTH8+3N3d8fLy4unTpxgbG+cKiS5duqSGRN7e3nTu3JmgoCBCQ0PV+8rMzCQiIoKEhAT8/Pzk27h/mf65pP9vTEwM8OcH35YtW9K9e3cCAgJYunQpkPM5WKRIEbp168aDBw+4evVqXne/QMvMzESj0ZCQkMDu3bvx9/dn69atXLt2TW0zceJEypQpQ7du3QgPD1cvXPQ1h2rVqsVbb73FyZMnARlBZGjZp6x89tlnQNbF56lTp/j999+BrOdmWloaAP3796dSpUrodDqmT5/OsWPHDNJv8Wf4eunSJZo1a8bZs2epWbMmJUqUYN26dTg6OnL9+vW/DNH1QaAwLCsrK6ZOncr48eNZsGABPj4+uUIi/fugra0tgCyCInLR5NMf8XISEAnxmugvZFxdXSlfvjxffvklK1euBLIuUDMyMnIERYcPH+b27du0bdsWkAsdQ0tLS+P8+fNs2LCBefPmPTckGjRokBoSlStXjilTprB//37ee+899X6MjIxo1qwZFy5coEGDBoY7oHxIP9IrJiYGT09P2rRpg6OjI5MmTeL27dtA1sWph4cHderUwcfHR11xLvtIripVqqDT6Z77Dat4PfTn7uHDhzg7O+Pm5sbkyZPp0aMHn376KZcvXwbAwcGBGTNmULhwYdq1a8ehQ4dITU0Fsl5j4+LiMDIyUld9FIaRfUptYGAg9+7do1KlSgD4+PigKAqzZs3izp076uqdADdu3MDU1JQ+ffpw/fp1li5dqoa8Im9pNBoSExMZMWIEtWrVYvXq1axbt46QkBCqV6/Oo0ePOHPmjKG7Kf4H2UOi+fPn5wqJ9GFfcHAwhQoVomvXroB8/hSioJOASIjXzN7envXr15Oeno63tzeLFi0Csi5Q9W/OJ0+exN/fn4SEBPr27QvIEF9DysjIwMTEhNDQUBo2bMhXX31FYGDgc0OioUOHcuHCBTIzM6lQoQJt2rQBctZqKFy4sFzA/sv0dUkePXpE8+bN2bp1KxqNhvLlyzN//nw+/fRToqOjAWjcuDEBAQG8/fbbjB8/Hk9PTxISEoCsEUf79++nbNmyco7yiP7cRUVF0bx5czIzM1m4cCHJyckEBASwd+9edRSeRqOhc+fOzJ8/n7Jly9K5c2dmz57NmTNnuHjxIkFBQdy+fRtHR0e1vch7+sD10qVLPHjwgNatW+Pu7g5AjRo1mDVrFvv27eOTTz7h0KFDANy+fZsdO3ZgYWHBtGnTGDlyJFu3buXOnTsGO46CLjY2lmvXrtGrVy/eeecdIGsa7pEjRwgKClK/wNK/v0mQ8OZ7XkiUlJSk7v/jjz/Ytm0bTZo0oU6dOoC8jgpR4ClCiDyxb98+xdraWtFoNEr37t2VTZs2KWFhYcqUKVOUZs2aKcWLF1fOnDlj6G4WCBkZGTn+m5qamquNfltycrLi7OyslCpVSvH19VWePHmiKIqipKWlKaGhoUqJEiWUGjVqKDExMXnUe6GXkJCgvP/++4qjo6MSGhqqKIqiJCUlKatWrVK0Wq0yb968HO1PnTqluLm5KSYmJkq1atWUDh06KB988IFiYmKiBAQEGOIQCqwnT54o3bp1Uxo1aqQcOXJEfS4+fPhQKVGihDJ79uwc7TMyMpRz584pgwYNUrRaraLRaBRLS0vFyspKmTt3riEOQTzD29tb0Wg0Svny5ZUvvvgix77Hjx8ry5YtU4oXL66Ympoq1atXVypXrqxoNBpl1qxZiqIoyokTJxSNRqMsWrTIEN0XiqKcO3dOKVKkiLJ3715FURRl4sSJiomJiRIUFKQ8ffpUUZSs975FixYpsbGxBuyp+F/Fx8crEyZMUDQajTJp0iTl6dOnyoULF5SOHTsqpUqVUi5evGjoLoo3SHx8vAIol25FKfdidfnq59KtKAVQ4uPjDf0wv7GM/ypAEkL8O9q2bcvx48eZNGkSoaGhbN26FYBixYrRvHlzVq5cKSt/5AF9Ec5Hjx4RHBzM0KFD1eXNP//8c9q0aUPdunUxMTEhLS2NQoUK8f3339OzZ08WL16MVqtl3LhxFC5cGEdHR7799ltu375NsWLFDHxkBc/27du5cuUKM2bMoHnz5kBWDYX27dtTs2ZN9u3bp9ZB0Wq11K9fn4CAANzc3Pj888958OABZcqUISgoCDc3N+DVlnMW/9zFixdJSkriww8/xNHRUR1NGR4eTkZGBvv27SMlJYW0tDTatWtH69atqV27NitWrGDgwIHcvn2bzMxMKlWqpJ57WQrdsJycnAgPD2fbtm0cOnQIV1dXbGxsgKxi1UOGDOH9999n3rx53Lt3D0tLS6ZNm8aAAQOArJG0ZmZmsjBDHtE/XzIyMjAyMkKj0VCkSBE0Gg1//PEHhw4dYuHChSxdupR+/fphbm4OwMyZM9m9ezedOnVSz6948+lHEkHWNNDo6GgiIyM5fPgwx44dk8+f4rk0mqyf/CS/Hc/roFEUGR8qRF5KSkoiKSmJM2fOoCgKdevWxcrKiiJFihi6a/mePhx68OABtWrVwsHBgeXLl1OmTBn27NlDhw4d6Nu3L97e3rz99ttAVi0ifVjUuHFjrl+/zqRJkxg3bhwWFhY5AgUJF/KWv78/X3zxBVeuXKFw4cI5Hn93d3cOHDjA6dOnsba2znVbfYHktLQ0taiqBAx5JykpiR07dtCzZ0+1Hs3Nmzdp3bo1pqamvPfeexQqVIgffviB6OhovvvuO7p37/7C+5Nzl7eyP9eyP/ZhYWHMmjWL0NBQgoOD6dOnT652+v+mpKRQqFAhICscGj9+PJGRkRw4cICyZcsa5sAKCP174c2bN9mwYQPFihXDzc0NMzMzvLy8CAgIIDMzk1WrVtGzZ0+1ePHvv//OhAkTKFmyJCtWrMDS0tLARyL+VwkJCcyZMwd/f3+MjY35/fffqVevnqG7Jd4wCQkJWFtbc/l2FJZWVobuzr8qMSGB6vZ2xMfHY5XPju3fIiOIhMhjRYoUoUiRIpQqVcrQXSlQsodDjRo1onr16vj6+lKmTBkA2rdvz5dffsnIkSNRFAVvb29q1qyJiYmJeiEzY8YM+vfvz5o1a0hKSsLPz09dLQtk3n5eGz58OJ06daJw4cK5Lj5Lly5NcnKyuqT282g0GjWcUP6/Lo54/RRFoUiRIvTp0wdALdjv7u5O2bJlWbZsGbVr1wagc+fOjBw5kjFjxvDOO+9QoUKF596nnLu8o38t1cv+utesWTOmTZtGcnIyI0aMoHDhwmrhWyMjoxzPM304NGHCBI4fP87ly5clHMoDmZmZaLVaLly4gLOzM9bW1vTu3RszMzMA+vbty/Xr19myZQuJiYkkJCRgYWFBSEgIS5Ys4cqVKwQHB0s49B9lZWWFh4cHlpaWdO/eXUYOCSFykYBICJHv6T8QP3jwgMaNG1O+fHkWL15Mo0aNcrQbMWIE6enpjBkzBkANifQXMnFxcdjb21O4cGGqVKmSIxwSec/a2lq9SHk2IKhQoQI6nY6EhASKFSumrtBz7949qlevrrbXX9xKuJd3nn2stVotWq2WpUuXYm5ujr29vbqvbdu2DB06lIkTJxIdHf3CgEjkjezhkJ+fH2fPnuXBgwcMGzYMR0dHKlasiIODA3PmzMHT05MBAwawZs0aNSR69txHRETw+PFjihQpwuHDh9WRm+L1MTIy4u7du3Ts2JFq1aoxZcoU3n33XXV/7dq1GTNmDJmZmYwePRo/Pz80Gg0ZGRlYWFiwZ88eqlWrZsAjEP+UjY0Nnp6eEqwLIZ5Lrm6EEPmekZERcXFx1KtXD1tbW5YsWaKu0AJw7do1PD092bJlC6NGjcLY2JiRI0cC8Nlnn9G4cWMePHjAb7/9RufOnfHz8zPUoRQ4+gtSnU6HoihqWKf37Adc/b/Nzc1JSEhQp8I8fvyYpUuXsnv3boKDg6lZs2aeHYP4a4qiUL169Rz/hqxAoWjRogASyBqYPmgHcHFxISwsjHLlymFmZsbAgQP58MMPGTt2LA0bNsTBwYG5c+fi6emJu7s7mZmZz50iWLFiRRYvXoyiKDIiJQ/oXw937dpFYmIin376KS1btgT+nHar0WhwdHSkZs2a9O3bl9DQUFJSUmjSpAnt2rXLEeCK/y4Jh8Sr0Pz/n/wkvx3P6yCftoQQBcJvv/2GkZFRjpozkFX35IMPPsDMzIzbt29jb2/PiBEjMDIyYty4cZw+fZoWLVoQFRXFzp07Wbx4sXpbqTn0eimKglar5eHDhzg7O+Pu7s7AgQNzhUTPU6hQIfX8JCUlERwczLRp05g2bZqEQ2+g7M+j7M+r+/fv89NPP1GvXj1KlChhqO4J/ryg/OSTTzh37hxLliyhbdu22NnZMWTIENavX09ycjLe3t40aNAABwcH/P39GTt2LD179uTatWtUrlw51/1K/b28o39eXbhwAUVR6NixIxqNJsfUv/T0dIyNjbGxsaFbt2506tRJwlkhhChAJD4WQhQILVu25Ouvv0an09GvXz9u3rxJREQErVq1wt7enh07duT4ZnTYsGF88803lC9fnpCQEM6ePUtAQADDhg1T20g49HppNBoSEhL48MMPOXXqFF5eXnzzzTfodLoX3kY/8sTW1hatVkt4eDiLFi1i8uTJzJw5E19f3xztxL8vMzMTyBr99VeynwdFUXKEQzdv3mTZsmVs27aNESNGULp06dfTYfHKTpw4wc8//8zw4cNxdnbGzs6OI0eOsHHjRqpUqcKOHTuYPn06p0+fBqBJkyYEBASwefPm54ZDwjA0Gg0pKSncvHlTfd5B1nNQHwZt2bIFnU4n4ZAQQhQwEhAJIQoEc3NznJycWLRoEXFxcTg5OeHo6EjlypVZsWIFVatWVdvqPyx3796d9evXc/r0afbt26fWJtJfAIvXKyMjgy+//JITJ04wYsQI3nvvPUaNGsXatWtfGBLpwwUzMzMyMjJYt24dvr6+zJgxA29vb+DPqRTi36dfMjsmJoaxY8eya9euF7bVh0GpqalcvnxZnd4CsGrVKry9vVmwYAHTp09n6NCh6m2E4RQpUgQHBwe6d++OtbU1586do0OHDnTp0oXTp08zbtw4tm/fzpw5c/jtt98AaN68OT169ADktfNN0bNnT1JSUli5ciUajQYjIyPS09PV59+aNWtYuXIlERERhu2oEEKIPCdfCwghCgwzMzM6dOjA4sWLmT59OlevXiU4OJgqVaoAf16wZg8P9PVP7Ozs1DYydz9v6HQ6wsPDKVWqFAsWLCA6OppRo0YxatQoAD7++GN15Z1n6c/hxo0bmTNnDh4eHoAsh/46ZZ8S+N577wFgYmJChw4dcgVy2cOhVq1a8fDhQ86cOYOlpSVxcXHMnTsXKysrFi9ezMCBAwE5d3nteVNoq1Spwrx587Czs+PRo0e4urrSokULfHx80Gg0DBw4kJUrV3Lw4EEeP37Md999p752gtQ9yUvZ67fpdDpMTEwwNzcHss5j//79mTt3LoULF2bKlCnqSKGTJ0+ybt06nj59muPcCSGEKBgkIBJCFChmZmY4OTmhKArjx49n8uTJVKpUiZo1a77SqBIZeZJ39BculpaWmJmZUapUKebOnYtGo2H06NHAi0OiOnXq0KNHD5o2bcr48eMBCRheN/2UQBcXF6ytrQkICMDR0fG5zxl9ONSxY0cePnxIUFCQWovGxsaG48ePExsbq05LknOXdzIzM8nMzFQDgydPnmBhYQFkFQq3tbUF4Pbt20RGRjJ+/Hh1BOatW7coV64c7777LtWqVZOAwUD04dCVK1eYOnUq586do0SJEjg6OjJ79mxKlSrFp59+SlxcHNOmTSMsLIyWLVsSExPD3r17uXv3LocPH6ZYsWKGPhQhhCFp/v8nP8lvx/MaSEAkhChwChUqhLOzMxqNhjFjxtC7d2++++47ateubeiuiWfUqFED+HMFpWrVqjFnzhwAdSSRq6ur+s24/mLW3NycoKAgdQSYBAx5IywsjIcPH7JgwQJatGiBkZER586dIz4+nszMTGrUqKEWm/b29ub333/nxx9/pFWrVmqQlJmZSdGiRdVzJ6P2Xr/NmzejKAoffvghRkZG6uP96aefcubMGYyNjdVpnvrzFxsbS0JCAgkJCQBERUWxZ88eihcvzsKFC9X7lmL+eUv/Wnnp0iXeffddihYtSv369Xn8+DFBQUH8/vvv7Nu3j0aNGjFz5kyaNm3KF198QWhoKDY2NtSuXZv169dLMX8hhCigNIpM6BdCFFA6nY7du3czZswYrK2tWb9+PbVq1TJ0t8QruHLlCpMnT2bnzp188cUXDBkyhBs3bjB79mzat29Pr1691LZygZp35s2bx7x587h37x5arZavvvoKT09PEhMTAahfvz6DBw9m5MiRnDx5Eo1GQ7169SQAMhBFUYiJiaFEiRKUL1+eRYsW0bVrVwA6derEoUOHqFu3LnFxcVy/fp3hw4czZswYKlasyOPHj2nZsiWJiYk0b96ctLQ0duzYQUBAgFqvTRjGgwcP6NKlCzY2NkyfPp2mTZsC4OTkxN69e2nevDlHjhxR2z9+/Jjbt29TrFgxihYtiqWlpaG6LoR4AyQkJGBtbc2VO9FYWlkZujv/qsSEBKqVtyU+Ph6rfHZs/xYZQSSEKLD0NYkAxo8fT48ePdi4cSP16tUzcM/EX6lWrRp+fn4AjB49mujoaG7dusXKlSupUKFCjrYSDuWdokWLotPpiI+P548//mDkyJF89tlnODg4kJmZydSpU5kyZQpJSUlqXShhWLa2thw/fpx27drh4eFBZmYm9evXJzo6mlWrVtGuXTusrKwYOXIka9asIT4+Hi8vL6pWrcrevXsZOHAgv/76K5aWlixcuJBPPvkEkGDWkA4ePMjDhw+ZOnWqGg55eXlx8OBB+vbty+bNm2ndujUHDx4EoFixYjKdTAghBCABkRCigNOHRJmZmQwcOJDz589LQPSG01941qhRg4ULF5KSkqKuUDZ37lwmTZpk4B4WXGXKlCE1NZUff/yRM2fO0KZNG7y9vSlevDgAdevWZejQoXz++efUrVtXDWhF3nq2IH+TJk3Yu3cvbdq0wcfHh/bt2/Po0SNatGihfsP65ZdfYmFhQXBwMBqNBk9PT9566y22bt2KTqcjLS2NkiVLAjKlM6/paw5FRkZibW1N48aNadq0KS4uLgAEBAQQEBBAcHAwLi4uGBsbs3btWpycnPjpp58M3HshxJtKShAVTPLuLYQo8MzMzOjYsSOnTp2iX79+hu6OeMazM6Gzj0pIT0/H2toagIULF6rhkCynbRgdOnSgSZMmzJw5kyNHjlC1alWKFy+Ooiikp6dTq1YtFi1aRHx8fI4pLiLvZGZmotFoSElJ4cqVK+zcuZMHDx7g4OBAaGgoN27cIDg4mPLly6uBT2pqKpAVNLi7u7N161YCAwO5du0aFhYWFCtWTG0rNaPynlarJTw8nMaNG7NhwwaqVq3Khg0bADh69Cj+/v5MmzaNbt26YWdnx4gRI7C3t2fv3r188MEHBu69EEKIN4m8gwshBFkhUfYVk8Tr8+zj+7JSeNmXQz969GiOfVFRUUydOpUNGzbg7++v1j2R0QuvT0ZGBvD854h+24wZM7CwsOD06dMcPHiQO3fuqKNVMjMzadSoEQ0bNiQ0NJSMjIyXnn/x78rIyMDIyIioqCjc3Nxo06YNn332GRs3biQ5OZnGjRtz+PBhAA4fPsyKFSsAMDU1VUOiwMBAhg4dyrp16/D29lbrS+nJtLK8o38+pqWlsXjxYipWrJhrsYXbt2+Tnp5Ot27dsLGxAWDPnj3Y2dmxfPlyvvzyy7zuthBCiDeYfIIWQohnSLjw+ujDm9jYWEJDQ4GsC8rnhQTZw6HWrVvTv39/YmJi1P12dnYUKVKEyZMnM3HixBz3L/59+tWRHj58SJcuXQgPD8+xX/+4N2rUiE8//ZS3336bGzduEBQUxIMHD9BqtRgZGXHnzh0SEhKoW7cuWq1WAoU8oiiKev4cHBy4du0aw4cP58iRIwwZMgRzc3MyMjJ45513OHToEIULF2bOnDn88MMPQM6QaO7cubi5udG6dWspaGxAWq2Wmzdv8u233/LHH3/Qu3dvmjRpkqONsbExiYmJREZGAnDixAl+/fVX3n33Xfr3789bb71liK4LIYR4Q0kNIiGEEHlCP/UkOjoaR0dHEhISWLFiBR07dlRDouxhgUajQafT0bFjRyIjIwkODlYLqerbLlu2TG0v4dDroz93cXFxdOzYkZMnTxIREcHmzZupUaNGjnbm5ub069cPExMT5s+fr65qNnbsWHQ6HT/++CPXrl1T60aJvKHRaIiPj6dLly7Y2try+eef4+joCPw5is/IyIiMjAzq169PaGgorVq1YvLkyWg0Grp166aGRKampjlGnkhB6ryXlpaGRqPB2dmZa9euUb58edq2bavuMzExAbJWDmzRogWdO3emQYMGPHz4kISEBAIDA9U2QgjxPBpN1k9+kt+O53WQT9JCCCHyhEaj4enTp4wYMYJr164B4OHhQUhIiLr/2ZFE8+fP59dff2XlypW8//776kXos22l7snrpdFoSEtLw9/fnytXrtCnTx+SkpJwcXHh0qVLOdopioKlpSX9+vVj+fLltGnThtWrV1OvXj2aNWvG559/jo+PDx999JEBj6hg2rlzJxEREYwZM4ZmzZoBOcMdjUaDVqslIyODRo0acejQIe7evYunpyc//vgjkDWS6NkphhIOvX53797ll19+ISgoCAATExOMjY3ZuXMn5cuXJyIigiVLlqj79OeoWrVq+Pv7M3r0aLRarbrEfc2aNQ12LEIIId5cGkUm/wshhMgDiqKwZcsW3NzcGDRoEE2bNsXDwwNra2v8/f3VFXeyX7BGRESQnJxM9erVJQAysPv379OtWzcyMjI4dOgQBw8eZNSoUWi1WkJCQnKNJMoeGuzatYvbt29jZmZG1apVadmyJSCjvvLakCFD2LlzJ/fu3XvuqD09fSFrjUbDb7/9Rrt27ShWrBhz586VYM8Atm/fzqJFiwgLC6Nhw4ZMmTIFJycn0tPTMTY25u7duzg6OhITE8Pnn3/OkCFDANT9ejqdDsiquSeEEC+SkJCAtbU11+5GY/n/K1nmF4kJCVQtZ0t8fLy6SqfISaaYCSGEyBP6EUT16tUjICAAc3NzNBoNkyZNwsPDAwAXFxe1mLGRkREVK1Y0bKcLuLi4OLWwrY2NDRMnTqRZs2ZYWFjw/vvvExgYyMSJE3FxcckREulDB/3y287OzrnuW8KhvKUoCtHR0VhbW5OWloZWq0Wr1T63rZGREYqiEBsbS5MmTdizZw+Ojo6kpKTkca/F2rVrGTFiBC4uLgQFBeHq6qoWpzY2NiYjI4Ny5cpx5MgRmjVrho+PD6ampgwYMEDdrz/PEgwJIYT4K/LJTAghRJ4ZMGAAP/zwg1oQt2/fvsyfP5/4+Pgc0830F6jZyYDXvLV582a6du2q/rtw4cJ07dqVsmXLkpGRgbm5OV26dCEwMJCMjIxc083gz3Omv6DNTsKhvKXRaLC0tOThw4ckJyerU8mepT9nV69exc/Pj5iYGJo2bcqDBw8YMGBAXne7QAsJCWHEiBG4u7szZ84cXF1dgT/PUXx8vBraVahQgV9++QVFUZg8eTJr1qwBsgpZy2unEEKIVyWfzoQQQvzrXnZBUqpUKeDPUSYfffRRjpBox44d6v7IyEguX76co73IG1WrVuXWrVv8+uuv6jb9dBX9RaeJickLQ6Jr164xZ84cLl269MKRKiJv6J+PH3zwAXFxcfj6+gJZ5/FF9YR27NjBli1b1H/b2dkB5GovXo/o6GgWL16Mo6MjI0eOpHLlysCf08YePXrEO++8Q8uWLUlMTASgYsWKhIWFoSgK06ZNU4v4y2unEOLv0OTTP+LlJCASQgjxr9LXL0lKSiIsLIwVK1bwyy+/qEvUZ18xSf/3jz76iAULFhAfH4+npyd79+4lJiaG6dOnM3bsWG7dumWw4ymoihUrhlar5eeffwZyBwP6Gjb6kCggIICMjAw6d+7Mvn37WLRoET4+Phw9etQQ3RfZ6AOCNm3aUL16dZYtW8by5cuBP1cuyy4iIoIDBw7g6OiIhYVFjlpFMvIrbzx8+JCwsDA6duxItWrVgKzXTmNjYx48eEDbtm1JSEjg0qVLODs75wiJjh8/TmRkJJ9//jnx8fGGPAwhhBD/MVKDSAghxL9GX1fm4cOH9O7dmwsXLhAVFYWxsTF9+/Zl3rx52Nraqu2zF8r98MMPARg/fjxjxoyhYsWK7NmzBz8/PypUqGCoQyqwKlSogJubG3PmzKFz587UqlUrV1Hj7CFR165dMTMzY8yYMXTp0oWUlBRmz56Nu7u7AY9CZFemTBnWrl1LmzZt8PHxIS0tjREjRuQY4RUZGck333zD8ePHCQoKkro1eUz/HDtx4gTJyck0bNgQ+HPkUHp6Oh9//DEpKSmEhIRw/PhxpkyZQocOHdi1axdWVlZUqFCB69evk5ycjLW1tYGPSAghxH+JfA0khBDiX6Ffaj4qKgpHR0d0Oh1+fn5ERkby8ccfs27dOo4cOQLkrEmjL0oN8OGHH+Lh4cH169fZs2cPAQEBTJ48Wb1/kbecnZ0pV64cAQEBxMTEPHeqSvaQqHr16lStWpWUlBQWLFiAp6cnINOS3iSNGzdmy5YtJCYmMnLkSNzc3Dhx4gT3799n165dTJ06lVmzZuHh4UHPnj0N3d0CR/8cK1u2LJmZmVy9ehX4c3qnsbEx/v7+7NixgyZNmuDm5sa8efM4d+4cXbt2JSMjg/T0dMqXL6+OPBJCCCFelQREQggh/hX6aWVubm7Y2toSGBjIwIEDKVmyJJMmTcLMzIyoqCgAdcSCPjjQT1u5ceMGV65cIT09nQULFjBhwgS1ndTRyHsNGzakffv2bNq0iS1btpCcnPzcdhqNhrt37+Ln58eBAwcICAjgs88+A2S1sjdR+/btOXbsGM2bN2fDhg00btyYcuXK4eLiwvHjx/n888/VlQUl3DMMW1tbTExM2LhxI3fu3Mmxr0GDBlSvXh1FUbCyssLV1ZXy5curK5ZlX9peCCH+Nk0+/REvJe8gQggh/hWKonDixAkSEhLo27cvjo6Oaqhz9epVChcuzP79+0lOTkZRFDp16kSVKlXUi5qUlBR++uknlixZwowZMyRgMDD9475w4UJOnz6Nr68vpUqVwsnJ6bnTjiwsLLh06RJeXl45gj05d2+mOnXq8OOPP3Lnzh2OHTtGeno6tWvXxt7enrfeeguQ82dI9erVY9SoUSxevJitW7cyZMgQzM3Nc7TRv74ePHgQQB3x9exUUCGEEOJVaRQZsy+EEOIfiIuLw8bGBoD79++zc+dO3NzcMDExAeDmzZu0bt2azMxMWrZsydOnTzl8+DAmJibs3buXunXrqvd14MAB4uLi6NGjByAXqIamD+9u3LhBr169ePjwIQEBAbi4uGBlZZXrQjQhIQErKytAzt1/nYQMhqN/7MPDwxkxYgSnTp1iwYIF9OzZk6JFi+Y4N2fPnmXChAk8fPiQnTt3Uq5cOQP3XgjxX5eQkIC1tTXX78Vg+f/v6flFYkICVcoWJz4+Xv28InKSgEgIIcTftnnzZr788ktCQ0Nz7cvIyCA1NZUWLVpgbm7Ol19+Sb169QBYt24dY8aMoUKFCuzfv5+iRYvmur0EDG+WCxcuMGjQIC5cuMDo0aNxd3enYsWK6sVq9vMl4YIQ/47du3fj4+PD+fPnGTZsGL1798bBwQGADRs2sHLlSn7//XeOHDlC7dq1DdxbIUR+IAFRwSYBkRBCiL/t1KlTdO/enQ0bNqgXLc86fPgw5cuXp1KlSjm2f/rpp6xcuZLw8PBc+8SbKSkpiUGDBnHw4EFKlSrFnDlzaN68+XMDPiHE35c9ZD169ChLlixh06ZNaLVa3n77bWJjY9HpdJQpU4Zvv/1WwiEhxL9GHxDdyKcBUWUJiF5KahAJIYT424oVK4ZWq+Xnn3/GwcHhuaNI3n33XbW9oijqameVKlUiJSUlx4pmIm/9LyN9FEWhSJEibNq0idWrV7Nt2zY6d+5M+/btqVWrFqNGjaJChQqvucdCFAz61QE1Gg0tWrSgRYsW9OzZk9DQUG7evEmDBg1o3749rVu3pnTp0oburhBCiHxCRhAJIYT4R2bNmkVAQABhYWHUqlXrhaFD9u137txh2LBhxMfHExISIiNQDEBfXwggLS0NRVHQarVotdoXTu/LfhuAvXv3cuzYMdasWcO3335LixYt8qz/QvyXveg5JtMzhRCGJiOICjYp7iCEEOIfcXZ2ply5cgQEBBATE/PCixv99lu3brF8+XIOHjzI4MGDJRwygOxBz9SpU+nWrRvvvfceAwcO5OrVqy+s/aTVasn+vdIHH3zA9OnTuX79uoRDQryijIwMjIyMuHfvHhs2bODgwYNcv34d+HPk0ItkZmaq++U7XiGEEP82CYiEEEL8Iw0bNqR9+/Zs2rSJLVu2kJyc/MK2mzZtwsfHh3nz5jFt2jQGDRoEyIVOXtKPFIKscG/JkiWkpaVRtGhRjh49yjvvvMPOnTtfeE6eFwBKMXEhXo3++XfhwgWaNm3KgAEDcHJyonPnzmzbtg14eUhkZGSkPgdlpJEQ4nXSaPLnj3g5+UQnhBDib8vMzARg4cKFNG3aFF9fX/bu3YtOp8vV9ubNmwwfPpzTp0/z+eefM3nyZPU+5EIn7+gf65kzZ3Lq1CmWL1/O9u3b2blzJ6NHjyYpKYnjx4+Tmpr6P9+nEOLF9K91iYmJ9OvXj2rVqrFmzRqWLFmCjY0NvXr1Yt26dcBfjyQSQgghXgcpUi2EEOJvMzIyUqcrrVixgl69ejFy5EgCAgJwcXHByspKvcipVKkSv/32G2lpabz99tuALGVvSKdOneKdd96hbdu2mJmZceTIEby8vBg4cCDu7u6YmZmpbZ+tPSSE+N/oi/NHRkZiYWFBxYoVGTp0KB06dACgXr16zJkzh/79+6PRaOjbt2+OQtVCCCFEXpBP5UIIIf4RfXBQuXJlvvnmG8qVK8fw4cMJCAggIiIixzfhVatWVcMh/QWT+Pc9O/IgLS0tx7+Tk5O5cOEC9vb2WFtbExoaipOTE926dWPWrFnqamTLly8nKipKwiEh/oaEhAT17xqNhujoaN555x0qVqzI1atXadSokbq/SZMm+Pj40LlzZ1xdXWUkkRBCCIOQT+ZCCCH+NTVr1mT//v04OTmxbNkyOnXqREhICPHx8bnayrfir49GoyE1NZXLly+TlJSEiYkJACtXruTu3buYm5tTr149rl69ysaNG3F2dqZbt24EBgaqS2YfPXqUmTNnsm/fPkMeihD/SSEhITRq1IizZ8+q20xNTenRowc2NjY8evSIqKgoAHU6Z/369fH19aVLly58/PHHrFy5EpDXSiGEEHlHAiIhhBAv9b98e60oCkWKFGHTpk0EBgZStWpVOnfuTN++fZkwYQK3bt16jT0V2e3atYuhQ4eyadMmFEXhvffeY968eSQmJgLQtWtXDhw4QJ8+fXB2dmbRokWUKVMGgPv377N+/Xqsra3VEV9CiFejKAo6nY7Hjx/j5ubG+fPnAbCysmLmzJl89NFHxMbGMnz4cCArOHo2JGrVqhVjx44lISFBRhAJIQxEk+/+gATuf0UCIiGEEC+UkZGhfnudlpZGamoqGRkZwJ8FqrPTaDTqfjc3N7Zu3cpPP/1EkyZN2LJlC3fu3Mm7zhdwzZs35/Hjx0ycOJE6depw6dIlAgMDqVixIgB9+vRh2rRpANjY2Kjh3ZkzZ/jqq69YsWIFI0aMoEGDBoY6BCH+kzQaDS4uLqxcuZKYmBj69u2rhkQ2NjZ4enoyadIkfv31V9q1a4eiKDlConr16rFo0SLCw8OxsrKSEURCCCHyjEaRryWEEEI8R/bCxFOnTuXUqVM8fvyYqlWrMnXqVN56660X3vZ5hVWl0HHe0T/+SUlJFC9eHI1Gw6BBg/Dz86No0aLquYiJiWH+/PnMnTsXGxsbypQpQ0xMDGlpaUycOBEPD48c9yeEeHU6nY7du3czZswYrK2t+e6776hduzYA8fHx+Pv7s3DhQlq0aKFO5UxNTcXU1NSQ3RZCFHAJCQlYW1tz8/5jrKysDN2df1VCQgKVyhQjPj4+3x3bv0UCIiGEELlkDwScnZ0JCwujSZMmGBsbc/HiRaKjo1m/fj3Ozs6vHBxIyJC3MjMzCQ0N5aOPPsLCwoKEhAQCAwPp3bs3FhYWOdqGhIQQGhrKlStXcHBwoFGjRrRv3169HykmLsTf8yoh0YIFC2jdujW7du2S10ghhMFJQFSwyTL3QgghctFfpMycOZNTp06xfPlyXFxcMDMz4/PPP2fcuHEcP35cXSL9f7lPkTeMjIxo06YNFy5cIDU1lS5dujBp0iTS09P5+OOPMTc3V8MfFxcXXFxccoVBEg4J8c+YmZmpS9mPGTOGvn37qiGRtbU1Hh4eaLVa/Pz86N69O1u3bjVwj4UQIotGk/WTn+S343kdJCASQgjxQqdOneKdd95Rg6AjR47g5eXFwIEDcXd3zxEOyRQyw8r++GcfrWVnZwfA7t27cXJywsvLC0ANiR4/fkx4eDgVKlTA3t4+x31KOCTEP/dXIdG4ceMwNTWlV69eBu6pEEKIgk4++QkhRAH17AzjtLS0HP9OTk7mwoUL2NvbY21tTWhoKE5OTnTr1o1Zs2ZRoUIFAJYvX05UVJSEQwaUPRzy9/fH1dWVjz76iODgYBISEoCsoGjPnj1UrFgRLy8vVq9ezYULFwgODqZnz55cv37dkIcgRL6mD4kWLVpEfHw8ffv2JTw8HEVRKFq0KFOmTKFGjRqG7qYQQogCTgIiIYQooDQaDampqVy+fJmkpCRMTEwAWLlyJXfv3sXc3Jx69epx9epVNm7ciLOzM926dSMwMJDSpUsDcPToUWbOnKkWWBV5LzMzUw2HXFxc8PPz48yZM5w4cYJp06bRr18/YmNjAbC1tWXv3r1UrlyZkSNH8sEHHzB16lSGDx9O69atDXkYQuR72UOiJ0+e4OTkxKVLlwCZgiuEEOLNIAGREEIUYLt27WLo0KFs2rQJRVF47733mDdvHomJiQB07dqVAwcO0KdPH5ydnVm0aBFlypQB4P79+6xfvx5ra2vefvttQx5GgaafBjZ8+HDOnj3LsmXLCAsL49q1a3To0IGdO3fSs2dPHj9+DEDx4sUJCwtjwoQJ9OrVi5UrVzJ9+nQgK2wSQrw+ZmZmODs7M3v2bIoWLfrKNdyEEEKIvCCrmAkhRAEWFRVFmzZtuH//PqVLlyY6OpoVK1bQpk0bzM3NAfD19WXGjBkMGjSITz75hIYNG3LmzBm2bNlCYGAgCxcuZMSIEQY+koLt+PHjuLq6Mnr0aNzc3LCysuL48eO8//771KhRg9u3b1O3bl2+//57bGxsnnsfUpBaiLyTmppKamoqRYoUMXRXhBAiB/0qZhGR+XMVs4qlZRWzl5Ei1UIIUUApioKdnR1hYWEUL16ca9euMWjQIBwdHTE3N1fr2owaNYrU1FTmzp3LDz/8QJkyZYiJiSEtLY3p06er4ZAsY2849vb2lChRgvfffx8rKyvOnTtH27Zt6dmzJ35+fsyePZuvv/6aPn368O2331K8ePFc50vCISHyjqmpKaampobuhhBCCJGDjCASQogCLDMzk9DQUD766CMsLCxISEggMDCQ3r17Y2FhkaNtSEgIoaGhXLlyBQcHBxo1akT79u3V+5GAwTD0Qc/Tp08pXLgwjx49om3btpQpU4alS5dSuXJl7t27R9OmTdHpdJQpU4Zff/1VprYIIYQQIhcZQVSwyQgiIYQowIyMjGjTpg0XLlwgNTWVLl26MGnSJNLT09Vl0PXhj4uLCy4uLrnCIAmHXh/9Y/uyx1g/Ckg/JfD69es8evQIT09PKleuDMCpU6fQ6XT06dOHBg0aSDgkhBBCCCFykU/0QghRgGRkZKh/zz6A1M7OjrJly7J79251GfS1a9eSnJyMkZERjx8/5siRI9y+fTtXUCHh0OtjZGRETEwMnTt35vTp0y9tqw+K0tLSiIqKUguNR0ZG8ssvv9CqVSv8/Pxwc3MDcp5/IYQQQojsNJr8+SNeTkYQCSFEAaGvKQTg7+/P2bNnSU9Pp23btnz00UdYWVlhZ2fHnj17aN++PV5eXmRmZtKqVSt27NjBggUL2LBhA/b29gY+koLl1KlTHDhwgPv377N27Vpq16790vb29va0aNECX19fjh49SkJCArt37yYgICBHQVypFyWEEEIIIbKTGkRCCFEAZJ+i5OLiwuHDh7G3tyclJYUnT57QqFEj1q5dS9GiRQGIiYnBycmJEydOUKZMGR49esTkyZPV5dBF3tHpdOzZs4dRo0ZhbW3Nd99995ch0aFDh1i2bBlHjx7F1tYWd3d3PvnkE0CKiQshhBDixfQ1iG49yJ81iCqUkhpELyMBkRBCFCDDhw9n165d+Pv74+LigqWlJYMGDWL16tW0bt2azZs3U6xYMQDS09Px8vIiLS2Nd955B1dXV0BqDhmCTqdj9+7djBkz5pVDoidPnpCcnIxOp6Ns2bKAnDshhBBCvJwERAWbBERCCFFAHD9+HFdXV0aPHo2bmxtWVlYcP36c999/nxo1anD79m3q1q3L999/j42NzXPvQwIGw3lZSJR9VNCVK1eYP38+zZo1U+sNPdtGCCGEEOJ59AHR7Qex+S5ESUhIwL5UUQmIXkI+5QshRAFhb29PiRIleP/997GysuLcuXO0bduWnj17sm3bNnr16sXBgwfp06cPMTExQO5CxhIOGY6ZmRkdOnRg0aJFxMfH07dvX86fPw/8WU/o+vXrLFq0iODgYB48eJDj9hIOCSGEEEKIl5ERREIIUQDoR488ffqUwoUL8+jRI9q2bUuZMmVYunQplStX5t69ezRt2hSdTkeZMmX49ddfZTn0N9CLRhLduHEDf39/goOD8ff3Z+LEiYbuqhBCCCH+Y2QEUcEmq5gJIcR/nH7a18umf+lHj5ibmwNZI00ePXqEp6cnlStXBrJWy9LpdPTp04cGDRpIOPSG0o8kAhgzZgyurq7MnTuXbdu2ERwczJw5c9RwSKYECiGEEEKIVyUBkRBC/McZGRkRExPDgAEDmDVrFvXr139hW31QlJaWRlRUFImJiQBERkbyyy+/0KpVK/z8/NTl0KVuzZspe0g0adIkOnXqREZGBnPmzMHDwwOQcEgIIYQQf59Gk/WTn+S343kdJCASQoh84NSpUxw4cID79++zdu3av1zhyt7enhYtWuDr68vRo0dJSEhg9+7dBAQEqOEQSN2aN5k+JMrIyGDSpEkMGzaMSZMmARIOCSGEEEKI/53UIBJCiHxAp9OxZ88eRo0a9crLoB86dIhly5Zx9OhRbG1tcXd355NPPgFk5NB/SXJyMvfv36dKlSqAhENCCCGE+Pv0NYjuPMyfNYjKl5QaRC8jAZEQQuQTL1sG/UWePHlCcnIyOp2OsmXLAhIw/JdJsCeEEEKIf0ICooJNrgCEECKfeNky6JBzyforV64wbNgwNm/ejK2trRoOKYoi4dB/mIRDQgghhBDi75KrACGEyEdeFhLpw4Pr16+zaNEigoODefDgQY7bS8AghBBCCCE0+fRHvJwEREIIkc+8LCS6ceMGAQEBfPXVV/j7++Pp6Wng3gohhBBCCCHeBLKKmRBC5EPZl0EfM2YMrq6uzJ07l23bthEcHMycOXOYOHEiIDWHhBBCCCGEEBIQCSFEvpU9JJo0aRKdOnUiIyODOXPm4OHhAUg4JIQQQgghhMgiAZEQQuRj+pAoIyODSZMmMWzYMCZNmgRIOCSEEEIIIV4gPxbtyW/H8xpIQCSEEPmcmZkZzs7O1K9fnypVqgASDgkhhBBCCCFykqsDIYQoAMzNzdVwSJayF0IIIYQQQjxLrhCEEKKAkaXshRBCCCGEEM+SKWZCCCGEEEIIIYRQaf7/T36S347ndZARREIIIYQQQgghhBAFnAREQgghhBBCCCGEEAWcBERCCCGEEEIIIYQQBZzUIBJCCCGEEEIIIYRKo8n6yU/y2/G8DjKCSAghhBBCCCGEEKKAk4BICCGEEP85ERERaDQa3Nzccmx/77330PxHviKsWLEiFStWfKW2oaGhaDQafH19/9Hv1Gg0vPfee//oPl7G19cXjUZDaGjoa/sdQgghhHg9JCASQgghxAvpg5jsP6amppQvX56+ffty9uxZQ3fxX+Xm5oZGoyEiIsLQXRFCCCGEyFNSg0gIIYQQf6lKlSq4uroCkJSUxPHjx1m/fj0//PADBw4coHnz5gbuYZa1a9fy9OlTQ3dDCCGEEOI/RwIiIYQQQvylqlWr5preNGXKFPz8/PD29n5jphTZ29sbugtCCCHEf57m/3/yk/x2PK+DTDETQgghxN8yatQoAH7//Xd1m77Gzb179/j4448pVaoURkZGOQKkw4cP06lTJ2xtbTEzM+Ott95iypQpzx35k5GRgb+/P1WrVqVQoUJUrVqVOXPmkJmZ+dw+vawG0bZt2/jggw8oXrw4hQoVomLFivTv35/z588DWTWB1qxZA0ClSpXUKXXP1uy5efMm7u7u2NvbY2ZmRunSpXFzc+PWrVsv/L2NGzfG3NyckiVLMmTIEGJjY5//oP6PDh48yKBBg6hevTpFihShSJEiNGrUiKCgoJfe7u7du/Tp0wdbW1sKFy5M8+bN2b9//3PbpqamsmDBAho2bIiFhQWWlpa0bNmS7du3/yvHIIQQQog3g4wgEkIIIcQ/8mwgExMTQ7NmzShWrBi9e/cmJSUFKysrAL766itGjhyJjY0NnTp1okSJEvzxxx/4+flx8OBBDh48iKmpqXpfQ4cOZeXKlVSqVImRI0eSkpLCggUL+OWXX/6nPo4fP54FCxZQrFgxunbtSokSJbhz5w779+/nnXfeoXbt2nz22WesXr2aM2fOMGbMGGxsbAByFJL+9ddfad++PU+ePMHFxYW33nqLiIgI1q1bx+7duwkLC6Ny5cpq+7Vr1zJgwACsrKzo378/NjY2hISE0LZtW1JTU3Mc69/h7+/PtWvXaNq0Kd26dSMuLo6ffvqJYcOGcfnyZebPn5/rNrGxsTRv3hw7Ozvc3d2Jiopi48aNODk5sWXLFrp27aq21el0ODk5ERoaSv369Rk8eDBpaWns3LmTLl268MUXX/Dpp5/+o2MQQgghxBtCEUIIIYR4gZs3byqA0r59+1z7pk2bpgBK69at1W2AAigDBw5U0tPTc7QPDw9XjI2NlXr16inR0dE59s2ZM0cBlHnz5qnbDh48qABKvXr1lKSkJHX73bt3FVtbWwVQBgwYkON+WrVqpTz78WbHjh0KoNSpUyfX701LS1MePHig/nvAgAEKoNy8eTPX8aampioVK1ZULC0tlZMnT+bYd+TIEUWr1SouLi7qtvj4eMXKykqxsLBQLl++nON+3n33XQVQKlSokOv3PI/+sfDx8cmx/caNG7napqWlKe3atVO0Wq1y69atHPv056dv375KZmamuv3MmTOKqampYmdnpzx9+lTd7uXlpQDK1KlTc7RPSEhQGjVqpJiamir37t1Tt/v4+CiAcvDgwVc6LiGEEG+W+Ph4BVAio+KUJ7rMfPUTGRWnAEp8fLyhH+Y3lkwxE0IIIcRfunbtGr6+vvj6+jJx4kTeffddZsyYQaFChfDz88vR1tTUlICAALRabY7ty5YtIz09nS+++ILixYvn2Ddp0iTs7OxYv369um3t2rUATJs2DQsLC3V72bJlGTNmzCv3fenSpQAsWrQo1+81NjamZMmSr3Q/ISEhREREMHHiRBo0aJBjX4sWLejSpQu7du0iISEBgB9//JGEhAQGDRpEtWrV1LYmJia5HrO/q1KlSrm2GRsbM3z4cDIyMjh48GCu/VqtltmzZ+cY+VW3bl369+9PVFQUu3btAiAzM5OvvvqKKlWqMH369BztLS0tmTZtGqmpqfzwww//yrEIIYR4g2jy6Y94KZliJoQQQoi/dP36daZPnw5kBRwlS5akb9++eHp6UqdOnRxtK1WqhK2tba77OH78OAB79uzhwIEDufabmJhw6dIl9d9nzpwBoGXLlrnaPm/bi/z222+YmZnRqlWrV77N8+j7f/ny5VwFuwEePHhAZmYmV65coVGjRi/tf7NmzTA2/ucfwxITE5k3bx4//vgj169f58mTJzn2379/P9dt7O3tqVChQq7tLVu2ZMWKFZw6dYoePXpw+fJlYmNjKVOmjHrus4uKigLIcc6EEEII8d8lAZEQQggh/lL79u356aefXqnti0bkPH78GOCVR8/Ex8djZGT03LDpVUf96O+nbNmyGBn9s4HT+v6vW7fupe30IU18fDwAJUqUyNVGq9XmGs30v0pNTeW9997j5MmTNGjQgP79+1O8eHGMjY2JiIhgzZo16HS6XLd70WOn367vt/54w8PDCQ8Pf2E/ng2lhBBCCPHfJAGREEIIIf5VL1pFTF+oOiEhAUtLy7+8H2trazIzM4mOjsbOzi7HvocPH75yf2xsbNTRPf8kJNL3f8eOHbi4uPxle2trawAePXqUa19GRgYxMTGULVv2b/dn27ZtnDx5ksGDB7N8+fIc+zZs2KCuyPasFz12+u36fuuPt0ePHmzZsuVv91MIIYQQ/w1Sg0gIIYQQecLBwQH4c6rWX6lXrx4AR44cybXvedtepEmTJuh0Og4dOvSXbfV1kzIyMnLt0/c/LCzslX7vy/ofFhZGenr6K93Pi1y/fh2ALl265Nr3ssfn9u3b3Lp164W30ddXevvtt7GysuKPP/4gLS3tH/VVCCHEf4smn/4RLycBkRBCCCHyxCeffIKxsTGjRo3i9u3bufbHxcVx6tQp9d/9+/cHYMaMGTmmMd27d49Fixa98u8dOXIkAGPGjFGnTemlp6fnGFFTrFgxAO7cuZPrfrp06YK9vT0LFizg8OHDufanpaVx9OjRHO2trKxYuXIlV65cydFuypQpr9z/F9HXEcr+OwEOHTpEcHDwC2+XkZGBl5cXiqKo286ePcs333yDnZ0dzs7OQFax6xEjRnDr1i0mTJjw3JDo/Pnzzx0hJYQQQoj/HpliJoQQQog8Ubt2bZYuXcqIESOoXr06zs7OVKlShcTERG7cuMGhQ4dwc3Pj66+/BqB169YMHDiQVatWUadOHbp164ZOp2Pjxo00bdqUkJCQV/q9zs7OTJgwgXnz5vHWW2/RrVs3SpQowb179zhw4AATJkzgs88+A6BNmzbMmzePoUOH0qNHDywsLKhQoQL9+/fHzMyMLVu20KFDB1q1akWbNm2oU6cOGo2GW7duceTIEYoXL64Wbba2tmbx4sW4ubnRuHFjevfujbW1NSEhIZibm1O6dOl/9Hh26tSJihUrEhAQwPnz56lduzaXL18mJCSEbt26vXBaWN26dTl69CiNGzembdu2REVFsXHjRtLT0wkKCsLc3FxtO336dE6ePMnixYvZuXMn7777rvrYnTt3jjNnzhAWFvbcOktCCCGE+G+RgEgIIYQQeWbIkCHUr19fHYWzY8cOrK2tsbe3Z+zYsQwYMCBH++DgYKpVq0ZwcDBLliyhXLlyjBs3jg8//PCVAyKAwMBAmjVrxpIlS9iyZQspKSmULl2aNm3a0K5dO7Vdhw4dCAgIIDg4mPnz55OWlkarVq3U0UyNGzfmzJkzBAYGsmvXLo4dO4aZmRlly5ala9eu9OnTJ8fvHTBgANbW1syaNYs1a9ZgbW1N586dCQgIUKdy/V1FihTh559/ZuLEiRw+fJjQ0FBq1arFunXrKFmy5AsDoqJFi7Jz504mTJhAcHAwT58+pUGDBkyfPj3HYwFgZmbG7t27WbFiBWvXruX7779Hp9NRsmRJatasyfDhw3OtYieEEEKI/yaNkn18sRBCCCGEEEIIIQqkhIQErK2teRAdry5WkF8kJCRQytaa+PhXPzadTse0adP45ptviI2NpW7dusyaNSvXFyr5hdQgEkIIIYQQQgghhEqjyZ8//ys3NzcWLFhAv379WLRoEVqtFmdn51z1//ILGUEkhBBCCCGEEEIIdQTRw5j8OYKoZPFXH0H022+/4eDgQGBgIBMmTAAgJSWF2rVrU6JECX755ZfX3eU8JyOIhBBCCCGEEEIIIbLZsmULWq2WoUOHqtsKFSrE4MGDCQsLe+6Kp/91UqRaCCGEEEIIIYQQqoSEBEN34V+nP6Znj83MzAwzM7Nc7U+dOkW1atVyjTZq0qQJAKdPn6Z8+fKvqbeGIQGREEIIIYQQQgghMDU1pVSpUrxVKX8FH3pFihTJFer4+Pjg6+ubq21kZCSlS5fOtV2/7f79+6+lj4YkAZEQQgghhBBCCCEoVKgQN2/eJDU11dBdeS0URUHzTLXq540eAkhOTn7uvkKFCqn78xsJiIQQQgghhBBCCAFkBSD6EKQgMzc3R6fT5dqekpKi7s9vpEi1EEIIIYQQQgghRDalS5cmMjIy13b9tjJlyuR1l147CYiEEEIIIYQQQgghsqlfvz5XrlzJVdT6119/VffnNxIQCSGEEEIIIYQQQmTTs2dPMjIyCAoKUrfpdDpWrVqFg4NDvlvBDKQGkRBCCCGEEEIIIUQODg4O9OrVi8mTJ/Po0SOqVq3KmjVriIiIYMWKFYbu3muhURRFMXQnhBBCCCGEEEIIId4kKSkpTJ06lW+//ZbY2Fjq1q3LzJkzad++vaG79lpIQCSEEEIIIYQQQghRwEkNIiGEEEIIIYQQQogCTgIiIYQQQgghhBBCiAJOAiIhhBBCCCGEEEKIAk4CIiGEEEIIIYQQQogCTgIiIYQQQgghhBBCiAJOAiIhhBBCCCGEEEKIAk4CIiGEEEIIIYQQQogCTgIiIYQQQgghhBBCiAJOAiIhhBBCCCGEEEKIAk4CIiGEEEIIIYQQQogCTgIiIYQQQgghhBBCiAJOAiIhhBBCCCGEEEKIAu7/ACe4xsB05v0LAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TCpZ_PYwip1S"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}