Untitled0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOO3CTm+MRbDrxxYpCd7cyg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/drshahizan/python-tutorial/blob/main/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONsEFVAVWSNs",
        "outputId": "f09cea5a-40d1-4840-9848-c6b385451ac0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/My Drive/foo.txt', 'w') as f:\n",
        "  f.write('Hello Google Drive!')\n",
        "!cat /content/drive/My\\ Drive/foo.txt\n",
        "\n",
        "drive.flush_and_unmount()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxHAVFT3W3hJ",
        "outputId": "f977448b-c0de-4e82-d027-ef38fcc8e8ce"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello Google Drive!"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "path = \"/content/drive/My Drive/data.csv\"\n",
        "\n",
        "df = pd.read_csv(path)\n",
        "\n",
        "df #displays dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "B_vAyMEUYT7l",
        "outputId": "9a99de01-a0d5-4114-bf9a-c0213272de0d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Make       Model  Year                Engine Fuel Type  Engine HP  \\\n",
              "0          BMW  1 Series M  2011     premium unleaded (required)      335.0   \n",
              "1          BMW    1 Series  2011     premium unleaded (required)      300.0   \n",
              "2          BMW    1 Series  2011     premium unleaded (required)      300.0   \n",
              "3          BMW    1 Series  2011     premium unleaded (required)      230.0   \n",
              "4          BMW    1 Series  2011     premium unleaded (required)      230.0   \n",
              "...        ...         ...   ...                             ...        ...   \n",
              "11909    Acura         ZDX  2012     premium unleaded (required)      300.0   \n",
              "11910    Acura         ZDX  2012     premium unleaded (required)      300.0   \n",
              "11911    Acura         ZDX  2012     premium unleaded (required)      300.0   \n",
              "11912    Acura         ZDX  2013  premium unleaded (recommended)      300.0   \n",
              "11913  Lincoln      Zephyr  2006                regular unleaded      221.0   \n",
              "\n",
              "       Engine Cylinders Transmission Type      Driven_Wheels  Number of Doors  \\\n",
              "0                   6.0            MANUAL   rear wheel drive              2.0   \n",
              "1                   6.0            MANUAL   rear wheel drive              2.0   \n",
              "2                   6.0            MANUAL   rear wheel drive              2.0   \n",
              "3                   6.0            MANUAL   rear wheel drive              2.0   \n",
              "4                   6.0            MANUAL   rear wheel drive              2.0   \n",
              "...                 ...               ...                ...              ...   \n",
              "11909               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11910               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11911               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11912               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11913               6.0         AUTOMATIC  front wheel drive              4.0   \n",
              "\n",
              "                             Market Category Vehicle Size  Vehicle Style  \\\n",
              "0      Factory Tuner,Luxury,High-Performance      Compact          Coupe   \n",
              "1                         Luxury,Performance      Compact    Convertible   \n",
              "2                    Luxury,High-Performance      Compact          Coupe   \n",
              "3                         Luxury,Performance      Compact          Coupe   \n",
              "4                                     Luxury      Compact    Convertible   \n",
              "...                                      ...          ...            ...   \n",
              "11909             Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11910             Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11911             Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11912             Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11913                                 Luxury      Midsize          Sedan   \n",
              "\n",
              "       highway MPG  city mpg  Popularity   MSRP  \n",
              "0               26        19        3916  46135  \n",
              "1               28        19        3916  40650  \n",
              "2               28        20        3916  36350  \n",
              "3               28        18        3916  29450  \n",
              "4               28        18        3916  34500  \n",
              "...            ...       ...         ...    ...  \n",
              "11909           23        16         204  46120  \n",
              "11910           23        16         204  56670  \n",
              "11911           23        16         204  50620  \n",
              "11912           23        16         204  50920  \n",
              "11913           26        17          61  28995  \n",
              "\n",
              "[11914 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-191eebd8-fa01-442a-961b-edc96cc86163\" 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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Fuel Type</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11909</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>46120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>56670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (recommended)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>Lincoln</td>\n",
              "      <td>Zephyr</td>\n",
              "      <td>2006</td>\n",
              "      <td>regular unleaded</td>\n",
              "      <td>221.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>front wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "      <td>28995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11914 rows × 16 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-191eebd8-fa01-442a-961b-edc96cc86163')\"\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-191eebd8-fa01-442a-961b-edc96cc86163 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-191eebd8-fa01-442a-961b-edc96cc86163');\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-b9dd28f6-a184-4983-a012-52359a3aab8e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9dd28f6-a184-4983-a012-52359a3aab8e')\"\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-b9dd28f6-a184-4983-a012-52359a3aab8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "x15U0yqfbJg2",
        "outputId": "dd493488-0711-465b-cc2d-963d519fae7a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Year    Engine HP  Engine Cylinders  Number of Doors  \\\n",
              "count  11914.000000  11845.00000      11884.000000     11908.000000   \n",
              "mean    2010.384338    249.38607          5.628829         3.436093   \n",
              "std        7.579740    109.19187          1.780559         0.881315   \n",
              "min     1990.000000     55.00000          0.000000         2.000000   \n",
              "25%     2007.000000    170.00000          4.000000         2.000000   \n",
              "50%     2015.000000    227.00000          6.000000         4.000000   \n",
              "75%     2016.000000    300.00000          6.000000         4.000000   \n",
              "max     2017.000000   1001.00000         16.000000         4.000000   \n",
              "\n",
              "        highway MPG      city mpg    Popularity          MSRP  \n",
              "count  11914.000000  11914.000000  11914.000000  1.191400e+04  \n",
              "mean      26.637485     19.733255   1554.911197  4.059474e+04  \n",
              "std        8.863001      8.987798   1441.855347  6.010910e+04  \n",
              "min       12.000000      7.000000      2.000000  2.000000e+03  \n",
              "25%       22.000000     16.000000    549.000000  2.100000e+04  \n",
              "50%       26.000000     18.000000   1385.000000  2.999500e+04  \n",
              "75%       30.000000     22.000000   2009.000000  4.223125e+04  \n",
              "max      354.000000    137.000000   5657.000000  2.065902e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84b738b4-284f-4d24-a4a1-70ba1c89da73\" 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>Year</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11845.00000</td>\n",
              "      <td>11884.000000</td>\n",
              "      <td>11908.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>1.191400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2010.384338</td>\n",
              "      <td>249.38607</td>\n",
              "      <td>5.628829</td>\n",
              "      <td>3.436093</td>\n",
              "      <td>26.637485</td>\n",
              "      <td>19.733255</td>\n",
              "      <td>1554.911197</td>\n",
              "      <td>4.059474e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.579740</td>\n",
              "      <td>109.19187</td>\n",
              "      <td>1.780559</td>\n",
              "      <td>0.881315</td>\n",
              "      <td>8.863001</td>\n",
              "      <td>8.987798</td>\n",
              "      <td>1441.855347</td>\n",
              "      <td>6.010910e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1990.000000</td>\n",
              "      <td>55.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>170.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>2.100000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>227.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1385.000000</td>\n",
              "      <td>2.999500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>300.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>4.223125e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>1001.00000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>5657.000000</td>\n",
              "      <td>2.065902e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84b738b4-284f-4d24-a4a1-70ba1c89da73')\"\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-84b738b4-284f-4d24-a4a1-70ba1c89da73 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-84b738b4-284f-4d24-a4a1-70ba1c89da73');\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-c9835764-b9ad-4194-8f35-b5c66c148dc5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c9835764-b9ad-4194-8f35-b5c66c148dc5')\"\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-c9835764-b9ad-4194-8f35-b5c66c148dc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=50, figsize=(15,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "JOXHi-oxbQn_",
        "outputId": "76adbe50-b6ce-462d-de12-fdba842fab56"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-86b371b29256>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 9 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMsAAANWCAYAAADgMguxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hT1/8H8HdACEMDokBAEXHUCQ6sGHcVQaTW1eEEK9XWolZx9+tAraJYV62jtlZshVbtsHUTJw5cKHX1Z9ViaVWgDoyCQoD7+0NyawSUQEIgvF/Pk0dz7rn3ns8BcpJPzj1XIgiCACIiIiIiIiIiIoKZsRtARERERERERERUXjBZRkRERERERERElI/JMiIiIiIiIiIionxMlhEREREREREREeVjsoyIiIiIiIiIiCgfk2VERERERERERET5mCwjIiIiIiIiIiLKx2QZERERERERERFRPibLiIiIiIiIiIiI8jFZRkRERFSBSSQShIeHG7sZRERUzpja+PB8PFFRUZBIJLhx44bezmGIY1LFxGQZ0QsEBASgevXqSE1NLbDtwYMHcHFxgY+PD/Ly8ozQOiIiKg80b6yLepw4ccLYTTQITdxnzpwpdHvXrl3RvHlzrbK6detq9Y2TkxM6deqEn3/+uSyaTERUpirr+KBx/fp1vP/++6hXrx6srKwgk8nQoUMHrFixAo8fPzZ284heqIqxG0BUnq1evRrNmzfHhAkTEBMTo7Xt448/xp07d7Bnzx6YmTHvTERU2c2dOxceHh4Fyhs0aGDQ8z5+/BhVqlSct3QtW7bExIkTAQC3bt3CF198gf79+2PNmjX44IMPjNw6IiL9q4zjw86dO/HWW29BKpUiKCgIzZs3R3Z2No4ePYrJkyfj0qVLWLduXanOMWzYMAwcOBBSqVRPrSb6T8V5Z0VkBB4eHpg9ezamTp2K4cOHw8/PDwBw+vRprF27FpMmTUKLFi0M2oYnT57A0tKSCTkionIuICAAbdq0KfPzWllZlfk5S6NWrVoYOnSo+DwoKAgNGjTAsmXLmCwjIpNU2caHpKQkDBw4EO7u7jhw4ABcXFzEbaGhobh27Rp27txZ6vOYm5vD3Ny81McxpIyMDNja2hq7GVQC/PRN9BJhYWHw8vLChx9+iCdPniA3NxcffPAB3N3dMXv2bPzf//0f3nzzTTg4OMDKygpt2rTBr7/+qnWMe/fuYdKkSfD09ETVqlUhk8kQEBCA3377TaveoUOHIJFI8P3332PGjBmoVasWbGxsoFKpyjJkIiIygBs3bkAikeDTTz/FunXrUL9+fUilUrz66qs4ffp0gfpbt25F06ZNYWVlhebNm+Pnn3/G8OHDUbduXa16z6/hEh4eDolEgmvXrmH48OGwt7eHnZ0d3n33XWRmZhY4z6ZNm+Dt7Q1ra2s4ODhg4MCB+Pvvv/UdfpHkcjmaNGmCpKSkMjsnEVF5YmrjQ2RkJB49eoT169drJco0GjRogI8++ggA0KVLlyInHzRq1Aj+/v5Fnqew9cXq1q2L119/HUePHkXbtm1hZWWFevXq4Ztvvimw/6VLl9CtWzdYW1ujdu3a+OSTT4pcXmf37t3o1KkTbG1tUa1aNQQGBuLSpUtadYYPH46qVavi+vXr6NWrF6pVq4YhQ4YAAK5evYoBAwZALpfDysoKtWvXxsCBA/HgwYMi4yPj4swyopeoUqUK1q1bh/bt22PevHlwcnLC2bNnsWfPHiQlJaFDhw6oVasWpk2bBltbW2zZsgV9+/bFjz/+iH79+gEA/vzzT2zbtg1vvfUWPDw8kJqaii+++AJdunTB5cuX4erqqnXOefPmwdLSEpMmTUJWVhYsLS2NEToREengwYMHuHPnjlaZRCJBjRo1tMpiYmLw8OFDvP/++5BIJIiMjET//v3x559/wsLCAsDTy1feeecdeHp6IiIiAvfv30dISAhq1apV7Pa8/fbb8PDwQEREBM6ePYuvvvoKTk5OWLRokVhn/vz5mDlzJt5++2289957+Pfff7Fy5Up07twZ586dg729fYniBgC1Wl2sdqrVavz9998F+omIyFRUtvFh+/btqFevHtq3b//StgwbNgwjR47ExYsXtda5PH36NP744w/MmDGj2HFpXLt2DW+++SZCQkIQHByMr7/+GsOHD4e3tzeaNWsGAEhJScFrr72GnJwc8XPcunXrYG1tXeB43377LYKDg+Hv749FixYhMzMTa9asQceOHXHu3DmtJGVOTg78/f3RsWNHfPrpp7CxsUF2djb8/f2RlZWFsWPHQi6X4+bNm9ixYwfS09NhZ2enc4xUBgQiKpYxY8YIFhYWQtWqVYVBgwYJgiAI3bt3Fzw9PYUnT56I9fLy8oT27dsLDRs2FMuePHki5Obmah0vKSlJkEqlwty5c8WygwcPCgCEevXqCZmZmQaOiIiI9GHDhg0CgEIfUqlUrJeUlCQAEGrUqCHcu3dPLP/ll18EAML27dvFMk9PT6F27drCw4cPxbJDhw4JAAR3d3et8wMQZs+eLT6fPXu2AEAYMWKEVr1+/foJNWrUEJ/fuHFDMDc3F+bPn69V78KFC0KVKlUKlOsSt+bRrFkzrX3c3d0FPz8/4d9//xX+/fdf4bfffhMGDhwoABDGjh37wvMREVU0lXF8ePDggQBA6NOnzwv7RiM9PV2wsrISpk6dqlU+btw4wdbWVnj06FGR8Wj6NykpSSxzd3cXAAhxcXFiWVpamiCVSoWJEyeKZePHjxcACCdPntSqZ2dnp3XMhw8fCvb29sLIkSO12peSkiLY2dlplQcHBwsAhGnTpmnVPXfunABA2Lp1a7H6hMoHziwjKqb58+fjhx9+QGZmJpYtW4Z79+7hwIEDmDt3Lh4+fIiHDx+Kdf39/TF79mzcvHkTtWrV0lp0Mjc3F+np6ahatSoaNWqEs2fPFjhXcHBwod9qEBFR+bVq1Sq88sorWmWFraXyzjvvoHr16uLzTp06AXg6Cxl4uuj9hQsX8PHHH6Nq1apivS5dusDT07PYl+Y/v/6X5q6TKpUKMpkMP/30E/Ly8vD2229rzXiQy+Vo2LAhDh48iI8//vil5yksbgCYOHEicnNzC5THxsbC0dFRfG5ubo5hw4ZpzWggIjIllWl80LShWrVqxWqLnZ0d+vTpg++++w4RERGQSCTIzc3F5s2b0bdv3xKt99W0aVOx7wDA0dERjRo1EvsRAHbt2oV27dqhbdu2WvWGDBmC1atXi2VKpRLp6ekYNGiQVl+Ym5vDx8cHBw8eLHD+0aNHF4gRAPbu3YtevXrBxsZG55io7DFZRlRMMpkMjRo1wp07d+Ds7IxTp05BEATMnDkTM2fOLHSftLQ01KpVC3l5eVixYgVWr16NpKQkrQ8PhV12UtjdcoiIqHxr27ZtsRZwrlOnjtZzzQej+/fvAwD++usvAIXfJa1BgwaFfsmi63lkMhmuXr0KQRDQsGHDQvfXXPLzMkXFXb169UIvz/Tx8cEnn3wCiUQCGxsbNGnSpFiXexIRVVSVaXyQyWQAoDWR4GWCgoKwefNmHDlyBJ07d8a+ffuQmpqKYcOGFfsYz3o+PuBpjJp+BJ72pY+PT4F6jRo10np+9epVAEC3bt0KPZcmXo0qVaqgdu3aWmUeHh4ICwvD0qVLER0djU6dOuGNN97A0KFDeQlmOcZkGVEJaRZ/nDRpUpELT2oGsgULFmDmzJkYMWIE5s2bBwcHB5iZmWH8+PGFLiLJWWVERKarqDt3CYJQpufJy8uDRCLB7t27C6377KwFfapZsyZ8fX0NcmwioorMFMYHmUwGV1dXXLx4sdjt8ff3h7OzMzZt2oTOnTtj06ZNkMvlJR4r9NmPms9q3377LeRyeYHtVapop1SkUinMzAreR3HJkiUYPnw4fvnlF8TGxmLcuHGIiIjAiRMnCiTXqHxgsoyohOrVqwfg6TcrL3sh/+GHH/Daa69h/fr1WuXp6emoWbOmwdpIREQVj7u7O4CnCxQ/r7Cykqpfvz4EQYCHh0ehl1ESEVH5UlHGh9dffx3r1q1DfHw8FArFS+ubm5tj8ODBiIqKwqJFi7Bt2zaMHDmyyKSXPri7u4uzxp515coVref169cHADg5OZX6ix5PT094enpixowZOH78ODp06IC1a9fik08+KdVxyTAKpjyJqFicnJzQtWtXfPHFF7h9+3aB7f/++6/4f3Nz8wLfZGzduhU3b940eDuJiKhicXV1RfPmzfHNN9/g0aNHYvnhw4dx4cIFvZ2nf//+MDc3x5w5cwqMUYIg4O7du3o7FxERlV5FGR+mTJkCW1tbvPfee0hNTS2w/fr161ixYoVW2bBhw3D//n28//77ePToEYYOHVr6QF6gV69eOHHiBE6dOiWW/fvvv4iOjtaq5+/vD5lMhgULFhR6l+dnP/MVRaVSIScnR6vM09MTZmZmyMrKKmEEZGicWUZUCqtWrULHjh3h6emJkSNHol69ekhNTUV8fDz++ecf/PbbbwCefrsyd+5cvPvuu2jfvj0uXLiA6OhocXYaERFVfLt378b//d//FShv3769zq/3CxYsQJ8+fdChQwe8++67uH//Pj7//HM0b95c6wNSadSvXx+ffPIJpk+fjhs3bqBv376oVq0akpKS8PPPP2PUqFGYNGmSXs5FRFSZVbbxoX79+oiJicE777yDJk2aICgoCM2bN0d2djaOHz+OrVu3Yvjw4Vr7tGrVCs2bN8fWrVvRpEkTtG7dWi+xFGXKlCn49ttv0bNnT3z00UewtbXFunXr4O7ujvPnz4v1ZDIZ1qxZg2HDhqF169YYOHAgHB0dkZycjJ07d6JDhw74/PPPX3iuAwcOYMyYMXjrrbfwyiuvICcnB99++y3Mzc0xYMAAg8ZJJcdkGVEpNG3aFGfOnMGcOXMQFRWFu3fvwsnJCa1atcKsWbPEeh9//DEyMjIQExODzZs3o3Xr1ti5cyemTZtmxNYTEZE+Pfu6/6wNGzbo/GGod+/e+O677xAeHo5p06ahYcOGiIqKwsaNG3Hp0iV9NBcAMG3aNLzyyitYtmwZ5syZAwBwc3ODn58f3njjDb2dh4ioMquM48Mbb7yB8+fPY/Hixfjll1+wZs0aSKVSeHl5YcmSJRg5cmSBfYKCgjBlypQSL+yvCxcXFxw8eBBjx47FwoULUaNGDXzwwQdwdXVFSEiIVt3BgwfD1dUVCxcuxOLFi5GVlYVatWqhU6dOePfdd196rhYtWsDf3x/bt2/HzZs3YWNjgxYtWmD37t1o166doUKkUpII+l4tkIiIiIgMomXLlnB0dIRSqTR2U4iIqBwxhfFhxYoVmDBhAm7cuFHoHS2JyhLXLCMiIiIqZ9RqdYH1TQ4dOoTffvsNXbt2NU6jiIjI6Ex1fBAEAevXr0eXLl2YKKNygZdhEhEREZUzN2/ehK+vL4YOHQpXV1f83//9H9auXQu5XI4PPvjA2M0jIiIjMbXxISMjA7/++isOHjyICxcu4JdffjF2k4gAMFlGREREVO5Ur14d3t7e+Oqrr/Dvv//C1tYWgYGB4roqRERUOZna+PDvv/9i8ODBsLe3x8cff8z1Mqnc4JplRERERERERERE+bhmGRERERERERERUT4my4iIiIiIiIiIiPKZ7JpleXl5uHXrFqpVqwaJRGLs5hARGYUgCHj48CFcXV1hZsbvR4yB4xEREccjY+NYRESk21hkssmyW7duwc3NzdjNICIqF/7++2/Url3b2M2olDgeERH9h+ORcXAsIiL6T3HGIpNNllWrVg3A006QyWQ67atWqxEbGws/Pz9YWFgYonlGwbgqHlONjXGVHZVKBTc3N/E1kcpeacajiqY8/g2UBcZdueIGKm/spYmb45Fx8bNRxcH+Llvs77JTHvpal7HIZJNlmunFMpmsRAOCjY0NZDKZSf3BMK6Kx1RjY1xlj5dcGE9pxqOKpjz/DRgS465ccQOVN3Z9xM3xyDj42ajiYH+XLfZ32SlPfV2csYgLBhAREREREREREeVjsoyIiIiIiIiIiCgfk2VERERERERERET5mCwjIiIiIiIiIiLKx2QZERERERERERFRPibLiIiIiIiIiIiI8jFZRkRERERERERElK+KsRtARGQqmofvRWTbp/9m5UoAADcWBhq5VUT6U3fazgJl/B0nIqLKjGMjkWnizDIiIiIiIiIiIqJ8nFlGRGQEhX0LCfCbSCIiIiIiImPjzDIiIiIiIiIiIqJ8TJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+ZgsIyIiIiIiIiIiysdkGRERERERERERUT4my4iIiIiIiIiIiPIxWUZERBVOXFwcevfuDVdXV0gkEmzbtk3cplarMXXqVHh6esLW1haurq4ICgrCrVu3tI5Rt25dSCQSrcfChQu16pw/fx6dOnWClZUV3NzcEBkZWRbhERERERGRETFZRkREFU5GRgZatGiBVatWFdiWmZmJs2fPYubMmTh79ix++uknXLlyBW+88UaBunPnzsXt27fFx9ixY8VtKpUKfn5+cHd3R0JCAhYvXozw8HCsW7fOoLEREREREZFx6ZQsi4iIwKuvvopq1arByckJffv2xZUrV7TqPHnyBKGhoahRowaqVq2KAQMGIDU1VatOcnIyAgMDYWNjAycnJ0yePBk5OTladQ4dOoTWrVtDKpWiQYMGiIqKKlmERERkcgICAvDJJ5+gX79+BbbZ2dlBqVTi7bffRqNGjdCuXTt8/vnnSEhIQHJyslbdatWqQS6Xiw9bW1txW3R0NLKzs/H111+jWbNmGDhwIMaNG4elS5caPD4iIiIiXdWdtrPQBxHprooulQ8fPozQ0FC8+uqryMnJwccffww/Pz9cvnxZ/IAxYcIE7Ny5E1u3boWdnR3GjBmD/v3749ixYwCA3NxcBAYGQi6X4/jx47h9+zaCgoJgYWGBBQsWAACSkpIQGBiIDz74ANHR0di/fz/ee+89uLi4wN/fX89dQEREpu7BgweQSCSwt7fXKl+4cCHmzZuHOnXqYPDgwZgwYQKqVHk6NMbHx6Nz586wtLQU6/v7+2PRokW4f/8+qlevXui5srKykJWVJT5XqVQAnl4eqlar9RxZ2ZKaCwXKno1J8/+KHqeuGHflihuovLGXJu7K1ldERFSx6ZQs27Nnj9bzqKgoODk5ISEhAZ07d8aDBw+wfv16xMTEoFu3bgCADRs2oEmTJjhx4gTatWuH2NhYXL58Gfv27YOzszNatmyJefPmYerUqQgPD4elpSXWrl0LDw8PLFmyBADQpEkTHD16FMuWLWOyjIiIdPLkyRNMnToVgwYNgkwmE8vHjRuH1q1bw8HBAcePH8f06dNx+/ZtceZYSkoKPDw8tI7l7OwsbisqWRYREYE5c+YUKI+NjYWNjY2+wjKKyLYFy3bt2lWgTKlUlkFryh/GXflU1thLEndmZqYBWkJERGQYOiXLnvfgwQMAgIODAwAgISEBarUavr6+Yp3GjRujTp06iI+PR7t27RAfHw9PT0/xAwfw9Jv60aNH49KlS2jVqhXi4+O1jqGpM378+CLbos9v8k3120LGVfGYamymGpfUTND6Fyg6xsJm6LyofkmZWh/rSq1W4+2334YgCFizZo3WtrCwMPH/Xl5esLS0xPvvv4+IiAhIpdISn3P69Olax1apVHBzc4Ofn59Wsq4iah6+t0DZxfD/vsRSq9VQKpXo0aMHLCwsyrJpRsW4K1fcQOWNvTRxa96bExERVQQlTpbl5eVh/Pjx6NChA5o3bw7g6TftlpaWBS5zcXZ2RkpKiljn2USZZrtm24vqqFQqPH78GNbW1gXaY4hv8k3120LGVfGYamymFte8Npp/88SywmbdAIXP0HlR/ZKqzN/kaxJlf/31Fw4cOPDSRJWPjw9ycnJw48YNNGrUCHK5vMCam5rncrm8yONIpdJCk20WFhYV/kN1Vq6kQFlhMZlCrCXBuCufyhp7SeKujP1EREQVV4mTZaGhobh48SKOHj2qz/aUmD6/yTfVbwsZV8VjqrGZalzec/dgXps8zDxjhqy8p0mFZ2fdPKuwGTovql9SlfWbfE2i7OrVqzh48CBq1Kjx0n0SExNhZmYGJycnAIBCocD//vc/qNVq8fdUqVSiUaNGRV6CSUREREREFV+JkmVjxozBjh07EBcXh9q1a4vlcrkc2dnZSE9P15pdlpqaKn4LL5fLcerUKa3jPf9NfVHf5stkskJnlQGG+SbfVL8tZFwVj6nGZmpxaRJkWXkScQZOUfEVNkPnRfVLypT691mPHj3CtWvXxOdJSUlITEyEg4MDXFxc8Oabb+Ls2bPYsWMHcnNzxZnLDg4OsLS0RHx8PE6ePInXXnsN1apVQ3x8PCZMmIChQ4eKibDBgwdjzpw5CAkJwdSpU3Hx4kWsWLECy5YtM0rMRERERERUNsx0qSwIAsaMGYOff/4ZBw4cKLDwsbe3NywsLLB//36x7MqVK0hOToZCoQDw9Jv6CxcuIC0tTayjVCohk8nQtGlTsc6zx9DU0RyDiIgqtzNnzqBVq1Zo1aoVgKfrj7Vq1QqzZs3CzZs38euvv+Kff/5By5Yt4eLiIj6OHz8O4OkXLN9//z26dOmCZs2aYf78+ZgwYQLWrVsnnsPOzg6xsbFISkqCt7c3Jk6ciFmzZmHUqFFGiZmIiIiIiMqGTjPLQkNDERMTg19++QXVqlUTv6m3s7ODtbU17OzsEBISgrCwMDg4OEAmk2Hs2LFQKBRo164dAMDPzw9NmzbFsGHDEBkZiZSUFMyYMQOhoaHizLAPPvgAn3/+OaZMmYIRI0bgwIED2LJlC3bu3Knn8ImIqCLq2rUrBKHwmyQAeOE2AGjdujVOnDjx0vN4eXnhyJEjOrePiIiIiIgqLp1mlq1ZswYPHjxA165dtb6p37x5s1hn2bJleP311zFgwAB07twZcrkcP/30k7jd3NwcO3bsgLm5ORQKBYYOHYqgoCDMnTtXrOPh4YGdO3dCqVSiRYsWWLJkCb766iv4++t3LR8iIiIiIiIiIqJn6TSz7GXf1AOAlZUVVq1ahVWrVhVZx93d/aV3fOvatSvOnTunS/OIiIiIiIiIiIhKRaeZZURERERERERERKaMyTIiIiIiIiIiIqJ8TJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+ZgsIyIiIiIiKqWFCxdCIpFg/PjxYtmTJ08QGhqKGjVqoGrVqhgwYABSU1O19ktOTkZgYCBsbGzg5OSEyZMnIycnR6vOoUOH0Lp1a0ilUjRo0ABRUVFlEBERUeXFZBkREREREVEpnD59Gl988QW8vLy0yidMmIDt27dj69atOHz4MG7duoX+/fuL23NzcxEYGIjs7GwcP34cGzduRFRUFGbNmiXWSUpKQmBgIF577TUkJiZi/PjxeO+997B3794yi4+IqLJhsoyIiIiIiKiEHj16hCFDhuDLL79E9erVxfIHDx5g/fr1WLp0Kbp16wZvb29s2LABx48fx4kTJwAAsbGxuHz5MjZt2oSWLVsiICAA8+bNw6pVq5CdnQ0AWLt2LTw8PLBkyRI0adIEY8aMwZtvvolly5YZJV4iosqAyTIiIiIiIqISCg0NRWBgIHx9fbXKExISoFartcobN26MOnXqID4+HgAQHx8PT09PODs7i3X8/f2hUqlw6dIlsc7zx/b39xePQURE+lfF2A0gIiIiIiKqiL7//nucPXsWp0+fLrAtJSUFlpaWsLe31yp3dnZGSkqKWOfZRJlmu2bbi+qoVCo8fvwY1tbWBc6dlZWFrKws8blKpQIAqNVqqNVqnWLU1Nd1v8pCai4UKCtNX5WmvwtrS2nbY+r4+112ykNf63JuJsuIiIiIiIh09Pfff+Ojjz6CUqmElZWVsZujJSIiAnPmzClQHhsbCxsbmxIdU6lUlrZZJimybcGyXbt2lfq4JenvwtoC6Kc9po6/32XHmH2dmZlZ7LpMlhEREREREekoISEBaWlpaN26tViWm5uLuLg4fP7559i7dy+ys7ORnp6uNbssNTUVcrkcACCXy3Hq1Cmt42rulvlsnefvoJmamgqZTFborDIAmD59OsLCwsTnKpUKbm5u8PPzg0wm0ylOtVoNpVKJHj16wMLCQqd9K4Pm4QVvtHAx3L/ExytNfxfWltK2x9Tx97vslIe+1syyLQ4my4iIiIiIiHTUvXt3XLhwQavs3XffRePGjTF16lS4ubnBwsIC+/fvx4ABAwAAV65cQXJyMhQKBQBAoVBg/vz5SEtLg5OTE4Cnsy5kMhmaNm0q1nl+ZpBSqRSPURipVAqpVFqg3MLCosQfUkuzrynLypUUKNNHP5Wkvwtri77aY+r4+112jNnXupyXyTIiIiIiIiIdVatWDc2bN9cqs7W1RY0aNcTykJAQhIWFwcHBATKZDGPHjoVCoUC7du0AAH5+fmjatCmGDRuGyMhIpKSkYMaMGQgNDRWTXR988AE+//xzTJkyBSNGjMCBAwewZcsW7Ny5s2wDJiKqRJgsIyIiIiIiMoBly5bBzMwMAwYMQFZWFvz9/bF69Wpxu7m5OXbs2IHRo0dDoVDA1tYWwcHBmDt3rljHw8MDO3fuxIQJE7BixQrUrl0bX331Ffz9eWkdEZGhMFlGRERERESkB4cOHdJ6bmVlhVWrVmHVqlVF7uPu7v7SBdi7du2Kc+fO6aOJRERUDGbGbgAREREREREREVF5wWQZERERERERERFRPl6GSURERCVWd9p/C0xLzQVEtn166/or8183YquIiIiIiEqOM8uIiIiIiIiIiIjyMVlGRERERERERESUj8kyIiIiIiIiIiKifEyWERERERERERER5WOyjIiIiIiIiIiIKB/vhklEVI48e2fBZ91YGFjGLSEiIiIiIqqcOLOMiIgqnLi4OPTu3Ruurq6QSCTYtm2b1nZBEDBr1iy4uLjA2toavr6+uHr1qlade/fuYciQIZDJZLC3t0dISAgePXqkVef8+fPo1KkTrKys4ObmhsjISEOHRkRERERERsaZZUREVOFkZGSgRYsWGDFiBPr3719ge2RkJD777DNs3LgRHh4emDlzJvz9/XH58mVYWVkBAIYMGYLbt29DqVRCrVbj3XffxahRoxATEwMAUKlU8PPzg6+vL9auXYsLFy5gxIgRsLe3x6hRo8o03rJW1AxHIiIiIqLKgMkyIiKqcAICAhAQEFDoNkEQsHz5csyYMQN9+vQBAHzzzTdwdnbGtm3bMHDgQPz+++/Ys2cPTp8+jTZt2gAAVq5ciV69euHTTz+Fq6sroqOjkZ2dja+//hqWlpZo1qwZEhMTsXTpUpNPlhERERERVWZMlhERkUlJSkpCSkoKfH19xTI7Ozv4+PggPj4eAwcORHx8POzt7cVEGQD4+vrCzMwMJ0+eRL9+/RAfH4/OnTvD0tJSrOPv749Fixbh/v37qF69eqHnz8rKQlZWlvhcpVIBANRqNdRqtb7DNQipuVCy/cwE8d+KEqs+aGKtTDEDlTduoPLGXpq4K1tfERFRxcZkGRERmZSUlBQAgLOzs1a5s7OzuC0lJQVOTk5a26tUqQIHBwetOh4eHgWOodlWVLIsIiICc+bMKVAeGxsLGxubEkRU9iLblm7/eW3ysGvXLv00pgJRKpXGboJRVNa4gcobe0nizszMNEBLiIiIDIPJMiIiIj2aPn06wsLCxOcqlQpubm7w8/ODTCYzYsuKr3n43hLtJzUTMK9NHmaeMUPCrJ56blX5pVaroVQq0aNHD1hYWBi7OWWmssYNVN7YSxO3ZpYtERFRRcBkGRERmRS5XA4ASE1NhYuLi1iempqKli1binXS0tK09svJycG9e/fE/eVyOVJTU7XqaJ5r6hRGKpVCKpUWKLewsKgwH6qzciWl2z9PUmFi1aeK9DPWp8oaN1B5Yy9J3JWxn4iIqOIyM3YDiIiI9MnDwwNyuRz79+8Xy1QqFU6ePAmFQgEAUCgUSE9PR0JCgljnwIEDyMvLg4+Pj1gnLi5Oa50dpVKJRo0aFXkJJhERERERVXxMlhERUYXz6NEjJCYmIjExEcDTRf0TExORnJwMiUSC8ePH45NPPsGvv/6KCxcuICgoCK6urujbty8AoEmTJujZsydGjhyJU6dO4dixYxgzZgwGDhwIV1dXAMDgwYNhaWmJkJAQXLp0CZs3b8aKFSu0LrEkIiIiIiLTw8swiYiowjlz5gxee+018bkmgRUcHIyoqChMmTIFGRkZGDVqFNLT09GxY0fs2bMHVlZW4j7R0dEYM2YMunfvDjMzMwwYMACfffaZuN3Ozg6xsbEIDQ2Ft7c3atasiVmzZmHUqFFlFygREREREZU5JsuIiKjC6dq1KwRBKHK7RCLB3LlzMXfu3CLrODg4ICYm5oXn8fLywpEjR0rcTiIiIiIiqnh0vgwzLi4OvXv3hqurKyQSCbZt26a1ffjw4ZBIJFqPnj2174h17949DBkyBDKZDPb29ggJCcGjR4+06pw/fx6dOnWClZUV3NzcEBkZqXt0REREREREREREOtA5WZaRkYEWLVpg1apVRdbp2bMnbt++LT6+++47re1DhgzBpUuXoFQqsWPHDsTFxWld1qJSqeDn5wd3d3ckJCRg8eLFCA8Px7p163RtLhERERERERERUbHpfBlmQEAAAgICXlhHKpVCLpcXuu3333/Hnj17cPr0abRp0wYAsHLlSvTq1QuffvopXF1dER0djezsbHz99dewtLREs2bNkJiYiKVLl3KtGCIiIiIiIiIiMhiDrFl26NAhODk5oXr16ujWrRs++eQT1KhRAwAQHx8Pe3t7MVEGAL6+vjAzM8PJkyfRr18/xMfHo3PnzrC0tBTr+Pv7Y9GiRbh//z6qV69e4JxZWVnIysoSn6tUKgCAWq2GWq3Wqf2a+rruV94xrorHVGMz1bikZoLWv0DRMUrNi15vqzAl7StT62MijbrTdhZafmNhYBm3hIiIiIhMjd6TZT179kT//v3h4eGB69ev4+OPP0ZAQADi4+Nhbm6OlJQUODk5aTeiShU4ODggJSUFAJCSkgIPDw+tOs7OzuK2wpJlERERmDNnToHy2NhY2NjYlCgWpVJZov3KO8ZV8ZhqbKYW17w2mn/zxLJdu3YVWjeyrW7HLuo4L5OZmVmi/YiIiIiIiCorvSfLBg4cKP7f09MTXl5eqF+/Pg4dOoTu3bvr+3Si6dOnIywsTHyuUqng5uYGPz8/yGQynY6lVquhVCrRo0cPWFhY6LupRsO4Kh5Tjc1U4/Keuwfz2uRh5hkzZOVJAAAXw/0Lrds8fK9Oxy7qOC+jmWVLRERERERExWOQyzCfVa9ePdSsWRPXrl1D9+7dIZfLkZaWplUnJycH9+7dE9c5k8vlSE1N1aqjeV7UWmhSqRRSqbRAuYWFRYk/jJdm3/KMcVU8phqbqcWlSZBl5UmQlfv0/0XFp9leXKV5HSMiIiIiIqLi0/lumLr6559/cPfuXbi4uAAAFAoF0tPTkZCQINY5cOAA8vLy4OPjI9aJi4vTWmtHqVSiUaNGhV6CSUREREREREREpA86J8sePXqExMREJCYmAgCSkpKQmJiI5ORkPHr0CJMnT8aJEydw48YN7N+/H3369EGDBg3g7//0EqImTZqgZ8+eGDlyJE6dOoVjx45hzJgxGDhwIFxdXQEAgwcPhqWlJUJCQnDp0iVs3rwZK1as0LrMkoiIiIiIiIiISN90TpadOXMGrVq1QqtWrQAAYWFhaNWqFWbNmgVzc3OcP38eb7zxBl555RWEhITA29sbR44c0bpEMjo6Go0bN0b37t3Rq1cvdOzYEevWrRO329nZITY2FklJSfD29sbEiRMxa9YsjBo1Sg8hExERERERERERFU7nNcu6du0KQRCK3L5378sXrXZwcEBMTMwL63h5eeHIkSO6No+IiIiIiIiIiKjEDL5mGRERERERERERUUXBZBkREREREVEJrFmzBl5eXpDJZJDJZFAoFNi9e7e4/cmTJwgNDUWNGjVQtWpVDBgwAKmpqVrHSE5ORmBgIGxsbODk5ITJkycjJydHq86hQ4fQunVrSKVSNGjQAFFRUWURHhFRpcVkGRERERERUQnUrl0bCxcuREJCAs6cOYNu3bqhT58+uHTpEgBgwoQJ2L59O7Zu3YrDhw/j1q1b6N+/v7h/bm4uAgMDkZ2djePHj2Pjxo2IiorCrFmzxDpJSUkIDAzEa6+9hsTERIwfPx7vvfdesZa/ISKiktF5zTIiIiIiIiICevfurfV8/vz5WLNmDU6cOIHatWtj/fr1iImJQbdu3QAAGzZsQJMmTXDixAm0a9cOsbGxuHz5Mvbt2wdnZ2e0bNkS8+bNw9SpUxEeHg5LS0usXbsWHh4eWLJkCQCgSZMmOHr0KJYtWwZ/f/8yj5mIqDJgsoyIiIiIiKiUcnNzsXXrVmRkZEChUCAhIQFqtRq+vr5incaNG6NOnTqIj49Hu3btEB8fD09PTzg7O4t1/P39MXr0aFy6dAmtWrVCfHy81jE0dcaPH19kW7KyspCVlSU+V6lUAAC1Wg21Wq1TXJr6uu5XWUjNC978rjR9VZr+LqwtpW2PqePvd9kpD32ty7mZLCMiIiIiIiqhCxcuQKFQ4MmTJ6hatSp+/vlnNG3aFImJibC0tIS9vb1WfWdnZ6SkpAAAUlJStBJlmu2abS+qo1Kp8PjxY1hbWxdoU0REBObMmVOgPDY2FjY2NiWKU6lUlmg/UxfZtmDZrl27Sn3ckvR3YW0B9NMeU8ff77JjzL7OzMwsdl0my4iIiIiIiEqoUaNGSExMxIMHD/DDDz8gODgYhw8fNmqbpk+fjrCwMPG5SqWCm5sb/Pz8IJPJdDqWWq2GUqlEjx49YGFhoe+mVnjNwwuuHXcxvOSXx5amvwtrS2nbY+r4+112ykNfa2bZFgeTZURERERERCVkaWmJBg0aAAC8vb1x+vRprFixAu+88w6ys7ORnp6uNbssNTUVcrkcACCXy3Hq1Cmt42nulvlsnefvoJmamgqZTFborDIAkEqlkEqlBcotLCxK/CG1NPuasqxcSYEyffRTSfq7sLboqz2mjr/fZceYfa3LeXk3TCIiIiIiIj3Jy8tDVlYWvL29YWFhgf3794vbrly5guTkZCgUCgCAQqHAhQsXkJaWJtZRKpWQyWRo2rSpWOfZY2jqaI5BRET6x5llREREREREJTB9+nQEBASgTp06ePjwIWJiYnDo0CHs3bsXdnZ2CAkJQVhYGBwcHCCTyTB27FgoFAq0a9cOAODn54emTZti2LBhiIyMREpKCmbMmIHQ0FBxZtgHH3yAzz//HFOmTMGIESNw4MABbNmyBTt37jRm6EREJo3JMiIiIiIiohJIS0tDUFAQbt++DTs7O3h5eWHv3r3o0aMHAGDZsmUwMzPDgAEDkJWVBX9/f6xevVrc39zcHDt27MDo0aOhUChga2uL4OBgzJ07V6zj4eGBnTt3YsKECVixYgVq166Nr776Cv7+XIeKiMhQmCwjIiKicqvuNM6cIKLya/369S/cbmVlhVWrVmHVqlVF1nF3d3/p3Qq7du2Kc+fOlaiNRESkO65ZRkRERERERERElI/JMiIiIiIiIiIionxMlhEREREREREREeVjsoyIiIiIiIiIiCgfk2VERGSS6tatC4lEUuARGhoK4Oliyc9v++CDD7SOkZycjMDAQNjY2MDJyQmTJ09GTk6OMcIhIiIiIqIywrthEhGRSTp9+jRyc3PF5xcvXkSPHj3w1ltviWUjR47E3Llzxec2Njbi/3NzcxEYGAi5XI7jx4/j9u3bCAoKgoWFBRYsWFA2QVRgRd3F8sbCwDJuCRERERGRbpgsIyIik+To6Kj1fOHChahfvz66dOkiltnY2EAulxe6f2xsLC5fvox9+/bB2dkZLVu2xLx58zB16lSEh4fD0tLSoO0nIiIiIiLjYLKMiIhMXnZ2NjZt2oSwsDBIJBKxPDo6Gps2bYJcLkfv3r0xc+ZMcXZZfHw8PD094ezsLNb39/fH6NGjcenSJbRq1arQc2VlZSErK0t8rlKpAABqtRpqtdoQ4emd1Fwo2X5mgta/hdG1D3RtizH6WHPOivLz1ZfKGjdQeWMvTdyVra+IiKhiY7KMiIhM3rZt25Ceno7hw4eLZYMHD4a7uztcXV1x/vx5TJ06FVeuXMFPP/0EAEhJSdFKlAEQn6ekpBR5roiICMyZM6dAeWxsrNZlnuVZZNvS7T+vTV6R23bt2mXQtuh6fH1SKpVGO7cxVda4gcobe0nizszMNEBLiIiIDIPJMiIiMnnr169HQEAAXF1dxbJRo0aJ//f09ISLiwu6d++O69evo379+iU+1/Tp0xEWFiY+V6lUcHNzg5+fH2QyWYmPawjNw/fq9XhSMwHz2uRh5hkzZOVJCq1zMdxfp2Pq2kZdj68ParUaSqUSPXr0gIWFRZmf31gqa9xA5Y29NHFrZtkSERFVBEyWERGRSfvrr7+wb98+ccZYUXx8fAAA165dQ/369SGXy3Hq1CmtOqmpqQBQ5DpnACCVSiGVSguUW1hYlLsP1Vm5hSe0Sn3cPEmRx9a1D3RtozH7uDz+jMtCZY0bqLyxlyTuythPRERUcZkZuwFERESGtGHDBjg5OSEw8MV3YUxMTAQAuLi4AAAUCgUuXLiAtLQ0sY5SqYRMJkPTpk0N1l4iIiIiIjIuziwjIiKTlZeXhw0bNiA4OBhVqvw35F2/fh0xMTHo1asXatSogfPnz2PChAno3LkzvLy8AAB+fn5o2rQphg0bhsjISKSkpGDGjBkIDQ0tdOYYERERERGZBibLiIjIZO3btw/JyckYMWKEVrmlpSX27duH5cuXIyMjA25ubhgwYABmzJgh1jE3N8eOHTswevRoKBQK2NraIjg4GHPnzi3rMIiIiIiIqAwxWUZERCbLz88PgiAUKHdzc8Phw4dfur+7u7tR765IRERERERlj2uWERERERERERER5WOyjIiIiIiIiIiIKB+TZURERERERERERPmYLCMiIiIiIiIiIsrHZBkREREREREREVE+JsuIiIiIiIiIiIjyMVlGRERERERERESUj8kyIiIiIiIiIiKifEyWERERERERERER5dM5WRYXF4fevXvD1dUVEokE27Zt09ouCAJmzZoFFxcXWFtbw9fXF1evXtWqc+/ePQwZMgQymQz29vYICQnBo0ePtOqcP38enTp1gpWVFdzc3BAZGal7dERERERERERERDrQOVmWkZGBFi1aYNWqVYVuj4yMxGeffYa1a9fi5MmTsLW1hb+/P548eSLWGTJkCC5dugSlUokdO3YgLi4Oo0aNErerVCr4+fnB3d0dCQkJWLx4McLDw7Fu3boShEhERERERERERFQ8VXTdISAgAAEBAYVuEwQBy5cvx4wZM9CnTx8AwDfffANnZ2ds27YNAwcOxO+//449e/bg9OnTaNOmDQBg5cqV6NWrFz799FO4uroiOjoa2dnZ+Prrr2FpaYlmzZohMTERS5cu1UqqERERkWmoO22nsZtARERERASgBMmyF0lKSkJKSgp8fX3FMjs7O/j4+CA+Ph4DBw5EfHw87O3txUQZAPj6+sLMzAwnT55Ev379EB8fj86dO8PS0lKs4+/vj0WLFuH+/fuoXr16gXNnZWUhKytLfK5SqQAAarUaarVapzg09XXdr7xjXBWPqcZmqnFJzQStf4GiY5SaC4WWF6WkfWVqfUxERERERGRoek2WpaSkAACcnZ21yp2dncVtKSkpcHJy0m5ElSpwcHDQquPh4VHgGJpthSXLIiIiMGfOnALlsbGxsLGxKVE8SqWyRPuVd4yr4jHV2EwtrnltNP/miWW7du0qtG5kW92OXdRxXiYzM7NE+xEZCmeQEREREVF5p9dkmTFNnz4dYWFh4nOVSgU3Nzf4+flBJpPpdCy1Wg2lUokePXrAwsJC3001GsZV8ZhqbKYal/fcPZjXJg8zz5ghK08CALgY7l9o3ebhe3U6dlHHeRnNLFsiIiIiIiIqHr0my+RyOQAgNTUVLi4uYnlqaipatmwp1klLS9PaLycnB/fu3RP3l8vlSE1N1aqjea6p8zypVAqpVFqg3MLCosQfxkuzb3nGuCoeU43N1OLSJMiy8iTIyn36/6Li02wvrtK8jhFVJkXNXLuxMLCMW0JEREREFZXOd8N8EQ8PD8jlcuzfv18sU6lUOHnyJBQKBQBAoVAgPT0dCQkJYp0DBw4gLy8PPj4+Yp24uDittXaUSiUaNWpU6CWYRERERERERERE+qBzsuzRo0dITExEYmIigKeL+icmJiI5ORkSiQTjx4/HJ598gl9//RUXLlxAUFAQXF1d0bdvXwBAkyZN0LNnT4wcORKnTp3CsWPHMGbMGAwcOBCurq4AgMGDB8PS0hIhISG4dOkSNm/ejBUrVmhdZklERERERGRMERERePXVV1GtWjU4OTmhb9++uHLliladJ0+eIDQ0FDVq1EDVqlUxYMCAAlfRJCcnIzAwEDY2NnBycsLkyZORk5OjVefQoUNo3bo1pFIpGjRogKioKEOHR0RUaemcLDtz5gxatWqFVq1aAQDCwsLQqlUrzJo1CwAwZcoUjB07FqNGjcKrr76KR48eYc+ePbCyshKPER0djcaNG6N79+7o1asXOnbsiHXr1onb7ezsEBsbi6SkJHh7e2PixImYNWsWRo0aVdp4iYiIiIiI9OLw4cMIDQ3FiRMnoFQqoVar4efnh4yMDLHOhAkTsH37dmzduhWHDx/GrVu30L9/f3F7bm4uAgMDkZ2djePHj2Pjxo2IiooSP18BTycoBAYG4rXXXkNiYiLGjx+P9957D3v36rYGKhERFY/Oa5Z17doVgiAUuV0ikWDu3LmYO3dukXUcHBwQExPzwvN4eXnhyJEjujaPiIiIiIioTOzZs0freVRUFJycnJCQkIDOnTvjwYMHWL9+PWJiYtCtWzcAwIYNG9CkSROcOHEC7dq1Q2xsLC5fvox9+/bB2dkZLVu2xLx58zB16lSEh4fD0tISa9euhYeHB5YsWQLg6dU6R48exbJly+DvX7KbABERUdFM5m6YRERERERExvTgwQMATycHAEBCQgLUajV8fX3FOo0bN0adOnUQHx+Pdu3aIT4+Hp6ennB2dhbr+Pv7Y/To0bh06RJatWqF+Ph4rWNo6owfP77QdmRlZSErK0t8rrk7tlqt1loXujg09XXdr7KQmhecSFKavipNfxfWltK2R9+KuiN8Se/8Xlr8/S475aGvdTk3k2VERERERESllJeXh/Hjx6NDhw5o3rw5ACAlJQWWlpawt7fXquvs7IyUlBSxzrOJMs12zbYX1VGpVHj8+DGsra21tkVERGDOnDkF2hgbGwsbG5sSxadUKku0n6mLbFuwbNeuXaU+bkn6u7C2APppj76U1zby97vsGLOvMzMzi12XyTIiIiIiIqJSCg0NxcWLF3H06FFjNwXTp0/XujmaSqWCm5sb/Pz8IJPJdDqWWq2GUqlEjx49YGFhoe+mVniFzZQqzSyp0vR3eZu1VZjy1kb+fped8tDXmlm2xcFkGRERERERUSmMGTMGO3bsQFxcHGrXri2Wy+VyZGdnIz09XWt2WWpqKuRyuVjn1KlTWsfT3C3z2TrP30EzNTUVMpmswKwyAJBKpZBKpQXKLSwsSvwhtTT7mrKsXEmBMn30U0n6u7C26Ks9+lJe28jf77JjzL7W5bw63w2TiIiIiIiIAEEQMGbMGPz88884cOAAPDw8tLZ7e3vDwsIC+/fvF8uuXLmC5ORkKBQKAIBCocCFCxeQlpYm1lEqlZDJZGjatKlY59ljaOpojkFERPrFmWVEREREREQlEBoaipiYGPzyyy+oVq2auMaYnZ0drK2tYWdnh5CQEISFhcHBwQEymQxjx46FQqFAu3btAAB+fn5o2rQphg0bhsjISKSkpGDGjBkIDQ0VZ4d98MEH+PzzzzFlyhSMGDECBw4cwJYtW7Bz506jxU5EZMo4s4yIiIiIiKgE1qxZgwcPHqBr165wcXERH5s3bxbrLFu2DK+//joGDBiAzp07Qy6X46effhK3m5ubY8eOHTA3N4dCocDQoUMRFBSEuXPninU8PDywc+dOKJVKtGjRAkuWLMFXX30Ff//ysxYVEZEpYbKMiIhMUnh4OCQSidajcePG4vYnT54gNDQUNWrUQNWqVTFgwIAC68EkJycjMDAQNjY2cHJywuTJk5GTk1PWoRARUTklCEKhj+HDh4t1rKyssGrVKty7dw8ZGRn46aefxLXINNzd3bFr1y5kZmbi33//xaeffooqVbQvAuratSvOnTuHrKwsXL9+XescRESkX7wMk4iITFazZs2wb98+8fmzHzwmTJiAnTt3YuvWrbCzs8OYMWPQv39/HDt2DACQm5uLwMBAyOVyHD9+HLdv30ZQUBAsLCywYMGCMo+FiIiIiIjKBpNlRERksqpUqVLg23sAePDgAdavX4+YmBh069YNALBhwwY0adIEJ06cQLt27RAbG4vLly9j3759cHZ2RsuWLTFv3jxMnToV4eHhsLS0LOtwiIiIiIioDDBZRkREJuvq1atwdXWFlZUVFAoFIiIiUKdOHSQkJECtVsPX11es27hxY9SpUwfx8fFo164d4uPj4enpCWdnZ7GOv78/Ro8ejUuXLqFVq1aFnjMrKwtZWVnic5VKBQBQq9VQq9UGirRkpOaCfo9nJmj9W54Ysu81xy5vP19Dq6xxA5U39tLEXdn6ioiIKjYmy4iIyCT5+PggKioKjRo1wu3btzFnzhx06tQJFy9eREpKCiwtLWFvb6+1j7Ozs3gns5SUFK1EmWa7ZltRIiIiMGfOnALlsbGxsLGxKWVU+hXZ1jDHndcmzzAHLoVdu3YZ/BxKpdLg5yiPKmvcQOWNvSRxZ2ZmGqAlREREhsFkGRERmaSAgADx/15eXvDx8YG7uzu2bNkCa2trg513+vTpCAsLE5+rVCq4ubnBz88PMpnMYOctiebhe/V6PKmZgHlt8jDzjBmy8iR6PbYhXAzXz13k1Go1lEolevToAQsLC70csyKorHEDlTf20sStmWVLRERUETBZRkRElYK9vT1eeeUVXLt2DT169EB2djbS09O1ZpelpqaKa5zJ5XKcOnVK6xiau2UWtg6ahlQqhVQqLVBuYWFR7j5UZ+UaJqGVlScx2LH1Sd8/j/L4My4LlTVuoPLGXpK4K2M/ERFRxWVm7AYQERGVhUePHuH69etwcXGBt7c3LCwssH//fnH7lStXkJycDIVCAQBQKBS4cOEC0tLSxDpKpRIymQxNmzYt8/YTEREREVHZ4MwyIiIySZMmTULv3r3h7u6OW7duYfbs2TA3N8egQYNgZ2eHkJAQhIWFwcHBATKZDGPHjoVCoUC7du0AAH5+fmjatCmGDRuGyMhIpKSkYMaMGQgNDS105hgREREREZkGJsuIiMgk/fPPPxg0aBDu3r0LR0dHdOzYESdOnICjoyMAYNmyZTAzM8OAAQOQlZUFf39/rF69Wtzf3NwcO3bswOjRo6FQKGBra4vg4GDMnTvXWCEREREREVEZYLKMiIhM0vfff//C7VZWVli1ahVWrVpVZB13d/cyuYuiodWdttPYTSAiIiIiqjC4ZhkREREREREREVE+JsuIiIiIiIiIiIjyMVlGRERERERERESUj8kyIiIiIiIiIiKifEyWERERERERERER5WOyjIiIiIiIiIiIKB+TZURERERERERERPmYLCMiIiIiIiIiIsrHZBkREREREREREVE+JsuIiIiIiIiIiIjyMVlGRERERERERESUj8kyIiIiIiIiIiKifEyWERERERERERER5WOyjIiIiIiIiIiIKF8VYzeAiKi8qjttZ6HlNxYGlnFLiIiIiIiIqKxwZhkREREREREREVE+JsuIiIiIiIiIiIjyMVlGRERERERERESUT+/JsvDwcEgkEq1H48aNxe1PnjxBaGgoatSogapVq2LAgAFITU3VOkZycjICAwNhY2MDJycnTJ48GTk5OfpuKhERkUmpO21noQ8iIiIiIio+gyzw36xZM+zbt++/k1T57zQTJkzAzp07sXXrVtjZ2WHMmDHo378/jh07BgDIzc1FYGAg5HI5jh8/jtu3byMoKAgWFhZYsGCBIZpLREREREREREQEwEDJsipVqkAulxcof/DgAdavX4+YmBh069YNALBhwwY0adIEJ06cQLt27RAbG4vLly9j3759cHZ2RsuWLTFv3jxMnToV4eHhsLS0NESTiYiIiIiIiIiIDJMsu3r1KlxdXWFlZQWFQoGIiAjUqVMHCQkJUKvV8PX1Fes2btwYderUQXx8PNq1a4f4+Hh4enrC2dlZrOPv74/Ro0fj0qVLaNWqVaHnzMrKQlZWlvhcpVIBANRqNdRqtU7t19TXdb/yjnFVPKYaW0WJS2ouFFpeVLulZoLWvy+sW8Sxi1LSvirvfUxERERERFTe6D1Z5uPjg6ioKDRq1Ai3b9/GnDlz0KlTJ1y8eBEpKSmwtLSEvb291j7Ozs5ISUkBAKSkpGglyjTbNduKEhERgTlz5hQoj42NhY2NTYliUSqVJdqvvGNcFY+pxlbe44psW3j5rl27Ci2f10bzb95L6xZ17KIUdZyXyczMLNF+RERE9HJxcXFYvHgxEhIScPv2bfz888/o27evuF0QBMyePRtffvkl0tPT0aFDB6xZswYNGzYU69y7dw9jx47F9u3bYWZmhgEDBmDFihWoWrWqWOf8+fMIDQ3F6dOn4ejoiLFjx2LKlCllGSoRUaWi92RZQECA+H8vLy/4+PjA3d0dW7ZsgbW1tb5PJ5o+fTrCwsLE5yqVCm5ubvDz84NMJtPpWGq1GkqlEj169ICFhYW+m2o0jKviMdXYKkpczcP3Flp+Mdy/0HLvuXswr00eZp4xQ1ae5IV1izp2UYo6zstoZtkSERGR/mVkZKBFixYYMWIE+vfvX2B7ZGQkPvvsM2zcuBEeHh6YOXMm/P39cfnyZVhZWQEAhgwZgtu3b0OpVEKtVuPdd9/FqFGjEBMTA+DpWO7n5wdfX1+sXbsWFy5cwIgRI2Bvb49Ro0aVabxERJWFQS7DfJa9vT1eeeUVXLt2DT169EB2djbS09O1ZpelpqaKa5zJ5XKcOnVK6xiau2UWtg6ahlQqhVQqLVBuYWFR4g/jpdm3PGNcFY+pxlbe48rKlRRaXlSbNQmyrDyJuG+RdYs4dlFK8zpGRIUr6k6hNxYGlnFLiKiiCggI0Jos8CxBELB8+XLMmDEDffr0AQB88803cHZ2xrZt2zBw4ED8/vvv2LNnD06fPo02bZ5OUV+5ciV69eqFTz/9FK6uroiOjkZ2dja+/vprWFpaolmzZkhMTMTSpUuZLCMiMhCDJ8sePXqE69evY9iwYfD29oaFhQX279+PAQMGAACuXLmC5ORkKBQKAIBCocD8+fORlpYGJycnAE8v1ZLJZGjatKmhm0tERERUqKKSa1fn+ZVxS4ioIkhKSkJKSorWes12dnbw8fFBfHw8Bg4ciPj4eNjb24uJMgDw9fWFmZkZTp48iX79+iE+Ph6dO3fWutGZv78/Fi1ahPv376N69eoFzs31nMtOYevQlqavStPfuq63awzlrY38/S475aGvdTm33pNlkyZNQu/eveHu7o5bt25h9uzZMDc3x6BBg2BnZ4eQkBCEhYXBwcEBMpkMY8eOhUKhQLt27QAAfn5+aNq0KYYNG4bIyEikpKRgxowZCA0NLXTmGBERERERUXmjWW+5sPWYn12vWTNBQKNKlSpwcHDQquPh4VHgGJpthSXLuJ5z2SlsHdqSrjX7rJL0t67r7RpDeW0jf7/LjjH7Wpf1nPWeLPvnn38waNAg3L17F46OjujYsSNOnDgBR0dHAMCyZcvEhSuzsrLg7++P1atXi/ubm5tjx44dGD16NBQKBWxtbREcHIy5c+fqu6lERGTCIiIi8NNPP+H//u//YG1tjfbt22PRokVo1KiRWKdr1644fPiw1n7vv/8+1q5dKz5PTk7G6NGjcfDgQVStWhXBwcGIiIhAlSoGn5xNRERUIlzPuewUtg5tSdeaBUrX37qut2sM5a2N/P0uO+Whr3VZz1nv7/S///77F263srLCqlWrsGrVqiLruLu7Gz2zTEREFdvhw4cRGhqKV199FTk5Ofj444/h5+eHy5cvw9bWVqw3cuRIrS9knv3GPTc3F4GBgZDL5Th+/Dhu376NoKAgWFhYYMGCBWUaDxERVSya9ZZTU1Ph4uIilqempqJly5ZinbS0NK39cnJycO/ePa01nTVrOD97jGfP8Tyu51x2CluHVh/9VJL+1nW9XWMor23k73fZMWZf63JeMwO2g4iIyGj27NmD4cOHo1mzZmjRogWioqKQnJyMhIQErXo2NjaQy+Xi49lv3GNjY3H58mVs2rQJLVu2REBAAObNm4dVq1YhOzu7rEMiIqIKxMPDA3K5HPv37xfLVCoVTp48qbVec3p6utbYdODAAeTl5cHHx0esExcXp7XWjlKpRKNGjQq9BJOIiEqP15AQEVGl8ODBAwCAg4ODVnl0dDQ2bdoEuVyO3r17Y+bMmeLssvj4eHh6emqtN+Pv74/Ro0fj0qVLaNWqVYHz6HNRZV0VtWhuWZGaCVr/VlRF/ZxetihxZVscuLLGDVTe2EsTt6n21aNHj3Dt2jXxeVJSEhITE+Hg4IA6depg/Pjx+OSTT9CwYUN4eHhg5syZcHV1Rd++fQEATZo0Qc+ePTFy5EisXbsWarUaY8aMwcCBA+Hq6goAGDx4MObMmYOQkBBMnToVFy9exIoVK7Bs2TJjhExEVCkwWUZERCYvLy8P48ePR4cOHdC8eXOxfPDgwXB3d4erqyvOnz+PqVOn4sqVK/jpp58APF04ubCFmTXbCmOIRZWLq6hFc8vavDZ5xm5CqRS1FERR/atZqLayLg5cWeMGKm/sJYlbl0WVK5IzZ87gtddeE59r1gkLDg5GVFQUpkyZgoyMDIwaNQrp6eno2LEj9uzZAysrK3Gf6OhojBkzBt27dxfXdv7ss8/E7XZ2doiNjUVoaCi8vb1Rs2ZNzJo1C6NGjSq7QImIKhkmy4iIyOSFhobi4sWLOHr0qFb5sx80PD094eLigu7du+P69euoX79+ic6lz0WVdVXUorllRWomYF6bPMw8Y4asvMLXJKkIilpkuKj+Pfe/bkZfsNYYysNCvcZSWWMvTdy6LKpckXTt2hWCUPRsWolEgrlz577wZmUODg6IiYl54Xm8vLxw5MiREreTiIh0w2QZEelF3Wk7Cy2/sTDQpM5JFc+YMWOwY8cOxMXFoXbt2i+sq1kf5tq1a6hfvz7kcjlOnTqlVccYiyoXV1GL5pa1rDxJuWlLSRT1c3rZosSVdXHgyho3UHljL0nclbGfiIio4uIC/0REZJIEQcCYMWPw888/48CBA/Dw8HjpPomJiQAg3rVMoVDgwoULWncqUyqVkMlkaNq0qUHaTURERERExsWZZURUaXAmWuUSGhqKmJgY/PLLL6hWrZq4xpidnR2sra1x/fp1xMTEoFevXqhRowbOnz+PCRMmoHPnzvDy8gIA+Pn5oWnTphg2bBgiIyORkpKCGTNmIDQ0tNDZY0TP4msOERERUcXEZBkRFYof8qiiW7NmDYCn68k8a8OGDRg+fDgsLS2xb98+LF++HBkZGXBzc8OAAQMwY8YMsa65uTl27NiB0aNHQ6FQwNbWFsHBwS9ce4aIiIiIiCo2JsuIKpHCEmBMfpGpetGCywDg5uaGw4cPv/Q47u7uRd4dkYiIiIiITA/XLCMiIiIiIiIiIsrHZBkREREREREREVE+JsuIiIiIiIiIiIjycc0yIjKa5uF7kZUr0SrjGmpERERERERkTEyWEVG5wrtwEhERERERkTHxMkwiIiIiIiIiIqJ8TJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+bjAP5EeFLUofVG4WD0RERERERFR+cRkGREREdEzdP0ChIiIiIhMC5Nl5UhRb845C6nyeP53QGouILKtkRpDREREREREVAkxWUZUhIqQvKwIbSQiIiIiIiKqSJgsIyIiqmB4mSAVhV+iEBEREZUek2UGxDesRERE9Dy+PyAiIiIq35gsI51U5Df4FbntRERUfjUP34vItk//zcqVlPg4hY1THKOIiIiIyh6TZRUY31QTERFRafCLJCIiIqKCmCzTAd9Q6o59RkREREREREQVCZNllUTdaTshNRcKXCbCpBURERERERER0X+YLHuB0q49UpHxTmtEREREREREVBkxWUblhq6XbGrqPz9jjrPliIiIiIiIiKikmCwjIiIiIi1cc5SIqOw8+5r77ESAK/NfN2KrqCxx3C1/zIzdACIiIiIiIiIiovKCyTIiIiIiIiIiIqJ8vAyTiIiIiEqlsMtHeOkIERERVVRMlhERERGVU/paw6Q83eWa67IQERFRecdkGRERUTlVnhIcRERERESVRblOlq1atQqLFy9GSkoKWrRogZUrV6Jt27bGbhYREVUyHI+ovKkIidSK0EaiiobjERFR2Si3C/xv3rwZYWFhmD17Ns6ePYsWLVrA398faWlpxm4aERFVIhyPiIioPOB4RKRfdaftRPPwvQCA5uF7UXfaTn7RQ6JyO7Ns6dKlGDlyJN59910AwNq1a7Fz5058/fXXmDZtmpFbR0RElQXHI6L/PPshQmouILIt8j9oSIzalmdx7TMyVcYej5qH70VWrvbfOv/eiMhUlctkWXZ2NhISEjB9+nSxzMzMDL6+voiPjy90n6ysLGRlZYnPHzx4AAC4d+8e1Gq1TudXq9XIzMxEFbUZcvNe/ubv7t27hZZXycko8/ovqlslT0BmZp5WXLq2pSj6iKmk8T8flzF+HobqL01sd+/ehYWFRanaWFR9Y/SXrn9jRR3HGH9jL6yvzijzv7GXefjwIQBAEIQS7V/ZGXs80vX3xNgKG2cqA8atn7gbTNpSaPnJ6d0LP38Rfx+6HqckNONYUeOzqSpN3ByPSkfX8aisPhuV9P1JeeATsb/Qcl1fK3R5f12cYzz72lqa45SmPYZUntqo62dkQ7elMOXpZ1da5WHs1GksEsqhmzdvCgCE48ePa5VPnjxZaNu2baH7zJ49WwDABx988MFHIY+///67LF6+TQ7HIz744IMP/T44HpWMruMRxyI++OCDj6IfxRmLyuXMspKYPn06wsLCxOd5eXm4d+8eatSoAYlEt287VSoV3Nzc8Pfff0Mmk+m7qUbDuCoeU42NcZUdQRDw8OFDuLq6GrsplYY+x6OKpjz+DZQFxl254gYqb+yliZvjUdniZ6OKi/1dttjfZac89LUuY1G5TJbVrFkT5ubmSE1N1SpPTU2FXC4vdB+pVAqpVKpVZm9vX6p2yGQyk/yDYVwVj6nGxrjKhp2dnbGbUGGVl/GooilvfwNlhXFXPpU19pLGzfGo5HQdj/jZqOJjf5ct9nfZMXZfF3csKpd3w7S0tIS3tzf27//vOvK8vDzs378fCoXCiC0jIqLKhOMRERGVBxyPiIjKVrmcWQYAYWFhCA4ORps2bdC2bVssX74cGRkZ4t1fiIiIygLHIyIiKg84HhERlZ1ymyx755138O+//2LWrFlISUlBy5YtsWfPHjg7Oxv83FKpFLNnzy4wdbmiY1wVj6nGxrioIjHmeFTRVNa/AcZdueIGKm/slTXu8sJY4xF/7mWL/V222N9lp6L1tUQQeP9mIiIiIiIiIiIioJyuWUZERERERERERGQMTJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+UwyWRYXF4fevXvD1dUVEokE27Zt09qempqK4cOHw9XVFTY2NujZsyeuXr2qVef69evo168fHB0dIZPJ8PbbbyM1NVWrzr179zBkyBDIZDLY29sjJCQEjx49MonY6tatC4lEovVYuHChweKKiIjAq6++imrVqsHJyQl9+/bFlStXtOo8efIEoaGhqFGjBqpWrYoBAwYUaHdycjICAwNhY2MDJycnTJ48GTk5OVp1Dh06hNatW0MqlaJBgwaIioqq8HEdOnSowM9LIpEgJSWlXMc1btw4eHt7QyqVomXLloWe6/z58+jUqROsrKzg5uaGyMhIg8SkUVax3bhxo9Cf2YkTJwwZHpHOyvL1uTxbuHAhJBIJxo8fL5aZatw3b97E0KFDUaNGDVhbW8PT0xNnzpwRtwuCgFmzZsHFxQXW1tbw9fUt8F7DGO+RSiM3NxczZ86Eh4cHrK2tUb9+fcybNw/P3gfLVOJ+2XtJfcVZ1uM3Gc6qVatQt25dWFlZwcfHB6dOnTJ2k0xSeHh4gfeFjRs3NnazTII+Xveo+F7W38OHDy/wu96zZ0/jNPYFTDJZlpGRgRYtWmDVqlUFtgmCgL59++LPP//EL7/8gnPnzsHd3R2+vr7IyMgQ9/fz84NEIsGBAwdw7NgxZGdno3fv3sjLyxOPNWTIEFy6dAlKpRI7duxAXFwcRo0aZRKxAcDcuXNx+/Zt8TF27FiDxXX48GGEhobixIkTUCqVUKvV8PPzE9sNABMmTMD27duxdetWHD58GLdu3UL//v3F7bm5uQgMDER2djaOHz+OjRs3IioqCrNmzRLrJCUlITAwEK+99hoSExMxfvx4vPfee9i7d2+FjkvjypUrWj8zJyenchuXxogRI/DOO+8Ueh6VSgU/Pz+4u7sjISEBixcvRnh4ONatW2eQuMoyNo19+/Zp/cy8vb31HhNRaZT161h5dPr0aXzxxRfw8vLSKjfFuO/fv48OHTrAwsICu3fvxuXLl7FkyRJUr15drBMZGYnPPvsMa9euxcmTJ2Frawt/f388efJErGOM90ilsWjRIqxZswaff/45fv/9dyxatAiRkZFYuXKlWMdU4n7Re0lAP3EaY/wmw9i8eTPCwsIwe/ZsnD17Fi1atIC/vz/S0tKM3TST1KxZM633hUePHjV2k0yCPl73qPhe1t8A0LNnT63f9e+++64MW1hMgokDIPz888/i8ytXrggAhIsXL4plubm5gqOjo/Dll18KgiAIe/fuFczMzIQHDx6IddLT0wWJRCIolUpBEATh8uXLAgDh9OnTYp3du3cLEolEuHnzpoGjespQsQmCILi7uwvLli0zeAxFSUtLEwAIhw8fFgThaRstLCyErVu3inV+//13AYAQHx8vCIIg7Nq1SzAzMxNSUlLEOmvWrBFkMpmQlZUlCIIgTJkyRWjWrJnWud555x3B39/f0CEJgmC4uA4ePCgAEO7fv18mcTyvJHE9a/bs2UKLFi0KlK9evVqoXr26GKcgCMLUqVOFRo0a6T+IIhgqtqSkJAGAcO7cOUM1ncggDPU6Vl49fPhQaNiwoaBUKoUuXboIH330kSAIphv31KlThY4dOxa5PS8vT5DL5cLixYvFsvT0dEEqlQrfffedIAjl4z2SrgIDA4URI0ZolfXv318YMmSIIAimG/fz7yX1FWd5GL9JP9q2bSuEhoaKz3NzcwVXV1chIiLCiK0yTUW9ZyT9KsnrHpXc8/0tCIIQHBws9OnTxyjt0YVJzix7kaysLACAlZWVWGZmZgapVCpm7rOysiCRSCCVSsU6VlZWMDMzE+vEx8fD3t4ebdq0Eev4+vrCzMwMJ0+eLItQCtBXbBoLFy5EjRo10KpVKyxevLhMLxt58OABAMDBwQEAkJCQALVaDV9fX7FO48aNUadOHcTHxwN4+jPx9PSEs7OzWMff3x8qlQqXLl0S6zx7DE0dzTEMzVBxabRs2RIuLi7o0aMHjh07ZuhwRCWJqzji4+PRuXNnWFpaimX+/v64cuUK7t+/r6fWv5ihYtN444034OTkhI4dO+LXX3/VT6OJDMjQr2PlTWhoKAIDAwuMHaYa96+//oo2bdrgrbfegpOTE1q1aoUvv/xS3J6UlISUlBStuO3s7ODj46MVd3l7j/Qy7du3x/79+/HHH38AAH777TccPXoUAQEBAEw37ufpK87yMH5T6WVnZyMhIUHr98HMzAy+vr5l9t65srl69SpcXV1Rr149DBkyBMnJycZukskrzuse6d+hQ4fg5OSERo0aYfTo0bh7966xm1RApUuWad7ITp8+Hffv30d2djYWLVqEf/75B7dv3wYAtGvXDra2tpg6dSoyMzORkZGBSZMmITc3V6yTkpJS4BK3KlWqwMHBwWDrRL2MvmIDnq639P333+PgwYN4//33sWDBAkyZMqVM4sjLy8P48ePRoUMHNG/eHMDT/ra0tIS9vb1WXWdnZ7G/U1JStD6QaLZrtr2ojkqlwuPHjw0RjsiQcbm4uGDt2rX48ccf8eOPP8LNzQ1du3bF2bNnDRoTUPK4iqM4sRuSIWOrWrUqlixZgq1bt2Lnzp3o2LEj+vbty4QZlWuGfB0rj77//nucPXsWERERBbaZatx//vkn1qxZg4YNG2Lv3r0YPXo0xo0bh40bNwL4r92FxfVs3OXtPdLLTJs2DQMHDkTjxo1hYWGBVq1aYfz48RgyZAgA0437efqKsyL+7lNBd+7cQW5u7gt/H0h/fHx8EBUVhT179mDNmjVISkpCp06d8PDhQ2M3zaQV53WP9Ktnz5745ptvsH//fixatAiHDx9GQEAAcnNzjd00LVWM3YCyZmFhgZ9++gkhISFwcHCAubk5fH19ERAQIC7i6ujoiK1bt2L06NH47LPPYGZmhkGDBqF169YwMyu/+UV9xhYWFib+38vLC5aWlnj//fcRERGhNSvNEEJDQ3Hx4kWTu0bfkHE1atQIjRo1Ep+3b98e169fx7Jly/Dtt9/q/XzPMtWfF2DY2GrWrKn1d/bqq6/i1q1bWLx4Md544w29n49IH0z57/15f//9Nz766CMolUqtGdumLi8vD23atMGCBQsAAK1atcLFixexdu1aBAcHG7l1hrNlyxZER0cjJiYGzZo1E9c1dXV1Nem4iaj80MxkBZ5+/vLx8YG7uzu2bNmCkJAQI7aMSL8GDhwo/t/T0xNeXl6oX78+Dh06hO7duxuxZdrKb+bHgLy9vZGYmIj09HTcvn0be/bswd27d1GvXj2xjp+fH65fv460tDTcuXMH3377LW7evCnWkcvlBRa2zMnJwb179yCXy8s0nmfpI7bC+Pj4ICcnBzdu3DBo+8eMGYMdO3bg4MGDqF27tlgul8uRnZ2N9PR0rfqpqalif8vl8gJ3IdM8f1kdmUwGa2trfYcjMnRchWnbti2uXbumpwgKV5q4iqOkseuDoWMrjI+Pj8F/ZkQlZYzXMWNKSEhAWloaWrdujSpVqqBKlSo4fPgwPvvsM1SpUgXOzs4mGbeLiwuaNm2qVdakSRPxUiBNuwuL69m4y+N7pBeZPHmyOLvM09MTw4YNw4QJE8RZhaYa9/P0FWdF/N2ngmrWrAlzc/MX/j6Q4djb2+OVV17he0MDK87rHhlWvXr1ULNmzXL3u14pk2UadnZ2cHR0xNWrV3HmzBn06dOnQJ2aNWvC3t4eBw4cQFpamjjjQ6FQID09HQkJCWLdAwcOIC8vDz4+PmUWQ1FKE1thEhMTYWZmZrC7KwqCgDFjxuDnn3/GgQMH4OHhobXd29sbFhYW2L9/v1h25coVJCcnQ6FQAHj6M7lw4YLWGzilUgmZTCa+8VcoFFrH0NTRHKOixlWYxMREuLi46Dmip/QRV3EoFArExcVBrVaLZUqlEo0aNdK6M5s+lVVshTHkz4yopIz5OmZM3bt3x4ULF5CYmCg+2rRpgyFDhoj/N8W4O3TogCtXrmiV/fHHH3B3dwcAeHh4QC6Xa8WtUqlw8uRJrbjL83ukwmRmZha4esDc3Fy8U7ipxv08fcVpjPGb9M/S0hLe3t5avw95eXnYv3+/wd47038ePXqE69ev872hgRXndY8M659//sHdu3fL3++6EW8uYDAPHz4Uzp07J5w7d04AICxdulQ4d+6c8NdffwmCIAhbtmwRDh48KFy/fl3Ytm2b4O7uLvTv31/rGF9//bUQHx8vXLt2Tfj2228FBwcHISwsTKtOz549hVatWgknT54Ujh49KjRs2FAYNGhQhY/t+PHjwrJly4TExETh+vXrwqZNmwRHR0chKCjIYHGNHj1asLOzEw4dOiTcvn1bfGRmZop1PvjgA6FOnTrCgQMHhDNnzggKhUJQKBTi9pycHKF58+aCn5+fkJiYKOzZs0dwdHQUpk+fLtb5888/BRsbG2Hy5MnC77//LqxatUowNzcX9uzZU6HjWrZsmbBt2zbh6tWrwoULF4SPPvpIMDMzE/bt21du4xIEQbh69apw7tw54f333xdeeeUV8Xdbc/es9PR0wdnZWRg2bJhw8eJF4fvvvxdsbGyEL774wiBxlWVsUVFRQkxMjPD7778Lv//+uzB//nzBzMxM+Prrrw0WG1FJlNXrWEXw7N0wBcE04z516pRQpUoVYf78+cLVq1eF6OhowcbGRti0aZNYZ+HChYK9vb3wyy+/COfPnxf69OkjeHh4CI8fPxbrGOM9UmkEBwcLtWrVEnbs2CEkJSUJP/30k1CzZk1hypQpYh1Tiftl7yX1Eacxxm8yjO+//16QSqVCVFSUcPnyZWHUqFGCvb291l1+ST8mTpwoHDp0SEhKShKOHTsm+Pr6CjVr1hTS0tKM3bQKTx+ve1R8L+rvhw8fCpMmTRLi4+OFpKQkYd++fULr1q2Fhg0bCk+ePDF207WYZLLs4MGDAoACj+DgYEEQBGHFihVC7dq1BQsLC6FOnTrCjBkzCtzCferUqYKzs7NgYWEhNGzYUFiyZImQl5enVefu3bvCoEGDhKpVqwoymUx49913hYcPH1b42BISEgQfHx/Bzs5OsLKyEpo0aSIsWLDAoL+8hcUEQNiwYYNY5/Hjx8KHH34oVK9eXbCxsRH69esn3L59W+s4N27cEAICAgRra2uhZs2awsSJEwW1Wq1V5+DBg0LLli0FS0tLoV69elrnqKhxLVq0SKhfv75gZWUlODg4CF27dhUOHDhQ7uPq0qVLocdJSkoS6/z2229Cx44dBalUKtSqVUtYuHChweIqy9iioqKEJk2aCDY2NoJMJhPatm0rbN261aCxEZVEWb4+l3fPJ8tMNe7t27cLzZs3F6RSqdC4cWNh3bp1Wtvz8vKEmTNnCs7OzoJUKhW6d+8uXLlyRauOMd4jlYZKpRI++ugjoU6dOoKVlZVQr1494X//+5/WeyhTiftl7yX1FWdZj99kOCtXrhTq1KkjWFpaCm3bthVOnDhh7CaZpHfeeUdwcXERLC0thVq1agnvvPOOcO3aNWM3yyTo43WPiu9F/Z2ZmSn4+fkJjo6OgoWFheDu7i6MHDmyXCbgJYKQv/I7ERERERERERFRJVep1ywjIiIiIiIiIiJ6FpNlRERERERERERE+ZgsIyIiIiIiIiIiysdkGRERERERERERUT4my4iIiIiIiIiIiPIxWUZERERERERERJSPyTIiIiIiIiIiIqJ8TJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+ZgsIyIiIiIiIiIiysdkGRERERERERERUT4my4iIiIiIiIiIiPIxWUZERERERERERJSPyTIiIiIiIiIiIqJ8TJYRERERERERERHlY7KMiIiIiIiIiIgoH5NlRERERERERERE+ZgsIyIiIiIiIiIiysdkGRERERERERERUT4my8jkHTp0CBKJBD/88IOxm1IsqampePPNN1GjRg1IJBIsX77c2E0iIqoUwsPDIZFIcOfOnZfWrVu3LoYPH16i89StWxevv/56ifYlIiIyBM1npkOHDhm7KUTlApNlpBdRUVGQSCSwsrLCzZs3C2zv2rUrmjdvboSWVTwTJkzA3r17MX36dHz77bfo2bNnkXUlEon4qFKlChwcHODt7Y2PPvoIly9fLsNWExFRZaIZ9yUSCY4ePVpguyAIcHNzg0QiKZAYfHbsMjMzg6urK/z8/Ar9gJaXl4dvvvkGPXr0QM2aNWFhYQEnJyf4+flh3bp1yMrKMlSIRESVXkxMDL+4p0qrirEbQKYlKysLCxcuxMqVK43dlArrwIED6NOnDyZNmlSs+j169EBQUBAEQcCDBw/w22+/YePGjVi9ejUWLVqEsLAwA7eYiKjyuXLlCszM+J2jlZUVYmJi0LFjR63yw4cP459//oFUKi10v2fHrqSkJKxevRrdunXDzp07ERAQAAB4/Pgx+vXrh71796J9+/aYNGkSnJ2dce/ePRw+fBgffvghTp48ifXr1xs8TiIiU9e5c2c8fvwYlpaWYllMTAwuXryI8ePHG69hREbCZBnpVcuWLfHll19i+vTpcHV1NXZzylRGRgZsbW1LfZy0tDTY29sXu/4rr7yCoUOHapUtXLgQvXv3xsSJE9G4cWP06tWr1O0qrry8PGRnZ8PKyqrMzklEVNaKSgJVNr169cLWrVvx2WefoUqV/95WxsTEwNvbu8hLWp8fu/r16wcvLy8sX75cTJZpZlovX74cH330kdb+EydOxNWrV6FUKg0QFRFR5WNmZsb370TP4FeipFcff/wxcnNzsXDhwhfWu3HjBiQSCaKiogpsk0gkCA8PF59r1pD5448/MHToUNjZ2cHR0REzZ86EIAj4+++/0adPH8hkMsjlcixZsqTQc+bm5uLjjz+GXC6Hra0t3njjDfz9998F6p08eRI9e/aEnZ0dbGxs0KVLFxw7dkyrjqZNly9fxuDBg1G9evUC36o/788//8Rbb70FBwcH2NjYoF27dti5c6e4XXNJiyAIWLVqlXiJSknUqFED33//PapUqYL58+drbUtLS0NISAicnZ1hZWWFFi1aYOPGjQWOkZGRgYkTJ8LNzQ1SqRSNGjXCp59+CkEQtOpJJBKMGTMG0dHRaNasGaRSKfbs2QMA+P777+Ht7Y1q1apBJpPB09MTK1asKFFMRERlJT09HcOHD4e9vT3s7Ozw7rvvIjMzU6tOYWuWnT9/Hl26dIG1tTVq166NTz75BBs2bIBEIsGNGzcKnOfo0aNo27YtrKysUK9ePXzzzTdabTA3N8dnn30mlt25cwdmZmaoUaOG1mvx6NGjIZfLxedHjhzBW2+9hTp16kAqlcLNzQ0TJkzA48ePxTqadp07d65AuxYsWABzc/NCl1V43qBBg3D37l2tpFV2djZ++OEHDB48+KX7a3h6eqJmzZpISkoCAPz999/46quv0LNnzwKJMo2GDRviww8/LPY5iIgqs5s3byIkJASurq6QSqXw8PDA6NGjkZ2dDaDgmmVdu3bFzp078ddff4mfS+rWrYtHjx7B1ta20Nfmf/75B+bm5oiIiCiyHZrPgZ9++ilWrVqFevXqwcbGBn5+fvj7778hCALmzZuH2rVrw9raGn369MG9e/e0jqFZ+zM2NhYtW7aElZUVmjZtip9++qnA+XQdm4k0OLOM9MrDwwNBQUH48ssvMW3aNL3OLnvnnXfQpEkTLFy4EDt37sQnn3wCBwcHfPHFF+jWrRsWLVqE6OhoTJo0Ca+++io6d+6stf/8+fMhkUgwdepUpKWlYfny5fD19UViYiKsra0BPL0EMiAgAN7e3pg9ezbMzMywYcMGdOvWDUeOHEHbtm21jvnWW2+hYcOGWLBgQYEk0rNSU1PRvn17ZGZmYty4cahRowY2btyIN954Az/88AP69euHzp0749tvv8WwYcPEy1NKo06dOujSpQsOHjwIlUoFmUyGx48fo2vXrrh27RrGjBkDDw8PbN26FcOHD0d6ero46AmCgDfeeAMHDx5ESEgIWrZsib1792Ly5Mm4efMmli1bpnWuAwcOYMuWLRgzZgxq1qyJunXrQqlUYtCgQejevTsWLVoEAPj9999x7NixIj/4EBGVB2+//TY8PDwQERGBs2fP4quvvoKTk5P4WlaYmzdv4rXXXoNEIsH06dNha2uLr776qsgZaNeuXcObb76JkJAQBAcH4+uvv8bw4cPh7e2NZs2awd7eHs2bN0dcXBzGjRsH4GlyTSKR4N69e7h8+TKaNWsG4GlyrFOnTuKxt27diszMTIwePRo1atTAqVOnsHLlSvzzzz/YunUrAODNN99EaGgooqOj0apVK622RUdHo2vXrqhVq9ZL+6pu3bpQKBT47rvvxBlhu3fvxoMHDzBw4ECtZN+L3L9/H/fv30eDBg3EY+Tm5haYOU1ERLq7desW2rZti/T0dIwaNQqNGzfGzZs38cMPPyAzM1Pr0kuN//3vf3jw4AH++ecf8b1/1apVUbVqVfTr1w+bN2/G0qVLYW5uLu7z3XffQRAEDBky5KVtio6ORnZ2NsaOHYt79+4hMjISb7/9Nrp164ZDhw5h6tSpuHbtGlauXIlJkybh66+/1tr/6tWreOedd/DBBx8gODgYGzZswFtvvYU9e/agR48eAHQfm4m0CER6sGHDBgGAcPr0aeH69etClSpVhHHjxonbu3TpIjRr1kx8npSUJAAQNmzYUOBYAITZs2eLz2fPni0AEEaNGiWW5eTkCLVr1xYkEomwcOFCsfz+/fuCtbW1EBwcLJYdPHhQACDUqlVLUKlUYvmWLVsEAMKKFSsEQRCEvLw8oWHDhoK/v7+Ql5cn1svMzBQ8PDyEHj16FGjToEGDitU/48ePFwAIR44cEcsePnwoeHh4CHXr1hVyc3O14g8NDS3WcV9W96OPPhIACL/99psgCIKwfPlyAYCwadMmsU52dragUCiEqlWriv2zbds2AYDwySefaB3vzTffFCQSiXDt2jWtNpiZmQmXLl0qcG6ZTCbk5OQUKxYiImPTvLaPGDFCq7xfv35CjRo1tMrc3d21xpqxY8cKEolEOHfunFh29+5dwcHBQQAgJCUlae0LQIiLixPL0tLSBKlUKkycOFEsCw0NFZydncXnYWFhQufOnQUnJydhzZo14jkkEok4lgnC03HreREREYJEIhH++usvsWzQoEGCq6ur1hh09uzZIsfnZz077n/++edCtWrVxPO+9dZbwmuvvSbGGhgYqLUvACEkJET4999/hbS0NOHkyZNC9+7dBQDCkiVLBEEQhAkTJggAhMTERK19s7KyhH///Vd83Llz54XtJCIiQQgKChLMzMyE06dPF9im+dyj+cx08OBBcVtgYKDg7u5eYJ+9e/cKAITdu3drlXt5eQldunR5YVs0nwMdHR2F9PR0sXz69OkCAKFFixaCWq0WywcNGiRYWloKT548Ecs04+iPP/4olj148EBwcXERWrVqJZbpMjYTPY+XYZLe1atXD8OGDcO6detw+/ZtvR33vffeE/9vbm6ONm3aQBAEhISEiOX29vZo1KgR/vzzzwL7BwUFoVq1auLzN998Ey4uLti1axcAIDExEVevXsXgwYNx9+5d3LlzB3fu3EFGRga6d++OuLg45OXlaR3zgw8+KFbbd+3ahbZt22pdqlm1alWMGjUKN27cMNidK6tWrQoAePjwodgOuVyOQYMGiXUsLCwwbtw4PHr0CIcPHxbrmZubi7MZNCZOnAhBELB7926t8i5duqBp06ZaZfb29sjIyOB6MkRU4Tz/2t6pUyfcvXsXKpWqyH327NkDhUKBli1bimUODg5FfrvetGlTrdlgjo6OBcavTp06ITU1FVeuXAHwdAZZ586d0alTJxw5cgTA09lmgiBoHUszWxp4ekn9nTt30L59ewiCoHXZZVBQEG7duoWDBw+KZdHR0bC2tsaAAQOKjPV5b7/9Nh4/fowdO3bg4cOH2LFjx0svwVy/fj0cHR3h5OQEHx8fHDt2DGFhYeIi0pq+1oxjGrt27YKjo6P4cHd3L3Y7iYgqo7y8PGzbtg29e/dGmzZtCmwvybIvvr6+cHV1RXR0tFh28eJFnD9/vtgzgt966y3Y2dmJz318fAAAQ4cO1VoD08fHB9nZ2QWWBnB1dUW/fv3E5zKZDEFBQTh37hxSUlIA6D42Ez2LyTIyiBkzZiAnJ+ela5fpok6dOlrP7ezsYGVlhZo1axYov3//foH9GzZsqPVcIpGgQYMG4rXqV69eBQAEBwdrvRF3dHTEV199haysLDx48EDrGB4eHsVq+19//YVGjRoVKG/SpIm43RAePXoEAGKS8K+//kLDhg0L3MHt+Xb89ddfcHV11Uouvqi9hfXDhx9+iFdeeQUBAQGoXbs2RowYIa5lRkRUnj0/3lSvXh0ACh1bNP766y/xEsJnFVZW2Dk053n2HJoE2JEjR5CRkYFz586hU6dO6Ny5s5gsO3LkCGQyGVq0aCHul5ycjOHDh8PBwQFVq1aFo6MjunTpAgBa41iPHj3g4uIiftjJy8vDd999hz59+hR4/X8RR0dH+Pr6IiYmBj/99BNyc3Px5ptvvnCfPn36QKlUYt++fTh58iTu3LmDJUuWiOOT5vyacUyjQ4cOUCqVUCqV8PPzK3YbiYgqq3///RcqlQrNmzfX2zHNzMwwZMgQbNu2TVzTMzo6GlZWVnjrrbeKdYzCPtsBgJubW6Hlz4/BDRo0KJDoe+WVVwBA/Hyn69hM9CyuWUYGUa9ePQwdOhTr1q3DtGnTCmwv6huM3NzcIo/57PXwLyoD8ML1w4qimTW2ePFirW8fnvX8N9zPfntfHl28eBHm5ubFTuqVVGH94OTkhMTEROzduxe7d+/G7t27sWHDBgQFBRV6QwEiovJCn2NLac7h6uoKDw8PxMXFoW7duhAEAQqFAo6Ojvjoo4/w119/4ciRI2jfvr2YZMrNzUWPHj1w7949TJ06FY0bN4atrS1u3ryJ4cOHa82QNjc3x+DBg/Hll19i9erVOHbsGG7dulWidcIGDx6MkSNHIiUlBQEBAS+9q3Pt2rXh6+tb5PbGjRsDeDqOPZsI1CTmAGDTpk06t5OIiPQjKCgIixcvxrZt2zBo0CDExMTg9ddf15ot9iJFjYNlMQYTFQdnlpHBaGaXFbYgsuZb+vT0dK1yQ82wAv6bOaYhCAKuXbuGunXrAgDq168P4OkUXl9f30IfFhYWJTq3u7u7eBnNs/7v//5P3K5vycnJOHz4MBQKhfgNvbu7O65evVrgctLn2+Hu7o5bt26Jl2+WtL2Wlpbo3bs3Vq9ejevXr+P999/HN998g2vXrpUqNiKi8sbd3b3Q17bSvt5pLrk8cuQIWrZsiWrVqqFFixaws7PDnj17cPbsWa0b2ly4cAF//PEHlixZgqlTp6JPnz7i5TKFCQoKgkqlwvbt2xEdHQ1HR0f4+/vr3M5+/frBzMwMJ06c0OkumEUJCAiAubm51iU+RESkO0dHR8hkMly8eFHnfV90iWbz5s3RqlUrREdH48iRI0hOTsawYcNK01SdXLt2rUAC7Y8//gAA8fOdocZmqhyYLCODqV+/PoYOHYovvvhCvG5cQyaToWbNmoiLi9MqX716tcHa880332glf3744Qfcvn1bvHuXt7c36tevj08//bTAZR/A0ynMJdWrVy+cOnUK8fHxYllGRgbWrVuHunXrFljvq7Tu3buHQYMGITc3F//73/+02pGSkoLNmzeLZTk5OVi5ciWqVq0qXqbTq1cv5Obm4vPPP9c67rJlyyCRSMQ+e5G7d+9qPTczM4OXlxcAICsrq8SxERGVR/7+/oiPj0diYqJYdu/evVInezp16oQbN25g8+bN4mWZZmZmaN++PZYuXQq1Wq21XpnmG/lnP0AIgoAVK1YUenwvLy94eXnhq6++wo8//oiBAwdqrRVTXFWrVsWaNWsQHh6O3r1767z/8+rUqYMRI0Zg9+7dBcYiDc4yICJ6OTMzM/Tt2xfbt2/HmTNnCmx/0Wupra1tgWVonjVs2DDExsZi+fLlqFGjRrE+I+jLrVu38PPPP4vPVSoVvvnmG7Rs2RJyuRyA4cZmqhx4GSYZ1P/+9z98++23uHLliniLe4333nsPCxcuxHvvvYc2bdogLi5O/DbAEBwcHNCxY0e8++67SE1NxfLly9GgQQOMHDkSwNOB5KuvvkJAQACaNWuGd999F7Vq1cLNmzdx8OBByGQybN++vUTnnjZtGr777jsEBARg3LhxcHBwwMaNG5GUlIQff/yxwBpiuvjjjz+wadMmCIIAlUqF3377DVu3bsWjR4+wdOlS9OzZU6w7atQofPHFFxg+fDgSEhJQt25d/PDDDzh27BiWL18uzkDr3bs3XnvtNfzvf//DjRs30KJFC8TGxuKXX37B+PHjxVl4L/Lee+/h3r176NatG2rXro2//voLK1euRMuWLcW1z4iITMWUKVOwadMm9OjRA2PHjhVvT1+nTh3cu3evRAsoA/+tW3blyhUsWLBALO/cuTN2794NqVSKV199VSxv3Lgx6tevj0mTJuHmzZuQyWT48ccfX7jeWlBQECZNmgQAJboEUyM4OLjE+xZm+fLlSEpKwtixY/H999+jd+/ecHJywp07d3Ds2DFs37690PVAiYhI24IFCxAbG4suXbpg1KhRaNKkCW7fvo2tW7fi6NGjRV467+3tjc2bNyMsLAyvvvoqqlatqvWFyODBgzFlyhT8/PPPGD16dImvwimJV155BSEhITh9+jScnZ3x9ddfIzU1FRs2bBDrGGpspsqByTIyqAYNGmDo0KGFrlE1a9Ys/Pvvv/jhhx+wZcsWBAQEYPfu3XBycjJIWz7++GOcP38eERERePjwIbp3747Vq1fDxsZGrNO1a1fEx8dj3rx5+Pzzz/Ho0SPI5XL4+Pjg/fffL/G5nZ2dcfz4cUydOhUrV67EkydP4OXlhe3btyMwMLBUcWkWOjYzM4NMJoOHhweCg4MxatSoAjPWrK2tcejQIUybNg0bN26ESqVCo0aNsGHDBgwfPlysZ2Zmhl9//RWzZs3C5s2bsWHDBtStWxeLFy/GxIkTi9UuzZp1q1evRnp6OuRyOd555x2Eh4eXKjlIRFQeubm54eDBgxg3bhwWLFgAR0dHhIaGwtbWFuPGjYOVlVWJjtuoUSM4OTkhLS1N647KmiRa27ZtIZVKxXILCwts374d48aNQ0REBKysrNCvXz+MGTNGa+2vZw0ZMgRTp05F/fr10bZt2xK10xBsbGywZ88efPvtt/j2228RGRkJlUoFe3t7tGjRAqtXr9Z7go6IyBTVqlULJ0+exMyZMxEdHQ2VSoVatWohICBA67PQ8z788EMkJiZiw4YNWLZsGdzd3bWSZc7OzvDz88OuXbvK9BJM4OnN21auXInJkyfjypUr8PDwwObNm7WWEjDU2EyVg0TgHHYiIiIigxg/fjy++OILPHr0qMhFi43tzp07cHFxwaxZszBz5kxjN4eIiCqQfv364cKFC2W6DljdunXRvHlz7Nixo0T7V4SxmYyP0zuIiIiI9ODx48daz+/evYtvv/0WHTt2LNdvxqOiopCbm1vmswKIiKhiu337Nnbu3Fmux4+KOjaT8fEyTCIiIiI9UCgU6Nq1K5o0aYLU1FSsX78eKpWq3M7WOnDgAC5fvoz58+ejb9++4t3DiIiIXiQpKQnHjh3DV199BQsLi1ItV2NoFW1spvKDyTIiIiIiPejVqxd++OEHrFu3DhKJBK1bt8b69evRuXNnYzetUHPnzsXx48fRoUMHrFy50tjNISKiCuLw4cN49913UadOHWzcuFG8+2R5VNHGZio/uGYZERERERERERFRPq5ZRkRERERERERElI/JMiIiIiIiIiIionwmu2ZZXl4ebt26hWrVqkEikRi7OURERiEIAh4+fAhXV1eYmfH7EWPgeERExPHI2DgWERHpNhaZbLLs1q1bcHNzM3YziIjKhb///hu1a9c2djMqJY5HRET/4XhkHByLiIj+U5yxyGSTZdWqVQPwtBNkMplO+6rVasTGxsLPzw8WFhaGaF6Fxz56OfZR8bCfXq40faRSqeDm5ia+JlLZe3484u+8YbBfDYP9ahiVsV85HhlXaT4bVQSm/jdl6vEBph+jqccHVIwYdRmLTDZZppleLJPJSpQss7GxgUwmK7c/ZGNjH70c+6h42E8vp48+4iUXxvP8eMTfecNgvxoG+9UwKnO/cjwyjtJ8NqoITP1vytTjA0w/RlOPD6hYMRZnLOKCAURERERERERERPmYLCMiIiIiIiIiIsrHZBkREREREREREVE+JsuIiIiIiIiIiIjyMVlGRERERERERESUj8kyIiIiIiIiIiKifEyWERERERERERER5ati7AYQEdF/6k7bWaBMai4gsq0RGkPlVmG/JwBwY2FgGbeEiIio+Dh+EVFFwZllRERERERERERE+ZgsIyIiIiIiIiIiysdkGRERERERERERUT4my4iIiIiIiIiIiPLplCzLzc3FzJkz4eHhAWtra9SvXx/z5s2DIAhiHUEQMGvWLLi4uMDa2hq+vr64evWq1nHu3buHIUOGQCaTwd7eHiEhIXj06JFWnfPnz6NTp06wsrKCm5sbIiMjSxEmERERERERERHRy+mULFu0aBHWrFmDzz//HL///jsWLVqEyMhIrFy5UqwTGRmJzz77DGvXrsXJkydha2sLf39/PHnyRKwzZMgQXLp0CUqlEjt27EBcXBxGjRolblepVPDz84O7uzsSEhKwePFihIeHY926dXoImYiIiIiIiIiIqHBVdKl8/Phx9OnTB4GBT2/tW7duXXz33Xc4deoUgKezypYvX44ZM2agT58+AIBvvvkGzs7O2LZtGwYOHIjff/8de/bswenTp9GmTRsAwMqVK9GrVy98+umncHV1RXR0NLKzs/H111/D0tISzZo1Q2JiIpYuXaqVVCMiIiIiIiIiItInnZJl7du3x7p16/DHH3/glVdewW+//YajR49i6dKlAICkpCSkpKTA19dX3MfOzg4+Pj6Ij4/HwIEDER8fD3t7ezFRBgC+vr4wMzPDyZMn0a9fP8THx6Nz586wtLQU6/j7+2PRokW4f/8+qlevXqBtWVlZyMrKEp+rVCoAgFqthlqt1iVMsb6u+1Um7KOXYx8VD/tJm9RcKFhm9rSsJH3EfiUiIiIiItKNTsmyadOmQaVSoXHjxjA3N0dubi7mz5+PIUOGAABSUlIAAM7Ozlr7OTs7i9tSUlLg5OSk3YgqVeDg4KBVx8PDo8AxNNsKS5ZFRERgzpw5BcpjY2NhY2OjS5gipVJZov0qE/bRy7GPiof99FRk26K3laSPMjMzS9EaIiIiIiKiykenZNmWLVsQHR2NmJgY8dLI8ePHw9XVFcHBwYZqY7FMnz4dYWFh4nOVSgU3Nzf4+flBJpPpdCy1Wg2lUokePXrAwsJC3001Ceyjl2MfFQ/7SVvz8L0FyqRmAua1yStRH2lm2RIREREREVHx6JQsmzx5MqZNm4aBAwcCADw9PfHXX38hIiICwcHBkMvlAIDU1FS4uLiI+6WmpqJly5YAALlcjrS0NK3j5uTk4N69e+L+crkcqampWnU0zzV1nieVSiGVSguUW1hYlPgDeGn2rSzYRy/HPioe9tNTWbmSIreVpI/Yp0RERERERLrR6W6YmZmZMDPT3sXc3Bx5eXkAAA8PD8jlcuzfv1/crlKpcPLkSSgUCgCAQqFAeno6EhISxDoHDhxAXl4efHx8xDpxcXFaa+0olUo0atSo0EswiYiIiIiIiIiI9EGnZFnv3r0xf/587Ny5Ezdu3MDPP/+MpUuXol+/fgAAiUSC8ePH45NPPsGvv/6KCxcuICgoCK6urujbty8AoEmTJujZsydGjhyJU6dO4dixYxgzZgwGDhwIV1dXAMDgwYNhaWmJkJAQXLp0CZs3b8aKFSu0LrMkIiIiIiIiIiLSN50uw1y5ciVmzpyJDz/8EGlpaXB1dcX777+PWbNmiXWmTJmCjIwMjBo1Cunp6ejYsSP27NkDKysrsU50dDTGjBmD7t27w8zMDAMGDMBnn30mbrezs0NsbCxCQ0Ph7e2NmjVrYtasWRg1apQeQiYiIiIiIiIiIiqcTsmyatWqYfny5Vi+fHmRdSQSCebOnYu5c+cWWcfBwQExMTEvPJeXlxeOHDmiS/OIiIiIiIiIiIhKRafLMImIiIiIiIiIiEwZk2VERERERERERET5mCwjIiIiIiIiIiLKx2QZERERERFRCaxZswZeXl6QyWSQyWRQKBTYvXu3uP3JkycIDQ1FjRo1ULVqVQwYMACpqalax0hOTkZgYCBsbGzg5OSEyZMnIycnR6vOoUOH0Lp1a0ilUjRo0ABRUVFlER4RUaXFZBkREREREVEJ1K5dGwsXLkRCQgLOnDmDbt26oU+fPrh06RIAYMKECdi+fTu2bt2Kw4cP49atW+jfv7+4f25uLgIDA5GdnY3jx49j48aNiIqKwqxZs8Q6SUlJCAwMxGuvvYbExESMHz8e7733Hvbu3Vvm8RIRVRY63Q2TiIiIiIiInurdu7fW8/nz52PNmjU4ceIEateujfXr1yMmJgbdunUDAGzYsAFNmjTBiRMn0K5dO8TGxuLy5cvYt28fnJ2d0bJlS8ybNw9Tp05FeHg4LC0tsXbtWnh4eGDJkiUAgCZNmuDo0aNYtmwZ/P39yzxmIqLKgMkyIiIiIiKiUsrNzcXWrVuRkZEBhUKBhIQEqNVq+Pr6inUaN26MOnXqID4+Hu3atUN8fDw8PT3h7Ows1vH398fo0aNx6dIltGrVCvHx8VrH0NQZP358kW3JyspCVlaW+FylUgEA1Go11Gq1niLWndRcKLS8tG3S7G/M2AzJ1OMDTD9GU48PqBgx6tI2JsuIiP6fvbuPi6rO////BIRB1AExYeQjIpubSl5j4Xwq1xIhozaTvbBMLS1XFy1lv2rsqqFWGJualulWpu0v+ZTuZrupKaOmrokXkaxXrVutxe7q4O4aTmoOI8zvj4azjlwIyDWP++3Grc77vOac9/s1I2fmxXvOGwAAoIaOHDkiq9WqS5cuqW3bttqwYYNiYmKUl5engIAAhYSEeMWHh4fLbrdLkux2u1ehrHR/6b7KYhwOh7799lu1bt26TJ8yMjI0b968Mu3Z2dkKCgqq8VivV+at5bdv3ry5Vo5vs9lq5TiNVXMfn9T8x9jcxyc17jFevHixyrEUywAAAACghrp37668vDydO3dOv/vd7zRu3Djt2rWrQfuUlpam1NRUY9vhcCgyMlIJCQkym80N1q9e6eXfZ+1o+vV9ndTlcslms2nYsGHy9/e/rmM1Rs19fFLzH2NzH5/UNMZYOsu2KiiWAQAAAEANBQQEqFu3bpKk2NhYHTx4UEuXLtVPf/pTFRUVqbCw0Gt2WUFBgSwWiyTJYrHowIEDXscrXS3zypirV9AsKCiQ2Wwud1aZJJlMJplMpjLt/v7+Dfoh1lnsU257bfWpocdX15r7+KTmP8bmPj6pcY+xOv1iNUwAQJO3cOFC+fj4eN2/5dKlS0pJSVGHDh3Utm1bJScnl/mwkZ+fr6SkJAUFBSksLEwzZszQ5cuXvWJ27typAQMGyGQyqVu3blqzZk09jAgA0FSVlJTI6XQqNjZW/v7+2r59u7HvxIkTys/Pl9VqlSRZrVYdOXJEZ86cMWJsNpvMZrNiYmKMmCuPURpTegwAQO2jWAYAaNIOHjyo3/zmN+rTp49X+/Tp0/X+++9r/fr12rVrl06dOqWRI0ca+4uLi5WUlKSioiLt3btXb775ptasWaO5c+caMSdPnlRSUpLuvPNO5eXladq0aXrssce0dWv5XyMBALQsaWlp2r17t7788ksdOXJEaWlp2rlzp0aPHq3g4GBNmDBBqamp+vDDD5Wbm6tHH31UVqtVgwYNkiQlJCQoJiZGY8aM0Z///Gdt3bpVs2fPVkpKijEzbNKkSfrb3/6mmTNn6i9/+YteeeUVrVu3TtOnT2/IoQNAs8bXMAEATdb58+c1evRovfbaa3rmmWeM9nPnzmnVqlXKysrSXXfdJUlavXq1evbsqX379mnQoEHKzs7W8ePHtW3bNoWHh6tfv35asGCBZs2apfT0dAUEBGjlypWKjo7WokWLJEk9e/bUnj17tGTJEiUmXt/9VQAATd+ZM2c0duxYnT59WsHBwerTp4+2bt2qYcOGSZKWLFkiX19fJScny+l0KjExUa+88orxeD8/P23cuFGTJ0+W1WpVmzZtNG7cOM2fP9+IiY6O1qZNmzR9+nQtXbpUnTt31uuvv851CADqEMUyAECTlZKSoqSkJMXHx3sVy3Jzc+VyuRQfH2+09ejRQ126dFFOTo4GDRqknJwc9e7d22uFscTERE2ePFnHjh1T//79lZOT43WM0pgrv+4JAGi5Vq1aVen+wMBALV++XMuXL68wJioq6pqrQQ4ZMkSHDh2qUR8BANVHsQwA0CS9/fbb+uSTT3Tw4MEy++x2uwICArxuqCxJ4eHhstvtRsyVhbLS/aX7KotxOBz69ttvy72xstPplNPpNLZLV91xuVzGT+l2TZn83OW2X88xm7rayCvKIq91oyXmtSWNFQDQ9FEsAwA0OX//+9/15JNPymazKTAwsKG74yUjI0Pz5s0r056dna2goCBj22az1fgcmbeW336tmQktwfXkFRUjr3WjJeX14sWLDd0FAACqjGIZAKDJyc3N1ZkzZzRgwACjrbi4WLt379bLL7+srVu3qqioSIWFhV6zywoKCmSxWCRJFotFBw4c8Dpu6WqZV8ZcvYJmQUGBzGZzubPKpO9u9pyammpsOxwORUZGKiEhQWazWS6XSzabTcOGDavxstq90qu3wMDR9OZ/X5vayCvKIq91oyXmtXSWLQAATQHFMgBAkzN06FAdOXLEq+3RRx9Vjx49NGvWLEVGRsrf31/bt29XcnKyJOnEiRPKz8+X1WqVJFmtVj377LM6c+aMwsLCJH03y8NsNismJsaIuXq2ls1mM45RHpPJZKxgdiV/f3+vD8VXb1eHs9inWvEt5cO4dH15RcXIa91oSXltKeMEADQPFMsAAE1Ou3bt1KtXL6+2Nm3aqEOHDkb7hAkTlJqaqtDQUJnNZk2dOlVWq1WDBg2SJCUkJCgmJkZjxoxRZmam7Ha7Zs+erZSUFKPYNWnSJL388suaOXOmxo8frx07dmjdunXatGlT/Q4YAAAAQL2hWAYAaJaWLFkiX19fJScny+l0KjExUa+88oqx38/PTxs3btTkyZNltVrVpk0bjRs3TvPnzzdioqOjtWnTJk2fPl1Lly5V586d9frrrysxsfl/rREAAABoqSiWAQCahZ07d3ptBwYGavny5Vq+fHmFj4mKirrmTfGHDBmiQ4cO1UYXAQAAADQBvg3dAQAAAAAAAKCxoFgGAAAAAAAAeFAsAwAAAAAAADwolgEAAAAAAAAeFMsAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4FHtYtk///lPPfzww+rQoYNat26t3r176+OPPzb2u91uzZ07V506dVLr1q0VHx+vzz77zOsYZ8+e1ejRo2U2mxUSEqIJEybo/PnzXjGHDx/WHXfcocDAQEVGRiozM7OGQwQAAAAAAACqplrFsq+//lq33Xab/P399cEHH+j48eNatGiR2rdvb8RkZmZq2bJlWrlypfbv3682bdooMTFRly5dMmJGjx6tY8eOyWazaePGjdq9e7cmTpxo7Hc4HEpISFBUVJRyc3P161//Wunp6Xr11VdrYcgAAAAAAABA+VpVJ/j5559XZGSkVq9ebbRFR0cb/+92u/Xiiy9q9uzZuv/++yVJv/3tbxUeHq733ntPo0aN0qeffqotW7bo4MGDGjhwoCTppZde0j333KMXXnhBERERWrt2rYqKivTGG28oICBAN998s/Ly8rR48WKvohoAAAAAAABQm6o1s+yPf/yjBg4cqB//+McKCwtT//799dprrxn7T548Kbvdrvj4eKMtODhYcXFxysnJkSTl5OQoJCTEKJRJUnx8vHx9fbV//34jZvDgwQoICDBiEhMTdeLECX399dc1GykAAAAAAABwDdWaWfa3v/1NK1asUGpqqn75y1/q4MGDeuKJJxQQEKBx48bJbrdLksLDw70eFx4ebuyz2+0KCwvz7kSrVgoNDfWKuXLG2pXHtNvtXl/7LOV0OuV0Oo1th8MhSXK5XHK5XNUZphFf3ce1JOTo2shR1ZAnbyY/d9k23+/aapIj8goAAAAA1VOtYllJSYkGDhyo5557TpLUv39/HT16VCtXrtS4cePqpINVlZGRoXnz5pVpz87OVlBQUI2OabPZrrdbzR45ujZyVDXk6TuZt1a8ryY5unjx4nX0BgAAAABanmoVyzp16qSYmBivtp49e+r3v/+9JMlisUiSCgoK1KlTJyOmoKBA/fr1M2LOnDnjdYzLly/r7NmzxuMtFosKCgq8Ykq3S2OulpaWptTUVGPb4XAoMjJSCQkJMpvN1RmmXC6XbDabhg0bJn9//2o9tqUgR9dGjqqGPHnrlb61TJvJ160FA0tqlKPSWbYAAAAAgKqpVrHstttu04kTJ7za/vrXvyoqKkrSdzf7t1gs2r59u1Ecczgc2r9/vyZPnixJslqtKiwsVG5urmJjYyVJO3bsUElJieLi4oyYX/3qV3K5XMYHQ5vNpu7du5f7FUxJMplMMplMZdr9/f1r/AH8eh7bUpCjayNHVUOevuMs9qlwX01yRE4BAAAAoHqqdYP/6dOna9++fXruuef0+eefKysrS6+++qpSUlIkST4+Ppo2bZqeeeYZ/fGPf9SRI0c0duxYRUREaMSIEZK+m4l299136/HHH9eBAwf00UcfacqUKRo1apQiIiIkSQ899JACAgI0YcIEHTt2TO+8846WLl3qNXMMAAAAAAAAqG3Vmll2yy23aMOGDUpLS9P8+fMVHR2tF198UaNHjzZiZs6cqQsXLmjixIkqLCzU7bffri1btigwMNCIWbt2raZMmaKhQ4fK19dXycnJWrZsmbE/ODhY2dnZSklJUWxsrG644QbNnTtXEydOrIUhAwAAAAAAAOWrVrFMku69917de++9Fe738fHR/PnzNX/+/ApjQkNDlZWVVel5+vTpoz/96U/V7R4AAAAAAABQY9X6GiYAAAAAAADQnFEsAwAAAAAAADwolgEAAAAAAAAeFMsAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDYhkAoMlZsWKF+vTpI7PZLLPZLKvVqg8++MDYf+nSJaWkpKhDhw5q27atkpOTVVBQ4HWM/Px8JSUlKSgoSGFhYZoxY4YuX77sFbNz504NGDBAJpNJ3bp105o1a+pjeAAAAAAaEMUyAECT07lzZy1cuFC5ubn6+OOPddddd+n+++/XsWPHJEnTp0/X+++/r/Xr12vXrl06deqURo4caTy+uLhYSUlJKioq0t69e/Xmm29qzZo1mjt3rhFz8uRJJSUl6c4771ReXp6mTZumxx57TFu3bq338QIAAACoP60augMAAFTXfffd57X97LPPasWKFdq3b586d+6sVatWKSsrS3fddZckafXq1erZs6f27dunQYMGKTs7W8ePH9e2bdsUHh6ufv36acGCBZo1a5bS09MVEBCglStXKjo6WosWLZIk9ezZU3v27NGSJUuUmJhY72MGAAAAUD8olgEAmrTi4mKtX79eFy5ckNVqVW5urlwul+Lj442YHj16qEuXLsrJydGgQYOUk5Oj3r17Kzw83IhJTEzU5MmTdezYMfXv3185OTlexyiNmTZtWqX9cTqdcjqdxrbD4ZAkuVwu46d0u6ZMfu5qxV/PuZqK2sgryiKvdaMl5rUljRUA0PRRLAMANElHjhyR1WrVpUuX1LZtW23YsEExMTHKy8tTQECAQkJCvOLDw8Nlt9slSXa73atQVrq/dF9lMQ6HQ99++61at25dbr8yMjI0b968Mu3Z2dkKCgoytm02W/UGfIXMW6sXv3nz5hqfq6m5nryiYuS1brSkvF68eLGhuwAAQJVRLAMANEndu3dXXl6ezp07p9/97ncaN26cdu3a1dDdUlpamlJTU41th8OhyMhIJSQkyGw2y+VyyWazadiwYfL396/ROXqlV+++aUfTm//XRmsjryiLvNaNlpjX0lm2AAA0BRTLAABNUkBAgLp16yZJio2N1cGDB7V06VL99Kc/VVFRkQoLC71mlxUUFMhisUiSLBaLDhw44HW80tUyr4y5egXNgoICmc3mCmeVSZLJZJLJZCrT7u/v7/Wh+Ort6nAW+1QrvqV8GJeuL6+oGHmtGy0pry1lnACA5oHVMAEAzUJJSYmcTqdiY2Pl7++v7du3G/tOnDih/Px8Wa1WSZLVatWRI0d05swZI8Zms8lsNismJsaIufIYpTGlxwAAAADQPDGzDADQ5KSlpWn48OHq0qWLvvnmG2VlZWnnzp3aunWrgoODNWHCBKWmpio0NFRms1lTp06V1WrVoEGDJEkJCQmKiYnRmDFjlJmZKbvdrtmzZyslJcWYFTZp0iS9/PLLmjlzpsaPH68dO3Zo3bp12rRpU0MOHQAAAEAdY2YZAKDJOXPmjMaOHavu3btr6NChOnjwoLZu3aphw4ZJkpYsWaJ7771XycnJGjx4sCwWi959913j8X5+ftq4caP8/PxktVr18MMPa+zYsZo/f74REx0drU2bNslms6lv375atGiRXn/9dSUmNv/7fwEAqiYjI0O33HKL2rVrp7CwMI0YMUInTpzwirl06ZJSUlLUoUMHtW3bVsnJyWW+5p+fn6+kpCQFBQUpLCxMM2bM0OXLl71idu7cqQEDBshkMqlbt25as2ZNXQ+vQXV9alO5PwBQH5hZBgBoclatWlXp/sDAQC1fvlzLly+vMCYqKuqaq0QOGTJEhw4dqlEfAQDN365du5SSkqJbbrlFly9f1i9/+UslJCTo+PHjatOmjSRp+vTp2rRpk9avX6/g4GBNmTJFI0eO1EcffSRJKi4uVlJSkiwWi/bu3avTp09r7Nix8vf313PPPSdJOnnypJKSkjRp0iStXbtW27dv12OPPaZOnTrxRxwAqAMUywAAAACgBrZs2eK1vWbNGoWFhSk3N1eDBw/WuXPntGrVKmVlZemuu+6SJK1evVo9e/bUvn37NGjQIGVnZ+v48ePatm2bwsPD1a9fPy1YsECzZs1Senq6AgICtHLlSkVHR2vRokWSpJ49e2rPnj1asmQJxTIAqAMUywAAAACgFpw7d06SFBoaKknKzc2Vy+VSfHy8EdOjRw916dJFOTk5GjRokHJyctS7d2+Fh4cbMYmJiZo8ebKOHTum/v37Kycnx+sYpTHTpk0rtx9Op1NOp9PYdjgckiSXyyWXy1UrY60Jk5+73Pby+lSd2NK2hhxbXWru45Oa/xib+/ikpjHG6vSNYhkAAAAAXKeSkhJNmzZNt912m3r16iVJstvtCggIUEhIiFdseHi47Ha7EXNloax0f+m+ymIcDoe+/fZbtW7d2mtfRkaG5s2bV6aP2dnZCgoKqvkgr1PmreW3l3dbhOrElrLZbDXpVpPR3McnNf8xNvfxSY17jBcvXqxyLMUyAAAAALhOKSkpOnr0qPbs2dPQXVFaWppSU1ONbYfDocjISCUkJMhsNjdYv3qlby23/Wh62a+SVifW5XLJZrNp2LBh8vf3v75ONkLNfXxS8x9jcx+f1DTGWDrLtioolgEAAADAdZgyZYo2btyo3bt3q3Pnzka7xWJRUVGRCgsLvWaXFRQUyGKxGDEHDhzwOl7paplXxly9gmZBQYHMZnOZWWWSZDKZZDKZyrT7+/s36IdYZ7FPue3l9ak6sVfua6wf0mtDcx+f1PzH2NzHJzXuMVanX7512A8AAAAAaLbcbremTJmiDRs2aMeOHYqOjvbaHxsbK39/f23fvt1oO3HihPLz82W1WiVJVqtVR44c0ZkzZ4wYm80ms9msmJgYI+bKY5TGlB4DAFC7mFkGAAAAADWQkpKirKws/eEPf1C7du2Me4wFBwerdevWCg4O1oQJE5SamqrQ0FCZzWZNnTpVVqtVgwYNkiQlJCQoJiZGY8aMUWZmpux2u2bPnq2UlBRjdtikSZP08ssva+bMmRo/frx27NihdevWadOmTQ02dgBozphZBgAAAAA1sGLFCp07d05DhgxRp06djJ933nnHiFmyZInuvfdeJScna/DgwbJYLHr33XeN/X5+ftq4caP8/PxktVr18MMPa+zYsZo/f74REx0drU2bNslms6lv375atGiRXn/9dSUmlr1/FwDg+jGzDAAAAABqwO12XzMmMDBQy5cv1/LlyyuMiYqKqnSVR0kaMmSIDh06VO0+AgCqj5llAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgwT3LAAAAAAANputTrOoJoHFhZhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADA47qKZQsXLpSPj4+mTZtmtF26dEkpKSnq0KGD2rZtq+TkZBUUFHg9Lj8/X0lJSQoKClJYWJhmzJihy5cve8Xs3LlTAwYMkMlkUrdu3bRmzZrr6SoAAAAAAABwTTUulh08eFC/+c1v1KdPH6/26dOn6/3339f69eu1a9cunTp1SiNHjjT2FxcXKykpSUVFRdq7d6/efPNNrVmzRnPnzjViTp48qaSkJN15553Ky8vTtGnT9Nhjj2nr1q017S4AAAAAAABwTTUqlp0/f16jR4/Wa6+9pvbt2xvt586d06pVq7R48WLdddddio2N1erVq7V3717t27dPkpSdna3jx4/rrbfeUr9+/TR8+HAtWLBAy5cvV1FRkSRp5cqVio6O1qJFi9SzZ09NmTJFP/rRj7RkyZJaGDIAAAAAAABQvhoVy1JSUpSUlKT4+Hiv9tzcXLlcLq/2Hj16qEuXLsrJyZEk5eTkqHfv3goPDzdiEhMT5XA4dOzYMSPm6mMnJiYaxwAAAAAAAADqQqvqPuDtt9/WJ598ooMHD5bZZ7fbFRAQoJCQEK/28PBw2e12I+bKQlnp/tJ9lcU4HA59++23at26dZlzO51OOZ1OY9vhcEiSXC6XXC5XtcZYGl/dx7Uk5OjayFHVkCdvJj932Tbf79pqkiPyCgAAAADVU61i2d///nc9+eSTstlsCgwMrKs+1UhGRobmzZtXpj07O1tBQUE1OqbNZrvebjV75OjayFHVkKfvZN5a8b6a5OjixYvX0RsAAAAAaHmqVSzLzc3VmTNnNGDAAKOtuLhYu3fv1ssvv6ytW7eqqKhIhYWFXrPLCgoKZLFYJEkWi0UHDhzwOm7paplXxly9gmZBQYHMZnO5s8okKS0tTampqca2w+FQZGSkEhISZDabqzNMuVwu2Ww2DRs2TP7+/tV6bEtBjq6NHFUNefLWK73sQiYmX7cWDCypUY5KZ9kCAAAAAKqmWsWyoUOH6siRI15tjz76qHr06KFZs2YpMjJS/v7+2r59u5KTkyVJJ06cUH5+vqxWqyTJarXq2Wef1ZkzZxQWFibpu9kSZrNZMTExRszmzZu9zmOz2YxjlMdkMslkMpVp9/f3r/EH8Ot5bEtBjq6NHFUNefqOs9inwn01yRE5BQAAAIDqqVaxrF27durVq5dXW5s2bdShQwejfcKECUpNTVVoaKjMZrOmTp0qq9WqQYMGSZISEhIUExOjMWPGKDMzU3a7XbNnz1ZKSopR7Jo0aZJefvllzZw5U+PHj9eOHTu0bt06bdq0qTbGDAAAAAAAAJSr2jf4v5YlS5bI19dXycnJcjqdSkxM1CuvvGLs9/Pz08aNGzV58mRZrVa1adNG48aN0/z5842Y6Ohobdq0SdOnT9fSpUvVuXNnvf7660pMTKzt7gIAAAAAAACG6y6W7dy502s7MDBQy5cv1/Llyyt8TFRUVJmvWV5tyJAhOnTo0PV2DwAAAAAAAKgy34buAAAA1ZWRkaFbbrlF7dq1U1hYmEaMGKETJ054xVy6dEkpKSnq0KGD2rZtq+Tk5DKLx+Tn5yspKUlBQUEKCwvTjBkzdPnyZa+YnTt3asCAATKZTOrWrZvWrFlT18MDAAAA0IAolgEAmpxdu3YpJSVF+/btk81mk8vlUkJCgi5cuGDETJ8+Xe+//77Wr1+vXbt26dSpUxo5cqSxv7i4WElJSSoqKtLevXv15ptvas2aNZo7d64Rc/LkSSUlJenOO+9UXl6epk2bpscee0xbt5ZdtRQAAABA81Dr9ywDAKCubdmyxWt7zZo1CgsLU25urgYPHqxz585p1apVysrK0l133SVJWr16tXr27Kl9+/Zp0KBBys7O1vHjx7Vt2zaFh4erX79+WrBggWbNmqX09HQFBARo5cqVio6O1qJFiyRJPXv21J49e7RkyRLuowkAAAA0UxTLAABN3rlz5yRJoaGhkqTc3Fy5XC7Fx8cbMT169FCXLl2Uk5OjQYMGKScnR71791Z4eLgRk5iYqMmTJ+vYsWPq37+/cnJyvI5RGjNt2rQK++J0OuV0Oo1th8MhSXK5XMZP6XZNmfzc1Yq/nnM1FbWRV5RFXutGS8xrSxorAKDpo1gGAGjSSkpKNG3aNN12223q1auXJMlutysgIEAhISFeseHh4bLb7UbMlYWy0v2l+yqLcTgc+vbbb9W6desy/cnIyNC8efPKtGdnZysoKMjYttls1Rzpf2XeWr34ay2q05xcT15RMfJaN1pSXi9evNjQXQAAoMoolgEAmrSUlBQdPXpUe/bsaeiuSJLS0tKUmppqbDscDkVGRiohIUFms1kul0s2m03Dhg2Tv79/jc7RK71690w7mt78vzJaG3lFWeS1brTEvJbOsgUAoCmgWAYAaLKmTJmijRs3avfu3ercubPRbrFYVFRUpMLCQq/ZZQUFBbJYLEbMgQMHvI5XulrmlTFXr6BZUFAgs9lc7qwySTKZTDKZTGXa/f39vT4UX71dHc5in2rFt5QP49L15RUVI691oyXltaWME3Wv61ObyrSZ/NzVnnUNAJVhNUwAQJPjdrs1ZcoUbdiwQTt27FB0dLTX/tjYWPn7+2v79u1G24kTJ5Sfny+r1SpJslqtOnLkiM6cOWPE2Gw2mc1mxcTEGDFXHqM0pvQYAAAAAJofZpYBAJqclJQUZWVl6Q9/+IPatWtn3GMsODhYrVu3VnBwsCZMmKDU1FSFhobKbDZr6tSpslqtGjRokCQpISFBMTExGjNmjDIzM2W32zV79mylpKQYM8MmTZqkl19+WTNnztT48eO1Y8cOrVu3Tps2lf2rNgAAAIDmgZllAIAmZ8WKFTp37pyGDBmiTp06GT/vvPOOEbNkyRLde++9Sk5O1uDBg2WxWPTuu+8a+/38/LRx40b5+fnJarXq4Ycf1tixYzV//nwjJjo6Wps2bZLNZlPfvn21aNEivf7660pMbP73AAMAAABaKmaWAQCaHLfbfc2YwMBALV++XMuXL68wJioq6porRQ4ZMkSHDh2qdh8BAAAANE3MLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA/uWVaJXulb5Sz2Mba/XJjUgL0BAAAAAABAXWNmGQAAAAAAAODBzDIAAAAAQK3p+tSmhu4CAFwXZpYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPBo1dAdAAAAdavrU5vKbf9yYVI99wQAAABo/JhZBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAQA3s3r1b9913nyIiIuTj46P33nvPa7/b7dbcuXPVqVMntW7dWvHx8frss8+8Ys6ePavRo0fLbDYrJCREEyZM0Pnz571iDh8+rDvuuEOBgYGKjIxUZmZmXQ8NAFo0imUAAAAAUAMXLlxQ3759tXz58nL3Z2ZmatmyZVq5cqX279+vNm3aKDExUZcuXTJiRo8erWPHjslms2njxo3avXu3Jk6caOx3OBxKSEhQVFSUcnNz9etf/1rp6el69dVX63x8ANBScYN/AAAAAKiB4cOHa/jw4eXuc7vdevHFFzV79mzdf//9kqTf/va3Cg8P13vvvadRo0bp008/1ZYtW3Tw4EENHDhQkvTSSy/pnnvu0QsvvKCIiAitXbtWRUVFeuONNxQQEKCbb75ZeXl5Wrx4sVdRDQBQe5hZBgAAAAC17OTJk7Lb7YqPjzfagoODFRcXp5ycHElSTk6OQkJCjEKZJMXHx8vX11f79+83YgYPHqyAgAAjJjExUSdOnNDXX39dT6MBgJaFmWUAAAAAUMvsdrskKTw83Ks9PDzc2Ge32xUWFua1v1WrVgoNDfWKiY6OLnOM0n3t27cvc26n0ymn02lsOxwOSZLL5ZLL5bqeYVWJyc9d5+fwOp/vd+erj7E1hNJxNdfxSc1/jM19fFLTGGN1+kaxDAAAAACakYyMDM2bN69Me3Z2toKCgur8/Jm31vkpymWz2RrmxPWkuY9Pav5jbO7jkxr3GC9evFjlWIplAAAAAFDLLBaLJKmgoECdOnUy2gsKCtSvXz8j5syZM16Pu3z5ss6ePWs83mKxqKCgwCumdLs05mppaWlKTU01th0OhyIjI5WQkCCz2Xx9A6uCXulb6/wcVzL5urVgYImGDRsmf3//ej13fXC5XLLZbM12fFLzH2NzH5/UNMZYOsu2KiiWAQAAAEAti46OlsVi0fbt243imMPh0P79+zV58mRJktVqVWFhoXJzcxUbGytJ2rFjh0pKShQXF2fE/OpXv5LL5TI+gNpsNnXv3r3cr2BKkslkkslkKtPu7+9fLx9incU+dX6O8tTX+BpKcx+f1PzH2NzHJzXuMVanX9zgHwAAAABq4Pz588rLy1NeXp6k727qn5eXp/z8fPn4+GjatGl65pln9Mc//lFHjhzR2LFjFRERoREjRkiSevbsqbvvvluPP/64Dhw4oI8++khTpkzRqFGjFBERIUl66KGHFBAQoAkTJujYsWN65513tHTpUq+ZYwCA2sXMMgAAAACogY8//lh33nmnsV1awBo3bpzWrFmjmTNn6sKFC5o4caIKCwt1++23a8uWLQoMDDQes3btWk2ZMkVDhw6Vr6+vkpOTtWzZMmN/cHCwsrOzlZKSotjYWN1www2aO3euJk6cWH8DBYAWhmIZAAAAANTAkCFD5HZXvPKjj4+P5s+fr/nz51cYExoaqqysrErP06dPH/3pT3+qcT8BANXD1zABAAAAAAAAj2oVyzIyMnTLLbeoXbt2CgsL04gRI3TixAmvmEuXLiklJUUdOnRQ27ZtlZycXGb1lvz8fCUlJSkoKEhhYWGaMWOGLl++7BWzc+dODRgwQCaTSd26ddOaNWtqNkIAAJqork9tKvcHAAAAQN2pVrFs165dSklJ0b59+2Sz2eRyuZSQkKALFy4YMdOnT9f777+v9evXa9euXTp16pRGjhxp7C8uLlZSUpKKioq0d+9evfnmm1qzZo3mzp1rxJw8eVJJSUm68847lZeXp2nTpumxxx7T1q31uwQxAAAAAAAAWpZq3bNsy5YtXttr1qxRWFiYcnNzNXjwYJ07d06rVq1SVlaW7rrrLknS6tWr1bNnT+3bt0+DBg1Sdna2jh8/rm3btik8PFz9+vXTggULNGvWLKWnpysgIEArV65UdHS0Fi1aJOm7VWL27NmjJUuWKDExsZaGDgAAAAAAAHi7rhv8nzt3TtJ3N6WUpNzcXLlcLsXHxxsxPXr0UJcuXZSTk6NBgwYpJydHvXv3Vnh4uBGTmJioyZMn69ixY+rfv79ycnK8jlEaM23atAr74nQ65XQ6jW2HwyFJcrlccrlc1RpXabzJ111uO/6bC3JSMXJUNeTJm8mv7E2CS38X1SRH5BUAAAAAqqfGxbKSkhJNmzZNt912m3r16iVJstvtCggIUEhIiFdseHi47Ha7EXNloax0f+m+ymIcDoe+/fZbtW7dukx/MjIyNG/evDLt2dnZCgoKqtEYFwws8drevHlzjY7TnNlstobuQqNHjqqGPH0n89aK99UkRxcvXryO3gAAAABAy1PjYllKSoqOHj2qPXv21GZ/aiwtLU2pqanGtsPhUGRkpBISEmQ2m6t1LJfLJZvNpjkf+8pZ4mO0H03nK6ClSnM0bNgw+fv7N3R3GiVyVDXkyVuv9LL3ZjT5urVgYEmNclQ6y7a52b17t379618rNzdXp0+f1oYNGzRixAhjv9vt1tNPP63XXntNhYWFuu2227RixQp9//vfN2LOnj2rqVOn6v3335evr6+Sk5O1dOlStW3b1og5fPiwUlJSdPDgQXXs2FFTp07VzJkz63OoAAAAAOpZjYplU6ZM0caNG7V792517tzZaLdYLCoqKlJhYaHX7LKCggJZLBYj5sCBA17HK10t88qYq1fQLCgokNlsLndWmSSZTCaZTKYy7f7+/jX+AO4s8ZGz+L/FMj7Il3U9+W0pyFHVkKfvXPk752o1yVFzzemFCxfUt29fjR8/3msRmVKZmZlatmyZ3nzzTUVHR2vOnDlKTEzU8ePHFRgYKEkaPXq0Tp8+bSxY8+ijj2rixInKysqS9F2hMSEhQfHx8Vq5cqWOHDmi8ePHKyQkRBMnTqzX8QIAAACoP9VaDdPtdmvKlCnasGGDduzYoejoaK/9sbGx8vf31/bt2422EydOKD8/X1arVZJktVp15MgRnTlzxoix2Wwym82KiYkxYq48RmlM6TEAAC3b8OHD9cwzz+iBBx4os8/tduvFF1/U7Nmzdf/996tPnz767W9/q1OnTum9996TJH366afasmWLXn/9dcXFxen222/XSy+9pLffflunTp2SJK1du1ZFRUV64403dPPNN2vUqFF64okntHjx4vocKgAAAIB6Vq1iWUpKit566y1lZWWpXbt2stvtstvt+vbbbyVJwcHBmjBhglJTU/Xhhx8qNzdXjz76qKxWqwYNGiRJSkhIUExMjMaMGaM///nP2rp1q2bPnq2UlBRjZtikSZP0t7/9TTNnztRf/vIXvfLKK1q3bp2mT59ey8MHADQ3J0+elN1u91ooJjg4WHFxccrJyZEk5eTkKCQkRAMHDjRi4uPj5evrq/379xsxgwcPVkBAgBGTmJioEydO6Ouvv66n0QAAAACob9X6GuaKFSskSUOGDPFqX716tR555BFJ0pIlS4x7vzidTiUmJuqVV14xYv38/LRx40ZNnjxZVqtVbdq00bhx4zR//nwjJjo6Wps2bdL06dO1dOlSde7cWa+//roSE7lnGACgcqWLxZS3UMyVC8mEhYV57W/VqpVCQ0O9Yq6eQX3lgjTt27cv9/zXWp25OivAlrc6am1qTqulsrJu3SCvdaMl5rUljRUA0PRVq1jmdl/7TXtgYKCWL1+u5cuXVxgTFRV1zZUlhwwZokOHDlWnewAANLiqrs5cldVNK1sdtTY0x1WeWVm3bpDXutGS8srqzACApqTGq2ECANAYlS4WU1BQoE6dOhntBQUF6tevnxFz5b0zJeny5cs6e/bsNRebufIc5bnW6szVWQG2vNVRa1NzWuWZlXXrBnmtGy0xr811dWYAQPNEsQwA0KxER0fLYrFo+/btRnHM4XBo//79mjx5sqTvFpIpLCxUbm6uYmNjJUk7duxQSUmJ4uLijJhf/epXcrlcxodZm82m7t27V/gVTKnqqzNXZXXTylZHrQ3N8UM6K+vWDfJaN1pSXlvKOAEAzUO1bvAPAEBjcP78eeXl5SkvL0/Sdzf1z8vLU35+vnx8fDRt2jQ988wz+uMf/6gjR45o7NixioiI0IgRIyRJPXv21N13363HH39cBw4c0EcffaQpU6Zo1KhRioiIkCQ99NBDCggI0IQJE3Ts2DG98847Wrp0qdesMQAAAADNDzPLAABNzscff6w777zT2C4tYI0bN05r1qzRzJkzdeHCBU2cOFGFhYW6/fbbtWXLFgUGBhqPWbt2raZMmaKhQ4caC9MsW7bM2B8cHKzs7GylpKQoNjZWN9xwg+bOnauJEyfW30ABAAAA1DuKZQCAJmfIkCGVLjrj4+Oj+fPne620fLXQ0FBlZWVVep4+ffroT3/6U437CQAAAKDpoVgGAAAAAKi2rk9tauguAECd4J5lAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHq0augMAAAAAAFyvXulb5Sz28Wr7cmFSA/UGQFPGzDIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPDgnmUAAAAAgGap61Obym3nXmYAKsPMMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPBo1dAdAAAADaPrU5vKbf9yYVI99wQAAABoPJhZBgAAAAAAAHhQLAMAAAAAAAA8+BomUMd6pW+Vs9jHq42vOAEAAAAA0DhRLAMAAAAAtCjctxNAZfgaJgAAAAAAAOBBsQwAAAAAAADwoFgGAAAAAAAAeFAsAwAAAAAAADwadbFs+fLl6tq1qwIDAxUXF6cDBw40dJcAAC0Q1yMAQGPA9ahp6PrUpnJ/ADQdjXY1zHfeeUepqalauXKl4uLi9OKLLyoxMVEnTpxQWFhYQ3cPANBCcD0CADQGDXk9akmFHlbJBCA14mLZ4sWL9fjjj+vRRx+VJK1cuVKbNm3SG2+8oaeeeqqBewcAaCla4vWIDwoA0Pi0xOtRY9KSCoYAGmmxrKioSLm5uUpLSzPafH19FR8fr5ycnHIf43Q65XQ6je1z585Jks6ePSuXy1Wt87tcLl28eFGtXL4qLvEx2v/zn/9U6zjNWWmO/vOf/8jf37+hu9MoVfQ6kngtXYnXkrdWly+UbStx6+LFkhrl6JtvvpEkud3uWulfS1MX16PqvObLez00pMb8u6s0r/1+9a6cV/3OlaT9aUMboFdNH7+j60ZLzCvXo+tT3etRbX42khrf9ehqpe+Vynvf3Zh0+3/rym2v6BoVl7FdkmTydWt2/5IKr3HX0hSugc3992JzHl9Fr9PG+LqrzrWoURbL/v3vf6u4uFjh4eFe7eHh4frLX/5S7mMyMjI0b968Mu3R0dG11q8bFtXaodDC8VpCdT10nY//5ptvFBwcXCt9aUka6/WooTTl311Nue9Ac8L1qGaqez1qzteiilzve6WGVJVr1PWMj2sg6suVr9PG/LqryrWoURbLaiItLU2pqanGdklJic6ePasOHTrIx6d61XeHw6HIyEj9/e9/l9lsru2uNgvk6NrIUdWQp2u7nhy53W598803ioiIqKPe4WrXuh7xmq8b5LVukNe60RLzyvWoftXmZ6OmoLn/m2ru45Oa/xib+/ikpjHG6lyLGmWx7IYbbpCfn58KCgq82gsKCmSxWMp9jMlkkslk8moLCQm5rn6YzeZG+yQ3FuTo2shR1ZCna6tpjvgLfs3V5fWI13zdIK91g7zWjZaWV65HNVfd61FdfDZqCpr7v6nmPj6p+Y+xuY9PavxjrOq1yLeO+1EjAQEBio2N1fbt2422kpISbd++XVartQF7BgBoSbgeAQAaA65HAFC/GuXMMklKTU3VuHHjNHDgQN1666168cUXdeHCBWP1FwAA6gPXIwBAY8D1CADqT6Mtlv30pz/Vv/71L82dO1d2u139+vXTli1bytzUsi6YTCY9/fTTZaYu47/I0bWRo6ohT9dGjhpWbV+PeD7rBnmtG+S1bpBX1ERDfj5q7Jr7v6nmPj6p+Y+xuY9Pan5j9HGzfjMAAAAAAAAgqZHeswwAAAAAAABoCBTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAo8UVyzIyMnTLLbeoXbt2CgsL04gRI3TixIlrPm79+vXq0aOHAgMD1bt3b23evLkeetswapKjNWvWyMfHx+snMDCwnnpc/1asWKE+ffrIbDbLbDbLarXqgw8+qPQxLek1VKq6eWppr6PyLFy4UD4+Ppo2bVqlcS3x9dRcLF++XF27dlVgYKDi4uJ04MCBhu5So7V7927dd999ioiIkI+Pj9577z2v/W63W3PnzlWnTp3UunVrxcfH67PPPvOKOXv2rEaPHi2z2ayQkBBNmDBB58+fr8dRND5Vuc5funRJKSkp6tChg9q2bavk5GQVFBR4xeTn5yspKUlBQUEKCwvTjBkzdPny5focSqNxresd+QSuX1V+dw0ZMqTMe8lJkyY1UI+rLz09vUz/e/ToYeyvyu+Sxqxr165lxufj46OUlBRJTe/5awnvUyobo8vl0qxZs9S7d2+1adNGERERGjt2rE6dOuV1jPKe94ULF9bzSKqvxRXLdu3apZSUFO3bt082m00ul0sJCQm6cOFChY/Zu3evHnzwQU2YMEGHDh3SiBEjNGLECB09erQee15/apIjSTKbzTp9+rTx89VXX9VTj+tf586dtXDhQuXm5urjjz/WXXfdpfvvv1/Hjh0rN76lvYZKVTdPUst6HV3t4MGD+s1vfqM+ffpUGtdSX0/NwTvvvKPU1FQ9/fTT+uSTT9S3b18lJibqzJkzDd21RunChQvq27evli9fXu7+zMxMLVu2TCtXrtT+/fvVpk0bJSYm6tKlS0bM6NGjdezYMdlsNm3cuFG7d+/WxIkT62sIjVJVrvPTp0/X+++/r/Xr12vXrl06deqURo4caewvLi5WUlKSioqKtHfvXr355ptas2aN5s6d2xBDanDXut6RT+D6VfUzyuOPP+71XjIzM7OBelwzN998s1f/9+zZY+y71u+Sxu7gwYNeY7PZbJKkH//4x0ZMU3r+WsL7lMrGePHiRX3yySeaM2eOPvnkE7377rs6ceKEfvjDH5aJnT9/vtfzOnXq1Pro/vVxt3BnzpxxS3Lv2rWrwpif/OQn7qSkJK+2uLg4989+9rO67l6jUJUcrV692h0cHFx/nWqE2rdv73799dfL3dfSX0NXqixPLfl19M0337i///3vu202m/sHP/iB+8knn6wwltdT03Xrrbe6U1JSjO3i4mJ3RESEOyMjowF71TRIcm/YsMHYLikpcVssFvevf/1ro62wsNBtMpnc//d//+d2u93u48ePuyW5Dx48aMR88MEHbh8fH/c///nPeut7Y3f1db6wsNDt7+/vXr9+vRHz6aefuiW5c3Jy3G63271582a3r6+v2263GzErVqxwm81mt9PprN8BNFKl1zvyCdSN8j6jXOs9VGP39NNPu/v27Vvuvqr8LmlqnnzySfeNN97oLikpcbvdTfv5awnvU64eY3kOHDjgluT+6quvjLaoqCj3kiVL6rZzdaDFzSy72rlz5yRJoaGhFcbk5OQoPj7eqy0xMVE5OTl12rfGoio5kqTz588rKipKkZGR15w91JwUFxfr7bff1oULF2S1WsuNaemvIalqeZJa7usoJSVFSUlJZV4n5eH11DQVFRUpNzfX67nz9fVVfHw8z10NnDx5Una73SufwcHBiouLM/KZk5OjkJAQDRw40IiJj4+Xr6+v9u/fX+99bqyuvs7n5ubK5XJ55bZHjx7q0qWLV2579+6t8PBwIyYxMVEOh6PF/N6uyNXXO/IJ1I2KPqOsXbtWN9xwg3r16qW0tDRdvHixIbpXY5999pkiIiL0ve99T6NHj1Z+fr6kqv1ubkqKior01ltvafz48fLx8THam/rzV6qlvk85d+6cfHx8FBIS4tW+cOFCdejQQf3799evf/3rJnGbgVYN3YGGVFJSomnTpum2225Tr169Koyz2+1eb14kKTw8XHa7va672OCqmqPu3bvrjTfeUJ8+fXTu3Dm98MIL+t///V8dO3ZMnTt3rsce158jR47IarXq0qVLatu2rTZs2KCYmJhyY1vya6g6eWqJryNJevvtt/XJJ5/o4MGDVYpvya+npuzf//63iouLy33u/vKXvzRQr5qu0td7Zf8W7Ha7wsLCvPa3atVKoaGh/HvxKO86b7fbFRAQUOaN7tW5LS/3pftaooqud3l5eeQTqGUVfUZ56KGHFBUVpYiICB0+fFizZs3SiRMn9O677zZgb6suLi5Oa9asUffu3XX69GnNmzdPd9xxh44ePVql381NyXvvvafCwkI98sgjRltTf/6u1BLfp1y6dEmzZs3Sgw8+KLPZbLQ/8cQTGjBggEJDQ7V3716lpaXp9OnTWrx4cQP29tpadLEsJSVFR48e9foeOLxVNUdWq9VrttD//u//qmfPnvrNb36jBQsW1HU3G0T37t2Vl5enc+fO6Xe/+53GjRunXbt2VVgIaqmqk6eW+Dr6+9//rieffFI2m63FLWYAoOHxXqj2VHS9A1D7KvrddeW9nnr37q1OnTpp6NCh+uKLL3TjjTfWdzerbfjw4cb/9+nTR3FxcYqKitK6devUunXrBuxZ7Vu1apWGDx+uiIgIo62pP38tmcvl0k9+8hO53W6tWLHCa19qaqrx/3369FFAQIB+9rOfKSMjQyaTqb67WmUt9muYU6ZM0caNG/Xhhx9ec8aKxWIps8pIQUGBLBZLXXaxwVUnR1fz9/dX//799fnnn9dR7xpeQECAunXrptjYWGVkZKhv375aunRpubEt9TUkVS9PV2sJr6Pc3FydOXNGAwYMUKtWrdSqVSvt2rVLy5YtU6tWrVRcXFzmMS359dSU3XDDDfLz8+O5qyWlOassnxaLpcziCZcvX9bZs2fJuSq+zlssFhUVFamwsNAr/urclpf70n0tUUXXO/IJ1K7qfEaJi4uTpCb7XjIkJEQ33XSTPv/88yr9LmkqvvrqK23btk2PPfZYpXFN+flrSe9TSgtlX331lWw2m9essvLExcXp8uXL+vLLL+ungzXU4oplbrdbU6ZM0YYNG7Rjxw5FR0df8zFWq1Xbt2/3arPZbJXed6kpq0mOrlZcXKwjR46oU6dOddDDxqmkpEROp7PcfS3tNVSZyvJ0tZbwOho6dKiOHDmivLw842fgwIEaPXq08vLy5OfnV+YxvJ6apoCAAMXGxno9dyUlJdq+fTvPXQ1ER0fLYrF45dPhcGj//v1GPq1WqwoLC5Wbm2vE7NixQyUlJcYb8JboWtf52NhY+fv7e+X2xIkTys/P98rtkSNHvN7kl75BZob1d0qvd+QTqB01+YySl5cnSU32veT58+f1xRdfqFOnTlX6XdJUrF69WmFhYUpKSqo0rik/fy3lfUppoeyzzz7Ttm3b1KFDh2s+Ji8vT76+vmW+gtroNOjyAg1g8uTJ7uDgYPfOnTvdp0+fNn4uXrxoxIwZM8b91FNPGdsfffSRu1WrVu4XXnjB/emnn7qffvppt7+/v/vIkSMNMYQ6V5MczZs3z71161b3F1984c7NzXWPGjXKHRgY6D527FhDDKHOPfXUU+5du3a5T5486T58+LD7qaeecvv4+Lizs7PdbjevoVLVzVNLex1V5OqVgHg9NR9vv/2222QyudesWeM+fvy4e+LEie6QkBCvFfDwX99884370KFD7kOHDrkluRcvXuw+dOiQscLSwoUL3SEhIe4//OEP7sOHD7vvv/9+d3R0tPvbb781jnH33Xe7+/fv796/f797z5497u9///vuBx98sKGG1ChU5To/adIkd5cuXdw7duxwf/zxx26r1eq2Wq3G/suXL7t79erlTkhIcOfl5bm3bNni7tixozstLa0hhtTgrnW9I5/A9bvW767PP//cPX/+fPfHH3/sPnnypPsPf/iD+3vf+5578ODBDdzzqvvFL37h3rlzp/vkyZPujz76yB0fH+++4YYb3GfOnHG73df+XdIUFBcXu7t06eKeNWuWV3tTfP5awvuUysZYVFTk/uEPf+ju3LmzOy8vz+vfZelKznv37nUvWbLEnZeX5/7iiy/cb731lrtjx47usWPHNvDIrq3FFcsklfuzevVqI+YHP/iBe9y4cV6PW7dunfumm25yBwQEuG+++Wb3pk2b6rfj9agmOZo2bZq7S5cu7oCAAHd4eLj7nnvucX/yySf13/l6Mn78eHdUVJQ7ICDA3bFjR/fQoUONN8RuN6+hUtXNU0t7HVXk6mIZr6fm5aWXXjJe57feeqt73759Dd2lRuvDDz8s93pU+u+hpKTEPWfOHHd4eLjbZDK5hw4d6j5x4oTXMf7zn/+4H3zwQXfbtm3dZrPZ/eijj7q/+eabBhhN41GV6/y3337r/vnPf+5u3769OygoyP3AAw+4T58+7XWcL7/80j18+HB369at3TfccIP7F7/4hdvlctXzaBqHa13vyCdw/a71uys/P989ePBgd2hoqNtkMrm7devmnjFjhvvcuXMN2/Fq+OlPf+ru1KmTOyAgwP0///M/7p/+9Kfuzz//3Nhfld8ljd3WrVvdkspcr5vi89cS3qdUNsaTJ09W+O/yww8/dLvdbndubq47Li7OHRwc7A4MDHT37NnT/dxzz7kvXbrUsAOrAh+32+2u9elqAAAAAAAAQBPU4u5ZBgAAAAAAAFSEYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPCgWAYAAAAAAAB4UCwDAAAAAAAAPCiWAQAAAAAAAB4UywAAAAAAAAAPimUAAAAAAACAB8UyAAAAAAAAwINiGQAAAAAAAOBBsQwAAAAAAADwoFgGAAAAAAAAeFAsA+rQkCFDNGTIkFo95pdffikfHx+tWbOmVo8LAAAAAAAolqGJW7NmjXx8fIyfwMBA3XTTTZoyZYoKCgoaunv1ZvPmzUpPT2/obgAArtOV17U9e/aU2e92uxUZGSkfHx/de++9Rvv58+f19NNPq1evXmrTpo06dOigfv366cknn9SpU6eMuPT0dK/rpr+/v7p27aonnnhChYWFZc7XtWtXr/iwsDDdcccd2rBhQ52MHwAAoDFo1dAdAGrD/PnzFR0drUuXLmnPnj1asWKFNm/erKNHjyooKKihu1eroqKi9O2338rf399o27x5s5YvX07BDACaicDAQGVlZen222/3at+1a5f+8Y9/yGQyGW0ul0uDBw/WX/7yF40bN05Tp07V+fPndezYMWVlZemBBx5QRESE13FWrFihtm3b6sKFC9q+fbteeuklffLJJ+UW6Pr166df/OIXkqRTp07pN7/5jUaOHKkVK1Zo0qRJdTB6AACAhkWxDM3C8OHDNXDgQEnSY489pg4dOmjx4sX6wx/+oAcffLCBe1c7Ll++rJKSEgUEBCgwMLChuwMAqEP33HOP1q9fr2XLlqlVq/++XcvKylJsbKz+/e9/G23vvfeeDh06pLVr1+qhhx7yOs6lS5dUVFRU5vg/+tGPdMMNN0iSfvazn2nUqFF65513dODAAd16661esf/zP/+jhx9+2NgeO3asunXrpiVLllAsAwAAzRJfw0SzdNddd0mSTp48qcuXL2vBggW68cYbZTKZ1LVrV/3yl7+U0+n0ekzXrl117733Kjs7W/369VNgYKBiYmL07rvvesWVfoXlaqVfnfnyyy8r7FdRUZHmzp2r2NhYBQcHq02bNrrjjjv04YcfesWV3pfshRde0Isvvmj0/fjx42XuWfbII49o+fLlkuT1VRm3262uXbvq/vvvL9OPS5cuKTg4WD/72c+umUsAQP178MEH9Z///Ec2m81oKyoq0u9+97syBbEvvvhCknTbbbeVOU5gYKDMZvM1z3fHHXd4HasyFotFPXv21MmTJ68ZCwAA0BRRLEOzVPpmv0OHDnrsscc0d+5cDRgwQEuWLNEPfvADZWRkaNSoUWUe99lnn+mnP/2phg8froyMDLVq1Uo//vGPvT6sXA+Hw6HXX39dQ4YM0fPPP6/09HT961//UmJiovLy8srEr169Wi+99JImTpyoRYsWKTQ0tEzMz372Mw0bNkyS9P/9f/+f8ePj46OHH35YH3zwgc6ePev1mPfff18Oh8NrpgAAoPHo2rWrrFar/u///s9o++CDD3Tu3Lky16+oqChJ0m9/+1u53e4ana/0Dz3t27e/ZqzL5dLf//53dejQoUbnAgAAaOz4GiaahXPnzunf//63Ll26pI8++kjz589X69at1aNHD02aNEmPPfaYXnvtNUnSz3/+c4WFhemFF17Qhx9+qDvvvNM4zl//+lf9/ve/18iRIyVJEyZMUI8ePTRr1iyjIHU92rdvry+//FIBAQFG2+OPP64ePXropZde0qpVq7zi//GPf+jzzz9Xx44djbarZ65ZrVbddNNNstlsZYpfY8eO1bPPPqt169Z5fVXmrbfeUteuXcvcCwcA0Hg89NBDSktL07fffqvWrVtr7dq1+sEPflDm/mMjRoxQ9+7dNXfuXK1atUp33nmn7rjjDt17770KCwsr99ilf0S5cOGCduzYoeXLl6tjx44aPHhwmViXy2V87fPUqVPKyMhQQUGBpk6dWssjBgAAaByYWYZmIT4+Xh07dlRkZKRGjRqltm3basOGDdq7d68kKTU11Su+9EbFmzZt8mqPiIjQAw88YGybzWaNHTtWhw4dkt1uv+5++vn5GYWykpISnT17VpcvX9bAgQP1ySeflIlPTk72KpRV10033aS4uDitXbvWaDt79qw++OADjR49utyvkwIAGoef/OQn+vbbb7Vx40Z988032rhxY5mvYEpS69attX//fs2YMUPSd7cFmDBhgjp16qSpU6eWue2AJHXv3l0dO3ZU165dNX78eHXr1k0ffPBBuYviZGdnq2PHjurYsaP69u2r9evXa8yYMXr++edrf9AAAACNADPL0CwsX75cN910k1q1aqXw8HB1795dvr6+2rBhg3x9fdWtWzeveIvFopCQEH311Vde7d26dStTQLrpppskfTejy2KxXHdf33zzTS1atEh/+ctf5HK5jPbo6OgyseW1VdfYsWM1ZcoUffXVV4qKitL69evlcrk0ZsyY6z42AKDudOzYUfHx8crKytLFixdVXFysH/3oR+XGBgcHKzMzU5mZmfrqq6+0fft2vfDCC3r55ZcVHBysZ555xiv+97//vcxms/71r39p2bJlOnnypFq3bl3usePi4vTMM8/Ix8dHQUFB6tmzp0JCQmp7uAAAAI0GM8vQLNx6662Kj4/XkCFD1LNnT/n6er+0a3MGVUXHKi4uvuZj33rrLT3yyCO68cYbtWrVKm3ZskU2m0133XWXSkpKysRX9MGlOkaNGiV/f39jdtlbb72lgQMHqnv37td9bABA3XrooYf0wQcfaOXKlRo+fHiVilRRUVEaP368PvroI4WEhHjNLi41ePBgxcfH68EHH5TNZlPr1q01evTocq9FN9xwg+Lj4zV06FBZrVYKZQAAoNmjWIZmLSoqSiUlJfrss8+82gsKClRYWGjcFLnU559/XubmyH/9618lfXezZem/Nz8uLCz0irt6llp5fve73+l73/ue3n33XY0ZM0aJiYmKj4/XpUuXqjOsMiorBoaGhiopKUlr167VV199pY8++ohZZQDQRDzwwAPy9fXVvn37yv0KZmXat2+vG2+8UadPn640rm3btnr66aeVl5endevWXU93AQAAmgWKZWjW7rnnHknSiy++6NW+ePFiSVJSUpJX+6lTp7RhwwZj2+Fw6Le//a369etnfAXzxhtvlCTt3r3biLtw4YLefPPNa/bHz89PkrwKcvv371dOTk5Vh1SuNm3aSCpbwCs1ZswYHT9+XDNmzJCfn1+5K4ECABqftm3basWKFUpPT9d9991Xbsyf//xn4wb8V/rqq690/PjxKs0kHj16tDp37sx9yAAAAMQ9y9DM9e3bV+PGjdOrr76qwsJC/eAHP9CBAwf05ptvasSIEV4rYUrf3Z9swoQJOnjwoMLDw/XGG2+ooKBAq1evNmISEhLUpUsXTZgwwSg+vfHGG+rYsaPy8/Mr7c+9996rd999Vw888ICSkpJ08uRJrVy5UjExMTp//nyNxxkbGytJeuKJJ5SYmFimIJaUlKQOHTpo/fr1Gj58eIWrowEAGp9x48ZVut9ms+npp5/WD3/4Qw0aNEht27bV3/72N73xxhtyOp1KT0+/5jn8/f315JNPasaMGdqyZYvuvvvuWuo9AABA00OxDM3e66+/ru9973tas2aNNmzYIIvForS0ND399NNlYr///e/rpZde0owZM3TixAlFR0frnXfeUWJiohHj7++vDRs26Oc//7nmzJkji8WiadOmqX379nr00Ucr7csjjzwiu92u3/zmN9q6datiYmL01ltvaf369dq5c2eNxzhy5EhNnTpVb7/9tt566y253W6vYllAQIB++tOf6pVXXuErmADQzCQnJ+ubb75Rdna2duzYobNnz6p9+/a69dZb9Ytf/KLMH4YqMnHiRD3zzDNauHAhxTIAANCi+bivvkET0EJ17dpVvXr10saNGxu6K3Vi+vTpWrVqlex2u4KCghq6OwAAAAAANErcswxoAS5duqS33npLycnJFMoAAAAAAKgEX8MEmrEzZ85o27Zt+t3vfqf//Oc/evLJJxu6SwAAAAAANGoUy4Bm7Pjx4xo9erTCwsK0bNky9evXr6G7BAAAAABAo8Y9ywAAAAAAAAAP7lkGAAAAAAAAeFS7WLZ7927dd999ioiIkI+Pj9577z2v/Y888oh8fHy8fq5efvzs2bMaPXq0zGazQkJCNGHCBJ0/f94r5vDhw7rjjjsUGBioyMhIZWZmVn90AAAAAAAAQDVU+55lFy5cUN++fTV+/HiNHDmy3Ji7775bq1evNrZNJpPX/tGjR+v06dOy2WxyuVx69NFHNXHiRGVlZUmSHA6HEhISFB8fr5UrV+rIkSMaP368QkJCNHHixCr1s6SkRKdOnVK7du3k4+NT3WECQLPgdrv1zTffKCIiQr6+TCZuCFyPAIDrEQCgaal2sWz48OEaPnx4pTEmk0kWi6XcfZ9++qm2bNmigwcPauDAgZKkl156Sffcc49eeOEFRUREaO3atSoqKtIbb7yhgIAA3XzzzcrLy9PixYurXCw7deqUIiMjqzc4AGim/v73v6tz584N3Y0WiesRAPwX1yMAQFNQJ6th7ty5U2FhYWrfvr3uuusuPfPMM+rQoYMkKScnRyEhIUahTJLi4+Pl6+ur/fv364EHHlBOTo4GDx6sgIAAIyYxMVHPP/+8vv76a7Vv3/6afWjXrp2k7y7IZrO5yn13uVzKzs5WQkKC/P39q/y4xorxNG6Mp3FrDuNxOByKjIw0fiei/nE9qn3kpmLkpmLkpnJ1nR+uRwCApqTWi2V33323Ro4cqejoaH3xxRf65S9/qeHDhysnJ0d+fn6y2+0KCwvz7kSrVgoNDZXdbpck2e12RUdHe8WEh4cb+8orljmdTjmdTmP7m2++kSS1bt1arVu3rnL/W7VqpaCgILVu3bpZvJFiPI0b42ncmsN4XC6XJPH1vwZUmnuz2VztYllQUJDMZnOTff3VFXJTMXJTMXJTufrKD9cjAEBTUOvFslGjRhn/37t3b/Xp00c33nijdu7cqaFDh9b26QwZGRmaN29emfbs7GwFBQVV+3g2m602utVoMJ7GjfE0bk15PBcvXmzoLgAAAABAk1InX8O80ve+9z3dcMMN+vzzzzV06FBZLBadOXPGK+by5cs6e/ascZ8zi8WigoICr5jS7YruhZaWlqbU1FRju3Sqd0JCQrX/km+z2TRs2LBm8VdHxtO4MZ7GrTmMx+FwNHQXAAAAAKBJqfNi2T/+8Q/95z//UadOnSRJVqtVhYWFys3NVWxsrCRpx44dKikpUVxcnBHzq1/9Si6Xy/iAarPZ1L179wrvV2YymcqsuilJ/v7+NfqQW9PHNVaMp3FjPI1bUx5PU+03AAAAADSUaq/bfP78eeXl5SkvL0+SdPLkSeXl5Sk/P1/nz5/XjBkztG/fPn355Zfavn277r//fnXr1k2JiYmSpJ49e+ruu+/W448/rgMHDuijjz7SlClTNGrUKEVEREiSHnroIQUEBGjChAk6duyY3nnnHS1dutRr5hgAAAAAAABQ26pdLPv444/Vv39/9e/fX5KUmpqq/v37a+7cufLz89Phw4f1wx/+UDfddJMmTJig2NhY/elPf/Ka9bV27Vr16NFDQ4cO1T333KPbb79dr776qrE/ODhY2dnZOnnypGJjY/WLX/xCc+fO1cSJE2thyAAAAAAAAED5qv01zCFDhsjtdle4f+vWrdc8RmhoqLKysiqN6dOnj/70pz9Vt3sAAAAAAABAjVV7ZhkAAAAAAADQXNX5Df7RvHR9alO57V8uTKrnngBAy9ArfaucxT5ebfzOBQAAAOoOM8sAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPCgWAYAAAAAAAB4UCwDAAAAAAAAPCiWAQAAAAAAAB4UywAAAAAAAAAPimUAAAAAAACAB8UyAAAAAAAAwKNVQ3cAuJauT20y/t/k51bmrVKv9K068ey9DdgrAAAAAADQHDGzDADQ5BQXF2vOnDmKjo5W69atdeONN2rBggVyu91GjNvt1ty5c9WpUye1bt1a8fHx+uyzz7yOc/bsWY0ePVpms1khISGaMGGCzp8/7xVz+PBh3XHHHQoMDFRkZKQyMzPrZYwAAAAAGgbFMgBAk/P8889rxYoVevnll/Xpp5/q+eefV2Zmpl566SUjJjMzU8uWLdPKlSu1f/9+tWnTRomJibp06ZIRM3r0aB07dkw2m00bN27U7t27NXHiRGO/w+FQQkKCoqKilJubq1//+tdKT0/Xq6++Wq/jBQAAAFB/ql0s2717t+677z5FRETIx8dH7733nrHP5XJp1qxZ6t27t9q0aaOIiAiNHTtWp06d8jpG165d5ePj4/WzcOFCrxj+kg8AqMjevXt1//33KykpSV27dtWPfvQjJSQk6MCBA5K+m1X24osvavbs2br//vvVp08f/fa3v9WpU6eM69ann36qLVu26PXXX1dcXJxuv/12vfTSS3r77beN69batWtVVFSkN954QzfffLNGjRqlJ554QosXL26ooQMAAACoY9W+Z9mFCxfUt29fjR8/XiNHjvTad/HiRX3yySeaM2eO+vbtq6+//lpPPvmkfvjDH+rjjz/2ip0/f74ef/xxY7tdu3bG/5f+JT8+Pl4rV67UkSNHNH78eIWEhHj9xR8A0DL97//+r1599VX99a9/1U033aQ///nP2rNnj1HEOnnypOx2u+Lj443HBAcHKy4uTjk5ORo1apRycnIUEhKigQMHGjHx8fHy9fXV/v379cADDygnJ0eDBw9WQECAEZOYmKjnn39eX3/9tdq3b1+mb06nU06n09h2OBySvvuDksvlqvIYS2NNvu4K97VUpeNv6XkoD7mpGLmpXF3nh7wDAJqSahfLhg8fruHDh5e7Lzg4WDabzavt5Zdf1q233qr8/Hx16dLFaG/Xrp0sFku5x7nyL/kBAQG6+eablZeXp8WLF1MsAwDoqaeeksPhUI8ePeTn56fi4mI9++yzGj16tCTJbrdLksLDw70eFx4ebuyz2+0KCwvz2t+qVSuFhoZ6xURHR5c5Rum+8oplGRkZmjdvXpn27OxsBQUFVXusCwaWlGnbvHlztY/THF39ngP/RW4qRm4qV1f5uXjxYp0cFwCAulDnq2GeO3dOPj4+CgkJ8WpfuHChFixYoC5duuihhx7S9OnT1arVd92pyV/yAQAtx7p167R27VplZWUZf1CZNm2aIiIiNG7cuAbtW1pamlJTU41th8OhyMhIJSQkyGw2V/k4LpdLNptNcz72lbPEx2vf0fTEWutvU1Sam2HDhsnf37+hu9OokJuKkZvK1XV+SmfZAgDQFNRpsezSpUuaNWuWHnzwQa8PCE888YQGDBig0NBQ7d27V2lpaTp9+rTx9Zma/CW/tr/20lymitf2eEx+Zb8OVJvHv9Y5S7+OZPJ1N4vniNdb49YcxtOU+16ZGTNm6KmnntKoUaMkSb1799ZXX32ljIwMjRs3zpi5XFBQoE6dOhmPKygoUL9+/SRJFotFZ86c8Tru5cuXdfbsWePxFotFBQUFXjGl2xXNjjaZTDKZTGXa/f39a/QB1FniI2exd7GMD/rfqWlOWwJyUzFyU7m6yg85BwA0JXVWLHO5XPrJT34it9utFStWeO278i/uffr0UUBAgH72s58pIyOj3A8YVVHbX3tpblP0a2s8mbeW316XXwkq75wLBpY0q68h8Xpr3JryeJrr114uXrwoX1/vNWr8/PxUUvLdVxajo6NlsVi0fft2ozjmcDi0f/9+TZ48WZJktVpVWFio3NxcxcbGSpJ27NihkpISxcXFGTG/+tWv5HK5jA96NptN3bt3Z5YzAAAA0EzVSbGstFD21VdfaceOHdf82klcXJwuX76sL7/8Ut27d6/RX/Jr+2svzWWKfm2Pp1f61nLb6/IrQVee0+Tr1oKBJZrzsa9y595dZ+esL7zeGrfmMJ7m+rWX++67T88++6y6dOmim2++WYcOHdLixYs1fvx4SZKPj4+mTZumZ555Rt///vcVHR2tOXPmKCIiQiNGjJAk9ezZU3fffbcef/xxrVy5Ui6XS1OmTNGoUaMUEREhSXrooYc0b948TZgwQbNmzdLRo0e1dOlSLVmypKGGDgAAAKCO1XqxrLRQ9tlnn+nDDz9Uhw4drvmYvLw8+fr6Gjdarslf8mv7ay/NbYp+bY3n6q8CXXn8ulLeOZ0lPjw/jRjjaTyaar+v5aWXXtKcOXP085//XGfOnFFERIR+9rOfae7cuUbMzJkzdeHCBU2cOFGFhYW6/fbbtWXLFgUGBhoxa9eu1ZQpUzR06FD5+voqOTlZy5YtM/YHBwcrOztbKSkpio2N1Q033KC5c+ey2AwAAADQjFW7WHb+/Hl9/vnnxvbJkyeVl5en0NBQderUST/60Y/0ySefaOPGjSouLjZWFAsNDVVAQIBycnK0f/9+3XnnnWrXrp1ycnI0ffp0Pfzww0YhjL/kAwAq065dO7344ot68cUXK4zx8fHR/PnzNX/+/ApjQkNDlZWVVem5+vTpoz/96U817SoAAACAJqbaxbKPP/5Yd955p7Fd+tXHcePGKT09XX/84x8lybhHTKkPP/xQQ4YMkclk0ttvv6309HQ5nU5FR0dr+vTpXl+h5C/5AAAAAAAAaAjVLpYNGTJEbnf5KyJKqnSfJA0YMED79u275nn4Sz4AAAAAAADqm++1QwAAAAAAAICWgWIZAAAAAAAA4EGxDAAAAAAAAPCgWAYAAAAAAAB4UCwDAAAAAAAAPCiWAQAAAAAAAB4UywAAAAAAAACPVg3dAdRc16c2lWn7cmFSA/QEAAAAAACgeWBmGQAAAAAAAOBBsQwAAAAAAADwoFgGAAAAAAAAeFAsAwAAAAAAADwolgEAAAAAAAAeFMsAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8WjV0B/BfXZ/aVG77lwuT6rknAAAAAAAALRMzywAAAAAAAAAPimUAAAAAAACAB8UyAAAAAAAAwINiGQAAAAAAAOBR7WLZ7t27dd999ykiIkI+Pj567733vPa73W7NnTtXnTp1UuvWrRUfH6/PPvvMK+bs2bMaPXq0zGazQkJCNGHCBJ0/f94r5vDhw7rjjjsUGBioyMhIZWZmVn90AAAAAAAAQDVUezXMCxcuqG/fvho/frxGjhxZZn9mZqaWLVumN998U9HR0ZozZ44SExN1/PhxBQYGSpJGjx6t06dPy2azyeVy6dFHH9XEiROVlZUlSXI4HEpISFB8fLxWrlypI0eOaPz48QoJCdHEiROvc8gNr6JVLwEAAAAAANCwql0sGz58uIYPH17uPrfbrRdffFGzZ8/W/fffL0n67W9/q/DwcL333nsaNWqUPv30U23ZskUHDx7UwIEDJUkvvfSS7rnnHr3wwguKiIjQ2rVrVVRUpDfeeEMBAQG6+eablZeXp8WLFzeLYhkAAAAAAAAap2oXyypz8uRJ2e12xcfHG23BwcGKi4tTTk6ORo0apZycHIWEhBiFMkmKj4+Xr6+v9u/frwceeEA5OTkaPHiwAgICjJjExEQ9//zz+vrrr9W+ffsy53Y6nXI6nca2w+GQJLlcLrlcriqPoTS2Oo+pLpOfu1rxFfWlvONcHVvb46mo7/WVL5Ov2/hvXZ6zvtTH660+MZ7Gpyn3HQAAAAAaQq0Wy+x2uyQpPDzcqz08PNzYZ7fbFRYW5t2JVq0UGhrqFRMdHV3mGKX7yiuWZWRkaN68eWXas7OzFRQUVO2x2Gy2aj+mqjJvrV785s2bq3ycimJrazwV9b2i89bVORcMLKnTc9a3uny9NQTG03hcvHixobsAAAAAAE1KrRbLGlJaWppSU1ONbYfDocjISCUkJMhsNlf5OC6XSzabTcOGDZO/v39ddFW90rdWK/5oemKVj3N1bG2Pp6K+V9TH2nDlOU2+bi0YWKI5H/sqd+7ddXbO+lIfr7f6xHgan9JZtgAAAACAqqnVYpnFYpEkFRQUqFOnTkZ7QUGB+vXrZ8ScOXPG63GXL1/W2bNnjcdbLBYVFBR4xZRul8ZczWQyyWQylWn39/ev0Yfcmj6uKpzFPtXuS1WPU1FsbY2nor7XZSGhvHM6S3yabPGiPHX5emsIjKfxaKr9rop//vOfmjVrlj744ANdvHhR3bp10+rVq42v+bvdbj399NN67bXXVFhYqNtuu00rVqzQ97//feMYZ8+e1dSpU/X+++/L19dXycnJWrp0qdq2bWvEHD58WCkpKTp48KA6duyoqVOnaubMmfU+XgAAAAD1w7c2DxYdHS2LxaLt27cbbQ6HQ/v375fVapUkWa1WFRYWKjc314jZsWOHSkpKFBcXZ8Ts3r3b6147NptN3bt3L/crmPWl61Obyv0BANSvr7/+Wrfddpv8/f31wQcf6Pjx41q0aJHXNaJ0deaVK1dq//79atOmjRITE3Xp0iUjZvTo0Tp27JhsNps2btyo3bt3ey0kU7o6c1RUlHJzc/XrX/9a6enpevXVV+t1vAAAAADqT7Vnlp0/f16ff/65sX3y5Enl5eUpNDRUXbp00bRp0/TMM8/o+9//vqKjozVnzhxFRERoxIgRkqSePXvq7rvv1uOPP66VK1fK5XJpypQpGjVqlCIiIiRJDz30kObNm6cJEyZo1qxZOnr0qJYuXaolS5bUzqgBAE3a888/r8jISK1evdpou/Jel6zODAAAAKCmqj2z7OOPP1b//v3Vv39/SVJqaqr69++vuXPnSpJmzpypqVOnauLEibrlllt0/vx5bdmyRYGBgcYx1q5dqx49emjo0KG65557dPvtt3v9lT44OFjZ2dk6efKkYmNj9Ytf/EJz587lgwkAQJL0xz/+UQMHDtSPf/xjhYWFqX///nrttdeM/ddanVnSNVdnLo0pb3XmEydO6Ouvv67rYQIAAABoANWeWTZkyBC53e4K9/v4+Gj+/PmaP39+hTGhoaHKysqq9Dx9+vTRn/70p+p2DwDQAvztb3/TihUrlJqaql/+8pc6ePCgnnjiCQUEBGjcuHENujqz0+mU0+k0tksXWXC5XF63F7iW0liTb9lrbnWO0xyVjr+l56E85KZi5KZydZ0f8g4AaEqazWqYAICWo6SkRAMHDtRzzz0nSerfv7+OHj2qlStXaty4cQ3at4yMDM2bN69Me3Z2toKCgqp9vAUDS8q0bd68uUZ9a25sNltDd6HRIjcVIzeVq6v8XLx4sU6OCwBAXaBYBgBocjp16qSYmBivtp49e+r3v/+9pIZdnTktLU2pqanGtsPhUGRkpBISEmQ2m6s8RpfLJZvNpjkf+8pZ4r0q8NH0xCofpzkqzc2wYcOa9YqvNUFuKkZuKlfX+SmdZQsAQFNAsQwA0OTcdtttOnHihFfbX//6V0VFRUnyXp25tDhWujrz5MmTJXmvzhwbGyup/NWZf/WrX8nlchkfHq+1OrPJZJLJZCrT7u/vX6MPoM4SHzmLvYtlfND/Tk1z2hKQm4qRm8rVVX7IOQCgKan2Df4BAGho06dP1759+/Tcc8/p888/V1ZWll599VWlpKRI+u7+maWrM//xj3/UkSNHNHbs2ApXZz5w4IA++uijcldnDggI0IQJE3Ts2DG98847Wrp0qdfMMQAAAADNCzPLAABNzi233KINGzYoLS1N8+fPV3R0tF588UWNHj3aiJk5c6YuXLigiRMnqrCwULfffnu5qzNPmTJFQ4cOla+vr5KTk7Vs2TJjf+nqzCkpKYqNjdUNN9zA6swAAABAM0exDADQJN1777269957K9zP6swAAAAAaoKvYQIAAAAAAAAeFMsAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDG/wDQCPS9alN5bZ/uTCpnnsCAAAAAC0TM8sAAAAAAAAAD4plAAAAAAAAgAfFMgAAAAAAAMCDYhkAAAAAAADgQbEMAAAAAAAA8KBYBgAAAAAAAHhQLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPCo9WJZ165d5ePjU+YnJSVFkjRkyJAy+yZNmuR1jPz8fCUlJSkoKEhhYWGaMWOGLl++XNtdBQAAAAAAALy0qu0DHjx4UMXFxcb20aNHNWzYMP34xz822h5//HHNnz/f2A4KCjL+v7i4WElJSbJYLNq7d69Onz6tsWPHyt/fX88991xtdxcAAAAAAAAw1HqxrGPHjl7bCxcu1I033qgf/OAHRltQUJAsFku5j8/Oztbx48e1bds2hYeHq1+/flqwYIFmzZql9PR0BQQE1HaXAQAAAAAAAEl1UCy7UlFRkd566y2lpqbKx8fHaF+7dq3eeustWSwW3XfffZozZ44xuywnJ0e9e/dWeHi4EZ+YmKjJkyfr2LFj6t+/f7nncjqdcjqdxrbD4ZAkuVwuuVyuKve5NLa8x5j83JU+pqoqOs61+lSV41wdW9l4aqK2clDTc5p83cZ/6/Kc9aW2n5+GxniuX23/G2suzwUAAAAA1Jc6LZa99957Kiws1COPPGK0PfTQQ4qKilJERIQOHz6sWbNm6cSJE3r33XclSXa73atQJsnYttvtFZ4rIyND8+bNK9OenZ3t9TXPqrLZbGXaMm8tP3bz5s3VOnZFx6lIRccv7zgVxZY3npqorRxc7zkXDCyp03PWt9p6fhoLxlNztf1v7OLFi9fRGwAAAABoeeq0WLZq1SoNHz5cERERRtvEiRON/+/du7c6deqkoUOH6osvvtCNN95Y43OlpaUpNTXV2HY4HIqMjFRCQoLMZnOVj+NyuWSz2TRs2DD5+/t77euVvrXcxxxNT6xWXys6TkUqOn55x7k6trLx1ERt5aCm5zT5urVgYInmfOyr3Ll319k560ttPz8NjfFcv9r+N1Y6yxYAAAAAUDV1Viz76quvtG3bNmPGWEXi4uIkSZ9//rluvPFGWSwWHThwwCumoKBAkiq8z5kkmUwmmUymMu3+/v41+pBb3uOcxT4VxlZHRceprC9VPU5FsTXNQ1XOWdl5a0N553SW+DSLYkyp2np+GgvGU3O1/W+sOT0PAAAAAFAffOvqwKtXr1ZYWJiSkpIqjcvLy5MkderUSZJktVp15MgRnTlzxoix2Wwym82KiYmpq+4CAAAAAAAAdTOzrKSkRKtXr9a4cePUqtV/T/HFF18oKytL99xzjzp06KDDhw9r+vTpGjx4sPr06SNJSkhIUExMjMaMGaPMzEzZ7XbNnj1bKSkp5c4cAwAAAAAAAGpLnRTLtm3bpvz8fI0fP96rPSAgQNu2bdOLL76oCxcuKDIyUsnJyZo9e7YR4+fnp40bN2ry5MmyWq1q06aNxo0bp/nz59dFVwEAAAAAAABDnRTLEhIS5Ha7y7RHRkZq165d13x8VFRUs1rpEAAAAAAAAE1Dnd2zDAAAAAAAAGhqKJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAKDJW7hwoXx8fDRt2jSj7dKlS0pJSVGHDh3Utm1bJScnq6CgwOtx+fn5SkpKUlBQkMLCwjRjxgxdvnzZK2bnzp0aMGCATCaTunXrpjVr1tTDiAAAAAA0FIplAIAm7eDBg/rNb36jPn36eLVPnz5d77//vtavX69du3bp1KlTGjlypLG/uLhYSUlJKioq0t69e/Xmm29qzZo1mjt3rhFz8uRJJSUl6c4771ReXp6mTZumxx57TFu3bq238QEAAACoXxTLAABN1vnz5zV69Gi99tprat++vdF+7tw5rVq1SosXL9Zdd92l2NhYrV69Wnv37tW+ffskSdnZ2Tp+/Ljeeust9evXT8OHD9eCBQu0fPlyFRUVSZJWrlyp6OhoLVq0SD179tSUKVP0ox/9SEuWLGmQ8QIAAACoexTLAABNVkpKipKSkhQfH+/VnpubK5fL5dXeo0cPdenSRTk5OZKknJwc9e7dW+Hh4UZMYmKiHA6Hjh07ZsRcfezExETjGAAAAACan1YN3QEAAGri7bff1ieffKKDBw+W2We32xUQEKCQkBCv9vDwcNntdiPmykJZ6f7SfZXFOBwOffvtt2rdunWZczudTjmdTmPb4XBIklwul1wuV5XHVxpr8nVXuK+lKh1/S89DechNxchN5eo6P+QdANCUUCwDADQ5f//73/Xkk0/KZrMpMDCwobvjJSMjQ/PmzSvTnp2draCgoGofb8HAkjJtmzdvrlHfmhubzdbQXWi0yE3FyE3l6io/Fy9erJPjAgBQFyiWodHo+tSmhu4CgCYiNzdXZ86c0YABA4y24uJi7d69Wy+//LK2bt2qoqIiFRYWes0uKygokMVikSRZLBYdOHDA67ilq2VeGXP1CpoFBQUym83lziqTpLS0NKWmphrbDodDkZGRSkhIkNlsrvIYXS6XbDab5nzsK2eJj9e+o+mJVT5Oc1Sam2HDhsnf37+hu9OokJuKkZvK1XV+SmfZAgDQFFAsAwA0OUOHDtWRI0e82h599FH16NFDs2bNUmRkpPz9/bV9+3YlJydLkk6cOKH8/HxZrVZJktVq1bPPPqszZ84oLCxM0nczKsxms2JiYoyYq2dx2Ww24xjlMZlMMplMZdr9/f1r9AHUWeIjZ7F3sYwP+t+paU5bAnJTMXJTubrKDzkHADQlFMsAAE1Ou3bt1KtXL6+2Nm3aqEOHDkb7hAkTlJqaqtDQUJnNZk2dOlVWq1WDBg2SJCUkJCgmJkZjxoxRZmam7Ha7Zs+erZSUFKPYNWnSJL388suaOXOmxo8frx07dmjdunXatImZsAAAAEBzRbEMANAsLVmyRL6+vkpOTpbT6VRiYqJeeeUVY7+fn582btyoyZMny2q1qk2bNho3bpzmz59vxERHR2vTpk2aPn26li5dqs6dO+v1119XYmLL/hokAAAA0JxRLAMANAs7d+702g4MDNTy5cu1fPnyCh8TFRV1zZvlDxkyRIcOHaqNLgIAAABoAnwbugMAAAAAAABAY0GxDAAAAAAAAPCgWAYAAAAAAAB4UCwDAAAAAAAAPCiWAQAAAAAAAB4UywAAAAAAAAAPimUAAAAAAACAB8UyAAAAAAAAwKPWi2Xp6eny8fHx+unRo4ex/9KlS0pJSVGHDh3Utm1bJScnq6CgwOsY+fn5SkpKUlBQkMLCwjRjxgxdvny5trta57o+tancHwAAAAAAADROrerioDfffLO2bdv235O0+u9ppk+frk2bNmn9+vUKDg7WlClTNHLkSH300UeSpOLiYiUlJclisWjv3r06ffq0xo4dK39/fz333HN10V0AAAAAAABAUh0Vy1q1aiWLxVKm/dy5c1q1apWysrJ01113SZJWr16tnj17at++fRo0aJCys7N1/Phxbdu2TeHh4erXr58WLFigWbNmKT09XQEBAXXRZQAAAAAAAKBu7ln22WefKSIiQt/73vc0evRo5efnS5Jyc3PlcrkUHx9vxPbo0UNdunRRTk6OJCknJ0e9e/dWeHi4EZOYmCiHw6Fjx47VRXcBAAAAAAAASXUwsywuLk5r1qxR9+7ddfr0ac2bN0933HGHjh49KrvdroCAAIWEhHg9Jjw8XHa7XZJkt9u9CmWl+0v3VcTpdMrpdBrbDodDkuRyueRyuarc/9LY8h5j8nNX+piqxldXdY5/dWxl46mJ6uagNo7tFePrNv5bW2NqSLX9/DQ0xnP9avvfWHN5LgAAAACgvtR6sWz48OHG//fp00dxcXGKiorSunXr1Lp169o+nSEjI0Pz5s0r056dna2goKBqH89ms5Vpy7y1/NjNmzeX215RfHVV5/gVxZY3npqobg5q49jlWTCwpFbO2VjU1vPTWDCemqvtf2MXL168jt4AAAAAQMtTJ/csu1JISIhuuukmff755xo2bJiKiopUWFjoNbusoKDAuMeZxWLRgQMHvI5RulpmefdBK5WWlqbU1FRj2+FwKDIyUgkJCTKbzVXur8vlks1m07Bhw+Tv7++1r1f61nIfczQ9sdz2iuKrqzrHvzq2svHURHVzUBvHvpLJ160FA0s052Nf5c69+7rP2dBq+/lpaIzn+tX2v7HSWbYAAAAAgKqp82LZ+fPn9cUXX2jMmDGKjY2Vv7+/tm/fruTkZEnSiRMnlJ+fL6vVKkmyWq169tlndebMGYWFhUn6blaH2WxWTExMhecxmUwymUxl2v39/Wv0Ibe8xzmLfSqMLU9F8TXpS1WPX1FsTfNQlXNWdt7aOHa5sSU+zaIYU6q2np/GgvHUXG3/G2tOzwMAAAAA1IdaL5b9v//3/3TfffcpKipKp06d0tNPPy0/Pz89+OCDCg4O1oQJE5SamqrQ0FCZzWZNnTpVVqtVgwYNkiQlJCQoJiZGY8aMUWZmpux2u2bPnq2UlJRyi2EAAAAAAABAban1Ytk//vEPPfjgg/rPf/6jjh076vbbb9e+ffvUsWNHSdKSJUvk6+ur5ORkOZ1OJSYm6pVXXjEe7+fnp40bN2ry5MmyWq1q06aNxo0bp/nz59d2VwEAAAAAAAAvtV4se/vttyvdHxgYqOXLl2v58uUVxkRFRTWrm7cDAAAAAACgafBt6A4AAAAAAAAAjUWd3+AfqG9dn9pUbvuXC5PquScAAAAAAKCpYWYZAAAAAAAA4MHMsgr0St8qZ7FPQ3cDAAAAAAAA9YiZZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPDgBv+oU12f2lRu+5cLk+q5JwAAAAAAANfGzDIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPDgnmVoEBXdywwAAAAAAKAhMbMMAAAAAAAA8KBYBgAAAAAAAHhQLAMANDkZGRm65ZZb1K5dO4WFhWnEiBE6ceKEV8ylS5eUkpKiDh06qG3btkpOTlZBQYFXTH5+vpKSkhQUFKSwsDDNmDFDly9f9orZuXOnBgwYIJPJpG7dumnNmjV1PTwAAAAADYhiGQCgydm1a5dSUlK0b98+2Ww2uVwuJSQk6MKFC0bM9OnT9f7772v9+vXatWuXTp06pZEjRxr7i4uLlZSUpKKiIu3du1dvvvmm1qxZo7lz5xoxJ0+eVFJSku68807l5eVp2rRpeuyxx7R169Z6HS8AAACA+sMN/gFUS0WLM3y5MKmee4KWbMuWLV7ba9asUVhYmHJzczV48GCdO3dOq1atUlZWlu666y5J0urVq9WzZ0/t27dPgwYNUnZ2to4fP65t27YpPDxc/fr104IFCzRr1iylp6crICBAK1euVHR0tBYtWiRJ6tmzp/bs2aMlS5YoMTGx3scNAAAAoO5RLAMaEQpRQM2cO3dOkhQaGipJys3NlcvlUnx8vBHTo0cPdenSRTk5ORo0aJBycnLUu3dvhYeHGzGJiYmaPHmyjh07pv79+ysnJ8frGKUx06ZNq7AvTqdTTqfT2HY4HJIkl8sll8tV5TGVxpp83RXua6lKx9/S81AeclMxclO5us4PeQcANCUUywAATVpJSYmmTZum2267Tb169ZIk2e12BQQEKCQkxCs2PDxcdrvdiLmyUFa6v3RfZTEOh0PffvutWrduXaY/GRkZmjdvXpn27OxsBQUFVXt8CwaWlGnbvHlztY/THNlstobuQqNFbipGbipXV/m5ePFinRwXAIC6QLEMANCkpaSk6OjRo9qzZ09Dd0WSlJaWptTUVGPb4XAoMjJSCQkJMpvNVT6Oy+WSzWbTnI995SzxqdJjjqa3jK+GluZm2LBh8vf3b+juNCrkpmLkpnJ1nZ/SWbYAADQFFMsAAE3WlClTtHHjRu3evVudO3c22i0Wi4qKilRYWOg1u6ygoEAWi8WIOXDggNfxSlfLvDLm6hU0CwoKZDaby51VJkkmk0kmk6lMu7+/f40+gDpLfOQsrlqxrKUVAGqa05aA3FSM3FSurvJDzgEATQmrYQIAmhy3260pU6Zow4YN2rFjh6Kjo732x8bGyt/fX9u3bzfaTpw4ofz8fFmtVkmS1WrVkSNHdObMGSPGZrPJbDYrJibGiLnyGKUxpccAAAAA0Pwws6wWVHRTdgBA3UhJSVFWVpb+8Ic/qF27dsY9xoKDg9W6dWsFBwdrwoQJSk1NVWhoqMxms6ZOnSqr1apBgwZJkhISEhQTE6MxY8YoMzNTdrtds2fPVkpKijEzbNKkSXr55Zc1c+ZMjR8/Xjt27NC6deu0aRO/9wEAAIDmipllAIAmZ8WKFTp37pyGDBmiTp06GT/vvPOOEbNkyRLde++9Sk5O1uDBg2WxWPTuu+8a+/38/LRx40b5+fnJarXq4Ycf1tixYzV//nwjJjo6Wps2bZLNZlPfvn21aNEivf7660pMbBn3BgMAAABaolovlmVkZOiWW25Ru3btFBYWphEjRujEiRNeMUOGDJGPj4/Xz6RJk7xi8vPzlZSUpKCgIIWFhWnGjBm6fPlybXcXANAEud3ucn8eeeQRIyYwMFDLly/X2bNndeHCBb377rvGvchKRUVFafPmzbp48aL+9a9/6YUXXlCrVt6TrocMGaJDhw7J6XTqiy++8DoHAAAAgOan1r+GuWvXLqWkpOiWW27R5cuX9ctf/lIJCQk6fvy42rRpY8Q9/vjjXn+9DwoKMv6/uLhYSUlJslgs2rt3r06fPq2xY8fK399fzz33XG13GQAAAAAAAJBUB8WyLVu2eG2vWbNGYWFhys3N1eDBg432oKCgMn/hL5Wdna3jx49r27ZtCg8PV79+/bRgwQLNmjVL6enpCggIqO1uAwAAAAAAAHV/g/9z585JkkJDQ73a165dq7feeksWi0X33Xef5syZY8wuy8nJUe/evRUeHm7EJyYmavLkyTp27Jj69+9f5jxOp1NOp9PYdjgckiSXyyWXy1Xl/pbGmnzdVX5MXauo/ya/sn28OrZ0uzo5qEx556zs+BXF1/j8nufF5Ouu9jlrKwe16ernpyn0vbI+1vbrraE1xHhq+zXQXJ4LAAAAAKgvPm63u86qQiUlJfrhD3+owsJC7dmzx2h/9dVXFRUVpYiICB0+fFizZs3Srbfeatx4eeLEifrqq6+0detW4zEXL15UmzZttHnzZg0fPrzMudLT0zVv3rwy7VlZWV5f8QSAluTixYt66KGHdO7cOZnN5obuTovkcDgUHBxc7efA5XJp8+bNmnnAT85inyo95suFSTXtZpNSmpt77rlH/v7+Dd2dRoXcVIzcVK6u81PT34UAADSEOp1ZlpKSoqNHj3oVyqTvimGlevfurU6dOmno0KH64osvdOONN9boXGlpaUpNTTW2HQ6HIiMjlZCQUO0PJzabTXM+9pWzpGofTura0fTyV13rlb61TNvVsaXjGTZsWK288SnvnNXt4/Uw+bq1YGCJ5nzsq9y5d1frnBX1sSFd/fw0hb5X1sfafr01tIYYT22/Bkpn2QIAAAAAqqbOimVTpkzRxo0btXv3bnXu3LnS2Li4OEnS559/rhtvvFEWi0UHDhzwiikoKJCkCu9zZjKZZDKZyrT7+/vX6EOus8Snyn/Jr2sV9b+8/lUUW9M8VOWclZ23rnLoLPGp9jkbc/Gm9PlpCn2vSh9r6/XWWNTneGr7NdCcngcAAAAAqA++tX1At9utKVOmaMOGDdqxY4eio6Ov+Zi8vDxJUqdOnSRJVqtVR44c0ZkzZ4wYm80ms9msmJiY2u4yAAAAAAAAIKkOZpalpKQoKytLf/jDH9SuXTvZ7XZJUnBwsFq3bq0vvvhCWVlZuueee9ShQwcdPnxY06dP1+DBg9WnTx9JUkJCgmJiYjRmzBhlZmbKbrdr9uzZSklJKXf2GAAAAAAAAFAban1m2YoVK3Tu3DkNGTJEnTp1Mn7eeecdSVJAQIC2bdumhIQE9ejRQ7/4xS+UnJys999/3ziGn5+fNm7cKD8/P1mtVj388MMaO3as5s+fX9vdBQAAAAAAAAy1PrPsWotrRkZGateuXdc8TlRUlDZv3lxb3QIAAAAAAACuqdZnlgEAAAAAAABNFcUyAAAAAAAAwKPWv4aJhtX1qU1e2yY/tzJvbaDOAAAAAAAANDHMLAMAAAAAAAA8KJYBAAAAAAAAHhTLAAAAAAAAAA+KZQAAAAAAAIAHxTIAAAAAAADAg2IZAAAAAAAA4EGxDAAAAAAAAPCgWAYAAAAAAAB4UCwDAAAAAAAAPCiWAQAAAAAAAB4UywAAAAAAAAAPimUAAAAAAACAB8UyAAAAAAAAwINiGQAAAAAAAPD/t3fvQVGd9x/HP8tlFw1dEYlcjCJqxEQFDUaKiVUrlVgmE5terLUOk0vTtNjRmqTF6VTjtB1MvMSmY9VOG6ntTIg0o5mYxEhRcGKISVapaCxVQzRNRBIJctEgss/vj677cwVUkGV32fdrZmfYc5495/t99vA8hy9nz7pQLAMAAAAAAABcwnwdANAXDM97rcPlH63M7uVIAAAAAADAzeDKMgAAAAAAAMCFYhkAAAAAKkvJ1wAAEmFJREFUAADgwscwgxwfHwSAvoMxHQAAALh5XFkGAAAAAAAAuFAsAwAAAAAAAFz8uli2fv16DR8+XBEREUpPT9e7777r65AAAEGI+QgAAAAIHn57z7KXXnpJS5Ys0caNG5Wenq5169YpKytLVVVVGjx4sK/DAwAEib4wH3EvMwAAAODG+W2xbO3atfrRj36khx56SJK0ceNGvfbaa3rhhReUl5fn4+gAAMGiL89HnRXROkNxDQAAAMHAL4tlFy9elMPh0NKlS93LQkJClJmZqfLy8g5f09LSopaWFvfzc+fOSZLq6urU2tp6w/tubW3V+fPnFdYaojanpZsZ9KyzZ892uDzsUvN1XxvmNDp/3qmzZ88qPDz8hrfR1X3eTIxdcTmfsNaQHouxR+Lq5j4vH2+X3x9fxN5V14rx6nwCnS/y6eljoLGxUZJkjOl2TMGM+cjTqCe33nDb/Utnei2OvjbW9CT6pnP0zbV5u3+YjwAAgcQvi2Wff/652traFBsb67E8NjZW//73vzt8TX5+vlasWNFueVJSkldi7E0xa27u9T/ohX3ebIxdcTmfmFVde11vxtjT+/RF7F0VCDEGspvt38bGRg0YMKBnggkizEfdx5gAoCPMRwCAQOCXxbLuWLp0qZYsWeJ+7nQ6VVdXp0GDBsliufH/yDc0NGjo0KH6+OOPZbfbvRFqryIf/0Y+/q0v5GOMUWNjoxISEnwdStBgPvI++qZz9E3n6Jtr83b/MB8BAAKJXxbLYmJiFBoaqjNnzngsP3PmjOLi4jp8jc1mk81m81gWFRXV7RjsdnufOpEiH/9GPv4t0PPhP/jdx3zk3+ibztE3naNvrs2b/cN8BAAIFCG+DqAjVqtVaWlpKikpcS9zOp0qKSlRRkaGDyMDAAQT5iMAAAAg+PjllWWStGTJEuXk5GjSpEmaPHmy1q1bp+bmZve3kQEA0BuYjwAAAIDg4rfFsrlz5+qzzz7TsmXLVFNTowkTJmjnzp3tbrLc02w2m5YvX97uIzSBinz8G/n4t76WD7qH+cj/0Dedo286R99cG/0DAMD/sxi+vxkAAAAAAACQ5Kf3LAMAAAAAAAB8gWIZAAAAAAAA4EKxDAAAAAAAAHChWAYAAAAAAAC4UCy7yvr16zV8+HBFREQoPT1d7777rq9D0t69e3X//fcrISFBFotF27dv91hvjNGyZcsUHx+vfv36KTMzU8eOHfNoU1dXp/nz58tutysqKkqPPPKImpqaPNocOnRIU6dOVUREhIYOHapnn33WK/nk5+fr7rvv1le+8hUNHjxYc+bMUVVVlUebL7/8Urm5uRo0aJAiIyP17W9/W2fOnPFoc+rUKWVnZ6t///4aPHiwnnrqKV26dMmjTWlpqe666y7ZbDaNGjVKBQUFPZ7Phg0blJKSIrvdLrvdroyMDL3xxhsBmcvVVq5cKYvFosWLFwdsPk8//bQsFovHY8yYMQGbD/qWrs45RUVFGjNmjCIiIjR+/Hi9/vrrvRRp7+tK3xQUFLT7PY+IiOjFaHvP9c4JOhIs41NX+6a0tLTdcWOxWFRTU9M7AfeiGzn36kgwjTkAAFyJYtkVXnrpJS1ZskTLly/XgQMHlJqaqqysLNXW1vo0rubmZqWmpmr9+vUdrn/22Wf1/PPPa+PGjdq/f79uueUWZWVl6csvv3S3mT9/vo4cOaLi4mLt2LFDe/fu1WOPPeZe39DQoFmzZikxMVEOh0OrVq3S008/rT/96U89nk9ZWZlyc3P1zjvvqLi4WK2trZo1a5aam5vdbX7+85/r1VdfVVFRkcrKyvTpp5/qwQcfdK9va2tTdna2Ll68qLffflt//etfVVBQoGXLlrnbVFdXKzs7WzNmzFBFRYUWL16sRx99VG+++WaP5nPbbbdp5cqVcjgcev/99/X1r39dDzzwgI4cORJwuVzpvffe06ZNm5SSkuKxPBDzGTt2rE6fPu1+vPXWWwGdD/qGrs45b7/9tubNm6dHHnlEBw8e1Jw5czRnzhwdPny4lyP3vu7Mx3a73eP3/OTJk70Yce+53jnB1YJpfOpq31xWVVXlcewMHjzYSxH6zo2ce10tmMYcAADaMXCbPHmyyc3NdT9va2szCQkJJj8/34dReZJktm3b5n7udDpNXFycWbVqlXtZfX29sdls5sUXXzTGGPPBBx8YSea9995zt3njjTeMxWIxn3zyiTHGmD/+8Y9m4MCBpqWlxd3ml7/8pUlOTvZyRsbU1tYaSaasrMwdf3h4uCkqKnK3OXr0qJFkysvLjTHGvP766yYkJMTU1NS422zYsMHY7XZ3Dr/4xS/M2LFjPfY1d+5ck5WV5e2UzMCBA82f//zngM2lsbHR3H777aa4uNhMmzbNLFq0yBgTmO/N8uXLTWpqaofrAjEf9B1dnXO+973vmezsbI9l6enp5sc//rFX4/SFrvbN5s2bzYABA3opOv9x9TlBR4J1fLqRvtmzZ4+RZL744oteicmfXH3u1ZFgGnMAALgaV5a5XLx4UQ6HQ5mZme5lISEhyszMVHl5uQ8ju7bq6mrV1NR4xD1gwAClp6e74y4vL1dUVJQmTZrkbpOZmamQkBDt37/f3eZrX/uarFaru01WVpaqqqr0xRdfeDWHc+fOSZKio6MlSQ6HQ62trR45jRkzRsOGDfPIafz48YqNjfWIt6GhwX1FV3l5ucc2Lrfx5vvZ1tamwsJCNTc3KyMjI2Bzyc3NVXZ2drt9Bmo+x44dU0JCgkaMGKH58+fr1KlTAZ0PAl935pxgOc66Ox83NTUpMTFRQ4cO9bi6N9gFy3FzMyZMmKD4+Hh94xvf0L59+3wdTq+4+tyrIxw7AIBgRrHM5fPPP1dbW5vHH8SSFBsb69f3rrgc27XirqmpafeRgrCwMEVHR3u06WgbV+7DG5xOpxYvXqx77rlH48aNc+/ParUqKiqqXTxdibezNg0NDbpw4UKP5lFZWanIyEjZbDY9/vjj2rZtm+68886AzKWwsFAHDhxQfn5+u3WBmE96eroKCgq0c+dObdiwQdXV1Zo6daoaGxsDMh/0Dd2Zczo7zvx5juqO7vRNcnKyXnjhBb3yyiv6+9//LqfTqSlTpui///1vb4Ts1xifOhcfH6+NGzfq5Zdf1ssvv6yhQ4dq+vTpOnDggK9D86qOzr06EixjDgAAHQnzdQAIbrm5uTp8+LDHPaQCUXJysioqKnTu3Dn94x//UE5OjsrKynwdVpd9/PHHWrRokYqLi/vMzbFnz57t/jklJUXp6elKTEzU1q1b1a9fPx9GBqCnZGRkKCMjw/18ypQpuuOOO7Rp0yb95je/8WFk8GfJyclKTk52P58yZYpOnDih5557Tn/72998GJl39ZVzLwAAvIkry1xiYmIUGhra7lvwzpw5o7i4OB9FdX2XY7tW3HFxce1uinzp0iXV1dV5tOloG1fuo6ctXLhQO3bs0J49e3Tbbbe5l8fFxenixYuqr69vF09X4u2sjd1u7/EiidVq1ahRo5SWlqb8/Hylpqbq97//fcDl4nA4VFtbq7vuukthYWEKCwtTWVmZnn/+eYWFhSk2Njag8ulIVFSURo8erePHjwfc+4O+oztzTmfHmT/PUd3RE/NxeHi4Jk6cqOPHj3sjxIDC+NQ1kydP7tPHTWfnXh0JljEHAICOUCxzsVqtSktLU0lJiXuZ0+lUSUmJx3+r/U1SUpLi4uI84m5oaND+/fvdcWdkZKi+vl4Oh8PdZvfu3XI6nUpPT3e32bt3r1pbW91tiouLlZycrIEDB/ZozMYYLVy4UNu2bdPu3buVlJTksT4tLU3h4eEeOVVVVenUqVMeOVVWVnoUAYuLi2W323XnnXe621y5jctteuP9dDqdamlpCbhcZs6cqcrKSlVUVLgfkyZN0vz5890/B1I+HWlqatKJEycUHx8fcO8P+o7uzDnBcpz1xHzc1tamyspKxcfHeyvMgBEsx01Pqaio6JPHzfXOvTrCsQMACGq+/oYBf1JYWGhsNpspKCgwH3zwgXnsscdMVFSUx7fg+UJjY6M5ePCgOXjwoJFk1q5daw4ePGhOnjxpjDFm5cqVJioqyrzyyivm0KFD5oEHHjBJSUnmwoUL7m3cd999ZuLEiWb//v3mrbfeMrfffruZN2+ee319fb2JjY01CxYsMIcPHzaFhYWmf//+ZtOmTT2ez09+8hMzYMAAU1paak6fPu1+nD9/3t3m8ccfN8OGDTO7d+8277//vsnIyDAZGRnu9ZcuXTLjxo0zs2bNMhUVFWbnzp3m1ltvNUuXLnW3+fDDD03//v3NU089ZY4ePWrWr19vQkNDzc6dO3s0n7y8PFNWVmaqq6vNoUOHTF5enrFYLGbXrl0Bl0tHrvw2zEDM54knnjClpaWmurra7Nu3z2RmZpqYmBhTW1sbkPmg77jenLNgwQKTl5fnbr9v3z4TFhZmVq9ebY4ePWqWL19uwsPDTWVlpa9S8Jqu9s2KFSvMm2++aU6cOGEcDof5/ve/byIiIsyRI0d8lYLXXO+cIC8vzyxYsMDdPpjGp672zXPPPWe2b99ujh07ZiorK82iRYtMSEiI+ec//+mrFLzmRs69gnnMAQDgahTLrvKHP/zBDBs2zFitVjN58mTzzjvv+Dok91ebX/3IyckxxhjjdDrNr3/9axMbG2tsNpuZOXOmqaqq8tjG2bNnzbx580xkZKSx2+3moYceMo2NjR5t/vWvf5l7773X2Gw2M2TIELNy5Uqv5NNRLpLM5s2b3W0uXLhgfvrTn5qBAwea/v37m29961vm9OnTHtv56KOPzOzZs02/fv1MTEyMeeKJJ0xra6tHmz179pgJEyYYq9VqRowY4bGPnvLwww+bxMREY7Vaza233mpmzpzpLpQFWi4dubpYFmj5zJ0718THxxur1WqGDBli5s6da44fPx6w+aBvudacM23aNPc4f9nWrVvN6NGjjdVqNWPHjjWvvfZaL0fce7rSN4sXL3a3jY2NNd/85jfNgQMHfBC1913vnCAnJ8dMmzat3WuCYXzqat8888wzZuTIkSYiIsJER0eb6dOnm927d/smeC+7kXOvYB9zAAC4ksUYY3rjCjYAAAAAAADA33HPMgAAAAAAAMCFYhkAAAAAAADgQrEMAAAAAAAAcKFYBgAAAAAAALhQLAMAAAAAAABcKJYBAAAAAAAALhTLAAAAAAAAABeKZQAAAECQ27t3r+6//34lJCTIYrFo+/btXd6GMUarV6/W6NGjZbPZNGTIEP3ud7/r+WABAPCyMF8HAAAAAMC3mpublZqaqocfflgPPvhgt7axaNEi7dq1S6tXr9b48eNVV1enurq6Ho4UAADvsxhjjK+DAAAAAOAfLBaLtm3bpjlz5riXtbS06Fe/+pVefPFF1dfXa9y4cXrmmWc0ffp0SdLRo0eVkpKiw4cPKzk52TeBAwDQQ/gYJgAAAIBrWrhwocrLy1VYWKhDhw7pu9/9ru677z4dO3ZMkvTqq69qxIgR2rFjh5KSkjR8+HA9+uijXFkGAAhIFMsAAAAAdOrUqVPavHmzioqKNHXqVI0cOVJPPvmk7r33Xm3evFmS9OGHH+rkyZMqKirSli1bVFBQIIfDoe985zs+jh4AgK7jnmUAAAAAOlVZWam2tjaNHj3aY3lLS4sGDRokSXI6nWppadGWLVvc7f7yl78oLS1NVVVVfDQTABBQKJYBAAAA6FRTU5NCQ0PlcDgUGhrqsS4yMlKSFB8fr7CwMI+C2h133CHpf1emUSwDAAQSimUAAAAAOjVx4kS1tbWptrZWU6dO7bDNPffco0uXLunEiRMaOXKkJOk///mPJCkxMbHXYgUAoCfwbZgAAABAkGtqatLx48cl/a84tnbtWs2YMUPR0dEaNmyYfvjDH2rfvn1as2aNJk6cqM8++0wlJSVKSUlRdna2nE6n7r77bkVGRmrdunVyOp3Kzc2V3W7Xrl27fJwdAABdQ7EMAAAACHKlpaWaMWNGu+U5OTkqKChQa2urfvvb32rLli365JNPFBMTo69+9atasWKFxo8fL0n69NNP9bOf/Uy7du3SLbfcotmzZ2vNmjWKjo7u7XQAALgpFMsAAAAAAAAAlxBfBwAAAAAAAAD4C4plAAAAAAAAgAvFMgAAAAAAAMCFYhkAAAAAAADgQrEMAAAAAAAAcKFYBgAAAAAAALhQLAMAAAAAAABcKJYBAAAAAAAALhTLAAAAAAAAABeKZQAAAAAAAIALxTIAAAAAAADAhWIZAAAAAAAA4PJ/vhPEnJ+XwdQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}