Starbucks-Product-Analysis-&-Prediction.ipynb

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        "# Starbucks Product Analysis & Prediction\n",
        "In this project, firstly i will try to analysis the Starbucks Product Nutrition Value dataset which i get from kaggle and then try to make prediction with DecisionTreeClassifier. Let's start with import neccesarry libraries."
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      "source": [
        "# Libraries for dataset\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Libraries for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "# Create pandas data frame\n",
        "df = pd.read_csv(\"starbucks.csv\", index_col=0)\n",
        "\n",
        "# Create a numeric data frame to make correlations\n",
        "numeric_df = df.select_dtypes(include=['number'])"
      ],
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        "df.head()"
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              "                          item  calories   fat  carb  fiber  protein    type\n",
              "1                 8-Grain-Roll       350   8.0    67      5       10  bakery\n",
              "2            Apple-Bran-Muffin       350   9.0    64      7        6  bakery\n",
              "3                Apple-Fritter       420  20.0    59      0        5  bakery\n",
              "4              Banana-Nut-Loaf       490  19.0    75      4        7  bakery\n",
              "5  Birthday-Cake-Mini-Doughnut       130   6.0    17      0        0  bakery"
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              "   calories   fat  carb  fiber  protein\n",
              "1       350   8.0    67      5       10\n",
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              "      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-d7a10720-7aa6-4656-964b-8e0154b3ce80 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-d7a10720-7aa6-4656-964b-8e0154b3ce80');\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-1c9cd4fa-44b3-4888-9c4b-a1fae6401b95\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c9cd4fa-44b3-4888-9c4b-a1fae6401b95')\"\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-1c9cd4fa-44b3-4888-9c4b-a1fae6401b95 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "numeric_df",
              "summary": "{\n  \"name\": \"numeric_df\",\n  \"rows\": 77,\n  \"fields\": [\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105,\n        \"min\": 80,\n        \"max\": 500,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          290,\n          140,\n          280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.095488010174427,\n        \"min\": 0.0,\n        \"max\": 28.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          2.5,\n          2.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 16,\n        \"max\": 80,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          57,\n          20,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 34,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          3,\n          25,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ss_SNRhPDyqY",
        "outputId": "77d2f1bd-42e3-4433-b216-b1479f54dac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 77 entries, 1 to 77\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   item      77 non-null     object \n",
            " 1   calories  77 non-null     int64  \n",
            " 2   fat       77 non-null     float64\n",
            " 3   carb      77 non-null     int64  \n",
            " 4   fiber     77 non-null     int64  \n",
            " 5   protein   77 non-null     int64  \n",
            " 6   type      77 non-null     object \n",
            "dtypes: float64(1), int64(4), object(2)\n",
            "memory usage: 4.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vIT3Qt9TEBCl",
        "outputId": "deaa1c3f-f71c-49f9-c264-66fc98289076"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         calories        fat       carb      fiber    protein\n",
              "count   77.000000  77.000000  77.000000  77.000000  77.000000\n",
              "mean   338.831169  13.766234  44.870130   2.220779   9.480519\n",
              "std    105.368701   7.095488  16.551634   2.112764   8.079556\n",
              "min     80.000000   0.000000  16.000000   0.000000   0.000000\n",
              "25%    300.000000   9.000000  31.000000   0.000000   5.000000\n",
              "50%    350.000000  13.000000  45.000000   2.000000   7.000000\n",
              "75%    420.000000  18.000000  59.000000   4.000000  15.000000\n",
              "max    500.000000  28.000000  80.000000   7.000000  34.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8014b90a-48e6-4395-acbf-39aaa1e3895e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>calories</th>\n",
              "      <th>fat</th>\n",
              "      <th>carb</th>\n",
              "      <th>fiber</th>\n",
              "      <th>protein</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>77.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>338.831169</td>\n",
              "      <td>13.766234</td>\n",
              "      <td>44.870130</td>\n",
              "      <td>2.220779</td>\n",
              "      <td>9.480519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.368701</td>\n",
              "      <td>7.095488</td>\n",
              "      <td>16.551634</td>\n",
              "      <td>2.112764</td>\n",
              "      <td>8.079556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>300.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-8014b90a-48e6-4395-acbf-39aaa1e3895e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-8014b90a-48e6-4395-acbf-39aaa1e3895e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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-e5f850a0-1243-4e57-a982-0e75eb867c10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163.82678773548426,\n        \"min\": 77.0,\n        \"max\": 500.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          338.83116883116884,\n          350.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.16636179952797,\n        \"min\": 0.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.766233766233766,\n          13.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.727746635712023,\n        \"min\": 16.0,\n        \"max\": 80.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          44.87012987012987,\n          45.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.44412830806075,\n        \"min\": 0.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          77.0,\n          2.220779220779221,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.39485984406494,\n        \"min\": 0.0,\n        \"max\": 77.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.480519480519481,\n          7.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find number of items in dataset\n",
        "df[\"item\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5gNQhnXD3iG",
        "outputId": "8ea48cef-5896-40bb-f6cf-8266acf2ab46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find names of items in dataset\n",
        "df[\"item\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD_sTXc3D9yG",
        "outputId": "acef4d73-f434-49bc-e30d-5fe956447b7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['8-Grain Roll', 'Apple Bran Muffin', 'Apple Fritter',\n",
              "       'Banana Nut Loaf', 'Birthday Cake Mini Doughnut',\n",
              "       'Blueberry Oat Bar', 'Blueberry Scone',\n",
              "       'Bountiful Blueberry Muffin', 'Butter Croissant ', 'Cheese Danish',\n",
              "       'Chocolate Chunk Cookie', 'Chocolate Cinnamon Bread',\n",
              "       'Chocolate Croissant', 'Chocolate Old-Fashioned Doughnut',\n",
              "       'Chonga Bagel', 'Cinnamon Chip Scone', 'Cranberry Orange Scone',\n",
              "       'Double Chocolate Brownie', 'Double Fudge Mini Doughnut',\n",
              "       'Everything with Cheese Bagel', 'Ginger Molasses Cookie',\n",
              "       'Iced Lemon Pound Cake', 'Mallorca Sweet Bread',\n",
              "       'Maple Oat Pecan Scone ', 'Marble Pound Cake',\n",
              "       'Marshmallow Dream Bar', 'Morning Bun', 'Multigrain Bagel',\n",
              "       'Old-Fashioned Glazed Doughnut', 'Outrageous Oatmeal Cookie',\n",
              "       'Petite Vanilla Bean Scone', 'Plain Bagel', 'Pumpkin Bread',\n",
              "       'Pumpkin Scone ', 'Raspberry Scone', 'Raspberry Swirl Pound Cake',\n",
              "       'Reduced-Fat Banana Chocolate Chip Coffee Cake',\n",
              "       'Reduced-Fat Cinnamon Swirl Coffee Cake',\n",
              "       'Reduced-Fat Very Berry Coffee Cake ',\n",
              "       'Starbucks Classic Coffee Cake', 'Zucchini Walnut Muffin ',\n",
              "       'Cheese & Fruit', 'Chicken & Hummus', 'Chicken Lettuce Wraps',\n",
              "       'Chipotle Chicken Wraps', 'Protein', 'Salumi & Cheese',\n",
              "       'Sesame Noodles', 'Tuna Salad', 'Apple Pie', 'Birthday Cake Pop',\n",
              "       'Brown Sugar Walnut Tart', 'Cherry Pie',\n",
              "       'Chocolate Creme Whoopie Pie', 'Chocolate Hazelnut Tart',\n",
              "       'Raspberry Truffle Cake Pop', 'Red Velvet Whoopie Pie',\n",
              "       'Tiramisu Cake Pop', 'Bacon & Gouda Artisan Breakfast Sandwich',\n",
              "       'Chicken Sausage Breakfast Wrap',\n",
              "       'Ham & Cheddar Artisan Breakfast Sandwich',\n",
              "       'Sausage & Cheddar Classic Breakfast Sandwich',\n",
              "       'Spinach & Feta Breakfast Wrap', 'Starbucks Perfect Oatmeal',\n",
              "       'Turkey Bacon & White Cheddar Classic Breakfast Sandwich',\n",
              "       'Veggie & Monterey Jack Artisan Breakfast Sandwich',\n",
              "       'Deluxe Fruit Blend', 'Chicken Santa Fe Panini',\n",
              "       'Egg Salad Sandwich ', 'Ham & Swiss Panini',\n",
              "       'Roasted Tomato & Mozzarella Panini', 'Roasted Vegetable Panini',\n",
              "       'Tarragon Chicken Salad Sandwich', 'Turkey & Swiss Sandwich',\n",
              "       'Greek Yogurt Honey Parfait', 'Peach Raspberry Yogurt Parfait',\n",
              "       'Strawberry & Blueberry Yogurt Parfait'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find how many of each item there are in dataset\n",
        "df.groupby(\"type\")[\"item\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "np5Ke6smEIY9",
        "outputId": "cb3b878a-c796-4544-a1ac-35d09481b780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "bakery           41\n",
              "bistro box        8\n",
              "hot breakfast     8\n",
              "parfait           3\n",
              "petite            9\n",
              "salad             1\n",
              "sandwich          7\n",
              "Name: item, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>item</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bakery</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bistro box</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hot breakfast</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parfait</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petite</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>salad</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sandwich</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize how many of each item there are in dataset\n",
        "df.groupby(\"type\")[\"item\"].count().plot()\n",
        "plt.title(\"Product Types\")\n",
        "plt.text(0.,50,\"Product Category Range\",bbox=dict(facecolor='yellow', alpha=1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "6jzNA4vSEZug",
        "outputId": "0a551f5e-34a4-4d55-80e0-57ee347f3e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize category numbers\n",
        "sns.countplot(x=\"type\", data=df, palette=\"Set1\")\n",
        "plt.title(\"Category Numbers\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "c7PEj18mIQEN",
        "outputId": "307b5135-5f73-4f86-d006-51156558aa2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-59329f4b4ca5>:1: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=\"type\", data=df, palette=\"Set1\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize calorie values\n",
        "sns.catplot(kind=\"bar\",x=\"type\",y=\"calories\",data=df)\n",
        "plt.title(\"Calorie Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "0F3iXNBQN_7A",
        "outputId": "41566c22-ab79-4d66-e16d-5356a0c00f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 511.111x500 with 1 Axes>"
            ],
            "image/png": 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SidGjRyMnJwe+vr6YOnWqdL2RkRFWrlyJadOmITAwEGZmZujSpQtGjx6tx6qLJi8vF0JoAAAajQZ5ebkMeyJ6bUqlAkZGSkzeegKxiek6XbYm94nW9KAfDkNpbKrTddStaoVZnzeHUqlg2OtQmQr7TZs2aU2XL18eU6dO1Qr459WsWROrV69+06UREb1VYhPTceVOqk6XqcjLgdUz09cT0iDKle2jqPSU3r9nT0TysWHDGnz+eQA2bFij71KI6BkMeyLSifzzUDSap+ehZGdn67skIvr/GPZEpBOFnYdCRGUDw56IiEjmGPZEREQyx7AnIiKSOYY9ERGRzDHsiYiIZI5hT0REJHMMeyIiIplj2BMREckcw56IiEjmGPZEREQyx7AnIiKSuTL1E7ekf0qlAkqlQufLLVdOWWD6+Xm6oNEI/gY2EdFzGPYkUSoVqFTJHEZGug9hY2PtAK5UqQIsLCrofD1qtQZpaVkMfCKiZzDsSaJUKmBkpMTkrScQm5iu02Vrcp9oTQ/64TCUxqY6XUfdqlaY9XlzKJUKhj0R0TMY9lRAbGI6rtxJ1ekyFXk5sHpm+npCGkQ5E52ug4iICscT9IiIiGSOYU9EREUilEoIPD2BV0ABoWSEvC14TxERUdEoyyHH1hkCCuTYOgNKfhL8tuA9RURERfa4lhce1/LSdxlUTAz7V+D3zomI6G3HsH8Jfu+ciIjkgGH/EvzeORERyQHDvgj4vXMiInqb8Wx8IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOb0GvZbt26Fv78/PD094enpicDAQBw/fly6PigoCCqVSusyZcoUrWUkJCRg8ODBcHNzg7e3N+bPn4+8vLzSboWIiKjM0usP4VSvXh3jx4+Hvb09hBDYu3cvRowYgT179qBevXoAgM8++wyjR4+WbmNmZib9rVarMWTIENjY2GD79u1ITExEcHAwjI2NMW7cuFLvh4iIqCzS6559y5Yt4efnhzp16qBu3boYO3YszM3NERERIY0xNTWFra2tdLGwsJCuO3nyJKKjo7FgwQLUr18ffn5+GDNmDLZs2YKcnBw9dERERFT2lJnP7NVqNQ4ePIisrCx4eHhI8/fv34+mTZuiU6dOWLRoER4/fixdFxERAScnJ9jY2EjzfH198ejRI0RHRxe7BoVC+yInz/dW2EVOitIvL7q/8D7Q/0VO9L0t9X3RJb3/nv3Vq1fRvXt3ZGdnw9zcHCtWrICjoyMAoFOnTrCzs0PVqlVx9epVLFy4ELGxsVi+fDkAIDk5WSvoAUjTSUlJxa7F2tryNbspmypXrqDvEkqVofVbVpiaar86WVtbah2JIyoOPo91S+9hX7duXezduxcZGRk4cuQIgoODsXnzZjg6OiIwMFAap1KpYGtri759+yIuLg61a9fWeS0pKRkQ4t9pIyOlLB5wDx5kQq3WvHKcofVLupWZ+UhrOiUlA0+eiBeMpjeFz2P5sLHR3Q6o3sPexMQE9vb2AAAXFxdcvHgRGzduxIwZMwqMdXNzAwDcunULtWvXho2NDSIjI7XGJCcnAwBsbW2LXYsQ0Ap7OZFrXy9iaP2WBc9vczk/n6h08PGjO2XmM/t8Go3mhSfXRUVFAfg3yN3d3XHt2jWkpKRIY06dOgULCwvpowAiIiJDp9c9+0WLFuGDDz5AjRo1kJmZiQMHDuDs2bNYu3Yt4uLisH//fvj5+aFSpUq4evUq5s6diyZNmsDZ2RnA05PxHB0dMXHiREyYMAFJSUlYsmQJevbsCRMTE322RkREVGboNexTUlIQHByMxMREWFpaQqVSYe3atWjWrBnu3r2L06dPY+PGjcjKykKNGjXQpk0bDB8+XLq9kZERVq5ciWnTpiEwMBBmZmbo0qWL1vfyiYiIDJ1ew37OnDkvvK5GjRrYvHnzK5dRs2ZNrF69WpdlERERyUqZ+8yeiIiIdIthT0REJHMMez0RSiUEnv4TEgEFhJJ3BRERvRlMGH1RlkOOrTMEFMixdQaUev+XB0REJFNMGD16XMsLj2t56buMUpF/JEMBwSMZRESljK+4VDp4JIOISG/4ikulxpCOZBARlSXcsyciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5/gc9IgOjVCqgVCp0vtxy5ZQFpp+fpwsajYBGI3S+XCI5Y9gTGRClUoFKlcxhZKT7EDY21g7gSpUqwMKigs7Xo1ZrkJaWxcAnKgaGPZEBUSoVMDJSYvLWE4hNTNfpsjW5T7SmB/1wGEpjU52uo25VK8z6vDmUSgXDnqgYGPZEBig2MR1X7qTqdJmKvBxYPTN9PSENopyJTtdBRCXDE/SIiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkTq9hv3XrVvj7+8PT0xOenp4IDAzE8ePHpeuzs7Mxffp0NG3aFB4eHhg1ahSSk5O1lpGQkIDBgwfDzc0N3t7emD9/PvLy8kq7FSIiojJLr2FfvXp1jB8/Hrt370ZoaCjef/99jBgxAtevXwcAzJkzB3/++SeWLFmCTZs2ITExESNHjpRur1arMWTIEOTm5mL79u2YN28e9uzZg6VLl+qrJSIiojJHr2HfsmVL+Pn5oU6dOqhbty7Gjh0Lc3NzREREICMjA6GhoQgJCYG3tzdcXFwwZ84chIeHIyIiAgBw8uRJREdHY8GCBahfvz78/PwwZswYbNmyBTk5OfpsjYiIqMwop+8C8qnVahw+fBhZWVnw8PDApUuXkJubCx8fH2mMg4MD7OzsEBERAXd3d0RERMDJyQk2NjbSGF9fX0ybNg3R0dF47733ilWDQqGzdsocOfdWGEPr1xDxPpa/ot7HSqUCirf4ASGEgEYj3ug69B72V69eRffu3ZGdnQ1zc3OsWLECjo6OiIqKgrGxMSpWrKg13traGklJSQCA5ORkraAHIE3njykOa2vLEnZRtlWuXEHfJZQqQ+vXEPE+lr/i3MdqjQZGyrf3fPPSqF/vYV+3bl3s3bsXGRkZOHLkCIKDg7F582a91JKSkgHxzJsrIyOlLF5UHjzIhFqteeU4Q+vXEPE+lj9Du4/z+5289QRiE9NLoTLdqlvVCrM+b15ovzY2utsB1XvYm5iYwN7eHgDg4uKCixcvYuPGjWjfvj1yc3Px8OFDrb37lJQU2NraAni6Fx8ZGam1vPyz9fPHFIcQ0Ap7OZFrXy9iaP0aIt7H8lec+zg2MR1X7qS+uWJKwZt8TJe54x4ajQY5OTlwcXGBsbExTp8+LV1348YNJCQkwN3dHQDg7u6Oa9euISUlRRpz6tQpWFhYwNHRsbRLJyIiKpP0ume/aNEifPDBB6hRowYyMzNx4MABnD17FmvXroWlpSW6du2KefPmwcrKChYWFpg1axY8PDyksPf19YWjoyMmTpyICRMmICkpCUuWLEHPnj1hYmKiz9aIiIjKDL2GfUpKCoKDg5GYmAhLS0uoVCqsXbsWzZo1AwBMmjQJSqUSo0ePRk5ODnx9fTF16lTp9kZGRli5ciWmTZuGwMBAmJmZoUuXLhg9erS+WiIiIipz9Br2c+bMeen15cuXx9SpU7UC/nk1a9bE6tWrdV0aERGRbJS5z+yJiIhItxj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7IlIJ4RSCQHF07+hgFDy5YWorOCzkYh0Q1kOObbOEFAgx9YZUJbTd0VE9P/x2UhEOvO4lhce1/LSdxlE9Bzu2RMREckcw56IiEjmGPZEREQyx7AnIiKSOYY9ERGRzDHsiYiIZE6vYf/jjz+ia9eu8PDwgLe3N4YPH44bN25ojQkKCoJKpdK6TJkyRWtMQkICBg8eDDc3N3h7e2P+/PnIy8srzVaIiIjKLL1+z/7s2bPo2bMnGjZsCLVajW+//RYDBgzAwYMHYW5uLo377LPPMHr0aGnazMxM+lutVmPIkCGwsbHB9u3bkZiYiODgYBgbG2PcuHGl2g8REVFZpNewX7t2rdb0vHnz4O3tjcuXL6NJkybSfFNTU9ja2ha6jJMnTyI6Ohrr1q2DjY0N6tevjzFjxmDhwoUYOXIkTExM3mgPREREZV2Z+sw+IyMDAGBlZaU1f//+/WjatCk6deqERYsW4fHjx9J1ERERcHJygo2NjTTP19cXjx49QnR0dLHWr1BoX+Tk+d4Ku8hJUfo1xIuc6HtbltWLnBh6v7pUZv5drkajwZw5c+Dp6QknJydpfqdOnWBnZ4eqVavi6tWrWLhwIWJjY7F8+XIAQHJyslbQA5Cmk5KSilWDtbXla3ZRNlWuXEHfJZQqQ+vXEPE+lj9Du4/fdL9lJuynT5+O69evY+vWrVrzAwMDpb9VKhVsbW3Rt29fxMXFoXbt2jqtISUlA0L8O21kpJTFA+7Bg0yo1ZpXjjO0fg0R72P5M7T7WM792tjobge0TIT9jBkzcOzYMWzevBnVq1d/6Vg3NzcAwK1bt1C7dm3Y2NggMjJSa0xycjIAvPBz/hcRAlphLydy7etFDK1fQ8T7WP4M7T5+k/3q5DN7tVqNqKgopKenF+t2QgjMmDEDv//+OzZs2IBatWq98jZRUVEA/g1yd3d3XLt2DSkpKdKYU6dOwcLCAo6OjsWqh4iISI5KFPazZ8/Grl27ADwN+l69eqFLly748MMPcebMmSIvZ/r06di3bx8WLVqEChUqICkpCUlJSXjy5AkAIC4uDitWrMClS5dw+/ZtHD16FMHBwWjSpAmcnZ0BPD0Zz9HRERMnTsSVK1dw4sQJLFmyBD179uSZ+ERERCjhYfwjR46gc+fOAIA///wTt2/fxq+//opffvkFixcvxvbt24u0nG3btgF4+o9znjV37lwEBATA2NgYp0+fxsaNG5GVlYUaNWqgTZs2GD58uDTWyMgIK1euxLRp0xAYGAgzMzN06dJF63v5REREhqxEYf/gwQPpMPrx48fRrl071K1bF127dsXGjRuLvJyrV6++9PoaNWpg8+bNr1xOzZo1sXr16iKvl4iIyJCU6DC+jY0NoqOjoVarceLECTRr1gwA8OTJExgZGem0QCIiIno9JdqzDwgIwBdffAFbW1soFAr4+PgAAC5cuIB3331XpwUSERHR6ylR2I8aNQr16tXDvXv30K5dO+lEOCMjIwwaNEinBRIREdHrKfH37Nu1awcAyM7OluZ16dLl9SsiIiIinSrRZ/ZqtRorVqxA8+bN4eHhgfj4eADAkiVLpK/kERERUdlQorD/4YcfsGfPHkyYMAHGxsbSfCcnJ/z88886K46IiIheX4nC/pdffsHMmTPRuXNnKJX/LkKlUuHGjRs6K46IiIheX4nC/v79+4X+CI0QAnl5ea9dFBEREelOicLe0dER//zzT4H5hw8fRv369V+7KCIiItKdEp2NP3z4cISEhOD+/fsQQuC3335DbGws9u7dix9//FHXNRIREdFrKNGefevWrbFy5UqcPn0aZmZmWLp0KWJiYrBy5Urpv+kRERFR2VDi79k3btwY69at02UtRERE9Abo5PfsiYiIqOwq8p69l5cXDh8+jCpVqqBJkyZQKBQvHHv27FmdFEdERESvr8hh/9VXX8HCwgIAMGnSpDdWEBEREelWkcM+///e5+XlQaFQwNfXFzY2Nm+sMCIiItKNYn9mX65cOUydOlXrB3CIiIio7CrRCXqurq6IiorSdS1ERET0BpToq3c9evTAvHnzcO/ePTRo0ABmZmZa1zs7O+ukOCIiInp9JQr7cePGAQBmzZolzVMoFBBCQKFQcK+fiIioDClR2B89elTXdRAREdEbUqKwr1mzpq7rICIiojekxP8uNy4uDhs2bEBMTAyAp7+E17t370J/+paIiIj0p0Rn4584cQIdOnRAZGQkVCoVVCoVLly4gI4dO+Kvv/7SdY1ERET0Gkq0Z79o0SL07dsX48eP15q/cOFCLFy4kL98R0REVIaUaM8+JiYGn376aYH5Xbt2RXR09GsXRURERLpTorCvUqVKoV+vi4qKgrW19WsXRURERLpTosP43bp1w5QpUxAfHw9PT08AwPnz57F69Wr07dtXl/URERHRaypR2I8YMQIWFhb46aef8O233wIAqlatipEjR6J37946LZCIiIheT4nCXqFQoG/fvujbty8ePXoEANLP3xIREVHZUuLv2edjyBMREZVtRQ77Tz75BAqFokhj9+zZU+KCiIiISLeKHPatW7d+k3UQERHRG1LksB85cuSbrIOIiIjekBJ9z56IiIjeHiU6QU+tVmP9+vX49ddfcffuXeTm5mpdf/bsWZ0UR0RERK+vRHv2y5cvx7p169ChQwdkZGSgb9+++Oijj6BQKHi4n4iIqIwpUdjv378fs2bNQv/+/WFkZIROnTph9uzZGDFiBC5cuFDk5fz444/o2rUrPDw84O3tjeHDh+PGjRtaY7KzszF9+nQ0bdoUHh4eGDVqFJKTk7XGJCQkYPDgwXBzc4O3tzfmz5+PvLy8krRGREQkOyUK++TkZDg5OQEAKlSogIyMDABAixYtcOzYsSIv5+zZs+jZsyd27tyJdevWIS8vDwMGDEBWVpY0Zs6cOfjzzz+xZMkSbNq0CYmJiVpHD9RqNYYMGYLc3Fxs374d8+bNw549e7B06dKStEZERCQ7JQr7atWqISkpCQBQq1Yt6TfsL168CBMTkyIvZ+3atQgICEC9evXg7OyMefPmISEhAZcvXwYAZGRkIDQ0FCEhIfD29oaLiwvmzJmD8PBwREREAABOnjyJ6OhoLFiwAPXr14efnx/GjBmDLVu2ICcnpyTtERERyUqJwv6jjz7C6dOnAQBBQUH47rvv0KZNG0ycOBFdu3YtcTH5RwisrKwAAJcuXUJubi58fHykMQ4ODrCzs5PCPiIiAk5OTrCxsZHG+Pr64tGjR/y5XSIiIpTwbPzx48dLf3fo0AF2dnYIDw+Hvb09WrZsWaJCNBoN5syZA09PT+kjguTkZBgbG6NixYpaY62traUjC8nJyVpBD0Cazh9TVEX8B4FvJTn3VhhD69cQ8T6WP0O7j99kvyUK+x9//BHW1tb49NNPAQDu7u5wd3fHzz//jFWrVmHw4MHFXub06dNx/fp1bN26tSQl6YS1taXe1v0mVa5cQd8llCpD69cQ8T6WP0O7j990vyUK+x07dmDhwoUF5terVw9jx44tdtjPmDEDx44dw+bNm1G9enVpvo2NDXJzc/Hw4UOtvfuUlBTY2tpKYyIjI7WWl3+2fv6YokpJyYAQ/04bGSll8YB78CATarXmleMMrV9DxPtY/gztPpZzvzY2utsBLdFn9klJSYUGaZUqVYp16FwIgRkzZuD333/Hhg0bUKtWLa3rXVxcYGxsLJ0fAAA3btxAQkIC3N3dATw9qnDt2jWkpKRIY06dOgULCws4OjoWqy8htC9y8nxvhV3kpCj9GuJFTvS9LcvqRU4MvV9dKtGefY0aNXD+/PkC4Xzu3DlUrVq1yMuZPn06Dhw4gO+//x4VKlSQ3ihYWlrC1NQUlpaW6Nq1K+bNmwcrKytYWFhg1qxZ8PDwkMLe19cXjo6OmDhxIiZMmICkpCQsWbIEPXv2LNY3A4iIiOSqRGHfrVs3zJkzB3l5eXj//fcBAKdPn8aCBQvQv3//Ii9n27ZtAJ6e0f+suXPnIiAgAAAwadIkKJVKjB49Gjk5OfD19cXUqVOlsUZGRli5ciWmTZuGwMBAmJmZoUuXLhg9enRJWiMiIpKdEoX9wIEDkZaWhunTp0v/F798+fIYOHAghgwZUuTlXL169ZVjypcvj6lTp2oF/PNq1qyJ1atXF3m9REREhqREYa9QKDBhwgQMHz4cMTExMDU1RZ06dXjYnIiIqAwqUdjnq1ChAlxdXXVVCxEREb0B/D17IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFP9AZt2LAGn38egA0b1ui7FCIyYAx7ojckOzsbhw8fgkajwZEjh5Cdna3vkojIQDHsid6QvLxcCPH096k1Gg3y8nL1XBERGSqGPRERkcwx7ImIiGTutX4Ih+htp1QqoFQq3siyy5VTFph+fp4uaDQCGo3Q+XKJSD4Y9mSwlEoFKlUyh5HRmznAZWysHcCVKlWAhUUFna9HrdYgLS2LgU9EL8SwJ4OlVCpgZKTE5K0nEJuYrvPla3KfaE0P+uEwlMamOl1H3apWmPV5cyiVCoY9Eb0Qw54MXmxiOq7cSdX5chV5ObB6Zvp6QhpEOROdr4eI6FV4gh4REZHMMeyJiIhkjmFPREQkcwx7ojdEKJUQePq1PgEFhJJPNyLSD776EL0pynLIsXWGgAI5ts6AkufDEpF+8NWH6A16XMsLj2t56bsMIjJw3LMnIiKSOYY9ERGRzDHsiYiIZI5hT0REJHMMeyIiIplj2BMREckcw56IiEjmGPZEREQyx7AnIiKSOYY9ERGRzDHsiYiIZI5hT0REJHMMeyIiIpnTa9iHhYVh6NCh8PX1hUqlwh9//KF1fUhICFQqldZlwIABWmPS0tLw5ZdfwtPTE40bN8akSZOQmZlZmm0QERGVaXr9idusrCyoVCp07doVI0eOLHRM8+bNMXfuXGnaxMRE6/rx48cjKSkJ69atQ25uLiZNmoQpU6Zg0aJFb7R2IiKit4Vew97Pzw9+fn4vHWNiYgJbW9tCr4uJicGJEyfw888/o2HDhgCAyZMnY/DgwZg4cSKqVaum85qJiIjeNmX+M/uzZ8/C29sbbdu2xdSpU/HgwQPpuvDwcFSsWFEKegDw8fGBUqlEZGSkPsolIiIqc/S6Z/8qzZs3x0cffYR33nkH8fHx+PbbbzFo0CDs2LEDRkZGSE5ORpUqVbRuU65cOVhZWSEpKanY61ModFV52SPn3gpjaP0ChtezofVriAztPn6T/ZbpsO/YsaP0d/4Jeq1bt5b29nXN2tpS58ssCypXrqDvEkqVofULGF7PhtavITK0+/hN91umw/55tWrVQuXKlXHr1i14e3vDxsYGqampWmPy8vKQnp7+ws/5XyYlJQNC/DttZKSUxQPuwYNMqNWaV45jv28vQ+u5qP0aIkO7j+Xcr42N7nZA36qwv3fvHtLS0qQg9/DwwMOHD3Hp0iW4uLgAAP7++29oNBq4uroWe/lCQCvs5USufb2IofULGF7PhtavITK0+/hN9qvXsM/MzERcXJw0ffv2bURFRcHKygpWVlZYvnw52rZtCxsbG8THx2PBggWwt7dH8+bNAQAODg5o3rw5vvnmG0yfPh25ubmYOXMmOnbsyDPxiYiI/j+9hv2lS5fQu3dvaTr/+/RdunTBtGnTcO3aNezduxcZGRmoWrUqmjVrhjFjxmh9137hwoWYOXMm+vTpA6VSiTZt2mDy5Mml3gsREVFZpdewb9q0Ka5evfrC69euXfvKZVSqVIn/QIeIiOglyvz37ImIiOj1MOyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyZxewz4sLAxDhw6Fr68vVCoV/vjjD63rhRD47rvv4OvrC1dXV/Tt2xc3b97UGpOWloYvv/wSnp6eaNy4MSZNmoTMzMxS7IKIiKhs02vYZ2VlQaVSYerUqYVev3r1amzatAnTpk3Dzp07YWZmhgEDBiA7O1saM378eERHR2PdunVYuXIl/vnnH0yZMqW0WiAiIirz9Br2fn5+GDt2LD766KMC1wkhsHHjRgwbNgytW7eGs7Mz/vOf/yAxMVE6AhATE4MTJ05g1qxZcHNzQ+PGjTF58mQcPHgQ9+/fL+12iIiIyqQy+5n97du3kZSUBB8fH2mepaUl3NzcEB4eDgAIDw9HxYoV0bBhQ2mMj48PlEolIiMjS71mIiKisqicvgt4kaSkJACAtbW11nxra2skJycDAJKTk1GlShWt68uVKwcrKyvp9sWhUJSw2LeAnHsrjKH1Cxhez4bWryEytPv4TfZbZsNeH6ytLfVdwhtRuXIFfZdQqgytX8Dweja0fg2Rod3Hb7rfMhv2tra2AICUlBRUrVpVmp+SkgJnZ2cAgI2NDVJTU7Vul5eXh/T0dOn2xZGSkgEh/p02MlLK4gH34EEm1GrNK8ex37eXofVc1H4NkaHdx3Lu18ZGdzugZfYz+3feeQe2trY4ffq0NO/Ro0e4cOECPDw8AAAeHh54+PAhLl26JI35+++/odFo4OrqWux1CqF9kZPneyvsIieG1i9geD0XpV9DvMiJoferS3rds8/MzERcXJw0ffv2bURFRcHKygp2dnbo3bs3fvjhB9jb2+Odd97Bd999h6pVq6J169YAAAcHBzRv3hzffPMNpk+fjtzcXMycORMdO3ZEtWrV9NUWERFRmaLXsL906RJ69+4tTc+dOxcA0KVLF8ybNw+DBg3C48ePMWXKFDx8+BCNGjXCmjVrUL58eek2CxcuxMyZM9GnTx8olUq0adMGkydPLvVeiIiIyiq9hn3Tpk1x9erVF16vUCgwZswYjBkz5oVjKlWqhEWLFr2J8oiIiGShzH5mT0RERLrBsCciIpI5hj0REZHMMeyJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRERDLHsCciIpI5hj0REZHMMeyJiIhkTq8/hENE9KYplQoolQp9l/FaNBoBjUZmP95OpYphT0SypVQqUKmSOYyM3u6DmGq1BmlpWQx8KjGGPRHJllKpgJGREpO3nkBsYrq+yymRulWtMOvz5lAqFQx7KjGGPRHJXmxiOq7cSdV3GUR683Yf2yIiIqJXYtgTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcyV6bBftmwZVCqV1qVdu3bS9dnZ2Zg+fTqaNm0KDw8PjBo1CsnJyXqsmIiIqOwpp+8CXqVevXpYt26dNG1kZCT9PWfOHBw/fhxLliyBpaUlZs6ciZEjR2L79u36KJWIiKhMKvNhb2RkBFtb2wLzMzIyEBoaioULF8Lb2xvA0/Dv0KEDIiIi4O7uXsqVEhERlU1l+jA+ANy6dQu+vr5o1aoVvvzySyQkJAAALl26hNzcXPj4+EhjHRwcYGdnh4iIiBKtS6HQvsjJ870VdpETQ+sXMLyeDa1fwPB6NvR+dalM79m7urpi7ty5qFu3LpKSkrBixQr07NkT+/fvR3JyMoyNjVGxYkWt21hbWyMpKalE67O2ttRF2WVO5coV9F1CqTK0fgHD69nQ+gUMr2f2q1tlOuz9/Pykv52dneHm5oYWLVrg119/hampqc7Xl5KSASH+nTYyUsriAffgQSbUas0rx7Hft5eh9Wxo/QKG1zP7BWxsdLcDWuYP4z+rYsWKqFOnDuLi4mBjY4Pc3Fw8fPhQa0xKSkqhn/EXhRDaFzl5vrfCLnJiaP0ChtezofULGF7Pht6vLr1VYZ+ZmYn4+HjY2trCxcUFxsbGOH36tHT9jRs3kJCQwJPziIiInlGmD+PPnz8fLVq0gJ2dHRITE7Fs2TIolUp06tQJlpaW6Nq1K+bNmwcrKytYWFhg1qxZ8PDwYNgTERE9o0yH/b179zBu3DikpaWhSpUqaNSoEXbu3IkqVaoAACZNmgSlUonRo0cjJycHvr6+mDp1qp6rJiIiKlvKdNgvXrz4pdeXL18eU6dOZcATERG9xFv1mT0REREVH8OeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkcwx7ImIiGSOYU9ERCRzDHsiIiKZY9gTERHJHMOeiIhI5hj2REREMsewJyIikjmGPRERkczJJuy3bNmCli1bomHDhujWrRsiIyP1XRIREVGZIIuwP3ToEObOnYsRI0Zgz549cHZ2xoABA5CSkqLv0oiIiPROFmG/bt06fPbZZ+jatSscHR0xffp0mJqaIjQ0VN+lERER6V05fRfwunJycnD58mUMGTJEmqdUKuHj44Pw8PBiLUupBIQoON/ZrgrMTN6+TWVvU1H6W1mMt3Xs9+1haD0bWr+A4fXMft8MhRCFxdvb4/79+/jggw+wfft2eHh4SPP/85//ICwsDLt27dJjdURERPoni8P4RERE9GJvfdhXrlwZRkZGBU7GS0lJgY2NjZ6qIiIiKjve+rA3MTFBgwYNcPr0aWmeRqPB6dOntQ7rExERGaq372yGQvTr1w/BwcFwcXGBq6srNmzYgMePHyMgIEDfpREREemdLMK+Q4cOSE1NxdKlS5GUlIT69etjzZo1PIxPREQEGZyNT0RERC/31n9mT0RERC/HsCciIpI5hj0REZHMMeyLICgoCLNnzy7x7c+cOQOVSoWHDx/qsKriK0ofLVu2xPr160unoFdQqVT4448/9F2GzhR3277u4644QkJCMHz48FJZ1/Nu374NlUqFqKioF45JSkpCv3794O7ujsaNG5didS+ni8fosmXL8PHHH+uoojfndV/HirOtyuI22bFjB/z8/ODs7Fzk53FZej1l2JOWn3/+GYGBgUUaW5YeyGXJ7t27Cw2k57dtabyZKYsvmiWxfv16JCUlYe/evThy5MhrL6+svAE3JCdPnsQHH3yg7zJK5NGjR5g5cyYGDRqE//u//yvya6Q+nvMvIouv3hmi3NxcGBsb63y5VapU0eny1Go1FAoFlG/yFx7eErretvqWk5MDExOTUllXfHw8GjRogDp16pTK+kj3bG1t9V1CsQkhoFarkZCQgNzcXPj5+aFq1apFvn1Zes7zFbiI1Go1ZsyYgUaNGqFp06ZYsmQJ8r+1uHfvXgQEBMDDwwPNmjXDl19+WeDf9z7r8ePHGDhwILp37y7tWezatQvt27dHw4YN0a5dO2zZskUan3+Y89ChQ+jVqxcaNmyInTt3wtPTE4cPH9Za9h9//AF3d3c8evSo2H0A2nvrQggsW7YMH374IVxcXODr64tZs2YBeHqI+c6dO5g7dy5UKhVUKhWAf/dqjx49ig4dOqBhw4ZISEhAeno6Jk6ciCZNmsDNzQ0DBw7EzZs3X7ndExMTMXDgQLi6uqJVq1YF+r169Sp69+4NV1dXNG3aFN988w0yMzMBPN17c3FxwT///CONX716Nby9vZGcnPzCdQYFBWHGjBkv3E45OTmYP38+mjdvDnd3d3Tr1g1nzpyR1vnVV18hIyND2i7Lli0rsG1btmwJABgxYgRUKpU0DTy9D7t06YKwsDDs3r0bPXr0QJMmTdCsWTNpWfkSEhIwbNgweHh4wNPTE2PGjJF62717N5YvX44rV65Itezevful23v58uV4//334enpiSlTpiAnJ6fAdpk9ezaaNm2KAQMGAACuXbuGgQMHwsPDAz4+PpgwYQJSU1Ol2/3f//0fevTogcaNG6Np06YYMmQI4uLiXliDWq3GV199hXbt2iEhIQEtW7bEkSNHsHfvXqhUKoSEhAB4+tPW/v7+cHd3h5+fH6ZNmybd9wBw584dDB06FE2aNIG7uzs6duyI48eP4/bt2+jduzcAoEmTJlCpVGjSpIn0GOrbty+ysrIQGRmJfv36oWnTpmjUqBF69eqFy5cvv3T7LViwAG3btoWbmxtatWqFJUuWIDc3V2vMqlWr4OPjAw8PD0yaNAnZ2dkvXeaLHD58GP7+/iWqW6VSYdeuXRgxYgTc3NzQpk0bHD16VGvM8ePH0bZtW7i6ukrP93xCCLz//vtaz8ePP/4Yvr6+0vQ///wDFxcXPH78WFrns3u19+7dw7hx4+Dl5QV3d3cEBATgwoULWjXs3bsXLVu2RKNGjTB27NgXvq7le9Vz91Wv1flHfI4fP46AgAA0bNgQ+/btg7+/PwCgdevWUKlUuH37NuLi4jBs2DDpvuzatStOnTqlVU9Rn/OlQtAr9erVS7i7u4tZs2aJmJgY8csvvwg3NzexY8cOIYQQu3btEseOHRNxcXEiPDxcBAYGioEDB0q3//vvv4WTk5NIT08X6enpIjAwUPTv319kZWUJIYT45ZdfRLNmzcSRI0dEXFycOHLkiPDy8hK7d+8WQggRHx8vnJycRIsWLaQx9+/fF5MnTxaDBg3SqnXo0KFi4sSJJepDCCFatGgh1q1bJ4QQ4tdffxWenp7i2LFj4s6dO+LChQvS2AcPHogPPvhALF++XCQmJorExEQhhBChoaGiQYMGIjAwUJw7d07ExMSIrKwsMXToUNG+fXsRFhYmoqKiRP/+/cVHH30kcnJyXrjdnZychJeXl9i5c6e4ceOGWLx4sahfv76Ijo4WQgiRmZkpmjVrJkaOHCmuXr0qTp06JVq2bCmCg4OlZcyfP1+0aNFCPHz4UFy+fFk0aNBA/PHHH691f3/99dciMDBQhIWFiVu3bok1a9YIFxcXERsbK7Kzs8X69euFp6entF0ePXpUYNumpKQIJycnERoaKhITE0VKSooQQoiwsDDh6ekpdu/eLT799FPh5uYmGjVqJGbOnCn27NkjVCqVOHnypBBCCLVaLT7++GPRo0cPcfHiRRERESG6dOkievXqJYQQ4vHjx2LevHmiY8eOUi2PHz8utOfg4GDh7u4uvvjiC3Ht2jXx559/ivfff198++23BbbL/PnzRUxMjIiJiRHp6eni/fffF4sWLRLR0dHi8uXLol+/fiIoKEi63eHDh8WRI0fEzZs3xf/+9z8xZMgQ0alTJ6FWq4UQ/z6+//e//4ns7GwxYsQI8cknn0jbJCUlRQwYMECMGTNGJCYmiocPHwohhFi3bp04ffq0iI+PF6dOnRJt27YVU6dOldY7ePBg0a9fP3HlyhURFxcn/vvf/4qzZ8+KvLw8ceTIEeHk5CTCwsJE/fr1xQ8//CDi4+PFlStXxObNm8WjR4/EqVOnxN69e0V0dLSIjo4WkyZNEj4+PiIjI0PrMfr7779L0ytWrBDnzp0T8fHx4ujRo8LHx0esWrVKuv7gwYPCxcVF7Ny5U8TExIhvv/1WeHh4iM6dO7/0Mfm8+/fvi/fee0+sW7euxHV/8MEHYv/+/eLmzZti5syZwt3dXTx48EAIIURCQoJwcXERc+fOlZ4DPj4+0uuYEEKMHDlSTJ8+XQghRFpammjQoIFo1KiR9Pz8/vvvRffu3QvdVo8ePRKtWrUSn3/+uQgLCxM3b94UBw8eFOfPnxdCCLF06VLh7u4uPbfDwsJEs2bNtB6PhdHVa7W/v784efKkuHXrlrh37544deqUcHJyEhcuXBCJiYkiLy9PREVFiW3btomrV6+K2NhYsXjxYtGwYUNx584daXlFec6XFoZ9EfTq1Uu0b99eaDQaad6CBQtE+/btCx0fGRkpnJycpBf5/AdQdHS08Pf3F6NGjRLZ2dnS+NatW4v9+/drLWPFihUiMDBQCPHvi+H69eu1xly4cEHUr19f3L9/XwghRHJysnjvvffEmTNnStzHsw/On376SbRp0+aFgfzs2HyhoaHCyclJREVFSfNiY2OFk5OTOHfunDQvNTVVuLq6ikOHDhW6bCGevjhMmTJFa163bt2kF/QdO3aIJk2aiMzMTOn6Y8eOCWdnZ5GUlCSEECI7O1t8/PHHYsyYMaJDhw5i8uTJL1xfvpdtpzt37oj69euLe/fuad2mT58+YtGiRdI2aNSoUYHlPr+9ng+K/OWsXLlSqqNHjx5i7969olmzZkIIIbp27SoWLFgghBDi5MmTon79+iIhIUG6/fXr16UXJSGevmgWJUiCg4OFl5eX9AZUCCG2bt0q3N3dpVDu1auX+OSTT7Rut2LFCtG/f3+teXfv3hVOTk7ixo0bha4r/0Xv6tWrQoh/H99hYWGiT58+okePHlKg5xs2bJjWm7jC/Prrr8LLy0ua7tSpk1i2bFmhY/Ofk2fOnBFOTk7i9u3bL122EE/fXHl4eIj//ve/0rzC7sNnrVmzRnTp0kWaDgwMFNOmTdMa061bt2KH/aVLl1677sWLF0vTmZmZwsnJSRw/flwIIcSiRYtEhw4dtJazYMECrbDfuHGj6NixoxBCiN9//11069ZNDBs2TGzdulUIIUTfvn21wvnZbbV9+3bh4eEhvbl43tKlS4Wbm5vWG5T58+eLbt26vbRXXb1WP3+f/u9//xNOTk4iPj7+pevv2LGj2LRpkzRdlOd8aeFn9kXk5uYGhUIhTbu7u2PdunVQq9WIioqSDpemp6dLh4zu3r0LR0dH6Tb9+/eHq6srFi9eDCMjIwBAVlYW4uLi8PXXX+Obb76Rxubl5cHS0lKrBhcXF61pV1dXODo6Yu/evRg8eDD27dsHOzs7NGnSpER95NeUr127dtiwYQNat26N5s2bw8/PDy1atEC5ci9/2BgbG0uH9QEgJiYG5cqVg5ubmzSvcuXKqFu3LmJiYl66rOd/zMjd3V06azsmJgYqlQrm5ubS9Z6entBoNIiNjYWNjQ1MTEywcOFCdO7cGXZ2dvjqq69eur58L9pO165dg1qtRrt27bTG5+TkoFKlSkVa9stcuXIF58+fx8qVK/HkyRMolUpcunQJ2dnZePz4MWxtbaXDjjExMahevTpq1Kgh3d7R0REVK1bEjRs34OrqWqx1q1QqmJmZSdMeHh7IysrC3bt3UbNmTQBAgwYNCtR75syZQn90Ki4uDnXr1sXNmzexdOlSXLhwAQ8ePNB6fjg5OUnjv/zyS1SvXh0bNmyAqanpK+s9deoUfvzxR9y4cQOPHj2CWq2WtpOZmRl69+6NadOm4eTJk/Dx8UGbNm3g7OystYx69erB29sb/v7+8PX1ha+vL9q2bQsrKyskJydjyZIlOHv2LFJSUqDRaPD48WMkJCS8sKZDhw5h48aNiI+PR1ZWFvLy8mBhYSFdHxMTg+7du2vdxt3dXfoYqKicnZ1fu+5nn6Pm5uawsLCQPn6JiYkp8Phxd3fXmm7SpAlmz56N1NRUhIWFwcvLCzY2Njh79iw+/fRThIeHY+DAgYXWHxUVhffee++lz5maNWtqbbuqVau+9OPRfLp4rW7YsOEr15OZmYnly5fj2LFjSEpKglqtxpMnT176+NAnhv1rys7OxoABA+Dr64uFCxeicuXKuHv3LgYMGFDgszo/Pz/89ttviI6Olp5oWVlZAICZM2dqhSGAAie1PRtq+bp164YtW7Zg8ODB2L17NwICArQe6K+jRo0aOHz4ME6dOoVTp05h+vTpWLt2LTZt2vTSkwNNTU11VoMuhIeHAwDS09ORnp5e6HYsqqysLBgZGSE0NLTAm6PXWe6zyx81ahTatGmD8ePHw8HBAcOGDQMAlC9fHgqFQusci9L27JsB4Gm9LVq0wPjx4wuMzT8ha+jQoahZsyZmzZqFqlWrQqPRoFOnToU+P/bt24fw8HB4e3u/tI7bt29jyJAh6NGjB8aOHQsrKyucO3cOX3/9NXJzc2FmZoZu3brB19cXx44dw19//YVVq1YhODgYQUFB0nKMjIywbt06nD9/Hn/99Rc2bdqExYsXY+fOnZg2bRrS0tLw9ddfw87ODiYmJggMDCxQd77w8HCMHz8eo0aNgq+vLywtLXHw4EGsW7euSNu2OHRR9/PPYYVCAY1GU+QaVCoVrKyscPbsWYSFheGLL76Ara0t1qxZg4sXLyIvL++FvzxalDdzhe1UvM5jvziv1c8/zgszf/58nDp1CsHBwahduzZMTU0xevToFz4+9I0n6BVRZGSk1vSFCxdgb2+PGzduIC0tDePHj0fjxo3h4ODwwnef48ePR5cuXdC3b19ER0cDAGxsbFC1alXEx8fD3t5e61KrVq1X1tW5c2ckJCRg48aNiI6ORpcuXUrUx/PBlc/U1BQtW7bE5MmTsXHjRoSHh+PatWsAnr5YFOXFwcHBAXl5eVon3zx48ACxsbFa76YLExERUaBeBwcHablXr16V3jABwPnz56FUKlG3bl0AT/cu58yZg5kzZ8LV1RXBwcFFqvlF26l+/fpQq9VITU0tcH/lh5uxsTHUavUr11HYuPfeew+xsbGwt7eHqakpLC0tpeU//+bPwcEB9+7dw927d6V50dHRePjwobSNinofAU9Pdnzy5Ik0HRERAXNzc60jB89r0KABrl+/jpo1axbYHubm5tL9PGzYMHh7e8PBwQHp6emFLqtHjx748ssvMXz4cJw9e/altV6+fBlCCISEhMDd3R1169ZFYmJigXE1atRAjx49sHz5cvTr1w87d+4E8G/Q5X9bpFGjRhg9ejT27t0LY2Nj/PHHHzh//jyCgoLg5+eHevXqwcTEBA8ePHhhTeHh4bCzs8OwYcPQsGFD1KlTp8BenoODQ4GT0J6fLipd1V0YBwcHXLx48aV1KhQK6WTc69evo1GjRlCpVMjJycGOHTvg4uLywjfA+f9XIS0trVh1FYUuXquLIjw8HF26dMFHH30ElUoFGxsbrZMYC1PU14Y3gWFfRAkJCZg7dy5u3LiBAwcOYPPmzejduzfs7OxgbGyMTZs2IT4+HkePHsX333//wuUEBwfD398fffr0kQ5hjx49GqtWrcLGjRsRGxuLq1evIjQ0tEh7BFZWVvjoo4/wn//8B82aNUP16tVL1Edhdu/ejV27duHatWuIj4/Hvn37YGpqCjs7OwBPD7OFhYXh/v37WmdfP69OnTpo1aoVvvnmG/zzzz+4cuUKJkyYgGrVqqFVq1Yvrffw4cP4+eefERsbi6VLlyIyMhK9evUCAPj7+8PExAQhISG4du0a/v77b8ycORMff/wxbGxsoFarMWHCBDRv3hxdu3bF3LlzcfXqVfz0008vXefLtlPdunXh7++PiRMn4rfffkN8fDwiIyPx448/4tixY9J2ycrKwunTp5Gamiqdjfy8mjVr4vTp00hKSpICcMSIEfjll1+wfPlyZGVlIT09HQcPHsTixYsL3N7HxwdOTk4YP348Ll++jMjISEycOBFeXl7SYciaNWvi9u3biIqKQmpqqtbZ9c/LycnB119/jejoaBw/fhzLli1Dr169Xvq1yc8//xzp6ekYN24cIiMjERcXhxMnTuCrr76CWq2GlZUVKlWqhB07duDWrVs4ffo05s2b98LlBQUFYcyYMRgyZIjWtyieZ29vj9zcXOl5t3fvXmzfvl1rzOzZs3HixAnEx8fj8uXLOHPmjPQmqGbNmlAoFNi4cSO+/fZbhIWFISEhAb/99htSU1Px7rvvok6dOti3bx9iYmJw4cIFjB8//qV7pPb29rh79y4OHjyIuLg4bNy4scB3qnv37o3Q0FCEhoZKj+nr16+/cJkvcuHCBaxcuRIXL1587boL0717d9y8eRPz58/HjRs3sH//fuzZs6fAOC8vLxw8eBD169dHhQoVoFQq0bhxY+zfv/+lHyd27NgRNjY2GDFiBM6dO4f4+HgcOXJEOgr3OnT1Wv0q9vb2+P333xEVFYUrV67gyy+/fOUb68Ke86WFYV9En3zyCZ48eYJu3bphxowZ6N27NwIDA1GlShXMmzcPhw8fRocOHbB69WoEBwe/dFmTJk1C+/bt0adPH8TGxqJbt26YNWsWdu/eDX9/fwQFBWHPnj145513ilTbp59+itzcXHTt2rXEfRSmYsWK2LVrF3r06IHOnTvj9OnTWLlyJSpXrgzg6ZuUO3fuoHXr1q887Dp37lw0aNAAQ4cORWBgIIQQWLVq1Sv/V8CoUaNw6NAhdO7cGXv37sWiRYukowFmZmZYu3Yt0tLS8Omnn2LMmDHw9vaWzn344YcfcOfOHUyfPh3A08/8Zs6ciSVLluDKlSsl3k5z587FJ598gnnz5qF9+/YYPnw4Ll68KO0Be3p6onv37vjiiy/g7e2NNWvWFLqO4OBgnDp1Ch9++KF0RKZ58+ZYuXIlTp48icuXL+PQoUNYv3699Jn5sxQKBb7//ntUrFgRvXr1Qt++fVGrVi2tNwZt27ZF8+bN0bt3b3h7e+PAgQMv7Nnb2xv29vbo2bMnvvjiC7Rs2RKjRo166XaqVq0atm3bBo1GgwEDBsDf3x9z5syBpaUllEollEolFi9ejMuXL6NTp06YO3cuJk6c+NJl9u3bF6NHj8bgwYNx/vz5Qsc4Ozvjq6++wurVq9GpUyfs378f48aN0xqj0WgwY8YMdOjQAQMHDkSdOnUwdepUqe5Ro0Zh27Zt+PHHH9G/f3+0bdsWS5YsQUhICPz8/DB79mykp6ejS5cumDhxIoKCgmBtbf3Culu1aoU+ffpgxowZ+PjjjxEeHi59BJOvQ4cOGD58OBYsWICAgAAkJCSgR48eL90ehbGwsEBYWBgGDx782nUXxs7ODsuWLcPRo0fx8ccfY/v27Rg7dmyBcV5eXlCr1fDy8nrpvOeZmJjgp59+grW1NQYPHgx/f3+sWrXqhUcYi0OXr9UvExISgooVK6J79+4YOnQomjdvXuCclucV9pwvLfyJWxnYu3cv5s6dixMnTpTaPzmRs6CgIDg7O+Prr7/WdylEVAx87r4YT9B7iz1+/BhJSUlYvXo1unfvzqAnIqJC8TD+W2zNmjVo3749bGxsMHjwYH2XQ0REZRQP4xMREckc9+yJiIhkjmFPREQkcwx7IiIimWPYExERyRzDnoiISOYY9kRUqKCgIMyePVvfZRCRDjDsiYiIZI5hT0QFhISE4OzZs9i4cSNUKhVUKhXee+89rF27VmtcVFQUVCoVbt26BeDpr5lt3boVAwcOhKurK1q1aoXDhw9r3ebu3bsYM2YMGjduDC8vLwwbNgy3b98utd6IDBHDnogK+Prrr+Hh4YHPPvsMJ0+exMmTJzFq1Cjs3r1ba1xoaCiaNGkCe3t7ad53332Htm3b4pdffoG/vz/GjRsn/cJjbm4uBgwYgAoVKmDLli3Ytm0bzM3NMXDgwJf+Ih8RvR6GPREVYGlpCWNjY5iamsLW1ha2trYICAhAbGys9Hvhubm5OHDgQIFfW2zXrh26deuGunXr4osvvoCLiws2bdoEADh06BA0Gg1mz54NlUoFBwcHzJ07F3fv3n3lb9gTUcnxh3CIqEiqVasGPz8//Pzzz3B1dcWff/6JnJwctGvXTmuch4eH1rS7uzuioqIAAFeuXEFcXBw8PT21xmRnZyMuLu7NNkBkwBj2RFRk3bp1w8SJEzFp0iTs3r0bHTp0gJmZWZFvn5WVhQYNGmDhwoUFrqtSpYouSyWiZ/AwPhEVytjYGBqNRmuen58fzMzMsG3bNpw4caLAIXwAiIiI0Jq+cOECHBwcAAANGjTArVu3YG1tDXt7e62LpaXlG+uFyNAx7ImoUDVr1sSFCxdw+/ZtpKamQqPRwMjICAEBAVi0aBHs7e0LHLIHgMOHD+Pnn39GbGwsli5disjISPTq1QsA4O/vj8qVK2PYsGH4559/EB8fjzNnzmDWrFm4d+9eabdIZDAY9kRUqP79+8PIyAgdO3aEt7c3EhISAACffvopcnNzERAQUOjtRo0ahUOHDqFz587Yu3cvFi1aBEdHRwCAmZkZNm/eDDs7O4wcORIdOnTA119/jezsbFhYWJRab0SGhr9nT0TF8s8//6Bv3744duwYbGxstK5TqVRYsWIFWrdurafqiKgwPEGPiIokJycHqampWLZsGdq2bVsg6Imo7OJhfCIqkgMHDqBFixbIyMjAxIkT9V0OERUDD+MTERHJHPfsiYiIZI5hT0REJHMMeyIiIplj2BMREckcw56IiEjmGPZEREQyx7AnIiKSOYY9ERGRzDHsiYiIZO7/ARoJ0XNljLLiAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize protein values\n",
        "sns.catplot(kind=\"bar\",x=\"type\",y=\"protein\",data=df)\n",
        "plt.title(\"Protein Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "36QjI3JHOs_3",
        "outputId": "52fcfb82-5996-4ae6-dcda-324e14675d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 511.111x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize carbohydrate values\n",
        "sns.catplot(kind=\"bar\",x=\"type\",y=\"carb\",data=df)\n",
        "plt.title(\"Carbohydrate Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "Tdgw-gGBO30l",
        "outputId": "810a8fd0-7582-41ce-accd-bf87ebe01e29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 511.111x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize fiber values\n",
        "sns.catplot(kind=\"bar\",x=\"type\",y=\"fiber\",data=df)\n",
        "plt.title(\"Fiber Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "ovcJ3uDVPCZx",
        "outputId": "17f3695f-f589-467f-b985-e981aa77f6a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 511.111x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Look at the correlation distribution in the dataset\n",
        "numeric_df.corr()"
      ],
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      },
      "execution_count": null,
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            "text/plain": [
              "          calories       fat      carb     fiber   protein\n",
              "calories  1.000000  0.758682  0.674999  0.260645  0.410398\n",
              "fat       0.758682  1.000000  0.144547 -0.028549  0.223470\n",
              "carb      0.674999  0.144547  1.000000  0.213044 -0.050789\n",
              "fiber     0.260645 -0.028549  0.213044  1.000000  0.488564\n",
              "protein   0.410398  0.223470 -0.050789  0.488564  1.000000"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"numeric_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2914285263016048,\n        \"min\": 0.260645076763742,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7586824985820176,\n          0.4103977117521641,\n          0.674999018871819\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4378354648767538,\n        \"min\": -0.0285485065607781,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          0.22347000302906136,\n          0.14454651200766985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42981914762209067,\n        \"min\": -0.050789235512843076,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.14454651200766985,\n          -0.050789235512843076,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38890569459942675,\n        \"min\": -0.0285485065607781,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.0285485065607781,\n          0.4885640025645622,\n          0.21304449422371893\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38771733924474966,\n        \"min\": -0.050789235512843076,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.22347000302906136,\n          1.0,\n          -0.050789235512843076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the correlation distribution in the dataset with a heat map\n",
        "sns.heatmap(numeric_df.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
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        "id": "BAFtDHSVWEIi",
        "outputId": "f31bfb55-f003-4127-f124-dfe1a44c13cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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6Tu6Qht8Bsl8Veizv9u8Q12sGsVNriCrbQLdRe4gdXJAXdEHFZ1P2JOeOQM+9K3RbdICOXXUYfDoBIgMD5P6zusBw6DTo9xour5/7168QGZvCYIAvRLbVIHZqDv3OA5F78YRiwyIR9Fp2yp8rUEG/zVzY8StaDPZAs/5tYVu7KvotHQl9YwP56oJBa8bhkxmD5PU9xvZE16leODjDDynRCTC1qQxTm8rQNzaQ1zm/7Tga9/gYLQZ5wqqmHVr5dEbDDm64svf3Mj8/VYrw+xU1hnjgg4FtYVK3Khp9OxJiYwM8+2d1gevGcag/J/+1k+XkIj0sWuGWm5YJaUYW0sOiIeTmr2rRM68EM6eaMK2Xv4+DSR17mDnVLNQDodF4CeN3W7NmDYYPH47p06crlK9evRqrV6+uMFcuvBf2CCMnzZTfX7lxGwCg9ycdsXTeNHWFpTbSR7eQa2QKvZY95ZsO5RzdCGTmjy2KTC3l32D/JTK3g7haXWQfWV90m+F3IDn3P+g17wpR+4EQUuIg+XUbZDHv3hBF0+TdvogcEzMYdB8KkakFZM8jkLl5PoT0VACAyNIGOv8ZEhBSE5H5/TwY9vsclWZvhpCahNzzv+SvJvgPsaMrdCxt5UlFRXT3xN8wsTRDlykDYGpjjpjQp9gxbIV8UqFFNWsI/3nvfezdCboGehi2dYpCO2fWHcKZdYcBAPd+u4nDc3fCc3wv9Fk4DPERMdgz7jtE3nxQdidWBmJ++Rv6VmZwnDEABjbmSLv/FNcGr4Dkn9fOqJo1IBPe0Yoiuy5N0WT9OPn9pn5fAAAerD6Eh6sPKy94NdKUqw0qi0gQhJK9CwC4uLjg+PHjqFWrlkL5kydP0KtXLwQHl3yNc25ixLsrUZFy961UdwgaS/ro2bsr0RstOlGBvgmWsXbZ6o5As/V8sV+l7Wdd3KW0tozaDldaW6pSqmECS0vLIpcPhoaGwsrK6r2DIiIiUisOE7ybl5cX5s+fj6ioKLi5uQEAbt++je3bt2P48OHKjI+IiKjscWnhu02YMAEmJib44YcfsHZt/sUrbG1tMXHiRPj4+Cg1QCIiojKnId/olaVUyYBIJMLw4cMxfPhwZGTkb+/57+WNiYiISLOUep+BfzEJICKiCofDBEXr06ePfPvYdzlypGJdKIWIiLQMhwmK1rFjR1XGQURERGpS7GRg4sSJqoyDiIio/OAwARERkZbjMMG7SaVS7Nq1C6dOnUJsbCxyc3MVHr9+veJdLIWIiKiiKtUOhJs2bYK/vz+6deuG9PR0DB8+HJ06dYJIJOJwAhERaT7uQPhux48fx5IlS9C+fXts3LgRPXr0QI0aNeRXLyQiItJoWjZnoFQ9A4mJiahXrx4AoFKlSkhPz79qnYeHB86fP6+04IiIiEj1SpUM2NnZISEhAQBQvXp1XL58GQAQHBwMfX195UVHRESkDhwmeLdOnTrh6tWraNy4MYYOHYqvvvoKhw4dQkxMDC9UREREmk/LhglKlQxMnz5d/nO3bt1QtWpVBAYGombNmvD09FRacERERGqhId/olaVUwwR+fn44dOiQ/L6rqytGjBiB5ORkbNu2TWnBERERkeqVKhn46aef4ODgUKi8bt26OHDgwHsHRUREpFaCTHk3DVCqYYKEhATY2NgUKre0tJRPLCQiItJYHCZ4N3t7e9y+fbtQ+a1bt2Bra/veQREREVHZKVXPgJeXF5YtW4a8vDy0bNkSAHD16lWsWrUKI0eOVGqAREREZU7LegZKlQyMHj0aqampWLRokfy6BAYGBhg9ejR8fX2VGiAREVGZEwR1R1CmSpUMiEQifPXVVxg/fjzCw8NhaGiIWrVqccMhIiIiDfRelzCuVKkSGjVqpKxYiIiIygctGyYo1QRCIiKiCk2N2xHv27cPnp6ecHFxgZeXF4KCgt5af9euXejSpQsaNWqEdu3aYdmyZcjJySnRczIZICIiKidOnjyJ5cuXY8KECThy5Ajq16+PUaNGISkpqcj6x48fx5o1azBx4kScPHkSS5cuxcmTJ7F27doSPS+TASIiooLUtOmQv78/Bg4ciP79+6NOnTpYtGgRDA0Ncfjw4SLrBwYGws3NDT179sQHH3yA1q1bo0ePHu/sTSiIyQAREVFBShwmkEgkyMjIULhJJJJCTymRSHD//n24u7vLy3R0dODu7o7AwMAiw2zSpAnu378v//CPiorChQsX0K5duxKd7ntNICQiIqqQlLi00M/PD5s2bVIomzhxIiZNmqRQlpKSAqlUCisrK4VyKysrREREFNl2z549kZKSgs8++wyCICAvLw+DBg3C2LFjSxQjkwEiIiIV8vX1xYgRIxTKlLUU/9q1a/Dz88OCBQvQqFEjPHv2DEuXLsXmzZsxYcKEYrfDZICIiKggJS4t1NfXL9aHv4WFBcRicaHJgklJSbC2ti7ymPXr16NXr17w8vICADg6OiIzMxPz58/HuHHjoKNTvNkAnDNARERUkBqWFurr68PJyQlXr179TxgyXL16FU2aNCnymOzs7EIf+GKxGAAglGCoo9z0DOTuW6nuEDSW3pAZ6g5BY/VtUvxuNCrsYuIVdYegsV7afaTuEDRaT3UHoCIjRozAzJkz4ezsjEaNGmH37t3IyspCv379AAAzZsyAnZ0dpk2bBgDw8PCAv78/GjZsKB8mWL9+PTw8PORJQXGUm2SAiIio3CjhkkBl6datG5KTk7FhwwYkJCSgQYMG2LFjh3yYIDY2VqEnYNy4cRCJRFi3bh3i4uJgaWkJDw8PTJkypUTPy2SAiIioAEGmvgsVeXt7w9vbu8jH9u7dq3BfV1cXEydOxMSJE9/rOTlngIiISMuxZ4CIiKggLbtQEZMBIiKigtQ0Z0BdOExARESk5dgzQEREVJAaJxCqA5MBIiKigjhngIiISMtpWTLAOQNERERajj0DREREBSnxEsaagMkAERFRQRwmICIiIm3CngEiIqKCuLSQiIhIy3EHQiIiItIm7BkgIiIqiMMERERE2k3gagIiIiLSJuwZICIiKojDBERERFpOy1YTMBkgIiIqSMt6BjhngIiISMuxZ4CIiKggLVtNwGSAiIioIA4TEBERkTZhzwAREVFBXE1ARESk5ThMQERERNqkxMmAj48P0tLSCpVnZGTAx8dHKUERERGpkyCTKe2mCUo8THD9+nXk5uYWKs/JycGtW7eUEpSq6TZqB92mnSEyNoMsMRq553+CLC6yyLoG/adC/EG9QuXSJ8HIObZZfl9kUQV6rftCXK0eoKMDWXIsJL/6QUhPUdVplGs37wTD/3+HEBL2GAlJyVi//Gt0aOuu7rDUruewHhjgOwCWNhaICI3A9/O34MGdh0XWrVmvBnymDUUdl7qoUt0OWxf64cjOo29se+B4L4yaPRJHdhzF1kV+KjqDsvX111MxYsRgmJub4erVm5g8eS7CwyPfeoyvrw+mTPkcdnY2CA4OxdSpC3Dz5l0AgIVFZXz99VR06NAG1atXQ2JiEo4fP4NFi9YgLS1d3kb79q2wYME0ODk54tWrTOzbdxgLFqyCVCpV5emqTPuhXdDFtxcq25gjKvQp9i/4AZF3HxdZt82gDvi4XztUdawOAHgaHIEjq/bL64t1xegzfRCc27vBpoYtstIzEXopGIe/3YeX8RXo7x2HCYoWFhaGsLAwAMDjx4/l98PCwhASEoJDhw7Bzs5OZYEqi7huU+i1GYDcayeQvX8ZhIRoGPSZBBiZFlk/58RWZG6fIb9l7V0EQSZF3qPb8jqiytYw9JoOITkO2YfXInvfYuRdOwkhL6+sTqvcycrKhmMdB8ydNl7doZQb7Xq2xedff4596/ZhQrdJiAh5gqV7l6CyVeUi6xsYGSL22Qv8sMIfSXHJb227XuN66D6kGyJCIlQRulpMmzYW48cPx+TJc9C2bW+8epWJ48f3wsDA4I3HDBjQA99+Ow9Ll67Hxx/3QFBQKI4d2wsbGysAgL29Hezt7TB79lI0bdoJY8ZMR6dO7bB160p5Gy4uDXD0qD/OnDmPli27YejQiejevROWLJml8nNWhWY93DFw3jAcXx+Axd1nIjrkKb7cMxemVmZF1nds6YTrxy5hzeBFWNFvLlJikzBl7zyY21kCAPSNDFDDyQG/bjyExT1mYsvY1bCrXRUTd8wsy9MiJRMJglCs9Kd+/foQiUQAgKIOMTQ0xLx58zBgwIBSBZK5fmypjispg09nQhb3FLnnD/xTIoLhqOXIu/sn8m7+9s7jdV09odeyJ7J2zATyJAAA/a6jAJkUkjO7VBf4W+gNmaGW5y0u51aflNuegR5NJpTZc60/9h0e3n2IzV9vAQCIRCL8eH0PfvE/hoPfB7z12N1XduHozqNF9gwYGhti86mN2DR3MwZPHoyI+xFl1jNwMTFEZW1HRNzAhg3bsW7dNgCAmZkpnj69ic8/n46AgONFx3PxKG7dCsKUKfMB5L/Gjx//jS1bdmH16i1FHtOvXzf88MM6WFk1gFQqxaJFX6FDhzZo3bqXvE63bh3w44/fo0YNN2RkvFLK+XnbfaSUdt5l9tFliLwbjv0LdgLIf02+vboV53afwuktR995vEhHB+vv+mP/gp24+vPFIuvUalQbc4+twEz3cUiOSVRm+G+0PfLt/2feV8ZXfZXWlsmqI0prS1WKPUxw9uxZCIKAjh07IiAgAJaWlvLH9PT0YGVlBbFYrJIglUZHDB3bGsi7cfo/hQJkz0KhU8WhWE3oOrWC9OFNeSIAiCD+0AW5t87AoM8k6NhUhywtCXk3TkMacVfpp0CaSVdPF3Vd6uLA5oPyMkEQEPjXHTRs2uC92p64ZAKun7uBwEt3MHjy4PcNtVyoVas67O1tce7cJXlZWlo6bty4gxYt3IpMBvT09NCkiQtWrfpeXiYIAs6du4SPPnJ743OZmZkhLS1DPgRgYGCA7OwchTpZWdkwMjJEkyYu+Ouvv9/39MqMWE8XNZ0dcOr71x9GgiAg9HIQarsVHv4sir6RPsR6uniVmvHGOkamxpDJZMhMU06iVC5o2dLCYg8TVKtWDR988AHCwsLg4uKCatWqyW+2trblPxEAIDIygUhHDCFTcQKkkJkOUaWiu8z+S8euFnSsqyHv/uXXhcamEOkbQq9ZF0ifhiD7yAZIw+9Av4cvdKrVVfYpkIYyszSDWFeM1ATFMdWUxBRY2FiUut12vdqhjktt/LDC/31DLFeqVLEFAMTHK37LjI9PhJ2dTZHHWFtbQFdXt8hjqlQp+hgrKwvMnj0JP/ywX172++8X0LJlUwwc2As6OjqoWtUOc+Z8AQCwt7ct9Tmpg4mFKcS6YqQlvlQoT0t4CTMb82K10X+WN1LjkhFyObjIx3UN9NB/ljduHLuM7Iys9w25/JAJyrtpgFLvM/D48WPExMQUmkzYoUOH9w6qvBI7uUOWGK0w2fDfoRNpxF3kBZ4FAOQlRkPH3gG6Lm0hef5IHaGSFrCxt8a4hb6Y/dkc5OYUntSrSQYN6oONG5fJ7/ftO0Llz2lqaoIjR/wRGvoYS5Z8Jy8/e/YvzJmzDBs2LMXOnd8hJ0eCFSs2oHXrFpBpyMxwZek6rg8+6tkKqwYtQF4R7zGxrhi+m6YCIuDHedvVECEpS4mTgaioKEyYMAEPHz6ESCSSzx/490MxNDRUuREqkZCVAUEmhchYsRdAZGwK4VXh5ZIKdPWhW685cv9W7J4UsjIgSKWQJcUqlie/gE7VOkqJmzRfWnIapHlSmBfoBbCwtkBKQulmYNdpVBcWNhbYfGqTvEysK4ZLC2f0Gt4TPWr30pgPrxMnfsf164Hy+wYG+gAAW1trvHgRLy+3tbVGUFDR8xQSE1OQl5cHW1trhfL8NhIUykxMKuHYsT1IT3+FTz/9HHkFJvtu2LADGzbsgL29LVJSXqJmzepYvHgWnjx59l7nWdYyUtIhzZPCzFpxkqqZTWWkJaS+9djOY3rik3F9sHbIN3geVvi8xbpi+G6eCqsPrLFm8KKK1SsAQNCQb/TKUuJ9BpYuXYoPPvgAV65cgaGhIX799Vf8+OOPcHZ2xt69e1URo/LIpJDFP4NO9fr/KRRBp3p9yF68fRa2uG5TQKyLvLBrhduMi4SOheJKCpG5HYT0JCUFTpouLzcPj4IfoUkrV3mZSCSCa2tXhNwqXQJ959IdfN5xLMZ1nSC/Pbj7EOeO/IlxXSdoTCIAABkZrxAR8VR+Cw19hNjYeHh4tJLXMTU1QfPmrrh27XaRbeTm5iIwMFjhGJFIBA+PVrh+/fUxpqYmOHHiR0gkEgwYMAo5OTlFNQcAiI2NR3Z2DgYO7IWoqOcIDLynhLMtO9LcPDy9F4EG7i7yMpFIhAbuLgi/XfSSVgDo4tsL3ScNwPphS/E0uPDfxn8TAdtaVbB2yOK3zifQWBwmeLvAwEDs3r0blpaW0NHRgUgkQrNmzTB16lQsWbIER48eVUGYypN3+w/odx4OWfxTyF5EQreJJ0R6+sgLuQIA0O88HEJGKnKvHFU4TtfJHdLwO0B24Qkyebd/h/4noyF+/hiy6AcQ13SC2MEFOYfXlsEZlU+ZmVl4Fh0jv/88Jg5hD8NR2cwU9lU0a9xVWX7efgTT107Dw6BHeHDnAfqO6gNDIwOcOfg7AOCr76Yh8UUS/L/dBSB/0mGNujUAAHr6urCqYgWHhg7IzsxCTGQssl5l4emDpwrPkZ2ZjfSU9ELlmmjz5p2YOXMSHj9+gsjIKCxYMA2xsfE4duyMvM7Jk//DsWO/YevW3QDyv9Fv374Gt24F4ebNu5g4cSSMjY2xZ0/+zPP8RGAvjIyMMGLEFzAzM4WZWf6y4oSEJHkCNWWKL86cOQ+ZTIbevT/B9Onj4O2tWQnWv37fcQIj10xAZHA4ntx5jI6jukPf2ACXA/4EAIxcMxEpcck4svJ/AICuY3uj15RPseOL9UiMTpDPLch5lY2czGyIdcUYu2Uaajh9iI2jVkBHrCOv8yo1A9Jc7V1SrclKnAzIZDJUqlQJAGBhYYH4+Hg4ODigWrVqePLkidIDVDbpo1vINTKFXsue8k2Hco5uBDLzNxwRmVoCBZZOisztIK5WF9lH1hfdZvgdSM79D3rNu0LUfiCElDhIft0GWUy4qk+n3LoX9ggjJ71ed7xyY/7ysN6fdMTSedPUFZZaXTh+EZUtK8NnmjcsbCwRERKOuUO/RmpiKgDAppotZP9571nZWWLLb683tvIaOwBeYwfg7tUgzBhY8dd0r1mzFcbGxti0aTnMzc1w5cpN9Orlo/BN3sGhBqysXg+9HDp0AtbWVpg/fyrs7GwQFBSC3r195JMKXV2d5SsLQkL+Ung+R8dWePYsGgDQuXN7zJgxAQYGBggODoGX1xicOXNexWesGjdPXIGppRl6T/kUZjbmiAqNxPphS5H+z6RCy2rWCsvF23l3hp6BHsZtna7QzrF1B3F8XQDMq1jCtVNzAMCCU6sV6qwatAAP/1bdctMypYGJ3/so9j4D//rss88wcuRIdOzYEdOmTcPLly8xbtw4HDx4EPfv38eJEydKFUhZ7TNQEZX3fQbKs7LcZ6AiUuU+AxVdWe0zUFGpep+B9PGfKK0t0+9PKa0tVSnWnIGwsDB599i4cePkWeTkyZMRHR2NIUOG4MKFC5g7d67qIiUiIiKVKNYwQd++fXHp0iVYWVlh4cKFOHToEACgZs2aOH36NFJTU1G5cmX5igIiIiKNpiET/5SlWD0DZmZmiI7OH0t7/vx5oe2Izc3NmQgQEVGFIQiC0m6aoFg9A507d4a3tzdsbGwgEonQv39/6OgUnUecPXtWqQESERGRahUrGVi8eDE6deqEZ8+eYcmSJfDy8pKvKCAiIqpwtGyYoNhLC9u2bQsAuH//Pnx8fGBiYqKyoIiIiNSKycDbLV++XBVxEBERlRvcjpiIiIi0SqmvWkhERFRhaVnPAJMBIiKigrRrN2IOExAREWk79gwQEREVoG0TCJkMEBERFaRlyQCHCYiIiLQcewaIiIgK0rIJhEwGiIiICtC2OQMcJiAiItJy7BkgIiIqiMMERERE2k3bhgmYDBARERWkZT0DnDNARESk5ZgMEBERFSDIlHcrqX379sHT0xMuLi7w8vJCUFDQW+unpaVh0aJFaN26NZydndGlSxdcuHChRM/JYQIiIqKC1DRMcPLkSSxfvhyLFi1C48aNsXv3bowaNQqnT5+GlZVVofoSiQQjRoyAlZUV1q9fDzs7O8TExMDMzKxEz8tkgIiISIUkEgkkEolCmb6+PvT19QvV9ff3x8CBA9G/f38AwKJFi3D+/HkcPnwYn3/+eaH6hw8fxsuXL3HgwAHo6ekBAD744IMSx8hhAiIiogKUOUzg5+eHpk2bKtz8/PwKPadEIsH9+/fh7u4uL9PR0YG7uzsCAwOLjPPcuXNwdXXFN998A3d3d/To0QNbt26FVCot0fmyZ4CIiKggJQ4T+Pr6YsSIEQplRfUKpKSkQCqVFhoOsLKyQkRERJFtR0VF4e+//0bPnj2xbds2PHv2DIsWLUJeXh4mTpxY7BiZDBAREanQm4YElEEQBFhZWWHx4sUQi8VwdnZGXFwcdu7cyWSAiIjofZRmFcD7srCwgFgsRlJSkkJ5UlISrK2tizzGxsYGurq6EIvF8jIHBwckJCRAIpEUOwnhnAEiIqIC1LG0UF9fH05OTrh69aq8TCaT4erVq2jSpEmRx7i5ueHZs2eQyV4/UWRkJGxsbErUG8FkgIiIqAB17TMwYsQIHDx4EEeOHEF4eDgWLlyIrKws9OvXDwAwY8YMrFmzRl5/8ODBSE1NxdKlS/HkyROcP38efn5+GDJkSImel8MERERE5US3bt2QnJyMDRs2ICEhAQ0aNMCOHTvkwwSxsbHQ0Xn9Pd7e3h47d+7E8uXL0atXL9jZ2cHHxwdjxowp0fMyGSAiIipIEKntqb29veHt7V3kY3v37i1U1qRJExw8ePC9nrPcJAPSR8/UHYLG6ttkgrpD0FgnAjerOwSN5ttshrpD0Fj+MVfUHYJG267i9tUxgVCdOGeAiIhIy5WbngEiIqLyQpCpb5hAHZgMEBERFcBhAiIiItIq7BkgIiIqQFDjagJ1YDJARERUAIcJiIiISKuwZ4CIiKgAriYgIiLScoKg7gjKFpMBIiKiArStZ4BzBoiIiLQcewaIiIgK0LaeASYDREREBWjbnAEOExAREWk59gwQEREVwGECIiIiLadt2xFzmICIiEjLsWeAiIioAG27NgGTASIiogJkHCYgIiIibcKeASIiogK0bQIhkwEiIqICuLSQiIhIy3EHwhISBAGCtr1qREREFUipewYCAgKwe/duREZGAgBq1aqFYcOGwcvLS1mxERERqQWHCYph/fr12LVrF7y9veHq6goAuHPnDpYtW4aYmBh88cUXyoyRiIioTGnb0sJSJQP79+/H4sWL0aNHD3lZhw4d4OjoiMWLFzMZICIi0iClSgby8vLg7OxcqNzJyQlSqfS9gyIiIlInbVtaWKoJhL1798b+/fsLlR88eBA9e/Z876CIiIjUSRCUd9MExe4ZWL58ufxnkUiEgIAAXL58GY0bNwYABAUFISYmBn369FF6kERERKQ6xU4GQkJCFO47OTkBAJ49ewYAMDc3h7m5OR49eqTE8FRDr20P6HfoD5GZBWTPnyA7YAtkTx+++QCjSjDoOQy6jd0hMjaFkBKP7EN+kIbcBABUWuQPHSu7QodJLp5AzsHvVXUaatFzWA8M8B0ASxsLRIRG4Pv5W/DgTtGvXc16NeAzbSjquNRFlep22LrQD0d2Hn1j2wPHe2HU7JE4suMoti7yU9EZlH837wTD/3+HEBL2GAlJyVi//Gt0aOuu7rDUznNoV3T17YXKNuaICn2KfQt24sndx0XWbTuoI9z7tUM1x+oAgKfBETi86n8K9d26tED7IZ1Ry8UBJhamWNBtOqJCIsviVMrEwgXTMWrkZzA3N8OVKzcxYdJsPH785K3HjBs7DNOmjkOVKjYICgrBF19+jRs378gfP/t7ANq1U3wv+m3biwkTZ8nvf7f2G7i7N4ezkyNCwx6jWfPOSj2vssIJhG+wd+9eVcZRZnTd2sKg7xhk/7QJssgw6Hn0gfGExXj1zecQMl4WPkCsC+OJSyGkpyJ75zLIUhOhY2kLIeuVvErmqi8AkVh+X6dqTRhPWoa8wL/K4pTKTLuebfH5159j45yNCAt8gL6j+mDp3iUY1X4MXiYVfu0MjAwR++wFLv56Cb7zP39r2/Ua10P3Id0QERKhqvA1RlZWNhzrOKBv9874cs4SdYdTLjTv4Y5P5w3D3nnbEBH4CJ1GdsfUPfMwx3My0pPSCtV3bOmEa8cu4fHtB8jNkaDb2D6YtvdrzOs0BalxyQAAA2MDPLoZihu/XsGIb8eV9Smp1FfTx2PihJEYMepLREZGYdHCr3DyxD64NPZATk5Okcd4efXC6lULMH7CLFy/EYjJk0bj5K/70NC5LRISkuT1tu/4EQsXrZbfz8zMKtTWrl0H8NFHbnBxaaD8kysjnDPwDrm5uWjYsCEePnzLN+lyTN+zL3KvnEbe379D9iIKOQc2QZDkQO/jorNXvY87Q2RsiqxtiyGNCIGQHA/p43uQPX+dYQsZaRDSU+Q3XeePIEuIgfRRcFmdVpnoN6YvTu8/hTMHf8ezR8+wYfZG5GTnoMunRb92D+8+xI6lO3Hh2AXkSnLf2K6hsSFmbvgK62auR/rLDFWFrzHafNwckz8fho7tWqk7lHKjy+ieuHjgD1wK+BMxj6OxZ+42SLJy0GagZ5H1t3+5Hn/++BuiQiLxIjwG/jO3QiQSoWErF3mdq0cu4viGQwi5HFRWp1FmJk8ajWXL1+P48TMIDg7F8BFfoGpVO/Tu3eWNx0z5Ygx27Pwfdu85iNDQRxg/YRYyM7MwYvgghXqZmdmIi0uQ39LTFf/PTpk6H1u27saTJ09Vcm6kGiVOBvT09GBvbw+ZTAMv9izWhU71OpA+uPO6TBAgfXAHOh/WL/IQXZcWkD4JhcGn41Fp2T4Yz/ke+p0HAqI3vHRiXeg290Du1TPKj1+NdPV0UdelLm5fuiMvEwQBgX/dQcOm75f9T1wyAdfP3UDgf9om+pdYTxc1nR0UPrQFQUDI5WDUdnMsVhsGRvoQ64nxKrXiJ5sfflgD9vZ2OHvukrwsLS0d168HomWLpkUeo6enBze3Rjh77nVvpiAIOHvuElq2VDzms8F98SImGHcCz2LpklkwMjJUzYmombZNICzVaoKxY8di7dq1SE1NVXI4qiUyMYNILIYsPUWhXEhLhY6ZZdHHWFWBbpPWgEgHWVsWQHL6APQ79IN+10FF1tdt9DFERibIvfaH0uNXJzNLM4h1xUhNUHztUhJTYGFjUep22/VqhzoutfHDCv/3DZEqKFMLU4h1xUhLVByKSktIRWUb82K1MWCWN1LjUnC/AvYCFFTFzhYAEBeXoFAeF5+IKlVsizzG2toSurq6iI9LVCiPj09AFTsb+f39B45i2PBJ6NjZC9+u3IQhnw3Ant0blXwG5YNMECntpglKtc/Avn378PTpU7Rp0wZVq1aFsbGxwuNHjhxRSnDlgUhHB0J6KnL2bwQEGWRRj5FjbgX9Dv0hOfW/QvX13DtDGnITwstkNUSrWWzsrTFuoS9mfzYHuTlvHkYgeh/dxvXBRz1bYeWghcirgO+zwYP7Ysvmb+X3e/X2Udlz7di5T/7zvXtheBEbj9/PHISDQ01ERFSsYQFtmzNQqmSgY8eOyo6jTAgZaRCkUuiYWuC/gxwiM3PI0or+8Ja9TAakeYDw+gjZiyjoVLYExLr5j/3bjoUtxI6uyN6+VFWnoDZpyWmQ5klhXqAXwMLaAikFeguKq06jurCwscDmU5vkZWJdMVxaOKPX8J7oUbuXZg5HkVKlp6RDmieFmXVlhXIzG3O8TEh967FdxvRCt3F9sXrIN4gOq1gfVv86fvwMrl8PlN83MNAHANjZ2eDFi3h5uZ2tNe7cvV9kG4mJycjLy4OtnbVCua2tDV4U6GH4r2vXbwMA6tSuVeGSAW1TqmRg4sSJyo6jbEjzIIt6DLFjY+QFXc0vE4kgrueK3IvHiz4kIgR6zdoDIpF88EfHthpkL5MUEgEA0Pu4E4T0l8i7f12VZ6EWebl5eBT8CE1aueLqb/mvnUgkgmtrVxzbdaxUbd65dAefdxyrUDZtzVREPY7CwS0BTAQIACDNzcPTexFo4O6CwDM3AOS/9xq4u+DcnlNvPK6rb2/0mNAPa4ctQWRweFmFW+YyMl4hI+OVQllsbBw8PVrj7j8f/qamJvjooybYum1PkW3k5ubi9u0geHq0xrFjvwHIf409PVrj+y1vHsJzbZy/xDz2P0lHRaEp3fvKUuqrFmoqybkjMBw6FdJnjyCLfAg9j94QGRgg9+/fAQCGQ6dB9jIJkmO7AAC5f/0K/bY9YTDAF5ILx6FjUxX6nQci90KBD0CRCHotO+XPFaigH2I/bz+C6Wun4WHQIzy4k7+00NDIAGcO5r92X303DYkvkuD/7S4A+ZMOa9StAQDQ09eFVRUrODR0QHZmFmIiY5H1KgtPHyh+m8jOzEZ6Snqhcm2SmZmFZ9Ex8vvPY+IQ9jAclc1MYf+GMd+K7rcdxzF6zUREBofjyZ3H6DSqOwyMDXAp4E8AwOg1k5ASl4TDK/OH7j4Z2wd9pnyKbV+sQ2J0Asz+mVuQ8yobOZnZAIBKlU1gWc0a5rb5vV1VHKoCAF4mpCLtHT0O5d2GjTswZ/ZkPHocIV9aGBMTh19++U1e58zpn3D0l1P4fssuAMB367fDf+d3uHU7CDduBGLypDGoVMkIu3b/BABwcKiJwYP64tSps0hKToGLSwOsWbUQFy9eRXBwqLzd2rVrwcSkEuzsbGFkZIjG/yQMISEPkZurOcM0GjLvT2lKlQxIpVLs2rULp06dQmxsbKFf8PXr5febcd7ti8gxMYNB96EQmVpA9jwCmZvnQ0hPBQCILG2g858hASE1EZnfz4Nhv89RafZmCKlJyD3/CyS/H1JoV+zoCh1LW3lSURFdOH4RlS0rw2eaNyxsLBEREo65Q79GamIqAMCmmi1k/5k6a2VniS2/bZbf9xo7AF5jB+Du1SDMGDizrMPXGPfCHmHkpNevz8qN2wAAvT/piKXzpqkrLLW6ceIKTC3N0GfKoH82HYrEd8OWyicVWlazhuw//289vDtDz0APE7Z+pdDOL+sO4pd1BwEArp2aYdTq172c4zZNLVRHU61a/T0qVTLG1u9XwtzcDJcv30D3nt4Keww4ONSEtfXridMBAcdgY22JhfOno0oVG9y9ex/de3gjPj5/UqFEkosOnq0xedJoVKpkhKioWBw5ehJLl61XeO5tW1cpbEx060b+yqradVvg6dNoVZ42vQeRIJR84cP69esREBCAkSNHYt26dRg7diyeP3+OP/74AxMmTICPT8knsKRP7FbiYyjfgF+0LYdVnhOBm99did7It9kMdYegsfbEXFV3CBotT/Jcpe1fse+vtLbcYw8rrS1VKVXPwPHjx7FkyRK0b98eGzduRI8ePVCjRg04Ojri7t27yo6RiIioTGnbaoJS7TOQmJiIevXqAQAqVaqE9PR0AICHhwfOnz+vtOCIiIhI9UqVDNjZ2SEhIX+5SfXq1XH58mUAQHBwMPT19ZUXHRERkRrIlHjTBKUaJujUqROuXr2Kxo0bY+jQofjqq69w6NAhxMTEYPjw4UoOkYiIqGwJ0K5hglIlA9OnT5f/3K1bN1StWhWBgYGoWbMmPD2LvnAIERERlU+lGibw8/PDoUOvl9a5urpixIgRSE5OxrZt25QWHBERkTrIBOXdNEGpkoGffvoJDg4Ohcrr1q2LAwcOvHdQRERE6iSDSGk3TVCqYYKEhATY2NgUKre0tJRPLCQiItJU2jZnoFQ9A/b29rh9+3ah8lu3bsHWVju3SyUiItJUpeoZ8PLywrJly5CXl4eWLVsCAK5evYpVq1Zh5MiRSg2QiIiorGnKkkBlKVUyMHr0aKSmpmLRokXy6xIYGBhg9OjR8PX1VWqAREREZU3bhglKlQyIRCJ89dVXGD9+PMLDw2FoaIhatWpxwyEiIiIN9F6XMK5UqRIaNWqkrFiIiIjKBQ4TEBERaTltSwZKtZqAiIiIKg72DBARERXACYRERERaTqZduQCHCYiIiMqTffv2wdPTEy4uLvDy8kJQUFCxjvv111/h6OiI8ePHl/g5mQwQEREVoK5rE5w8eRLLly/HhAkTcOTIEdSvXx+jRo1CUlLSW4+Ljo7Gt99+i2bNmpXqfJkMEBERFSAo8VYS/v7+GDhwIPr37486depg0aJFMDQ0xOHDh994jFQqxfTp0zFp0iRUr169hM+Yj8kAERFRATIl3iQSCTIyMhRuEomk0HNKJBLcv38f7u7u8jIdHR24u7sjMDDwjbFu3rwZVlZW8PLyKvX5cgIhERGRCvn5+WHTpk0KZRMnTsSkSZMUylJSUiCVSmFlZaVQbmVlhYiIiCLbvnnzJg4dOoSjR4++V4xMBoiIiAqQiZS3nMDX1xcjRoxQKFPG9v0ZGRmYMWMGFi9eDEtLy/dqi8kAERFRASUd638bfX39Yn34W1hYQCwWF5osmJSUBGtr60L1o6Ki8Pz5c4wbN05eJpPl753YsGFDnD59GjVq1ChWjEwGiIiIygF9fX04OTnh6tWr6NixI4D8D/erV6/C29u7UH0HBwccP35coWzdunV49eoV5s6diypVqhT7uZkMEBERFaCuaxOMGDECM2fOhLOzMxo1aoTdu3cjKysL/fr1AwDMmDEDdnZ2mDZtGgwMDFCvXj2F483MzACgUPm7MBkgIiIqQF07EHbr1g3JycnYsGEDEhIS0KBBA+zYsUM+TBAbGwsdHeUvBGQyQEREVI54e3sXOSwAAHv37n3rsStWrCjVczIZICIiKqCkOwdqOiYDREREBShzNYEm4A6EREREWq7c9AwsOlFZ3SForIuJV9QdgsbybTZD3SFoNL+bK9Udgsaa3PRLdYdAb6FtlzAuN8kAERFReaGupYXqwmSAiIioAM4ZICIiIq3CngEiIqICOGeAiIhIy2nbnAEOExAREWk59gwQEREVoG09A0wGiIiIChC0bM4AhwmIiIi0HHsGiIiICuAwARERkZbTtmSAwwRERERajj0DREREBWjbdsRMBoiIiArgDoRERERajnMGiIiISKuwZ4CIiKgAbesZYDJARERUgLZNIOQwARERkZZjzwAREVEBXE1ARESk5bRtzgCHCYiIiLQcewaIiIgK4ATCt8jNzUXDhg3x8OFDVcVDRESkdjIISrtpghIlA3p6erC3t4dMpm2jKURERBVXiecMjB07FmvXrkVqaqoKwiEiIlI/mRJvmqDEcwb27duHp0+fok2bNqhatSqMjY0VHj9y5IjSgiMiIlIHzejcV54SJwMdO3ZURRxERETlhqZ8o1eWEicDEydOVEUcREREpCal2mcgLS0NAQEBWLNmjXzuwP379xEXF6fM2IiIiNRCJlLeTROUuGcgLCwMI0aMgKmpKZ4/f46BAwfC3NwcZ86cQWxsLFauXKmKOImIiMqMpiwJVJYS9wysWLECffv2xZkzZ6Cvry8vb9euHW7evKnU4FTFfWgnzLm0Acsf7Mbko4tRvXHtN9ZtMcgT4w8uwDd3t+Obu9vx+Y9ziqxvW7sqRmyfjsVBO7EsxB9f/LIE5lWtVHkaZebrr6ciIuIGkpMf4Ndf96F27VrvPMbX1wdhYZeQkvIAFy8eRbNmjeWPWVhUxtq1i3D37jkkJz/Aw4dXsGbNQpiZmSq00b59K/z558+Ij7+PJ09uYMmSWRCLxco+vTLlObQrVl76Hn4P/od5R5fjw8Z13li37aCOmHVwMTbe3YWNd3dh+o/zC9V369ICU/d8jQ2B/vgh8hCqN6yl4jMo/27eCcaEGQvg0WsInFt9grMXr6g7JLWz9umGhpe2o9GDQ6h7dBWMG9ct1nHmPdvA9ekxfLhtjkK5rrU5aqz+Ak7X/dEoLAAOuxdCv5a9KkKnMlLiZCA4OBiDBg0qVG5nZ4eEhASlBKVKjXu0RK95Q/H7+sNY130OYkKeYsyeWTCxMiuyfu2WDXDn2BVsHbwEG/stwMvYJHy+dzbM7Czkdaxq2GLCoYWID4/BlsGLsabrTPy+8QjycnLL6rRUZtq0sRg/fjgmT56Dtm1749WrTBw/vhcGBgZvPGbAgB749tt5WLp0PT7+uAeCgkJx7Nhe2NjkJ0f29nawt7fD7NlL0bRpJ4wZMx2dOrXD1q2ve5VcXBrg6FF/nDlzHi1bdsPQoRPRvXsnLFkyS+XnrCrNe7jj03nDcGx9ABZ1n4GokEhM3TMPpm947zm2dMK1Y5ewcvBCLO03B8mxiZi292uY21nK6xgYG+DRzVAErPixrE6j3MvKyoZjHQfMnTZe3aGUC+Y9WqPqvFF4sf4AHvSYgqzQSDjsXQRdq8pvPU7/A1tUnTsCGdfuF3rsw+1zoF+jCiJGL8WDbl9C8jwedfYtho7Rm/8uaBpBiTdNUOJkQF9fHxkZGYXKIyMjYWlpWcQR5Uu70d1x7cA53Ai4gLjHz3F47k7kZknQfGD7Iuv/78vNuPLj74gJeYqE8BgcnLkNIpEIdVs5y+t0/epThP15B7+u+B9i7kci6Vk8Qv64hYyktDI6K9WZMGEUvv12E06c+B337oVh9OipsLe3Ra9end94zOTJo+HvfwB79wYgLOwRJk2ag6ysLAwbNhAAEBLyEIMHj8XJk2fx5MkzXLhwBQsXrkK3bh3k3/wHDOiBe/fCsHz5BkREPMWlS9cwd+4y+Pr6wMSkUpmcu7J1Gd0TFw/8gUsBfyLmcTT2zN0GSVYO2gz0LLL+9i/X488ff0NUSCRehMfAf+ZWiEQiNGzlIq9z9chFHN9wCCGXg8rqNMq9Nh83x+TPh6Fju1bqDqVcsBndG0kHziA54CxyHkUhes73kGXlwHLgW1aG6eigxvppePHdfkievVB4yODDqqjkVh/Rc79HVtBj5EQ8R/TcLRAZ6sO8d1sVn03Z0bZ9BkqcDHh6emLz5s3IzX39rTcmJgarV69G585v/oAoD8R6YlRz/hAPL9+TlwmCgEeX76GmW/G6zfSNDCDW00Vman5CJBKJ0MCjCRKexGLMnllYeHMrJh9dDKfOzVRyDmWpVq3qsLe3xblzl+RlaWnpuHHjDlq0cCvyGD09PTRp4qJwjCAIOHfuEj76qOhjAMDMzAxpaRmQSqUAAAMDA2Rn5yjUycrKhpGRIZo0cSmqiXJNrKeLms4OCh/agiAg5HIwars5FqsNAyN9iPXEeJVaOBknKopITxfGLnWQcenO60JBQMalu6jkVv+Nx1X54lPkJaYi+affC7eprwcAkP2351MQIEhyYdKsobJCpzJW4mRg1qxZyMzMhLu7O3JycjB06FB07twZlSpVwpQpU1QRo9JUsjCDWFeMjMSXCuXpCS9hZmNerDa6z/oML+NS8OifhMLE2gyGJkbwHNcLYRfuYpvPcgT/dgPDtk6BQ4sGyj6FMlWlii0AID4+UaE8Pj4RdnY2RR5jbW0BXV3dIo+pUqXoY6ysLDB79iT88MN+ednvv19Ay5ZNMXBgL+jo6KBqVTvMmfMFAMDe3rbU56QuphamEOuKkVbgvZeWkIrKxXzvDZjljdS4FNxnLwAVk9jCDCJdMXITUxXKcxNTofuG912lZg1g+WknRM3aVOTj2eHRkETHw36mD8RmlSDS04Xt2H7Qr2oDXVuLIo/RRNp2bYISryYwNTWFv78/bt68iQcPHiAzMxNOTk5wd3dXRXzlise4XnDt+TG2DFosnw8gEuXnU/d+v4W/dp4CAMSEPEUtt3r4eEhHRFwLVVu8JTVoUB9s3LhMfr9v3xEqf05TUxMcOeKP0NDHWLLkO3n52bN/Yc6cZdiwYSl27vwOOTkSrFixAa1bt9DKa2N0G9cHH/VshZWDFlaIuShUPulUMkKNdVMRNWsTpCnpRVfKk+KJ73LUWDkJLsH7IeRJkX7pLtL+vAmINGQdXTFoxke48pT6EsbNmjVDs2aa1RX+KiUN0jwpTKwVJ86Y2lRGWkLqW49tN6Y7PMf1gt+QZYgNe6bYZm4e4h49V6gfH/4ctZoVr/u3vDhx4ndcvx4ov29gkL9axNbWGi9exMvLbW2tERQUUmQbiYkpyMvLg62ttUJ5fhuKE0xNTCrh2LE9SE9/hU8//Rx5eXkKj2/YsAMbNuyAvb0tUlJeombN6li8eBaePHkGTZOekg5pnhRmBd57ZjbmePmO916XMb3QbVxfrB7yDaLDnqowSqpopClpEPKk0LM2VyjXszZHXhHvO4OaVWBQ3Q4OO79+XaiT/wHfOPwIQj3GQfLsBbLuheNBty+hY2oMkZ4upMlpqHt0FTKDH6vwbEiVSrXp0NWrV+Hr64uOHTuiY8eO8PX1xZUr5X/5jjRXiuf3nqCu++vJfyKRCHXcnfD09qM3Htfetyc6TuqH7cNWIDo4olCbUUERsHVQXFZj/aE9Up4rdpWXdxkZrxAR8VR+Cw19hNjYeHh4vJ6IZWpqgubNXXHt2u0i28jNzUVgYLDCMSKRCB4erXD9+utjTE1NcOLEj5BIJBgwYBRycnKKag4AEBsbj+zsHAwc2AtRUc8RGHjvjXXLK2luHp7ei0AD99fzHUQiERq4uyD89oM3HtfVtzd6TuqPtcOWIDI4vCxCpQpEyM1DZvBjmLR6vbQXIhFMWjXCq9thhepnh0cjrNNEPPjkC/kt7ffryLgajAeffIHcWMW/abL0TEiT06Bfyx7Gjeog7cw1VZ9SmdG2CYSlulDRsmXL0KVLF/j4+AAA7t69i88//xyzZ8/GkCFDlB6kMl3Y8SsGrRmH6OAIPLvzGG1GfQJ9YwPcCLgAABi0ZhxexqXg1MoDAACPsT3RZYoX9n2xCSnRCTC1yf9ml/MqG5LM/A+w89uOw3vjF4i4HobHV++jfrvGaNjBDVsGLVbPSSrR5s07MXPmJDx+/ASRkVFYsGAaYmPjcezYGXmdkyf/h2PHfsPWrbsB5H+j3759DW7dCsLNm3cxceJIGBsbY8+eAAD/JgJ7YWRkhBEjvoCZmal8j4GEhCT5MMCUKb44c+Y8ZDIZevf+BNOnj4O39wSNHSb4bcdxjF4zEZHB4Xhy5zE6jeoOA2MDXAr4EwAwes0kpMQl4fDK/wEAPhnbB32mfIptX6xDYnSCfF5Lzqts5GRmAwAqVTaBZTVrmP8zVlvFoSoA4GVC6jt7uyqqzMwsPIuOkd9/HhOHsIfhqGxmCvsqmjff5H0l7PgFNdZ8icygx8i8+xA2I3tBx9gQyQFnAQA11n6J3BfJiF25B0JOLrIfKva8SdNeAYBCeeVurSBNfgnJ8wQY1q+FDxaMxssz15D+150yOy9V05SxfmUpcTLg5+eH2bNnw9vbW6Hczc0NW7duLffJwN0Tf8PE0gxdpgyAqY05YkKfYsewFfJJhRbVrCEIr98EH3t3gq6BHoZtVZwceWbdIZxZdxgAcO+3mzg8dyc8x/dCn4XDEB8Rgz3jvkPkzTd/49MUa9ZshbGxMTZtWg5zczNcuXITvXr5KHyTd3CoASur1xOHDh06AWtrK8yfPxV2djYICgpB794+8kmFrq7O8pUFISF/KTyfo2MrPHsWDQDo3Lk9ZsyYAAMDAwQHh8DLawzOnDmv4jNWnRsnrsDU0gx9pgxCZRtzRIVG4rthS+WTCi2rWUMmvE50PLw7Q89ADxO2fqXQzi/rDuKXdQcBAK6dmmHU6tfXCxm3aWqhOtrmXtgjjJw0U35/5cZtAIDen3TE0nnT1BWW2qSeuARdq8qwn/oZdG0skBUSgQifhcj7Z1KhflUbQFayDz49WwtU+3okdK3NkRefguSf/0Tchp9UEL36aFcqAIiE/37yFUOTJk1w9OhR1KxZU6E8MjISffv2RWBg4BuOfLvptQaX6jgCNseV/yGa8mqwXXN1h6DR/G5y+/HSut/0S3WHoNFcnx5TaftTahXeXK+0vos8oLS2VKVU+wz8/nvhtadnz55F+/btlRETERGRWnHOQBH27Nkj/7l27drYunUrrl+/DldXVwD5cwZu376NESNUvxSNiIhI1QQtGygoVjKwa9cuhftmZmZ4/PgxHj9+vYzE1NQUhw8fxvjx3A+ciIhIkxQrGTh37pyq4yAiIio3NKV7X1lKvekQERFRRcWlhUVYvnw5vvjiCxgbG2P58uVvrTt79mylBEZERERlo1jJwJEjR+Dr6wtjY2OEhBS9DS2Qv6MaERGRptOufoFiJgNpaWnyjXhiYmJw6NAhWFhUnKtTERER/Ze2DRMUa5+BypUrIzo6f1e458+fo4T7FBEREVE5Vqyegc6dO8Pb2xs2NjYQiUTo378/dHSKziPOnj2r1ACJiIjKGlcTFGHx4sXo1KkTnj17hiVLlsDLywuVKlVSdWxERERqwU2H3qBt27YAgPv378PHxwcmJiYqC4qIiEid2DPwDu9aWkhERESapcQXKiIiIqroBCX+K6l9+/bB09MTLi4u8PLyQlBQ0BvrHjx4EJ999hmaN2+O5s2bY/jw4W+t/yZMBoiIiApQ11ULT548ieXLl2PChAk4cuQI6tevj1GjRiEpKanI+teuXUP37t2xZ88eHDhwAPb29hg5ciTi4uJK9LxMBoiIiMoJf39/DBw4EP3790edOnWwaNEiGBoa4vDhw0XWX7NmDYYMGYIGDRqgdu3aWLJkCWQyGa5evVqi5+W1CYiIiAqQKXE/HYlEAolEolCmr68PfX39QvXu378PX19feZmOjg7c3d0RGBhYrOfKyspCXl4eKleuXKIY2TNARERUgKDEm5+fH5o2bapw8/PzK/ScKSkpkEqlsLKyUii3srJCYmJiseJevXo1bG1t4e7uXqLzZc8AERGRCvn6+mLEiBEKZQV7BZRh27ZtOHnyJPbs2QMDA4MSHctkgIiIqABlXpugqCGBolhYWEAsFheaLJiUlARra+u3Hrtz505s27YN/v7+qF+/folj5DABERFRAepYWqivrw8nJyeFyX//TgZs0qTJG4/bvn07vv/+e+zYsQMuLi6lOl/2DBAREZUTI0aMwMyZM+Hs7IxGjRph9+7dyMrKQr9+/QAAM2bMgJ2dHaZNmwYgf2hgw4YNWLNmDapVq4aEhAQAgLGxcYkuG8BkgIiIqAB1bUfcrVs3JCcnY8OGDUhISECDBg2wY8cO+TBBbGyswoUCDxw4gNzcXEyePFmhnYkTJ2LSpEnFfl4mA0RERAUoc85ASXl7e8Pb27vIx/bu3atw/9y5c0p5TiYDREREBWjbVQs5gZCIiEjLsWeAiIioAF7CmIiISMsJStyOWBNwmICIiEjLsWeAiIioAHWuJlAHJgNEREQFcM6AmrTLVncEmuul3UfqDkFj+cdcUXcIGm1y0y/VHYLGcrq1Tt0hEMmVm2SAiIiovNC2fQaYDBARERWgbXMGuJqAiIhIy7FngIiIqABt22eAyQAREVEBXE1ARESk5bRtAiHnDBAREWk59gwQEREVoG2rCZgMEBERFaBtEwg5TEBERKTl2DNARERUAIcJiIiItBxXExAREZFWYc8AERFRATItm0DIZICIiKgA7UoFOExARESk9UrdM5CWloagoCAkJSUVWo/Zp0+f942LiIhIbbiaoBjOnTuH6dOnIzMzEyYmJhCJRPLHRCIRkwEiItJoTAaK4dtvv0X//v0xdepUGBkZKTsmIiIiteIOhMUQFxcHHx8fJgJEREQVQKmSgdatWyM4OFjZsRAREZULMghKu2mCUg0TtGvXDqtWrUJ4eDjq1asHXV3FZjp06KCU4IiIiNRB23YgLFUy8PXXXwMANm/eXOgxkUiE0NDQ94uKiIiIykypkoGwsDBlx0FERFRuaNsEQu5ASEREVICmjPUrS7GTgT179uDTTz+FgYEB9uzZ89a6Pj4+7x0YERERlY1iJwO7du1Cz549YWBggF27dr2xnkgkYjJAREQajcMEb3Du3LkifyYiIqpotG2Y4L0uVCSRSBAREYG8vDxlxUNERERlrFTJQFZWFubMmQNXV1f06NEDsbGxAIDFixdj27ZtSg2QiIiorAlK/KcJSpUMrFmzBmFhYdizZw8MDAzk5R9//DFOnjyptOCIiIjUQSYISrtpglIlA2fPnsX8+fPRrFkzhfK6devi2bNnSglMlWqN6IQONzagW+RutD65GOZNahfruKq9P0bPF/vR3H+qQnmVbs3R8sBsdAnZhp4v9sPMqaYqwi4X2g/tguWXNuP7B/sw++gy1Gpc54112wzqgBkHv8G6u/5Yd9cfU378WqG+WFeM/rOGYMHpNdgUsherrvlh5JqJqGxrURanUmYWLpiOqKe3kf7yMX47dQB16nz4zmPGjR2Gxw//RkZaOK5cOo7mzVwVHj/7ewDyJM8Vbps3rVCo893ab3Dt71N4lR6BmzfOKPOU1MLapxsaXtqORg8Ooe7RVTBuXLdYx5n3bAPXp8fw4bY5CuW61uaosfoLOF33R6OwADjsXgj9WvaqCF1j3LwTjAkzFsCj1xA4t/oEZy9eUXdIasOegWJITk6GlZVVofKsrCyFyxmXR1V7t0TDhUPxcM1hXOw8B2n3n6LF/lnQtzZ763FG1a3RcMEQJF0tvLuirrEBkq4/QOiS/aoKu1xo1sMdA+cNw/H1AVjcfSaiQ57iyz1zYWpV9Gvn2NIJ149dwprBi7Ci31ykxCZhyt55MLezBADoGxmghpMDft14CIt7zMSWsathV7sqJu6YWZanpVJfTR+PiRNGYvzEWXBv3ROvMjNx8sQ+hR61gry8emH1qgVYvGQtmrfoirtBITj56z7Y2Cj+n9u+40dUq+4qv82avaRQW7t2HcDBgONKP6+yZt6jNarOG4UX6w/gQY8pyAqNhMPeRdC1qvzW4/Q/sEXVuSOQce1+occ+3D4H+jWqIGL0Ujzo9iUkz+NRZ99i6Bi9+XdT0WVlZcOxjgPmThuv7lCojJUqGXB2dsb58+cLlQcEBMDV1fU9Q1ItB9/ueLbvHKIOXEDGw+cImrET0iwJagxq/+aDdERw2zwRD1YdQuaz+EIPRx+6hEdrf0bCXxX74k2dRvfAXwfO4krAecQ+jsaPc7dBkiVBq4GeRdbf8eUGnP/xDKJCIvEiPAa7Z26FSCRCg1bOAICs9Ex8N3Qxbv56FXERMYgIfIT983eiVqPasKxqXZanpjKTJ43GsuXrcfz4GQQHh2L4iC9Qtaodevfu8sZjpnwxBjt2/g+79xxEaOgjjJ8wC5mZWRgxfJBCvczMbMTFJchv6ekZiu1MnY8tW3fjyZOnKjm3smQzujeSDpxBcsBZ5DyKQvSc7yHLyoHlwI5vPkhHBzXWT8OL7/ZD8uyFwkMGH1ZFJbf6iJ77PbKCHiMn4jmi526ByFAf5r3bqvhsyq82HzfH5M+HoWO7VuoORe04TFAMU6ZMwdq1a7FgwQJIpVLs2bMHI0eOxM8//4wpU6YoO0alEemJUbnRh0i8eO91oSAg8a97sGj25i7HetP6IycxDVH7z6s+yHJKrKeLms4OCL0cJC8TBAGhl4NQ261esdrQN9KHWE8Xr1Iz3ljHyNQYMpkMmWmv3jtmdfvwwxqwt7fD2XOX5GVpaem4fj0QLVs0LfIYPT09uLk1wtlzf8nLBEHA2XOX0LKl4jGfDe6LFzHBuBN4FkuXzIKRkaFqTkTNRHq6MHapg4xLd14XCgIyLt1FJbf6bzyuyhefIi8xFck//V64TX09AIAsJ1ehTUGSC5NmDZUVOmkwDhMUQ7NmzfDLL79AKpWiXr16uHz5MiwtLXHgwAE4OzsrO0al0bc0g46uGDkJLxXKcxJewsDWvMhjLD9yRI3B7RE0fXsZRFh+mViYQqwrRlqi4muXlvASZjbmxWqj/yxvpMYlI+Ry0T0ougZ66D/LGzeOXUZ2Rtb7hqx2VexsAQBxcQkK5XHxiahSxbbIY6ytLaGrq4v4uESF8vj4BFSxs5Hf33/gKIYNn4SOnb3w7cpNGPLZAOzZvVHJZ1A+iC3MINIVIzcxVaE8NzEVum9471Vq1gCWn3ZC1KxNRT6eHR4NSXQ87Gf6QGxWCSI9XdiO7Qf9qjbQrWBzVoiKo9TXJqhRowaWLCk8RlmRiCsZosmm8Qiavh2S5HR1h6PRuo7rg496tsKqQQuQ999vY/8Q64rhu2kqIAJ+nKeZidfgwX2xZfO38vu9eqtuJ84dO/fJf753LwwvYuPx+5mDcHCoiYgIzR8WeB86lYxQY91URM3aBGnKG/7f5knxxHc5aqycBJfg/RDypEi/dBdpf94Eyvm8JyobmtK9ryylSgYaNGiAS5cuFZpEmJKSAnd393J7CWNJchpkeVIY2ChOOjKwqYyc+NRC9SvVsoNxDVs03/OVvEykk/+Honv0j/iz1VRkPi08h6AiykhJhzRPCjNrxdfOzKYy0hJS33ps5zE98cm4Plg75Bs8Dyu82kSsK4bv5qmw+sAaawYv0thegePHz+D69UD5fQMDfQCAnZ0NXrx4/T6xs7XGnbuFJ7QBQGJiMvLy8mBrpzhnwtbWBi8K9DD817XrtwEAdWrXqnDJgDQlDUKeFHrW5grletbmyCvivWdQswoMqtvBYefXrwv/+X/bOPwIQj3GQfLsBbLuheNBty+hY2oMkZ4upMlpqHt0FTKDH6vwbEhTaEr3vrKUKhl4057NEokEenp67xWQKgm5UrwMegLrNs54cfpmfqFIBOvWToj8ofDSq4zHMTjf/iuFsvozB0JsYoT7X+9GVkxSWYRdLkhz8/D0XgQauLvgzpkbAPKvQ9HA3QXn9px+43FdfHuh24T+WD9sCZ4GRxR6/N9EwLZWFawevOit8wnKu4yMV8jIUJzrEBsbB0+P1rj7z4e/qakJPvqoCbZuK/piX7m5ubh9OwieHq1x7NhvAPJfZ0+P1vh+i/8bn9u1sVP+872oeMmpkJuHzODHMGnVGC/PXMsvFIlg0qoREnf/Wqh+dng0wjpNVCizn+4NHRMjPF+4HbmxikMwsvRMAIB+LXsYN6qDF2v2gUjblCgZ+PdqhSKRCAEBATA2NpY/JpPJcOPGDTg4OCg3QiWL8PsVruvHIfVuBFIDH8NhzCcQGxvg2YELAADXjeOQHZuCsGUHIMvJRXpYtMLxuWn5fzj+W65nXglG1axhWCV/rNGkTv5a5Zz41ELzEzTZ7ztOYOSaCYgMDseTO4/RcVR36Bsb4HLAnwCAkWsmIiUuGUdW/g8A0HVsb/Sa8il2fLEeidEJ8rkFOa+ykZOZDbGuGGO3TEMNpw+xcdQK6Ih15HVepWZAmqv521xv2LgDc2ZPxqPHEYiMjMKihV8hJiYOv/zym7zOmdM/4egvp/D9ll0AgO/Wb4f/zu9w63YQbtwIxORJY1CpkhF27f4JAODgUBODB/XFqVNnkZScAheXBlizaiEuXryK4ODXvXK1a9eCiUkl2NnZwsjIEI3/SRhCQh4iN7fwUE15lrDjF9RY8yUygx4j8+5D2IzsBR1jQyQHnAUA1Fj7JXJfJCN25R4IObnIfqjYAyX9Z0Lqf8srd2sFafJLSJ4nwLB+LXywYDRenrmG9L/ulNl5lTeZmVl4Fh0jv/88Jg5hD8NR2cwU9m+Y51JRcZjgLf69WqEgCDhw4AB0dF7PP9TT08MHH3yARYsWKTVAZYv55W/oW5nBccYAGNiYI+3+U1wbvAKSfybGGVWzBmQlexPYdWmKJuvHye839fsCAPBg9SE8XH1YecGr2c0TV2BqaYbeUz6FmY05okIjsX7YUqT/89pZVrNW6DVq590ZegZ6GLd1ukI7x9YdxPF1ATCvYgnXTs0BAAtOrVaos2rQAjz8O0TFZ6R6q1Z/j0qVjLH1+5UwNzfD5cs30L2nN3JycuR1HBxqwtraUn4/IOAYbKwtsXD+dFSpYoO7d++jew9vxMfnf6OVSHLRwbM1Jk8ajUqVjBAVFYsjR09i6bL1Cs+9besqtGvnLr9/65+Nh2rXbYGnTxWT3PIu9cQl6FpVhv3Uz6BrY4GskAhE+CxE3j+TCvWr2pT4/62erQWqfT0SutbmyItPQfLPfyJuw08qiF5z3At7hJGTXu/zsXJj/vbyvT/piKXzpqkrLLXQtmECkVCK6zQOHToUmzZtQuXKb9/woySOVxmstLa0zTFDzf8GrS7+Mdq7w5oy3LQveokkvZvTrXXqDkGj6VmrthfawbqJ0tqKSAx8dyU1K9Wcgb1798p//jeXKO87DxIRERWXIMjUHUKZKvXSwqNHj2Lnzp2IjIwEANSqVQujRo1Cnz59lBQaERGResi0bJigVMmAv78/1q9fjyFDhuDLL78EANy6dQsLFy5Eamoqhg8frsQQiYiIylYpRtA1WqmHCRYuXKjQC9ChQwfUrVsXGzduZDJARESkQUqVDCQkJKBJk8KTK5o0aYKEhDdvjEJERKQJtG2YoFTXJqhZsyZOnTpVqPzkyZOoVavW+8ZERESkVoIgKO2mCUrVMzBp0iRMmTIFN27cgJubGwDg9u3b+Pvvv7Fu3TplxkdEREQqVqpkoEuXLggICIC/vz/Ons3fAczBwQEBAQFo2JCX/yQiIs3GHQjfITc3F/Pnz8f48eOxevXqdx9ARESkYbRtB8ISzxnQ09PDmTOFL+pDREREmqlUEwg7duwoHx4gIiKqaNQ5gXDfvn3w9PSEi4sLvLy8EBQU9Nb6p06dQteuXeHi4oKePXviwoULJX7OUs0ZqFmzJjZv3ozbt2/DyckJRkZGCo/7+PiUplkiIqJyQV1LC0+ePInly5dj0aJFaNy4MXbv3o1Ro0bh9OnTsLKyKlT/9u3bmDZtGqZOnQoPDw8cP34cEyZMwM8//4x69eoV+3lLdaEiT0/PNzcoEpWq14AXKio9Xqio9HihovfDCxWVHi9U9H5UfaEim8qOSmvreUIwJBKJQpm+vj709fUL1fXy8oKLiwvmz58PAJDJZGjXrh2GDh2Kzz//vFD9L7/8EllZWfDz85OXDRw4EPXr18c333xT7BhL1TNw7tw5+c+8UBEREVU0ytwfwM/PD5s2bVIomzhxIiZNmqRQJpFIcP/+ffj6+srLdHR04O7ujsDAoq98eOfOnUK7/rZu3Rp//PFHiWIs9YWKAgICsHv3boULFQ0bNgxeXl6lbZKIiKhcUObSQl9fX4wYMUKhrKhegZSUFEil0kLDAVZWVoiIiCiy7cTERFhbWxeqn5iYWKIYS5UMrF+/Hrt27YK3tzdcXV0B5Gcny5YtQ0xMDL744ovSNEtERFQuKLNn4E1DAuVJqZKB/fv3Y/HixejRo4e8rEOHDnB0dMTixYuZDBAREZWQhYUFxGIxkpKSFMqTkpIKffv/l7W1daFegLfVf5NSLS3My8uDs7NzoXInJydIpdLSNElERFRuyCAo7VZc+vr6cHJywtWrV1/HIZPh6tWrRV4cEABcXV3x999/K5RduXJF3mtfXKVKBnr37o39+/cXKj948CB69uxZmiaJiIjKDXXtMzBixAgcPHgQR44cQXh4OBYuXIisrCz069cPADBjxgysWbNGXt/Hxwd//fUXfvjhB4SHh2Pjxo24d+8evL29S/S8pZ5AeOjQIVy+fBmNGzcGAAQFBSEmJgZ9+vTB8uXL5fVmz55d2qcgIiLSKt26dUNycjI2bNiAhIQENGjQADt27JB3+8fGxkJH5/X3eDc3N6xevRrr1q3D2rVrUatWLWzevLlEewwApdxnYOjQocVrXCTCnj17ilWX+wyUHvcZKD3uM/B+uM9A6XGfgfej6n0GTIw/VFpbGZlPlNaWqpSqZ2Dv3r3KjoOIiKjc4IWKiIiISKuUes4AERFRRaXMTYc0AZMBIiKiApS56ZAm4DABERGRlmPPABERUQHaNoGQyQAREVEB2jZMwGSAiIioAG1LBjhngIiISMuxZ4CIiKgA7eoXKOV2xERERFRxcJiAiIhIyzEZICIi0nJMBoiIiLQckwEiIiItx2SAiIhIyzEZICIi0nJMBoiIiLQckwEiIiItx2SAiIhIyzEZICIi0nJanQxs3LgRvXv3fu92fv75ZzRr1kwJEWkmQRDw9ddf46OPPoKjoyNCQ0PVHZJWGDp0KJYuXaruMFSu4PurWbNmCuft6emJXbt2qS9AAsDfg6bjhYqUoFu3bmjXrp26w1Cbixcv4siRI9izZw+qV68OCwuLt9aPjo5Ghw4dcPToUTRo0KCMoiRNVfD9paOjAwMDA3WHVWF5enrCx8cHw4cPL9Fxhw4dgpGRkWqCIpVjMvCecnNzYWhoCENDQ3WHojZRUVGwsbGBm5ubukPRChKJBPr6+uoOo8yo4/1VEV9jVZ+TpaWlytom1dP4YQKZTIbt27ejU6dOcHZ2Rvv27bFlyxYAwKpVq9ClSxc0btwYHTp0wLp165Cbm/vWtjZt2oS2bdvC2dkZvXv3xsWLF+WPR0dHw9HRESdPnoS3tzdcXFxw/PjxIocJ/vjjD/Tt2xcuLi7o0KEDNm3ahLy8PAD53Z4bN25E+/bt4ezsjNatW2PJkiUqeHVUb9asWVi8eDFiYmLg6OgIT09PXLx4EYMHD0azZs3QokUL+Pr64tmzZ/JjOnToAADo06cPHB0dMXToUHWFX2be533673BWQEAAPD090ahRI/ljUqkU33zzDZo2bYoWLVpg3bp1qEgXIi3q/VXU8MirV68wdepUuLq6ok2bNti3b5/C42lpaZg7dy5atmwJNzc3+Pj4ICwsTP74217j8mro0KH45ptv3vj79/T0xObNmzFjxgy4ublh/vz5AIDffvsN3bt3h7OzMzw9PfHDDz8otPn8+XMsX74cjo6OcHR0lD928+ZNfPbZZ2jUqBHatWuHJUuWIDMzU/54wWECR0dHBAQEYMKECWjcuDE6d+6Ms2fPqvhVoVITNNzKlSuF5s2bCz///LPw9OlT4caNG8LBgwcFQRCEzZs3C7du3RKioqKEs2fPCu7u7sK2bdvkx27YsEHo1auX/L6/v7/g5uYmnDhxQggPDxdWrlwpODk5CU+ePBEEQRCioqKEevXqCR4eHsJvv/0mPHv2TIiLixMOHz4sNG3aVN7OjRs3BDc3N+Hnn38Wnj17Jly6dEnw8PAQNm7cKAiCIJw6dUpwc3MTzp8/Lzx//ly4e/eu8NNPP5XBq6V8aWlpwqZNm4S2bdsK8fHxQlJSknD69Gnht99+EyIjI4WQkBDB19dX6NGjhyCVSgVBEIS7d+8K9erVE65cuSLEx8cLKSkp6j2JMvC+71NXV1dh1KhRwv3794XQ0FBBEATB29tbcHV1FZYsWSKEh4cLv/zyi9C4cWONfS8Vpaj3l7e3t7BkyRJ5HQ8PD6FJkyaCn5+fEBERIezZs0do0KCBcOnSJXmd4cOHC76+vkJQUJDw5MkTYcWKFcJHH30kf++96TUuz971+/fw8BDc3NyEnTt3Ck+fPhWePn0qBAcHC/Xr1xc2bdokRERECIcPHxYaNWokHD58WBAEQUhJSRHatm0rbNq0SYiPjxfi4+MFQRCEp0+fCq6uroK/v7/w5MkT4datW0KfPn2EWbNmyePx8PAQ/P395ffr1asntG3bVjh+/LgQGRkpLF68WHB1ddWK/++aSKOTgfT0dMHZ2Vn+R/VdduzYIfTt21d+v2Ay0Lp1a2HLli0Kx/Tv319YuHChIAivk4Fdu3Yp1CmYDAwbNkzYunWrQp2jR48KrVq1EgRBEH744Qehc+fOgkQiKVbc5Z2/v7/g4eHxxseTkpKEevXqCQ8ePBAE4fXrGBISUlYhqpUy3qdOTk5CUlKSQj1vb2/hk08+EWQymbxs1apVwieffKKcwMuJgu+vopKBUaNGKRzz5ZdfCqNHjxYE4XVynpOTo1CnY8eOwoEDBwRBePNrXJ696/fv4eEhjB8/XuGYqVOnCiNGjFAo+/bbb4Vu3brJ7xf8UBcEQZgzZ47w9ddfK5TduHFDqF+/vpCdnV3kcfXq1RO+++47+f1Xr14J9erVEy5cuFDicyXV0+g5AxEREZBIJGjZsmWRj588eRJ79uxBVFQUMjMzkZeXBxMTkyLrZmRkID4+vtC4pJubm0J3IgA4Ozu/Na6wsDDcvn0bW7dulZdJpVLk5OQgKysLXbt2xe7du9GxY0e0adMG7dq1g4eHB3R1NfrXIRcZGYkNGzbg7t27SElJkXdbxsbGol69emqOruwp431atWrVIsdkGzduDJFIJL/v6uoKf39/SKVSiMVi5Z5IOebq6lro/u7duwEADx48QGZmJlq0aKFQJzs7W2H46k2vcXn2tt8/UPhvVUREhHyY7l9ubm7Ys2fPW98zYWFhePDgAY4fPy4vEwQBMpkM0dHRqF27dpHH/XeYwdjYGCYmJkhOTi7ZSVKZ0OhPn7fNKA4MDMT06dMxadIktG7dGqampvj111/h7+//3s9rbGz81sczMzMxadIkdO7cuciY7e3tcfr0aVy5cgVXrlzBokWLsHPnTuzduxd6enrvHZ+6jR07FtWqVcOSJUtga2sLmUyGHj16vHW+RkWmjPcpZ2mX3qtXr2BjY4O9e/cWeszU1FT+c0V8jZV1TpmZmRg0aFCR83vs7e3feFzBv2cikQgymUwpMZFyaXQyUKtWLRgaGuLvv/9G9erVFR4LDAxE1apVMW7cOHlZTEzMG9syMTGBra0tbt++jY8++khefvv27RJPJmrYsCGePHmCmjVrvrGOoaEhPD094enpic8++wyffPIJHj58CCcnpxI9V3mTkpKCJ0+eYMmSJfJJlTdv3lSo8+8fiH+/vVR0ynyfFhQUFKRw/+7du6hZs6ZW9QoA+edd8P6/31adnJyQmJgIsViMDz74QB3hqUxJf/8ODg64ffu2Qtnt27dRq1Yt+TF6enqFPrAbNmyIx48fv/VvGmk2jU4GDAwMMGbMGKxatQp6enpwc3NDcnIyHj16hJo1ayI2Nha//vorXFxccP78efzxxx9vbW/UqFHYuHEjatSogfr16+Pnn39GWFgYVq9eXaK4JkyYgLFjx6Jq1aro0qULdHR0EBYWhocPH2LKlCn4+eefIZVK0bhxYxgZGeHYsWMwNDRE1apV3+flKBcqV64Mc3Nz/PTTT7CxsUFMTAzWrFmjUMfKygqGhob466+/UKVKFRgYGCh8Q6tolP0+/a+YmBgsX74cn376KUJCQvDjjz9i5syZKjyb8un27dvYvn07OnbsiCtXruD06dPw8/MDALi7u8PV1RUTJkzAV199hVq1aiE+Ph4XLlxAx44d4eLiouboS6+kv/+RI0diwIAB2Lx5M7p164Y7d+5g3759WLBggbxOtWrVcOPGDXTv3h16enqwtLTEmDFj8Omnn+Kbb76Bl5cXjIyM8PjxY1y5ckW+SoE0m0YnAwAwfvx4iMVibNiwAfHx8bCxscGgQYPg5eWFYcOG4ZtvvoFEIkH79u0xbtw4bNq06Y1t+fj4ICMjAytWrEBycjJq166N77//HrVq1SpRTG3atMHWrVuxefNmbN++Hbq6unBwcICXlxcAwMzMDNu2bcOKFSsgk8lQr149bN269Z2b9WgCHR0dfPfdd1iyZAl69OiBDz/8EPPmzVPoXtTV1cW8efOwefNmbNiwAc2aNSuyC7ciUeb79L/69OmD7OxseHl5QSwWw8fHB59++qmKz6b8GTFiBO7du4fNmzfDxMQEs2bNQps2bQDkd01v27YN69atw+zZs5GSkgJra2s0a9YM1tbWao78/ZT09+/k5IR169Zhw4YN2LJlC2xsbDB58mT069dPXmfy5MmYP38+OnbsCIlEggcPHqB+/frYu3cv1q1bh88++wwAUL16dXTr1k3l50hlQyQIFWhRMhGRlhg6dCjq16+PuXPnqjsUqgA0ftMhIiIiej9MBoiIiLQchwmIiIi0HHsGiIiItByTASIiIi3HZICIiEjLMRkgIiLSckwGiIiItByTASIiIi3HZICIiEjLMRkgIiLScv8HlKCLubZwA/IAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the correlation between calories and fat\n",
        "plt.title(\"Calorie and Fat Correlation\")\n",
        "sns.scatterplot(x=\"calories\",y=\"fat\",data=df,s=20,edgecolor=\"red\")\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "rX62DythYVPT",
        "outputId": "9a184b86-969d-4433-cfbc-9bfeb03ec75f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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mTZqkf//730pJSdGIESNUUlKijIwMtWnTpsrbLNO6dWu1bNlSjzzyiA4dOqT69evrrbfeqvAXcvv27SVJs2fPVnJysgIDA9W/f/8zbvv222/Xnj179Nxzz+mjjz5Sv379FBkZqdzcXG3cuFE7duxw3Q/njjvu0Lp16zR27FiNGjVKYWFhevXVV5Wdna0FCxZUeLO7yrriiis0bNgwLV68WLt27XK957799lutX79e9913n6677roKf/aaa67RP//5T02YMEHXXHONsrOzlZmZqTZt2qigoMC1XkhIiNq0aaM333xTF198sS688EK1bdvW7byeMu3atdPgwYP10ksv6dixY+rSpYv++9//as2aNerTp4+uuuqqKu8rcD4IMsBpevfurddee01LlizR22+/rRdffFHBwcGKi4vTtGnTdPPNN0sq/UU6f/58Pf7443rkkUcUGRmpESNGqFGjRpo+ffpZnyMkJETLly9XWlqa1qxZo+PHj6tVq1aaO3euKyhVRZs2bbRq1SotXLhQa9as0dGjR9WoUSNddtllmjBhwll/tmvXrnr44Yf1zDPPaM6cOYqOjtbUqVN14MCBKoeOdu3aacmSJZo7d67mz5+viy66SJMmTVJOTs55B5mgoCA9/fTTmj17thYvXqy6devq2muvVUpKSrk7Afft21ejRo3SunXr9Nprr8myrLMGmYCAAP3lL39R7969tXLlSj333HM6fvy4wsPD1aVLF/3xj390HXqMjIxUZmamHn30UWVkZOjUqVOKi4vT008/Xe7eO1Uxa9YsxcfHKzMzU3/7298UGBio5s2b67e//e1Z73c0ZMgQ5ebm6qWXXtLmzZvVpk0bPfroo1q/fr22bt3qtu7s2bP10EMPae7cuSoqKtLEiRMrDDJl60ZHR2vNmjXauHGjIiMjdeedd2rixInnva9AVTmsmjo7DwAAoJpxjgwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYKxac2ffvLx8mXDrP4dDiohoYEy9tRm9Mgv9Mge9Moc3e1W27XOpNUHGsmTUB8K0emszemUW+mUOemUOX/aKQ0sAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMFatmbUEAEBtVbJtm75b9qJyCqWoIKnp6BEKTEo6r3XL1tvhDFC4w3nWbXoTQQYAAD9Wsm2b1i3K1Oz2A2U5AuSwnJqxKFP9J6hc8Kjsup5s09s4tAQAgB/7btmLmt1+gCxH6a98yxGg2e0H6LtlmVVe15NtehtBBgAAP5ZTKFfgKGM5ApRTaFV5XU+26W0EGQAA/FhUsOSwnG7LHJZTUcGOKq/ryTa9jSADAIAfa5o6QjN2rnUFD4fl1Iyda9U0dXiV1/Vkm97msCyr5v8O5AO5ufkyYU8dDikysoEx9dZm9Mos9Msc9Kr6lV5hlKmcQktRwQ41TR1+jquWzr1u2Xo/OB0KD7DOus2qKHsfnHM9goy98AE2B70yC/0yB70yhzd7Vdkgw6ElAABgLIIMAAAwFkEGAAAYiyADAACMxYgCAABswpOZSChFkAEAwAbsNL/IJBxaAgDABuw0v8gkBBkAAGzATvOLTEKQAQDABuw0v8gkBBkAAGzATvOLTMLJvgAA2EBgUpL6T5AST59zNKF65xf5I4IMAAA2EZiUpOikJEX7uhCDcGgJAAAYiyADAACMRZABAADGIsgAAABjcbIvAMCvMb/IvxFkAAB+i/lF/o9DSwAAv8X8Iv9HkAEA+C3mF/k/ggwAwG8xv8j/EWQAAH6L+UX+j5N9AQB+i/lF/o8gAwDwa8wv8m8cWgIAAMYiyAAAAGMRZAAAgLF8eo7M4sWL9c9//lN79+5VSEiIEhISNHXqVLVu3dq1zqhRo7R161a3nxs2bJhmzZpV0+UCAACb8WmQ2bp1q1JSUtShQweVlJToscce05gxY7Ru3TqFhoa61rv55ps1efJk1/cXXHCBL8oFABioKCNDWemZOhQYqibFJxRz6wgFjRzp67JqlD/Pm/JpkFmyZInb9/PmzVPXrl21c+dOdenSxbU8JCREUVFRNV0eAMBwRRkZWvPKJqX1m+qatTT1lRUaLNWaMOPv86Zsdfl1fn6+JCksLMxt+euvv67XXntNUVFR6tmzp8aPH+/xX2UchtzEsaxOU+qtzeiVWeiXOaqzV1npma4QI5WOJ0hLTlFieprajqodQaZ03tTAcvOmEpdlqkWX8wsy3vxcVXabtgkyTqdTc+bMUWJiomJjY13LBwwYoGbNmqlx48bavXu30tLS9M0332jhwoUebT8iokF1l+xVptVbm9Ers9Avc1RHrzbXqVfhrKXDdeqpa2TteC/scAZU+Br84HQooZpeA19+rmwTZGbOnKmvvvpKL7zwgtvyYcOGuf47Li5OUVFRuvXWW7Vv3z61bNmy0tvPy8uXZcCMMIej9A1hSr21Gb0yC/0yR3X2qknxCTksp9svcoflVOPiE8rNzT/PSs0QHuCs8DUID7DO+zXw5ueqbNvnYosgM2vWLL377rvKyMjQRRdddNZ1O3XqJEnKysryKMhYloz6x8u0emszemUW+mWO6uhVzOjhmvrKCqUlp/xyjszmFYoZPbzWvA+apo7QjEWZmt1+wC/nyOxcq6YTqu818OXnyqdBxrIsPfTQQ9qwYYOWL1+uFi1anPNndu3aJUmc/AsAOKegkSM1WFJielrpVUslBYoZPbzWnOgr+f+8KZ8GmZkzZ2rt2rV68sknVa9ePeXk5EiSGjRooJCQEO3bt0+vv/66evTooQsvvFC7d+/W3Llz1aVLF7Vr186XpQMADBE0cqTajBypNr4uxIf8ed6UT4PMiy++KKn0pnenmzt3roYMGaKgoCBt2bJFy5YtU0FBgZo2baq+fftq/PjxvigXAADYjE+DzO7du8/6eNOmTZWRkVFD1QAAANMwawkAABiLIAMAAIxli8uvAQDwlD/PD0LlEWQAAMbx9/lBqDwOLQEAjFM6P2hAuflB3y3L9HFlqGkEGQCAcXIKVeH8oJzCWnK7XrgQZAAAxokKLp0XdDqH5VRUMOPNaxuCDADAOE1TR2jGzrWuMOOaH5Q63MeVoaZxsi8AwDj+Pj8IlUeQAQAYyZ/nB6HyOLQEAACMRZABAADGIsgAAABjEWQAAICxONkXAAC4mDbDiiADAAAkmTnDikNLAABAkpkzrAgyAABAkpkzrAgyAABAkpkzrAgyAABAkpkzrDjZFwAASDJzhhVBBgAAuJg2w4pDSwAAwFgEGQAAYCyCDAAAMBZBBgAAGIuTfQEAsAnT5hzZAUEGAAAbMHHOkR1waAkAABswcc6RHRBkAACwARPnHNkBQQYAABswcc6RHRBkAACwARPnHNkBJ/sCAGADJs45sgOCDAAANmHanCM74NASAAAwFkEGAAAYiyADAACMRZABAADG4mRfAECVmDIXqCgjQ1npmToUGKomxScUc+sIBY0c6euyUE0IMgAAj5kyF6goI0NrXtmktH5TXXVOfWWFBkuEGT/BoSUAgMdMmQuUlZ6ptOQUtzrTklOUlW6vOlF1BBkAgMdMmQt0KDC0wjoPBYb6qCJUN4IMAMBjpswFalJSUGGdTUoKfFQRqhtBBgDgMVPmAsWMHq6pm1e41Tl18wrFjLZXnag6TvYFAHjMlLlAQSNHarCkxPS00quWSgoUM3o4J/r6EYdlWfY6oOklubn5MmFPHQ4pMrKBMfXWZvTKLPTLHPTKHN7sVdm2z4VDSwAAwFgEGQAAYCyCDAAAMJZPg8zixYs1dOhQJSQkqGvXrho/frz27t3rts6pU6c0c+ZMXXnllUpISNCkSZOUm5vro4oBAICd+DTIbN26VSkpKVq5cqWWLl2q4uJijRkzRgUFv1zfP2fOHL3zzjt6/PHHtXz5ch0+fFgTJ070YdUAAJOUbNum7MlTtP2uKcqeNEUl27b5uiRUI59efr1kyRK37+fNm6euXbtq586d6tKli/Lz87Vq1SqlpaWpa9eukkqDzQ033KDPPvtMl19+uQ+qBgCYwpSZUKg6W91HJj8/X5IUFhYmSfr8889VVFSkbt26uda55JJL1KxZM4+DjMNeN5s8o7I6Tam3NqNXZqFf5qjOXpXOhBpYbiZU4rJMtehCkDlf3vxcVXabtgkyTqdTc+bMUWJiomJjYyVJubm5CgoKUsOGDd3WjYiIUE5Ojkfbj4g497XodmJavbUZvTIL/TJHdfRqhzOgwllLPzgdSqjEPUpQOb78XNkmyMycOVNfffWVXnjhBa9sPy/PjBsrORylbwhT6q3N6JVZ6Jc5qrNX4QFOOSynW5hxWE6FB1jKzc0/z0rhzc9V2bbPxRZBZtasWXr33XeVkZGhiy66yLU8MjJSRUVFOnbsmNtfZfLy8hQVFeXRc1iWjPrHy7R6azN6ZRb6ZY7q6FXT1BGasShTs9sP+OUcmZ1r1XTCcN4H1ciXnyufBhnLsvTQQw9pw4YNWr58uVq0aOH2eHx8vIKCgrRlyxb169dPkrR3714dPHiQE30BAOdkykwoVJ1Pg8zMmTO1du1aPfnkk6pXr57rvJcGDRooJCREDRo00NChQzVv3jyFhYWpfv36mj17thISEggyAIBKCUxKUnRSkqJ9XQi8wqdB5sUXX5QkjRo1ym353LlzNWTIEEnS9OnTFRAQoMmTJ6uwsFDJycl64IEHarxWAABgP0y/thmmvpqDXpmFfpmDXpmD6dcAAADngSADAACMZYvLrwEA3lOybZu+W/aicgqlqCCp6egR1XLVjre2awp/3X/T9osgAwB+zFuzhmr7DCN/3X8T94tDSwDgx0pnDQ0oN2vou2WZttyuKfx1/03cL4IMAPixnEJVOGsop/D8LjHx1nZN4a/7b+J+EWQAwI9FBZfOFjqdw3IqKvj8xhV7a7um8Nf9N3G/CDIA4Meapo7QjJ1rXb+cXLOGUofbcrum8Nf9N3G/uCGezXAjKHPQK7PU5n6VXoVy2qyh1OqZNeSt7ZrSK2/tv695sl92uCEeQcZmTPkAg16Zhn6Zg16Zww5BhkNLAADAWAQZAABgLIIMAAAwFkEGAAAYixEFAPAzk2bMFGVkKCs9U4cCQ9Wk+IRibh2hoJEjfV1WhUx6XT1h0n55UqtJ+yURZABAklkzZooyMrTmlU1K6zfVVevUV1ZosGS7MGPS6+oJk/bLk1pN2q8yHFoCAJk1YyYrPVNpySlutaYlpygr3X61mvS6esKk/fKkVpP2qwxBBgBk1oyZQ4GhFdZ6KDDURxWdmUmvqydM2i9PajVpv8oQZABAZs2YaVJSUGGtTUoKfFTRmZn0unrCpP3ypFaT9qsMQQYAZNaMmZjRwzV18wq3WqduXqGY0far1aTX1RMm7ZcntZq0X2UYUWAz3JrbHPTKLJXpl0mzc9yuWiopUMzo4bY70beMp6+rKZ8tk94vntTKrCWbsvsHoowpH2DQK9PQL3PQK3PYIchwaAkAABiLIAMAAIxFkAEAAMYiyAAAAGMxogCA3yubHbPDGaBwh9P2s2NMYdpMntrMn3tFkAHg10ycHWMCXldz+HuvOLQEwK+ZODvGBLyu5vD3XhFkAPg1E2fHmIDX1Rz+3iuCDAC/ZuLsGBPwuprD33tFkAHg10ycHWMCXldz+HuvGFFgM9ya2xz0yhxls2N+cDoUHmDZeiaOSbw1a4jPVvUzsVfMWvoVUz4QfIDNQa/MQr/MQa/MYYcgw6ElAABgLIIMAAAwFkEGAAAYiyADAACMxYgCADBQUUaGstIzdSgwVE2KTyjm1hEKGjnS12UZz59nEvkrggwAGKYoI0NrXtmktH5TXbNzpr6yQoMlwsx58PeZRP6KQ0sAYJis9EylJae4zc5JS05RVrp/zM7xFX+fSeSvCDIAYJhDgaEVzs45FBjqo4r8g7/PJPJXBBkAMEyTkoIKZ+c0KSnwUUX+wd9nEvkrggwAGCZm9HBN3bzCbXbO1M0rFDPaP2bn+Iq/zyTyV5zsCwCGCRo5UoMlJaanlV61VFKgmNHDOdH3PAUmJan/BCnx9JlEE5jLZXfMWrIZZoyYg16ZhX6Zg16Zg1lLAAAA54EgAwAAjEWQAQAAxvJpkPn444911113KTk5WXFxcdq4caPb49OmTVNcXJzb15gxY3xULQAAsBufXrVUUFCguLg4DR06VBMnTqxwne7du2vu3Lmu74ODg2uqPADAWTCXCHbg0yDTo0cP9ejR46zrBAcHKyoqqoYqAgBUBnOJYBe2v4/M1q1b1bVrVzVs2FBXXXWV/u///k/h4eEeb8dhyI0Zy+o0pd7ajF6ZhX5Vr9K5RAPLzSVKXJapFl3OL8jQK3N4s1eV3aatg0z37t117bXXKjo6Wvv379djjz2msWPH6qWXXlJgYKBH24qIOPe16HZiWr21Gb0yC/2qHjucARXOJfrB6VBCJe79URn0yhy+7JWtg0z//v1d/112sm+fPn1cf6XxRF6eGTdWcjhK3xCm1Fub0Suz0K/qFR7glMNyuoUZh+VUeICl3Nz889o2vTKHN3tVtu1zsXWQ+bUWLVooPDxcWVlZHgcZy5JRHwjT6q3N6JVZ6Ff1aJo6QjMWZWp2+wG/nCOzc62aThheba8vvTKHL3tlVJD5/vvvdfToUU7+BQAfYy4R7MKnQebEiRPat2+f6/vs7Gzt2rVLYWFhCgsL08KFC9WvXz9FRkZq//79evTRRxUTE6Pu3bv7sGoAgFQaZqKTkhTt60JQq3l8Q7zU1FQdO3as3PLjx48rNTXVo219/vnnGjRokAYNGiRJmjt3rgYNGqT58+crMDBQX375pcaNG6frrrtO9913n9q3b68VK1ZwLxkAACCpCn+R2bp1q4qKisotP3XqlD755BOPtnXllVdq9+7dZ3x8yZIlnpYHAABqkUoHmS+++ML133v27FFOTo7re6fTqX//+99q0qRJ9VYHAABwFpUOMoMGDZLD4ZDD4dDo0aPLPR4SEqIZM2ZUa3EAAABnU+kg8/bbb8uyLPXp00cvv/yyGjVq5HosKChIERERHt+kDgBgL6fmzFHW6xv0/QVhuujkUcUM7Ku606ef1zaZyQRvqnSQad68uST3Q0wAAP9xas4cvfrhXv116AOue8NMeS9Dg+bMqXKYYSYTvK3Kl1/v2bNHBw8eLHfib+/evc+7KABAzct6fYMrxEilIwf+evVIJayaqdgqBpmzzWSKJsigGngcZPbv368JEyboyy+/lMPhkPXzrfwcP0932rVrV/VWCACoEd9fEFbh/KRDF4QptorbzClUhdvMKbS4/wyqhcf3kXn44YcVHR2tDz74QCEhIVq3bp0yMjIUHx+v5cuXe6NGAEANuOjkj3JYTrdlDsupJid/rPI2o4JV4TajghltjerhcZDZvn27Jk+erEaNGikgIEAOh0NJSUn6wx/+oNmzZ3ujRgBADYgZeK2mvJfhCh5l58jEDLy2yttsmjpCM3auddvmjJ1r1TR1eLXUDHh8aMnpdKpevXqSpPDwcB0+fFitW7dW8+bN9c0331R7gQCAmlF3+nQNmjNHCatm6tAFYWpy8kfFDLz2vK5aYiYTvM3jINO2bVvt3r1bLVq0UKdOnfTss88qKChIK1euVIsWLbxRIwCghtSdPl2x06dX+ZyYijCTCd5UqUNLX3zxhZzO0j8Ljhs3znWC7+TJk5Wdna2UlBRt2rRJ9913n/cqBQAA+JVK/UVm8ODB2rx5syIiIvTggw/qlVdekSTFxMRo/fr1Onr0qMLCwlxXLgEAANSESv1FpmHDhsrOzpYkHThwwPUXmTIXXnghIQYAANS4Sv1Fpm/fvho5cqSioqLkcDg0dOhQBQRUnIHefvvtai0QAADgTCoVZB566CFde+212rdvn2bPnq2bbrrJdeUSAKDmmTS/yNe1+vr54V2Vvmrp6quvliTt3LlTqampql+/vteKAgCcmUnzi3xdq6+fH97n8Q3x5s6dS4gBAB8qnV80oNz8ou+WZfq4svJ8Xauvnx/e53GQAQD41tnmF9mNr2v19fPD+wgyAGAYk+YX+bpWXz8/vI8gAwCGMWl+ka9r9fXzw/s8HlEAAPAtk+YX+bpWXz8/vM9h/frudn4qNzdfJuypwyFFRjYwpt7ajF6ZhX6Zg16Zw5u9Ktv2uXBoCQAAGIsgAwAAjEWQAQAAxiLIAAAAY3HVEnAaZrL4p7K+7nAGKNzhrJa++vq94snz+7pWwJsIMsDPmMnin7zRV1+/Vzx5fl/XCngbh5aAnzGTxT95o6++fq948vy+rhXwNoIM8DNmsvgnb/TV1+8VT57f17UC3kaQAX7GTBb/5I2++vq94snz+7pWwNsIMsDPmMnin7zRV1+/Vzx5fl/XCngbIwpshltz+1bp1R2nzWRJPfNMFnpljrK+/uB0KDzAOmtfPd1mZd4r3uDJ8/u6Vk/x2TKHHUYUEGRshg+wOeiVWeiXOeiVOewQZDi0BAAAjEWQAQAAxiLIAAAAYxFkAACAsRhRAJymKCNDWemZOhQYqibFJxRz6wgFjRzp67KAcvx1fpI35mLBvxFkgJ8VZWRozSublNZvqmsmzdRXVmiwRJiBrfjr/CR/3S94F4eWgJ9lpWcqLTnFbSZNWnKKstKZSQN78df5Sf66X/Auggzws0OBoRXOpDkUGOqjioCK+ev8JH/dL3gXQQb4WZOSggpn0jQpKfBRRUDF/HV+kr/uF7yLIAP8LGb0cE3dvMJtJs3UzSsUM5qZNLAXf52f5K/7Be9iRIHNcGtu33K7aqmkQDGjh5/xRF96ZRZ/65dp85MqyxtzseA9dhhRQJCxGX/7x9af0Suz0C9z0Ctz2CHIcGgJAAAYiyADAACMRZABAADG8mmQ+fjjj3XXXXcpOTlZcXFx2rhxo9vjlmXpiSeeUHJysjp27Khbb71V3377rW+KBQAAtuPTEQUFBQWKi4vT0KFDNXHixHKPP/PMM1q+fLnmzZun6OhoPfHEExozZozeeOMN1a1b1wcVw0T+OpOmtvOkr/44v4f3NVDKp0GmR48e6tGjR4WPWZalZcuWady4cerTp48k6S9/+Yu6deumjRs3qn///jVZKgzF7Bb/5Elf/fE94I/7BFSVbYdGZmdnKycnR926dXMta9CggTp16qTt27d7HGQchtwYsqxOU+q1u9LZLQPLzW5JXJapFl3O7x98euU7nvTVm+8BX/HHfTodny1zeLNXld2mbYNMTk6OJCkiIsJteUREhHJzcz3eXkTEua9FtxPT6rWrHc6ACme3/OB0KKES9yeoDHpV8zzpa028B2qaP+5TRfhsmcOXvbJtkKlueXlm3FjJ4Sh9Q5hSr92FBzjlsJxu/+g7LKfCAyzl5uaf17bple940ldvvgd8xR/36XR8tszhzV6VbftcbHv5dVRUlCQpLy/PbXleXp4iIyM93p5lmfNlWr12/jrb7BZ6Ze6XJ3319nvA7vtv6hefLXO+vNmryrDtX2Sio6MVFRWlLVu26NJLL5UkHT9+XP/5z380YsQIH1cHUwQmJan/BCnx9Jk0E5jdYjpP+nr6uq75PYa/B3hfA7/waZA5ceKE9u3b5/o+Oztbu3btUlhYmJo1a6bU1FQ99dRTiomJcV1+3bhxY9dVTEBlBCYlKTopSdG+LgTVypO+BiYlqUWXJCX40fwe3tdAKZ8Gmc8//1ypqamu7+fOnStJGjx4sObNm6exY8fq5MmTuv/++3Xs2DF17txZzz77LPeQAQAAkph+bTtMfTUHvTIL/TIHvTIH068BAADOA0EGAAAYy7ZXLQFnU5SRoaz0TB0KDFWT4hOKuXWEgkaO9HVZwHmr7AylqsyaYi4T/BFBBsYpysjQmlc2Ka3fVNecmamvrNBgiTADo1V2hlJtnzUFnI5DSzBOVnqm0pJT3ObMpCWnKCs908eVAeendIbSgHIzlL5bllml9TxdFzARQQbGORQYWuGcmUOBoT6qCKgeOYWq8L2dU2hVaT1P1wVMRJCBcZqUFLhuzV7GYTnVpKTARxUB1SMqWBW+t6OCHVVaz9N1ARMRZGCcmNHDNXXzCrc5M1M3r1DM6OE+rgw4P2eboVSV9TxdFzARN8SzGW4EVTluVy2VFChm9PAaP9GXXpnFlH6VXmF02gyl1IpnKFV2PU/XtQNTegV73BCPIGMzfIDNQa/MQr/MQa/MYYcgw6ElAABgLIIMAAAwFkEGAAAYiyADAACMxYgC2AbzYPwTffUOf31dy/ZrhzNA4Q6n3+wXvIcgA1tgHox/oq/e4a+vq7/uF7yLQ0uwBebB+Cf66h3++rr6637BuwgysAXmwfgn+uod/vq6+ut+wbsIMrAF5sH4J/rqHf76uvrrfsG7CDKwBebB+Cf66h3++rr6637BuxhRYDO1+dbczIPxT3bpq7/1yy6va3Ur268fnA6FB1h+s1/+yg4jCggyNuNv/9j6M3plFvplDnplDjsEGQ4tAQAAYxFkAACAsQgyAADAWAQZAABgLEYUwMWk2S2e1GrSfsEcvK8AeyDIQJJZM048qdWk/YI5eF8B9sGhJUgya8aJJ7WatF8wB+8rwD4IMpBk1owTT2o1ab9gDt5XgH0QZCDJrBknntRq0n7BHLyvAPsgyECSWTNOPKnVpP2COXhfAfbBiAKb8eWtuU2a3eJJrd7aL26jbpbq7pdJnxfT8Nkyhx1GFBBkbIYPsDnolVnolznolTnsEGQ4tAQAAIxFkAEAAMYiyAAAAGMRZAAAgLEYUQAXZscAAExDkIEkZscAAMzEoSVIYnYMAMBMBBlIYnYMAMBMBBlIYnYMAMBMBBlIYnYMAMBMnOwLSaUn9PafICWePjtmArNjAAD2RpCBS2BSkqKTkhTt60IAAKgkDi0BAABjEWQAAICxCDIAAMBYtj5HZsGCBVq4cKHbslatWmn9+vU+qggAANiJrYOMJLVt21ZLly51fR8YGOjDalCGuUzwR7yvAfPYPsgEBgYqKirK12XgNMxlgj/ifQ2YyfZBJisrS8nJyapbt64uv/xyTZkyRc2aNfN4Ow5DblBbVqed6y2dyzSw3FymxGWZatGl9vyDb0Kv8Itz9Yv3tX3w2TKHN3tV2W3aOsh07NhRc+fOVatWrZSTk6NFixYpJSVFr7/+uurXr+/RtiIiGnipSu+wc707nAEVzmX6welQQqR96/YWO/cK5Z2pX7yv7YfPljl82StbB5kePXq4/rtdu3bq1KmTevbsqTfffFM33XSTR9vKy8uXZcD8Q4ej9A1h53rDA5xyWE63f/QdllPhAZZyc/N9WFnNMqFX+MW5+sX72j74bJnDm70q2/a52DrI/FrDhg118cUXa9++fR7/rGXJqA+EnettmjpCMxZlanb7Ab+cS7BzrZpOGG7bmr3Jzr1CeWfqF+9r++GzZQ5f9sqoIHPixAnt37+fk399jLlM8Ee8rwEz2TrIPPLII+rZs6eaNWumw4cPa8GCBQoICNCAAQN8XVqtx1wm+CPe14B5bB1kvv/+e/3hD3/Q0aNH1ahRI3Xu3FkrV65Uo0aNfF0aAACwAVsHmb/97W++LgEAANgYs5YAAICxCDIAAMBYtj60BPgD5vf4XlkPdjgDFO5w0gPAjxBkAC9ifo/v0QPAv3FoCfCi0vk9A8rN7/luWaaPK6s96AHg3wgygBflFKrC+T05hdyutKbQA8C/EWQAL4oKLp3XczqH5VRUMGN9awo9APwbQQbwoqapIzRj51rXL1LX/J7U4T6urPagB4B/42RfwIuY3+N7gUlJur73F7osPU2H6tRTk6ITirmVHgD+giADeBnze3yrZNs2vfn2Z5rdb+ovVy29vVb927UjzAB+gENLAPwaVy0B/o0gA8CvcdUS4N8IMgD8GlctAf6NIAPAr3HVEuDfONkXgF87/aqlw3XqqTFXLQF+hSADwK9x1RLg3zi0BMCvcdUS4N8IMgD8GlctAf6NIAPAr3HVEuDfCDIA/BpXLQH+jZN9USUl27bpu2UvKqdQigqSmo4ewYmTsKXT51394HQoPMBi3hXgRwgy8FjJtm1atyhTs9sP/OUqkEWZ6j9B/HKALQUmJalFlyQlRDZQbm6+LE6PAfwGh5bgMa4CAQDYBUEGHuMqEACAXRBk4DGuAgEA2AVBBh7jKhAAgF1wsi88dvpVIDmFlqKCHVwFgmrB1XAAPEWQQZUEJiUpOilJ0b4uBH6Dq+EAVAWHlgDYAlfDAagKggwAW+BqOABVQZABYAtcDQegKggyAGyBq+EAVAUn+wKwBa6GA1AVBBkAtsHVcAA8xaElAABgLIIMAAAwFkEGAAAYiyADAACMxcm+AGyDWUsAPEWQAWALzFoCUBUcWgJgC8xaAlAVBBkAtsCsJQBVQZABYAvMWgJQFQQZALbArCUAVcHJvgBsgVlLAKqCIAPANpi1BMBTHFoCAADGIsgAAABjEWQAAICxjAgyK1asUK9evdShQwfddNNN2rFjh69LAgAANmD7IPPGG29o7ty5mjBhgtasWaN27dppzJgxysvL81lNJdu2KXvyFG2/a4qyJ01RybZtPqsFAIDazPZBZunSpbr55ps1dOhQtWnTRjNnzlRISIhWrVrlk3rK5sEMaTlQd7S9UUNiBmrdokzCDAAAPmDry68LCwu1c+dO3Xnnna5lAQEB6tatm7Zv3+7RthzVdHPQ0nkwA8vNg0lclqkWXc7/fhdldVZXvfAeemUW+mUOemUOb/aqstu0dZD54YcfVFJSooiICLflERER2rt3r0fbiohoUC017XAGVDgP5genQwmR1fMcUvXVC++jV2ahX+agV+bwZa9sHWSqU15evqxqmD0XHuCUw3K6hRmH5VR4gKXc3Pzz3r7DUfqGqK564T30yiz0yxz0yhze7FXZts/F1kEmPDxcgYGB5U7szcvLU2RkpEfbsixVy4vcNHWEZizK1Oz2A2Q5An6ZBzNheLU2sbrqhffRK7PQL3PQK3P4sle2DjLBwcFq3769tmzZoj59+kiSnE6ntmzZopEjR/qkJubBAABgH7YOMpJ022236d5771V8fLw6duyo9PR0nTx5UkOGDPFZTcyDAQDAHmwfZG644QYdOXJE8+fPV05Oji699FI9++yzHh9aAgAA/sf2QUaSRo4c6bNDSQAAwL5sf0M8AACAMyHIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGMuLOvtXB4fB1BZVTVqcp9dZm9Mos9Msc9Moc3uxVZbfpsCyGpAMAADNxaAkAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQqQEff/yx7rrrLiUnJysuLk4bN250e9yyLD3xxBNKTk5Wx44ddeutt+rbb791W+fo0aOaMmWKEhMTlZSUpOnTp+vEiRM1uBe1w+LFizV06FAlJCSoa9euGj9+vPbu3eu2zqlTpzRz5kxdeeWVSkhI0KRJk5Sbm+u2zsGDB3XHHXeoU6dO6tq1qx555BEVFxfX5K7UCi+88IIGDhyoxMREJSYmatiwYdq0aZPrcXplX3//+98VFxenhx9+2LWMftnDggULFBcX5/Z13XXXuR63W58IMjWgoKBAcXFxeuCBByp8/JlnntHy5cv14IMPauXKlbrgggs0ZswYnTp1yrXO1KlTtWfPHi1dulRPP/20tm3bpvvvv7+mdqHW2Lp1q1JSUrRy5UotXbpUxcXFGjNmjAoKClzrzJkzR++8844ef/xxLV++XIcPH9bEiRNdj5eUlOjOO+9UUVGRMjMzNW/ePK1Zs0bz58/3xS75tYsuukhTp07V6tWrtWrVKl111VWaMGGCvvrqK0n0yq527NihzMxMxcXFuS2nX/bRtm1bbd682fX1wgsvuB6zXZ8s1KjY2Fhrw4YNru+dTqf1m9/8xnr22Wddy44dO2bFx8dba9eutSzLsvbs2WPFxsZaO3bscK2zadMmKy4uzvr+++9rrvhaKC8vz4qNjbW2bt1qWVZpb9q3b2+9+eabrnXK+rN9+3bLsizr3Xfftdq1a2fl5OS41nnhhResxMRE69SpUzVaf23UpUsXa+XKlfTKpo4fP2717dvXev/9962RI0das2fPtiyLz5adzJ8/3/rtb39b4WN27BN/kfGx7Oxs5eTkqFu3bq5lDRo0UKdOnbR9+3ZJ0vbt29WwYUN16NDBtU63bt0UEBCgHTt21HjNtUl+fr4kKSwsTJL0+eefq6ioyK1fl1xyiZo1a6bPPvtMkvTZZ58pNjZWkZGRrnWSk5N1/Phx7dmzp+aKr2VKSkq0bt06FRQUKCEhgV7Z1KxZs9SjRw+3vkh8tuwmKytLycnJ6t27t6ZMmaKDBw9Ksmef6lT7FuGRnJwcSVJERITb8oiICNcxx9zcXDVq1Mjt8Tp16igsLMz186h+TqdTc+bMUWJiomJjYyWV9iIoKEgNGzZ0WzciIsLVi9zcXLcPsCTX9/Sr+u3evVvDhw/XqVOnFBoaqkWLFqlNmzbatWsXvbKZdevW6X//+59eeeWVco/x2bKPjh07au7cuWrVqpVycnK0aNEipaSk6PXXX7dlnwgywBnMnDlTX331lduxYdhPq1at9Oqrryo/P19vvfWW7r33XmVkZPi6LPzKd999p4cffljPPfec6tat6+tycBY9evRw/Xe7du3UqVMn9ezZU2+++aZCQkJ8WFnFOLTkY1FRUZKkvLw8t+V5eXmuBBsZGakjR464PV5cXKwff/zR9fOoXrNmzdK7776r9PR0XXTRRa7lkZGRKioq0rFjx9zWz8vLc/UiMjKy3Bn8Zd/Tr+oXHBysmJgYxcfHa8qUKWrXrp2WLVtGr2xm586dysvL05AhQ3TZZZfpsssu09atW7V8+XJddtll9MvGGjZsqIsvvlj79u2zZZ8IMj4WHR2tqKgobdmyxbXs+PHj+s9//qOEhARJUkJCgo4dO6bPP//ctc6HH34op9Opjh071njN/syyLM2aNUsbNmxQenq6WrRo4fZ4fHy8goKC3Pq1d+9eHTx4UJdffrkk6fLLL9eXX37pFk4/+OAD1a9fX23atKmR/ajNnE6nCgsL6ZXNXHXVVXr99df16quvur7i4+M1cOBA13/TL3s6ceKE9u/fr6ioKFv2iUNLNeDEiRPat2+f6/vs7Gzt2rVLYWFhatasmVJTU/XUU08pJiZG0dHReuKJJ9S4cWP16dNHUumJVN27d9ef//xnzZw5U0VFRXrooYfUv39/NWnSxFe75ZdmzpyptWvX6sknn1S9evVcx3MbNGigkJAQNWjQQEOHDtW8efMUFham+vXra/bs2UpISHB9iJOTk9WmTRv9v//3//THP/5ROTk5evzxx5WSkqLg4GAf7p3/+etf/6qrr75aTZs21YkTJ7R27Vpt3bpVS5YsoVc2U79+fde5ZmVCQ0N14YUXupbTL3t45JFH1LNnTzVr1kyHDx/WggULFBAQoAEDBtjyc+WwLMuq9q3CzUcffaTU1NRyywcPHqx58+bJsizNnz9fK1eu1LFjx9S5c2c98MADatWqlWvdo0eP6qGHHtK//vUvBQQEqG/fvpoxY4bq1atXk7vi9359X4syc+fO1ZAhQySV3gxq3rx5WrdunQoLC5WcnKwHHnjA7U+mBw4c0IMPPqitW7fqggsu0ODBgzVlyhTVqcP/O1Sn6dOn68MPP9Thw4fVoEEDxcXFaezYsfrNb34jiV7Z3ahRo9SuXTvdd999kuiXXdxzzz36+OOPdfToUTVq1EidO3fWPffco5YtW0qyX58IMgAAwFicIwMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBoAtLViwQDfeeON5b2f16tVKSkqqhooA2BG3QgTg12644Qa3ab4A/AtBBoDfKioqUkhIiEJCQnxdCgAv4dASAK9xOp165plndO211yo+Pl7XXHONnnrqKUnSo48+qn79+qlTp07q3bu3Hn/8cRUVFZ11WwsXLtTVV1+t+Ph43XjjjXrvvfdcj2dnZysuLk5vvPGGRo4cqQ4dOuj111+v8NDSxo0bNXjwYHXo0EG9e/fWwoULVVxcLKl0AvqCBQt0zTXXKD4+XsnJyZo9e7YXXh0A1YG/yADwmr/+9a96+eWX9ac//UmdO3fW4cOH9c0330iS6tWrp7lz56px48b68ssv9ec//1n16tXT2LFjK9zWsmXLtHTpUs2aNUuXXnqpVq1apfHjx2vt2rW6+OKLXeulpaVp2rRpuvTSS1W3bl1t3rzZbTvbtm3TvffeqxkzZigpKUn79u3Tn//8Z0nSxIkT9dZbb+n555/XY489prZt2yo3N1dffPGFd14gAOeNIAPAK44fP65ly5bp/vvv1+DBgyVJLVu2dP11ZPz48a51o6Oj9c0332jdunVnDDJLlizR2LFj1b9/f0nSH//4R3300UdKT0/XAw884Fpv9OjR6tu37xnrWrhwoe644w5XTS1atNDdd9+tRx99VBMnTtR3332nyMhIdevWTUFBQWrWrJk6dux4fi8GAK8hyADwir1796qwsFBXXXVVhY+/8cYbWrZsmfbv36+CggIVFxerfv36Fa57/PhxHT58WImJiW7LExMTy/21JD4+/qx1ffHFF/r000/19NNPu5aVlJTo1KlTOnnypK677jqlp6erT58+6t69u3r06KGePXuqTh3+uQTsiE8mAK+oW7fuGR/bvn27pk6dqkmTJik5OVkNGjTQunXrtHTp0vN+3tDQ0LM+XlBQoEmTJlX4V5u6deuqadOmWr9+vT744AN98MEHmjlzppYsWaLly5crKCjovOsDUL0IMgC84uKLL1ZISIg+/PBDtWjRwu2x7du3q1mzZho3bpxr2cGDB8+4rfr166tx48b69NNPdcUVV7iWf/rppx4f9rnsssv0zTffKCYm5ozrhISEqFevXurVq5duueUWXX/99fryyy/Vvn17j54LgPcRZAB4Rd26dTV27Fg9+uijCgoKUmJioo4cOaKvvvpKMTEx+u6777Ru3Tp16NBB7777rjZu3HjW7Y0ZM0YLFixQy5Yt1a5dO61evVpffPGF0tLSPKprwoQJuuuuu9SsWTP169dPAQEB+uKLL/Tll1/qnnvu0erVq1VSUqJOnTrpggsu0GuvvaaQkBA1a9bsfF4OAF5CkAHgNePHj1dgYKDmz5+vw4cPKyoqSsOHD9dNN92k0aNHa9asWSosLNQ111yjcePGaeHChWfcVmpqqo4fP6558+bpyJEjuuSSS/Tkk0+6XbFUGd27d9fTTz+tRYsW6ZlnnlGdOnXUunVr3XTTTZKkhg0b6u9//7vmzZsnp9Op2NhYPf300woPDz+flwKAlzgsy7J8XQQAAEBVcEM8AABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIz1/wHcsP8IquzbEAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of calories\n",
        "sns.displot(x=\"calories\",data=df,color=\"red\",kde=True)\n",
        "plt.title(\"Calories Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "FQ778-68Y99g",
        "outputId": "1dd9c376-6670-4804-e45b-021dd88bf384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of proteins\n",
        "sns.displot(x=\"protein\",data=df,color=\"green\",kde=True)\n",
        "plt.title(\"Proteins Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "qaCqNGKvZbcO",
        "outputId": "30fead8e-cb6f-408c-90d9-bd069d9b1d10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of fats\n",
        "sns.displot(x=\"fat\",data=df,color=\"purple\",kde=True)\n",
        "plt.title(\"Fats Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "zoCOSjEpoEGu",
        "outputId": "7629f711-695e-4783-e334-2bc743918d54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of carbohydrates\n",
        "sns.displot(x=\"carb\",data=df,color=\"violet\",kde=True)\n",
        "plt.title(\"Carbohydrate Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "vyX_pMXBoK6w",
        "outputId": "360d9d25-25a9-402a-9ad0-8fd40ad76cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling\n",
        "Lets start with importing libraries to modelling step."
      ],
      "metadata": {
        "id": "b0ufM39Ly1HG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "jl3GKmESoVD9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define dependent and independent variables\n",
        "X=df[[\"calories\", \"fat\", \"carb\", \"fiber\", \"protein\"]]\n",
        "y=df[\"type\"]"
      ],
      "metadata": {
        "id": "7cnrI0xkou-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "frqDMiaCpCwH",
        "outputId": "2279c863-6a15-4fda-bd1c-4e665ae63c51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    calories   fat  carb  fiber  protein\n",
              "1        350   8.0    67      5       10\n",
              "2        350   9.0    64      7        6\n",
              "3        420  20.0    59      0        5\n",
              "4        490  19.0    75      4        7\n",
              "5        130   6.0    17      0        0\n",
              "..       ...   ...   ...    ...      ...\n",
              "73       420  13.0    46      6       32\n",
              "74       390  13.0    36      2       34\n",
              "75       300  12.0    44      0        8\n",
              "76       300   4.0    57      3       10\n",
              "77       300   3.5    60      3        7\n",
              "\n",
              "[77 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eeab2bbc-4412-4c9f-bdd5-0dfdba9658f0\" 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>calories</th>\n",
              "      <th>fat</th>\n",
              "      <th>carb</th>\n",
              "      <th>fiber</th>\n",
              "      <th>protein</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>350</td>\n",
              "      <td>8.0</td>\n",
              "      <td>67</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>350</td>\n",
              "      <td>9.0</td>\n",
              "      <td>64</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>420</td>\n",
              "      <td>20.0</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>490</td>\n",
              "      <td>19.0</td>\n",
              "      <td>75</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>130</td>\n",
              "      <td>6.0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>420</td>\n",
              "      <td>13.0</td>\n",
              "      <td>46</td>\n",
              "      <td>6</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>390</td>\n",
              "      <td>13.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>300</td>\n",
              "      <td>12.0</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>300</td>\n",
              "      <td>4.0</td>\n",
              "      <td>57</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>300</td>\n",
              "      <td>3.5</td>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77 rows × 5 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-eeab2bbc-4412-4c9f-bdd5-0dfdba9658f0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eeab2bbc-4412-4c9f-bdd5-0dfdba9658f0 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-eeab2bbc-4412-4c9f-bdd5-0dfdba9658f0');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78702182-34cc-4871-88b5-5d02679bf912')\"\n",
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              "            style=\"display:none;\">\n",
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              "    </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",
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              "      --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",
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              "      --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",
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              "    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-78702182-34cc-4871-88b5-5d02679bf912 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_809a0313-209c-4a6e-acb0-398e7f143d79\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_809a0313-209c-4a6e-acb0-398e7f143d79 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 77,\n  \"fields\": [\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105,\n        \"min\": 80,\n        \"max\": 500,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          290,\n          140,\n          280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.095488010174427,\n        \"min\": 0.0,\n        \"max\": 28.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          2.5,\n          2.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 16,\n        \"max\": 80,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          57,\n          20,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 34,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          3,\n          25,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "-cNDu2EupD0e",
        "outputId": "a24c2a97-d24e-45e9-872c-a6c5275e17f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       bakery\n",
              "2       bakery\n",
              "3       bakery\n",
              "4       bakery\n",
              "5       bakery\n",
              "        ...   \n",
              "73    sandwich\n",
              "74    sandwich\n",
              "75     parfait\n",
              "76     parfait\n",
              "77     parfait\n",
              "Name: type, Length: 77, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>sandwich</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>parfait</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>parfait</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>parfait</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Tk6WOvxXpGLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup model\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "S4-iwaJ8pPb3",
        "outputId": "182ca05a-4927-4d3d-8edc-df58586e150b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = model.predict(X_test)"
      ],
      "metadata": {
        "id": "uI-BLv_Ipize"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets look models accuracy\n",
        "accuracy = accuracy_score(y_test, y_prediction)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwyQKMIbpqg7",
        "outputId": "0990ad21-9336-4186-a25e-f4fd5320f8cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a variable to make a prediction\n",
        "prediction = model.predict([[250, 2, 40, 7, 9]])"
      ],
      "metadata": {
        "id": "h_fDr7tup7e8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Prediction:\", prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8iaQTOMqGuu",
        "outputId": "0e4222f8-83f9-4d89-d568-ae3031d49e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: ['parfait']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Tree\n",
        "Let's import sklearn library to create the model tree."
      ],
      "metadata": {
        "id": "LT9TRsyLzjcm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import plot_tree"
      ],
      "metadata": {
        "id": "kcMV3JmYqgSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize model tree\n",
        "plt.figure(figsize=(15, 10))\n",
        "plot_tree(model, filled=True, feature_names=X.columns, class_names=model.classes_)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "AZRjW9GIqkkh",
        "outputId": "ec4326a7-54f1-4e88-bc5a-85e2eae3e6b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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IiIg88uYM62rrEEREREQeSko8iYiIiE2Ehkcz/uu1fDHkxQcyXp/Ji0gzmhjb8xmK5Pfj1Ylf42BvR3KqkRdb1uGpupU4eSmMSYt+xcHeDqPJwvCuT1K2SP5b9pmcmsa0oD8IvhqF0WTm3e5PU/xfC5PfcORsKNOXrQcg1WjkUlgUf3426JZ9f/nTZrYfOYu9nR0VihVg6AstMRgMmba9EhHD6PmrrN8fPBPMumkD8XJ3vWX/3/2+m22HT2Mym3m+SU1a1alI0Po9LPptF/3bNaF1vcq3vFZEREQkq5R4EhERkUfGpH7tres4ffl2Vxwd7IlPSuHFMfN4qm4lShbMw/zh3TEYDPx17DzzV2/jo/4dbtnfvFVbaVytDA2rlv7PsSuXLGStrPp111EOnLp02/ZP1a1E3+caAzB89nL+On6eOhVKZNq2gL+3te+Dp4OZv3rbbZNOO46cJSI2PkPSL7B5baLjEv/zXkRERESySms8iYiIyH3z6ZI/+XPvcQCMJjNd3p+L0Whi5vKN9Pv4O14aO59lG/ZmuO6Dr1ZZEzF7jl9g3MI1AJwOucprU7+n38ff8c6s5SSnpN23WB0d7AFITE6lZKG8ADg42FuriuISkykTkO+2few8eo69Jy7QZ/IipgX9gdFkztLYa7Yf/s+KomI3VU45OjhgZ8jaY9uaHf/d9+9//Y3ZbOG1qd/zzqzlhMfEZ6lvERERkTuliicRERG5b9o2qMLM5ZtoUas8O4+e5bEKxXFwsOfVNvVxdXYiNc1Ilw/m0a5R9Sz199Gi3xjX61kK+HuzZP0elm/ez4st61jPpxlNDJj2Q4brqpcJ4LX2TW/bd3JqGm98spizoeG8/vw/bQ+dCWZa0B+ERcYy5bXnb9vHmdBr9GzbgIGdWjD5+99Ys+MwzzWsdttrwmPiCY2IpmqpgNu2u2HP8QtcjYqlZtmi/9k2zWhi++EzDA584rbtrkXH4e7ixBdDXuSPPceYvmw9Y3s+m6V4RERERO6EEk8iIiJy35QunI/ouEQiYxNYvf0QPVrXB2DZxn1s3H8SezsDkbEJRP5rOpeBm9cusli/Ohsabl27KM1oola59MkXRwf7u14Y3MXJkbnvdCM6LpHuExbQsnYFPNxcqFoqgIXv9uDwmRAmf/8734x65ZZ9eLm7UL9KKQAaVi3NtkNn/nPcX3ce5ck6lbIU4/ELV5i5fCOfvNkJO7vM13e62eaDp3isfHFcnBxv287L3dUad6NqZZi3amuW4hERERG5U0o8iYiIyH311OOVWLphL6HhMZQvWoDYhCRWbTvEDx/0wmgy8/zI2VgslnTXeLm7cCUyFoCj5y5bj5cqnIcP+7Qjj48HcD35dLO7rXhKM5qwt7PDzs6Ai7MjTg4OODk6kJJmxNnx+uORp7uLNYGTnJpGUkoqvp7u6fqpU744x85fpnqZIhw5G0rRAn4ARMYm4O7qbO3rZmt3HmHyTetGGU1mouISyOvjma7dudBwJnyzlo8HdMTHw816PCY+CUcHe9xcnDL2veMIXVrUTnfsSkQMBfy9M8T99/nL1K9ciiNnQ6xxi4iIiNxvSjyJiIjIffVUnUq0GTaDfv9fGNvTzYUSBfPQa9K3lCiUB2+PjItet2tUnZFzf+LXXUcplOefJMmwl57kgwWrrGsndX/qcepXLmU9f7cVT1ciYhi7cA12dgbSjCZ6P9sQJ0cH/thzjCXr91jXeRrSpSUAB04Fs+XgKd5+sVW6fl5/vhnjv15LSpoRPy83xvx/utq0oD/o2qou5YsVSNf+dPBVXJwcCcjnaz0WGh7NJ0v+5JM3OqVrO3HRryQkp/LevJ8BeKlVHZpUL8ui33dRoVgBmtcqn659dHwip4OvUqtcMesxo9HEW9OXsHhM73Rt29Svwoff/kLfKYuwWOC9l1vf8WsoIiIikhUGy78/chQRERG5Awn7VmFOiLR1GP9pxJcruBYdx/s92lIk/51V+CxYu52GVUpTpsjtFxu/eayJfdtnqe0vO4/g7uJM4+plstT+/fmrGNn9aZwyqab6twOnLnH84hW6tHgsS30Hrd/DT1sO0OfZRjStUQ4AO3c/3Gs+k6XrRURERP5NiScRERG5J7kl8SR3R4knERERuRdZ25dXRERERERERETkDinxJCIiIrlaaHg0r0393mbjtxsxK8ttv/xpM2t3HMnGaERERERyFiWeRERERHIwi8WSYRdAERERkdxCu9qJiIhIrjJz+Ub2HL+Ak6M9nZvXpsJNO8f9vvtvlm/eT0qqkeIF/Rndow0mk5kRX64gKj4RB3t7erVtQKE8Poya+zOODnZYLPDxgOfxcs+4215WpJlMjFmwmothkRQr4M97L7cmLjGZ4bNXYDSZMZpMjO7RhuIF81ivSUpJZfT8VTxVtxKVShRi0qJfSU5Nw8nBnvdfaYu/twftRsyi5WMVOHw2hNrliuHoYM/LT9cD4JUPv+aTNzrh4+l2by+miIiISDZT4klERERyjW2HzxB8LYqvRnTHYDBgMpsJi4y1nm9UrTSt6lQEYMTsFew7eZH8fl7EJiYz753uAJjNFlZtP0TdisXp+1xjgAwVRZsOnOS733dnGP+19k2oXqZIumPhMfH0bNuAgLy+jJ7/M9sOn+HxiiX4bGAgjg72bDt8hq9/2cn7r7YFICI2niEzltHn2UZUL1OEEV+uoGebBlQpVZith06z8JcdDOnSEpPZTIMqpRjQoSkJySn0m/IdLz9dj2PnL1Mwj7eSTiIiIpIrKPEkIiIiucbp4KvUqVAcg8EAgL1d+lUD9p28xLe/7cRsNnM5IpbG1ctQq1wxWtauwKi5P+Hq7Eivtg1pWbsCC9ZuZ9Tcnyjo702fZxvh6GBv7adJ9bI0qV42SzH5e7kTkNcXgKqlAjh/OYJKJQry0Xe/ERGTQJrJhJuzk7X94j/38GyDqtYE1pmQa3z+4wYAzBYzBf19ALCzM1CtdAAA7i7OlC9WgH0nL/LbrqO0b1T9zl88ERERERtQ4klERERyjVKF8/LLziO0a1TdWvF0sxk/buDzt7qQx8eDEbNXYLFAapqR9k1q0LFZLVZvP8z363bTv10TBnRoCsCYBavZceQsjauXsfZzJxVPEbEJhIZHUyiPD0fOhtCiVnnW7jhKuaIFeKV1fbYeOs336/7pq99zjdn99zmWbthLp2a1KFkoDz1a16d80etTBtOMJgAMGKwJNoDnm9Zk/uptXA6PYXjXp+7thRQRERF5QJR4EhERkVyjYdXS7Dt5kVcmfo2LkyOdmtVKt8ZTm3pVeG3a95QomAcL16fPXY6IYdzXa7Az2GE0mRj6Qiu2HDpF0Po92BnscHK0p3qZgHTj3EnFU15vDxas3c7Z0HCK5vOjYdXSnA0NZ9Tcn9h/8hIlC+VJ197ezo4xPZ/lw29/YdFvu3ir8xN89N1vJKekYgHa1qtC2wZVM4xTvmgBwiJjeaJ2+XQJKREREZGczGDRNikiIiJyFyIiIvD39ydh3yrMCZG2DueR0Pujb5nUrz3+3h4PbEw7dz/caz6D2WzGzk4bIouIiMid0dODiIiIZJnJZGLz5s28/vrr9OnTx9bhPDKuRcfx2tTvqVmu6ANNOt2sf//+zJ49m6tXr9pkfBEREcmdNNVORERE/lNYWBg//vgjy5YtIyQkhLJly9K/f39bh/XIyOvjyRdDXrRpDJUrV2bWrFlMnz6d5s2b07lzZxo2bKgqKBEREbktTbUTERGRTJlMJrZu3cqSJUvYsGEDjo6OtG7dmsDAQKpVq2ZdZ0hT7R5uN6baAcTFxfHzzz+zePFiTp48SeHChenUqRMdOnQgf/78No5UREREciIlnkRERCSdf1c3lStXjsDAQJ599lk8PT0ztFfi6eF2c+LpBovFwqFDhwgKCmLt2rWkpqbSrFkzaxWUvb29jaIVERGRnEaJJxEREbFWNwUFBbFx40acnJys1U1Vq1a97S5qScc3Y06MeYDRyoNk5+aNa/nGtzx/owoqKCiIEydOULhwYTp27Mjzzz+vKigRERFR4klERORRFhYWxrJly1i2bBmhoaH/Wd30bxaLGYNBa/w87LLyc7ZYLBw+fJjFixerCkpERESslHgSERF5xNyquqlLly5UqVLlttVNIlkRFxfHqlWrCAoK4vjx46qCEhEReYQp8SQiIvKICAsLY+nSpSxbtozLly9Tvnx5AgMDeeaZZ7JU3SRyp25UQQUFBbFmzRpSU1Np2rQpgYGBqoISERF5RCjxJCIi8hAzmUxs2bLFWt3k7OxMmzZtCAwMVHWTPFDx8fHWtaCOHz9OoUKF6NixIx07dlQVlIiIyENMiScREZGH0JUrV6xrN91c3fTss8/i4eFh6/DkEXajCmrJkiWsWbOGlJQUVUGJiIg8xJR4EhEReUjcqG5avHgxmzZtwsXFhTZt2tC5c2dVN0mOFB8fb10L6tixYxQsWJCOHTvSqVMnVUGJiIg8JJR4EhERyeX+Xd1UoUIF69pNqm6S3ODfVVDJycnWKqhGjRqpCkpERCQXU+JJREQkFzKZTGzevJmgoCBVN8lDJT4+ntWrVxMUFMTff/9trYLq2LEjBQoUsHV4IiIicoeUeBIREclFLl++zI8//sjSpUu5cuUKFStWJDAwkLZt26q6SR4qFouFI0eOsGTJElavXk1ycjJNmjShS5cuqoISERHJRZR4EhERyeGMRiObN29myZIl1uqmtm3b0rlzZypXrqzqJnnoqQpKREQk91LiSUREJIe6fPmyde0mVTeJXHfkyBGCgoLSVUEFBgbSuHFjVUGJiIjkQEo8iYiI5CA3qpuCgoLYvHmztbopMDCQypUr2zo8kRwjPj6eNWvWEBQUxNGjRylQoIC1CqpgwYK2Dk9ERET+T4knERGRHODy5cssXbqUZcuWERYWRqVKlQgMDKRNmzaqbhL5DzfWglq1apW1Cqpz5840btwYBwcHW4cnIiLySFPiSURExEaMRiObNm1iyZIl1uqmZ555xrp2k4jcmfj4eNauXcvixYtVBSUiIpJDKPEkIiLygIWGhlrXblJ1k0j2+HcVVOPGja1rQakKSkRE5MFR4klEROQBuFHddGPtJldXV1U3iTwAN6qggoKCOHLkCPnz57dWQRUqVMjW4YmIiDz0lHgSERHJRiEhIdbqpqtXr1K5cmUCAwNp3bq1qptEHrCjR48SFBSUrgqqc+fONGnSRFVQIiIi2USJJxERkfvMaDSyceNG69pNN6qbAgMDqVSpkq3DE3nkJSQkWNeCUhWUiIhI9lLiSURE5D7JrLqpS5cutG7dGnd3d1uHJyKZOHr0qHUtqMTEROtaUKqCEhERuT+UeBIREbkHN6qbgoKC2LJlC25ubta1m1TdJJJ73KiCCgoK4vDhw+TLl89aBVW4cGFbhyciIpJrKfEkIiJyF4KDg63VTdeuXaNKlSrWtZtU3SSSu/3999/WtaASExNp1KgRgYGBNG3aVFVQIiIid0iJJxERkSxKS0tj06ZNLF68mK1bt1qrmwIDA6lYsaKtwxOR++xGFdSSJUs4dOgQ+fLl4/nnn6dTp06qghIREckiJZ5ERET+Q3BwMEuXLuXHH3/k2rVrVK1alc6dO6u6SeQRcuzYMYKCgvj5559VBSUiInIHlHgSERHJRFpamnXtphvVTc8++yyBgYFUqFDB1uGJiI0kJiZa14I6dOgQefPmta4FFRAQYOvwREREchwlnkRERG5yq+qmNm3a4ObmZuvwRCQHOXbsGEuWLOHnn38mISGBhg0b0qVLF5o0aYKjo6OtwxMREckRlHgSEZFHXlpaGhs2bCAoKIht27bh7u7Os88+S+fOnVXdJCL/6UYV1JIlSzh48CB58+a1rgWlKigREXnUKfEkIiKPrEuXLrFs2TJrdVO1atWsazepuklE7sbx48eta0HdqIK6sRaUqqBERORRpMSTiIg8Um5V3RQYGEj58uVtHZ6IPCQSExP55ZdfWLJkCQcOHFAVlIiIPLKUeBIRkUfCpUuXrGs3hYeHU716dTp37szTTz+t6iYRyVbHjx9nyZIl/PTTTyQkJNCgQQMCAwNp1qyZqqBEROShp8STiIg8tNLS0li/fj1Llixh69ateHh48Nxzz9G5c2dVN4nIA3erKqiOHTtSpEgRW4cnIiKSLZR4EhGRh46qm0Qkpzt+/DhLly7lp59+Ij4+XlVQIiLy0FLiSUREHgo3qpturN3k6enJc889R6dOnVTdJCI5VlJSkrUKav/+/eTJk8e6FpSqoERE5GGgxJOIiORqFy9eZOnSpSxfvpzw8HBq1KhhrW5ydXW1dXgiIll24sQJ61pQcXFxNGzYkM6dO9O8eXNVQYmISK6lxJOIiOQ6qamp1rWbbq5u6ty5M+XKlbN1eCIi9yQpKYlff/2VoKAgVUGJiEiup8STiIjkGhcvXmTJkiUsX76ciIgIatSoQWBgIE899ZSqm0TkoXTixAmWLl3KypUriYuLo0GDBnTu3JkWLVqoCkpERHIFJZ5ERCRHS01N5c8//2TJkiVs374dT09P2rVrR6dOnVTdJCKPjMyqoDp06ECnTp0oWrSorcMTERG5JSWeREQkR7pw4YJ17SZVN4mI/OPkyZPWtaBiY2OpX78+gYGBNG/eHCcnJ1uHJyIiko4STyIikmPcqG4KCgpix44deHl5WdduKlu2rK3DExHJUZKSkvjtt99YvHgx+/fvx9/fnw4dOtC5c2dVQYmISI6hxJOIiNjchQsXrGs3RUZGUqNGDbp06cKTTz6p6iYRkSzIrArqxlpQqoISERFbUuJJRERsIjU1lT/++IMlS5aouklE5D5JTk62rgW1b98+axVUp06dKFasmK3DExGRR5ASTyIi8kD9u7qpZs2a1rWbXFxcbB2eiMhD49SpUyxZsoSVK1cSGxtLvXr1CAwMVBWUiIg8UEo8iYhItrtR3RQUFMTOnTvx8vKiXbt2dO7cmTJlytg6PBGRh9qNKqglS5awd+9e/Pz8rGtBqQpKRESymxJPIiKSbc6fP8+SJUtYsWIFkZGR1KpVi86dO6u6SUTERm5UQf3000/ExMRQr149OnfuzBNPPKEqKBERyRZKPImIyH11o7pp8eLF7Nq1C29vb+vaTapuEhHJGZKTk/ntt98ICgqyVkG1b9+ezp07U7x4cVuHJyIiDxElnkRE5L44f/48QUFBrFixgqioKGrVqkVgYCBPPvmkqptERHKw06dPW9eCiomJ4fHHHycwMFBVUCIicl8o8SQiInctNTWVdevWERQUZK1uurF2U+nSpW0dnoiI3IGUlBTrWlB79uzB19fXuhaUqqBERORuKfEkIiJ37Ny5c9a1m6Kioqhdu7a1usnZ2dnW4YmIyD06c+YMQUFB1iqounXr0qVLF1VBiYjIHVPiSUREsuRGddPixYvZvXu3tbopMDCQUqVK2To8ERHJBikpKfz2228sWbKEv/76C19fX+taUCVKlLB1eCIikgso8SQiIrd19uxZli5dyvLly4mOjuaxxx6jc+fOqm4SEXnEnDlzxroWVHR0NHXr1iUwMJCWLVuqCkpERG5JiScREckgNTWV33//naCgIHbv3o2Pj4917SZVN4mIPNpSUlKs7xGqghIRkf+ixJOIyCPKYrFgMBjSHcusuikwMJBWrVqpuklERDI4c+YMS5cuZcWKFURHR1OnTh26dOmiKigREbFS4klE5BG0atUqJk+ezLp16zAYDPz+++8sWbLEWt3Uvn17OnXqpOomERHJkhtVUDe/l3To0IFOnTpRsmRJW4cnIiI2pMSTyCPKYjFjMNjZOgzJZpn9nLdv306fPn1o2rQpAQEB6T6l7ty5s6qbRETknmRWBXWjetbJyYnx48fj5eXFm2++aetQRUTkAVDiSeQRFrf9O0wxYbYOQ7KJvXd+POu/lO7YwYMH6datGy4uLsTExKi6SUREsk1KSgrr1q1Lt15g+/btcXd3Z8aMGQwfPpxXXnnF1mGKiEg2U+JJ5BEW/cs0TFEhtg5Dsom9b2F8nh6c7li1atVITk7GycmJgIAASpUqxZtvvknZsmVtFKWIiDwK/r2GYMGCBbl8+TKTJ0/mueees3V4IiKSjRxsHYCIiDw4Q4cOJTg4GIDIyEhiYmJITk62cVQiIvKwK1myJIMHD8bb25vt27dz4sQJAIYNG0aJEiWoWrWqjSMUEZHsosSTiOQa41ce4JmaRalW1C/bx5q06hBnwmIBaFS+AN0blgZgy4krfL3lNI72dnR4rBgtKxdOd93l6ER6fLmF0vk9AehSrySNyhXI9nizqlu3brYOQUREHlEpKSn8+eefWCwWqlevjqOjIykpKQQEBNg6tLs2fPhwOnbsSO3atR/YmMOGDePq1assXLgQgL59+xITE4PJZOLFF1+kffv2Ga756aef+P777wEYNGgQjz/++AOLV0REiScRyRVM5rufFZyYasTN6c5+3b1YvyRF/T0wWyz0/WobzSsWpJCvGzPXHWN+70Y4O9jR96tt1C+TH3fn9H2XK+jN9O56oBMREbmZh4cHS5cutXUY943JZLrraxMSEnB3d7/j644ePUp8fHy6Y8OHD6dEiRKkpKTQtm1bWrdunW6TkNjYWL766iuWLFlCQkICr7zyCitWrMDOTpvMiMiDocSTiDxws/88zt7z4Tg72PP8Y8WpVcKfUUv3YTKbSTNZePe5ahTP48G8jSe4EpNEbGIajcrlB2DV/ot8veUUcclpjG5XnSL+HpmOkWYys/1kGL8fCSXNZGZyl8fuKMai/+/XzmDAwc4OezsD0Ymp+Lg5WRNNhXzdORYaTe0SedJdezoslv4LtpPf25WBT1bE1107xImIiORkn3zyCTt37sTJyYmuXbvy+OOPM3DgQEwmE2lpaUyYMIFSpUrx+eefExoaSlRUFC1atADgxx9/ZPbs2cTFxfHRRx9RvHjxTMdITU1l06ZNrFq1irS0NGbNmnXHcc6cOZN+/foxbdo067ESJUoA4OjoiJ2dHQaDId01Bw8e5LHHHsPZ2RlnZ2fy5ctHSEgIRYoUuePxRUTuhhJPIvJA7Th1lZCoROa82gCDwYDJbMFisTD1pTo42tux49RVFm07zajnqgNgbzAw+YXrSaODFyMp5OPGqOeqs+dcOLPXn2BCp1rp+j8aHMXq/ZcIjkrg8VL5eLNVRfJ7uwJwJSaJcSv2Z4ipWcWCdKxTItN4fz0UTD4vFwr6uGG2WIhOTOVabBKuTg4cCY6iWcX00+j8PZxZ+mZz3J0dWL3/IjPWHeO9dtXv8VUTERGR7LJp0yYuXrzI4sWLrz+bmEyYzWbmzJmDk5MTmzZtYt68eUycOBEAe3t7Zs+eDcDevXspXLgwEydOZMeOHUybNo3p06en6//gwYP8+OOPXLhwgUaNGjFixAgKFiwIQGhoKO+8806GmJ588km6du2a7tiGDRuoWLEifn6ZLznw5Zdf8uSTT+Lk5JTueHR0NN7e3tbvvby8iIqKUuJJRB4YJZ5E5IE6czWW2iX8rZ/G2dsZiEpIZeraI0TEp2A0mXG7aerav9dzqlLE1/rvp78ezdD/lhNhHA2J4vnHitOsYkG8XP95+Crg7crMHvWzHOuOU1f55WCwNfFlZzAwrG0Vxqw4gJuTA2UKeJHX0yXdNU4O9tyY1deqSmF+2HEuy+OJiIjIg3fy5Enq1av3z7OJvT0xMTGMGTOG8PBw0tLS0k2Lq1Ur/YdeNWvWtP774YcfZuh//fr1HDhwgK5du9KqVSt8fHys5woVKsS33377nzGaTCYWLFjA7NmziYyMzHB+6dKlnD59milTpmQ45+PjQ0xMjPX7uLg4fH19/3NMEZH7RYknEXmgSubz5LdDITxbs6i14um3QyGULeBN90al2X4qjMU3JWvs7NKXix8Jjuaxknk5GhxFUf+MayP0a1Ge7o1Ks+nYFcauOIC9nYGWlQvzROVCd1TxtP9CBAu3nGLqi3VwdrC3Hq9ZPA81i+chIcXIu0v2UKGQT7rr4pPT8HBxBGDf+YhMYxQREZGco2zZsvz888906tTJWvH0888/U7FiRfr27cumTZtYsGCBtf2/10Y6cOAA9evX58CBA9ZpbzcbNGgQffr0Yd26dbzzzjvY29tb12LKasVTeHg4sbGxvPHGG6SkpHDq1ClmzJjB66+/ztq1a1m3bh0zZ87MdN2matWqMW3aNFJTU0lMTCQsLIzChQtnaCcikl2UeBKRB6p+mfzsPx9Jn/nbcHa8vsZTnVJ5+WD5Pg5ejKB4Xs/bXn8tNolBi3YRn5zGqFtMYXNzcuDpagE8XS2AiPhktpwIA+6s4mncigO4OzvwzuK/ABjQsgIVC/syc93fHAuNwd7OQP8W5XGwv/6A98HyfXzQoSZ7z0ewYNNJ3JwdcLS3Y/gz2h5aREQkJ2vSpAm7d+8mMDAQFxcXXnrpJRo0aMCQIUPYs2cPpUuXvu31V65coWfPnsTFxTFp0qRM27i7u9OuXTvatWvHtWvXWL9+PZD1iqf8+fOzcuVKAIKDgxk1ahSvv/46qampDBs2jPLly/Pqq68C8NFHH1GoUCGGDBnC1KlT8fLy4uWXX7bubDt8+HAtLC4iD5TBYrHc/VZRIpKrRf8yDVNUiK3DkGxi71sYn6cH2zoMERERERF5hCnVLSIiIiIiIiIi2UKJJxERERERERERyRZa40lEcpyTV2L460w4LzUodcs2n/56lJcblcbX3fmuxgiLSWLCTwdJM5mpWNiHN1pVzNCmz/xtONgbSE4z0eXxkrSqUpg0k5kxy/cTEZ9CqtFE3+blqVMqL5HxKYxbeYA0k5kCPq6807YqjvbK7YuIiOR2x44dY9u2bfTq1euWbSZMmED//v3x8/O7ZZvbuXz5MiNGjCA1NZVq1apluuB4SEgIEyZMID4+HicnJ+bNmwfA9OnT2b59O46Ojrz33nuULVuWmJgYBg0aRGpqKkajkdGjR1OxYsZnHRGRB0FrPIk8wh7lNZ7e/3Efz9QsSu0SeXj/x308W7MotUrkSdcmzWTG0d6OhJQ0us/ezI8DW7DtZBgbjl1m1HPVCYtJYnjQHhb0acSnvx6lUoAPLSsXZsHmU+T1dKFtjSI2urvrtMaTiIhI7jBkyBA6duxIvXr1GDJkCJ06deLxxx9P16ZPnz6MGzeO/PnzW48dO3aMqVOnMm/ePIKDgxk5ciRff/01ixYtIjo6mtdff509e/bw9ddf8/nnnz/o2xIRATTVTkRsyGyxMHbFfvov2M7UtYfp8OmfAOw7H87Enw8CMH7lASatOsTbP+ymx5ebuRQRD8CAhdu5Gpt012OfuBxD7f8nmhqXL8C+8xEZ2tyoWEpMMVLi/7vtFfZ1I81oxmKxEJechq+7EwCXIuKpUMgHgAqFvNl3PvyuYxMREZEHz2w2M2zYMF588UXGjh1L8+bNAdi1axcjR44Eru8I995779GvXz/at2/P+fPnAejWrRtXrly567GPHj1KvXr1AGjRogW7du1Kdz40NJSkpCQmTZpE165dWbp0KQDnzp2jUqVKAAQEBHDmzBmMRiOlSpUiPv76M1NMTMxdV2KJiNwPmmonIjaz5fgVHO3tmPVKfYIjE1i171Km7Yrl8WD4M1VZc+ASq/Zf4rUnKmTa7kpMEuNW7M9wvFnFgnSsUyLdsZtrPT1dHIlJTM1wXUqaibcW7eLctTj6tygPQCFfN5LTTLwwcyNxyWl81OUxAErm82Tn6at0rFOCnaevEZuUlqXXQERERHKGP//8EycnJ77//nsuXrxoTe78W8mSJRk3bhzLly9n2bJlDB06NNN2oaGhmU6Ze/LJJ+natWu6YzdPQvH29iY6Ojrd+bCwMI4cOcKaNWvw9/enW7duPPbYY5QpU4ZvvvmG1NRUTp06RXh4OLGxsVSsWJHPPvuMtm3bEhsby3fffXeHr4aIyP2jxJOI2MzFiAQqB/gCEODnjrebU6btKhTyBqCgjyuHLkbesr8C3q7M7FE/S2MbDP98HZ+clunYzo72zHqlPtGJqfScu4UWlQrxx5FQ8nm58FGXx7gcncjwoD183bcx3RqWZtovR9h0/Aql8nmRx9MlS3GIiIhIznDu3DmqVasGQNGiRfH19c20XZUqVQAoXLgw+/btu2V/hQoV4ttvv83S2IabHkxiY2Px8fFJd97b25syZcpQqFAhAOrUqcPJkydp1aoVbdu25ZVXXqF48eKULVsWX19fpk2bRsuWLenZsycHDhxgzJgx1jWhREQeNCWeRMRmivi7s+v0NZ6pWZTQqMRMq44g/cPY7dxJxVPZgt7sPx9BjeL+bDkZRpvq6ddjSjOZsbczYGcw4Opoj5ODPU4O16fe+bhdX9Dc08WRpFQjAF6uTnzQoSYAs/44Rr0y+bIUs4iIiOQMxYsXZ+vWrXTq1IlLly4RFRX1n9fcbrncO6l4qlixIrt376ZOnTqsX7+eDh06pDtfrFgxUlNTiYuLw8PDgyNHjvDMM88A0LVrV7p27crJkyeZP3++9bnpxvQ6Pz8/4uLi/vNeRESyixJPImIzjcoVYOOxK/RfsJ2S+Tzx97y7HepuuJOKp/4tyvPhzwcxbrBQvqA3tYr7A//slpeQYmTCTwextzOQajTzapMyODnY82TVwrz/435eW7id5LTru9oB7DkXzsLNpwCoUyovdUvlvad7ERERkQerRYsW/P7777z44ouULVuWvHnv7b38TiqehgwZwrvvvsunn35K5cqVrQuL37xb3vDhw+nbty8mk4nGjRtTrlw5AF599VWMRiO+vr68//77wPVk1LBhw1i+fDnJycm3nA4oIvIgaFc7kUdYTtjVzmgy42BvR3BkAu8t28eCPo1sGs/DRLvaiYiI3Jm0tDQcHR25cOECgwYNYvny5bYOSUQk11PFk4jY1HvL9hGTlEpSqok3WmW+aLiIiIjIgzBo0CCio6NJTEzMdJqciIjcOVU8iTzCckLFk2QfVTyJiIiIiIit2dk6ABEREREREREReTgp8SQiD51O09c/0PFW7btIo3FrrN/P23iCXvO20verbUz75chtd7wRERGRh1/Lli0fyDh79uzhmWeeoUqVKly5csV6fP/+/XTp0oWXXnqJBQsWPJBYRERuUOJJROQeJKeZ2Hj8Cvm9Xa3HWlUpzLxeDfny1QZExqew91yEDSMUERGRR0W5cuVYvHgx1atXT3d8/PjxTJs2jUWLFrF161YuXrxomwBF5JGkxcVF5IG5HJ3IB8v342hvh8ViYWJgbc5cjWP+xpOYzBY8XRwZ17Emzo72DFi4nbIFvDkXHk+a0cRztYqx9mAwUfEpjOtUk6L+HgxYuJ0SeT25FJmA2WJhzPM18XN3to5nNJmZsuYwIVGJGE1mBrSsSJUivizYfIqtJ67g6uRAg7L5eaFeybu+p++3n6FTneJMXXvEeqyov4f1a0cHO+yU4hcREcmRgoODGTp0KI6OjgDMmDGDkydPMn36dEwmE15eXnz66ae4uLjQrVs3KlSowJkzZ0hNTaVz586sXLmS8PBwPv30U0qUKEG3bt0oXbo058+fx2w2M23aNPz9/a3jpaWlMWbMGC5evEhaWhrDhg2jRo0afPHFF6xfvx43NzeaNWvGK6+8clf34+npmenxuLg4ChUqBECFChXYvXs3RYsWvasxRETulBJPIvLA7D0XzmMl89CraTkALBYL5Qt6M+PlegDMXHeMP4+G0rp6EQCqF/dn4FOVmPjzQY6FRPNp17r8eiiY1fsv8doT13fAqxTgy9A2VVi9/yLfbzvD660qWsdbtf8Shf3cGfFsNaISUhi2+C/m9mzI74dD+Kp3Q1ydHDBnMg1uRNAeYpNS0x0L+H8/N4uIT+b45RhebVI20/vddz6ca7HJVC/mn+l5ERERsa2dO3fSoEED3njjDeD6s0mlSpX49ttvAZgyZQq//PIL7du3B6BOnTq8++67jBw5ksOHDzN//nx++uknfvzxR4YOHQpAtWrVeP/991m2bBnz5s1LtzvesmXLKFq0KOPHjycyMpL+/fsTFBTEqlWr+PHHH3Fzc8NsNmeI8/XXXycmJibdsaJFizJhwoQs3aevry/Hjx+nZMmS7Nq1C19f3zt/sURE7pISTyLywDSvVIhvtpzmg+X7KODtRs+mZTl3LY4v158gzWQmMj4Fd+cAa/vyBb0ByOvlQmFfdwDyebmw//w/U9eqFLn+4FS5iB+bjl/hZmfCYjkcHMWu01cBiE82AjDk6cpMXXsEo9lC+9rFqFbUL911EwNrZ+l+5m88SY9GZTI9d+JyDLP+PM7HL9TBzmDIUn8iIiLyYD399NN8+eWXDBkyhMKFC/P6669z+vRpPv30U1JTUwkPD8fD459K5kqVKgFQoEABihQpYv36r7/+srapUaOG9d8//vgj3XgnT55k//79bNmyBYDY2FgARo8ezdixYzEajXTp0oXatdM/i8yYMeOe7nPs2LFMnjwZgGLFipEvX7576k9E5E4o8SQiD4ydwUC/FuUBmPDTQXadvsbP+y/Sq2k5qhTxZea6v7m5/ujmfM2tcjdHgqMI8HPnaHBUuiluACXyeRLg506X/0+lSzNd/wSxchFfapfMw5WYJEYE7WFBn0bprstqxVNwZCJzN5wAICI+hXeX7OHDzrU5fy2Oj1YdYlKX2ni7OWXptREREZEHz87OjsGDBwMwYsQItm7dytKlS3njjTeoUaMGkydPTrdJiOGmB5Kbv765zYEDByhWrBgHDx6kRIkS6cYrXbo0xYoVo0ePHgCkpl5/3qhevTr16tUjNDSU119/neXLl6e77l4rnsqVK8f8+fNJTU3ljTfeoGHDhlm6TkTkflDiSUQemG0nw1i2+zx2BnB0sKdqUV8SU41M/PkgxfJ44O7sgJuz4x31eeJyDL8cDMZktjD2+Zrpzj1XsyhT1x7h9a93AFCmgBcDn6zE8KA9pBpNpBrNPP9YsQx9ZrXiaXr3x61fd5q+ng87X79uyprDJKQYGbN8PwBd6pWkUbkCd3RfIiIikv02bNjAokWLsLe3x8nJiVq1apGYmMjIkSMpWbIkHh4e6SqesuLvv/9m5cqVmEwmpk2blu5c586dGTduHN26dQOgYsWKjBgxgtdff52UlBRSUlJ48cUXM/SZ1YqnM2fOMG7cOI4fP87gwYNp3bo1Xbt2ZcGCBaxfvx6DwUCvXr001U5EHiiDRft8izyyon+ZhikqxNZh3LUBC7fzfoca5PNy/e/GjyB738L4PD3Y1mGIiIg8Mrp168aUKVMoUEAfOImI3KC9lkREREREREREJFuo4knkEZbbK57k9lTxJCIiIiIitqY1nkTkjlyOTmTiz4fSrW+UnQYs3E6qycz77WsQ4OdOn/nbcLA3kJxmosvjJWlVpTCHL0Xx+e9/42BvwM5gYMSzVa274GUmLjmNcSsPEJeURmFfN955piqO9rcuAB238gB/nblGvTL5Miww/m8r91xg1f6LODnYk9fLhVHPVcPJwT7TtmkmM299u9P6/dHgaOb3bkip/F6ZtrdYLEz//W+Oh8bgaG/HqHbVyOflyvq/Q5mz/gRPVg3glcaZ77InIiLyKAgODmbUqFEsXLjwgYzXrVs3UlNTmTx5MsWKFeOdd95h27ZtNGnSJEsLfy9cuJCNGzdiMpl44YUXaN269S3bRkVFMX78eMLDwzGbzcyaNeu260/NmzePP/74Azs7O95//33KlSt3y7axsbG88847xMTEULRoUcaOHYuT0603SAkJCWHChAnEx8fj5OTEvHnzSEhIoF+/fly5coV169b9572LyKNDiScRyfEmdKplXcdpZo96ONrbkZCSRvfZm2lVpTAFfFz5rFtdXJ0c2H4qjHkbT/J++xq37O+7bWdoXC4/bWsUZfafx/ntUAhtaxS5Zft+zcvRpnoAvx367+qw2iXz8GytotgZDMxc9ze/HQrhmZpFM23raG/HzB71Abgam8SgRbtvmXQC2H02nJjEVGa9Up9dZ67x5foTvNeuOs0rFiIp1cTV2OT/jE9ERETur88++8y6ptPgwYPp0KEDP//8839et2XLFq5du5blJNnEiRPp3bs35cuX/8+2586dY9OmTfzwww+cPXuWDz74gG+//faW7efNm0eLFi3o2LEj06ZNY9WqVTz//PO3bD9mzBjGjRtH/vz5rcfc3d359ttvadmyZZbuR0QeHVrjSUT4/Pe/2fD3ZQCMZjPdZm3CaDIz+8/jvP71Dnp8uZnlf53PcN34lQc4eDESgH3nw5n480EAzlyN5c1vdvL61zsYuXQvyWmm+xbrjcqkxBQjJfJ6ApDX0wVXp+t5dCd7e+xv2t44M3vPh9O4/PUHxMbl87PvfPht2+e9g8XLA/zcsfv/+I729tjZ3T6WG349FMKTVQrfts2+c+E0+X/cdUrm4e+Q6CzHJSIiklt99NFH/PbbbwAYjUaeeeYZ0tLSmDZtGt27d6d9+/Z8//33Ga4bPnw4e/bsAWDXrl2MHDkSgJMnT9KjRw+6d+/Om2++SVJS0n2L9eZEzH9Zu3YtZrOZHj168Oabb3Lt2rVbtjWZTJw4cYJFixbRtWtXZs2addu+d+3aRYsWLTAYDJQqVYqoqCiMRuNt299IGD3xxBPs2rXrlm1DQ0NJSkpi0qRJdO3alaVLl/7HnYrIo04VTyJC6+oBfPnnCZpVLMiu09eoVSIPDvZ2vNyoNK5ODqQaTXSbtZlnb1G5828frznC+x1qUMDblR93n+envRcIfLyk9fy/p5jdULWoH32b3/5TvJQ0E28t2sW5a3H0b5G+bVKqkTkbTvB2myq37SMuKQ1PF0cAPF0ciUlKy9J93Ynz1+LYefoqM3vUy1L73w+H8PGLdW7bJiYpDU/X63EbDAbMZi3RJyIiD7/27dszbdo0nnzySbZu3crjjz+Oo6Mj/fr1w83NjdTUVJ555hk6deqUpf7GjBnDlClTKFSoEN999x1BQUH06NHDej41NZWePXtmuK5mzZoMGjToft0WV69exd3dnYULF/Lrr78yZcoUJk+enGnbiIgITpw4wYQJE6hYsSIDBgxgx44d1KuX+XNGdHQ0+fLls37v4eFBbGwsfn5+t2zv5XW96trLy4vo6Ohbxh0WFsaRI0dYs2YN/v7+dOvWjccee4zixYtn7cZF5JGjxJOIUCqfF1GJKUQmpPDLwWC6NSwNwIo9F9h8/Ap2dgaiElKISkhJf+FNxTw3b1Nw7loc41bsByDVZKZmMf90l908xexOOTvaM+uV+kQnptJz7hZaVCqEh4sjqUYT7y7ZS/eGpSlT4NbT1QA8XR2JS07Dy9WJuGQj3v9P5twvl6MTGbfyAOM61bRWYt3O8dBofNycKOB9+8oqb1dH4v6fJLNYLNhnsZpKREQkNytbtixRUVFERESwYsUK+vbtC8APP/zAH3/8gb29PREREURGRqa7znBTBfTN+ymdPn2ad955B7ieZKpTJ/0HP05OTredlna/eHt707hxYwCaNWvGzJkzb9nWx8eHPHnyULlyZQAaN27M8ePHb5l48vHxITY21vp9QkKCNbF0u/be3t7Exsbi4+Nz27jLlClDoUKFAKhTpw4nT55U4klEbkmJJxEB4MkqhVm++zyXoxMpV9Cb2KRU1hy4xDf9mmA0m+kyYyP/rq/xcnEiLOZ6efqx0Gjr8ZL5PBnzfE3yeLoA1yucbna3FU9pJjP2dtcXEHd1tMfJwR4nBzuMZjOjf9xHy8qFaFjunxJ3o9lMVEIqef8fxw01i/mz9UQYrasXYeuJMGoWv54Yi01KxcHeDrcsJIsArsQkZUgWRcQnM3LpXt59tlq6Bc4TUoyYzGa8XDMu1PnroRCeqpp+ml1mfdco7s8fR0NpUqEge89FUL6Qd5biFBERye2eeeYZvvvuO0JCQqhYsSIxMTEsX76cn3/+GaPRyFNPPcW/N+v29vbm8uXrSwkcPnzYerxMmTJMnTrVWhGUmpqa7rrsqHgKDQ21JmpuqFevHkeOHKFx48YcPHiQEiVKABAfH4/JZMLb+5/3eScnJ0qUKEFwcDABAQEcOnSIVq1aAdcrkPLkyYO9/T+bmdSpU4exY8fy8ssvc+HCBXx8fHBwcMBoNBIREZFhSmCdOnVYv3497du3Z/369dZkXHR0NI6Ojri7//NMU6xYMVJTU4mLi8PDw4MjR47wzDPP3NXrIiKPBiWeRASAVlUK0+6TP+nVtCxwfQpa8bye9FuwjRJ5PDOtCnq2ZhHeX76f3w+HUMjHzXp8SOvKjP/pIMb/J5y6NijF46X/Kfe+24qnsJgkJvx0EHs7A6lGM682KYOTgz1rDlxiz9kI4pLSWHPgEsXzevJ2mypcjkrk89+PMfmFx9L181KDUoxfeZBV+y9R0MeVV5pc3wnu++1nKV/Im6YVCqZrP+uPY+w+G05EfDJvfrOTiYG1cHaw5+3vd/Nt/yb/anucqPgUPvnlCAAtqxSmXa1i/HEkhMRUEy/UK5muvdFsZvPxK/RuVjbd8UGLdvHDgKbpjtUtlZedp6/Rf8F2HO3tGPnc7XfYExEReVi0bduWpk2b8uabbwLXp4OVKlWKF198kVKlSmVaodOpUyeGDBnC6tWrCQgIsB4fPXo0I0aMIC3tehVxr169rJVHcG8VT1OnTmXbtm1cu3aNHj16MGPGDJydnenbty+rVq1K1/a5557j/fffp1u3bgCMHz8euL72U0JCAq+88kq69qNGjWLEiBEYjUbKli1L06ZNARg0aBAzZsxIN42uZMmS1K9fnxdeeAGDwcD7778PXN/1b9KkScyePTtd37169eKdd95h2bJlFC5cmNdeew2ABQsWUKlSJWuSC8De3p7hw4fTt29fTCYTjRs3vu2OeSIiBsu/PxoQkUdG9C/TMEX9905ttvTesn2ExyUz8rlqBPi5//cFN/ntUDDuzo7pqqBuZ9zKA7zTtgpODvb/2fbQxUhOXI6hU90SWer7k1+O8HLjMvi5O/9n22uxSfyw4xxvPlkxS32v/zuURVvP0Lp6AB3r/BOPvW9hfJ4enKU+RERE5M4NGjSIsLAwJk6cSLFixTJts3fvXv7++29rgum/jB8/nv79++Pv7/+fbdPS0hg5cuQt14b6t59//hkPDw+aN2+epfbvvPMO48aNw8kpY8X2vyUkJNCvXz+ABzJVUURyDyWeRB5huSHxJHdPiScREREREbE1O1sHICIiIiIiIiIiDyclnkTEJi5HJ/LmNxkXGH9QOk1fn+W28zae4NdDwdkYjYiIiDwIwcHB9OjRw2bjt2zZMsOxa9euMWnSpFtes3z5cuLj4+/rmCIiD5ISTyIi2cBisWTYXUdERETk3/Lmzcvw4cNveX7FihWZJp5MJlN2hiUict9oVzsReSBm/3mcvefDcXaw5/nHilO+0D9bBP9xJJSVey+QYjRRPI8n7z5bFZPZwnvL9hGdmIq9nYFXGpehkK8bHyzfj6O9HRaLhYmBtfFy/e/FLjNjNJmZ8NNBLkXEU9TfgxHPViUuOY1RS/dhMptJM1l497lqFM/jYb0mKdXIuJUHaFm5MBUL+zBlzWGS00w4Odgx6rnq+Hk402n6elpUKsSR4ChqFffH0cGOrg1KA9B7/lamvFAHH7e7i1lERETuzCeffMLOnTtxcnKia9euVKpUyXpu7dq1LF68mJSUFEqWLMmHH36I0Whk0KBBREZG4uDgwGuvvUZAQABDhw7F0fH6Dr8zZszA29v7VkPeVlpaGiNGjOD8+fOUKFGCCRMmEBISwqhRo1i4cCHLli3jhx9+wN3dnSpVqtCwYUOOHTvGwIEDKV26NP3792fgwIGUKlUKo9HIwIEDee+99zCbzbi7uzNp0iR8fX3/c0yAsWPHcvz4cUwmE2+88QZ169bl5ZdfZsyYMeTPn59XX32VL774gvz5s7ZJi4jIrSjxJCLZbsepq4REJTLn1QYYDAZMZgtXY5Os5xuUzccTlQsB8N6yvRy4EEk+bxfiktOY9Up9AMwWC2sPXOKxknno1fT6lr3/rijacuIKi3eczTB+n+blqVbUL92x8PgUejQuTWFfd8au2M/2U1epWyovU1+qg6O9HTtOXWXRttOMeq46AJHxKQwP2sOrTcpSragf7y3bR4/GZagc4Mu2k2F8s/U0bz1VCZPZQv0y+ejXojwJKUbe+HoHXRuU5nhoNAV93JR0EhEReUA2bdrExYsXWbx48fXnD5OJy5cvW883bdqU1q1bA/DWW2/x119/UbBgQWJiYvj+++8BMJvNLF++nAYNGvDGG28AGZ8//vzzTxYuXJhh/IEDB1K7du10x65du8Zrr71GkSJFGDZsGJs2baJ06dLW8ytWrOCzzz4jICAAs9mMnZ0dFSpUYMqUKRQoUIDg4GBCQkJYuHAhnp6eDBgwgAEDBlC3bl2WLFnC3LlzGTZs2H+OaTQaSUpK4ocffiAyMpIXXniBX3/9lUmTJvH222/j5+fHW2+9paSTiNwXSjyJSLY7czWW2iX8MRgMANjbGdKdP3Ahku+2n8FssXAlOomGZZOoUdyf5hUL8sHyfbg4OvBqkzI0r1SIb7ac5oPl+yjg7UbPpmVxtP+nr0blCtCoXIEsxeTn7kxhX3cAqhTx40J4PBUL+zB17REi4lMwmsy4Of/zK3LprnO0qV7EmsA6ezWWWX8cA8BsgQI+rgDY2RmoUuT6J43uzg6UK+jN/gsR/HE4lGdrFr2bl09ERETuwsmTJ6lXr94/zx/29unO//XXX8yfPx+z2UxoaCjNmzenTp06PP300wwZMgQ3NzcGDBjA008/zZdffsmQIUMoXLgwr7/+Ok5O/3yQ1KJFC1q0aJGlmPz9/SlSpAgANWrU4OzZs+kSTyNGjOCLL74gJSWF1q1bZ9pvmTJl8PT0BODcuXPUqlULgFq1arF+fcY1LDMb02g0Wq/z8/PDy8uLmJgYihUrRunSpTl37hyNGjXK0j2JiPwXJZ5EJNuVzOfJb4dCeLZmUWvF081m/XmMaS/VJY+nC+8t24sFSDWaeK5WMTo8VpxfDgazeMc5+jQvR78W5QGY8NNBdp2+RsNy/3wSdycVT5EJKVyOTqSgjxtHg6NoVrEgvx0KoWwBb7o3Ks32U2Es3nHO2r5Xs3L8dTacH/86z/OPFadEXk+6NSxNuYLXS+3TTGYADGB9wAVoV7sYX285xeXoJIa2qXxPr6OIiIhkXdmyZfn555/p1KmTteLpZlOnTmXevHnky5ePt956C4vFQmpqKoGBgbz44ousXLmShQsXMnDgQAYPHgxcTwxt3bqV5s2bW/u5k4qniIgIgoODCQgI4ODBgzz55JPpzpcqVYoPP/yQlJQUWrVqRYsWLXB0dMRoNFrb3JxAK1GiBHv27OHxxx9n7969lChRIkMcmY1pMplYt24dnTp1IjIyktjYWLy9vdm5cyeRkZGUKVOGlStX0q5duyy/3iIit6LEk4hku/pl8rP/fCR95m/D2THjGk9PVQ1g4Le7KJ7Xw1q+fiUmiYk/H8LOAGkmC4OersS2k2Es230eOwM4OthTtWj6NQzupOIpj6cL32w5zblrcQT4u1O/TD7O+cTzwfJ9HLwYQfG8nuna29sZGN2+Oh+tOsT328/wRquKTF17hKQ0IxYLtK4WQOvqRTKMU66gN2ExSTSrWDBdQkpERESyV5MmTdi9ezeBgYG4uLjw0ksvpVvjqV27drz66quULFnSeiwkJISRI0dib29PWloaI0eOZMOGDSxatAh7e3ucnJyslUI33EnFU758+ZgzZw6nTp2iePHiNG3alJCQEOv5MWPGEBwcTFpaGl26dAGu70r37rvvUqlSJV566aV0/Q0dOpTRo0czY8YMXF1d+eijj7I0JsCWLVvo0qULJpOJd999l8jISKZMmcLcuXNxd3fn5ZdfpkqVKpQqVSpL9yYicisGi7ZdEnlkRf8yDVNUyH83lHvSf8F2JnSqhZ+H8wMd1963MD5PD36gY4qIiIiIiNxMFU8iItnkWlwy41ceoEYx/weedBIREREREckJlHgSEckmeT1d+Kzb47YOQ0RERERExGbsbB2AiIiIiIiIiIg8nJR4EhERERERERGRbKGpdiKPMHvv/LYOQbKRfr4iIiIiImJr2tVO5BFlsZgxGFT0+LDTz1lERERERGxJf42IPKIeRDLi4sWL9OrVi759+xIcHJzt4+V0hw8f5qWXXmLo0KFERUU9kDGVdBIRkYdBTEwMX3zxBR06dGDw4MEcO3bM1iEJcOnSJT744APat2/PhAkTuHLliq1DEpEcSBVPIpIt9uzZQ//+/SlYsCBz584lf35N+wI4fvw4vXr1wsXFhfnz51OsWDFbhyQiIpJjmUwmli5dyieffILJZGLQoEF06dIFe3t7W4cm/2exWPjtt9+YOHEikZGR9O3b1/qsIyICSjyJSDb4448/GDx4MNWqVeOLL77A09PT1iHlKCEhIfTs2ZOYmBjmzJlDlSpVbB2SiIhIjnPo0CE++OADjh49SocOHRg6dCj+/v62DktuITExkVmzZrFgwQLy58/PqFGjaNasma3DEpEcQHMwROS+Wrx4MW+88QbNmjVj/vz5SjplonDhwvzwww8ULVqU7t27s3XrVluHJCIikmNERkYyatQoOnfujNlsZvHixUycOFFJpxzOzc2NIUOG8PPPP1OsWDH69etHv379uHTpkq1DExEbU8WTiNwXFouFGTNmMGPGDLp168a7776LnZ1y27eTlJTEoEGD2LJlCx9++CHPPfecrUMSERGxGZPJxJIlS/jkk0+wWCy89dZbmlaXS1ksFn7//XcmTpxIREQEffr0oXfv3pp+J/KIUuJJRO6Z0WhkzJgxLFmyhCFDhtC7d28MBoOtw8oVjEYjo0eP5scff2TYsGG8+uqreu1EROSRc/DgQcaMGcPRo0d5/vnnGTJkiCqcHgKJiYnMnj2br776ivz58zNy5EiaN29u67BE5AFT4klE7klSUhKDBw9m06ZNjB8/ng4dOtg6pFzHYrHw2WefMWvWLHr06ME777yjajEREXkkREZGMnXqVJYtW0bFihUZPXo0NWrUsHVYcp+dPXuWCRMmsHXrVpo2bcrIkSMpWrSorcMSkQdEiScRuWvR0dH069eP48eP89lnn9GkSRNbh5Srfffdd4wbN47WrVszadIknJycbB2SiIhItjCZTAQFBfHpp59isVgYNGgQgYGBmlb3ELNYLKxbt46JEycSHh5O79696dOnj6bfiTwClHgSkbsSGhpKr169iIyMZM6cOVStWtXWIT0Ufv/9d4YMGUKtWrWYMWMGHh4etg5JRETkvjpw4ABjx47l6NGjdOzYkSFDhuDn52frsOQBSUxM5Msvv2T+/Pnky5fPOv1OSw2IPLyUeBKRO3by5El69eqFg4MD8+fPp0SJErYO6aHy119/0b9/fwICApg7dy558+a1dUgiIiL37OZpdZUqVWL06NFUr17d1mGJjZw7d47x48ezdetWmjRpwqhRozT9TuQhpcSTiNyRG0mRwoULM3fuXPLly2frkB5KJ06coHfv3jg6OjJv3jwl90REJNcymUwsXryYTz/9FIPBwFtvvaVpdQJcn373xx9/8OGHHxIeHk6vXr3o06cPrq6utg5NRO4jJZ5EJMt+++03hg4dSs2aNZk5c6amgWUzTWcUEZHcbv/+/YwbN46jR4/SqVMnBg8erGl1kkFSUhKzZ8+2Tr979913adGihabfiTwktG2SiGTJ999/z8CBA2nZsiVz585V0ukBKFSoEN9//z3Fixene/fubNq0ydYhiYiIZElERAQjRoygS5cuACxZsoTx48cr6SSZcnV1ZdCgQaxevZqSJUsyYMAA+vTpw4ULF2wdmojcB6p4EpHbslgsfPbZZ8yaNYuXX36Z4cOHY2ennPWDlJyczKBBg9i8eTMTJkygXbt2tg5JREQkU/+eVjd48GA6deqkaXWSZRaLhT///JMJEyZw7do1evXqRd++fTX9TiQXU+JJRG7JaDQyevRofvzxR95++2169uypkmcbMRqNjBkzhiVLljBkyBB69+6tn4WIiOQo+/fvZ+zYsRw7doxOnToxaNAgVTjJXUtKSuLLL79k3rx5mn4nkssp8SQimUpKSmLQoEFs2bJFVTY5hMViYcaMGcyYMYNu3brx7rvvqvpMRERsLiIigo8//pjly5dTuXJl3n//fa1LKPfN+fPnmTBhAps3b6Zx48aMGjWKYsWK2TosEbkDSjyJSAZRUVH069ePkydPMn36dBo1amTrkOQmixcvZsyYMTz55JNMnjwZJycnW4ckIiKPIKPRyOLFi/nss8+ws7Nj0KBBmlYn2eLG9LsPP/yQq1ev0rNnT/r166fpdyK5hBJPIpJOSEgIPXv2JCYmhjlz5lClShVbhySZ+OOPPxg8eDDVq1dn5syZeHp62jokERF5hOzdu5exY8dy4sQJOnfuzKBBg/D19bV1WPKQS0pKYs6cOcybN488efLw7rvv8sQTT2j6nUgOp8STiFgdP36cXr164eLiwvz581XGnMPt2bOH/v37U6hQIebOnUu+fPlsHZKIiDzkwsPD+fjjj1mxYoWm1YnNXLhwgQkTJrBp0yYaNmzIe++9R/HixW0dlojcghJPIgLArl27eO211yhatChz5swhb968tg5JsuDUqVP06tULe3t75s2bR8mSJW0dkoiIPISMRiM//PADn332Gfb29gwePJiOHTtqWp3YjMViYf369UyYMME6/a5v3764ubnZOjQR+RclnkSEX375hbfffpvHHnuMzz//HA8PD1uHJHfg8uXL9OrVi/DwcObMmUO1atVsHZKIiDxENK1OcrLk5GTmzJnD3LlzyZMnDyNGjKBly5aafieSgyjxJPKIW7RoEePHj6dNmzZMnDhRC1XnUjExMfTv35+jR4/y2Wef0bRpU1uHJCIiuVx4eDhTpkxh5cqVVKlShdGjR2taneRYFy9eZMKECWzcuJGGDRsyatQoSpQoYeuwRAQlnkQeWRaLhU8//ZTZs2fzyiuvMGzYMOzs7GwdltyD5ORkhgwZwoYNGxg3bhzPP/+8rUMSEZFcyGg08v333/PZZ5/h4ODA0KFDef755/WcILnC+vXrGT9+PFevXuXVV1+lX79+mn4nYmNKPIk8gtLS0hg9ejTLly/nnXfe4dVXX7V1SHKfmEwmxowZQ1BQEIMGDaJv374qNRcRkSzbs2cPY8eO5eTJkwQGBvLWW29pWp3kOsnJycydO5c5c+bg7+/PiBEjaNWqlZ6JRGxEiSeRR0xiYiIDBw5kx44dfPjhhzz77LO2DknuM4vFwhdffMH06dN56aWXGDlypBZ/FRGR27p5Wl3VqlUZPXo0VapUsXVYIvfk4sWLfPjhh2zYsEHT70RsSIknkUdIZGQkffv25fTp08yYMYMGDRrYOiTJRkuXLmX06NG0bNmSKVOm4OzsbOuQREQkhzEajXz33XdMnz5d0+rkoXVj97uwsDBNvxOxASWeRB4Rly5dolevXsTFxTF37lwqVapk65DkAfjzzz8ZNGgQ1apVY+bMmXh5edk6JBERySH27NnDmDFjOHXqFIGBgQwaNAgfHx9bhyWSLZKTk5k3bx5z5szBz89P0+9EHiAlnkQeAceOHaN37964uroyf/58ihYtauuQ5AHat28f/fr1I3/+/MybN4/8+fPbOiQREbGha9euMXnyZH7++WeqVavG6NGjqVy5sq3DEnkgLl26xIQJE9iwYQMNGjRg1KhRlCxZ0tZhiTzUlHgSecjt2LGDAQMGULx4cebOnYu/v7+tQxIbOHPmDD179sRgMDBv3jxKlSpl65BEROQBMxqNLFq0iM8//xxHR0eGDBmiaXXyyNqwYQMTJkzgypUr9OjRg/79++Pu7m7rsEQeSko8iTzE1q5dy7Bhw6hbty7Tp0/Xm+kjLiwsjF69enH16lVmz55NjRo1bB2SiIg8IH/99Rdjx47l1KlTdOnShbfeekvT6uSRl5KSYt39ztfXl+HDh/PUU09p+p3IfabEk8hD6uuvv7buWjdhwgScnJxsHZLkALGxsbz22mscPnyYTz75hObNm9s6JBERyUZXr15l8uTJrFq1StPqRG7h0qVLfPjhh6xfv5769eszatQoVYeL3EdKPIk8ZCwWC1OnTmXu3Ln07NmToUOHqoRe0klJSWHo0KH8+eefjBkzhk6dOtk6JBERuc/S0tKsu9U5OTkxdOhQOnTooGcCkdvYuHEj48eP1/Q7kftMiSeRh0haWhqjRo1i5cqVjBgxgh49etg6JMmhTCYT48eP5/vvv2fgwIH0799fZeUiIg+JG9PqTp8+zQsvvMDAgQPx9va2dVgiuUJKSgrz5s3jyy+/xNfXl3feeYenn35az0ki90CJJ5GHREJCAm+++Sa7du3io48+ok2bNrYOSXI4i8XC7Nmz+fTTT+nSpQujR4/G3t7e1mGJiMhdCgsLY/LkyaxevZoaNWowevRoKlasaOuwRHKlS5cuMXHiRP7880/q1avHe++9p+l3IndJiSeRh0BERAR9+vTh3LlzzJw5k3r16tk6JMlFli1bxujRo2nevDlTp07F2dnZ1iGJiMgdSEtLY9GiRUyfPh0XFxeGDh1K+/btNa1O5D7YtGkT48ePJzQ0lB49evDaa69p+p3IHVLiSSSXu3TpEj179iQhIYG5c+fqk025Kxs2bOCtt96icuXKfPHFF5qSISKSS+zatYtx48Zx5swZTasTySYpKSnMnz+f2bNn4+Pjw/DhwzX9TuQOKPEkkosdPXqUPn364OHhwbx58yhSpIitQ5Jc7MCBA/Tt25e8efMyb948ChQoYOuQRETkFjStTuTBCw4OZuLEifzxxx88/vjjjB49WtPvRLJAiSeRXGr79u0MGDCAUqVKMWfOHPz8/GwdkjwEzpw5Q+/evTGZTMyfP5/SpUvbOiQREblJWloa3377LZ9//jkuLi68/fbbtGvXTtPqRB6gTZs2MWHCBEJCQujevTsDBgzAw8PD1mGJ5FhKPInkQqtXr2b48OHUq1ePTz/9VPPM5b4KCwujd+/eXLlyhVmzZlGrVi1bhyQiIlyfVjd27FjOnj3Liy++yMCBA/Hy8rJ1WCKPpJSUFL766itmz56Nl5cXw4cPp3Xr1pp+J5IJJZ5EcpmFCxcyceJE2rdvz7hx43B0dLR1SPIQiouL47XXXuPgwYNMmzaNJ554wtYhiYg8ssLCwvjoo49Ys2YNNWrU4P3336dChQq2DktEgJCQECZOnMi6deuoW7cuo0ePVsW4yL8o8SSSS5jNZqZMmcJXX31Fnz59GDx4sD5RkWyVmprK22+/ze+//84HH3xAYGCgrUMSEXmkpKWl8c033zBjxgxcXFwYNmwYzz33nKbVieRAmzdvZvz48Zp+J5IJJZ5EcoHU1FTeffddVq9ezbvvvkv37t1tHZI8IkwmEx9++CGLFi3ijTfeYMCAAUp4iog8ADt37mTcuHGcPXuWl156iTfffFPT6kRyuNTUVOvud5p+J/IPJZ5Ecrj4+HjefPNNdu/ezeTJk2ndurWtQ5JHjMViYe7cuUydOpXAwEBGjx6Ng4ODrcMSEXkohYWFMWnSJNauXUvNmjUZPXq0ptWJ5DIhISFMmjSJ33//nbp16/Lee+9RpkwZW4clYjNKPInkQN988w1ms5m2bdvSp08fLly4wMyZM3n88cdtHZo8wpYvX86oUaNo2rQp06ZNY8qUKTRo0IDmzZvbOjQRkVwvNTWVb775hpkzZ+Lq6mqdVqdKCZHca8uWLYwfP57g4GBNv5NHmhJPIjlMcnIyjRo1onXr1mzfvp2kpCTmzZtH+fLlbR2aCJs2bWLgwIFUqFABT09Prl27xooVK2wdlohIrrNkyRIiIyPp168fO3bsYOzYsZw/f17T6kQeMqmpqSxYsIAvvvgCT09Phg8fTps2bVizZg0nT55k8ODBtg5RJNsp8SSSw/z0008MGzYMHx8fvL29mTlzpkpzJUfZtWsXb775Jq6urly+fJkff/yRypUr2zosEZFc4/Tp07Rr146XXnqJsLAwfvnlF2rVqsXo0aP1QZPIQyo0NJRJkybx22+/UadOHZo0acKUKVOYM2cOTZo0sXV4ItlKiSeRHOaZZ57h5MmT+Pn54eDgQEREBDt37tQnn5IjHD58mI4dO1K4cGGio6NJSEjgqaee4rPPPrN1aCIiuYLZbOaFF17gwoULJCcn4+7uzttvv61pdSKPiK1btzJu3DguXrxIwYIFMZlMrF27Fnd3d1uHJpJttBerSA4SHh7OyZMnAfD19aVNmzYsXLhQSSfJMSpXrszMmTNp2LAhnp6eAKxbt87GUYmI5B4ff/wxBw4cICoqiqJFi1KjRg18fHyUdBJ5RPj4+FC+fHkqV67MlStXuHLlCkOGDLF1WCLZStsSieQgefLkYfz48dSrV4+AgABbhyOSgcFg4IknnuCJJ57AYrFw7NgxLly4YOuwRERyjQ0bNmAwGPDz88NgMBAfH4/JZLJ1WCLygFgsFuv/+zx58hAeHs6ePXtsHZZIttJUOxERERGRB+TGo7cqnETkBovFot8J8lBT4klERERERERERLKF1niSWzKblZN82Ohn+uixmDV941Ghn/WjR58dPlz08xR5NFj0PP5Q0c8za1TxJLe1eWEcMVf0x8zDwLuAPY17eNo6DLGB6NWTMEZcsnUYko0c/Ivg03a4rcMQG0g0RWC2GG0dhtwjO4MDbvb+tg5DRB6QhKV/Y7qaYOsw5B7Z53PHvVNFW4eRK2hxcbmtmCsmIi8p8SSSmxkjLmEMO23rMEQkG5gtRsyk2ToMuVf6GFjkkWK6moDpcrytwxB5YDTVTkREREREREREsoUqniTH2HxmHn5uAVQu+FSW2p8J30liWhRVCj6dzZHdXkTCRVYeGU1k4iW61JhGEZ9qwPX7ORb2J+5OfgC8UPNT7O0cuRp3ml+Pf4wFM+XzNaVusRcz9Hnk8q/sDf4RgCal+lHcr9aDuyGRB+DT7RGcjkylYyUvmpZwz3D+clwaZyPTaFDMLdtjmb83ij/PJPB954AM5wauuUyqCZzsoZiPE4MbaCqMCMDk8dM5deIMXbp2YMOf2xg/+V327j7AL6v/YNTYobYOz2rmp/P4dfWfFCkWwBfzp2Ta5tuvgtj45zbs7Ay8895ASpct+YCjFBHJPnOPriLAIx9PF6ubpfY7rhwlOiWOp4s9ns2R/XccX/29Bgc7e+ywY3Sdl8nv5seCY2vZdeVvTBYzAR55GVm7Ow529vTfOJU0kxFHewdKeBVkWM2Mf2MtOvE7m0IOYGew4+2aL1Dau7AN7uzRpMST5Epmi4lSee7/L0OLxYzRnIqjvUuWr/FyyceLNafzx8npGc41KPFyhkTaryem0rbSSHxdC7No7wDK5m2Mr9s/f/Amp8Wx88IP9HhsDqmmJL7fN5CedRdgMKhAUR4eu0OS+L5TxkTPDVfijWy7mJjlxFOK0YyjvQG7O9yK+FqCkeDY26+PM6ZFXvK56+1S5GY7t+1h+S/fANDiySb33J/ZbMbO7vbvc4kJSbi5u95Rv51fbMdzz7fmww8+yfT8hfOX2LZ5F/MWfcaFc5eYNPZTZi+cdkdjiIg8LEwWM/UKVLrv/ZotZlJNRlwcnLJ8Te185ayxrDq3jSWnNvBGtefpWq4Vr1RoDcCY3QvYFfY3DQpWAeDDen3I5+abaX8X48LYfvkIc5q9zYW4MCbv+54vmg6+xzuTrNKTtGS7jadncyFqL/Z2ztQOeJ5ifrVYcXgUZrMJkyWNNhXfJY978XTXHAxdzf7glYCBsvkaUb94dy5E7mPHhW9xsnfHw9mfAp5liU2+SsOSr3Ahaj+bz8zFgAE/twCerjCcmOQr/HzkA+zsHMFi4flqE3F19Mo0xqtxpzly5TcuRR+kZdm3KOSd9UXiHO1dbpmo2nHhO/YFr6R8vqbUKdYFozmVVGMCfm5FACidpwEXow6kSzyFxh6lqG91HOydcbB3xtM5D9FJl/F1U0ZeHg7TtkVwLcHEwDWXGVTfn8WHY7kcbyQh1Uz36t40Lu7OkiOxHL+WysA1l+lZy5eqBTL+HzOZLey7nMwfZ+K5HGdkypP5cXa4s8TTgn3RdK/uzch1VzNvYDAwdsM17O0MdKvmTe3Cd/ZHr8jDaNLYz7gado2+PQbzzqg3GfL6e6z49VsAQoKv8Pabo7kcGkaHzm3p0PkZ4uPimfD+NKKjYrBYLIz4YBDFihehb4/BVKhUljOnzvP6oF6Uq1A6w1iJCUlsXL+Vdb9sJF/+PIx4f9AdxZo3Xx5CQ67c8vze3Qdo3Kw+BoOB4iWLEhUVg9FowsHB/s5eFBGRB2zW4ZXsvXoCJ3tHOpZuSu185Xh3x1xMFhNGs4mRtbtT3KtAumtWn9vO8rObMWCgSeFqdC//FHuvnuDb47/h7uiCv4s3ZX2LcDUxmlcrtmbftZPMPbIKDFDEIx8janXlcmIE7+/6Ckc7ByxY+Kh+P7ycMlavA5yKDubXi7s4FH6GQdU7U9GveJbvz9Hun1RFXFoSpX0Kpztutpgxma9XPQEYMDBq5zwc7OzpUeFp6uSvkK6/vVdP0KhQ1eu/770KEJ0Sj9FswsFOv+8fBCWeJFudCd9BVFII3WvPwWAwYLaYsFgsBFafir2dI2fCd7Dz/CLaVhplvSYhNYrdF4N4pc587Az2fLf3DcrkaQhAbPJVetZdiL2dI4dC1wDXtx/+48SnvFR7Ji4OHvx5agYnr20mOS2O4n6P0bhUL2u7myWkRnEodDVnI3bh51aMSgVa0qz0axj+XzHx6/EphMefT3eNk4Mrnat/nKV7r12kE41K9sRkTmXpwXfI51kGf/eiuDj8s7Oci6MnSWkx6a5LSovNtI0vSjzJw2FwA3/2hCTxWZuCAAys54erox0xySbeXHOFxsXd6VzZi3WnExjWKE+G689HpbL6RDynIlKoXtCFl6r6UNTHEYA0k4Whv2b8I7NKfhd61U7/CdiZyFQASvje+tO3Mc3z4uNiz7UEI4N/ucKsZwvh4aTqQ3m0DR89kF3b9/BlJpVB4dcimD57IhYsdO/8Gi1aNeGbr4Jo9kRDWrVuzpnT5/l86hw+/nwcAOUqlOatt/tl6Gfntj2s+fl3oqNiadysHqPHv42vnw8ARw8dZ/q0ORmu6fzCc3dcfRUTHUuevP9MofXwcCcuNs46lohITrT98hFCEsKZ23wYBoMBk8WMxWLhk0av42jnwPbLR/j2xG+899jL1muiUuL44dSfLGgxHHs7e17f+AkNC1YFICwpim8ajsTRzoHV57cD1/92+vTAUr5oOhgPR1c+P/gjm0MPEpuaQJ38Fehd6Rlru5tFpcSx6tx2dl35m6Ke+Xmy6GO8XqWD9W+syfu+51zs5XTXuDm4MLXhgAz3uTFkPwuP/UJ8WhKfNHrDenzu0VX8dmE3RTzzkd/1+rImH9brg4+zB1eTonhj06fMbzEcD8d/PjCMSU0gj6u39Xt3Rxfi0hLxddau3w+CEk+Sra7Gn6G4X23rLxo7gz0JaVH8dnwqCakRmMxGnBzST6WJTgoln0cpHOyu/zFY2LsSEYkXcXXwopBXBeztHNO1T0yLJib5CssOvANAmjkJb5eCVC34NNvPf8NPRz7A26UAjUr2xN7wz7WRiRc5GLqGMnkaUKXg0+TzTP9J61Pl376ne3dzuv6LzcHemfL5mnIl9jgB3pVJNv6zg0VKWjyujt7prnN19CLZGGf9PjktLkMbkYeF2WJh4f5ojl5Nwd4AYQn/vS38vsvJ7ApO5JnynjQv4U6em6bBOdobrAmt//LV3igG1r/9mk0+Ltc/Bcvr7kAZf2cuxaRRIa9zlvoXeRSVr1gGR6fr77UlSxcjNOQKZ06dY/+eg/y4ZDUAN9clVq9ZJdN+fl39ByGXQun8YnsaN6ufbopdparlM0163Q1vHy/iYv95X05ISMTTS3+EiEjOdiYmhNr5yln/xrI32BGVGsfH+34gIjmWNLMJN4f0zysh8eGU9i6Mk/3139GV/EtwIS4MLyc3KvoVT1dhBBCdGs/lhAje3vYFAMnGVAq6+9O6+ON8fexXRu+aT0E3f3pVaouj4Z9rL8SFsfrcdhoWqsLTxR6njE/6pRUyW3vpVpoWrkHTwjVYd/EvZh9eyYR6fQDoXekZelVsy5T9P7D6/HY6lm6Kj7MHAPlcfSnnU5SLcWHpKqy8ndyJS020fp+YloynY/avJSrXKfEk2SqvR0mOXP6N6oWetVY8Hb38GwU8y1K/RHdOh29n98XF6a7xcS3E1bjTGM2p2BscCIk5SpWCT5OYGo3BkLEU0s3RBx/XQnSuPsWaxDKZjZgtRpqWvv4p6uqjEzgbsYsyeRtaryviU42+9X4gOOYQe4NXEJ5wjpL+dahRuB1uTj73XPGUnBaHi6MnFouFC1H7qFigJQ72zjg5uBGVGIKPayFOR+ygdYXh6a4r5FWJDadnYzSnkmZKJi4lHB/XrP0hLZLbnI5I5WxkKjPaFiQ62cSLS4IBcLQzYLJkvr94h4petC3nyY6LiUzfGUmS0ULjYm60LuuB2UKWK55C4oxM2RIOQESSiU+2RzDopkSUxWIhMc2Cu5MdiWlmzkSmUtBDb5sit3Pi71MY04xYLBbOnr5AwcL5KVmqGFWqV6LZE9ffg9NS06ztb7Wu0wcThxMdHcO6Xzby9sDR+Ph407bdk9Rr+Nh9rXiq+Vh1Jo+fzgvdn+fSxRC8fbw0zU5EcrxS3oX49cJunivR0Frx9OuFXZT1KcrLFZ5i++XDfH/yz3TXFPbIw+noYFJNaTjY2XM04hytiz1OVEocdpmsJevj5EEh9zxMbTgAN4frSx4Yzden8fWv0g6A8X99w84rf9OoUFXrddXzlCboqQ84GHGG5Wc2cz72MnUKVKB9ycb4OHtkueIpxZSG8/+TZJ5ObjjbO6U7bjAY8HB0xcXB6fozmzEZd0dXEo3JnIoJpqB7+g8Xa+Yry5R9i+lSpgWX4q/i5eyuaXYPkJ6gJVuVzlOfi1H7+fqvPjjaO1Mr4HlK+NfhpyMfcCn6YIa1nQDcnXx5rGhnvt3THwMGyuRtSF6PklyI3JfpGAaDgRZl32TJwWHXv8dAizKvE5kUzJ5Ly7DDDns7RwJ8qmZ6bRGfahTxqYbJbORMxA4SUiNxc/LJcsVTUlosKw6PIjz+POEJZynhV5empfuy7uRnRCZewGKBIr7VKJu3EQCtyr7FqqPjsGCmXN4m+P1/faefjnzAc5U/wMXRkzpFAvlu7/Vfvi3KvqGFxeWhVdTHEaMZ3lxzmdJ+TtZpbCV9nQiJNTL6z6u8UNU7Q5WRk72BJiXcaVLCnbgUExvPJWI0W3B2sMtyxdPCDv9MX31xSbA16fTdwWgeL+JGMR9H3lp7BWcHA2kmC92qe+PjqgcUkdvJXzA/I4aM5XJoGM8HPoOPjzev9n2JiWM+Zcl3KwB4vEFtXu71wn/25ePjTacXnqPTC88REnyZg/uOAHdW8fTDtz/y52+bOX/2Aq/1fJt33nuTYsWLMGrYh4yf/C7FSxShbr2a9Oo6EIPBwLBRb979zYuIPCD1C1Zh37VT9Fo/GRd7J54v3YS6+Ssyetd8DoSfooRXxmchX2dPOpdpTr8NH4PBQKNCVSnpXYi9V09kOobBYGBg9Y4M3Xq94snOYOCNqs9zKf4qS09vxM5gwMnOkWp5SmV6bfU8pamepzRGs4ntV44QmRyLj7NHliuefjq7lQ0h+zBgwMnOgXdqvQTAlH0/EJJwDbPFQlGPfPSp9Cwmi5nXNn6Cs70jRrORVyq0tk6hG71rPmPr9qSYZwHq5K9Anw1TMGDg7Zr//T4k94/B8u9JmSI3WTUpmshLJluHIfeBXxF7nhnuY+swxAbCvx6AMey0rcOQbOSQvzR5Xp5p6zDEBuKNYZhJ+++GkqPZ4YiHQ35bhyEiD0jszL8wXY7/74aSo9kX9MBrwGO2DiNXUBmFiIiIiIiIiIhkCyWeREREREREREQkW2iNJ8mVwuJOci7iLx4v/tIt26w78Sn1S7yMu5PvLdvcTmxyGKv/noDRnEZhr4q0KPtGhjZf/9UHO4MDRnMydYp2oVKBVpy8toXdF64vmJ5sjMfOYMerdRfcVQwiD7NTESnsCUnmhaq33rXx850RdKvmc9drK12NNzJpSzipJgsV8zrzWl2/DG0+3HSNPaHJPB7gyrBGeQCITzXz9q9XuBiTxsB6/rQq7ZHumjUn4pi6LYL1rxa/q7hEHkYnjp1m9469dHs18JZtpk6cyat9X8LXz+euxrhy+SpjR00hLTWVSlUr8Nbb/TK0ea7VSxQodH3aWu061en9WncA/tq5nwVzv8dsMlG5WkVeH9SL5KRk3h8xiejoWLy9vRg9figenh4Z+hQReVidjL7E7rBjdC3X6pZtPjmwhB4Vnraum3SnwhIjGffXN6SZjVTyK86b1Tpm2i7NbOSFX8fQung9Xq3Y2no8JiWejr+MZnCNQJ4uVheLxcLEvYu4GBeGi70T7z7WjXyud/c3nzwYSjxJrpTfsyz5Pcvetk3Lcm/d0xjrT31B/eLdKe5Xm5WH3+d85F6K+9VK16ZrrZnY2zmSYkxg3s7uVCrQirJ5G1kXEt927mvsMtmJT0SgjL8zZfydb9vmjcf9b3v+v8z+K4qu1bypWciVsRuusS80iZqFXNO16V3bl6dj01h3OsF6zMXBwISW+fnpWGyGPpONZjafTyS/drgTSadchdKUq1D6tm2GjBhw2/P/Zca0ubzS+0Uee7wGo96ewJ5d+6ldt0a6NnZ2dhkWH4+OjuG7r5fy6awJODk5WY8vX7qGCpXK0aP3C6xdtY7vvl5G39d73FOMIiK5SVmfIpT1KXLbNoOqd76nMWYeWkGPCk9RO195Ru+cz96rJ6iVr1yGdstOb6R4JgujLzi2lqo3LWK+OfQg9gY7ZjcbyoHw08w+/BOj6/S4pxgle2mqneRoFouZn4+M5Zs9/fnt+FRmbu0AwIXIfaz5eyIAq46OZ+2xSSw58Dbzd/UgMvESAIv2DCA2+epdj30l7gTF/WoDUDZfYy5EZdxVz97u+hafqcZE8nqUyHD+6JXfqVzgybuOQeRhYLZYmLDpGq+vvsyn2yMIDLr+f3T/5SQmbwkHYOLma0zZGs7w38PotTKU4JjriyUPXHOZqwnGux77VESKNdHUsJgb+y8nZ2iT1z1jAsnBzoDfLaqsFh+OpUMlTwx3HZVI7mY2m3l/xCR6dxvI5PHTebbl9R2K9u4+wPjRHwPwwbsfMeH9aQx6bSRdO/bl4oVgAPr2GEzYlWt3PfbxY6d47PHriaYmLRqwZ/eBDG0sWOjXYzBv9H6H43+fBGDbpl34+Hoz9I3RvNbzbQ4f/BuAi+eDqVD5+gdZFSuVY28m/YmIPAzMFjNjdi+g74aP+XjfD7Rb8y4Ae6+eYMKebwEYu3shE/csYsjWmXRfN4GLcWEA9N84lauJUXc99onoi9TOVx6AxoWrZbqTXlxqIn+FHadpQPV0xy/FXyUmNYHyvkWtxy7GhVHetxgAFXyL3XJnPsk59HGt5Ggnr23Bwc6R7rVnEZUYzIHQVZm283crRusKwzkUuoaDIatoVua1TNvFJF9h1ZFxGY6Xz9+M2kX+XfL5z4aPrg6eJKXFZLguzZTC4v1vEZ5wjqal+6c7FxrzNx7O/ni65P2PuxR5uG29kIijnYEZbQsSEpvGmpNxmbYr6u3I2w3z8MvJONacjKPvYxmnxQGExRv5cFPGP1yblHCnQ0WvdMfMN+3b6ulsR2yK+e5vBIhINHIyPIUeNXyAyHvqSyS32rR+O46Ojsz99jOCL4by049rM21XvEQRRo4ZzKoVv/LTj7/wxuDemba7EhrG++9+lOF4i5aN6fxSu3THLOZ//g97eXkQE52xKnHhDzPx8fXm9KlzvDt4LEtWLeDq1XDOn73Il19/QmREFAP7DmfJqgWUKlOcHVv/om69Wmzf+hcxMZn/fhIRye02hx7E0c6BL5sNJTj+Gj+f25Zpu2JeBRhRuyurz29n1bntDKjaPtN2VxIjGbM743IizQNq0ql0s3THzJZ/Hsg8ndyISU3492UsPPYL3co/SUhC+me8uUdW0afys/xyYaf1WCnvQqw+v4NnSzRg55WjxKYm3vrGJUdQ4klytMjEixTyrgyAr1sAbo6ZrwVT0KsCAN4uBQmOPnTL/rxdCtC1dla3HP+nniHZGI9rJmM72jvTrfYsElOjWbC7JxXyt8DF4fraEIcv/0rlAk9lcSyRh1dwjJFK+a5PqSvs5Yi3c+aVROXzXm9TwNOBw2Ept+wvv4cDn7XJWIadGbubypLiU814Od9boe/C/dF0q+5zT32I5HYXz1+iSrXr77sBRQvh45v5e3PFKtenURQqXICD+4/esr8ChfJnmBp3Kwa7f/4Px8Ul4O3jlaHNjXhKlymBh5cHUZHReHt7UqtOdZydnShYKD/uHu7ERMfy3PNPM23SLPr1GEyV6hXJm+/epveKiORUF+OuUtm/JAABHnnxds58PbuK/68kKujmz6HwM7fsr4CbH7OaDsnS2HaGfx7I4lOT8HZyT3c+NCGc0IRwauQtky7xdCj8DF7O7gR4pP8gv37BKhyKOMtrG6dRwa8YxbzyZykOsR0lniRH83UrwtmIXVQv/AzRSaEkZlJ1BGC46ZeZJdMW191JxVMBz7JciNpPMd8anLq2haqF2qQ7bzKnYWewx2Cww9HeFQc7JxzsnP5/zsjp8K00K51x0VORR02AtwO7g5NoU86Ty3FpxKSYMm2X1alrd1LxVNrfmQOXk6le0IVtFxJpXfbeFg0OiTXy1d7rpeYRSSZG/3mVsS3y3VOfIrlNkWIB7Nz2F88935qQ4MtER93ivTnd/+pbvzvfScVTufKl2fvXQWo9Vo3NG7bzTLv0H/CkpqZisYCzsxPh1yKIjozB28eLWnWqs37d51gsFuLjEoiNjcPL2xODwcDw0QMBWL5kFQULFcjaiyAikssU8cjHrrC/ebZEA0ITwolJic+8YRb/rrqTiqeyPkXYd+0kNfOWZUvoIdqWqJfu/ImoS4Qnx/DW5ulcS4om1WyklHchriRGciY6hLc2Tyc4/houDk4EeOSlin9J+lV+DoCdV47iaKe0Rk6nn5DkaGXzNuLE1Y18s6c/+TxK4uF0b59E3knFU7PS/Vn994dsshgp6FmeYr7XFxa/sVteijGBNX9PwGCwx2ROpWHJV62Jp7MROynsXQUnB7d7ilfkYdCgqBubzyfy+urLlPR1ws/13t567qTiqW9tXz7aEs68vRbK53GmRkEXIP1ueXP+iuKvkCQik0wM/uUK41rkw93JjmG/hXExJg0XewMHLiczrFEepj39zx+lLy4JVtJJHklNmtdnwx9b6N1tIKXKlCRP3nt7b76TiqfXB/dm7KgpzJ7+FRUqlaN23erAP7vlpaSkMvSN93B1dcFkMvHO6IHY2dlRrHgR6jesQ9+XB5GamsagYf0xGAycPX2ej8ZPx87OjrLlS/HmkD73dC8iIjlV48LV2Biyn74bPqaUdyH8XW69q3BW3EnF02tV2jNhzzd8af6ZCr5FqZX3ekXsjd3ymgXUoFnA9fX7Vp/fztXEaJoUrg5AYJnmAMz9H3t3GSBV1cdx/Du5NdvNNt3dKSCigBKCggqChJR0SkooHSKlggiC8KAgICChdHd3Ltvdu5PPi5GFcXdhl7q7y/m8cZ05997fzDBx//fEpS34ajyo4FqURG0KIw8vQSGT42XryrAqHZ/rsQgvn8xkMj2pkCm85rZMiyc2OPveCa+KwahHIVcSm/qAPy6M47NaWSvrwtO5+Cl4d5ST1DEECUT/3A99xE1JM+iNJpRyGQ8SdXz1TxQ/tCkiaZ7CRulZHLdPczuMWChMkvURGNG98uPqdXqUKiXB90L4cthkVq1f8sozFCZyVGiUYqiIILwuEheewBCWQ4+jl0hvNKCUKwhOjmTskR/5udmXrzxDYaLw1uDQr4bUMQoE0eNJyPc2XhhHmi4BnSGNpiW+kDqOIAjPYOI/USRmGEjTmehby1nqOIIgPKfRwyaTEJ9IWmoag4aLYeWCIAgFwZijP5CYkUKqPoMBld6XOo7wGhGFJyHfa1/pG6kjCILwnKa8KYakCUJhMnP+V1JHEARBEPJoel1xoUCQhig8Ca+txYc60Kfe+pd+nOSMWP68PBm9UYeTtRfvlBmJQq566ccVhNfBR/97wJoPfF/6cbZeT2L79WRkgJ1azrg33LFTy5l3OIbbcVoy9CYqe1vTp6bLS88iCIVV27c7s/GvVS/9OGdPXWDa5PkE33vAhu2r8PQyr5Y0dcIcbl6/DUCjJvXo2rPTS88iCIJQmLTfNo7fWmRdyOlFW3Z5KycjrwIQlhJDY9+qDKzU/ilbCVIShSdBeMmO3F1JBe8WlPNqxsHbP3ExfAeVirSSOpYgCHnwVjENLUvaA7DsVBw7bybTtqwD/Wq5oFKYV38ZsDWMO3FagpzVUkYVBOEpipcMYvnqBQzuN8bi9k+6dSAg0A+j0UiPTwbwZvNG+PqL+egEQRDym+5lW9K9rHnF8QH75/OmXzWJEwlPIwpPQr4TnxbG5osTkctVYDLxfqVviEq+xf7byzCaDNgo7WlTYTIqhRW/nOyHp31JolPuYDDqqOLbmvOh20jRxtG2wmRc7fz55WQ/3DRBxKYGYzIZaVPhK+zUj3olGIx6dlydSVxaCAajniYl+uHrVIGDt3/iRvRB1AobirvVo1bAs135jE0NppqfuQLv7VCGS+E7ReFJKPTCknRM2RuNUiEDk4kpb3pwO1bH8jPxGIwm7K3kTGzsjpVSzsCtYZRwteJuvBadwUSr0vbsuJFMXJqBiU088HNUMXBrGIHOaoITdJhMML6xO842iszj6Y0m5hyKITRJj95ooncNZ8p7WrPyTDyH7qdio5JTx8+GDys82wouD4tLAGk6E4FFVBa36wwmrJRy3GwV2W4vCAVZaEg440Z8jUqlxIR5mN3NG3f4/rsVGAwG7B3s+Xr2OKytrfi86xBKlS7OnVv30Op0tG3fkq2bdhITE8c3c8YREOjH512HULRYAPfvPcBoNDF15hhcXB/N/abX6Zk2eT4hwaHodHoGDOtFxcrlWLbkF/bvOYytrQ31G9Xm464dnunxaOw12d4eEOgHgFwuR6FQIFfIn2n/giAI+U1oSjQTji1HJVdiwsT0ur25lRDKD5e2YDAZsVfZMqVOD6wVavrsnU0pJz/uJIajNepoU7QB2+4eJTY9kal1euJv70mfvbMp6lCE+0kRmDAxqVZ3XKwdMo+nNxqYcXoNIcnR6Ix6vqj0PhVci7L88jYOhp7DRmlNvSIV+Kjkm8/1uKLS4olMjaOcS9DzPkXCSyYKT0K+cy/2FIEuNWhYrAcAJpMJL4fSfFLtOwD+ubGQKxF/U7FICwD8nSvTrNRAtl7+htCEK3SqOo+LYX9xPvRPGpfoC4CPQzneLj2McyF/cvTeGpqW6J95vHOhW3Cy9aFF2dGkaOP47dwIPq3xA5fCd9Kt1nLUChtMJmOWnL+dG026LtHiNmdbX1qWHW1xm7umKLdjjuJi257bMUdJ11tuIwiF0emwdKr7WNOtqvlk0mQyUdJNzfwWXgAsOR7LnjupvF3CfAJYycuK/rVdmHEgmmtRWma97cXOm8lsu57E5zXMheIy7lYMruvK1utJrL2QYDGsbeu1JHwclIxo4EZ8moHRuyJZ/J43u26l8H1rb2xUcozZLOI6dnckSRmWK3f6OKgY0cAtS9s/riTyx+UkrJQyPqn0qIA162A0R4PTqOFrg51anKgKhc/JY2eoVbcavfp9Cpjfz2XKlmDJijkALJj9Pbv/2kurNs0BqFqjIkNG9WXK+FlcvniNBT9MZ9uWXWze8BdfDOkJQPmKZRg5biCbN2xn1U//Y+CwzzOPt2nDdnz9ijB20lDiYuMZ2n8sy9d8x19b/2blukXY2NpgNGb9Xh4+cAKJCUkWt/n5F2HspGF5erzbtuzC08uDIj5eedpOEAQhvzoVeY2anmXoWe5dwPw5XtrZn0VvDAHgu/Mb+Dv4FC0D6wBQxb0kgyp/wNSTq7gSe5f5DQew/d4xttw5TL+KbQEo5xLE8Kqd2HLnEKuv7eKLxyYr33znEL4ad76s3pm4jCSGH1zEj01HsuP+cVa8ORobpRXGbM6vRh5eQqI2xeI2X40HY6p3zvZx7bh/nLf8az7/EyS8dKLwJOQ7ZTybcPjuSjZdnIijtRcNinYnOvkO+24tRW/UkaKNxUppl9ne26E0APZW7jjb+vz7twf3485ktvFxqvDvf8tzLWqfxfEik28REn+B29HHAEjXmZc2fav0UHZcnY3RpKeqb1v8nCpZbJfbSc/rBHZm57U5XIvch4emGBqrrCe0glDYNA6y45dzCUzeG4WnRkm3Kk7cjdfx48k4dEYTcWkGiyJNKTcrANztFBSxV2X+fTbs0Y+S8p7mNuU9rDhwN9XieLfjdFyMyOD4gzQAkrXm7QbVdWHekRgMRnivtD0VvawttsvLpOdtyjjQpowDq8/F8+tjha9h9d3QG02M3R3J8Qdp1PazzfU+BaEgeLP5G/z0w2rGjvga7yIe9Or7Kbdv3mPxguVotTpiY+Kw0zz6Xi5dtiQAHp7u+PqZh6p5erpz+sT5zDYVK5fL/O+e3Qctjnfz+m3On73MkUMnAEhKMp+EDB/zBdOnfIvBYOD9D96lcrUKFtu9iAnPDx04xrbNu5j93ZTn3pcgCEJ+0dSvGj9f+Yvxx5bhbetKj3KtuJ0YytKLm9EZ9MRmJGKnevQbqbSzPwAeNk74atwz/z4TdT2zTQW3oub/uhZjb8hZi+PdSgjhQsxtjoRfAiBJZ/59NqxKR2ae/hWDyUjbYg2p7FbcYru8Tn7+171jzKjXJ0/bCNIQhSch35HJ5LxR3Pyh8+elqdyOOcbZkM00KNoDX6cK/HNjISaLnguyx/569PfjLUITLuJi60towiVcbf0tjuduF4SLjS81AzoCYDDqAPB1LE+QS3US0sP5/dxoPqv1k8V2ue3xZKNyoHX5iQDsubmYYm51cv1cCEJBJZdBr+rm3k7T9kdzPCSNrdeS6FbVifKe1iw+HksOb2NkMrJ1OTIDXwcVlyMz8He0nKA/0EmFj4OSD8qbeyLpDOadl/OwoloRGyKS9YzdHckPbSzna8ltj6cMvRErpblQZm+lQPvv/h/erpTLsFHJsVLmEF4QCjC5XEa/QeZeyJPGzuTIoRNs+n07vfp9SsXK5fh21lKL72XZ429ii7fEozYXzl/GL8CHC+cuExDkZ3G8osUD8fP34aNPzcPUdVrz93LFSmWpWbsq4aERDB84gVXrl1hs97w9nk6fPM/yJauZv+RrrKzEXG2CIBQecuT0qdAGgCknVnI0/DKb7xykZ7l3qeBalAXnf7f4XSaz+PDO/vzqYswd/DQeXIy9TYC9p8Xxghy88dW40+nfoXQ6ox6ACq5FqeFZmvDUWEYeWsLPzb602C4vPZ6uxwejUdlQxE5c1C8IROFJyHduRB/iZPBvyJGjkKvwdaqI1pDK1ivf4GobgJXSDrVN3noUhCdd43zYdkwmA63LT7K4r7JPa3Zcm80vp8zD77zsS/BmyYH8dm4UeqMWg1FLNd/3s+wztz2e7sae5OCdFciAINeaFHWtlafsglAQHb6fxobLiShk5nmQKnpakaYzMuNgDP6OKuzUMuxUeRuWdj1ay183wjH+O8fT494tbc+8wzEM3BYOQAkXNf1ruzB2dyRagwmtwUSbsvZZ9pnbHk8/n03gUkQ6YC48jWzgCsD4v6NI1xvRGc3DBat42+TpMQlCQXBg31H+t3ojcoUCtUpF5aoVSEtNZ8q4WQQU9cfOztaix1NuXLt8g62bdmI0GJky03KS77btWzJj6rf07moeAlKqTHEGj+zL8AETyNBq0WZo6dCpdZZ95rbH051b95j59XfcuHaLscOn0OztxnzwcRsmjp6GncaWoV+MB2DAkF6Uq1g6T49LEAQhPzoYdp71N/cil8lQy1VUcitGmj6DqSdWEuDghZ3SGjtN3n7DXIu7z7a7RzCaTEyq3d3ivjZFGzDrzFr67jUPyS7p5Megyh0YeXgJWoOODKOO9sUbZdlnXno8bb93jLcDxHlVQSEzmbKZ9EIQ/rVlWjyxwYanN8zHfjnZj/fKT8DBOvdDagojFz8F745ykjqGIIHon/uhj7gpdYznMnBrGGPecMfDTlwvyY7Sszhuny6UOoYggWR9BEZ0UsfIk8+7DmHStNF4erk/vfFrQo4KjdLz6Q0FQSgUEheewBCWLHWMZ9Zn72y+qvkZHrbOT29ciCm8NTj0qyF1jAJBzIIqCIIgCIIgCIIgCIIgvBTi0rFQ6H1SXfQCEISCbn5Lb6kjCILwgiz9dzU8QRAEoWBa/MZQqSMIBYzo8SQIgiAIgiAIgiAIgiC8FKLHk/BSxKeFse3KN3xU9dtXcrxfTvbDYNLybrkJuNj6suXSZO7EnKCYW50sq8z9l96oZdvlacSnh2GrcqBVuXFYKzU5tt97cwkXw3fgYuv31MdnMpn4+8a3hCZeRSlX0ars2CfONfX4SnkRyTd4r9x4SrjXz7H90buruR61H2Ry3i49DA9NMYLjz7Hj6hy8HUo/9bELwpOEJemYeTCGOe94vZLjDdwahtYAY95ww9dBRd8tYSjlkK438UF5B94spiFZa2T4X+HcT9AxsI4rbxXP+b0KEJmsZ9qBaLQGE2Xdrehby+WJ7XfeTGbjZfOqWD2rO1G1SM4TbW6+msTWa0moFTLc7JSMbuiGWpHzqna3YrXMPRyD0QSNAm35sILjE7P8ej6Bg/dSkclgcF1XirmoOR+ezvwjMZRys8qy8p4g5FZoSDhTxs9m0bKZr+R4n3cdgk6r5atvRuMX4MPxo6dZ8u1yAD78uB3NWzZ54vY7t/3Dxt+2YTIaadS0Hp06Z13w46GJX07nxrXbaOztcHC0f+qE46uWr2Pv34eQy2WMHDeQ4iWL5tg2KTGZiV9OJzEhER+/IoyZOASVWpVj+4mjp3HsyGnqNayZubJe8L0Qxo/6Bhtbm1f2/AuCUDiEpkTz9clf+K7RoFdyvD57Z6Mz6JlQqxt+Gg8mHV/B8Ygr1PEun+0qc4/TGnR8c+oXQlNicFTbMb5mVzSqnH9TLb7wBzvuH8dX4/HUx2cymZh/7jeuxN1DJVcyvsanT5xrKiI1lsknVqIz6innEsiASu2fuP9Gv39BWddAAJr716RN0Qacjb7J7NNrKe0S8NTHLmRP9HgSCo22FabiYusLwBvFetO6/MRcbXchdBuONt50qb6YYq51OH5v7RPbV/N7n4+rfperfd+JPU6qNoEu1RdTO+Bj9t1a+sT27St9wyfVF9KxyhyslRqCXGvm2DYm5T43Y47QufoSWpQZyc5rcwHwc6pEs5IDc5VPEPKbr5q64+tgPpGb38KLb1t6M/cdL344GQ+AtVLG1GaetC/nkKv9LTkRxyeVHPmulTfRqQZOh6bl2DYpw8C6CwnMa+HJN295sPBYLMYnrL9RtYg1i9/zZkErbzzsFOy6+eRJQucejmFUAze+a+XFwXuphCTmPCF0cIKOow/S+K6VF8Pru/Lt0VgAKnpZ07/2k4tngpAffTNnAn4BPhgMBuZNX8ycRVNZtHw2Py9bS0pKao7b3bl1j/17j7LwxxksWTHniUWnh4Z/2Z+lK+Y8teh0724wh/Yf48df5jPmq6HM+vrJ3+0rl6+lUZO6/LBqPu4ermzf+vcT2/cd1J0pMyyXCvcL8GHqrLFPfQyCIAj5wdd1euGnMV80712hNV/V+ixX2227dxRvW1eWNh5GXe/y/Hp99xPbty/+BgsbDc7Vvo9HXCFBm8zSxsPoXOotllzc9MT2C89vpGuZt1naeBjRaQmcirz2xPbuNk4sfmMoi98YSpuiDQCo7FacQZU75CqfkD1ReBJy7e/rC7gasQcAo1HPD0c7YzDq2XtzCatP9WfZsa6cerAhy3ZbLk0hOP4cAPdiT7P18jcARCbfYs3pAaw+1Z8N58egM6S/sKz21rlfKede3GlKuTcEoKRHQ+7FnXryvq3ckcly99a5F3eaUh7mpUKDXGoSmnA5V9tdi9pPkGstlHL1E/dd0q0+MpkMN7tAUrVxGI36XO1feH0tOhbLvjspAOiNJrptCEFvNPH9yTgGbQunxx+h/HElMct23+yP4ny4+T16JiyNGQeiAbgdq2XI9nAGbQtn/N+RpOuNLyyr6t/eQ6k6I0HO5mKUUi7DxUaR633ciMnI7LVUP8CWM2E5f85cidJSycsaK6UcJ2sFrrZKwpNyfk/5OqiQy8wZ1QoZ8pw7O6E1mEjVGfF1NG9Tx9+Ws+E5ZzkTlk49fxtkMhkBTmoS0gzojWIRWiFn82Yu4Z+d+wHQ6w10atsDvU7Pwnk/0qfbUD5p/zm/rd2cZbuJX07n7KkLAJw6fpYp42cBcPPGHfp2H06fbkMZNfgr0tNezHd08P1QvH28cHJyxNraioqVy3H5Qs4nAbt37EOjsWPA56MY1OdL7t4Jfuox5s5YTM/OA/nrzycXhk4dP0vDxnWRyWQEFvUnLi4BvT7nlXxPHT/LG03NvZDfaFKfU8fPPnH/Hp5i1T5BEJ7s23O/8c+D0wDojQY+3jkZvdHA4gt/0G/vXLrsmsrvt/Zl2W7S8RWcjTavWHwq8hpTT64C4FZCCP33zaPf3rl8eeR70vXaF5bVwyb3K9idirxGI5/KADQsUilXxR6Z7Ak/pB7fd9Sjfdf0LMOl2LtPbH8t/j7VPUqbs/g8PUtMeiJ99sxmxKHFhCRH5SqT8HRiqJ2QaxWKtGDfzaWU9mzM7dhjBDpXQyFXUjfoU9QKG/RGLT8e7UzlIu/lan87rs7ivfITcLT24mTw75wJ2URN/w8z7zcYdfx6elCW7XydKvJG8c9f1MMiTZeAtcrce8Ja6UCaLutJ93PtW2kPgEwmw0TuTsovhv1F3aAuT923vdWjoTbWSg1p+iTs1K/3sqbCk71dQsMPp+JoFGTHiQdpVC1ijVIuo3MlR2xUcrQGczGqVSn7XO1v7uEYxr7hjqdGycbLiWy5mkSH8o+GkOkMJob9FZ5luwqe1vSo/uR/qxl6I8P+iuBuvI5eT2mbk8drNfZWchIzcn4PJmQYsLeSW7RPyDBS5CnHuBuv5diDNOa3yHlIYmK6AXv1Y/tWy0lMf0KWdANuto8KbHZqOckZRpzyUHQTXi+t2jRn0bxlNHmrIUcPnaB6zSooVUo+6/UxNrY2aLVaOrXpQZv3W+RqfzMmz2fStNF4FfHkf2v+YMP6rXzU5VFvI51WR/9eI7NsV7lKefoMzPmKeEJ8IvYOj4bIOjhqSEjI+Xs3KjKauNh4vl06jSuXrvPNV3OfODn5oOG9cXJ2JCkxmb7dh1G2Qin8A3xzzOLm7pr5/xqNHUmJSTi7OGXfPiEpM7u9o4aE+Bf3e0EQhNdTy8A6LL6wiSa+VTkWcZnqHqVQyhV0LfMONkortAYdH++cTOugnKfeeNzM078ysdZneNm68NvNvfxx+wAdSzbNvF9n1DNg//ws21VyK07v8q1f2ONK0KZgr7YFwEFtR4I25cXtOyMFe5V53zKZDKPpyedXj/det1fbPjXLxpZTcbLScCLiKlNOrhQTqb8govAk5JqHphipujhStLGcD91O3UDz+NbTDzZyPXI/MpmcFG0cqdo4i+0er12bePTGj065w5aLkwEwmLT4O1e12E4hV72SFelsVI7meZVsvEnXJ2Gjyt0QnlzvW2+eL8ZkMiGTPf2kMTkjlri0B/g6Vnz6vnVJmf+foU/BRpm7YoHw+irqoiY+zUhcmoG/bibzcUVzkWjT1SQO3ktFLoP4dANxaf+96v/onfz46LO78Tq+3me+GqQ1QBVva4utVArZM69IZ6WUs6CVN/HpBj7fFEbjonZo1HnrqPt4L6RkrREHq5y3d7RScCUj41H7DCOOT2gP5nmwvtkXzcTG7tiocm5rbyUnWfvoh1Gy1oiD9ROyWCtIeqx9is6I5ilZhNdb8RJBxMfFExsTx9ZNO+nasxMAv63bwr6/DyFXyImNjSc29j/f0bLH39uP3ty3b95lwpfTAdBptVStUdliO5Va9Uyr0zk6OZCU+GhYalJiMo6OOX/vOjg6UKpMCeRyOeUqlCYqIvqJ+3dyNn+m2TtoqF2vOtev3sqx8PTfLCkpqdg75Pw96uhoT1JiMg7//tfR6cX9XhAE4fVUzNGH+IwkYtMT2Xb3CF1Kvw3Ahlv72BdyDoVMTlxGErEZloXunHoH3U4M46vjPwGgM+ip6lHS4n6VXPlKCimOajuStKlgB0m6VBzVdi9u31Z2JOnMQ7RNJhOKp4xEkT/2XCVr056axcnKfIGhhmdppp9a/ZxphYdE4UnIk3JezTkVvIGE9DC8HEqRpkvkfOhWetReidGoZ8mRjhbFJQBrlQOJ6REAhCVeybzdza4obSp8hebfXjsGo+V8Jy+jx1NCejiO1pa9Evydq3Ij+iBeDqW4EXUwswCWoU/BaDLkuhCV3b4DnKtwOXw3pTwacTfuFN725m6eOkMGWkNqtr2TLkfsooznmxZfKEnpUdhZuSB/rHAV4FyFHddmU8P/Q+LSHmCjckQuF29p4emaFbdj45VEwpP0lHSzIinDwPbrySxvWwS90UTn30L476AuBys5ESnmYWdXox912w5yVjG+sTuutuZ/ezqD5ZbP2uNJZzChkJt/LNgoZagVsidO3J2hN5KmM2XpEVTc1YqzYelU9rbm0L1UWpQ0/5iISzNgq5JhpXz0Y6WMu5rvT8ahNZhI1xuJTtXjZW9+XBHJejw1lu+vmFQ9E/6JYmQDN4o4PJpgOFVrxGAyYW/1KIuVUo6NSk5oog5veyVHH6QxvL65p0VUih4XGwWKx6pklb2smXckhg7lHAhJ1ONoJUf5pLF8ggC83fJN1v+6idCQcEqVKUFiQhJbNv7Frxt/QK830L7lp/x32jIHR3vCwyIBuHzx0fCDoiWCmDpzTGaPIJ3W8jv6WXs8+fkXISwknMSEJKxtrDh/9jL9BvcAIDw0Aq8inhbta9Suwq7te4B3eRAcioOjuTCUnp5BWmpalt5JSYnJ2Dto0Ov0nDt9kWZvNwYgMiIKVzcXFIpH78uqNSozY8q3dOryPsH3Q3B0ckCpVKDXG4iLjcPdw3IC/6o1KrN/z2FatWnO/j1HqFajEmDuOaVSqbC1y3niXEEQhJw0D6jJ77f2EZYSQylnfxK1Kfx59wi/vDUOvdHAh39N4L8/zBzUdkSkmud/vPzYULNiDkWYXKsHbjbmIrzuP9NwvIweT+GpsXjZWs4/WdW9JAdCz1PK2Z8Doeep6m4ugKXo0jGYDDjkshCV0753BZ/kDZ8qnIy8RhmXAADSDVrS9Bk4W1leQCjp5MfpqOuZmVoF1QEgNj0RO5UNVorHfsPp07FSqFHI5NxKCMHh315bwvMTZ6lCnpTzeovvDrShYTHzj0RrpT1udoGsOtkbN7sgbFRZV2mq7PMemy5M4FL4ThytHw1aaV56KFsuTcFoMn8g1g74hGJutTPvf54eT3tuLuZOzHGStTGsOT2A9yt+g1Juxf/ODqdn7VUWbSsWacG2y9+w6mQfrJX2vFtuHACXI3aj1adSK6CTRfvj99dxNWIPMal3WXN6AM1LDcPVzp+1pwfzed1fLdoGudTiVvRRVp7s8++qdmMAeBB/jhvRB3mr1JAs2S+G/UXr8pYTom68OJ72Fb/BVu2UeZurXQBBLjVYdbI3IKN5adENVMidN4vZ0WHtAz6r6gSARi0nwElF/z/DCXRW4WCdtWdey1IaJu+J5u9bKZnFGIBBdVz5Zn80D6d26lTRgVq+j76kn7XHU0SKnun7o5HLZegMJrpWccwsPI3YEcH9BB3WChlnw9IZ0cCN8xEZHL6fysA6rhb7+by6M9MPRPPjKROl3awye2R9dyyWD8s7UNLNKrOtvZWC9uUcGLjVXCjrW8sFuUyG3mhi1M4IfmrnY7Hv70/GEZdm4NsjMQA0LabhvdL2/H07hVSdMcuqdQNqu/D1/miMJmgQYJs5ifpXe6KY8qYHTo897/5OKqoXsaH/n+HIZObnWRCepnnLJrR6sxOf9/8UMPf6KVosgJ6dBxJULCDbHjpt3m/B2BFfs2Pb3xTxefReHTF2AF99OQO93vwd3fmzD6nb4NGCF8/a40mhUDBw+OcM6mNedfWTbh3QaMwnIF98Por1W36yaF+zdlWOHTppXhlPp2P4l18AcO70BQ7sPcqwL/tbtP9y6GRSU9PQ6/W89U5jSpYuBsCYYVOY+e2kzB5RAIFBftSqU5UenwxEJpMxYuwAAEJDwpg3YwlzFk6x2HeXzz5k4pfT2bxhO94+XvTo/QkAq39eT+myJWnSrIFF++/m/sixwyeJiY6lb/fhzJg/MfOxCoIgPPSWf01a/zmanuXeBcBeZUuQgzef/zOTIAfvbIs07wXVY8Kx5ey8fwJvu0e/EYZV7cjkEz+jN5p7rn9S+i3qeJXLvP95ejwturCRY+FXiE1PoP++eUyv+zlWCjVDDy5k9VvjLNq2DKzD1yd/ofeeWdirbRlfoysAu4JPkKrP4KOSb1q0X3fjb/4OPs3dpHD675vHiKqd8Lf3ZOD+b1n39kSLtrU8y3Ik/BKf75mFSq5kXA3z9CTnom9yMPQCQ6t8aNG+b4W2TD25kqXGzZRx9qeaeykA5p1bz0clm1Ha2T+z7Z3EMKafWoOt0vz7cGS1j5/puRKykplMT1iyR3jtbZkWT2xwzhNt5hcbL4wjKSOaVmXHZK5s91/B8ecJT7xGDf/crUiw89pc6gV9ip366StIJaVHcez+r7xZckCu9n34zkqKu9XFw774U9sajHq2Xv6a98qPz9W+g+PP8ff17whwrkLjEn0zb3fxU/DuKKdc7UMoXKJ/7oc+4qbUMZ7qq38iiUo1MKqhW2ZRJrd+ORdPHT9birnkPCH/f481oYlHrtpeiEjnerSW93O5kt63R2LoXNkJ51zMx6Q3mph+IJoxjXI3CfH58HQWH4+lsrc1n9d49Nmk9CyO26cvf2iykP8k6yMwkvMKifnBl8MmExURzfgpI/AL8Mm2TWREFKt//o3BI/rkap8//bCG+g1rUaJUsae21ev0TB4/i6++GZWrfW//czd2drY0bFw3V+0njp7Gl18NQa1++udP8L0QvhozA28fTyZPf7TinRwVGqXnE7YUBKEwSVx4AkPYk1fDldrYoz8SlRbP2BpdMle2+69z0Te5FhfMByUa52qfc86so2uZd3Cxfvpvqsi0OH69/jcDK7XP1b5/vvIXdb3LU8Ip+3PB/xp79Eem1O6Rq7Zno2+y4NzvVHUvSb+KbTNvV3hrcOhXI1f7eN2JwpPwRAWl8CQ8nSg8vb4KSuFJeHai8PT6KgiFJ+HpROFJEF4vBaHwJDydKDzlnpipVBAEQRAEQRAEQRAEQXgpROFJyLfi08JYczp3Q9ekdi/2NJFJj3qUbLo4ETA/hutRByRKJQj5Q1iSjiHbs04w/qp89L8HuW770+k4dt4UVyCF11toSDh9uw+X7Pht3+6c5bboqFjmzlic4zZbNv5FcvKzL9ed3TEFQRAKotCUaPrvmyd1jBfih0tb2H7v2FPbHQm/xPZ7R3O8v8/e2USmxuV4v/DyicKTILwA9+JOE5n8qPDUuvxEABLSwrgRdVCiVIIgvAomkwkxal0QXi43d5cnzv/056adpCSnZrndYBDTBQiCIBR2dbzK8U5A7ac3FCQjVrUT8o29N5dwL+4UCrkV1X3fx8uhdOZ9l8N3cybkD/TGDFxtA2lZ9kuMJgMbL4wjVRePQqagXlA3nGyKsPniRORyFZhMvF/pG2xUuZsQ+L++O9CWQJfqxKYG42rnT4syo5HJZOy9uYTg+PMYTTrqBHTGz7kyF8K2oZRbcTZkCx9UnsmyY5/Sp956jt9fS2jiFX452Y9GxXthpdSw+/p8TCYjNipH3i03DpXC+kU9hYKQL/xwMo7ToemolTLalbGnpNujCXf/uZ3C5qtJZOhNBDipGNnAFYMJJv4TRXy6AaVcRpfKjnjbK5myNxqlQgYmE1Pe9MDe6umTdWdHZzQxbX80DxJ1+DmqGFHflWStkQn/RGEwmtAZYWQDVwKcHuVM0xn5en80bxa1o4y7FXMOx5CuN6FSyBjd0A0XGwUf/e8BjYvacSkygyre1qgUMj6qaF4xq8/mML55y3KlOkHIrxbNX8bJY2dQq9V88FEbSpcrmXnfzu172Pi/P8nIyCCwaADjJg/DoDcwethk4mPjUSqVdO/9CUV8vRk34mtUKiUmYOb8r3BwtM/5oE+g0+mZNHYm9+8+ICDIl7GThhEWGsGU8bNZtGwmmzds57e1W7Czs6Fs+VLUrleD61dvMnrIVxQtHshnn3/CqMGTCCrmj16np/cX3fh64hwMRiN2djZM+HokTk6OTz0mwIwp33Lj2m0MBgO9+n1KtZqV6PvZcEZNGISHhxv9e45k9neTcPdwe+bnXxAE4XksvvAHpyKvoVaoaF/8DYtV2nYFn+SPWwfIMGgJdPBmTPXOGExGxhz9gfiMZJQyBZ+VbYG3nSsTji1HJVdiwsT0ur2zXU3vaZK0qYw+8j0GkwEZMoZX7YRcJmPGqV8xmozIZDIm1+6Bq7UDk46vQCVXEp2eQFRaPFNq98Df3pO9IWdYdmkrnrYumDDhq/HgXPRNdt4/wfCqnZh0fAVWChUjq33MlBMraV/8DW4mPCAyNZ7PyrbgdNR1llzYhEIux0/jwZfVzT1a11zfxd3EcJJ1acyq3w8nK80Lew2EpxOFJyFfuBV9hLi0ELpU/x6ZTIbRZCAxPTLz/uLu9SjrZV52c+OFcdyPP4uDlQfp+iS6VDd3vTeZjJwP3UagSw0aFuvx722WvRCuRx3g+L21WY7fqHgv/JwqWdyWrI2mXlBXnG192HxxErdiDiNDQZoukc7VF6E3ZLDiRC+61/qJCt4tcLH1pbz32xb7qOnfkYvhO2hZ1rxs9KqTfXiv/AQcrb04Gfw7Z0I2UdPfcslPQSjIjganEpqoY9G7XshkMgxGE5Ep+sz76/jZ0KSo+YfMxH8iOReegYedgiStke9amZdyN5pMbL+RTHUfa7pVdQayvpcP3UvlfxcTshy/ezVnKnpZFnNjUg10qexIEQcVU/dFcTQ4jRq+Nkx/yxOVQsbR4FTWnE9kdEPziWNsmoExuyPpWsWJil7WfPVPJJ0rO1LOw5oj91NZfS6eL2q7YjCZqO1nQ8/qzqRqjQzaHs5HFR25Fp2Bl71SFJ2EAuHQgWM8CA5l2eoF5veswUBEeFTm/Q0a1eatd8yrFY0eOpnTJ8/j6eVOUkISP6yaD4DRaOTPP3ZQq241evX7FMj6nt33z2HWrPwty/H7fNGNytUqWNwWHR3DZ59/jK9fESaMnsah/ccoWjww8/4//9jBtLnjKeLjhdFoRC6XU7J0cSZNG42nlzuhIeGEhYazaNkMNPYahg8YT48+nalWszIb129l5Y9rGTDs86ce02AwkJaWzo+/zCc+LoHuH3/Bb1t/ZsLUEYwf9Q3OLk70GdBNFJ0EQZDM4bCLhKRE80OTEebPcJORiNTYzPvre1egmV91AMYc+YEz0TfwtHEmSZvK0sbmArvRZGTr3SPU9CxDz3LvAlk/w/eHnuPX67uzHP/z8q2p7PZole5r8ffxsHFifM2umfvWGvUsaDQQuUzO77f2sfHWfnqUawVAgIMXo6t/wp93D7PlzmH6VGjNovN/sPzNUdgqrei3by4A5VyCmHv2fwDEZiTBv/muxwdT0smXmwkPMnNPP7WahY2G4GbjiMFkzMxWya0Egyp/wA+XtvDPg9O0K9bwGZ914VmIwpOQL0Qm3yLQpToymQwAuczyhC047ixH763GaDKSmB5OCbf6BDhXoYxnEzZdnIhKYU39oM8o49mEw3dXsuniRBytvWhQtDsK2aNl2Uu6N6Cke4NcZbJTu+Bsa1722depAjEp9zCZTNyPO8MvJ/sBYDBqSdHG5/pxRqfcYcvFyeZtTVr8navmeltBKAhux+moWsQm872skMss7j8Xns7aC4kYTSYikvXU89dT2duaxkG2TN4bhbVSxqdVnGgcZMcv5xKYvDcKT42SblWcUD32sVAvwJZ6Aba5yuRio6CIg/lzoLyHFfcTdJRxt2LukRhiUw3ojSZsVI9Gnv9+KZF3SmoyC1i343R8f8I8L4DBBF4a81enXCajvIcVALZqOSVd1ZwLS2f37RTeLSWuogkFw63rd6hRq+qj96zC8vv39Knz/LL8fxiMRsJDI2jYuA7ValTizbffYOyIr7GxsaZHn8682fwNfvphNWNHfI13EQ969f0UlfrR92+jJnVp1KRurjK5ujrj61cEgIqVy3H3zn2LwtPgkX1ZtmQVGelamr3TONv9FiseiMbe/D68dyeYSlXNxa3KVctzYO+RXB3ToDdQuWp5AJycHbF3sCcxIQm/AB+KFg/g3p0H1KkvVjMSBEE6txJCqO5R6tFnuMxyJp0zUTdYfW0XRpORsNQYGqRWpKp7SZr6VWP8sWXYKKzoXrYlTf2q8fOVvxh/bBnetq70KNcKlexRqaBhkUo0LGJ5kT47VdxLcinmLuOPLcNJraFnuXeJy0hi/rnfSNWlk6xLo4xLQGb7ss7mv71tXTkffYu4jGScre3RqGwAKO9SFAClXIGzlT0nIq4SYO+J1qDjRMRViti5IX/sMcdlJOGgtsPNxjHL81H23+N627kSlRaf6+dYeDFE4UnIF9w1RbkYtoPKRd7L7PH0uD03F9Oxyhw0Vm5svDAOMKE3aqni05pqvu24ELqd4/fX0qhYL94o3huAPy9N5XbMMUq418/cT156PKVoY4lPC8PJxpuQhEuU9miMTCYjyLUmb5UaDIDBqEMhV6GQq7JkBrLc7mZXlDYVvkJj5Za5vSAUJkWdVey8mUKrUprMHk+P+/5kHDObe+Jqq2TiP5GYAK3BxLul7GlTxoG/biSz/mIi3as50au6ubfTtP3RHA9Jo57/o0JTXno8xaYZCEvS4W2v4nJUBo0C7dh5K5kSrmo+aeLE0eBU/ncxMbP9Z1WdOBWazsbLibQt60Cgs4qPKzpS0s1cZNIZzI9JBpk/9ADeK23PqnMJhCfpGVLX5bmeR0F4VYqVCGL7lt20ad8is8fT4xbO+ZFvv5+Gm7sro4dOxmQyodVqaduhJe07vsfWTTtZs/I3+nzRjX6DzL2NJ42dyZFDJ2jY+FFBKC89nmJi4ggNCaeIjxcXzl2m6VuWV6WDivozbvJwMjK0tGvRhUZN6qJSKS2yyx8roAUE+XH21Hmq16rC2dMXCQjyy5Iju2MajUb27DpAm/YtiY9LICkxCQdHe04eO0NsTDzFSgSybfMuWrzXLA/PuCAIwotTzLEIf907Tuug+pk9nh636MJG5jUYgJuNI2OO/PDv7y4dbYo24P1ijdh29yi/3vibz8u9R58KbQCYcmIlR8Mv06BIxcz95LbHk86o59My5hEgyy5vZfu9YzxIjqS5f03e8q/Bbzf3ci0++NEOHvsdZQKcrDTEpieSokvDVmnN5di7FHU0XxSo7lGKJRc30a3MO2QYdCy5uIl3AmpZ5HG2sidRm0JMeiKu1g4YTMbM4pOMx44lpuZ85UThScgXirvV5X7cGX4+0QuVwopq/5njqYL326w5PRA3u0BMmD8pEtLC2XrlG+TIMZh0vFVqMDeiD3Ey+DfkyFHIVfg6VbQ4Tl56PGms3Dh8dyXRyXdwsfWluFtdZDIZD+IvsOpkX2QyORq1K20qfEWQSw12X5/PjehDvFdufOY+3DVFiUsL4ffzX1In4BOalx7KlktTMJrMQ49qB3xCMTcxEZ5QeNT2s+VsWDp9t4RhpZTT9j9zPDUvrmHo9gj8nR71hIhI1jP9QDQKGeiMMKC2C4fvp7HhciIKGagUMip6WlkcJy89ntxsFaw+l8DdeB0+Dirq+NlwN17J5L1RnA/PIPCxLGDupfVlIzdmHYxh3YUE+tV0Ye6RGNJ15k+ft0toeLtE1h5NJd2siEzW80aQrUVBShDys3oNa3H6xDk+++gLrK2t6NCptcUcTy1aN6NfjxEEFfXPHHoRFhrBlHGzkCsU6HQ6ho3uz4F9R/nf6o3IFQrUKhWVq1oWk/LS48nd3ZUVP6zh9s17+AX4UL9RbcJCIzLvnzZ5PqEh4eh1Ot7/wDwspPGbDZg8dialy5WkQ6fWFvvrP6Qn30ycy/eLVmJjY81X34zK1TEBjhw8QfePB2AwGBgyqh9xsfF8O/sHvl36DTa2NvT5bChly5cisKh/ln0KgiC8bHW9K3A66gY9/pmBtULN+8UbWczx9E5AbQbsn0+gg1fmZ3h4aixTT65CLpOhNxoYUvlDDoadZ/3NvchlMtRyFZXcilkcJ7c9nq7G3mPRhT9QyhUYTUbG1+xKWEoMs86sZef9E3jYOj1xe4VMTp8Kbei9ZzYets44qB/91qvuUZpFF/6gintJ9EY9Y4/+yNgaXSy2l8nM80qNPLQYlUJpMceTIC2ZSSzFIzzBlmnxxAa/nivCLD7UgT711ksd44Vx8VPw7ignqWMIEoj+uR/6iJtPbyg8ty/+DOOrph642Lza+Z2UnsVx+3ThKz2mkD8k6yMwInrPFnRyVGiUnlLHEAThFUlceAJDWLLUMYTnpPDW4NBPDLnODdHjSRAEQRCeU3SKnq/3R1PZ2/qVF50EQRAEQRAEIT8ThSdByEFh6u0kCMLL5WanZM47XlLHEARBEARBEIR8R/70JoIgCIIgCIIgCIIgCIKQd6LwJAiCIAiCIAiCIAiCILwUYqid8ESOXmKuksJCvJavL6Vr1qXDhcJFvMavL7lMCWKZmAJPLhM/yQXhdaLwsJM6gvACiNcx98SqdkKOjEYTcrlYErwwEa/p68dkNCCTi6Lj60C81q8fk8mETCY+0wsL8XoKwuvBZDQhE7/HCw3xeuaOGGon5Ci/FyiuX79O27Zt2b17t6Q5Tp8+Tdu2bTl27JikOXIjv7+mwotXkAoRv//+O+3btycsLEzSHCtWrKBTp07ExsZKmiOvCtJrLbwY+blIYTKZGD9+PP3790en00mWw2g0MmzYMEaOHEl+v9aan19PQRBenPxepJg/fz5du3YlNTVV0hxTp06ld+/ekn6H5EZ+fz3zC1F4Egokk8nEhAkTMBgMNG7cWNIsVapUwdnZmfHjx+f7D0ZByK9iYmKYOnUqVapUwdvbW9Isbdu25fbt28yZM0fSHIJQkO3bt49169bRrl07VCqVZDnkcjkdOnTgjz/+YPv27ZLlEARBKAguXbrEokWLePvtt7G1tZU0S8eOHdm/fz+//PKLpDmEF0MUnoQCadeuXZw+fZqRI0eiUEh7lV8mkzFixAju3bvHunXrJM0iCAXVggULUCgU9O3bV+ooODo60rdvX37//XeuXbsmdRxBKHD0ej0zZsygVq1akl8cAqhVqxZNmjRh1qxZaLVaqeMIgiDkSyaTiWnTplGsWDHat28vdRyKFStGhw4dWLRoEfHx8VLHEZ6TKDwJBY5Wq2XWrFk0aNCAevXqSR0HgNKlS9OuXTsWLFhAYmKi1HEEoUC5desW//vf/+jTpw/Ozs5SxwGgU6dO+Pn5MXPmTKmjCEKB89tvv3Hr1i1GjhyZb4aPDRs2jPDwcFatWiV1FEEQhHxpz549HD9+nBEjRqBU5o8FDwYMGIDBYGDRokVSRxGekyg8CQXOr7/+SnBwMCNGjJA6ioWBAweSkZHB0qVLpY4iCAXKzJkz8fb25pNPPpE6Sia1Ws2wYcM4cOAABw8elDqOIBQYycnJfPvtt7Ru3Zpy5cpJHSdTsWLF6NixI4sXLyYuLk7qOIIgCPmKTqdjxowZ1KlTh0aNGkkdJ5Orqyu9evVizZo13Lt3T+o4wnMQhSehQElISGDRokW0b9+ekiVLSh3HgqenJ927d2flypU8ePBA6jiCUCAcOXKEPXv2MHToUNRqtdRxLDRr1oxq1aoxffp0DAaD1HEEoUD44YcfSE5OZvDgwVJHyaJ///4YjUZx5VwQBOE/1q9fz927d/NVT9WHunbtiqurK7Nnz5Y6ivAcROFJKFCWLFmCVqtlwIABUkfJ1meffYajoyNz586VOoog5HtGo5EZM2ZQuXJl3nnnHanjZCGTyRg5ciTXr19nw4YNUscRhHwvLCyMn376iW7dukm+SEB2XFxc6N27N2vWrOHu3btSxxEEQcgXkpOTWbBgAW3btqVMmTJSx8nC2tqawYMHs2PHDk6dOiV1HOEZicKTUGAEBwezatUqevTogbu7u9RxsmVnZ8fAgQP5888/OX/+vNRxBCFf27x5M5cvX86XV9ceqlSpEi1btmT+/PmkpKRIHUcQ8rV58+ah0Wjo2bOn1FFy1KVLFzw8PMSVc0EQhH99//33pKamMnDgQKmj5Oi9996jXLlyTJ8+HZPJJHUc4RmIwpNQYMyZMwdnZ2e6desmdZQnateuHSVLlmTatGnig1EQcpCWlsbcuXNp3rw5VatWlTrOEw0ZMoT4+Hh++uknqaMIQr516dIlNm3axBdffIFGo5E6To4eXjnfuXMnJ0+elDqOIAiCpEJDQ1mxYgXdunXDy8tL6jg5ksvljBgxgnPnzrF9+3ap4wjPQBSehALh7NmzbNu2jcGDB2Nrayt1nCdSKBSMGDGCU6dOsXv3bqnjCEK+tGLFCmJiYhg2bJjUUZ7K19eXLl268OOPPxIRESF1HEHId0wmE9OnT6do0aJ06NBB6jhP1apVK8qXL8/06dMxGo1SxxEEQZDM3Llzsbe3p0ePHlJHearatWvTuHFjZs2ahVarlTqOkEei8CTkeyaTiWnTplG6dGlat24tdZxcadCgAfXr12fmzJnig1EQ/iM6Oprvv/+ejz/+GH9/f6nj5Erv3r2xsrLi22+/lTqKIOQ7e/fu5dixY/lqCe4nkcvljBo1ivPnz4sr54IgvLYuXrzI5s2bGTBgQL7uqfq44cOHEx4ezi+//CJ1FCGPROFJyPd27NjBmTNnGDVqFAqFQuo4uTZixAiCg4NZu3at1FEEIV/59ttvUalU9OnTR+oouebg4ED//v35/fffuXr1qtRxBCHfeLgEd+3atfPVEtxPU6NGDZo2bcrs2bPJyMiQOo4gCMIr9bCnaokSJXj//feljpNrxYoV48MPP2Tx4sXExcVJHUfIA1F4EvI1rVbLrFmzaNSoEXXq1JE6Tp6UKlWKdu3asXDhQhITE6WOIwj5ws2bN1m/fj19+/bFyclJ6jh50rFjRwICApgxY4bUUQQh31i/fj137tzJ14sE5GT48OFERESwatUqqaMIgiC8Uv/88w/Hjx9n+PDhBaKn6uP69++PwWBg4cKFUkcR8kAUnoR8bc2aNYSEhDB8+HCpozyTgQMHkpGRwZIlS6SOIgj5wsyZM/Hx8eGjjz6SOkqeqVQqhg8fzqFDhzhw4IDUcQRBcg+X4G7Tpg1ly5aVOk6eBQUF0bFjR5YsWUJsbKzUcQRBEF4JnU7HzJkzqVu3Lg0bNpQ6Tp65urrSu3dvfv31V+7evSt1HCGXROFJyLfi4+NZtGgRHTp0oESJElLHeSYeHh706NGDlStXEhwcLHUcQZDUkSNH2Lt3L8OGDUOtVksd55k0bdqU6tWrM2PGDAwGg9RxBEFSD5fgHjRokNRRnlm/fv0wmUwsWrRI6iiCIAivxP/+9z/u3r1bIHuqPtSlSxc8PDyYPXu21FGEXBKFJyHfWrJkCTqdji+++ELqKM/ls88+w8nJiblz50odRRAkYzAYmDZtGlWqVKF58+ZSx3lmMpmMkSNHcv36dTZs2CB1HEGQTEFZgvtpXFxcMq+c37lzR+o4giAIL1VSUhILFiygbdu2lC5dWuo4z8za2prBgwezc+dOTp48KXUcIRdE4UnIl+7fv88vv/xCz549cXd3lzrOc7G1tWXQoEFs3bqVc+fOSR1HECSxefNmrl69WqCvrj1UsWJFWrVqxfz580lJSZE6jiBIYu7cuWg0mgKxBPfTdOnSBU9PT2bNmiV1FEEQhJdq6dKlpKenF+ieqg+1atWKcuXKMX36dIxGo9RxhKcQhSchX5o9ezYuLi5069ZN6igvRNu2bSlVqhTTpk3DZDJJHUcQXqm0tDTmzp3LO++8Q5UqVaSO80IMGTKEhIQEli1bJnUUQXjlCuIS3E9iZWXFkCFD2L17NydOnJA6jiAIwksREhLCzz//zGeffYanp6fUcZ6bXC5n1KhRnD9/nu3bt0sdR3gKUXgS8p0zZ87w119/MXjwYGxsbKSO80IoFApGjhzJ6dOn2bVrl9RxBOGV+umnn4iNjWXo0KFSR3lhfHx8+PTTT1m2bBkRERFSxxGEV+bhEtzFixenffv2Usd5YVq0aEGFChXElXNBEAqtuXPn4ujoSPfu3aWO8sLUrFmTpk2bMnv2bDIyMqSOIzyBKDwJ+YrJZGLatGmUKVOG1q1bSx3nhapXrx4NGjRg1qxZaLVaqeMIwisRFRXFDz/8QOfOnfHz85M6zgv1+eefY2Njw7x586SOIgivzMMluEeMGFHgluB+kodXzi9cuMDWrVuljiMIgvBCnT9/ni1btjBw4EDs7OykjvNCDRs2jIiICFatWiV1FOEJROFJyFf++usvzp49y8iRI5HLC98/zxEjRhAcHMyvv/4qdRRBeCUWLFiASqWid+/eUkd54ezt7fniiy/YuHEjV69elTqOILx0BX0J7qepXr06zZo1Y86cOeLKuSAIhYbJZGLGjBmULFmSdu3aSR3nhStatCgdO3ZkyZIlxMbGSh1HyEHhO7MXCiytVsusWbN44403qFOnjtRxXoqSJUvSvn17Fi1aREJCgtRxBOGlunHjBuvXr6dv3744OjpKHeel+OCDDwgMDGT69Oli/jah0CsMS3A/zdChQ4mMjOTnn3+WOoogCMIL8ffff3PixAmGDx+OQqGQOs5L0a9fP0wmE4sWLZI6ipADUXgS8o3Vq1cTFhbGiBEjpI7yUg0YMACtVsuSJUukjiIIL9WMGTPw9fXlo48+kjrKS6NSqRg+fDiHDx9m//79UscRhJemsCzB/TRBQUF06tSJpUuXiivngiAUeA97qtavX79Q9lR9yMXFhc8//5xff/2VO3fuSB1HyIYoPAn5Qnx8PIsWLaJDhw4UK1ZM6jgvlbu7Oz169GDVqlUEBwdLHUcQXopDhw6xf/9+hg0bhlqtljrOS9WkSRNq1qzJjBkz0Ov1UscRhJfi+++/Jy0trVAswf00ffv2RSaTsXDhQqmjCIIgPJd169Zx7949hg8fLnWUl+7TTz/Fw8OD2bNnSx1FyIYoPAn5wqJFizAYDAwYMEDqKK9Et27dcHZ2Fh+MQqFkMBiYPn06VatW5a233pI6zksnk8kYMWIEN2/eZMOGDVLHEYQXLiQkhBUrVhSaJbifxsXFhd69e7N27Vpu374tdRxBEIRnkpiYyIIFC3j//fcLdU/Vh6ysrBgyZAi7du3ixIkTUscR/kMUngTJ3bt3jzVr1tCrVy9cXV2ljvNK2NraMnjwYLZv386ZM2ekjiMIL9Qff/zBtWvXGDVqVKGdB+a/KlSowHvvvcf8+fNJTk6WOo4gvFBz587FwcGBHj16SB3llencuTOenp7MmjVL6iiCIAjPZOnSpWRkZLw2F/YBWrZsSfny5Zk+fTpGo1HqOMJjROFJkNzs2bNxdXXl008/lTrKK9W6dWtKly4tJiUWCpXU1FTmzZtHixYtqFSpktRxXqnBgweTlJTEsmXLpI4iCC9MYV6C+0msrKwYOnQof//9N8ePH5c6jiAIQp48ePCAn3/+me7du78WPVUfksvljBo1igsXLrB161ap4wiPEYUnQVKnTp1ix44dDB48GBsbG6njvFIKhYJRo0Zx5swZduzYIXUcQXghli9fTlxcHEOGDJE6yitXpEgRunbtyvLlywkPD5c6jiA8t8eX4H7//feljvPKtWjRgooVKzJt2jRx5VwQhAJl7ty5ODk50b17d6mjvHI1atTgzTffZM6cOWRkZEgdR/iXKDwJkjGZTEyfPp1y5crx3nvvSR1HEnXq1KFRo0bMmjULrVYrdRxBeC6RkZEsW7aMzp074+fnJ3UcSfTq1QtbW1vmz58vdRRBeG6vwxLcTyKTyRg5ciSXLl1iy5YtUscRBEHIlfPnz/Pnn38ycOBAbG1tpY4jiWHDhhEZGcnKlSuljiL8SxSeBMls376dc+fOMWLECOTy1/ef4vDhwwkJCWHNmjVSRxGE57JgwQLUajV9+vSROopkNBoN/fv3Z+PGjVy5ckXqOILwzB4uwV2vXj0aNGggdRzJVK9enbfeeou5c+eSnp4udRxBEIQnMplMTJs2jZIlS9KuXTup40gmKCiITp06sWTJEmJjY6WOIyAKT4JEtFots2bNonHjxtSuXVvqOJIqUaIEHTp0YNGiRcTHx0sdRxCeyfXr1/ntt9/o168fDg4OUseR1AcffEBQUJCYv00o0B4uwT1ixIjXZpGAnAwdOpSoqChx5VwQhHxv9+7dnDp1ipEjR76WPVUf17dvX2QyGQsXLpQ6ioAoPAkSWbVqFeHh4QwfPlzqKPnCgAED0Ol0LF68WOoogvBMZsyYgZ+fHx07dpQ6iuRUKhXDhw/nyJEj7N+/X+o4gpBnD5fgbteu3WuxBPfTBAYG8tFHH7FkyRJiYmKkjiMIgpAtrVbLzJkzqV+/PvXr15c6juRcXFzo3bs3a9eu5fbt21LHee2JwpPwysXFxbF48WI+/PBDihUrJnWcfMHNzY1evXqxevVq7t+/L3UcQciTgwcPcuDAAYYPH45arZY6Tr7QuHFjatWqxYwZM9Dr9VLHEYQ8ebgE98CBA6WOkm/07dsXhULBd999J3UUQRCEbK1du5bg4GBGjhwpdZR8o3Pnznh6ejJr1iypo7z2ROFJeOUWLlyI0Wikf//+UkfJV7p27YqLiwuzZ8+WOoog5JrBYGD69OlUr16dN998U+o4+cbDSYlv3rzJb7/9JnUcQci1Bw8esHLlytduCe6ncXZ2pk+fPqxbt45bt25JHUcQBMFCQkICCxcu5P3336dkyZJSx8k3rKysGDp0KH///TfHjx+XOs5rTRSehFfq7t27/Prrr/Tu3RtXV1ep4+QrNjY2DB48mL/++ovTp09LHUcQcmXjxo1cv36dkSNHvvbzwPxXuXLlaN26Nd9++y3JyclSxxGEXJk7dy6Ojo589tlnUkfJdz755BO8vLyYOXOm1FEEQRAsLFmyBK1Wy4ABA6SOku+0aNGCihUrMm3aNIxGo9RxXlui8CS8UrNnz8bd3Z0uXbpIHSVfat26NWXKlBGTEgsFQkpKCvPmzaNVq1ZUrFhR6jj50uDBg0lOTuaHH36QOoogPNXjS3Db2dlJHSffUavVDBs2jD179nD06FGp4wiCIAAQHBzMqlWr6N69Ox4eHlLHyXce9kK/dOkSf/75p9RxXlui8CS8MidPnmTnzp0MHjwYa2trqePkS3K5nJEjR3L27Fm2b98udRxBeKKffvqJhIQEBg8eLHWUfMvb25tu3brx008/ER4eLnUcQciRWII7d9555x0qVarEjBkzxJVzQRDyhTlz5uDs7Cx6qj5B9erVeeutt5gzZw7p6elSx3kticKT8EoYjUamTZtGuXLlePfdd6WOk6/VqVOHxo0bM3v2bLRardRxBCFbkZGR/Pjjj3Tp0gVfX1+p4+RrPXv2RKPRMG/ePKmjCEKOxBLcufP4lfPNmzdLHUcQhNfc2bNn2bZtG4MGDcLW1lbqOPna0KFDiYqKYuXKlVJHeS2JwpPwSmzbto0LFy4wcuRI5HLxz+5phg8fTlhYGKtXr5Y6iiBka/78+VhZWfH5559LHSXf02g0fPHFF/zxxx9cvnxZ6jiCkIVYgjtvqlWrRvPmzZk7dy5paWlSxxEE4TX1sKdqqVKlaNOmjdRx8r3AwEA6derEkiVLiI2NlTrOa0dUAISXLiMjgzlz5tCkSRNq1aoldZwCoVixYnTo0IFFixYRFxcndRxBsHD16lV+//13+vfvj4ODg9RxCoQOHTpQtGhRpk2bJuZvE/Kdh0twjxgxQuooBcbQoUOJiYnh559/ljqKIAivqZ07d3LmzBnRUzUP+vXrh1wuZ8GCBVJHee2IwpPw0q1atYrw8HCGDx8udZQCZcCAARgMBhYvXix1FEGwMHPmTAICAujYsaPUUQoMpVLJiBEjOHbsGPv27ZM6jiBkSkxMzFyCu1SpUlLHKTACAgL46KOPWLp0KdHR0VLHEQThNaPVapk1axYNGzakXr16UscpMJydnenTpw/r1q3j1q1bUsd5rYjCk/BSxcbGsmTJEjp27EjRokWljlOguLq60qtXL9asWcO9e/ekjiMIABw4cICDBw8yfPhwVCqV1HEKlEaNGlG7dm1mzJiBXq+XOo4gALB48WKxBPcz6tu3L0qlUlw5FwThlVuzZg0PHjwQPVWfQefOnfHy8mLmzJlSR3mtiMKT8FLMnTuXyZMns2jRIkwmE/3795c6UoHUtWtXXF1dmT17ttRRhNfcxx9/zLZt25gxYwY1atSgadOmUkcqcGQyGaNGjeL27dusX79e6jiCIJbgfk5OTk707duX9evXc+bMGRo2bEhwcLDUsQRBKKTS0tJo2rQpJ06cYNGiRXTo0IESJUpIHavAUavVDBs2jD179nD06FGp47w2ROFJeCmuXbvGjRs3+PXXX+nevbu4uv+MrK2tGTx4MDt27ODUqVNSxxFeYxcvXmTXrl1cv36dPn36kJycLHWkAqlMmTK0adOGBQsWiOdQkNzcuXPFEtzPwWg00rhxY4oUKcK8efOIiIgQPZQFQXhpYmNjefDgAStXrkSn09GpUycyMjKkjlUgvfPOO1SqVIkZM2ZgNBqljvNaEIUn4aVITU3l7t27ODk58ccff9C3b1+pIxVY7733HuXKlWP69OliUmJBEgaDgfT0dPbv30/FihX54osv+OGHH6SOVWANGjSIlJQU8RwKkjp37hxbt24VS3A/h4sXL9K8eXN8fX0zr5qLVe4EQXhZHn6+/P3331SoUIH27duzfft2iVMVTDKZjJEjR3Lp0iU2b94sdZzXgig8CS9FdHQ0ERERxMTE4OTkxIwZM6SOVGDJ5XJGjBjBuXPnxJeLIImHP3SSk5M5f/48b7/9Nr169ZI4VcHl5eVFt27d+OmnnwgLC5M6jvAaEktwvxgVKlRg0qRJnDt3LnPOu5SUFIlTCYJQWKWmpgLmz/CTJ0/Sq1cvWrZsKXGqgqtatWo0b96cuXPnkp6eLnWcQk8UnoSXIiIiAplMxsCBA1mzZo2YWPw51a5dm8aNGzNr1iy0Wq3UcYTXzMNu3DY2NixevJivv/4ajUYjcaqCrUePHtjb2zN37lypowivoV27dnH69GmxBPdzkslkfPjhh2zevJlixYoBcOXKFYlTCYJQWD28WOXq6sratWsZOHCgWOjlOQ0dOpSYmBh+/vlnqaMUejKTGLsjvATr1q3Dz8+PunXrSh2l0Lh16xbvvvsuQ4cORalUotFoeP/996WOJbwGTCYT8+bNo2PHjnh7e0sdp9BYt24d48ePZ8OGDaxcuZJu3bpRunRpqWMJhZROp2Po0KEMGjSI3r17ExAQIIZ7vkAGg4GFCxfSrl07fH19pY4jCEIhlJaWxoIFC+jfv78YIv0Cff311/z2229s3ryZqVOnMnXqVFxcXKSOVeiIwpMgFCBfffUVW7ZsoXz58mg0Gr777jupIwmC8Iz0ej2tW7fGxcWFM2fOMHr0aD7++GOpYwmFVEREBA0bNuTDDz9k/fr1bN68WayGJAiCILz24uPjadasGY0aNWLLli2sWLGCOnXqSB2r0BFD7QShgOjcuTMPHjzAYDAQEhKSOc5bEISCJz4+nnr16lGqVCmOHz+OSqUSc8MIL9XDf19bt26lXr169OrViy1btkicShAEQRCkc+nSJZo3b06dOnXYunUrgDjHeklE4UkQCogePXpw8eJFTCYT9+/fJy4uTupIgiA8I0dHRz799FN27NiBnZ0daWlpovAkvFQPFwlITU3lwIEDlChRgvr160ucShAEQRCkU6JECZo3b86OHTsy58sSq5O+HKLwJAgFRKNGjfjzzz8zu34+ePBA4kQFmxhlXPgUpNdUJpPRt29f1q1bh4uLCyaTiYsXL0odSyjEwsPDAVAoFEyZMoWlS5fi7Owscaq8M5mMUkcQXjDxmgqCIBW1Ws2kSZP4/vvvsbKyAuDatWsSpyqcxBxPglDAmEwmvvvuO6Kiopg0aZLUcQo07YNLGLWil0lhIFfbofYtJ3WMZ5KRkcHw4cNp1KiRWDBAeGkiIyMZMGAAU6ZMoXjx4lLHeS43UreTZoiVOobwAtgoXChh+47UMQRBEIiLi2PgwIEMGDCA6tWrSx2n0BGFJ0EQXlvpt49jSk+WOobwAsisNVgXrSl1DEEQXoHzSatJNUZKHUN4AWzlHlS0F4sqCIIgFHZiqJ0gCIIgCIIgCIIgCILwUojCUyGxcuXKp7aZOnUqsbGvtmv6mTNn6NixI5988gldunQhODg4S5sNGzbQpEkTOnfuTOfOnQkNDX2lGQXhVfvm+7V0+3IWuw6fZvTc5QCcuHidrxaukjiZpS+mLOTT0TP5ZMR0Nv9zJNs2tT4cQPexc+g+dg6/7TzwihMKgiC8WitnHmBqzz848fetbO+PCk3k9P67LzXDib9vMeqDtfSo/0O290/ttYllU/Zme9+h7deZ9NlGJn22kSsnQ15iSkEouPLreVVSUhIffPAB1atXZ9OmTRb3rVixgq5du9K5c2e2bduWZdtRo0bRpk0bOnfuTP/+/V9VZEHIpJQ6gJB7BoMBhUKR7X2rVq2iS5cuT9x+zJgxz50hJSUFOzu7XLcvUqQIy5cvx9bWln379vHtt98yc+bMLO3at29P3759nzvf8zIaTMgVMqljCC9QfnxND5+9zJZF5vm5mtWt+tz7MxqNyOVPvo6QmpaOrY11nvY7rFt7Anw8ydDqaD9wMs3rV8dKrbJo4+HixLIpQ/Kc+WUyGQ3I5Nl/VgqFy8PX2mAyopCJa2mFSX59TS8eDWbG7x/leH90WBJn9t+lasPAXO1Pm65HqVYgl+f+e6p0tSJMWtWeMZ3+l+W+k3vuYGuvzna7lKQM/vrlHOOWtyUjTceMfn/y1ar2eTq2IBQWBfG8ytramkWLFvHrr79a3H7gwAGioqJYsWLFE7cfO3asZHMXmQxGZIr895kuPLu8vqai8JRPPHjwgAEDBuDv709ISAj169dn4MCBHDt2jO+//x6NRoO7uzvvvfceX3/9NQqFAk9PT6ZNm8bq1auJiIigc+fONG/enNatWzN27Fji4uIwmUxMmjSJoKAgOnfuzMyZM9Hr9QwYMIDixYtz69Yt6taty9ChQ3PMdu/ePbZs2cKhQ4fo1asXjRs3zvXj8vT0zPxbrVbn+AG/adMmDhw4QM2aNRk4cOBTT6JfFrlCxoaJcUTf1UtyfOHFcgtU0m5i/lq1aerSX4mMiaf72Dl8+XknBn29mC2LzUWokIgYBk9bQlhULO3fakD75g1ISklj0qJfiE9MxgSM6/0RAT6edB87h3LFA7h5P5QBndtQOsgvy7FS09LZc+wcOw6dwsPFibF9cj5Zyk6Aj/n9q1IqkMlkyLI5N4mOT6T7mDnYa2wZ1u19fL3c8/ycvGgyuYK4DRPRR9+VOorwEindAnFuNxEAhUzOxLgN3NVHS5pJeDEClW5MdG4ndYwsfp6+n9jIFL7pvYkuIxqwffU5okKTSEvW8t5n1ajeOIi/1pznzuUovum9ifd716RkZe8s+zEajFw+GcKRv24QFZrEsPktUVvn/ie5vZNNtrcb9Eb+2XCJdz6qxPFsemTdvhhJqareqK2UqK2UOLnbEh2WhIePQ+6fBEEoAArreZVKpcLNzS3L7du2bcPJyYmuXbvi4ODAuHHjcHfP+nts2rRpqNVqOnXqxLvvvpvr474IMoWcuIl70d9NeKXHFV4OZaAjzhPfyNs2LyeK8CxCQkJYsWIFGo2GTz/9lOvXrwPmJZA3btyIWq3m/fffZ86cOQQEBDB37lzWr19Pt27dWLNmDatWmYfpzJo1i7feeouWLVty48YNZs6cyaJFiyyOFRkZyS+//IK1tTXNmzenb9++2Ng8+iGTkpLCxo0b2bNnD25ubrRo0YLevXujVJr/yXz33XccO3Ysy2NYsmRJtpX71NRU5s+fz8SJE7Pc17RpU1q3bg2YK/EbN26UdGWn6Lt6wq/rJDu+ULiN+bwTR89eybaXUFRsAgvH9ceEiY+GTaNZ3aqs2LiTprUr83aDGty8H8rclRuZN7o3AKWC/BjSNet75fDZy/y55xjxSck0qlGRif074+JoD8CF63eZt3JDlm06tngjx95Xy37fwZt1q6BWqbLct23pFJwdNBw7f5UJ363KN72f9NF30YVflzqG8Ard1UdzXRcudQyhEPt0ZEMuHnvA6CXm3yydh9XHykZFcnw6Uz/fRPXGQbz9UUUOb79B97FvZNk+5HYs+zZd5d61KEpXLUKrrlXxDnACQK8zMPOLP7NsU6KSN+375G7hhH82XKbu2yVQqrO/eJeckI6dvVXm/9vZW5GckC4KT0KhVJjPq/4rMjISOzs7VqxYwV9//cXMmTOZMWOGRZsRI0bg4uJCYmIiXbt2pUKFCgQGBubpOX1e+rsJ6K7HvNJjCvmHKDzlI0WLFsXBwfzlX6lSJe7cuYOTkxMVK1ZErTZ3m05KSiIgIACAqlWrcuBA1jlVrl+/zokTJ1i7dm2OxypWrBi2trYAeHl5kZiYaPEBGRkZybp166hUqRLvv/8+VapUsdi+f//+uR4frNVq+eKLL+jVqxelS5fOcr+jo2Pm3y1btuTvv/8WS4oLr6UyxfxRqcwfy8X8vQmJjObG/VBOXbrB+h3m97rssW5HVcoUy3Y/2/Yd50F4FB1bvsEbNSpaDLGrUDIwT8WhDbsOcis4lK8Hdcv2fmcHDQC1KpZmyuI1ud6vIAhCQWY0mvjjh5PcuBCBQiEjNuLpK6RePhnK+cP3eaNtGWo1K46z+6MTSqVKkVnQehZpyVpO77vD8AWtuHYm+7kyNY7WpCRlZP5/arIWjWPehmALQkFRWM+rsuPo6EjDhg0BaNy4MQsXLszSxsXFBQAHBwfq16/PlStXXnnhSXi9icJTPnL79m2SkpLQaDScP3+ed999l/j4eIthZ/b29ty9e5fAwEBOnz5NUFAQYHkyWrx4capUqUKzZs0Ac+Hnv2T/GTNjMpks/j8oKIgtW7Zw5coVNm/ezIwZM6hevTodO3bEx8cn15V5vV7P4MGDadWqFU2aNMn2cScmJmZ+MRw9ejTzMQk5i9XfJFx3krI2HXNsczLlO8rbfIK13OmZjpFiiORoynQMJh1uyjJUteuTpU247hTnUs2TY5eybkegVdNnOpZgdvX2fXR6A5hM3LofRhF3V4r5eVOpVFGa1K4MgE73aBioIochqVMGdiU+MZkdh04xePpSnO01vNekDnWrlM1Tj6cdB0/yz7FzzB3VO9vhr6lp6Vip1SgUcm7eC8FBY/scj/71djNWz8lwHR3LZj+EBuC7kyl8Ut4GJ+tnG4ocmWJg+tEUdAYTZdyU9Kma9SrqqXAdy8+lAtCulDVNA62ytBGenf5mLLqT4dh0LJtjm5TvTmLzSXnkTs9WEDBEppAy/SgmnQFlGTfs+mTtyag7FU7q8nMAWLcrhVXTwGc61uss+EYMwTdjGftDG5Li0xje1jznilKlwGgwZrtNsw/K80abMpw9eI9fZh8iI01H9cZBNHy3NEaj6bl6PIXejSMtRcucQdtITkwnITqVPRsu07jdo39rRct7sH7RMXRaAxlpOuIiU3Dztn/GZ0AQ8rfCeF6Vkzp16nDx4kUaNmzIuXPnsj2Xeni+pdPpOHXqFC1atHjqfl9HN/URnNTdpaNNrRzbfJeym09s6uIkf7bfvZGGRKanbENnMlBG6U0fu6znyKd0d1meai6EtrOuRlOrnH83FBSi8JSP+Pr6MnHiRO7fv0/dunUpVapUlg+hcePGMWrUKBQKBe7u7pkTclepUoW+ffvy5ptv0qdPHyZMmJDZRbR+/fr06tXrmTKVKVOGMmXKYDQaOXbsGJGRkfj4+OS6Mr9582aOHDlCYmIiGzZsoFixYkycOJErV65w6NAhevTowbJlyzh8+DAKhYLAwEAGDBjwTFlfJy7K4rgoiz+xTXW751ux4kzqUsrZfIyXqioHkyYTrjuDl+rRFRqjycCplEW86TAHhcyanQn98FHXQSUTxYdn5eXuwohZPxAWFUuHtxvi5KChR/t3mLpkDb9u3QNA3Spl6dau+VP35eSg4cN3GvHhO40IiYjmzBXzfB+57fGk0+kZM38FpQJ96T1xPmAuaHm7uzB67nK+GfwZt4LDmbJkNXb/9qga1+fjZ33or73iLkqKuzz5K7l/9dxPQJqdpWdS+bicDVW9VEw+mMSZcB1VvB4NnzQYTSw6lcKcNx2wVsjotzOBOj5qbFVi4uEXRVncBWVxlye2sev/fBO/pi49g83H5VBV9SJp8kF0Z8JRVfHKvN9kMJKy6BQOc95EZq0god9O1HV8kNlmHUor5MwrwBGD3sjXn2/Cv6Rr5oTevsVciHyQyIJRO2nZpTJFy3pYbKdSK6jRpCg1mhQlJSmDE3/fQq8zorZW5rrH05VTIWxefpr4qFSm99tC47ZlqflmMSaueD/z/sPbb2QWnZaM203vyW9iZ2/FWx0r8E3vzQB0HFhHTCwuFFqF8bwKoGfPnty+fRsbGxtOnDjBlClTaN26NRMmTKBz584ATJkyBTCvHO7p6Um9evUYPHgwKSkp6PV6WrZsme0oFAGKKz0prvR8Ypv+dm8+1zGWpu7lY5s6VFUFMDlpM2d096iiCsi832AysijlH+Y4dMJapqRfwi/UURfDVlawLwbKTP8tyQqSePDgAWPHjn3qagTCy/d916h8M8eTyWTkSPI0ko3hOCuKEaI7QhvntUToznInYxe1NcM5kjwNOUrSjLGkGqOpbz8eB4UvuxIGUU8zBlvFs032vCWuC+86m5eTvZexh3jDHSrZfpZ5f6LhPmdSltLIYSoAx5Pn4m/VCC/V86/S9qJ4lVTRa0XOjz/99nFM6U8fHiHkfzJrDdZFH/UKiPq+a76c48loMjHtSDLhyUaKOSs4EqJjbRtnzkbo2HUng+G1NUw7koxSDrFpRqJTjYyvb4+vg4JBuxIYU0+Du+2zrdjXZUscK981T7a/514Gd+INfFbpUaH4fqKBpWdSmNrI3AN17vFkGvlbUdUrfxYkVF4lce+1IvP/u0Z9n2/meDIZTSRPO4IxPBlFMWd0R0JwXtsG3dkIMnbdQTO8NsnTjoBSjjE2DWN0Kvbj66PwdSBh0C40Y+qhcH+2In5cly04rzRPGpux5x6GO/HYflYp837D/URSlp7BYWojAJLnHseqkT+qql7Z7k8KJVVerHDP+cTufNJqUo2RrzCR8LLYyj2oaC8uWggvljivyp+ium7KN3M8GU0mpiVvJdyYQDGFB0d0N1nr3IezuvvsyrjEcM07TEveihIFscZkoo3JjLd/D1+FC4MS1jBG0wp3xbPNj9cl7gdWOvcEYE/GFe4YovnMtkHm/fcNMSxN2ctUB/PFhLnJO2lkVYqqjxWnpKYq6Yr7irwNDxc9ngQhH3ugPYRcpuYtx29JMoRwM2Nrtu0cFP7U0gzjVvpf3ErfRhW77H+wpxgiOJz8TZbb/dWNKGXT1uI2E49q0mqZPRnGRIv7M4yJqOWPuuir5VnbCIJg6dADLWq5jG/fciQkycDWmxnZtvN3UDCsloa/bqWz7VY6vapk39spIsXAN4ezFk8b+atpW8py2N7jl5ns1TISMyyHAyVmGLF/bFJie7U8Sxshd7SHHiBTy3H89i0MIUlkbL2ZbTuFvwOaYbVI/+sW6dtuYderSrbtDBEpJH9zOMvt6kb+2LQtZXnjYy+0zF6NMdHy35gxMQP5v71zAOTZtBEEQRCEwuyQ9gZqmYJvHT8mxBDH1oxz2bbzV7gwTPM2f6VfYFv6eXrZvZFtuwhDIt8kZx0u3UhdirY21Sxue/wcy15mTaIxzeL+RGMa9vJHw+3t5VnbFESi8JRP+Pr6iqq8kEWi8QFuyjIA2Ct8sJI7ZtvOVWnuLqtReBGlv5jj/uwUnjRznJerY8t41P1ea0rGSm5Z1beSO6A1Jj1qY0zGSiVWxhGEJ3mQaKSMm/mr18degaNV9vM1lXY1t/HSKLgYpc+2DYCnnYJ5zbL/XPivx6egSNaacPjPsR2s5CRpjY+1MeJglT97O+V3xgeJKMuYl7xW+Ngjd8y+e7yytKu5jZcG/cWoHPen8LTDcV6z3B38sRfalKxF7mB5bLmDFcakR3OUGJO1qBwKdvd9QRCEx4nzKuFpHhhjKaMsAoCPwhlHefZzbJZWegPgpXDkov5BjvvzVDgwz/GjXB378XOsZFMGDv85toPchiRj+qM2xnQcVDnPAVpQiMJTIfP43Ek5mTp1Kn369Mlc3SCvwsLCGD16NFqtlkqVKjFy5MgsbZo0aYKPjw8AtWrVeq6VGl5n9nIfwnQnKE5Lkg1hZBgTcmj5+BwNOY+ezUuPJ2dlCSJ0Z/FUVeaB9jDFrN+2uF8j9yHZGE6GMQmlzIpo/SWq2PbM7UMTnuDqnWCOnr1C17Zv5dhmxrL/0aP9O7g4PtvEsOHRsUxYsBKtTk+FkkEM6Zr9SpI6nZ52AybxXpPa9OzQApPJxLQf1nHl1n30RiMft2xMyzdynoBRsORjL+dEmI6WxSEs2UBCDj2KcveOzluPpxLOSs5G6KjsqeLwAy1vF7OcvNpHIyc82UhShhErpYxL0Xp6VhFztj0LuY89uhNh0LI4hrBkjAk59CjK5Qudlx5PyhLO6M5GoKrsifbwA6zftlz9Uu6jwRiejDEpA5mVEv2laGx7Zt/TSni6e9ejuXTsAS06V86xzeo5h3i3W1UcnJ/txCEmIpkfJ+1BrzVQtLwnnQbWybadXmdg9IfrqN+yFK27m6+wb/j+BJeOPUCpktN5eAN8iz367Xf5RAjT+21h7pZPcPHUPFM2QXhdiHOswsVH7swJ3R1aUokwQzwJOfQoerxI9MTfY3no8VRC6clZ3X0qq/w5rL3J29YVsmQLNyaQZEzHSqbkkj6EnraNcv/g8ilReCpkHk5a9yRjxox5rmPMmjWLzz//nDp16jB06FCOHj1K7dq1LdooFIrMSfiEZ+errkew9gA7EwbgpAjCRu76XPvLS4+nyrY9OZo8g3Msx1VZCk+l+cTk8dXyqtr2YW/SKADK2HyASv58kx8LZqWD/Cgd5PfENiO6f/Bcx5i3ciPd33+bmhVLM2rOMk5cuEaNCqWytFu7fR9F/R7N/XLrfii3gsNYOX0EaekZtB80WRSe8qCer5oDwVoG7EwgyEmBq82zrVD3UF56PPWsbMuMo8ksPwelXJVU8TT/BHh8tbw+VW0Ztdfck/GDMjbYqZ4v3+tKXc8X7YFgEgbsRBHkhNz1+a5U5qXHk23PyiTPOArLz6Es5YqyinmS1MdXy7PtU5WkUXsBsPmgDHI70bPtWQWUdCOgpNsT23w8pN5zHeN/C47ybteqlK3hw+Kxu7lyMoQy1X2ytNu9/iJFgpwz///e9WhuX4pk3LK2RIUmsnzKPkYuMs//ZTSa2L76LEFlnm0eSEF43YhzrMKlnroEB7TXGZCwmiCFO67y5yu+56XHU0/bRsxI3s5yDlBK6UUVpT9guVpeH9vGjEpaD8AHNjWxkxf8nsmi8FRAGY1GRo0axYMHDyhdujR79+7ln3/+4dixY2zevJmpU6cyatQoVCoVUVFRREREMHfuXAIDA+ncuTMzZ87Ey+vZJhK9dOkSs2fPBqBp06YcO3Ysy4eiyWSic+fOqNVqhgwZQrly5Z77Mb+O5DIFdTQjkcuUJBlCiNFfAcBTVRlPVWUA6mhGZbZ//PbcFphyolF48abjnCy3P75anre6Ot7q51uB6XVmNBoZv2AlIRHRlAz0Zf/JC2z/fionLl5n275jTOjXmXHf/oxKqSQ6LoHI2HimD+1BQBEPuo+dw9eDuuHp5vz0A2Xj6u1galY0D9FsXKsyJy5cz1J4SkxJ5dj5qzSrW5XImDgA3F2cUCkV6PQGUtLScdSIYmNeKOQyRtbRoJTLCEkycCXGPIyusqeKyp7mk/9RdR79+Hn89twWmHLipVEw582s+3h8tbzq3mqqe6uztBHyRqaQoxlZB5lSjiEkCf0V82SqqsqeqCqbC0GaUY96rTx+e66H1OVA4aXBcU7WFXceXy1PXd0bdXXv5zrO68ZoNPHDV3uIDk3Er6Qr5w7eY/amTzJXkOs+9g1++OofFCoFCdGpxEWl0Hfqm3j5O/FN7018/lXTZ+5VdO9aNGVrmAtNVRsFcuVUaJbCU0pSBpdPhFCjaVFiI1IACL8XT2Bpc2HJvYgDoXfjMOiNKJRyDm+/TpUGgRzblf38Y4LwuhLnWK8HhUzOSE0LlDIFIYY4ruhDAais8qeyylwIGqVpmdn+8dtzW2DKiZfCkTmOHbPc/vhqedXVQVRXBz3XcfIbUXgqoP7++2/UajVr1qzh/v37rF+/Ptt2RYsWZfLkyWzYsIHffvuNYcOGZdsuNDQ02+6czZs355NPPrG47fGFEB0dHYmPj8+y3f/+9z9cXFy4fv06gwYNYtu2bXl4dMLjDiZPIsOYgN6URlW7vlLHEV6gvcfPo1Iq+enrYQSHRbFx96Fs2wX5eDK+78ds+ucIf+w+xMAubbNtFxYVy9j5K7Lc3qxuVTq2eMPiNqPx0fvYwc6W+KSsw7V+XL+dbm3f4kFE9KO2Glt8vdxp3W8CaekZjOsjViPKq0kHk0nIMJKmN9G3qijcFVbJkw5iTMjAlKbHrm/+We1TeDan991FpZYz5oc2RDxIYP+mq9m2KxLgxGdfNuLAn1fZv/kqH/SvnW27mPAkvp/4T5bbqzcpRrMPylvc9vjntZ2DFckJ6f/djC0/naZF58pEhT5a5MOnqAs7111ArzPw4FYsCTGppCZnYGWt4tC26wyb31IUngThP8Q51utjUvJmEoxppJm09LVrInWcQk8UngqoO3fuUKmSeXlkf39/nJ2z7/VQoYJ5zKiPjw+nT5/OcX9FihTJdbdN2WMTlyYmJuLk5JSlzcOxzSVLlsTBwYHY2NhnHu/8umtoP0nqCMJLci80goqlzFcz/LzdcXLI/mp4uRLm5VOLeLhy9sqtHPfn7e7CsilDcnVsufzR+zgpJRUne8tjh0TGEBoZQ7VyJSwKT0fOXiEqJp4tiyaRnJpGty9nUb9aOdQqMVQntyY1fLZ5uYSCxX5SQ6kjCC9QxP14ipU390rz9HVE42Sdbbugsh4AuHnbc+NceI77c/WyZ/SS3C1F/fjndWqSFo2j5bGjQhOJCk2idNUiFoUn32Iu1H6rBNP7/YmXvyO+xVzROFrz589naNKuLAqlGEorCP8lzrFeH5Pss7+QK7wcovBUQAUGBnLw4EE6dOhAcHAwcXFxT93m8Sr6f+WlGl+2bFmOHz9OzZo1+eeff2jXrp3F/VqtFpPJhJWVFZGRkcTGxmb7wSm8epviPqa18+qXfpwLqT8TrjsDQIoxHD91Q6qJ3lpZ+Ht7cPjsZdq+WY+QiGjiE7P2OgLLiQ2fNLVhXno8lQ7y4+TF61QvX5J9J87TuonlZLVXb90nMjaBvpMWEBkTj1anp5h/EazVahzs7VAo5NjaWKMzGDAYnzTdovCifbwpjtWtn22IZV5svpHO9lvpqOUy3GzljKyjQa2QcS1Gz/wTKagUYK2QMa6+Bo1anMC+THEfb8J5de6KFM8jfdstMrbfAjnI7NRoxtQT8z/9y9PfkQtHg2nUugxRIYkkx2ftdQSWq0c+4WdXnno8+Zdy4+rpUEpXLcKZA3dp0Kq0xf33rkUTH5XCrAFbiYtKQac14FvMhWpvBNHsg/I0+6A8D27Fsm3VWWQyGSG347h2Oox9m64SfDOWpRP/Ycicd7CyEa+1IIhzLOFZfBy3lNXOn7/04/yVfoGf0w7h+e9q46M1rfBUFIxVxUXhqYBq2rQpO3fu5KOPPqJkyZK4uz/f5JB5qcYPHTqUL7/8knnz5lG+fPnMsccPV3LIyMigb9++2NjYYDAYmDhxInK5OCl5nVSw/ZQKfArA34nDCVA3ljhR/vRGzUr8ffQs3b6cRfEAH9ycn28On7z0eBrQuQ0Tv1vFwjWbKVs8IHN+p4er5TWtU4WmdcwTym/65wiRMXE0qVUZg8HI9gMn+HT0THR6A51avIGNlZgTqDCq5qWiVXEr5DIZS06nsOtOBi2LW7PmUhq9qthS2VPFqgup7LyTQbtSBX+ZXwGsmgVi3cK8Cl7q8nNod97Gum3WRQdeR1UbBnJyzx2m9vwD3+IuOLo+34qPeenx9EG/WiybvJffdccJLONOmermJcAfrpZXvXFRqjcuCsCBP68SG5FCtTfMvWlnfPEnRr0RjZM1XUY0AKD3pKaZ+/6m9yY+n9hEFJ0E4V/iHEvI71pYVaSzbV2pY+SZzPSkEq2Qr+l0OlQqFffu3WPw4MFs2LBB6kiFwvddowi/rnulx0w2hHMoeQoKVJgw0dB+MvGG21xIXYERA2qZPfXtx6OUWbErYRAuyhIkGO5iMOkobt2KOxk7SDfGUd9+Ag4KP3YlDMJREUiS8QEmk5H69uOwljtn9ngymvQcT5lHsiEUIzqq2PbGXVWOC6mrCNEeRimzwUddmzI2z7dyWqoxmr8Th/Gu04oX80TlkVdJFb1W5PyDIf32cUzp2fcyelV0egMqpYL7YZGMnPUjv87+UtI8BZXMWoN10ZqZ/x/1fVd04ddfaYbwZANTDiWjUph7OkxuaM/teAMrLqRiMIK9Wsb4+vZYKWUM2pVACRcldxMM6AwmWhW3ZsedDOLSjUyob4+fg4JBuxIIdFTwIMmI0WRiXH17nK3lmT2e9EYT846nEJpsQGeE3lVsKeduLgYdDtFio5RR20fNB2Wevyi0/FwqRTRy3i5mzU/nUynhrKS+n5pFp1Io7aqkSeCrX21F5VUS914rMv+/a9T3XNflPLTpZTCEJ5M85RAPX3T7yQ0x3I4ndcUFMBiR2auxH18fmZWShEG7UJZwwXA3AZPOgHWr4mTsuIMxLh37CfVR+DmQMGgXikBHjA+SMBlN2I+rj9zZOrPHk0lvJGXecQyhyaAzYtu7Cqpy7qSuuoD2cAgyGyXq2j7YfPDklZdyI2XhKdT1fDMnPX+VSqq8WOHeK8f7zyetJtUY+QoTmen1BpRKBRHBCSwcs4tJK9u/8gyFja3cg4r2Yq5AIX8R51gvR1TXTeiux7zSY4YbEpiSvAUVCkyYmGzfjtuGKFakHsSAEXuZNePt38NKpmJQwhpKKD25a4hGZzLQyroyOzIuEGdMZYJ9a/wULgxKWEOgwo0HxliMJhhn/y7OcrvMHk96k4F5KTsJNcSjw0Bv28aUU/mwKvUwh7U3sZGpqK0uxgc2NZ8ePht/pV9gddoRnOS2VFL585lNA+SPd7V9RVQlXXFfkbee2KLHUwE2ePBg4uPjSU1NzbYLp1BwROhO462qTkXbroC5y66rsiRvOs4F4EzKUu5n7KGo9dsAeKgqUc2uH0eTZxKrv0YTh5ncydjFrfTtVLEz/1h3U5WhptUgbqVv43LaOqra9c483s2MbdgrilBbM4x0Yzz7ksbQ3HEhd7W7ecdxCUqZDSaTMUvO/UnjyTAmWtxmr/ChtmZ4to/rbsZuAtVNs71PMBs560cSklJITU9nSDdxElOQnY7QUd1bRdeK5p4QJpOJkq5K5v67ktzSMynsuZ/B20XN87NU8lDRr5odM48mcy1Wz8wmDuy6k8H2W+n0qmKedLyMm4pBNa3YdiuddZfT6P3YZOTbbmZQxF7BsNoa4tONjNmXxMLmjuy+q2XJO47YKGUYs7m2NH5/EokZlu9vH3sFw2tnP8fYvQQ9x0K1mavqNfBVM2ZfEsvOpWKrktGryvP1/CjIdKcjUFX3xrZrRcD8mitLuuI417wyTcrSM2TsuY/12+beKKpKHtj1q0byzKPor8XiMLMJGbvukL79Fna9zD0MVWXcsBpUk/Rtt0hbdxm73o8mJs/YdhNFEXs0w2pjjE8nacw+HBc2R7v7Lo5L3kFmo8SUzdDXpPH7MSZmWNym8LFHMzzrxNfpm66T/sd1sFZi87FYLelxi77cTXJCOulpOjoNrPP0DQRBKJDEOVbhcVp3j+qqQLra1gf+/W2m9GSuYycAlqbsZU/GVd62Ns/ZVUnlRz+7psxM3s41fRgzHT5kV8Yltqefp5fdGwCUURVhkNVbbEs/z7q04/S2ezSyY1vGeYoonBmmeYd4Yypjkn5noWNndmsvscTxU2xk6ux/myVtJNGYZnGbj8KZ4Zp3LG6rpy5BMyvzd/OslO3syLjAO9YVX8yT9ZKJwlMB9t1330kdQXhB/K0acyntFw4lTcFO4UkFm67E6+9yLm05RpOWdGMcKqtHJ5wuipIA2Mrd0SiKZP4dYTqX2cZdaZ4jwk1VnmDtQYvjxetvE62/RKj2OABak7nXTw27AZxImY/RZKCE9Xt4qCpYbJfXic7vZOyikf3UPG3zupkz6uWPBxdejcb+VvxyKY0ph5LwtFPQtYINd+P1LD+XhtZoIi7diJ3qUc+gki4KANxt5RTRPPr7XMSjHyTl3c1f0+XdVBwM1loc73a8nkvReo6Hmm9P1pq3G1DDjvknUjAYTbxXwpoKHpZDaPIyuXl4soFpR5KZUN8eG6X5itrcEylMamhPKVclay6lsf5KOp3KvZ5D7awa+5P2yyWSphxC4WmHTdcK6O/Gk7b8HCatEWNcOlaPzZGkKGmeAFbubouiiCbzb9O5iMw2yvLmXpqq8m5oDwZbHE9/Ox79pWi0x83LPpuSza+93YAapMw/gclgxPq9EqgqeFhsl5eJzq1bl8S6dUnS1lwiba1l4et1N2BGc6kjCILwCohzrMKjsVVpfkk7wpSkLXgqHOhqU5+7+miWpx1AazIQZ0zB7rEpI0oqvABwl9tTROGU+fc50/3MNuWVPub/qnw4qLXsXX9bH8UlfQjHtbcBSDaZ5wMcYNeM+Sm7MJiMvGddhQoqX4vtcjvRub380eISTdRlOaS9wTuIwpMgCLkkQ0Zl254AHEmeTpjuBLfSt1LRpivuqnKcTlmC6bFJpR+fbDqniaej9ZexV/gQrbuMg8LP4nhOykDsFT6UsekAgMFkHlropiyHl6oaKYYI9ieN4x2n7y22y0uPp1j9TVQyDZp/P8AFobCTyaBnZXPvn+lHkjkRpmPrrXS6VrShnLuKJadTLCYbfrxntMWExI/t83K0Hh97BZejdfg5KCyOF+ikxMdeQYd/h9LpDOYty7kpqealIiLFwLj9SXz/jpPFdrnt8RSbZmTigSSG19ZQxN7y2I5Wssz/hiYZnvi8FGoyGbY9KwOQPP0IuhNhpG+9hU3XiqjKuZOy5DR5fdH1l6NR+NijuxyNws9ywlBloBMKH3tsOpiH0pl05udeWc4NVTUvDBEpJI3bj9P3lldIc9vjyaQ1IFObX2uZRo1M+xq/toIgCEKBJ0NGT9tGAExP3sYJ3R22pp+jq019yql8WJKyx+J3l+w/2z5k8dtMH4qPwpnLulD8FJYrCgYq3fBRONPBpgYAOpP5e7Sc0odqqkAiDImMS9rA905dLbbLbY+nZGM6mn+LT2d097IcPz8ThScBgGbNmrFr166Xfpy1a9fy22+/YWVlhaenJ9OmTUOtFhMTh2iPcC19IzLkKGQq3JUV0FulcTRlJo4Kf1QyW1Qyu6fv6DGx+uvcztiByWSknv1Yi/uKW7XiRMp8dicMBsBZWZxqdv3YnzQOg0mLAS0lrdtk2WdeejzdydhJkNWbecosvBjv9hnPlsV56532LDb9c4Sl67bi7W7+0psysGvm36+jIyFaNl5LRy4DlUJGBXclaXorZh5Nwd9Rga1Khp0qb+Pwr8fq2XE7A6PJxNh6lj2VWhW3Yv6JFAbvTgCguLOSftXsGLc/Ca3BhNYAbUpmXfI9tz2evj+bSly6iQUnUwBoGmjFuyWs6VnZlkkHk1ErQC6DL+vmvgdVYaM9EkL6xmsglyFTKVBWcMcqTU/KzKMo/B2R2aqQ5XFVOP31WDJ23DbP8TS2nsV9Vq2KkzL/BAmDdwOgLO6MXb9qJI3bj0lrAK0B6zYls+wztz2e0n6+gO5SFAByByvsRmQdiie8XMPbrWHmho9e+nESYlL54as96HQG3Lzt6Ta6IUqV4ukbCoKQa+L8SnpHtDfZmH4aOTJUMgUVlL6kWWmZmbIdf4UrtjI1drK8zVN5XR/OjowL5t9m9u9Z3NfKqhLzU3YxOOFXAIorPehn15RxSRvQmgxo0dPGOmtP4tz2eFqbfpxTursokOOrcKabdYM8ZZeSmFxcAF7dB+O9e/fw8/NDLpczY8YMgoKC6NChw0s/bl5IMbn4i7YrYRD1NGOwVTzfShwFXUGYXPxleJWFp8iYOHp2aPHSj/U0+WFy8Rdt0K4ExtTT4G4rTgazkx8mF3/REgbtQjOmHgr313feLMi/k4u/Cq+q8LR6ziGKlfek9lvF2bTsFM4edjR8t/RLP+5/icnFhcJMnF9ZkmJy8RdtUMIaxmha4a5weHrjQkxMLl4IPXjwgGHDhqFSma+Yfvfdd1y/fp1vv/0Wg8GAg4MD8+bNw9rams6dO1OmTBlu3bqFVqvlgw8+4I8//iA6Opp58+YRFBRE586dKV68OHfv3sVoNDJnzhxcXV0zj6fT6fjqq6+4f/8+Op2OESNGUKVKFRYtWsQ///yDra0tjRs3plu3bs/0eAICAjL/VqvVKBTihEp4fYRExvDl3OWolEpMJhNzR33OjXuhLF77p/n9rLFl+tAeWFup6T52DqWL+nE7OAytTs/7b9Vny56jxMQnMXNYDwJ8POk+dg7F/Ly5FxaJyWhi2pDPcHF69EWo0xv4eumvPAiPQqc3MPjTdlQqXZQf1m9j7/Hz2Fpb0bB6BTq3fvaeaVv2HOPQ6ctUK1eCfh+9K5b1FQShUIoKTWTJ+L/NvYJMJgbMeJvgmzFs/OEkRr0RWwcr+k1thtpayTe9N+Ff0o3QO3HodQbeaFOWg1uvkRibRr+vm+EV4MQ3vTfhU9SF8PvxmIzQZ8qbOLg8mitNrzewcvoBIh8kotcb6fhFbYpX9GLTslOcOXAXaxsVlesH8PbHlZ7p8YTfj+fNDua5IIPKunPkr5uSFJ4EQQri/EoQXj1ReMrnjh49Sr169fjiiy8A80z85cqVY9WqVQDMnDmT7du307atuXtezZo1+fLLLxkzZgwXLlxg2bJlbNq0id9//51hw4YBUKlSJSZMmMBvv/3Gjz/+aLFaw2+//Ya/vz9TpkwhNjaWPn36sG7dOrZs2cLvv/+Ora0tRmPW1c769+9PQkKCxW3+/v5MnZr9xNK3bt1i//79/PLLL8//JAlZNHOcJ3UEIRsnzl+jdqUy9OnYCjC/n8sW8+fHyeYhj/NWbmTnoVO818S8WlK1ciUY/lkHvlq4iks377F4wgC27j3GH38fZmAX83u+Qskgvvy8E3/sPsyKP3YxpOv7mcf7Y/ch/LzcmdDvE2ITkhj09WJWTh/Btn0nWDNrFDbWVtm+n4dMW0pCcorFbf7e7kzo19nitsY1K9KqUS0Avlr0C5v3HKVN07ov6NkSHq4iJ7w+HOc1kzqCkIMrJ0MpX9OXtr3M83aYTCaCyrgzerF5mMW6BUc5vvsW9VuVAqB01SJ8PKQey6bs5fblSIYvaMWh7dfZv+UqH/Q3D2EsWs6DLiMasH/zVbb9cpaOAx6tlLd/01U8fB35bMwbJMalMW/oX4xf3pYjO27w1c/vY2WjwpjNCobfjthBSmK6xW0evo50H/uGxW2+RV04fySYZn6OXDgSnGUbQSjMxPmV8KzmOb78HqmFlSg85XPvvPMOS5cuZejQofj4+NC/f39u3rzJvHnz0Gq1REdHo9E8mhC2XDnz8opeXl74+fll/n3ixInMNlWqVMn87+7duy2Od/36dc6cOcOBAwcASEw0TyQ9fvx4Jk2ahF6vp2PHjlSvXt1iu7ys/vDgwQNGjhzJ/PnzsbV9vYcTCK+Xt+pV5cff/2L03OV4u7vQ58NW3LwfxsI1m9Hp9MQkJGJn82hOnrLF/AHwcHXGz8s8ZNDDzZmTl25ktqlUumjmf/85dtbieDfuhXDu2m0OnbkEQFKKedLCUb0+5Ovv12IwGOnQvAFVyha32C63K+05aB7NO/Z2g+rsPXZOFJ4EQSiUar5ZjC0rTrNk3G5cve1p27M6D27H8vuSE+i1BhJj07B5bD6vwDLmz2wXDzs8fB0z/752OjSzTYmK5sU3ilf05NTeOxbHe3Arlhvnw7lwxLySUmqyeXL4zsPqs3LGAQwGE03alaVkZW+L7XK78l6rrlVZNfMAp/fewbe4C07ueZtHUhAKMnF+JQivnig85XNyuZwhQ4YAMHr0aA4ePMj69ev54osvqFKlCjNmzODxabpkj62S8/jfj7c5e/YsAQEBnDt3jqCgIIvjFS9enICAALp27QqAVmteqrly5crUqVOH0NBQ+vfvz4YNGyy2y21FPioqioEDBzJ16tTMD+7CItkQzrGUmTR1mP1KjrcrYRBGdNTVfIm9wodw3SnOpS4HoJR1OwKtmua4rclk4nTqImL011DIVNSxG/XE+aBSDJEcTZmOwaTDTVmGqnZ9npjtTsYurqf/AUAl2x54qark2PZC6s+E686Yj2MMx0/dkGp2fXNsn93j1JnS2Js4mlRjFK2dVz8xm5RkcjkDPmkDwIQFKzl89jIbdx2iT8dWVCpdlLk/b4AcVy983KM2F67dwd/bg/PX7xDo42nRqph/Efy8Pej8nvnfgk6nB6BiqaLUqliasKhYhkxbwq+zv7TYLrc9nhJTUnGwM/+4OXH+WpbjF0ThyQZmHkthdtNXM3Z/0K4EdEb4sq4GRysZ4/cnoTOCwQifVrChlo8ak8nEtydTuB5rwGA08X5pG5oF5TwRZrLWyDdHkknKMFHEXsHQmnaoFDlPan4qXMfyc6kAtCtlTdPAnPdtMplYdDqVazF6VAoZo+rYPXEOqsgUA9OPpqAzmCjjpqRP1Sef3O66k8Ef1809L3pUsqWKl4qQJANTDyVjo5K9lNfFEJ5MysxjOMzO+TPzRUoYtAt0RjRf1jWvXncqnNTl5wCwblcKq6aBOW5rMplIXXQa/bUYZCoFdqPqPHE+qOT5JzDcjsekNaCq5IFd76wTmj4ube1ltAeDQS7DblANlEWdc2xrTNaS/M0RTEkZKIrYYze0JrInTE6d3eM0pelIHL0XY1QqzqvzNlfEqyaTQ4e+5h6eP07ew4WjwezbdJV2PatTvKIX6749YrmAocXGj/58vM3NixF4+jly62IEXgFOFsfzKeqMh68Db39kHkqn/3cFw+IVPSlX05eY8CTmj9jBpJXtLbbLbY8nOwcrek82D7P+38JjVKzrn7snQhAKAXF+VbCEGxKYmbKd2Q4dX8nxBiWsQYeBLzWt8FE4c0p3l+Wp5qJhO+tqNLUqm+O2JpOJRan/cE0fjkqmYJRdiyfOBxVpSGR6yjZ0JgNllN70sWvyxGy7Mi7xR/ppAHrYNqSKKiDHtlqTnlnJfxFuTMBBZsMoTUs08px/4z183CoUBCjcGKx5izSTltGJvxFlTGK1c+4uTOdEFJ7yuT179vDLL7+gUChQq9VUq1aN1NRUxowZQ9GiRdFoNBYV+dy4fPkyf/zxBwaDgTlz5ljc98EHHzB58mQ6dzafYJYtW5bRo0fTv39/MjIyyMjI4KOPsnYxzG1Ffvbs2cTExGR+YLZq1YoPP/wwT/mFRxpoJmKrcMdoMnAqZRFvOsxBIbNmZ0I/fNR1UMmyPyEJ150kw5jAW47fEqY9wdm0H6mrGZ3jcc6kLqWczcd4qapyMGky4bozORaTtMZkrqSto7njInSmNP5JHMo7jt8jk2U/908F20+pwKcA/J+9+45vov7jOP66zKZN2jJKC6XsvfcQkD1FERQEEQURWbKnCoqKyp7KkqUgov4UQYYMUbYsWSJT9mhZHUnaZt39/oi0xA7KvNJ+n4+HH40NTgABAABJREFUDyH55u6dhMvdfe573++vccMoaGiYZo703mfToGmsjM7cA5Ru3XeE5Wt+Q6PRYNDrqFy6KPGJDj74fAmFwsMI8PfD7J9yFrL0HDtzkVW//4HskRk3pLvPc+2a1mXcF8t5Y/RUAEoVjmDo6y8yeNwcnE43DpeLl1o2SLHMjPZ4+nLFRnYfPo5Wq6FA3jz06fTsPWUXvMbU8w4g7nArvP2U98+xiTL9NsRSM9zAuVgP52I9fN48iAS3Qvc1MekWnr75O5E6+Q20KurHFwfj2XTOQcuiqf+78sgKs/bbmdIkED+tRN8NsdQON+Cfxux7+yJdxDpkZjQLYu9VJ/MPJvD2U2nvg+YeiKdzWRNVwvR8tN3KgUgXlcNSn+XN5pT59lgCs5oHkeBSGLI5jnktgwi3aHmvrpmJu+2pvu5JZB5TD22IP4pHxj5rP4FTmiD5aYntuwFD7XAk/9Q/I9e+SORYB0EzmuHce5WE+Qcxv512L8OAPlWSikGxAzbiPhuDrnBwqm09F+Nw7r5C4MxmyBfjsE3dS9DUtMd/S/zmbwx18uPXqijxXxzEsekcfi2Lpto2vfcZNK0p0Z1XprmezOLgtvNs+u4vNFoJnV5LiYp5SYx3seDjLeQtGIwpQI9fwL3NInX++A12rDmB7FHoPdb3s67/fGmWTNzOp71XAVCwRG5eHvQUM4atx+X04HJ6aPJiuRTLzGiPp7/3Xmblwv1IQLlaEZSvlfVOVgUhLeL8SribMeY2hGgD8Sgys+ybmRLYCT9JR9/YpdQ2FMU/jZnw9rnOESsnMCOoM3udZ5mfsI23zc+kuZ658b/T2VSbKvqCfGRdxQHX+TSLSTY5kW8T9jArqAsJipMhcd8yL6grGin1Y7b1jr8I0wbxjqU1Pyce5PvEPXTzT38WvNvv+zaTZGBa0Mt0jp6b7usyQhSeMrlWrVrRqpXvjFGtW7emdevWKdrevi8ZSLpnGaBmzZrUrFkz6e/dunUjLCzM57W3Z1zQ6/V8+GHK2bAWLFhwf2/gP8aNG/dQlvO4/GmfTW5dGQoY6yMrHtbFvknLoLkcTljEDdcxnIqNYn7PUMLP90rtLts4ihqfIY++PFGug5x1bKSWeRgx7rPsj/8MRVEwagKpbR6JTrq3QkNqbPJlzJowjBpvd/7curLcdB8nTJ/61e1I10EiDN4fnjB9NfbFp79ji3afSlpWhKEuUekUnm64/yaPviJayYBWMmDS5MYmR2LR5kt3HfHyDeLl6+TWl35o7zOzaV6nKs3rVPV5rGW96rSsVz1F2wVjByf9+faYUADVy5Wgernk6dK7PNeY0Ny+vRJuz2in12kZ3TtlMW72+/3v7w38R79X2tCPzN1LAWD2n3bK5NZRv4ARj6zw5rpY5rYMYtHhBI7dcGFzKjxTzI82JXy3xXG7bDxT1Ej5PHoORrnYeNbBsFpmzsa4+Wx/PIqiEGjUMLK2GT9d2j2KMsqokwjReYsEBp2U1Esil0mDTiPhlhUSXAoWQ/rrOhjlomNpCwB18+tZcTIxzcLTZZtMmFlDkNFbGC6bW8fxm26qpFEcOhjpol6E9+S6Wpiez/bFp5vlVHTysupGGDgQlXbh6e8bbirm0WPQShi0ErlNGiJtMvks9z5Iqn32n+jK5MZYvwCKRyb2zXUEzW1JwqLDuI7dQLE58XumGH5tSvi8zjZuF8ZniqIvnwfXwSgcG89iHlYL99kY4j/bj6IoaAKNmEfWRvJ78EMo+bINTZgZTZD3AFZXNjfu4zfRVwlLtb3rYCSGet4Cgb5aGPGf7Ut3+beLTorLg+SnRZPblGZb14EoDE+FI0kS2gJBKDGJKB4ZSZv6RQPXwSgsHb2/1/q6+UlccTLNwtO9vs/MqGbTYtRs6ntbcu3mxandvHiKtm/PSf5dvD0mFEDpquGUrhqe9PcWL1cgZ6jvye3tGe10Oi3d3q6fYtnDZqY8/rsfZaqHU6Z6+N0bCkIWJM6v1DfbvpkyunDqG0viUWTejF3M3KDXWJSwnWOuK9gUB8/4VaSNn+/5xjjbGp4xVqS8Pj8HXRfY6DjKMHNLzrqv81n8r95jM423d4+flPrxxr24LEcTpgkiSOPdf5bVhXPcHUmVNIpDB10XqGfwHltU0xfis/hNqba77ZQ7KmlZdQ3FOeC6kGbh6W/3FSrqIzBIOgySjtwaM5FyLPm0wWlm6WiqmbTsMdaf0s0iIfGhbRU6tLxiqk1VQ6F0298rUXgShHQUMbbgYPx8Chjrc9W1h1B9ZTSSjnKmV9D5m/AoTtbEdKeYMe1K9p322KdRx/wOAdpQTiSu4HTiakqZkrvJexQXm+OGpXhdiL48lfy7p3j8Nocch0FjSfq7QWPBIcel2d6pxCa1lyQJRUk5oOGdlDtu7TJI6S/bocRhkO7IIplxKnFA+oWnc45NFDKkf6vLvb5PQQBoUcTI/IPx1C9gZM9VF5VD9eg0Eq+UM2HS+eP0eHsRPVMs7V5Ed5q2x847dcyEBmhZcSKR1acTebFU8gm9y6MwbHPKf5flQ/R0r5SxcRc+32en/b/LtBgk8pk1dFkVQ6JbYXDN9G9XszpkzP8WpywGDXGOlAMQ3xbnkLEYkgsL3vZp/x7EOpWk9pIkIStpLxt8byuyGKR0lx3n8C2qmQ0ScU7lLr8cqTO2KEL8/IMY6xfAtecq+sqhSDoNplfK4W/SoTg9xHRfg/GZYndfGGCftgfzO3XQhgaQuOIEiatPY3oxeQYwxeUhbtjmFK/Tlw/Bv3ulNJcrxznQWJJ7yWgsBuQ4R5rtlVhnUntJklBSGVz6v2yTd+P64zL66vmQ0umRI8c5fApTUoAexepECk69aClbHUhmQ1JuJZ3c9/o+BUEQhKythbE88+O3Ut9Ykj2us1TWF0AneQseJn8DTsVN95iFPGOskKHlTbNv4B3zs4RqA1mR+CerEw/yoim58O9SPAyL+zbF68rr89Pd/+k0lxsnJ2DRJO8HLRo/4uSENNvHKsntM3ScdMc5lkVKf9lxSiKWOzosmCU/4pQE8hGcRvuEpPYWyY9YJe1lA4yxtCFI4891j5UhccuZpXs13Vvz7pUoPGUzd1bthbsL1hXGocSQKEdzxrGesiZv75GTiau45NyOhIZEJZpEOfo/r7zj/u87flBiPefYafsUABkXeXSVfF6llfT3NSOdUROIU7Ym/d0p2zDq076f2CAFJbVXFAVJSr9HwZ1jDTkVG0ZN2ss2SoHcVKw+7Q3S3cdlOevYSH1L6rN0JC37Ht9nVndnryghbYWDdcQ4FKITZdafcdC5rPfketXJRLZfcqKRIDrR+/yd7uxXdOdxw7lYD5/utAHgkqFSHt9dqV4rPdCMdAsPxWMxSjxb3HuwsPeqixsJMkufC8buUui/IY6a+QwY0hi3yWLUYHMqWIwSNpdCoDHtHlKBRg1WZ/L7tjllAo1pXyEMMkhJ7RVFQZtG9+7b7nza5vT2EEs7i4T1puLb/i69u9KiKxyMEuNAjk7Esf4Mps7egWETV53Euf0SaCSU6ETk6P/M5JXGl+45F4vt053ev7hkdJXy+L5Mr72vGek0gUZkqzPp77LNiT4w7YM8KciQ1F5RFKR0xu66zTykJopbxvreVlx7rmColXovF02gEeWOLEq8C8mSdqFKYzGi2JxIFiOKzYWUTu57fZ/ZwZ29ogRBeLKJ86t7V1gXQowST7RsZ73jCJ1N3lk9VyUeYLvzFBo0RCvxRMu+Pat9dtN3nGOd89zgU9tqAFx4qKTzHbdOL2nva0a6QI0Jq5x8rGCTEwnUp917OEhKbu89Tkr7uAd8z7FsioNATdrLDpT8sCp3ZFESCZTSa2/CqiQSRhA2xUFQOm0BgjTei6MhWgvFdaFckm9RSpM33dfcC1F4egwuXbrEqFGjWLx48WNZX5cuXXA6nUyYMIGCBQsyYsQIduzYQf369dOcfvM2p9PJqFGjuHTpEsHBwYwfPx6LxZJm+ylTpvDzzz9TsGDBu74/RVEYN24cR44cwWAwMG7cuBRdUu909epV3n77bZxOJxUrVvSZljQ1K1euZNmyZQAMGjSIWrVqcf78eYYNG4a/v/99f/6FDE04mfgTdk8UOXXFcchWzjjW0SpoAQoeVsW8muI1BsmCXY4CynPTfTzp8WBtIepaRmPS5AK8PZzudL89nsyacGxyJA7Zik4ycsN9lMr+PQCwe6II0PoO+hyqr8h5529EGOsR5f6TXFpvl1C34sCtJOCnCfZpn0NXnCjXQUL1lbjk3ElRvxYAJMrR6KUAtFLyyUluXRkOxs/HozhxK4kkyDcwa8LSzAJwy30avWTGrE3+9+CQ49BIevR3/Eim9z4fl8vXbvLh50uY+8HAx7K+7qOm4HK5GTuwKwXy5uHVkRPRa7UkOJ10ea5xqrfp3eZ0ufhw1tdcjrpBkMXMR/1fwxKQ9k7n5LlLfDJ3ObKi0KR2ZV5tk/b4LoeOn2Hy4h/Qa7VoNBLv932F/GFpD1AfZ4/nvelfEmuzkz8shPd6d0avT3sXtPvwcT7/2ju2SafWDWlZrzrxCYn0+3gW127GJN1OmFFNChn46WQiUXYPxXPqsDpk1p1xsKBVEB4FXl0Vk+I1FoNElF2mPHD8pjvp8ULBWkbXtZDL5D2YcHl8r2Y9SI+n5X8ncCNBZngt39tvAg0atBoJfz24FQVZ8Y7PFJ2okNvf96CmUh4dOy47aVHEj52XnFQK9RaS4hwyeo2E6Y7xm8LN3tvZrA4Zo07i6A03PSp7M0bZPYQG+BalK4bq+e28k3oRRv6MclMil/d5h1shwa0Q7OebpXgOHQejXFQK1bPzkpMW/97yF50oE6CXfIpnZXLrmH8wHqdHIdGtcCPBexvg/TI0KUTiTyfxRNnRFc+JbHXgWHeGoAWtwKMQ8+qqFK+RLAbkKDuUB/fxm0mPawsFYxldF00u7/aj/DvQ82332+NJE25GjrR5ew8ZdbiP3sC/h/e2Ak+UHW2ob+82fcVQnL+dx1gvAvefUWhLePclisONkuBG85/eSYrTg2TQIuk0SCZd0u2BnuvxaHL6+dxGp68Uin36XvxeLIV82YoUaETSalA8Mkp0Iprcvv92dZXy4NxxGb8WRXDuvIS+kve3XY5zIOk1SKbkAmZ67/NxuX4ljoUfb2HE549nLLpPe63E5ZTp+UEjQiOCOLrnEj/M8c6A1fSlcqneonenPzacZstPx5AVhar1C9GsY9pX/Rd+soVLp73/XqvUL0zr19L/bHesO8mv33tnO23fpwalq6V9y53L6WHhx1u4cSUOc5AfPcY0xN+cdtHw+1m72fXLKUIjgpI+68R4F1MHr+XWNXvSrYSC8KDE+ZXXk3p+BdDEUIafEv8kyhNHcV0oVjmRdY4jLAh6HQ8yr8Z8wZ0T6gBYJBNRcpz32MwdmfR4IW0Ioy3PkUvjPX5yKb776fvt8RSuyUGkHItVTsQo6TjqvkwPf+9t0FGeOEL/M3B4RX0EvzmPUc9Ygj/d5ynx77mNQ3GRoLgI1vjuS4vrQjnoukAlfQF2Ok/Twq88ANGynQDJiEFKPlYuo8vH/PitOBU3iYqLG7KNsH+HH0k9SwF2Ok9RXBfKDucpKuq9xbh4xYFHUXx6cimKQrziJEBjJEFx8o/nWtKyHxZReMqipk+fnvSjM3jwYNq1a8eqVSkPsv9rxYoVhIeHM2HCBL799lsWLVpE//5pjwfTuXNn2rdvz+jRo++67B07dhAdHc2yZcvYvn07U6dOZfz48Wm2nzRpEj179qR27doMGTKEP/74g1q1aqXaNi4ujoULF/Ldd99ht9vp1q0bK1asoGDBgkyZMoVRo0bdNV9aChkbsyL6JSr6dwO8t44FaQuxMa4/QdqCGFPpzVPM+Aw7bGM55/gVsza5Ulw9YCC7bOOQ//0xLGPqSD5DjaTn77fHk0bSUsW/N79bRwJQ2tQBvcZ7wrLZOpxng7/0aZ9XX50rrj1siO2PVtJTK8C707nuOsJl106qBfh+55X8e/CHbQKHWEguXUlCdd6D2v32zylt6kBOXfI4KQaNmVKmF9gUNwiAKgG9kSQNsuLmd+s7PBOc8n72s44NFDb6FjmOJX5HTm0JChiTdwbpvc+sbOKwHknjOC34aBB6vQ5bfAIdBn2cbuFp1W9/kC8kF2MHdOV/G7axZNWmdAcA/3Tecj7o9yoRYbl5Y/RUGtaoSETe1ItJefPkZO6Y/pj8jGzb/xezl6/m44Hd0lz24hUbaFCjIs83eYoZS39izdY9PN849UGRPR6ZyYv+x7wPBuJnNPDqyIk8Xa08ASY/FowdzLO930tzPWlpXMjISyui6VbRu8M3GyQKBWnpvzGOgkHaVHsFPVPMyNgdNn495yCvObkAM7B6AON22fD8e5tTxzImauRLLr7eb4+nqzYP8w7EUy5Ex8CN3llsJjYKpGqYns3nnby1Pha3rNC2hB9+OolLcR5m/2nn4wa+v0Edy5gYt8vGutMOQs0aupTzFku+O5ZIiZxani6QfNKo1Uj0ruLPyN+9PQk7lDYRoPcWI4ZvtvLls8E+y66eV8+eKy76b4hFr5UYUcu7/R257mLnZRf9q/lujz0q+TPhDxsLD0HJXDoqh3oPOz7fb6dDaRMlciYfhpgNGl4oZWLQJm/RrneVgDQHzMwIY+NCRL+0Av9u3lnBJLMBbaEg4vpvRFswKNUeOsZnimEbuwPHr+fQ5k0u/gUMrI5t3C4Uj7e3l6ljGQw1km8CvN8eT5JWg3/vKlhH/u5dbofSaAK8BRvr8M0Ef+m7veqr58W15wqx/Td4Z7Ub4d0nuo5cx7XzMgH9fafitr63FSXRDW4ZXYU8ScUh20fbsXz0NFJQ8kGntkAg+mphxPXbAJJEwADvb4t81YZ99p8EftzAZ9mmjmWwjduFY91pNKFmTF28A10nfncMbYmcGJ9Ovtqc3vvMyt76tCk5Q83IHplvpu1i5KxnMfjp+Kj7CirVLYgpjVsfr5yN5uC2cwz7rDUazd23gVadKxJWMBhZVhj7xk9Ub1yE0Pyp/wbZrQ5+WXqI0Qvb4khwMaHvaj5Y8mKa69m+5gQh+Sz0/KARv634m1+WHabdm2nvd5q0L0f9NqVZ9MmWpMf8/PW8PacNw9otu+t7EYTMTJxfPbzzK4DGxjK8FD2bbv51ATBLRgppc9M/7msKanOl2pvnGWMFxtp+5lfH3+TVJv/ODQxoyjjbGjz/Dh/S0VSTGoYiSc/fb48nraSht39DRlq/B6CDqQYB/95+Ntz6LV8G+14Ar64vzB7XGfrHfo1e0jIiwDuW2BHXJXa6/qF/gO/5Tg//+kywrWMh2yipC6Pyvz21PrdvpoOpOiV0yUVEs8aPF0zVGBT3DQC9AxqikSTciod3rP9jQfDrPstuYSzHJNsvDIj9GrPklzTI+WbHMeIVJx1MyeegHmQGxX2DUdLhVmS6mJ5KUSR7UKLwdJ/Gjx9PpUqVaN68OW63m7Zt2/Ljjz8yc+ZMDh48iNVqpX379ilmKBg5ciQvvvgi1apVY/fu3axatYqPP/6YkydP8sknnyDLclIl3GRKvztcRoWGhnLu3LkMtd29ezdvvPEGAE2aNEn3R/H2si9dupThZTdt6j0wr1Onzl2vDhw9epTJkycD0LhxY3bv3p3mD+OhQ4eoXr06RqMRo9FInjx5uHz58kOZUtSoCaRjrnVJf5ckiXqWMam2bRzozRukK0ir4C9SPB+sK0yjwIkPnAnApMnJdttH1DaPwKINJ6+hGnkNvicd8Z7r5NPXTPFaSZKoFvBWisdvek5QNJXxqszaMJoETUnxuILsU3S6rYixOUWMvjPr3HAfS3MsrKoBfVI8Fi9fp7wpZW+y1N6nS0ng97i38dek3eMmPVMW/0CFkoVpUrsKbo+HTkM+Zdmkt5m9/GeOnDiLNT6Bdk3r0KGF70Cvo2d8Sbsmdahcphh7/zrJ2i27eb9vF06fv8zEhd8jy4q3p9GA1zAZ7222o7Tc7iVkT3BQNCL97q/7jpyka9tmADSqUZGhE1P+m7zN6XJhT0ikYD7vLUT1qpVn/9FTaRae8uQMTvqzQa9Do0m/Z8q+Iyfp+rx3+29YoxLfrvs9zcLTxchr5AvJRXCg98S/YqkiHD11jhoVSqXaPiMCjRrWdcyV9HdJkhhTL/WrjZMbews5BYN0fNEqOMXzhYN1TGz0cG7xzGnS8NF2GyNqmwm3aNncOVeq7UbWTjm7zrGbbp4plnL8HYtRk6IYBXA9XubV8in3K9XyGqiW1/Cfth5q5ktZGJAkibeqpSz2nrjp4ZmiKQs5YWYtU5qkPAGWFXyKTrc1L2KkeRHf5Vy2ehi3y0ZYwL31ftIEGsm1LnnqZUmSsIxJfTaXwMne8eV0BYMI/qJViud1hYMJnJj+FMcZzpXThO2j7ZhH1EYbbsFQLS+Gar7bsud6PPqaKUe3kiSJgLeqpXjcc+ImxmdSDuwdOC7lLKGKW/53oO+U/3ZMncpi6lTW5zH3sZv4pTIWlsZiTFGMApCvx2N6tXyKx1N7n0qCi7i3f0cTcn8Ht99M30Wx8qFUb1QEj1vmvVf/xwdfvcCKefv450gU8TYn9duUpvGLvu/piw82U79NaUpUysux/ZfZue4U3Uc14NI/t/h6yg4UWUnq3WP0e/ACWdSlOHLns2D+t0dasQqhnP37epqDe+/59R9MZgOTBqxBp9PQaeBT5C0YnObyw/59TqOR0GqldItVZ/66RskqeTEYdRiMOoJD/Llx1Uqe8NR/047tv8IzXSoBULV+YWaO3ABvpv1ec4QEcP2KGHtRuD/i/Cp52Vn9/Aq8t7Gty5U8bIQkSYyxPJ9q28mB3v15QV1uvghOeaGzsC6EiYEPZya/nJoAPrL9zAhzK8K1OahmKEw1Q2GfNtc9VmrqU+53JUnirYCUdwyc8ESmOl5VmDaIKUEdUzwuI/sUnW5rbixHc6PvjKbH3Fd5xlgxRVuDpOMdS8oB8896bvDKv7c23qaTtMwL7pqibYLi5O24/xGiSbuHXkaJwtN9atu2LVOmTKF58+Zs376dWrVqodfr6dWrF/7+/jidTp599lnat2+foeV98MEHTJw4kXz58vH111/z7bff0rVr16TnnU4n3bunvNWqSpUqDBo06GG9LWJiYggM9B58BAYGEhMT80iWLUkSsnyXAa3vGF8jKCgo3SwxMTEEBSWf3AQGBhIdHf3Qfhgzo7qWu/f68NeGpFrUSUs5U8oZ0B40w2159OXJo095MpKWp8xvZ7itXjLdV0+x255rWIuZX6+kSe0q7DzwN9XLl0Cv09LjxZaY/Iw4XS5eHDCWtk3qZmh5n8xbzscDu5E3JCfL1/7ODxu28cqzyQOnu1xuen0wI8XrKpcuylud0x/3I9HhpM+HM/nnwlX6d3k+3bYxVjsWs/eEzmIOIMaa9nT0MVY7loDkk7/AAH9irLZ0lw+QkOjg82U/827PTum2i7UlLz/Q7H/3LOb/Zkm7/ZPsvbr3vyNvWvjexsh5+6mMTw0d4q+lT9WM9ybsXO7eDuTv5X2HW7TMbPZwu3uryfLe3X9HtCH+BPSpetd2t5k6l7t7o39JOg2Wd1Iv+qbG2LTw3Rvdwfx2xpctmfT31VPstnqtS/L9rN1Ub1SEI39cpEy1cHQ6Lc91q4LRpMfl9PBup++o/3zGitZfjt9Grw8bkSvMwqbv/+L3Fcdo3in5RMHt8jCx3+oUryteMS8v9q6R4vHbbLGJBNwxXlaAxYgtNjHN9tHX7cTdSmDo9Gc4d/w6iz/dkqExoXasO0nOUDMh+dIujHuzJP923M6SVuHJFpuI/7/Z/QMN6eYWhAclzq8ebNni/OrheM9y99/bEK2FPgEZvyDV+T+FnoeR4bby+vyU1+fPcPt+qRTG0mKSDPfVUyw1ovB0n0qUKEF0dDQ3b95kxYoV9OzZE4BvvvmGTZs2odVquXnzJrdu3fJ5nXTHbQN3bvinT59OusfW6XRSo4bvAYzBYHgsA9cFBwcTF+e9UmW1WgkODn4ky1YUBa32LgNa3/FZxcXFpZslODiY2NjYpL9brVZy5MiRZntBuFOxguFEx9q4FRPHz7/9QfcXvGNYfffLVn7bfQitRsOtWCu3Yq0+r7vz3+idAxH/c+Eqo6YvBrxFpmrlfHuF6fW6+x4Y3M9oYOHHQ4iOs9F52Dia1ama5rhNwZYArLZ4yJMLqz2eYEvahYQgcwBWe/JsF9726RcqnC4XQybMo/sLzSlZOP0dnnf58QT++//0siTl9smS9W+pFATh3uQvmhNrdCJxtxLYvuYEz3atAsCvPxzlzy3n0GgkrNEJxN36z0w+Pr/dyX+8fOYW88Z4x+pyOWVKVfXtdabTa+9rUHBzkB/2OwZXj7c5MafS4+y2gEA/CpbIjUYjUaRMHqKv373wfmjnBXasPcnASS0ykCV5RsG7ZTEH+RH/b/Z4a/ptBeFBifOrB1u2OL8SMjNReHoAzz77LF9//TWXL1+mTJkyxMbG8uOPP7Jq1SrcbjctWrTw+fEDb2X56tWrABw5ciTp8eLFizN58mTy5PHe5uJ0On1e9ygq8leuXCFfPt+Dqho1arB582bKlCnDr7/+mvQDbbPZ8Hg8PlXv+1n22rVradq0KX/88Qflyv07HkRiIvHx8eTMmdOnfZkyZdizZ09Spnbt2gFw8+ZNzGYzRmPyFbuKFSsyZcoUnE4n8fHxREVFER6e9mCZmZXNE8lu+8SkW/YysyjXQQyShRw6bzfTHdax1LGMwuaJJMbzD/kNdVROeG9a1a/B8nVbuHLtJqWKRBBns7Ny8y6+nzoKt8fD82+N8Z3aDAgy+3P1xi0qA3+dPp/0eLEC+Rg3uDshOb3bi8vl9nnd/fZ4crncaLUaNBoNJqMBo16P0eD9Gb96/RZ5Q3y3oWrlSvD73kOUKhLBlr2HqVbWWwCzJyTi8XgINCcXc4wGPQEmI5cirxMempvtfx7lvT7eHnBRN2PIHRyI9o6BiN0eDyMmL6BF3WrUr17B5/FbsVafW/GSsuw5zHONavtkibXa0eu0+JuST2YiwvJw5fpN4mx2/IwGDp04c9feXZlNpM3DxN32pNv2HrfOK6P5uk3GDg4XH44n3KK95x5UWYUn0oZ94u6k2+0et+jOK8nxdcaKGa6DUUgWA7qiKb/be1lORtfl2HgW8zDfWzAcG8+S8MNxDNXzpjtw+p08kTY8/8RgqJPxK7IZVbtFcTb97y9uXLVSsGRu7HEOtv18go+XtcftlhnZfvl/x6bFHGjkZqS3R+eZv68lPZ6/aE56j21CcG7vb6P7P4PI32+Pp9D8gdy4YsUe58Dgp+PU4Uja9/HeBn8z0kquMN8egGWrh/PHhtMAXLscR0Cg9/fRmegmMcFFYA7fiw3H/7zCqoX7GTKtFQZj8qF93K0E/AL0Po8VKZeH72ftxuX04EhwEX3NTu68ljSzlK6Sjz+3nqNgydwc2HqO0v8W4xLsTmSPQkA2n51QePjE+dW9L1ucX0GkJ5aJ9nVJt+Q9yRbHbydcm4OmxrLpttvjPEOMEk8zY+q9ngfGLuNdc2tCtOoci/6XKDw9gNatW9OgQYOk+3QDAwMpWrQoL7/8MkWLFk21gty+fXuGDBnC6tWryZ8/+QDsvffe4+2338bl8s5y9sYbb/D008mDKj9IRX7y5Mns2LGD69ev07VrVz777DOMRiM9e/bk559/9mnbrl073n33XTp37kxgYGDS4HRr165NGlTuTl9++SW//PILZ86coWvXrrz//vsULlyY7t27s27dOp+2devWZevWrbz88ssYDAY+/fRTAPbv389vv/2WYoC6IUOG8M477zBt2jTKlSuXdP/xJ598wuuvv07ZsskbY2BgIK+99hpdunQBvPd6323MGeHBRLkOYtGGJxWe6li8359djuSSc+cTV3hqWa86zXu8kzT4tiXAnyL589LtnUkUichLUCo9bto2qcPbUxeybutewkNzJz3+9psv8d7ML3G7vSctXds2o06V5H+v99vj6eqNW4z5bAkajQany03Pl1ph0OtxuT30/3gW30/z3Yaea1SLDz5fyuvvTiYwwJ8PB7wGwC/b9hGfkEiX/8xaN+KNlxg1/UtkRaFRzYoUyOs9UBs5eT5TRvYiR2ByD6g1W/aw5/AJ4mzxrNy8iyIReXm3ZycuR91kyuL/Mf0d31s8u7ZtyugZX/LTrzvJlycXb7b3jqPz1cpNlClagMa1k2dh0mo1DHrtBd766HMAXm3TFLP/wxmTQXg4bh/0Sw8w+Ldw71wHo9CGW1ItPN2N4pF9ZrG7X44NZ7G8X89n8PW7kSPtOHdeejSFp+bFGPTsUtr19A547W8xEF44B2N7rCS8cI5Ue+jUb1Oa2aM3sWv9KZ/b0l4dXo8vPvgNj9t7q0qrVytRoXbyIOn32+NJo9XQcUAtJg9cC0DLVypiMntvX5vYfw3jvvM9USpTPZy//rjIp71W4nbJvDLUuz89eegqB7ed55WhvrdrfvHBZvwCDEwf+gsAHfrVomjZUJZN3UGLzhUpVCp5rL4Ai5FmHcvzaS/vgMgdB9RGo5Fwuz1MGbyOj5d18Fl23dYlWfjx73z85koCAo30eN87btjujadJtLto0dl3XJENyw+z59czXDkbzfi+P/Pa8HpJY1AJQkaI8ytxfiVkzJ2Dpz8JJOW/JWPhiTdo0CCioqL49NNPKViwYKpt9u/fz99//530Q3I3Y8eOpXfv3uTKlfrAt3eKiopi0aJFjBw5MkPLnjt3LvXr16dUqYyNwTBo0CCmTp2aobbnz59n5MiRhIeHM2nSpAy9Zl7X60SedGWo7YM6GD+fKNcBtBgo4fc8OXUlk3o8nXNs5nTiajw4CNQWoFbAcBQ8bLd9SKIcgwYd5fy7YNbkZYdtLFr0KCg8bfkI430OALci+iXC9FWwei4RqI2gZsAwJEniYPwXXHf9hYybMqZO5NFVYF3sm2glI35SDhoEfsramDdok+Nrfo97l5vu4wRqI6jo3x2DZGZ//GcoioJRE0ht80h00uPpqh9WQs+bi9MecDzxzB6UxLuPY6SmEZPnc+1mDB/0ezWpEPRfB479w/EzF+j0TMqBhFMzfv539HixBTmD734FxOX28MHnSxg7oGuGlr1myx7M/n4+vaDSM2r6Yt7r0xmD/u4D+MYnJNLv41kAKQp3kp8ZvyLJPQ6uz+uKK/JkhjI8DPMPxnMgyoVBC8+X8KNkTl1Sj6fN5xysPp2IwwMFArUMrxWAR4EPt9uISZTRaaBLOX/ymjWM3WFDr/V2rvvoaQsW4/0d4L20IpoqYXouWT1EBGoZVjMAm1NhzHbvbHxuGYbXMlMgSJvU46luhIFPd9poXMhA6Vw6pu6x4/Ao6DUSI2qbyWnS0HllNA0LGvn7hotKoXr0GolOZb1Fwb7rY/mkvoUgv8dzUKoPK0HIm4uT/t71+jxOuiLTfsG/4ucfxHUgCgxa/J4vga5kzqQeT47N50hcfRocHrQFAgkYXgs8CrYPtyPHJIJOg3+XcmjymrGN3cHtL8vy0dNoLPfX8yO64wr0NcPxnIlBExaA5d06KIqCffpePP/EgEfG1LUCulK5iH1zHZJRi5TDj8BPGyCZkreb6JdWoK8ShueSFW1EIAHDaiJH2bGO2Ya2QBB4ZPx7VsY+dQ+Kw4Ok12AeUds72Pn4XXgi7Sh2J6Yu5THWi/Dp8RS/+DBKohttPgvx8w6gLRiEX9sSyDEOnDsvodhdGGp4e0DJNie297d5ZwCUJAIGVid+3gHcx2+ijQjEv3tF9OVT/y27rYQ+jMUhaY9gfdj6NfHytTSfzwxmvbuR6Gt23nivIaERqfdmuHXNxvplh+k0MGPjYv28+E8q1ilIgeJ3Pxa7naHPxxkbQ+vkoaucP36Dpi9lbCzGpZO381y3qgTmvPtFgcR4F1MHe4tu/y3c+WvyUMFyb+NLCoIaxPlVsvs5vwK43nUlrpM3M9w+PfPjt3LAdR4DOp73q0JJXVhSj6fNjmOsTjyIAzcFtLkYHtASDzIf2lYRI8ejQ0MX/6fIqwlmrO1n9GhRUPjI0g6L5t7PUWxyIu/bfsKjyEhIDAxoiiRJTLNtwIOCBonRlufIqQlgnG0NOrTckm3ckG28Z3mO/NqcbHOc5MuEHYRqAlFQaGgsTZgmiF8dfzPQ3IxxtjUY0DHY3JzxtrW09avCGfd1rstWuvg/xUHXBRbEb0WLhvzaHAw1t2Rg7DKK60I577mJXXHwieUFgh7STHX6ErkIWXxvF2JE4UkQ/uNxFZ4uO3dz1rGeOubR3sEAFQ/x8vWkwpNbSUD37zSi26wfUMKvDf6aPOy2TaRJkHfHoCgyZxy/YJevUcG/67+PKT69EC45d3As4fsU66/o3z3FYN/Lbjbh2eCvsGjzsdP6CQWNDZHQctG5nZrmwXgUJ+tj+9AyaB5HEr7Cog2nsNF7ULsyujNtcnxNlOsgZx0bqWUeBsCG2AHUMb9DgDaUE4krUBQPpUwvPvTPMzVZofAkZIyahafdl52sP+tgdB0zkiThkRWux8tJhacEt4JJ590mP9hmpU0JP/L4a5i428bUf2d8kxWFX844uGaX6VrBe1Dw3215xyUn3x9LSLH+7hX9KZ/Ht3DXZNlNvno2mHwWLZ/stNKwoJHqefUoCui1ErsvO/n9gpMRtc0sPhyPSSex96qL18qbKJ9Hz4fbrbxYyo8yufXsuuxk/1UXb1ULoONP0bz7lJnyefTEuxQGbYplbstgTtx0892xBEY/wGDp9+p+Ck/O3ZdxrD+LeXQdJElC8cjI1+OTCk9KghvJ5O0Mbv1gG35tSqDJ449t4m6Cpnp7CCqyguOXM8jX7Ph39RZY//tdOXdcIuH7YynWn1rR5Wazb8ixrA2a3P7EDtyIeXANPBficG6/hHlkbeTYRGLf2kDwV8+S8OURtOGWVAf8vtlkGcFfPYs2nwXrJzsxNiyItnAQsb1+IXjpc2jMBqwfbsfvxVLoy+TGuesyrv1XCXirWtL7lmMdxA3YSPDi1t7C07p/QKtBWywHpnYlAbwZ362DNsQ/6XWKohD31gbMo+sgX7Hh2HAW88jaSZ+X+/C1VG/bS0tWKDwJGSMKT4KQfTyswtNu5z+sd/zFaPNz3uMuRea6bE0qPCUoTkySt1fpB9aVtPGrTB5NIBNt65ga5J0MR1YUfnEc4ZocR1d/by/SFMddzlN8n7A3xfq7+z/tM5j3n67zbHD8xUjzM0nLduFGjw6NJLEy8QAxsp3X/OsyzraGItoQOphq8EviES54bvKGf326xsxnVtCr+EsGBsd9wzN+FWloKEXf2KXMDX6NEXHfoQATAjvQI2YRc4O6ssHxF9dlK6+YavNazHymBnUil8aMR5HRShoGxi6jnV9VnjaWZHH8dnJqAnjOr3KK93M/7qfwJG61EwSVxHjOEKavkvQDp5F8BwOMch3mWMK3KHiwy1HEy08Rqq9EAWMDdljHopP8KG96jQLGhhxNWMoO61gCtKGUN3VFS/JJaH5DnQzf9uanyYFF6713PLe+HHGeiyjIXHMfYmPsQAA8uEhUYjL8PmM959hp83b7lXGRR1cpw68VhCfBmRgPVcL0Sduy9j9TmR+OcvHtsQQ8CkTZZZ6Kl6kUqqdBASNjd1jx00m8Vt5EwwJGlh5NYOwOK6EBWrqWN6G/42ehTn4DdfIbyIgcfhryWbwvLpdbz8U4D6Vz6Zi21050goxLBn99cs4fTyTSoqgxqYB1JsbDvAPeAd5lBcICvL2YtBKUC/EeOvjrJUrk1HEoysXm8w6eKZb5Bx32nIlBXyUs6bv6761nrsNRJHx7DDwKcpQd+al49JVCMTYogHXsDiQ/HabXymNsWICEpUexjt2BNjQAU9fy3PllGerkz/BtZZoQfzS5vcVGbZgZOdaB52IcuvLeorkmyA+N2YBidaa3GDQ5/NDm8xb+9OVy47kYh7ZwENrCwWj+va3LcyaG+HkHvC+QFTRhASiyQvyXh3EfvQFaCc+15IGsXQei0OS3EDC0ZqrrdG69QOLa04CE56oN+Xo8uop5cB+7gXXsDjRBRkzdMtb7URAEQRAy4oznBlX0BZOPuyTffflh10W+TdiDB4UoOZan5GJU0heggbEUY60/4yfpec30FA2NpViasIux1p8J1QbS1VQXPcn78jqG4tQxFL9rnoq6CI65rzDW+jNBGhPdTHWJUeKZZd+MXXFiVxyU0oUltS+lywtAmDaIv9yXiFHiCdb4Y9Z4e06X0XnPxXSSlhwaf/a7zhGhzYlT8bDfdY682mA0dxTIYpR4AjUmcmnMKT6PpHVpgrgh+06S9LiJmzQzuUuXLvlM+/m4NW2a8WmOZ86cycqVKx9hmqwlWFuYSNeBpLFSZMV3ENOD8fOoY3mXpkHTyaUrDYBHcVLM2Jo6llGE6MtzLPF7JCQq+fegjmUUCfItrrp8K/OXnDvYGDswxX/XXEf4r0Q5GpvH22PgpvtvLNoIgrSFyKuvRtOgaTQNmkaroPmYNDnRoEuRGUCDDoXkx4O1hahrGU3ToGk0D/o8qWdWdnT52k16vj9NtfU/2/u9DLedvXw1a37f/QjTZB2Fg7UciHQlbcse2bcj8byD8bxbx8L0pkGUzuUt2jg9Cq2LGRlVx0L5ED3fH0tEkqBHJX9G1bFwK0Fm71Xfnpc7LjkZuDE2xX9HrqXsoRmdKBNp826Hf990E2HRsvGsg+I5dExvFsSr5U0+4y13q2DiitXDTye9U6UXCtLSt2oA05oGMaNZEMNqJY/nc+fVwOeK+/HDiUSO3XRTOTTzX8vSFg7GdSAy6btSPL7TTsfPO4jl3ToETW+KrrT31gfF6cHYuhiWUXXQlw8h8ftjIEn496iEZVQd5FsJuPZe9VmOc8clYgduTPGf60jGeuloIwJxH/a2lWMTkW1OJIsBdJoUmW+ToxPx/Dtgtvvvm2gjvEUo6Y5CqLZQEAF9qxI0rSlBM5phHlYLzz/ReM7EEDSzGZYP6vm011fPi7FeBLZxO1Ndb/yiQwROaETg1CbJYz65ZUydy2EZVQcp0Ihjw1nQacCTuTvYX78Sx/i+P9+94SMyrN2yFI/F3Ijnm2k703zNttXHSbClX5C813UKQnYgzq+ebIW1uTngupB83KX47p/mxW/hXcuzTA96mdL/FnGcipvWxoqMsjxLeX1+vk/ch4RED//6jLI8yy3Zzl7XWZ/l7HCeYmDsshT/HXFd8mnnxkNnU21GWZ4lUDKxwXGUFYl/0thYhulBL9Par6LPMZfEHbMwAkGSiWjZjl12oCgKx93JxxSV9QVYEL+VqvpCVNEXZEH8VirrfW/1DJb8iZMTuCXbU3wed14KVXsvnPmPEoUsLTsPUBtuqMU11yHWx/VFhx8l/NqQU1cy6fkixub8GjeUIG1BlH9/KuxyFH/YJiKhQcZNtYB+XHbu4kTiCiQ0aCU9ITrf2+fupceTSZObowlfE+s5h0Wbn3B9LSRJ4rr7KBtjByChwU+Tk7qW0eQ1VGOf/TMuu3bxlPntpGUE64pgjb/CVuv7lDV1onrAQHbZxiUVqcqYOpLPkPbsP8KTK7tuz7XCDRy65qLv+jj8dNDm3zGebmtexMjQX+MoGKRNmhgxyi4z8Q8bGgncMvSrFsCuy05WnEhEI3lvhysf4ruLvpceT7lNGr4+msC5WA/5LVpqhes5F+sdQ+rIdRcFg3x7WGo1Em8/ZWbKbjvfHUugTxV/pu+1k+D2Bm5WxEiLIil7NBXPqeN6vIcGBYxPxPduqBWO69A14vquBz8dfm28YzzdZmxehLihv6ItGJQ0i6UcZcc28Q9uf1kB/arh3HWZxBUnQCMh6bVJvZOS1nMPPZ5So6+TH+feq8S+tR48CgF9qyJJEoZqebF/tg/XrsuY334KyS/534gmt4mEr4/iOReLNr8Ffa1w5Ci7z3L9+1TBPn0vSoJ3pk1jsyIYGxRAccvEDtiIrlgOJLPvbZt+bUuCnw7b2B2Y3/XdlxjqFSC23wa0EYFJtyi6T9wi/ouD3mKTLGMe+RSaICPxV6xY39+KqVNZdKUyNkZRdhec2z/d8Z+2rz5B2er5kwYqv032yGgewkDygiA8mbL68VgtQ1EOuS7SN24Jfuhp8+8YT7c1N5ZjaNy3FNTmSjqHipLjmGhbhwYJNx76BTRhl/M0KxL/RIOEXtJSXue7385oj6cT7ki+iN+CDi0yMiPNzxApxzLdvpHNjmPkvsvYu1pJwxv+9RkQt4w8GovPOFNV9IX4In4rFfURuBSZD20rGRHQyuf1kiQx0NyM0dYf0aNNGuMpsxFjPGUyU6dO5Y8//sBgMPDKK69QtmxZRo0axeLFi1m7di3Lly/H4XBQpEgRPvnkE9xuN4MGDeLWrVvodDr69OlD/vz5GTp0KPp/B+v97LPPMjxN5381aNCA2rVrc+7cOQoXLszHH39MXFwcAwYMwOPx4HK5+PjjjylatCgzZ86kQIECNG3alBEjRtC6dWsqVKjAmDFjSEhIwGAwMG7cOHLnzk3Tpk1p1aoVBw4coGbNmhgMBnr06AHASy+9xOzZs1NM//m4PM7BxTOb2+M0ZRVqj/E0c+lK9h45gcGgp2Or+pQuWpAPP1/C3A8Gsn77Pv63fhsOp4vC+cMY81YX3B6ZEZPmEx1nRafV8maHVuQLzc07Uxei13nHUZk6sieB5pQz7GVEix7vULNCKc5fuUah8FDe7/sKVns8wyZ+gdsjewcSf6sLhfOHMXv5agqEhdCoViVGTV9My6erU654IT6e8w2JDicGvY4P+79GruBAnu39Hs3qVOXwiTNUL18SvV5Ht7bNAHh1xASmv9vHZ0a8R0HtwcWzswEbYnm/noWcpsd7onu/g4sLmd/jGOPpf7N28/e+K+iNWpq0L0ehUrlZ+PEWRnz+LLs3nua3H//G6fCQr3Aw3Uc1wOORmfXOJqzRCWh1Gtp0r0rufBbmvPcrun8Hme8/oQUBgfc3yPygZ5dStkY4kRdiyVsgmNdH1efGVWtSpq2rjrP5h6P4+espXCYP5WtFMHPEevIWykF4kRw893oVPn97I3kL5cDjkXmhZ3UWfbIFWVbw89fz5vuNMAf73XWdAEsmbufCqZvIHpm2PapTqmo+xvf9ma4jnyZHSAAT+61mwKQW5Ai5v33RncQYT8LjIM6v1D+/goc7uLigLjHG0xNuy5YtXLhwgeXLl3sHSvN4uHo1uatdgwYNaNXKW+EcOHAge/fuJW/evMTGxrJsmbe7tCzL/Pjjj9SpU4d+/foByVXv23799VcWL16cYv0DBgygWrVqPo9dv36dPn36EBERwfDhw9myZQt16tRh3rx5GAwGtmzZwvz585Om7rxx4wZ9+/alb9++VKtWjUGDBtG7d28qVarE77//zty5c3n33XfxeDw8/fTTDBo0CJvNxquvvkqPHj3466+/CA8PV/VHURAehu37/+JS1HW+HDfs3+1ZJvJmdNLzT1crT/O63u1t+KT57D96irDcOYizx7Pok6GAd3tetfkPalUsTe+OrYGU2/Pvew6xZNWvKdb/1svPUblMMZ/HbkTH0qN9S/KHhTBq+mK27f+L2pXK8Nmot9DrdWzf/xeLVmzgw36vetvHxDFw3Bx6dXiGymWKMWLyfHq0b0mFkkXYuu8IC374heHdO+D2eKhbtRz9XmmDPSGRHqOn0q1tM/7+5zz58uR65EUnQR034mXG7bJRMVT/2ItOgvAgDu28wLXLcYxe8Lx3cg+PzM2o5IsQleoWpGZT7+/n5+9s5MSBq+QMNWO3Onj3i+cBkGWF7atPUK5Gftq+WR1I+fv859ZzrF92KMX6X+hVgxKV8vo8Fnsjnuder0qe8EDmvr+ZQzsuEF4kR9Lz21afoO+nTQnJF4gsK2g0EgVK5KLnB43JGWrm+pU4bly1Mvzz1vibjUwf9gtt3qhK6arh/P7T36z56gAv9a9913XKHhlHgotRXzyPNSaBj7r/xPj/daTHew2Z+/6vWHKYeKFXjYdSdBKEx0GcX4nzKyFzEIWnTOTkyZPUrl07eaA0re+tEHv37mXBggXIssyVK1do1KgRNWrUoGXLlgwZMgR/f3/69u1Ly5YtmTt3LkOGDCE8PJy33noLgyG5G3bjxo1p3LhxhjLlypWLiIgIACpXrsyZM2eoUKECH3zwATdu3MDlchEQkHzwsWTJEtq1a5f0A3vy5EkmT54MgMfjITw8HACNRkOVKlUAMJvNlCtXjr1797J69Wrat29/Px+f8BBkpd5Oajt14Qo1ype6Y3v2PTHff/QUX/60EVlWuHL9JvWrV6BauRI0q1OFt6cuxGQ00rNDK5rVqcL8H37h7akLyRuSk94vtUavT/7pblCjIg1qVMxQppzBgeQP8/YAq1iyCOcuR1GueCE+nbecmzFxuNxu/E3JV8S/WfMbzzWqnVTAOnX+CtOX/AR4D8Ly5fHeLqPVaKhUqggAASY/ShctwP6jp/hl217aNc3YbZ7Ckye3v4ZJjQPVjiEI9+zS6ZuUqR6ePLnHf36fjx+4yrolB5FlhZuRVio/XZBSVfJRo3FR5ozehMGk5/nuVanRpCg/L/6TOaM3kSuvhbY9qnl7P/2rytOFqPJ0oQxlCsxlIk+4d3sqXiGUq+djfApPLw96ilUL/sTpcFOzWbFUlxteNCf+Zm+Pq8gLsZSo6C1ulaiYlwPbzmdonbJbpvi/r7MEmwgINGKPcxAaEUR44ZxcvRBD+doRGXpPgpAZiPMrcX4lZA6i8JSJlChRglWrVtG+ffukivydJk+ezPz588mTJw8DBw5EURScTicvvfQSL7/8Mj/99BOLFy9mwIABDB48GIC3336b7du306hRo6Tl3EtF/ubNm1y6dIn8+fNz6NAhmjdvzqpVqyhTpgw9e/Zky5YtLFq0KKl9//792blzJ19//TWdO3emWLFi9OzZkzJlygDgdHoHwZQkyee+444dOzJ79mwuX77MmDFjHuhzFITMoFiBfKzZspt2Tesk9Xi604ylP/H56H6E5Axi+KT5oCg4XS5eaFqPDi3q8/Nvf7D051/p8/Jz9H/leQDen/kVOw/+Tf3qybNE3UuPp1sxcVy+dpPwPLk4fPIsTWtXYc2WPZQqEkH3F1qwbf9fLF21Kal9707PsvvQcb5dt4WXWtanaEReur/QglJFvAdLLpd3nJj/bs/tmz/NF9+v5cq1m7zTs9ODfZCCIAgPWf6iOdm57hT125RO6vF0p+8/283QGa0Izh3A5+9sRFHA5fTQsG1pGr9Ylu1rTrD+m8O061Wd9n28M/7N/+g3jvxxkcr1CiUt5156PMXdTOD6lThC8gXyz19RVG9U1Of5fIWD6T66AU6HmxEvfEOVpwuh1Wt99i2aOwaGDysQxMmDVyldLZyTh66St2BwihyprVOWZfb9dpYGz5fGGpOAPc5BQKCRY/suExedQP4iOdmx9iR1WpXI8OctCGoS51fi/ErIHEThKROpX78+e/bs4aWXXsLPz4/OnTtTtmzZpOeff/55Xn/9dYoUKZL02OXLl3n33XfRarW4XC7effddfvvtN5YuXYpWq8VgMFC1alWf9dxLRT5PnjzMmzePU6dOUahQIRo0aMDp06cZMmQI+/bto1gx3xNbrVbLhAkTeO+991i4cCEjR47kgw8+ICEhAUVRaNu2LW3btk2xnjJlynD16lVatGiRZQfCE7KXelXLsf/oKV4dOQE/g4GX/h3j6bbWDWrRa8x0CufPy+15Jq5eu8WYz5eg0WhwuT2MeKMDW/cdYfma39BoNBj0OiqX9j0ZuZceTyE5g1n4wy/8c+EqBfLloV61cuS7eJV3pi7kz79PUzTC90RIp9EwdsBrfDR7GV+t3MSQbi/w6bzlJCQ6URSFZxvW4rlGtVOsp1SRCCJvRNP0qapiexYEIdOpWKcgx/+8ykfdV2Aw6mj87xhPt9V5pgQT3lpNvkI5kmYBunnVyoKxv6PRSrhdMq8MqcPBbefZ9N1faLQSOr02qYfRbffS4yk4xJ/VXx7g8ploQiOCqFi3ADeuJk99/dX4bVy/YsXtkmnYznuyWa1BYRZ89DuFSofQ+MWyPst76a1aLBq3lRVf7MNo0vHmmEb8V2rrBDi86yIfvbEC2aPQefBTWKMT+XbmHwyZ3gqjSc/4Pj9TuEwI+QrlSLFMQchsxPmVOL8SMgcxuLiQaXTu3Jnp06eTO3fuuzd+hH4cE82Nc25VMwgPR+5COtqNSfvA+FEPLp6dvf7uZCYO60Gu4MdzK9Z/BxeP/nEM7hvnHsu6BXXochciR7sxSX8fE/0j59w3VMsjPDyFdLkZk6Ndms8/jMHFhcxBDC4uCI9WZjm/Aoge8zvuc7FqxxAeAl2hIHKMaXBvr3k0UQQh46Kiohg5ciTVq1dX/UdR9ijpFiqEJ4/sUdBoxVWex+XarRhGz/iSqmWLP7ai038pssenICFkXYrsQdJo8ShyuoUK4cnjUWS0khi0XhAE4X5kpvMrAMUj33OhQsjcFI+MpM34flr0eBIEIdsSPZ6yjv/2eBIEIesSPZ6yDtHjSRAEIXsQl5IEQRAEQRAEQRAEQRCER0IUngRBEARBEARBEARBEIRHQozxJAhCtqUxBCDfvZnwBNAYAtSOIAjCY2LS5lQ7gvCQiO9SEAQhexBjPAmCkC0piiKmls1ixHcqCFmfoshIYtDxLEV8p4IgCFmf+JXPIjZs2EDbtm05deqUqjl++OEHXnzxRS5fvqxqDkG4m8xeoJg8eTLdu3cnISFBtQyKojBmzBj69u2Ly+VSLUdGZfbvVBCEB5fZCxSLFi2iU6dOREdHq5pj5syZvPbaa8THx6uaIyMy+3cqZF8ej4dBgwYxcuRI1Oyr4XK56NWrF2PHjlUtgyA8KPFLnwXY7XY++OADihYtSvHixVXN0rp1a27evMknn3yiag5BeJIdPHiQefPm0apVK0wmk2o5JEmiY8eO/Prrr3z77beq5RAEQXgSXLhwgcmTJ/P000+TI0cOVbO0b9+egwcPMmfOHFVzCMKTbNWqVaxdu5aXXnpJ1Ytber2eNm3asGTJEnbu3KlaDkF4EKLwlAUsWLCAuLg4Bg0apHYUjEYjgwcPZtOmTezdu1ftOILwxFEUhXHjxlGyZEmef/55teNQqlQp2rZty8yZM7FarWrHEQRByLQmT55Mzpw56dq1q9pRCAsLo1u3bixevJgrV66oHUcQnjgJCQlMnTqVFi1aULlyZbXjJOUYP348Ho9H7TiCcM9E4ekJFxUVxYIFC+jatSvh4eFqxwHgmWeeoVy5cowfPx5ZFkM3C8K92LBhAwcOHGDkyJFotVq14wAwcOBAHA4Hc+fOVTuKIAhCpnTgwAF++eUXBg0apGpP1Tu98cYbWCwWpk6dqnYUQXjiLFq0iFu3bjFkyBC1owDeXugjRozg+PHjrFy5Uu04gnDPROHpCTdt2jRMJhNvvvmm2lGSaDQaRo4cyZEjR1izZo3acQThieF0Opk0aRJPP/00Tz31lNpxkoSGhvL666/z5ZdfcunSJbXjCIIgZCq3e6qWLl2aNm3aqB0nidlspn///qxatYq//vpL7TiC8MS4fv06X3zxBa+88goFChRQO06SypUr06JFC6ZOnarqGKCCcD9E4ekJdvz4cVasWEG/fv2wWCxqx/FRvXp1mjRpwpQpU3A4HGrHEYQnwrJly7h06RLDhw9XO0oK3bt3JygoSFw5FwRB+I9ffvmFgwcPMmLECDSazHVo/cILL1C8eHHGjx+v6uDIgvAkmTlzJnq9nl69eqkdJYWhQ4cSHR3NokWL1I4iCPckc+0dhQxTFIXx48dTqFAhOnTooHacVA0dOpRr167x5Zdfqh1FEDK9mJgYZs2aRfv27VWfJCA1AQEBDBgwgNWrV3P48GG14wiCIGQKt3uqNmzYkNq1a6sdJwWdTsewYcPYs2cPmzdvVjuOIGR6p06d4vvvv6dPnz4EBwerHSeFiIgIXnnlFb744guuX7+udhxByDBReHpCbd26lZ07dzJs2DD0er3acVJVuHBhOnXqxNy5c7l165bacQQhU5szZw4ul4t+/fqpHSVN7dq1o0SJEuLKuSAIwr++/vprrl69yrBhw9SOkqbbt29PnDgRl8uldhxByNQmTJhA/vz5efnll9WOkqbevXuj1+uZOXOm2lEEIcNE4ekJ5Ha7mTBhAjVq1KBRo0Zqx0lXnz59APj8889VTiIImdfFixdZunQpPXr0ICQkRO04adJqtQwfPpx9+/bx66+/qh1HEARBVXf2VC1atKjacdJ0e1Dic+fO8d1336kdRxAyrR07drB161aGDBmCwWBQO06agoKC6NOnD99//z0nT55UO44gZIgoPD2BfvjhB06fPs2IESOQJEntOOnKmTMnvXr1Yvny5Zw5c0btOIKQKU2aNImcOXPSrVs3taPcVb169ahbty4TJ07E6XSqHUcQBEE1s2bNwuPx0L9/f7Wj3FWpUqVo27YtM2fOxGq1qh1HEDIdj8fD+PHjqVy5Ms2bN1c7zl29/PLL5M+fn4kTJ6odRRAyRBSenjA2m40ZM2bQpk0bypUrp3acDHn11VcJDQ1l0qRJakcRhEwnM07BfTfDhw/nwoULfPvtt2pHEQRBUMX58+dZtmwZb775Jrly5VI7ToYMHDiQxMRE5s2bp3YUQch0fvrpJ06cOMHIkSMz/YV9AIPBwNChQ9m6dSs7duxQO44g3JUoPD1h5s+fj9VqZeDAgWpHyTCj0cjgwYP59ddf2bNnj9pxBCHTuHMK7ueee07tOBlWsmRJ2rVrx2effUZcXJzacQRBEB67yZMnkytXLrp27ap2lAwLDQ3l9ddfZ/HixVy+fFntOIKQacTHxzNt2jRatmxJpUqV1I6TYc2aNaNy5cqMHz8ej8ejdhxBSJcoPD1BIiMjWbRoEV27diVfvnxqx7knrVq1onz58owbNw5ZltWOIwiZwp1TcGu1WrXj3JP+/fvjcDiYPXu22lEEQRAeq/3797N+/XoGDRqEn5+f2nHuSffu3QkKCmLKlClqRxGETGPhwoVER0czZMgQtaPcE0mSGDlyJCdOnGDFihVqxxGEdInC0xNk+vTp+Pv78+abb6od5Z5pNBpGjhzJ0aNHWb16tdpxBEF1TqeTyZMn06BBg0w5BffdhIaG8sYbb7BkyRIuXbqkdhxBEITHQlEUxo8fT9myZZ+onqq3BQQEMGDAAFavXs2RI0fUjiMIqrt27RoLFiygS5cuREREqB3nnlWqVIlWrVoxffp04uPj1Y4jCGkShacnxLFjx1ixYgX9+vXDbDarHee+VKtWjaZNmzJlyhQSExPVjiMIqvr666+5cuVKpp6C+25ef/11goODxZVzQRCyjXXr1nHo0CGGDx+ORvNkHka3a9eOEiVKMH78eBRFUTuOIKhq5syZGAwGevXqpXaU+zZ48GCio6NZuHCh2lEEIU1P5h4zm7k9DkzhwoXp0KGD2nEeyNChQ7l+/TpfffWV2lEEQTV3TsFdrFgxtePcN39/fwYOHMiaNWs4dOiQ2nEEQRAeKafTyaRJk2jYsCG1atVSO85902q1DB8+nL179/Lrr7+qHUcQVHPy5En+97//0adPH4KCgtSOc98iIiLo0qULCxYs4Nq1a2rHEYRUicLTE2DLli388ccfDB8+HJ1Op3acB1KoUCE6derEnDlzuHnzptpxBEEVs2bNwu12069fP7WjPLC2bdtSokQJxo0bJ66cC4KQpS1dupTIyMgnuqfqbfXq1aNu3bpMnDgRl8uldhxBUMWECROIiIigU6dOakd5YL169cJgMDBjxgy1owhCqkThKZNzu91MmDCBmjVr0qBBA7XjPBR9+vRBo9Hw2WefqR1FEB67O6fgzp07t9pxHphWq2XEiBH8+eefbNy4Ue04giAIj0R0dDSzZ8/mpZdeomjRomrHeSiGDRvG+fPn+fbbb9WOIgiP3fbt29m2bRtDhw7FYDCoHeeBBQUF0adPH3744QdOnDihdhxBSEEUnjK5//3vf5w5c4aRI0ciSZLacR6KnDlz0rt3b7799lv++ecfteMIwmP1JE7BfTd169alXr16TJo0CafTqXYcQRCEh27WrFl4PB7eeusttaM8NKVKleKFF15g5syZWK1WteMIwmPj8XgYP348VapUoWnTpmrHeWg6depEREQEEydOVDuKIKQgCk+ZmM1mY8aMGbRp04YyZcqoHeeheuWVVwgLCxM/jEK2cucU3CaTSe04D9Xw4cO5ePEi33zzjdpRBEEQHqpz586xbNkyevXqRa5cudSO81D1798fh8PBnDlz1I4iCI/NihUrOHnyZJa6sA9gMBgYOnQo27ZtY/v27WrHEQQfovCUiX3xxRfYbDYGDRqkdpSHzmg0MnToUH777Tf++OMPteMIwiN3ewruMmXKPJFTcN9NiRIlePHFF5k1axaxsbFqxxEEQXhoJk+eTEhICK+++qraUR660NBQ3njjDb766isuXbqkdhxBeOTsdjvTpk2jVatWVKxYUe04D13Tpk2pWrUq48ePx+PxqB1HEJKIwlMmdfXqVRYtWkS3bt0ICwtTO84j0bJlSypWrMiECROQZVntOILwSN2egnvEiBFP7BTcd9O/f3+cTqe4ci4IQpaxb98+NmzYwKBBg/Dz81M7ziPx+uuvExQUxNSpU9WOIgiP3KJFi4iJiWHw4MFqR3kkJEli5MiRnDx5kh9//FHtOIKQJGue/WQB06ZNw2w206NHD7WjPDKSJDFixAiOHj3KqlWr1I4jCI+M0+lk8uTJT/wU3HcTEhLCG2+8wZIlS7h48aLacQRBEB6ILMuMHz+esmXL8uyzz6od55Hx9/dn4MCBrF69msOHD6sdRxAemWvXrjF//nxeffVVIiIi1I7zyFSoUIHWrVszffp07Ha72nEEARCFp0zp6NGjrFy5kv79+2M2m9WO80hVrVqV5s2bM3XqVBITE9WOIwiPxNKlS7l69WqWmIL7brp160aOHDmYMmWK2lEEQRAeyLp16zh8+DAjR47Msj1Vb2vbti0lSpRg/PjxKIqidhxBeCSmT5+O0WikV69eakd55AYNGkRMTAyLFi1SO4ogAKLwlOncHgemSJEivPjii2rHeSyGDBnCzZs3Wbx4sdpRBOGhuz0Fd4cOHbLMFNzpuX3lfO3atRw8eFDtOIIgCPfF4XAwefJkGjduTI0aNdSO88hptVpGjBjBvn372LRpk9pxBOGhO378OD/88AN9+/YlMDBQ7TiPXP78+Xn11VeZP38+UVFRascRBFF4ymx+//13du/ezfDhw9HpdGrHeSwKFizIyy+/zNy5c7lx44bacQThobo9BXe/fv3UjvLYPP/885QqVYpx48aJK+eCIDyRlixZQmRkJEOHDlU7ymNTt25d6tWrx8SJE3E6nWrHEYSHauLEiRQsWJCOHTuqHeWx6dWrF0ajkRkzZqgdRRBE4SkzcblcTJgwgdq1a1O/fn214zxWffr0QafTMXPmTLWjCMJDc3sK7p49e2a5KbjTc/vK+YEDB9iwYYPacQRBEO7JrVu3mDNnDh07dqRIkSJqx3mshg8fzsWLF1m+fLnaUQThodm2bRvbt29n6NChGAwGteM8NoGBgbz11lv88MMPHD9+XO04QjYnCk+ZyPfff8/Zs2cZMWIEkiSpHeexCg4Opk+fPnz//fecPn1a7TiC8FDcnoL7tddeUzvKY/fUU09Rv359Jk2aJK6cC4LwRJk1axaKovDWW2+pHeWxK1GiBC+++CKff/45cXFxascRhAfm8XiYMGEC1apVo0mTJmrHeew6duxIwYIFmThxotpRhGxOFJ4yCZvNxsyZM2nbti2lS5dWO44qOnfuTN68eZk0aZLaUQThgWWHKbjvZtiwYVy6dIlly5apHUUQBCFDzp49yzfffEPPnj3JmTOn2nFU0a9fP5xOJ3PmzFE7iiA8sB9//JGTJ09mywv7AHq9nmHDhrF9+3a2bdumdhwhGxOFp0xi3rx5xMfHM2DAALWjqMZgMDBkyBB+++03du3apXYcQbhvtycJyOpTcN9N8eLFad++PbNmzSI2NlbtOIIgCHc1efJk8uTJky17qt6WJ08eunfvzldffcXFixfVjiMI981utzN9+nRat25NhQoV1I6jmsaNG1OtWjUmTJiAx+NRO46QTYnCUyZw5coVFi9eTLdu3QgLC1M7jqpatmxJpUqVGD9+PLIsqx1HEO7L2rVrOXz4MCNGjMjyU3DfTb9+/XC5XMyePVvtKIIgCOnau3cvGzduZPDgwRiNRrXjqOr1118nR44cTJ06Ve0ognDfFi5cSGxsLIMGDVI7iqokSWLEiBGcPHmSH3/8Ue04QjaVvc+IMompU6disVh444031I6iuts/jMeOHWPlypVqxxGEe3Z7Cu5GjRpRs2ZNteOoLiQkhB49erB06VJx5VwQhExLlmXGjx9PuXLleOaZZ9SOozp/f38GDhzImjVrOHTokNpxBOGeRUVFsWDBAl599VXy58+vdhzVVahQgdatWzN9+nTsdrvacYRsSBSeVPbXX3+xatUq+vfvj9lsVjtOplClShVatGjB1KlTSUhIUDuOINyTpUuXEhkZybBhw9SOkml069aNnDlzMnnyZLWjCIIgpGrNmjUcOXKEkSNHZvueqrc9//zzlCpVinHjxqEoitpxBOGeTJ8+HaPRSK9evdSOkmkMHjyY2NhYFixYoHYUIRsSe1YV3R4Hpnjx4rzwwgtqx8lUhgwZwq1bt1i8eLHaUQQhw27dusXs2bOz5RTc6TGZTAwaNIh169Zx4MABteMIgiD4cDgcTJkyhSZNmlC9enW142QaWq2WESNG8Oeff7Jx40a14whChh0/fpwff/yRfv36YbFY1I6TaYSHh/Paa6+xYMECoqKi1I4jZDOi8KSizZs3s2fPHoYNG4ZOp1M7TqZSoEABOnfuzLx587h+/bracQQhQ7LzFNx306ZNG0qXLs348ePFlXNBEDKVr776imvXrjF06FC1o2Q6Tz31FE8//TSTJk3C6XSqHUcQ7ur2hf2CBQvy0ksvqR0n0+nZsycmk4lp06apHUXIZkThSSUul4uJEycm7dCFlHr37o1er2fmzJlqRxGEuxJTcKdPo9EwYsQIDhw4wC+//KJ2HEEQBMDbU3XOnDl06tSJwoULqx0nUxo+fDgXL17km2++UTuKINzVtm3b2LlzJ8OGDUOv16sdJ9OxWCz069ePFStWcPz4cbXjCNmIKDyp5LvvvuPcuXOMGDECSZLUjpMpBQcH06dPH77//ntOnTqldhxBSJeYgvvuateuTYMGDZg8ebK4ci4IQqbw+eefI0kSffr0UTtKplW8eHHat2/PrFmziI2NVTuOIKTJ7XYzYcIEqlevTuPGjdWOk2l16NCBQoUKiV7owmMlCk8qsFqtzJw5k7Zt21KqVCm142RqL7/8Mvnz52fixIlqRxGENO3bt09MwZ1Bw4cP58qVKyxbtkztKIIgZHNnzpxh+fLl9OrVS/RUvYt+/frhdDqZM2eO2lEEIU0//vgjp06dEhf270Kv1zNs2DB27tzJ1q1b1Y4jZBOi8KSCefPmkZiYyMCBA9WOkukZDAaGDBnCli1b2Llzp9pxBCEFWZYZN26cmII7g4oWLZp05TwmJkbtOIIgZGOTJk0iNDSULl26qB0l0wsJCaFHjx4sWbKEixcvqh1HEFKw2+1Mnz6dZ599lvLly6sdJ9Nr1KgRNWrUYMKECbjdbrXjCNmAKDw9ZpcvX2bx4sW8/vrrhIaGqh3nidC8eXMqV67M+PHj8Xg8ascRBB9r164VU3Dfo379+uFyuZg9e7baUQRByKb27NnDr7/+Knqq3oNu3bqRM2dOJk+erHYUQUhh/vz5xMXFMXjwYLWjPBEkSWL48OGcPn2aH374Qe04QjYgzpIes6lTpxIUFET37t3VjvLEkCSJkSNHcvz4cVauXKl2HEFIIqbgvj+5c+emZ8+efP3111y4cEHtOIIgZDO3e6pWqFBB9FS9ByaTiUGDBrFu3ToOHDigdhxBSBIVFcXChQvp2rUr+fLlUzvOE6N8+fI899xzzJgxA5vNpnYcIYsThafH6PDhw/z8888MGDCAgIAAteM8USpVqkTLli2ZOnUq8fHxascRBMA7BXdUVJSYgvs+vPbaa+TKlYtJkyapHUUQhGxm9erVHD16VIwDcx/atGlD6dKlxaDEQqYybdo0TCYTb775ptpRnjiDBg3CarUyf/58taMIWZwoPD0miqIwYcIESpQoQbt27dSO80QaMmQI0dHRLFq0SO0ogiCm4H5At6+cr1+/nj///FPtOIIgZHGKotC6dWu2bNnC1KlTadasGdWqVVM71hNHo9EwYsQIDhw4wPr169WOIwgcO3aMFStW0K9fPywWi9pxnjj58uWja9euLFq0iMjISLXjCFmYKDw9Jr/++it79+5l2LBhaLVateM8kSIiIujSpQvz58/n+vXrascRsrlZs2aJKbgf0HPPPUfZsmXFlXNBEB65hIQETp06xU8//cS1a9fo3r07drtd7VhPpNq1a9OgQQMmTZqE0+lUO46QjSmKwvjx4ylUqBAdOnRQO84T680338Tf35/p06erHUXIwkTh6TFwuVxMnDiRunXr8vTTT6sd54nWq1cvDAYDM2bMUDuKkI2dPXuWb775RkzB/YA0Gg3Dhw/n4MGDrFu3Tu04giBkYQkJCYD3QmDFihXp0qUL3333ncqpnlzDhw/nypUrLFu2TO0oQja2detWdu3axfDhw9Hr9WrHeWKZzWb69evHihUrOHbsmNpxhCxKFJ4eg2+//Zbz588zbNgwtaM88YKCgujTpw//+9//OHnypNpxhGxKTMH98NSqVYuGDRsyefJkceVcEIRH5vb4kE6nk/3799OpUyc6deqkcqonV9GiRWnfvj2zZs0iJiZG7ThCNuR2u5kwYQI1atSgYcOGasd54rVv357ChQszbtw40QtdeCRE4ekRs1qtzJw5k3bt2lGqVCm142QJnTp1IiIigokTJ6odRciG9uzZw6ZNm8QU3A/RsGHDuHr1KkuXLlU7iiAIWVRsbCwAFouFr776infeeQc/Pz+VUz3Z+vXrh8vlYs6cOWpHEbKh//3vf5w+fVpMEvCQ6PV6hg0bxh9//MHWrVvVjiNkQaLw9IgsXryYH374gTlz5uBwOBgwYIDakbIMg8HA0KFD2bp1Kzt27GDs2LHs3btX7VhCFqYoCiNGjODo0aOMHz9eTMH9kBUtWpSXXnqJ2bNnc+nSJXr16iWuoAuC8FCVKlWKN954gw0bNlCzZk2142QJuXPnpmfPnixdupSzZ8/Sv39/Ll68qHYsIQuz2+307t2bixcvMmPGDNq0aUO5cuXUjpVlNGzYkJo1azJ+/HhOnz7NoEGD8Hg8ascSsghReHpENmzYwG+//cZXX31F9+7dCQ0NVTtSltK0aVOqVq3K+PHjWb9+Pbt371Y7kpCFuVwufvrpJ7777jv++usvcXXtEXjrrbfweDzMmDGD3377jdOnT6sdSRCELESn0zFs2DBy5MihdpQs5bXXXiNXrlxMmjSJ9evXc+TIEbUjCVnYxYsX2bx5M7Nnz8ZmszFo0CC1I2UpkiQxYsQIzpw5w5IlS1i7di1xcXFqxxKyCFF4ekTi4+M5efIkZrOZvXv30q9fP7UjZRlOp5N69eqRJ08eTpw4gcfjETPTCI/U7X9f69evp1q1arz77rssWbJE5VRZx4EDB2jdujV16tRhzZo1QPJ4LIIgCELmdPHiRerXr0+VKlXYtGkTIH67hUfr9vHYzz//TN26dWnfvj3bt29XOVXW8eOPP9KvXz9q1aoljseEh04Unh6RmJgYzp8/j91u58KFC7z22mtqR8oyDAYDvXr1YvPmzQQEBBAdHY3ValU7lpCF3Z4NKTo6mn379hESEkKzZs1UTpV1lC5dmgYNGrBhwwY0Gu9u6fZnLgiCIGRO4eHhvPDCC6xbtw6TyQSIk1Th0bp9bCDLMr/++is1atSgUqVK6obKQurXr0+hQoXYtWsXNpsNEMdjwsMjCk+PyM2bNwFo2bIlP//8M9WqVVM5UdbyyiuvsGLFCsLDw5Flmb///lvtSEIWdnu8IY1Gw8iRI/nqq6/E7bMPkZ+fH59++imzZs3CYDAAiFvtBCEDxMxDWcuT9n1qNBpGjBjBkiVLMJvNABw9elTlVEJWdv78ecA7EPbUqVOZMmVK0r894cHlypWLBQsW8P7776PVagG4du2ayqmErEKndoCsqlmzZlSoUEH0dHqEihYtyo8//sioUaMoWbKk2nGELKxgwYJUqVKF4cOHU7lyZbXjZFmNGzdmw4YNDBw4kHr16qkdRxAyPUmSOOq8hF12qh1FeEABGgNlDfnVjnFfqlevzi+//MKgQYNEb2DhkapZsyZVq1Zl8uTJ5M2bV+04WZIkSbz88stUrlyZDz74QMzKLjw0kvKkXV4RBEEQBEEQANiTeAabkqh2DOEBmSU/avgVUTuGIAiCIDwS4lY7QRAEQRAEQRAEQRAE4ZEQt9oJgiAIgiAID2T5p/O4dPIcjV5uTZWmT6V4/ubla1w6dY6KDWo8sgxR5y/z9YezkSSJAmWK8sLgrinaHN99mFWffw1Aw06tqd5S3NYrCIIgCI/affV4UmTPw84hqCy179SjyCokER6l1L5TsT1nPWl9px5Z3Fmd1YjvVMgs/t55kKGLPkm16ARw88o1Dm/Zm+HlORMdyPK9HYesmPoVzw/owqD5HxFvtXNi7xGf52WPh/9NXkSfGe8ycN6HbFj0I4n2zD9jk6yI/XRWk9p3Ko7Hsp40j8fEOVaWI77Tu7uvHk+SRkv0j2Nw3zj3kOMIatDlLkSOdmNSPK6VNIyJ/pFz7huPPZPw8BXS5WZMjnYpHhfbc9aS1vYMoNVIjPk9mnOx7seaSXg0CgXpGNMgh9oxBIFvPp5LzLWbTOk+ik7v9GTjVz9x8/I1EmzxtHqzPZUa1WLT0lWcP3qaKd1H8dxbL1OscpkUy5E9Hk7sOcKetVu5eSWKtz5/D4OfMcM5oi5coWCZYgAULFOUk3uPULJ6+aTnr12MJFe+EMzBgQAUqViKc0dPUapGhQf8BB4tjaTl9+gxxLrPqR1FeAiCdIVokGNMisfF8VjWku7xmDjHylLSOscSfN33rXbuG+dwRZ58mFmETOic+wYnXZFqxxAeMbE9Zx/nYt2cvOlSO4YgCFlIp3d7cuyPgwxeMBaAl0b2wGjywxYTx5TXR1GpUS2avPIcu9duocv7fVO8/uo/F9mxYiMXjp2hRLVyNO/+AmGFwgFwu1zM6PVBitcUrVyaNm919nksX9EC/L3zT8rVq8axXQcJCsnp87w9xoq/JXnqdf/AAOwx1gd+/49DrPscN11iP53VieOx7EOcYwnZjRjjSRAEQRAEQXgoZFlmzZxvOXPoOBqtlluR1+/6mhN7j/DX9j+p90IzqjavS3Ce5IKRTq9PKmjdzQtDuvHtuHls/noNufOHpig8BQRbiLfakv4eb7UTEGzJ4DsTBEEQBOF+ZenC0+lbbvZFuuhYxpRmm8/22XmlnIlgv/ub4O+a3cP4P+y4PAqlc+voXSUgRZv9kS4WHooHoF1JPxoXyni3ceHu3Kdv4doXialjym77t9k/24fplXJogv3uax2ea3bs4/9AcXnQlc5NQO8qKdq49kcSv/AQAH7tSmJsXOi+1iWkTmzP2Yc76jSuc/sw1eyYZhv7ps8wPfUKGv/g+1qHJ+4a9rXjUTwudHlLE9Cod4o2rnP7id+2EAC/qu0wlml8X+sShOzk8slzXD51nqGLP8UWHcfoZ73bllavQ3anPt5Jg46tqNOuKUe27uO7CfNxJCRSuVEtnnq+MbIsZ7jHU86w3PSe9g6KorB41HQqNarp83yeiDBuXrmOPc6Gwc/ImUMneL5/l4f0zoWo027O7XNRs2Pa++lNn9l56hUT/sH3t5+Ou+Zh7Xg7HpdC3tI6GvVOuZ8+t9/FtoXe/XTVdn6UaSz20w+TOB7LPsQ5lvAwZenCU7GcOorlTP8tvlUt5Q/ZvZh7IJ7OZU1UCdPz0XYrByJdVA7TJz3vkRVm7bczpUkgflqJvhtiqR1uwF8vPdB6hWS6YjnRFcuZbpuAt6o90Dri5x7A1Lks+iphWD/ajutAJPrKYUnPKx4Z+6z9BE5pguSnJbbvBgy1w5H89eksVbgXYnvOPnShxdCFFku3TUCTtx5oHfG/z8VUuzP6glWwrvoI1/kD6AtWTnpekT3YN88isNMUJJ0fsUv7YihaG8no/0DrFYSsLrRgOB63mynd3yV/icL4W7y/y+HFC3L9UiTzhk6gWde2FCpX3Od1eoOeKk1qU6VJbeLj7Py5cQdulxuDnzHDPZ72rN3Kjh83AlDruYbkLRIBwHcTFtDyjRex5AzihUGv8flbHwHQ9NU2mMxim35YQovpCC2W/n66yVsPtp/+fW48tTubKFhFz6qPrJw/4KJg5eT9tOxR2DzLTqcpgej8JJb2jaVobQNGf7GffljE8Vj2Ic6xhIcpSxSeZEVh3C4bkTaZojm07LrsYvnzOTgY5WLjWQfDapkZt8uGTgO3EmRuxMu8V9dC/kAtAzfG8m4dMyH+2vta96loN1X+/SGsG2HgQJTvD+Nlm0yYWUOQ0VvxL5tbx/Gbya8RMk6RFWzjdiFH2tAWzYFr12VyLH8e18EoHBvPYh5WC9u4XaDTIN9KQL4Rj+W9umjzBxI7cCPmd+ugDbm/A0z3qWj0Vbw/goa6EbgORPn8KMqXbWjCzGiCvFdbdGVz4z5+M+k1QsaJ7Tn7UBQZ25pxyLGRaPMUxXV6Fzl6L8d14SCOoxsxtxyGbc040OqQbbeQbTewPPce2pz5iV02EHPrd9EGhtzXut1Rp9AX9F5VMxSvi+uCb+FJjr6MJigMjSkIAF14WdyRx5NeIwiCrw9/ng2AwWRk4LwPkx7vMOINAPwCTAxd9EmGluUfGEDdF5rdc4YarZ6mRqunUzzeYXj3pD+Xrl2J0rUr3fOyhWSKrLBmnI3YSJk8RbWc3uWi9/IcXDjo4uhGBy2HmVkzzoZWB7ZbMrYbMs+9ZyFnfi3LBsbS+l0zgSH3t5+OOuWmYBXvPrd4XQMX/lN4ir4sExSmwRTk3U+Hl9UReTz5NULGieOx7EOcYwmPQ5YoPO245MSgkZjRLIjLVg9rTjtSbVcgUMvQmmZ++SeRtf8k8mbl1KvxUXYPn+60pXi8fgEDbUv6ditV7pjJ2mKQiHP4TqUY55CxGDR3tNGkaCNkjHPHJSSDhqAZzfBctuJYczrVdtoCgZiH1iTxl39IXPsPAW9WTrWdJ8qO7dOdKR431C+AqW1J3wfv+KIliwE5zvffmBznQGMxJP1dk0obIWPE9px9OE/tQNIaCOo8A0/0ZRyH1qTaTpuzAOYWQ0k88guJh9cS0ODNVNt54qKwrf40xeOGkvUxVW3r++Cd27SfBTkhzudpOSEOjV/y2C+aVNoIgiBkR6d2ONEaJDrPCCL6sodDa1LfT+csoKXFUDNHfknk8NpEGryZ+n46LsrD6k9T7qdL1jdQtW3a+2k/i0RCnO8+OCFOxs+iuaONJkUbIWPE8Vj2Ic6xhMchSxSeLsXJlM7tfSvhFm1S9fu/SuXytgkza/nretrTiYcGaJnWNChD65bu6NFpcyoE/mfdgUYNVqd8RxuZQKOoxt8P+VIcutK5AdCGW5Iq3/+lK5XL2ybMjPuvtAc11YYGEDStacZWfscXrdicaAJ9160JNCJbnclZbU70geJe8/shtufsQ751CV2+0gBoc4Qn9S76L13eUt42QWG4L/2V5vK0gaEEvTwtYyu/c5t22NCYAn2e1pgCkROTZ7uSE23o/9NGEAQhO7p1SSZfae8+OEe4Nql30X/lLeVtExSm5dJfae+nA0O1vDzt3vfTDpuCKdB33aZADYnW5P10ok3GFCj20/dDHI9lH+IcS3gcskThKdyiYe9VF88Ug6s2D7FpVLzvvOtXSbWF171U5Ivn0HEwykWlUD07LzlpUdR3YLVws4ZIm4zVIWPUSRy94aZHZTGewP3QhFtw7b0KzxTDc9WGHJtGtTuDX/S9VON1xXPgOhiFvlIozp2X8GtR9D/ZzMiRNmSrA8mow330Bv49Ur8KIKRPbM/ZhyZHOK6ze6HiM3hiriInxKbeUMrYt30vPZ50ocVxXTiIvkAlnKd34le+RYpscmwkcqIVSWfEffko/vV7ZPi9CUJ2dPH4WY79cZBmXdum2ebO8Zbux63IG3z1/kzcTheFy5fghcFdU7S5eeUa309YQII9Hp1BT7/P3+P6xat89f5nKIoCikL7Yd0pWLYYNy9f4+OOg8lfohAAjbs8R8UGNe4rW3aRI1zD2b0uKj4DMVc9JMSmsZ/O4I76Xno8hRbXceGgiwKV9Jze6aR8C9/9dI5wDbGRMolWGZ1R4vJRN/V7iP30/RDHY9mHOMcSHocsUXiqk9/AtotO+m+IpXCwllym+5tB4bZ7qcj3qOTPhD9sLDwEJXPpqBzq/UjvnM2hdxV/Rv7uvXLeobSJAP2D5cuuDHXy49x2kdj+G9AWDkaTK+3ZNDLiXqrx/j0qYZvwByw8hK5kLnSVQwHfmRz8e1fBOvJ3AEwdSqMJEFde7ofYnrMPQ/E6OE9uI/br/mhDCqMx53qg5d1Ljyf/+j2wrZsA2xaiCyuJroD3IObO2fL8G/bG+v1IAEw1OqAxPthgqYKQ1UWUKkxEqcLptrlzvKX7sWLaV7To/gKlalRgwcgpnNh7hJLVy/u0Wf7JPDqP7k1waPJviskSQM8pIzAHB3L1n4t8PXZ20phTBcoUZeDclDPnCakrXsfAyW1Ovu4fS0hhLeZcD7YfvJceT/V7+LNugo1tCyGspI4Clb376Ttny2vY25/vR3r30zU6mDAGiP30/RDHY9mHOMcSHgdJUZT0itNpuj6vK67Ikw87z31zywo6jcRlq4cPtluZ1zJY7UhPDH1YCULeXJzqc12vz+OkK/LxBkqH4paRdBo8l61YP9hO8LyWakd6YpTQh7E4JPWxccT2nHWktz0DdF15nZM3XY8v0F0oHjeSVocn+jLWlR8Q3HWe2pGeGCVy6Vnc5v4GVxeyjj2JZ7ApiY91nbIs89V7M7lxOYr8JQpxZOs+Pl43j5N7/2L32i10eb8vX46egU6vI/ZGNDHXbvHG+CHkKZiPKd1H0e2TgeQIzX1f6x7z/FuM+ekzAPav38GVf87zbJ+Xk56/dfU6i0dNJzB3DuJu3KJm64bUadvEZxnXL15lyZjPGbxgLDcvX2PcK8MILRROrrwhvDj09fvujfUgzJIfNfyKpPn8yutduenKPPtpj1tBq5OIvuxh5QdWus4LVjvSEyOXvgRtQhan+pw4Hss67no8Js6xsoz0zrGEZFmixxPAh9ttxDpkEtwKfaqIq9JZle3D7cixDpQENwF9xOxSWZXYnrMP26oPkRNiUZwJBDTqo3YcQRAy4PDve9DpdQxd9AnXL15lx4pNqbYLLRxO5/f6sGvlZnb8tIm2A15Ntd3tYtF/VWn6FA06tvJ5TJGTb/fxDwzAFmP1eT7m2k0uHPuH936YgSVnEFPeGE3xKmXIUzAfALLHw3fjF9D89XYABIbk4KPVc/ALMLHzp038MGUxXccOyPiHkU2t+tBGQqyMM0GhUR+xn86qxPFY9iHOsYRHLesUnp623L2R8MSzfJhymmQh6xHbc/Zhafvh3RsJgpCpRJ2/QuEK3nE6QiLyYg5OfeD9gmWLA5ArXx7+OXgszeXlzBvC4AVjM7RuSZN8O0281Y452Hd/4R9kIW/RAuTM6+0NWKJaWS6fPk+egvlQFIWv3v+Mck9Xo2wd74mV3qBHb/DetlG9VX02LVmVoRzZXdsPxX46OxDHY9mHOMcSHjVxM+x/dF4Z/VjWc+Sai9dXx9Dsm5tcj/c8lnUKvqI7r3ws63EduUbM66u52ewbPNfjH8s6hWSPa5te+08i/TbEMmBDLO/8HofdJab1fdyi53Z+LOtJPLiKmK96E/v1AKyrPkJxO+/+IkHIQvIUyMu5v04BcONyFLaYuFTb3Tm4dHrjOty6ep0p3Uel+O/35WtTtI0oVZiT+7yzWx7espfi1cr5ZosIw+10kWC1oygK5//+hzwFvL2dvh33BXkK5KV+h+TJBBKs9qQ/n9x7hNB/e0YJj8/czo/p2PuXROZ0imbZwFiWDYwlLkocfz9O4hwrexDnV0JaskyPpydN4WAtnzcP4u3fUz9YE7IObeFggj5vTtzbv6sdRXiEmhYy0urfWVcWHopnwxknbUv63eVVwpNIX7AqxoqtkSQN9t/m4Di6Eb+Kz6gdSxAem4oNanDw1z+Y1O0dwosVJCh3jgda3r30eHq+fxeWjPmMVZ8vo2CZYkkDi985W96LQ7vxef+PkT0eytWtSnjxgpzc+xfbf9hIkYolOb77MAFBZnpOGcmJvX+xdt53+AX4odPr6Txa3PKblVVoZeSpLmL2s6xMnGNlD+L86snzxBSeIm0exu6wodeCosBHT1s4E+Nh8ZF4PDJYDBLv1bVg1EkM3BhL8Zw6zsV6cHkUWhfzY/1ZB9GJMu/XtRARqGXgxlgKBWm5ZJWRFYXRdS3k8EvuAOaWFabtsXPF5sElQ6/K/pQN0bPkSDw7Lzsx6SRqhRvoUPr+Rv03G0Rns7R4Im3Yxu7g9pdt+ehpPGdiiF98BDwyksWA5b26SEYdsQM3oiueE8+5WBSXB7/WxXCsP4scnYjl/bpoIwKJHbgRbaEg5EtWFFnBMroumhzJBQHFLWOftgfPFRu4ZPx7VUZfNoT4JUdw7ryMZNJhqBWOqUPp+3o/GrPhYX00WUpW26b12uRL+wluhSrB2gf+jLIKT2wktp/Hglbv3abbfYTn+hnity8G2YPkZ8Hy3HtIeiOxywaiCy2O58Y5FI8Lv0qtcRxZjxwfjaXN+2hzRhC7bCDa3IWQb11CQcby7Gg0AcknvorHjX3DNDwxV8Djwr9hL/ThZYnfuQTn6Z1IehOGorUw1ehwX+9HmyM86c+S1gAa8V0L2YtGq+XVD/qh1eu4duEqZ//yDoZcono5SlT39kB67aP+Se3vfDyjBaa05MqXh4HzUt6ie+dseSWrl6fkIt+Z7kpUL8dn+75P8bpKjWpSqVHNB8qUVcVGevh5rO32TzftPrJw/YyH7YvjkT3gZ5F47j0LeqPEsoGxhBbXceOcB49LoVJrP46sdxAfLdPmfQs5I7QsGxhL7kJabl2SQVZ4drSFgBzJ+2mPW2HDNDsxVzx4XNCwlz/hZfXsXBLP6Z1O9CaJorUM1Ohw/zNu/bXBwZk9LgpU1FPvdROSRrr7i7K4rHY8Js6xUifOrwS1PTGFpz+jXFTLq6drBe9VCkVRKJFLx9Qm3plH5h6w89sFBy2KeP/BV8yjp2/VACb+YePELTcTGwWy8ayDdf8k8mZl7+B4pXPrGVjDyNp/Evn27wR63TFo3trTDvJZtAytZSYmUebdLVY+bx7EpnNO5rQMwqSTkFOZEPC9rVbiHL632IRbtAyrZX4kn0tW5PozCn21vPh3rQB4v2tdiVwETfXOSmOfewDHbxfwa+Gd/UVfMQ8Bfatim/gH7hO3CJzYCMfGsySu+4eAN71TpOtL58Y4sAaJa/8h4du/CeiVPGieY+1ptPksmIfWQo5JxPruFoI+b45z0zmC5rREMulQ5JTftfW9rchxDp/HtOEWzMNqPZLPJavJitv0ypOJ/HQyET8ddC77YFPRZiWu83+iL1QN/7pdgX+36dASBHWaCoD997k4jv+GX3nv7S/6iIoENO6Lbd1E3FdPEPjSRBxHN5J4eB0BDbyzhujzlcbYbCCJh9eSsOdbAhr2Slqf4/BatDnyYW45FDk+BusP7xLU5XOcRzcR9NocJIMJRUl5K6R1xXvICb5XSLU5wjG3HJbq+3LfOI/z7G6Cak170I9IEJ4480dMwh5rJTE+kRcHd1M7jvAInP/TRaFqeup2Td5Ph5bQ0Wmqdz/9+1w7x39zUL6Fdz8dUVFP474BrJto4+oJNy9NDOToRgeH1yXS4E3v/jhfaT3NBho5vDaRPd8m0LBX8n768FoHOfJpaTnUTHyMzA/vWunyeRBHNzl5bU4QBpOU6vHYivesJMT5/qbnCNfScpjvfrp4HQNlmxoBWDfJzpH1Diq0FD2Ts+LxmJCSOL8S1PbEFJ4aFjCy9GgCY3dYCQ3Q0rW8iXMxbhYeSsApK0QnygTojUntS+T0XoEO8deQz5z850NRyf/Ay4V433653Hq2X/Qdo+NMjJujN9zsueJ93Ob0vq5/9QCm77XjkRWeK+5H+Tx6n9eJQfgenLFhARKWHsU6dgfa0ABMXcvjPhdDwsJDKE4ZOToRY0Dy564tkRMATYg/2nzmpD8rh6KS2ujKeQcZ1ZfLjXP7RZ/1uc/E4D56A+eeKwAoNu93HtC/Ovbpe1E8Mn7PFUdfPo/P68QgfA8mK27TbUr40aaEH8uOJrD8Pwda2ZmxVEMSdi3F+vNYtIGhmOp2xX3jHAnbFqJ4nMj2aIyG5M9KG1YCAI0lBG1wvqQ/KxcOJbXRhXt7T+jDy+E8ud1nfe7rZ3BfPorzzB4AlEQbAAFN+2PfOB1F9uBX+Tn0+X17RNzLQOee2Ehsa8dhafM+kkEUGYXsp+eUkWpHEB6xUg2N7FqawM9jrQSGaqnb1cSNc262LUzA41SwR8sYApL302ElvPtmS4iG4HzJf75wKHk/HV5O9+//9Zzc7rufvn7GzeWjbs7s8T6eaPO+rmn/ADZOtyN7FCo/50f+8r776YwOdO5nSe4JU6aRgVM7nFQQM8ZnyeMxISVxfiWo7YkpPEkS9KjkrcSP32Vj71UXa/5JpGsFE2VD9Mz5086dxfE7B7RMa3DLv2+4Cbdo+fuGi4hA31slCgXrCLdoaf9vN0+Xx/vKsrl1VA3TE2X3MHqrlXktg31eJ6rxD4Ek4d+jEgC28btw7b1K4pp/MHWtgL5sCPY5f3KvX7b77xtowy24/r6BNsJ39h1doWC04RZM7b1dPRWXdyBCXdnc6KuG4YmyYx29leB5vkcnoiL/YLLaNu30KBj+vd3ObJBwekT3/SSShH/9HgDY1o7HdXYviYfWYKrbFX14Wey/zcH3m0zjy76jjfvK32hzhOO68jfanBE+q9PlLoQ2Rzim6u29r/K4vI+Hl0VfqCqeuCisP44muOs8n9dltMeTbLuF9acxmFsOSyqMCYIgZDWSBPV7ePfTa8fbOLvXxaE1idTtaiK8rJ7f5tjv9aebK3+7yRGu5crfLnJG+O6ncxfSkSNcS/X23v20x+V9YXhZHYWq6omL8vDjaCtd5wX7vC6jPZ4SbTJ+Zm/x6fyBlOvPrrLa8ZiQBnF+JajsiSk87brsZMWJRDSSdyyV8iE6EtxGJv5hp0CQFn+9RID+3k70Tt5ys/6MA1lRGFXHt4reupiR6XvtDNoUC0CxHDr6Vg1g9FYrTo+C0wPPl0jZPTej1fjzsW5m7IvnnxgPH2230bCggbYlxVVzAOeuyySuOAEaCUmvRVc+BGOCG/vEP9AWCELy1yMF6O++oDu4T97Csf6M9x7kUXV8njO2LoZ9+l5iB20CQFcsBwF9q2IdvRXF6QGnB7/nS6RYZkYr8u7zscTP2IfnnxhsH23H0LAgprYl7yl/VpTVtukvjyRw9Lq3wBFo1DC8lujtdJvz9C4S/1wBkgZJq0eXvzxGZwL2dRPR5iqAZPBHMt7b5+WOPInjyHoURcby3Cif54wVW2PfOJ3YbwYBoMtTjIDGfbH+OBrF4wS3E78qz6dYZkZ7PMVvmYdij8a+aaZ3fWUa41fp2XvKLwhC+t57tjcf/jz7sa1vx4pNLPt4Dp/v+99jW2dmd3qXkz9XJCJpQKuXyF9ehzPByLqJdnIV0GLwlzAG3Nt+OvKkmyPrHSiywnOjfPevFVsb2TjdzjeDvPvpPMV0NO4bwI+jrXicCm4nVHk+5X46oz2e9ixP5Nx+Fxot5MivpV43cZsdZL3jMXGOlTpxfiWoTVKUVG6izYDr87riijz5sPM8NgM3xvJuHTMh/uJqhz6sBCFvLk71ua7X53HSFfl4Az1ksQM3Yn63DtqQ7D2LSQl9GItD3kz1uSd9ewaxTd+W3vYM0HXldU7edD2+QI9A7LKBmFu/izYwRO0oqiqRS8/iNtn7MxBgT+IZbEqi2jEeusdZeHImOPhi2EQiz13io9VzHss6/8ss+VHDr0iaz6+83pWbrid7P71sYCyt3zUTGJK999O59CVoE7I41efE8VjWcdfjsSf8HEucXyVL7xxLSPbE9HgSBEEQBEEQ1HXz8jUWvjMVnV6Hoij0nDqSK6fOs3r2cjweD/6BZt4YPwSDn5Ep3UcRUaoIV89cxO10UfeFZvzx829Yb8bwxsShhBYMZ0r3UeQtGsG181dRFJnXxw0mMGdw0vo8LjfffDKX65ci8bjctBv0GkUqlmLtF99z+Pc9GP39KP90NZp0aXPf72njVz/RoFMrvh33xUP4hARBEARB+K9sW3ia1jRI7QjCYxI0ranaEYTHQGzT2UfQy9PUjiAI2daJvYcpXasirXt3BLwzIxUoU5RB8z8CYMW0r9i/YQe1n2sEQPGqZWk/7HWWfPA554+epv/s99m95nd2/vQrbQe8CkDh8iXo9E5Pdv60iY2Lf+KFwV2T1rfjp02ERITxyvt9sd6KZfbATxj+1Xj2rt3CyGWTMJr8kOWUs1TOHTwOe6zN57GQAnnp8n5fn8dib0Rz4dg/PNPzpYf2GQmpe3ma2E9nB+J4LHsQ51fCvcq2hSdBEARBEATh3lRpVodf5v/AwrenkjNvCK17v8TV0xdY9fky3C4XcTdj8QtIHk+lQJmiAOTIk4uQiLB//5ybU/uOJrUpUrFU0v8Pbt7ts77Lp85z5tAJju44AECC1Q7ASyPfZPkn85A9Huq1b06xymV8XpfRWffWzFlOyzfa38tHIAiCIAjCPXqshadIm4eJu+1Mbhx498YPwcCNsbhkeOcpM+EWLZ/utLE/0knNfIa7zoDg9ChM2m0j0iYTaJQYWduM2aBJs/0/0W6m7fXO+vB0AQMdSqc/iN3yvxPYftGJRoKB1QMokiPtr8LmlPl0lw2rQyGfRcuQGgHotWkP8rc/0sXCQ/EAtCvpR+NCRhJcCm//Hsf1eJmv2+RIN9vD4Im0YZ+4m8DJjR/5usB7nzEuGfM7T3lnV9gfSfxC79Trfu1KYmxcKM3XKopC/Kw/cZ+4iaTXEjCydrr3Kyf8eMI7OJ+skOPru3ftT1j+t3eKUY1EwMDq6Iqk/fnLNie2T3ehWB1o81kIGFIDSZ/2PfKpvU8lwUXc278jX4/PUL4HofY2/db6WLQacLgV2pc20biQMc3Xim36/nliI7Gvm0hgx8mPdD23xS4bCB4X5tbveGeuO7ef+G0LAfCr2g5jmbR/VxRFIX7zLNyRJ5C0egJajUx3LChP3DXsa8ejeFzo8pYmoFHvtNtGX8a2dgKggKIQ0PgtdHnTHshScTux/TIJOTYSyRSI+ZmRaIxp73vc1/7BvmEaKAqGkk9jqtEBgLj/vY3rwkFyDV6X5msF4XHRSBqe7/8KAF+9P5O/dx5kx4qNtO7dkSIVS/Hj1C99Jzq786ctjRmwzh45QZ4CeTl7+CShhcJ91pevaAHyROSlcZfnAHC7vGPUFalQklI1K3Dr6nXmDB7HO9/4/j5ltMfTtYuR/DzrGwBir0czb+gE3pw0PIOfRuYXG+lh3UQ7HSc/nv30soGxeFzQ+h0zOcK1nNvvYttC7/6rajs/yjROez994aCLVR9Zk2aba9DTn3yl0x7s+No/bjZM8+6nSz5toEaH9PfTu5cncGq7E0kDzQYGEFIk7f10ok1mzac2Eq0Kwfm0tBgSgDadAbZTe5/OBIX/vR2H9bpMz68f7X5a7eMxcY71mI7HxPlVcvssfH6VVWX5Hk9j6iUPbvdGJRMtrUY2nnXc5VWw/oyDsAAt7zxl4edTiXx/PJFuFdLeWKbvtTOilpl8Fg2DNsZRJ7+BcEvq/6AvxnnYfcXJzGaBXIyTmbrXxtQmaXdL/ebvROrkN9CqqB9fHIxn0zkHLYumPhOHR1aYtd/OlCaB+Gkl+m6IpXa4AX+9xLSmQXReGX3X9/6kMo+phzbEH8UjY5+1n8ApTZD8tMT23YChdjiSf+oHL659kcixDoJmNMO59yoJ8w9ifvupNNdjbFAAv+eKE/Paz3fN5LkYh3P3FQJnNkO+GIdt6l6CpjZJs33iN39jqJMfv1ZFif/iII5N5/BrWTTVtum9z6BpTYnuvPKu+Z5Ed27TU5sEotdK2F0yb6yJTbfwJLbpJ4u5zRi0gSEosgf75lkEdpqCpPMjdmlfDEVrIxlT/+5c5/YhJ8QS1HkGzrN7Sdg2H/Mzb6e5nvjf52Kq3Rl9wSpYV32E6/wB9AUrp9pW8jNjafchGlMQ7hvnsK+fQlDnGWku2/HXerRBYVhav0PiwZ9J3PM9/vW6pdnevnE65lYj0OTIR9yyQRiK10GbI5zAFz8lem7nNF8nCI/Tka37+G35GjQaDTqDnqKVS+OIT2TJB58TVigcvwB//Mz3NtjsxWNn+GPV78iyh+7jhvg8V7ddU5aP+4Kpb4wGIKJUYV4c+jpzBo/D7XTicrho8FLLFMvMaI+ngXM/SPrze8/2zlJFJ7W0GeMdQFz2KGyeZafTlEB0fhJL+8ZStLYBo3/aJ/ZFaxloOSz9wsVtG6fbaTXCTI58GpYNiqN4HQM5wlPfT9+66OHMbiedZwZy66LMhqk2Ok1Nez+9+5tEitcxUKGVH1u+iOfoJgcVWqa+n07vfb48LYi5nbPmflqcY2WT4zFxfpUtzq8ehwcuPM3+006Z3DrqFzDikRXeXBfL3JZBLDqcwLEbLmxOhWeK+dHmP9Nijttl45miRsrn0XMwysXGsw6G1TJzNsbNZ/vjURSFQKOGkbXN+OnubQrPtIT4a7lsTTkOQGoORrnoWMZbUa8bYWDMVitUSL2t06NgdynkD/T+CNYKN3DomivNH8UDUS6eCjcgSRIFgrTEJCp4ZAWtJvX3eTDKRcfS3ilE6+bXs+JkYpo/ipdtMmFmDf9n777jo6jWP45/Zms2yaYQIKEFkC4iRVBAELCi2CteLCB2BbEAdr3KvTbsnWvBLld/V2kCotgoCipNLEhvJrS0TbJ15vfHaoophLokfN+vly9h58yZZ3fZnZlnn3NOsjv6y0HH+g5+3RGmW8buLY9ZmcIXf8RxeH3c/TKxIiZ5V88g+eVTKX59GaFftmP5gsQNak3cWeWXxvQ9vAD3oFY4OzUktCSbwOy1JI7uSXhtLkXP/YBlWdiS3CTe3gsjbu9zoeZmH7aMRGzJ0SSEo2N9wr/uwNkto9L2oSVZuPo2A8DZPYOi576vtn9bvZovyRpanI2rdxMMw8CemYyV68eKmBj2yn/ZCS3Jxju4QzSWPk3xf7Syyi/G3X2eNVWbPtN//SpVFLJokVz96in6TJdXOOdFHE0Ox92uH5YZIW/i1SRf/jLFc18ntOUXrICPuM6DiOta/lcd3/SHcXcehLNpJ0IblhBYMZvEU0cT3raWos+fi36e/6zuMZx7v1S1mbMZW3IGNk/0wtHRpCPhrF9xNu9WafvQhiW42vYFwNmiO0WfPVdt/+Hs30v6crXpQ2hD1Ymnv2IAMOxOMKr+hfavWDzHDC7pu+Dj+6tsa4WDWIFC7PWaRtu37klo41LsqU2q3EckFo465ViOOqX8stk9Tu1Lj1P7Vmh7y6vjSv7815xQAG17HEHbHkeU/P2ES88kNb1+uX3/WtHO7nQw5J6KlYgjX7xvz55ANQ7UKnp7a86LhTQ53EG7fm7MiMXEq/O4/OVk5r5ezJZfQgR8Fp0HxdH1rPLfwdMf9tF5kJumnZxsWBJixewAp45OZNvaMJ8/Fz1Pe5JsDLo9EWfc3p+nczabJGfY8CRHvyubdHSQ9WuY5t2qPn+t/T7E2yPyaHiYnQHXJVQZRzhoESi0qNc0el5u3dPFxqWhKhNPGxaHaN07ep5Oy7RTlGthRixsVVS3bFgS4pjB0fN0mz5OfvzIX2XiaU+eZ03Upusx3WPtOd1fRdX1+yvZB4mngYe5eWVJEf0y3Sz8I0TXdCcOm8ElR3jwOOIJRiyGT89lUOuqKxHKemphIXcem0h6gp2PfvMzbZWf89uX/kMMRSxGz8mvsF+nBk6Gd9l3yznmByy8ruiXlNdlkBe0atQWwOs2yA9U196kvqf0g5HgNCgIWqRUcXItCJgklsRi22Xf3jLlqtH2NTsR7Ip74GEUvbIEd79MQgv/wNk1HcNhw3PJEcR7HFjBCLnDp+Me1LpG/RU+tZDEO4/Fnp6A/6Pf8E9bhef89iXbrVCE/NFzKuzn7NSA+OFdquzXzA9g87pK/m7zujDzq/4FxsoLlrQ3DAPLrPr13V1mfgBb/dJ/v0aCE6sgiJFSxa9mBQGMRFdJ3FY1ce/u86yp2vSZDoQtxszJZ11ehKu6Vt9Wn+ny3J0GUvT1K7jb9SO0diHOzK4YdgeeXpcQ7/JghYPkvjYc95GDatRf4adPkXjGndiT0vH/+BH+JdPw9Di/ZLsVCZE/aXSF/ZxNOxF/3PAq+zWL87HFeUv+bovzYhZX/PdScpzivJL2hmFgWbt4razS197YRd8lu5gRCj97Fk/Pf1Tfrjgf469Y4rxYxXk1agtguL1YNYhFRA49nQa6+fqVItr1c7N2YYjMrk7sDoNel3hweeIJBy1eG57LkYNqdp7+9KlCzrgzkaR0Oz9+5GfJND89zi89T0dCFpNGV/w+atrJyXHDqxk6k28S5y09f8V5bRTnV/2dnN7WwdVvpeBwGXzzWhHfvldM32GV91+cbxHnLT3Hur0GxflVn0uL800S65fG4k4w8BdYxKdUfp72F5i4E40ycVff9+48z5qqTddju0PXY+Xp/mr31cb7K9kHiaeWKQ5yAxY5fpNZawIM6Rj9RzBlpZ+5m6Lja3P80e1llf34l7nuZ11ehIfmR8fkh0zo0rB8iE67cUBWS0hyR7+oMgBf0CLZVfUvAkluA1+o9En4ghZJ7ura2ygo8yVbFCr/pfp3XrcNX9DC++dxdt136WvtC5okufe+2gnA0TIFKzeAmeMnMGsNniEdAfBPWUlw7iawGVg5fswcf/kdq3izI+vy8D00P/qXkImjS8Pyuznte7Rigi3JjVkQLPm76QviTKr6pGwku0raW5aFUc3Y7j2JxSoTi1UUwijzZVahvdeN5QtieN1YvhBGNXHv7vOsqdr0mXY7DJ4+OZk8v8m1M/Pon+mqcp4AfabLczRoiVWUi1mYQ2D5LDy9okO5/IunEPx9LthsWEU5mEV/Lx0vO0lLmc/z9nX4pj30519CODK7lN/L7tyj1ehsniRMf0HJ302/D6en6jksDE9ySXvLsjBs1VfClZ2Axgr4sFXT9199Fs54FFfrXrgOO7r6rj1JWP4CSM7ACvgwPFX/Ozc8SViB0vloou0PzFwdIrFUtipKaqZBSwdFuRaFOSbLZwXoNSR6nl48xc/vc4PYbFCUY1GU87eb4sq/vtm+LsK0h6LfP5EQZHYpf562O409WpHOk2TDX6YKxu8z8SRVff4qOwSv40luPn++sJq+DQK+0icR8Fl4kqo+l0ZjKdO+qHzi6u/ivDYCvmibmvVd8+dZU7Xpemx36HqsPN1f7b7aeH8l+2iOpxNbuPh4pZ/swght6jkoCJjMWBPg1dOSiVhw2ZTcCvt4XQbZhSadgF93hEseb5Fi554+XtL+zFaHIuWzo/sjG59dGCE9ofzNSed0J/M3BWlTz8G8TUE6p0e/WIpC0ZJNr7v05tZlN4h3GGwpiNAo0cZ3W4Lcdkx0fPq2ogj14mzlSjy7pDt5elEh57ePY3NBdGI9u80gYlrk+C3qx5e/ce7S0MG8zUEGHhbH/E1BuvwZS37AxGkz8JSZ7LBJoo0sn0lBwMTtMFixPbzLSpDd4TqxBf6PVxLJLsTRph5mQYDAjDUkv3oaRCxyL5tSYR/D68LMLoROEP51R8nj9hYpeO/pgy0teiK1QpFy++1pRt7WJBEzyxfNbrsdhFdsJ/6q6NCZSHYh9vSE8v11Tif4xXrcfZsR/jEbe9u06PEDYaziMLYqsud/F9lWhK1eXLkyT2eXdAqfXkTc+e0xNxdgJLkx7DasiImV48dWv/x74+jSkOC8zcQNPIzg/E04u6QD0ey74bRheEpPcNU9z71VGz7ToYiF3QY2wyDOYeCyR/8DfaZrynX4ifh//JhIfjaO9DaY/gICy2eQfMWrYEbI/c9l5WcABgyPFzM/G+hEOOvXksftDVrgPfMebIl/fn4ioXL77WnFky21CWZeFqa/AMPhJrx5BfH9rgIgkp+NPSm9fH/NOhP85QvcbfsSXv8j9oxoaboVCmCFirHFp5Rr70hvQ2jDEpyZXQiumk9cp4EAmIU5GO4EDEf5C5nCz57BltKkwhDESmPJ7Ezw9/k40tsQ/H0ezszO0VgCRVhWpFwll+FwYbjiieRuwZbciOCa70gceFuVr4vI3tixeStvPfB8ufmN9qcnht9NOBRi6LhRNMxsxK/fLWPK8+8AMODi0ysdpveX7PWbeXXsE2xdv4UbX7inwup1f/f8iHEU5RdiRiIcd+FAep15fLXtP534EUu/WIhhM7j4zmto0qZ5lW2L8gt5496nKczz0aBpBkPuvQ6Hs+obz4l3P82v3y2lY5+jSiY137rhD16/6ync8XF79foffqKLHz/2k58dIb2NA3+ByfIZAa54NRkzAv+5LPfvX994vAb52SZ0gqxfS8/TDVrYOfMeL4lp0XNVJFR+zz2teEptYiMvy8RfYOJwG2xeEabfVdH2+dkRktLLn6cDPhN3YjSG9YtDJZOMB4osrIhVrqrI4TJwxRvkbomQ3MjGmu+CDLwtep4u2BYhoZ6t3DC6Zl2czH66kO7nx5Gz2cSTZGCzG5gRi8IcC2/98ufpzC4Ofp8XpNPAOH6fHySzS/R9Ls43sTsNXJ7Svqt7nnurNlyPVUfXYzWj+6uq1aX7q0PdPkk8ndDCzUUf5TCsc/RNTnQZtEi2M3J2Ps2T7ZVmjwe1djNuno/P1wVolFj6hTSqRwIPL/AR+bMcb/DhHo5uXHrhvzfZ+P8sLmRRVoicYpNbP8/ngeMScdsN7vyygFcHpZRrO/AwN+O/9XHTp3kkug3u6BX9kpuzPkBRyKqwosKI7gk8tMCHZUXHK/819vjBuT4ePM5LcpkSz8wkO90znIz4NB/DgJt6RD+of/hMXvyxkH/1L/8r9+DDPTy8wMeMVQHSE21cekT02P/9xU/benaOyyzNwtptBtd1i+f2L6O/+F/YwUOCs/p5SHaH+4QW5Fz0EfHDojdQRqILe4tk8kfOxt48udIMsntQa3zj5hH4fB32RqUTRiaM6oHv4QVYkeivB57Bh+M6unHJ9j3NyBt2G/HXdaPg9i+j/V7YAVtC9AulYMwcUt44o1x7Z49GhBZuIW/kp9FVF8b2BCC0fBuh+ZtJGNm9XPvAnHUEpq/G3FFM/q2f47m8E84jG+J7cC7eB4/DSC79IrVnJuHsnkH+iE/BMEi4qQcA5h8+Cl/8kaR/9S/Xt2fw4fgeXkBgxips6Yl4Lo3OgeH/7y/Y29bDfVxmjZ7n3qoNn+mtRSaPLvBhs0EoApd38uCyG4RNS5/pGnIffgI5L15EfJ/ohNeGOxF7/RbkvzMSe1rzSitu3EcOwjd1HIGfP8ee3Kjk8YSTRuGb/jCWGb3A8RwzuFxF0J5WPBk2O/EDrqPgg+hEwZ6jL8Tmjr6+BZPGkHLVG+XaO1v2ILRmIXnvjPxzVbuxAIQ2LSe0ej4JJ44s1z6+31X4ZjwK37yGI6MdjszoxUXhnOfx9LgQR0bpnAqhDUsILJmGo0lH8tb/iM2ThPecB7AiYQo+vJOUK14t/1odMRDfzPHkvXMTRlxiySTngV/mYAWLSlatK3kNTxwRrRqzLFxt+2h+J6lTrnpsNKnp9TEjET58/HVGTfgnrjg3j112O52O605cQuXze6Q2rM9NE/7Jh4+9VqPjnH/bMNKbNyEUCPLg+TfR/ZQ+ON2V/xKevX4zK+b+yG0T/032us2896+XufmVB6vs+9OJH9G5/9H0PvtEPn7mbRZO/5reZ1e9utTZIy5h61kn8N0nX5U81jCzEVc+fCtvPfB8jZ5PVQ4/wc2LF+XQ58+haO5Eg/ot7LwzMp+05vZKK3SOHORm6jgfP38eILlR6Xn6pFEJTH/Yh/lnIuKYwR4OO7r0NdvTiieb3WDAdfF8cHv0/HX0hR7cCdHz16QxBVz1Rkq59is+C7J8ph+H28DjNTh1TPQ8/cucAMEiq8KqdSeOSGDaQ9HzdNs+pROLT3nQxzkPeolPLn0N0jLttOju5J0R+WDAyTdFzyO5f5h88WIh5/2r/PnumMEepj/sY9mMAMnpNo69NHrsRf/1k97WTrvjSs/T1T3PvVUbrsdA91h7S/dXh8b91aHOsCzr7z+I1Mi2CUMJZa3c1/HsUw/MLWB7kcnYXolVTkK3fGuI33PCnNuuZhOaPft9IZcc4SE1btdfNGHT4tFvfdzZ27vLtgCz1wZIcBr0blp1qWBZD833cesxCSVVHtX5a6lPoMJJxZnRlgZXT6x0v6HbJrAylFWjeGKp4IG5mNuLSBzbC3uTyl/vyLYi/B/8QsL1R9Woz+J3fsLZswmOVrteGtUKm/ge/RbvnVWv1lBWYPZajAQnrt5Na9Te99B8Em49BsO1i2FDULLcJ1DuxNLWmcHEBldXuk9t+DyDPtNlVfWZru7zDDB08jZW7ghVuf1gUDD5AUzfdhJPG1tl8iVSsA3/og9IOP76GvVZvOAdnK164mhY+WSSlcXgPeveGrUNbVpOOPt3PEedW6P2hZ89i6fXJdgSarbscv6HdxDJ2UzqVW+We7xtmpOJZzWoUR9Sdy30r8Fn+fm/JybS8sh2dDuxF5FwhIcuvpU73h3P1BffZ+3y3yguKOLYc0+i34UDy1U8vXHPMxx77om07no4Kxf9xHeffMWl993A5lXr+eCx17BMk8RkL5c/eBMuz54NN3hi+N0M+/coUtPrk7VuMx899QbXPXUnAO/962W6ntSL9kdXMbvwn8rGWROmafLPs2/k7g+fxumq/Kbhmw9nESwOcMKlZwLwwHkjuWvSk9gdlZ9fHr1sLDc8dw8JSYmsW76SLyfNYOi4m6qNo+xr+pfKKs4SjTiOjjusyn4mbxvKjtDBf56e/EABvu1mdLW5Kib6LtgWYdEHfo6/PqHS7X/32bOF9LrEQ0Lqrs/TkbDFjEd9nH5nzc7TK2YHcCcYtO5ds/P09Id8nHJrAo5qhm/9JVhs8eEd0fN02cRdmrMtZzWYWOk+uh47hK7HasE9lu6vysRSxf0VVH+PJaX2ScXTwerePrv+MurU0EmnhjXPYo7oXrOTJIDDZtT4CxHgpJa7d0F3R++aLTcL4HEemHHbseK9t88u29gbxNf4SxHAM+SIXTf6k+Gw1fhLEcB9UssatwWqXX60Qiwe5x79klEb6DNdqi5/pmuS8LF7G9Q46QSUzGG1L2P4i7NpJ5xNO9W4fcKJI3YrlqTzH9qt9nJo6nnmACY/+w7dTuzFz/MX07ZHJ+xOB6dedT5uTxyhYIhx599En3OqXnK6rPf/PYFh/xpFvUYN+PL9T/jm/z7lhEtKf9EOh0I8c23FoWKtunbgrBur/rwV5hYQ7y39rotPSqAwt6DK9ntq1qv/R9cTe1eZdALw5RaQ0qBeyd89iQkU5fvw1qv8u7Uwz0e8N3rOiE9K3C9x13Zn3bvrc6S3gb3GSSeIVjbVlN1h1DjpBNG5pHbHoDtqfp52efasUqw20PVYqTp9Pab7q9JY6vD91YFSpxNPIiIiIoeCJq2b48vJI39nLt9O/YKBw88D4Ov/zmTpF99hs9so2JlHwc7yqysaZSfYLzMr0B+rNzDx7qeBaJKpbffyNwsOp3OPJgZPSPFSVFA6kX5RQSEJKTW/gayJuf+bzZbVGxn271HVtktM8VKUXxqLv7CI+KSqbzgTkhOj8SYl7pe4RURE6qp9N/nPfpbli3Dr57FbWnrI5L+v7FS1icuKmL320F52MZLlI//Wz2N2/Jwhk2vcNrQkm/Dqyt/f3emnpsfyPfZthccDs9eSe+0Mil5dUuO+Ilk+gvM27cPoDix9pg8dkbws8t+/NdZh7JHwtrXkvnkd+R/eUWWbws+ewyzKxfT7CPw06wBGJ1Le0af146v3Z7Bjy1aatT+MwnwfCybP4ZZXxzHihfvweBPKJZcA4pMT2fnHdgDW/7Sq5PHGrTMZ/vAt3PLqOMa8+QinXze43H7hUIgnht9d4b/Jz71TbYwNm2WwY8s2CvN9hIIh1iz9jeaHR5cJ3/nHtho/V39hMYVlkkZ/+X7WXJbO+Y6hD47EZiu9zM3N3oEZKT/JbpvuR7B87g9YlsXW9VtISPZid9iJhCPkbt1Zoe+23Y9g2ZcLAVj21SLado+uPlWYV4C/qLjGsR9M8rIivH9r7M7FLw+peC727TSZ80LVK9otn+knULjny9hXdsxDma7Hag/dX1V9LN1fHfxU8XSQ+GuqrbK/PMqBEVqSjb2Jt0Zjjf/OipjlVlnYU4FP1+K9r2+5yQF3xcwqJDh/E65jazaOWQ4sfaZrP8uMEFo1n7guZxJ35KlVtks48UYgmmAL/PQp7iNOOVAhipTT49S+3HnKVZxx/cUAxHsTaHRYU8YPu5NGhzUjIblihc6x55zIa3c8yaIZX1O/SenKjBfdcTVv3PsskXB0VayTh55Dx2O7lWzf04onm93OeTdfzvM3RifxPumys/AkRidOfvb6f3LfR8+Va1+Y7+M/ox8ja81G/li9kQ69u3DWjUP4fuY3+IuKOfHS0hUmw6EQE+96mqbtWvD0tfcDMHTcTdRr1IBXbn+ca5+4ncTU0smFM1o0oUPPzowfeieGAYPviM7RsWNzNh8+MZHrn76zXCwnDT2HN+55hvkff05a44acdvUFAHz25mQyD29F1xN6lWv/8TNv8fOCJeRvz+Wpa+7jmsfHljxXqVpiPVu1Q/GWzwzQ/Cgn7r81MSNWuZXsREDXY7Gi+yv5u4M28fTKkiIWZ4dw2eHstnG0q1ca6px1Aaat8hOIRFcvGNMzgYgFD8z1kes3cdjg0iPiaZRoY9w8H047WBY8eJy33BKduyNswiMLfGwqiNAsyc7oYxLwBS3unxtdHSJswpieiWQml05OVhy2eGi+jxNauOiQ5uDJhYUEIhZOm8HYXonU89gYMjmHAc3d/Lw9RJd0J06bwcUdo5Pw3TArj3/385Jcg0n2Yq3olSWEFmeDy07c2W1xtCudMyEwZx3+aasgEMGemUTCmJ4QsfA9MBcz1w8OG/GXHoGtUSK+cfP46w3zPngcNu+eTWRKxMT35EIia3KxZSTgvetYLMui8OlFRFbnQsTEM/RIHO3TCMxcg+G245++iqSH+pdbUpOwie+RBUQ2FWBvlkTC6GMwswspuP8b7JnJEDGJv6YrhU8uxApEMJw2Esf2wlbPE90vqxCrMIjn0k64+zYr/5pNXIblD2Nv7CX8y3Z84+YRd05bzNwAwfmbsApDuI5uRPzwLpi+IL77vomuUGEYJIzqQfEHvxD+dQd5o2YTP7wzzk4N9+y1OkD0ma5dn+m9UfT1K4TWLwaHi7huZ+PIaFeyLfDLHPxLpkE4gD0tk4RTx4AZwTflAcyiXLA5iO99KbaURvimjgO7M/p9cO6D2OL2bFhLzgsX4WzRjcjOTdjrNSPh1NFYAR++j++PrsIXCZN42hjsaZkUzZ2ImZeN6c/H2eQI/EumYjjcRHasx9W6F0VzJ4IZwYjz4j3zXgynm7x3R5F4+l34F/2XcNZK8t4dRVy3c3C377dvXlCRGkpI9vL0t++X/N0wDK4aP6bStn9NbN3osGbcNemJCtubtG7OyBfv2ydxJdVP5dXbn+Cyf46gYWYjOvTqQodeXcq1yc3eQcc+FecJSUhKLDcJ91+2rNrAwKvOL/eYw+nkue8/qNA2EgpTv0l6uaTTX04Zdi6nDCu/MMC6n36nz7kV5/JISEqskIwCyMnewWnXXFTh8bNHXsrZIy8t99jWDX/w5r3PkNY4Nufsr18pYv3iEA4XdDs7jox2pefiX+YEWDLNTzgQXQXu1DEJmBGY8oCPolwTmwN6XxpPSiMbU8f5/vp65twHvcR59+y8FgnDJ4/42LkpQr1mdk4dnUB+tsmMxwoZ/HgSyz7xs3iKH1e8QaN2Dlr2cLF1VYTJ9/uo38JO70s9fHx/AWmZdswI9L0inpmP+7Ai4Io3GHR7Ip5k2y6PCTD76UK2ro5gRqDPUA+ZXZy8f2s+p9ySgLe+jUmj8zl3XBLe+rX7HK7rsdpzPab7K91f1SUHZeLpu81BtvgiPHdyEoZhEDEtthWVltT2auri+BbRD8w/vylg6dYwDeNtFARNnjk5OrmbaVnMXBOgeyMnQ4+M/rr09wX85m0K8sEvFUujh3eOrzAZ3o5ik0uP8NDYa+ff8wv4dkuIHo2cPNzfi9Nu8N3mIO/9XMzYP5cE3Vlscs9XBVzeyUOnhk4emFvApZ08HF7fyYLNQd5dUcyN3aNf5sc0dnJll3iKQhY3f5bHxR09/LYjTEaC7aD/QgQIfreZyBYfSc+djGEYWBETc1tRyXZXr6a4j28BQME/vyG8dCu2hvGYBUGSnzkZAMu0CMxcg7N7I+KHRle2+fv7FZy3ieIPfqlw/Mq+FMyd/uiXbf148kbNJrIhj8iGfPBHSH72ZMw8P3k3fkrKm2fgHngY9ibeSiekM3cU47n0COyNvRT8ez6hb7dgb5mMmVVI0vgTsCW6KHhgLp5LO+E8vD7BBZspfncFCTd2J2FkDwyPAzMvQP5Ns0u/GP9cocHeOrXkuQbmrCPxrmOxN4jHKg7jOb89lmWRf+OnRAb5MLf4sDWIJ/H2XiWvl+eCDgRmryVxdM89eNcOLH2ma9dnem8EV39HJGcLSZc8F/0+MCOYBaXDZ1yteuHucDwABZP/SXjjUmxJDTH9BSQPeQYAyzIJLJ+Js0V34vsM/fOxv30f/D6P4kUVbzDjjxteYaJvs3AHnt6XYk9pTMG0fxNa8y3OFj3wXvAwht1JcPV3FH/7HomDxkZ3sNlJOu9f0eOGA9hTm+DueBJWsJjki58EoPDLlwn8+gVxnQaWHCeux4VEtq8nafDje/EKitQ9Vz6y66G2KelpnH/rsBr3eeHYK2vc1u507HL1ubKOHrR7SePd6bthZiNumxibBQNWfxckZ0uES56LnovNiEXBttJzcateLjocHz0XT/5nARuXhklqaMNfYDLkmei52DItls8M0KK7kz5DKz8X/z4vyKIPKp6LjxseT9NO5c/FhTtMel/qIaWxnWn/LmDNtyHqtyxNMCyfFeDs+70kZ9ixTAvDZtCwtZ3T70okqYGdvKwIeVkmg8cn4U608b978jn2sngyuzhZMs3Pt+8VM+DahF0e04xAyA+XPJtMUZ7J2zfmcdWbKQy6PZGp//KRkGrQ94r4Wp900vVY7bke0/2V7q/qmoMy8bQmN0K3DGdJSaTdVr40cll2iEm/FBOxILvQpHeRSZd0J/0z3YybV0Ccw+DyTh4GZLp5e0Ux4+YVkJ5gZ2gnD84yqyUe29TFsTVcVjM1zkbjP5cLPaK+k435ETqkOXhqUSE5xSYhE+KdpXH+7zc/A1u5S75c1+RGmLA4+mVhWpCREP2ysxtwRIPo2xDvNGhbz8HS7BBz1gcY1DpuD169Ay+yJhdnt4yS9+vvpZGhZdkUT/oFIhZmdiFm7yKcXdJx98+kYNw8jDgHnss74R6QSfHbKygYNw97egKeoZ0o+4a5jm1a47JHW4N4bPWjJ0N7RiJmXoDIxnwcnaJLj9uS47AlurAKgtX3kxqHvXG0ysJ5RH0iG/Oxt0zG3jIFW6Kr5PkXTVgc3cG0sGUkYJkWRW8sI7xiO9gNIltL5yoILc7G1tRLwm3HVHrM4Ncb8H+yCjCI/OHD3FaEo3NDwr9sp2DcPGzJbjzDql92+mCjz3Tt+kzvjcj2NTibdyv9PrCVX6I2tHEZxQsngRWJVha17o0zswvu9v0pmDoOwxmHp/fluNsPoHjB2xRMHYc9KR1Pn6HR6qc/udoci6vNsTWKyRafij2lMQDOJkcQ2bERR6MOFH76FGZhDkRCGK7S4S+OppWvuBLevo7ib17DigQxC3Nwu2q+Ao+ISKxtXxOhebfSc/Hfh6VtXBZi4aRirAjkZZu07m2S2cVJ+/5upo4rwBln0PtyD+0HuFnwdjFTxxWQlG6nz1BP2a9n2hzros2xNTsXx6faSGkcPU80OcLJjo2RcomnE65PYN6bxYQDFh2Od1fab4OWdtyJ0XPwzo0mTTtFz8FNOzlYvaDidV5lxzQjlOwXn2wjLtGGv8AitYmd+i3s7NwY4bCja/acDma6Hqs912O6v9L9VV1zUCaeWqbYmb02wKBW7pJsfFkTlhTx6PFJpHls/POb6FK2wYjF6a3dnNU2jllrAnzwi58rOsdzVZfoh+ORBT4W/RGid5kvwd3Jxuf4TbJ8ETIS7fy8I0y/Zi5mrw3QJtXBkL4evt0c5INf/SXthx3p4YesEB+v9HN22zhaJNsZ0tFDmz/LWUOR0udUdszxmW3ieOunYrIKI4zqUTtuauwtUwjMXot7UKuSjHxZRROWkPTo8djSPBT88xsArGAE9+mtiTurLYFZa/B/8AvxV3Qm/qouAPgeWUBo0R+4epd+Ee5ORr7SOJslEfxmIwxqjZnnx/QFMbwucNgqxPwXM8dPJMuHPSOR8M87cPWLZtWNMidqe4tkPEM64mgTLX+1QhEiq3OIrMktyf7nDplS0t7ZoxGO1qn4Hp5P4theFU4kRa8vJeWNM8BpJ3/Ep9EHw2bJ8qNFbywn8OlaHG3rQaT8Z+Ngpc907fpM7w17/ZYEVszG3XlQScVTWUVfTSDpwkexJaZRMDk6fMYKB3F3Pp24rmcR+GkW/u8/IL7vFcT3uwoA3yePEFq7CFfr0mVvd6viqSiHSF4W9uQMwlt+xtWuH4EVs3Gkt8HTawjB1d/iL9PX35Nlfyle8DaePkNxNulI4Rcvwd8maTZsjgrPV+RgtGPzVt564PlKh7AdCPeecR0PTH2xRm1XLvqJ+OREmrZtsVf91PRY333yFZfed0O5x7+b/iVfvDO9ZH6pmtixeSubfl9H5/5H77P49lb9lnZWzA7QeZC7pOKprK8mFHHho0kkptmY/M/ouTgctOh8upuuZ8Xx06wA33/gp+8V8fS7Knou/uQRH2sXhWjdu/RcvDsVT0U5JnlZEZIz7Gz5OUy7fuWTFWnN7Zw2JpFw0GLCJbm0OdaF3WlglfmqLXtNVq+ZjY3LwjTv6mTT8jD1mlX8Pq/smFYEVn4TpPMgKMoz8ftM4rwG6xeHKMo1adDSzk+fBjji5D0conSQ0PVY7bke0/2V7q/qmoMy8dSziYulW0PcMCufOAec9bfxx6cc5ua2z/Npnmznr2rB7EKTx771YTOiY4VHdE9gweYgH/3mx2aA027QqUH5p7s72fj6HhvvrChmXV6Epl47PZs4WZcXHd+8fFuI5snlT2x2m8EdvRN54rtC/vtLMdd3i+fpRYUUh6MBn3yYm4GHVcy2t6nnYFtRhP6Z7lozCZ6rZxNCS7eSf8MsiHMQd1b5McjuUw4j/7bPsTdP5q83zMwujK4+8OcbljCiO8EFm/F/9BvYDAynvSR7XnKc3cjIV8Z5bFOCi/4g78ZZELFIuOEoDMPA1b0Rhc99T2jBZhLv6I0RV/rvxFbfQ/E7K4isy8Pe1IuzZxPM7PIrrcRf343CpxdhFUcnYHWffBju/plYYZO8m2bjaJ2KkVj+JBt3TjuIc+AbN4/Eu8pXbbj6ZpI34lPszZIwPNFYwr/tpOg/S8BhA9Mk8fbe2JLdFG0poOC+r/Fc3BFH+7Q9fm32N32ma9dnem+4WvUktHEp+W/dAM444rqdVW6OJ/cRp5A/6Tbsac1Lvw/ys/HNeAwMG0TCJJw4guCqBfh//AgMG4bdieNvyaTdqnhKrE/xgneIbF+HPbUpzlY9sW1fh2/qOEKblkdjqQF3h+MpnPEY9rRMDFc8xt9mtjUS62E43BR8dC/uI0/F1apXFT2JSE2t/P4nGmRmVJp42hUzEsFmrzyRvDu+m/YVVz52W7nJ13dlx5atLPtq0UGVeGrV08XGpSHeuiEfZxx0O6v8HE9HnOJm0m35pDUvPRdH51vy/fX1zIkjEli1IMiPH/kxbGB3GiWVQn/ZnYqnxPo2FrxTzPZ1EVKb2mnV00l+dunN6qdPFZL3R4RIGLqcGU36tO3r4pNHfWS0ddDt7PLn3f5XJzDrCR/zJoLTYzDojoqTCld2TIA1i4K8fWMeZgROuCGBolyLL18u5IJHknB5DN67JZ9G7R2kZe79v6lY0fVY7bke0/2V7q/qGsP6+0DPGto2YSihrJX7Oh4Bbvo0j/v6eqnnOTBjj50ZbWlw9cRKtw3dNoGVoawDEofsX22dGUxscHWl2/R53r8O5Ge6us8zwNDJ21i5I7Tf4ziY5Lw8hNRrql/ivTZqm+Zk4lkNdt1Q6rSF/jX4LH+1bSY/+za/LVqO0+Wi3+DTaN6hVUnF0/ez5vLNh7MIBYJktGzKpfffiBmO8MrY8RTk5GN32Dnt6gup3zid1+58EofTgWVZXPPk7SQk1XyloLLuOvVqOvbpxpbf15PWuCHD/n0zlmXx/kMT2LxyHZGIyRnXDaZ5x9b8e/CtuOJceOulcP2zdxEX7ynp586BV9H+mCPZun4L6S2acMl9N7Bzyzb+M/oxMg5rSiQc5pxRl/Pev14i6A/gcDm5/IGRJKWl8OZ9z7Jj81aKfUWcdvUFdDm+Z7mKp2kvvk/QH6BBsww+eupNMg5rSv/Bg/Dl5LH0y4X4C4s5vHdXzrpxCEX5hUwY/ShmOIJhGAy+82o+evot1q9YRXrzxpx54z9o3fXwal+TRCOOo+MOq/o93DaUHSGdp+uCNGdbzmowsdJtuh7bvw6q6zHdY9UZ1d1jSamDsuLpULW9yOThBT46pzsPWNJJRPYffaZFJNZ+mvsD2zZlM/qNh/8cXhUhJ2tHyfZOx3Wn+yl9AHhlzHh+/2EFqRn1Kcov5LbX/w2AaZp8O2UOHXp25vTrBgMVJ6hd+uVCPn9rCn9XWdIlf0cup111ISkN6/HE8LvJWruJ7HWbCRYHuG3iQ/hy8nns8tu5f/Lz9DrzeBpkZnDMoP4V+s7bnsOpV11Ag6YZTLz7aX765gcat8pkxx9buenl+/F4E3hl7OOcetUFHHZkO5Z//T0zX/0/LhwznItuvwq3Jw5fbj5PXHE3XY6PTiQbCYV56/7naNquJadfHH2ui2Z8w7B/jyI1vT6BYj/HDzkDy7J47PLb2bHlJLZtyiK1YRqXPziy5PU68ZIzKx22JyIHnq7HRGJPiaeDSP14G+NPqLjUr4jUTvpMHxzqYrWTSE1t+X0D7Y/uVGZC6fLDVn7/YQWz3/gYyzTZsWUbR/brQdvuR9Dt5GN57Y4ncXvcnHbNhXQ7+VhmvvJ/vHbHk9Rr1IDTr7sIh7N0mEPn/kfXeFhZSsN6pDSMDhlJa9yQwtwCstZtplXXDgAkpiYRn5RIUb6v2n6S6qXQoGkGAId1bkf2us00bpVJ41aZeLwJfz7/9Xz89FtANCGU1rghpmky/aVJrFn6Kza7nZ1ZpStvrly0nAaZjRhy7/WVHnPxZwuY99FnGEZ0Hqfc7B20Paoj65b/zmt3PEliipfTr7u4Rq+DiBwYuh4TiT0lnkRERETqqMatM/lu+lcce+5JJRVPZX38zNuMeP4ekhvU45Ux47GwCAVD9D3vJPpdOJBvp37B529P5czr/8HZIy8B4M37nuXn+Us4sl+Pkn52p+Lp7/OrWFhktGjC4s+/pc+5J+HLyaco30d8UiJ2pwMzXPkEtfk7c9mxeStpTRqydtlKup0UnVet7KSyjVo1Y+Dw82jWPjqMLRwKsXnlOjb/vr6kuuqeM64rad+hd1eatWvJG/c8w+UPjKiQqJv6wnvc99GzOFxOxg+9AwuLcCjMwOHnATD95Ul8N/1LMju0wgxrsQERERFQ4klERESkzjqi71H8/sMKHr3sdlxxLvpdFJ3j6S89T+/P09feT6OWTUvWadz5x1beuv95bDYbkVCYC8deyfKvv+eL96djs9lwuJwl1Ul/2Z2Kp8oc2f9oVsxfzGOX34EZiXDB6OEYhkGHnp354LHXWP7N9wx98CZcntJVxVIa1GPma//HH6s30DCzMUf07c7OLdvK9XvercN4/6EJBIv9WJZFzzMGcNRJxxIJh3li+F00bduSeG/5hQL6Dz4NV5yb1+54kmH/GlVuW5fjezJ+6J2kt2iC2xOdb2r9z6uZ/Mxb2B0OTNPk8gdHkpiSxLZNWUy47VFOHnoOLY5os8evjYiISG23x5OL5/zvfsLb1+3jcCQWHPVbkHru/ZVuuz/nf6wLbz+g8cj+0cJRn/tTz610mz7PdUd1n2eA+7/MYV1e+IDFI/tPi2QH9/dPjXUYEmM1mVxcDn67mlz8y5z7yQuvO3AByX6T7GhB/9T7K92m67G6Y5fXY7rHqjOqu8eSUnuUeLLMCIat9i4lKhVV9p5GLBO7oQn46pLK3lN9nuueqt7TiGlhtx38SwhLzek9FSWe6obqEk+mFcFm6Dxdl1T2nup6rO6p8npM91h1jt7TXdujV0dfinVPZe+pPjx1T2XvqT7PdU9V76kSFHWP3lORuk9Jp7qnsvdU12N1T5XXY7rHqnP0nu6aXiEREREREREREdkvlHgSEREREREREZH9QqvaiYiIiNRSCTYXmLGOQvZWgs0V6xBERET2mz1e1U5EREREYseyLAxD83zVFXo/RUSkrtJQOxEREZFa6GBOUliWxb333suNN95IKBSKWRymaXLbbbcxduxYDvbfWg/m91NERGRvKPEkIiIiIvvUV199xaRJkzj33HNxOp0xi8Nms3HBBRfw8ccfM2PGjJjFISIicijTUDsRERER2WfC4TBnnnkm9evX54033jgoKnmuu+46fvvtN2bOnInLpfmUREREDiRVPImIiIjIPvPhhx+yevVqxo4de1AknQBuu+02srKyeOutt2IdioiIyCFHiScRERER2Sd8Ph/PPPMMZ511Fh07dox1OCVatWrF4MGDefHFF8nJyYl1OCIiIocUJZ5EREREZJ/4z3/+g8/n4+abb451KBXceOONmKbJCy+8EOtQREREDilKPImIiIjIXvvjjz94/fXXGTZsGI0aNYp1OBXUq1ePa6+9lnfffZd169bFOhwREZFDhhJPIiIiIrLXnnrqKRITE7nqqqtiHUqVLrvsMho2bMjjjz8e61BEREQOGUo8iYiIiMheWbFiBZMnT2bEiBEkJibGOpwqxcXFcfPNN/Ppp5/y/fffxzocERGRQ4JhWZYV6yBEREREpHayLIvLL7+c7du3M2XKFBwOR6xDqpZpmlxwwQXYbDYmTZqEzabfYUVERPYnnWlFREREZI99+eWXfPfdd4wZM+agTzoB2Gw2br/9dpYtW8aMGTNiHY6IiEidp4onEREREdkjoVCIM888k4YNGzJx4kQMw4h1SDV2/fXX8+uvvzJjxgzcbneswxEREamzVPEkIiIiInvkgw8+YO3atYwdO7ZWJZ0ARo8eTXZ2Nm+99VasQxEREanTlHgSERERkd3m8/l49tlnOfvsszn88MNjHc5ua9myJYMHD+all15i586dsQ5HRESkzlLiSURERER224QJEygqKmLUqFGxDmWP3XDDDViWxQsvvBDrUEREROosJZ5EREREZLds2bKFiRMnMmzYMDIyMmIdzh6rV68e1157Le+99x5r166NdTgiIiJ1khJPIiIiIrJbnnzySRITE7nyyitjHcpeu+yyy0hPT2f8+PGxDkVERKROUuJJRERERGrsp59+YsqUKYwcOZLExMRYh7PX3G43t9xyC5999hmLFi2KdTgiIiJ1jmFZlhXrIERERETk4GdZFpdddhk7d+5k8uTJOByOWIe0T5imyYUXXgjAf//7X2w2/TYrIiKyr+isKiIiIiI1MmfOHBYuXMiYMWPqTNIJwGazcfvtt7N8+XKmT58e63BERETqFFU8iYiIiMguhUIhzjjjDBo1asRrr72GYRixDmmfu/HGG1mxYgUzZ87E7XbHOhwREZE6QRVPIiIiIrJL//3vf1m3bh1jx46tk0kngFtvvZWtW7fyxhtvxDoUERGROkOJJxERERGpVkFBAc8++yznnHMO7du3j3U4+03Lli25+OKLefnll9m5c2eswxEREakTlHgSERERkWpNmDCB4uJiRo0aFetQ9rvrr78ewzB4/vnnYx2KiIhInaDEk4iIiIhUafPmzUycOJErrriC9PT0WIez39WrV49rr72W999/nzVr1sQ6HBERkVpPiScRERERqdKTTz5JUlISV155ZaxDOWAuvfRS0tPTGT9+fKxDERERqfWUeBIRERGRSi1btoypU6dy0003kZCQEOtwDhi3282tt97K559/zsKFC2MdjoiISK1mWJZlxToIERERETm4WJbFpZdeSl5eHh9//DF2uz3WIR1QlmVx4YUXEolE+PDDD7HZ9HutiIjIntAZVEREREQq+Pzzz1m0aBGjR48+5JJOAIZhMHbsWFasWMHUqVNjHY6IiEitpYonERERESknFApx+umn06RJE1599VUMw4h1SDEzYsQIli9fzsyZM4mLi4t1OCIiIrWOKp5EREREpJxJkyaxfv16xowZc0gnnQBuvfVWtm3bxptvvhnrUERERGolJZ5EREREpER+fj7PPvss5557Lu3bt491ODHXokUL/vGPf/DSSy+xY8eOWIcjIiJS6yjxJCIiIiIlXn75ZQKBADfddFOsQzloXH/99djtdp577rlYhyIiIlLrKPEkIiIiIgBs2rSJN998k+HDh5Oenh7rcA4aqampXHfddUyaNInVq1fHOhwREZFaRYknEREREQHgySefJDk5mSuuuCLWoRx0LrnkEjIyMnjsscdiHYqIiEitosSTiIiIiLBs2TKmTZvGTTfdREJCQqzDOei4XC5uu+02vvjiC7799ttYhyMiIlJrGJZlWbEOQkRERERix7IshgwZQkFBAR9//DF2uz3WIR2ULMvioosuIhwO8+GHH2Kz6TdcERGRXdHZUkREROQQ99lnn/HDDz8wduxYJZ2qYRgGY8eOZcWKFUyZMiXW4YiIiNQKqngSEREROYQFg0FOP/10mjVrxquvvhrrcGqFkSNHsnTpUmbOnInH44l1OCIiIgc1VTyJiIiIHMLef/99Nm7cyJgxY2IdSq1x6623smPHDt54441YhyIiInLQU+JJRERE5BCVn5/P888/z3nnnUe7du1iHU6t0bx5c/7xj3/w8ssvs3379liHIyIiclBT4klERETkEPXiiy8SDAYZOXJkrEOpda6//nocDgfPPvtsrEMRERE5qCnxJCIiInII2rhxI2+99RbDhw+nYcOGsQ6n1klJSeH666/ngw8+YPHixRx33HFs3Lgx1mGJiIgcdJR4EhERETkEPfnkk6SmpnLFFVfEOpRayTRNBgwYQOPGjXnqqafIzs5m/fr1sQ5LRETkoKPEk4iIiMghZunSpUyfPp1Ro0YRHx8f63BqpZ9++olTTjmFpk2b8u233wJQXFwc46hEREQOPko8iYiIiBxCLMvi4Ycfpl27dpx99tmxDqfW6tSpEw888ABLly7F6XQCUFhYGOOoREREDj5KPImIiIgcQmbPns2PP/7I2LFjsdvtsQ6n1jIMg4suuogpU6bQqlUrAH755ZcYRyUiInLwMSzLsmIdhIiIiIjsP6FQiFtvvZVRo0Zx7bXX0rx5c/7zn//EOqw6IxKJ8Pzzz3PuuefStGnTWIcjIiJyUHHEOgARERER2b927tzJrFmzSElJYePGjTz//POxDqlOsdvtjBw5MtZhiIiIHJQ01E5ERESkjvtr7qHp06dz7LHHcvXVVzN16tQYRyUiIiKHAiWeREREROq4v1ZbKyoq4ptvvqFNmzb06dMnxlGJiIjIoUBD7URERETquKysLCA6JOyBBx7g/PPPxzCMGEe1+0zLwlYL45aq6T0VEan7lHgSERERqeM6depE165dGTduHK1bt451OHvMZhh8WfwLuWZRrEORfSDFFk9/T4dYhyEiIvuZVrUTERERkVrj48If2GH6Yh2G7ANptkTOTjgq1mGIiMh+pjmeRERERERERERkv9BQOxERERGRGpj9yLts+30TRw0+gXYnVqzUyduyna2/b6JNvy77LYbfPvuBb16aTN6W7dw6/4WSx79+/iN+nvEdqZkNueiFWyqNbeKQcTRs0xSA7kNO3K9xioiI/EWJJxEREanVTCuCzbDHOgzZhw7W93Tttz9z9Ufjqtyet2UHq75eVuOETsgfxOFyYNhqPgihWfe2DH3nHl676P5yj3e9cABHntWHWf9+q8p9MzpkVpqUEhER2Z+UeBIREZFazWbY+TLnfvLC62IdiuwDyY4W9E+9P9ZhVPDpw+/g25rDe1eP56Tb/8Gitz4ld8t2Ar5iel95Om0HdGXRO7PJ+nkd7109nr7Xn0XTLm0q9GNGTDYs+pWfZ35H7pbtXPDMTTjjXDWOIz7FW+nj3gYp5G3ZXu2+W1du4t0rHyWpURrH33Ih8amV9yUiIrIvKfEkIiIitV5eeB07QitjHYbUYSffPoR13/7MxRNuA+CEMRfj8rgpzvXx7tWP0XZAV3oMOYkVM77j1Hsuq7D/9jVbWPbxXLJ/3UCzo9pyzNBTSWuRAUAkFOa/NzxVYZ+mXVrT9/qz90n8CfWTuXryv3EnxLFs8ly+eOpDBv1z2D7pW0REpDpKPImIiIiI7AbLNJk/YSqbl63GZreTn7Vzl/tsWPQra+Ytp/O5x9H+5B54G6SUbLM7HSUJrf3F4XLicDkBOPzUY/j+nc/26/FERET+osSTiIiIyAGQvSrMuu9DHDPYU2Wbz54rpPclHuJT9mzh4fytET55pJBIyKJRBwfHX5dQoc26H0J881oRAEedG8fhJ7j36FiHsq0rN7Ft1WaGvDqWotwCJpx1FxBNIFmRSKX7dLvoeI48py+rv1nG54+9T8gfoO2AbnQ681gs09zvFU8BXxHuxHgANnz/G6nN0/dJvyIiIruixJOIiIjIAZDe2kF66+ovvU68sWKiaHd8+XIRvYZ4aN7NyZQHC1i/OETzrs6S7WbEYs4LhVz8RBKOOIO3b8ijVS8X7nhjr457qKnXIp1IOMK7Vz9GettmxHmjCZ36rZuQu2kbH495iWMuH0ijji3K7edwOWl3wlG0O+Eo/AVF/PbZD0RCYZxxrhpXPG34/jcWvDYd37ZcJl3/RLSC6sTufP/e5/z22ffsWJfFpOuf4KSxQ6jXPJ2pd7/CGeOuZMP3vzHvP9NwJcThcDo45a5L9/XLIiIiUinDsiwr1kGIiIiI7I3J24YeNHM8WabF9Id95GWZNGxlZ9WCENe9n8qGJSFWzA5w6uhEpj/sw+4A304T33aTM+/1Uq+pnXdH5XH6XYkkNdizFd3+c1kOV72ZCsAvXwTYvjZC3yviS7bv2BDhy5cLOe9fSQB8+qSPdv3cNO/mrLS/WEhztuWsBhOr3P5x4Q/sMH0HLiDZb9JsiZydcFSswxARkf1MFU8iIiIi+9Dv84LYXQZDnkkmZ3OEpdMDlbarl2ln4G2JLJ/pZ9knfvpfXXm1U352hGkPVUy0tOvn4qhzyg/bK/tzYpzXoDjfLLe9ON8kzmsr08ZWoY2IiIjIvqTEk4iIiMg+tHOTSeMO0Uus1CZ2PMmVz9fUqH20TXKGnU0/havsLyndzj+eSq7RsY0yI+YCPgtPUvlje5Js+AtKE01+n4kn6eCpdhIREZG6Z89mrhQRERGRSqU2sfHHr9FEUu4fEYrzKq8oKpskopqJD/KzI7w7Kq/Cfz98VFyhbXobBxuWhABYNT9IZpfySaXUJjbyskz8BSbhoMXmFWEy2u3ZsD6B7N828t2bs6pt8/njkyjKKdjjY+Rn7WTS9U/w7pWP8sVTH1TZLhIKM+Hsu5j/yvRyjxfn+nh6wChWfPJtucd3rMti/DHXsmnJ73scm4iISE2o4klERERkH2pzrIuV3wR5Z2QeDVraSUzbu9/5dqfiqd9V8cx41Mc3r0FGOweZXaOXemVXyxtwXTwf3B5NhBx9oQd3gn6H3FPp7ZqR3q5ZtW1OuPWivTrGV8/+j57DTqN5j/ZMvesV1n//K827t6/Q7sf/fkFay0YVHp//6nSadmld4fF5E6bS7Kh2exWbiIhITSjxJCIiIrIP2ewGp41NxO4wyNkcYcsv0eqnzC7OkgqkQbcnlrQv+3hNE0xVSc6wM/iJin2UXS2vZXcXLbu79uo4hxrLNPnk/onkbdlOgzZNWT13OddOfYgN3//Gihnfceo9l/HJ/a9jczoo3J5HwdYcznzoauplpvPe1eM5/cHheNNT9+jY2b+up3mPKwFo078LG77/rULiyV9QxPqFv9DuxO4UZOeUPJ6zcSv+vELS22eWa7/xx5UkN07D4dKtgIiI7H8624iIiIjsY1Me8FGcZxIstjj++sonDZfa4/evlmJ3OfjHK2PI2bSVZZPnVtourXkGA++6lOVT57N88jz6jTi30nb5WTuYfu/rFR5vd0I3ul10fLnHyi5AHeeNx59XWGG/Ba99wjGXDyR38/Zyj899aQp9rjuTFdNLh9lZlsW3r8/gjH9fxZzHJ1X9pEVERPYRJZ5ERERE9rFzHvDGOgTZh3LWZ9P4iMMASG3aEE9KYqXtMjq2ACC5URqbl66qsr+kjDQunnBbjY5tlJkMzO8rIi65fCIzb8t28jZvp1m3tuUST5uXrsaTnEBq04bl2v8yaxHNj+5AnDe+RscXERHZW0o8iYiIiBzEXh6SwzXv7Nkwrd2xaXmIWU8WkrMpwtXvpJDUQJOO/yU1syFrF6zgyLP7kLt5O8W5vkrblZ0w3qpuwvjdqHhq2C6TDT/8RuZR7Vj11VI6nXlsue3Zv27Atz2XD0Y8TcHWHCKhMA1aNyY/aydbV23igxFPk7NxK06Pm5SmDdi6ciNZv6xn/cJf2LZqMzvWZXHGuCtJadqg5i+IiIjIblDiSURERERo0NLOpc8n8+Ed+bEO5aDTul8XVn6xmHevfJT6rZuQWH/v5uLanYqnfiPOZcYDbzD3xcmkd2hOZvfohOCfPz6JXlecRtvju9H2+G4ALJ86n4LsHNr07wrAUYNPAGDuy1NIbdaQJke2osmRrUr6/uT+1zny7D5KOomIyH5lWFZ1v8eIiIiIHPwmbxvKjtDKA3rMvKwIU8f5sDuj1S3nPuhl25oIcycWYUYgzmtw5r1enG6Dd0flkd7GwfZ1ESIhiy6nx7F8VoCiHJOz7vNSr5mdd0flUb+FnZ2bTDAtzrjHS0KqraTiKRK2+PSpQnK3RIiEYMC18TTp6GT+W0Wsmh/E6TFo1dPF0Rd69up5vTsqj9PvSoxZxVOasy1nNZhY5faPC39gh1l5xdH+FAmHsTsc5GzcypQ7JnD523cf8BjqmjRbImcnHBXrMEREZD9TxZOIiIjIHlj/Y4gW3Z30GRqdK8eyLNLbOrj4yWg1zJcvF/LrFwE6DYwDoFlnJyfckMCMx3z88VuYix5LYsXsAMtm+Ol/dXTensYdnJw8ys2yT/wsnFTMgGtL5/NZ9kmA1MZ2Tr0tkaJck/+7q4BLn09mxWdBLn8pGZfHwDIr/p740b0FFOeb5R5LbWLn1NGVz1MklZtyx38ozvMRKgowYNQFsQ5HRESk1lDiSURERGQPtB/gZsHbxUwdV0BSup0+Qz1sXxfmm9eKiQQtCnNMXAnukvYZbaMVRN4GNlIal/55w9LSZFGTIxx//t/JyrnBcsfbtibM5hVh1iyMPu73Rfc7aWQCs58uxIxYdD0zjqadnOX200Tn+8Y5j10X6xBERERqJSWeRERERPaAYUC/q6LVTp884mPtohBLp/vpM9RDk45OvnipEMoWIBnl9y1Rps2Wn8OkNrGz5ecQ9ZqVH+pWv4WD1CZ2elwQHUoXCUV3bNLRQYujnORnR/jfPQUMnZBSbj9VPImIiEgsKfEkIiIisgdWLQjy40d+DBvYnQZNOzkIFruZ8VghaZl2XPEG7gRj1x2VkbUyzPJZASzT4sy7y1cqdT7dzeynC3nv5jwAGrZ2cMINCfzvngIiQYtwELqdHVehz5pWPG1fH+azZ4rYujrC1Ad9tB/g4qhz9m6+KNk9E86+i6s//td+P86yyXNZPmUehs2GO9HD6Q8Ox52o91pERPYPTS4uIiIitV4sJhff12I9qffB4mCdXPxAOFCJp0gojN0Z/f35m5cmk1AviW4XDtjvx/07TS4uInJoUMWTiIiIiEg18rZsZ9rdr2Jz2sGCc8Zfx7ZVm5n78hSsiInbG8+ZD12NM87Fe1ePp2G7ZuxY+weRYJjO5/blp2kLKNqZz5kPXUO95um8d/V46rdqzM712ViWxRn/upKEekklx4uEw8x++F1yN20jEgrT/6bzaXJkK+a/Mp1VXy/FFe+mVZ8j6XHJSXv0fP5KOgGEigLU7954r18jERGRqijxJCIiInIQ+MdTybEOQaqwftGvND+mA32uORP4cwXDDs25+OXbAPjymf/jt8++54jTewOQeVRbTrj1ImY8+CZ/rFjHhc+NYsUn37J8yjz6jTgXgEYdW3LS2H+wbPJcFr71KQNuOr/keMs+nkdK0wYMvPsyinIK+N8tz3PJ67fz88zvuOytu3B53Fimyd99NPpF/HmF5R5LadaQU++5rELbxR9+yeL/fokjzkXPYafumxdKRESkEko8iYiIiIhUo/1J3fn29RlMvfsVkhulcezVZ7Bj9Ra+efFjwqEwRTvycSeUzq+V3r45AN6GKaQ2bfDnn1PZ+GPpcNAmnVuV/P/3L5eUO9721ZvZvHQ1a+evAMBfUATASWMu5rNH3sWMmHQ57ziadmlTbr/dWXmv6/n96Xp+f76dOIOFb86if5nEl4iIyL6kxJOIiIgcsvKyIsx4rJDBjyftuvE+8O6oPCIhOP3ORFKb2Fn3Q4hvXosmFY46N47DT3BXue/OjRGmPFjAzo0RLnw0iaadnNUea+vqMJ8+VYhlQbvjXBx9YfWTR3/3fjG/zw1i2ODkUQk0OKzqy0S/z2T6Qz78BRYpje0MvDUBu7PqidQre57BYosP78inYJvJNe+kVhtbrBk2g+NuOAeAGf+cyNoFK1j28VyOveZMmhzZii+f/pCys6YaRuVLGJZts2X5GlKbNWTL8rXUa55e7nj1D2tMStMG9BgSHUoXCYUBaHzkYTQ/ugP5WTv46LYXufztu8vtV9OKp3AghMMd/fcT540nHAjtxqshIiKye5R4EhERETmAzro/OoG4GbGY80IhFz+RhCPO4O0b8mjVy4U7vvIEjreBjcGPJ/H580U1Os7spws5bWwiqY1tvHtzPm2OdZHapPKJy3dujLDmuyBDnk1i50aTT5/0cfGTVQ/9++49P22OdXHkaXF89Z8iVnwW4MhTK66oB1T7PP/xVDIvD8mp0fOJpdXfLOfHSXMwbDbsLgdNu7QmWBRg5oNvUq9FBu6EOFwJu7cqXPavG/hp2gIs0+SMf11VbtuR5/Ths0fe471rxgOQ3i6T42+5kI9ue5FwMEQkEKLrBf0r9FnTiqf5r0xj89LVAMQlJ3DqvZfvVuwiIiK7Q4knERERqVPmvFhIk8MdtOvnxoxYTLw6j8tfTmbu68Vs+SVEwGfReVAcXc8qnyiZ/rCPzoPcNO3kZMOSECtmBzh1dCLb1ob5/LkiLMvCk2Rj0O2JOOOqru6pqZzNJskZNjzJNgCadHSQ9WuY5t0qr2Ryxhk1Pm44aBEotKjXNJpoat3TxcaloSoTTxsWh2jd24VhGKRl2inKtTAjFjZ75cfbsCTEMYO9ALTp4+THj/xVJp5293kejNqf1J32J3Uv99jhA4/m8IFHV2h78YTbSv7815xQAJnd25HZvV3J33sMOQlvevlKr79WtLM7HJxy16UV+r7wuVF7FP/f/VW9JSIiciAo8SQiIiJ1SqeBbr5+pYh2/dysXRgis6sTu8Og1yUeXJ54wkGL14bncuSgqoe1lfXpU4WccWciSel2fvzIz5JpfnqcX1rdEglZTBqdX2G/pp2cHDc8vsp+i/NN4ry2kr/HeW0U51ecMHpPFOdbxHlLk0Zur0FxvlVNe5PE+qWxuBMM/AUW8SmVJ578BSbuRKNM3NX3vb+ep4iIiBz8lHgSERGROqVBSwdFuRaFOSbLZwXoNSSaJFo8xc/vc4PYbFCUY1GU87fkR5kcS9m5eLavizDtIR8AkRBkdil/+WR3Gnu0Ip0nyYa/oDQGv8/Ek7RvqoA8SQYBX+mTCPgsPElVV0tFYynTvqh84urv4rw2Ar5om5r1vX+eZ21VtipKRESkrlPiSUREROqcw0908ePHfvKzI6S3ceAvMFk+I8AVryZjRuA/l+Xy9xodj9cgP9uETpD1a7jk8QYt7Jx5j5fEtGjVTiRUfs89rXhKbWIjL8vEX2DicBtsXhGm31XR9vnZEZLSKx8W93eBIgsrYpWrKnK4DFzxBrlbIiQ3srHmuyADb0sEoGBbhIR6tnLD6Jp1cTL76UK6nx9HzmYTT5KBzW5gRiwKcyy8ZaqhIJp8+31ekE4D4/h9fpDMLtFEUnG+id1p4PKU9l3d8zxQ8rZsZ+a4N7nohVsOyPHeu3o8kVCYQQ9cQWqzhqxf+AvfvDgZgG4XHV/pEL2/WJbFF09+QNbP67C7HJx237AKQ/LKmvmvt9j2+yYA2vTvQs+hp1Yb24pPvmXxB18C0PeGs2nevX2VbcPBELPGvUXelu3EJScw6J/DcCdW/d59/fxH/DzjO1IzG5a81sEiP/836jkKtuaUDCUUEZFDixJPIiIiUuccfoKbFy/Koc+w6E2yO9Ggfgs774zMJ625vdIKnSMHuZk6zsfPnwdIblSa9DlpVALTH/ZhRqIJp2MGezjsaFfJ9j2teLLZDQZcF88HtxcAcPSFHtwJ0QTPpDEFXPVGSrn2/gKTj+/3sWN9mO3rwrTs4eK44fH8MidAsMiqsGrdiSMSmPaQD8uCtn1KJxaf8qCPcx70Ep9c+hqkZdpp0d3JOyPywYCTb0oAIPcPky9eLOS8f5Vf9e+YwR6mP+xj2YwAyek2jr00euxF//WT3tZOu+NKhzFW9zzrsrMevgZveipmxGTOkx8w+MVbcMS5eOeKh2nV90jcCZXPibXuu58pzvXxj1fGsPbbFXz9wscM+uewKo9z9CUnU695OpZp8s7wR2l34lGkNm1YaVt/QRGL3v6US16/g2Cxn/9e/ySXv303hq3y9+OnaQtIapzGoAeuYMn/vmbRO5+Vm7fq77peOIAjz+rDrH+/VfKYKz6OiyfcxoSz76pyPxERqduUeBIREZE6x5Nk45YZaSV/NwyDs+/3Vtp28OPRpEr95g6G/SelwvYGLR1c9FhShcf3REI9G1Mf9EVXm2tip2V3Fy27u8q1KdgWodUxFYeixXltJbGWtX1thF6XVFxRLb2Ng0ueLZ8Qi4QtkjNsxCdXTDT0vNhDz4vL9/PHL2E6D6qYIInz2ioko6Kxm/S+rGIslT3PYLHFh3fk422wZ0moL576gMadDqPdCUdhhiO8cck4Lnv7Lua9PJUty9fgLyii8zl96Xp+/3L7fXL/6xx5dh+admnDhu9/Y8WM7zj1nsvYtmozc56YhGVaeJITOO2fw3DG1WwesOrkbNxKcqM0PCnRirPGR7Yi6+d1NO9ReaXRhu9/o82ArgC0OOZwPh8/qdr+6zVPB8Cw2bDZbdiqSCIB/PHTWpp1a4vD7cThdpJYP4W8LTtIadqgyliOuewUIFpNNXnsy3BN1bF4G6SQt2V7tfGKiMihR4knERERkQPkrHsrT36V5W1g5/jrE2rc54kjat7W7jA4/c5dx/CXjiftXuJl0B2JNW7r8uxZpdhfjji9N18//xHtTjiKtd+uILNHe+wOBz2vOA2Xx004GOL1wf/kyLP71Ki/2Y+8y+kPXkFSRho//vcLlv7vG7r/48SS7ZFQmP/e8FSF/Zp2aU3f68+usl9/XiFxSaXD0+K88RTn+apsX5xXSJw32t4wDCyzZhOxr/jkW7zp9UhuXL+avn24vaWxuJPiKc4rrDLx5M/zlcQel1R93CIiIlVR4klEREREap0GrZtQnOujcGc+P01bUDK30ZIPv+L3Lxdj2G0U7SygaGdB+R2N0iGGVpmZvrav2cL0e18HokmmZke1Lbeb3enYo0nB45IT8OcXlfw94CvGk1x1gs6TnIC/INresixs9l3P9bVm3nJWTF/AuU/cWG07T3IigYK1pbEUFOFJrjpxGZeciD+/iOTGECioPm4REZGqKPEkIiIiUoW8rAgzHiusdIjbgfDykByueaf8xNK+nSYL3y+usipq+Uw/bfu69ngepcqOebA6fODRLP7gS/K27CC9fSb+/EKWT53HsPfuwwxHeOW8e/j7LPJxSQnkZ+0EIGvFupLH67dqzBn/vpLE+ilANPlU1p5WPKU2a0jeHzvw5xfiiHOxeelqjrvhHADys3aQlJFWrn3mUe345dNFtB3QlQ2LfiWjQ3MAQv4goeIA8anlK9Y2/riSBa9+wvnPjMDhLh2iWbgzH3eCp9xjjY5oyVfP/Y9wMESoOIhvWy7JjdOqiaUtq75eSnr7TFZ9tbQkGRco9GNFIsQl1bzaTkREDl1KPImIiIjUIon1bNUOxVs+M0Dzo5y4/9bEjFjlVrKrCzoMPIaXBo3l2GujE167vfHUb9mId698lLSWjYirpJqn89l9mHrXK/w8cyEpZYalnTTmH3xy/0TMcASAoy87hcN6H1GyfU8rnmx2GwNuOp8Pb3oWgB6Xnow7MToP1n9vfJorP3ygXPsWPQ9nzfyfePfKR7G7HJx671AANi9ZxapvlnHi6MHl2k+/73XcCXH875YXAOg/8jwaHdGSOU/8lx6XnERG++YlbeO88XS/+ETev+ZxAAaMugDDZiMSDvN/o55j2Pv3lev7iDN6M/PBN3n3qseI88Zz2p+TnP/66SKChX56XHJSufbfv/c5v332PTvWZTHp+ic4aeyQkjmoRETk0GVYlvX31YRFREREapXJ24ayI7Ryr/v5+pUi1i8O4XBBt7PjyGjnKKl4+mVOgCXT/IQD0VXgTh2TgBmBKQ/4KMo1sTmg96XxpDSyMXWcD7sTLAvOfdBLnHfPqo9euCiHFt2c7NwUoV4zO6eOTiA/2yyJadknfhZP8eOKN2jUzkHLHi4+ureAtOZ26rew0/tSDx/fX0Baph0zAn2viGfm4z6sCLjiDQbdnojnbxONV3ZMgNlPF7J1dQQzAn2Gesjs4uT9W/M55ZYEvPVtTBqdz7njkvDW37sV69KcbTmrwcQqt39c+AM7zIN7rqEpd0zAty2XU+8bSmqzyleYK9iaw6J3PuP4my+oUZ/fvj6Dw/p0omGbpjWO4cyHrq5R201LVpH96waOGnx8jdp/Pv59el5xGgn1dl0JGCzy83+jngOokLhLsyVydsJRNTqmiIjUXqp4EhEREQFWfxckZ0uES55LwjAMzIhFwbbSiZ1b9XLR4fjoZNuT/1nAxqVhkhra8BeYDHkmOkm2ZVosnxmgRXcnfYZGJ2X++298v88LsuiD4grHP254PE07lV/NrnCHSe9LPaQ0tjPt3wWs+TZE/Zalc/4snxXg7Pu9JGfYsUwLw2bQsLWd0+9KJKmBnbysCHlZJoPHJ+FOtPG/e/I59rJ4Mrs4WTLNz7fvFTPg2oRdHtOMQMgPlzybTFGeyds35nHVmykMuj2Rqf/ykZBq0PeK+L1OOtUVNUn4eBum1jjpBNBz2Kn7PIa/NO3SmqZdWte4/Qm3Dd51oz+54uP2qFJMRETqDiWeRERERIDtayI07+bE+HPy6b8PS9u4LMTCScVYEcjLNmnd2ySzi5P2/d1MHVeAM86g9+Ue2g9ws+DtYqaOKyAp3U6foR7sZfJJbY510eZYV41iik+1kdI4mmhqcoSTHRsj5RJPJ1yfwLw3iwkHLDoc76603wYt7bgTowmhnRtNmnaKXv417eRg9YJgjY5pRijZLz7ZRlyiDX+BRWqTaGXVzo0RDju6Zs9JREREDi36WUpEREQEqN/SzobFoZIKJTNSvlLpqwlFnHGXl388nUzjDtEkTDho0fl0N2fc7aVpJyfff+DHMKDfVfGccbeXwp0maxeFyvXz+7wg747Kq/DfpuXl2wEU5ZjkZUXnHNryc5h6zcqvcJbW3M5pYxI5bWwis58uBMDuNLAipW0MW2kCrV4zGxuXRSfN3rS8Yn9VHTOtmZ1Nf+5XlGfi95nEeQ3WLw5RlGvSoKWdnz4N7OIVjp28LduZdP0TMTv+hLPvqnHbDd//xtbfN+11PzU91owH36zw+IpPvuXNy/7FNy98XOO+8rZs5/evluy74EREpM5QxZOIiIgI0Kqni41LQ7x1Qz7OOOh2VnSOp78ccYqbSbflk9bczl+j56LzLfkwbBAJw4kjEli1IMiPH/kxbNEk0F+VQn/ZnYqnxPo2FrxTzPZ1EVKb2mnV00l+dunwv0+fKiTvjwiRMHQ5MzoMsG1fF5886iOjrYNuZ8eV66//1QnMesLHvIng9BgMuiOxRscEWLMoyNs35mFG4IQbEijKtfjy5UIueCQJl8fgvVvyadTeQVpmxWSW1NyGH34jtVnDGs/lVJYZMbHZ9/535RWffMuZD11DSpP6u278p7wtO1j19TLa9Ouy18cXEZG6RZOLi4iISK23ryYXl9jbF5OLf/38R2z4/jfsLgfdLhxARofmzBz3Jhe9cAu/fLqIpf/7mnAgRFqLDAbeezlmJMKUO/5DcU4BNoedXlcOIqVxfabd/So2px0sOGf8dcQlVb2aYHVeOv12Dju2E9tWbSK5cX1Of3A4lmXx2aPvsXXlRsyISZ9rziTj8Ba8cck4HG4nCfWSOO+pG3HFlyYPXxw0lhZHd2DnhmzqNc9g4D2Xkf/HDibf/jJpLRoRCUfoP/I8Pn34HcL+IHaXg9PuG0ZCWhIz/jmR3C3bCfiK6X3l6bQd0JUN3//Gihnfceo9lzH35SmE/UFSmjbgq2f/R1rLRnS7aABFOT5WfbWEQKGfw3p1pO/1Z+MvKGLy2JcwwyYYcPLtQ/jymf8j6+d11GueQd/rz6Jplza7fF00ubiIyKFBFU8iIiIiUmesmbec3E3bGPLa2D8niTcpyN5Zsr1V3yPpcHIPACbfMYGNP64kKb0e/vxC/vHKGAAs02T51Pk0P6YDfa45M/rY3yeJ/2oJ37/zWYXjV5Z0KdyZT68rB+FtkMJ7V49nx7o/2Lkum1BxgCGvjqUot4B3rniEK//vQY44vRepzRrS8bSeFfou3J5Hr+GDSGnagOn3vsaaecupf1hj8rbs4KIXbsadGM+UOybQe/ggGnc6jNVzl/HtxBmccOtFnDDmYlweN8W5Pt69+jHaDugKgBmOMOOBN2jYthlHXRNd1e6XWYs4/cHheNNTCRYH6H7xCViWxTtXPMKRf+wgd9M2vA1TOe3+YSWvV48hJ5UksURERMpS4klERERE6oxtqzbT/OgOZSaJLz/0bNOPK1n41qeYpkn+HztpfVxnMo9qR/uTujP17ldwetwce+XptD+pO9++PoOpd79CcqM0jr36DOzO0kvnNv261HhYmbdBCt4GKQAkN06jOLeQneuyaPLnSnLxKV7ivPH484uq7Sc+LYmUpg0AaHxkK3asy6L+YY2p36ox7sToKorbVm/mq2f/B0QTQkmN0rBMk/kTprJ52Wpsdjv5WaWJuA3f/0pqs4YMvPvSSo+58vMfWTZ5LhjReZwKsnNo1q0tf/y0lql3v0J8SiLH/pmcExERqYwSTyIiIiJSZ9Rv1YSfZ3zHkWf3Kal4Kuur5z7igmdHklg/hcl3TADLIhwM0fmc4+h6fn9+mraARe9+Rt/rzuS4G84BYMY/J7J2wQpaH9e5pJ/dqXjCMP7WyqJeiwxWfrGYzmf3pSi3AH9BEXFJ8didjgox/6VoRz55W7aT3Lg+f/y0hrYnRIeplU2u1T+sMT2Hnkp6+0wAIqEwW1duYtuqzSXVVRPOKp2kvEXPjqS3a8b0+17ntPuHVUjUzX1pMsM/fACHy8E7wx8FLCKhMD2HnQrAvP9MY8X0b8no0BwrEkFEROTvlHgSERERkTqjVZ9ObPxxJW8PexhnnIuuF/Qno0Pzku0dB/Xkv9c/RVrLjNJJ4v/YycwH38Cw2YiEw5xw22BWf7OcHyfNwbDZsLscNP2zOukvu1PxVJnW/TqzdsEK3rniEcxIhONvuQjDMGhxzOHMeXwSq79ZxqAHhuGMc5fsk9gghW9fn8H2NVtIzUynVZ9O5P+xo1y/A0ZdwOxH3iXkD4JlccTpvWh34lFEwhHevfox0ts2I84bX26fbhcOwBnnYurdr3D6g1eUf54DuvLO8EdIa9EIpycaS9Yv6/n6uf9hc9ixTIvT7h+GJyWR3E3b+HjMSxxz+UAadWyxx6+NiIjULZpcXERERGq9L3PuJy+8LtZhyD6Q7GhB/9T7q9xek8nFpXbQ5OIiIocGVTyJiIhIrWZakWoTFVL7mFYEm2GPdRgiIiKyD9h23URERETk4KUERd2j91RERKTuUOJJRERERERERET2CyWeRERERERERERkv9AcTyIiIiJSa6TY4nfdSGoFvZciIocGrWonIiIiIrWCaVnYDCPWYcg+pPdURKTu01A7EREREakVDvYExeuvv87FF19MTk5OTON49tlnufzyyykqKoppHDVxsL+nIiKy95R4EhERERHZSxs2bODxxx+nb9++pKamxjSWCy64gCVLlvDSSy/FNA4RERFQ4klEREREZK89/vjj1KtXj2HDhsU6FDIyMhg2bBgTJ05ky5YtsQ5HREQOcUo8iYiIiIjshcWLFzNz5kxuvvlmPB5PrMMB4Morr8Tr9fLkk0/GOhQRETnEKfEkIiIiIrKHLMvi4YcfpkOHDpx11lmxDqdEYmIiI0eOZMqUKfz000+xDkdERA5hSjyJiIiIiOyhmTNnsmTJEsaOHYvNdnBdWp933nm0adOGRx55BC1kLSIisXJwnR1FRERERGqJYDDI+PHj6d+/P7169Yp1OBU4HA5Gjx7NwoULmTNnTqzDERGRQ5QSTyIiIiIie+Cdd97hjz/+YPTo0bEOpUrHHXccvXv35rHHHiMUCsU6HBEROQQp8SQiIiIisptyc3N54YUXuOCCC2jdunWsw6mSYRiMHTuWdevW8d///jfW4YiIyCFIiScRERERkd30wgsvEA6HGTFiRKxD2aX27dtzzjnn8Oyzz1JQUBDrcERE5BCjxJOIiIiIyG5Yv3497777Ltdccw3169ePdTg1MmrUKPx+PxMmTIh1KCIicohR4klEREREZDc8/vjjpKWlcfnll8c6lBpLT0/niiuuYOLEiWzevDnW4YiIyCFEiScRERERkRr64YcfmDVrFjfffDMejyfW4eyW4cOHk5yczBNPPBHrUERE5BCixJOIiIiISA1YlsUjjzzC4YcfzplnnhnrcHZbQkICN910E9OmTWP58uWxDkdERA4RSjyJiIiIiNTAjBkzWLp0KWPHjsVmq52X0eeeey5t27blkUcewbKsWIcjIiKHgNp5xhQREREROYCCwSDjx49nwIAB9OzZM9bh7DG73c6YMWNYtGgRn3/+eazDERGRQ4ASTyIiIiIiu/DWW2+RlZXF6NGjYx3KXuvbty99+vThscceIxQKxTocERGp45R4EhERERGpRk5ODi+++CIXXnghrVq1inU4+8To0aNZv349kyZNinUoIiJSxynxJCIiIiJSjRdeeAHTNBkxYkSsQ9ln2rdvz3nnncezzz5LQUFBrMMREZE6TIknEREREZEqrFu3jnfffZdrrrmGtLS0WIezT40cOZJAIMBLL70U61BERKQOU+JJRERERKQKjz/+OA0aNODyyy+PdSj7XHp6OldeeSVvvvkmmzZtinU4IiJSRynxJCIiIiJSie+//55PP/2Um2++mbi4uFiHs19cccUVJCcn8+STT8Y6FBERqaOUeBIRERER+RvTNHnkkUfo2LEjZ5xxRqzD2W/i4+MZNWoU06ZNY9myZbEOR0RE6iAlnkRERERE/mbGjBksW7aMsWPHYrPV7Uvmc845h7Zt2/LII49gWVaswxERkTqmbp9FRURERER2UyAQ4PHHH+f444/nmGOOiXU4+53dbmfs2LF8//33fPbZZ7EOR0RE6hglnkREREREynjrrbfIyspi9OjRsQ7lgOnTpw99+/blscceIxgMxjocERGpQ5R4EhERERH5086dO3nppZcYPHgwhx12WKzDOaDGjBnDxo0bef/992MdioiI1CFKPImIiIiI/OmFF17AsixuvPHGWIdywLVt25bzzz+f559/nvz8/FiHIyIidYQSTyIiIiIiwNq1a3nvvfe45pprqFevXqzDiYkRI0YQDAZ56aWXYh2KiIjUEUo8iYiIiIgAjz/+OA0bNuTyyy+PdSgx07BhQ4YPH86bb77Jxo0bYx2OiIjUAUo8iYiIiMghb9GiRcyePZtbbrkFt9sd63Bi6oorriA1NZUnn3wy1qGIiEgdoMSTiIiIiBzSTNPkkUce4YgjjmDQoEGxDifm4uPjGTVqFNOnT2fp0qWxDkdERGo5JZ5ERERE5JD2ySefsHz5cm6//XZsNl0eA5x99tm0b9+ehx9+GMuyYh2OiIjUYjqzioiIiMghKxAI8MQTT3DiiSfSo0ePWIdz0LDb7YwdO5Yff/yR2bNnxzocERGpxZR4EhEREZFD1ptvvkl2dja33XZbrEM56PTu3ZvjjjuO8ePHEwwGYx2OiIjUUko8iYiIiMghaefOnbz00ktcfPHFtGzZMtbhHJTGjBnDxo0bee+992IdioiI1FJKPImIiIjIIen555/HMAyuv/76WIdy0GrTpg0XXHABL7zwAnl5ebEOR0REaiElnkRERETkkLNmzRref/99rr32WurVqxfrcA5qI0aMIBgM8tJLL8U6FBERqYWUeBIRERGRQ8748eNJT0/n0ksvjXUoB70GDRpw1VVX8dZbb7Fx48ZYhyMiIrWMEk8iIiIickhZuHAhn3/+ObfccgtutzvW4dQKw4YNo169ejz++OOxDkVERGoZJZ5ERERE5JBhmiaPPPIIRx55JIMGDYp1OLWGx+Ph5ptvZsaMGSxevDjW4YiISC2ixJOIiIiIHDKmTZvGTz/9xNixYzEMI9bh1CpnnXUWHTp04JFHHsGyrFiHIyIitYQSTyIiIiJySPD7/Tz55JOcfPLJdO/ePdbh1Do2m42xY8eyePFiZs2aFetwRESkllDiSUREREQOCW+++SZbt27l1ltvjXUotVavXr3o378/48ePJxgMxjocERGpBZR4EhEREZE6b+fOnbz88sv84x//oEWLFrEOp1YbM2YMW7Zs4d133411KCIiUgso8SQiIiIidd5zzz2HYRhcf/31sQ6l1mvVqhUXXHABL7zwArm5ubEOR0REDnJKPImIiIhInbZ69Wref/99rrvuOlJTU2MdTp0wYsQIQqEQL730UqxDERGRg5wSTyIiIiJSJz388MMsWLCA8ePHk5GRwaWXXhrrkOqM+vXrc8011/D222+zdu1aRo4cycaNG2MdloiIHISUeBIRERGROumjjz5i+vTpzJkzh9tuuw2XyxXrkOqUyy+/nLS0NMaPH8+sWbNYvnx5rEMSEZGDkBJPIiIiIlInFRUV8c0339CmTRsmTJjA448/HuuQ6oyNGzfSr18/unXrxmeffQZEX28REZG/U+JJREREROqcSCRCMBgkKyuLNWvWYFkW55xzTqzDqjOaNGnCeeedx4wZM/B4PIASTyIiUjklnkRERESkzikuLi7581VXXcUHH3zAYYcdFsOI6habzcbYsWN56623SExMBGDFihUxjkpERA5GjlgHICIiIiKyryUkJNCzZ08uv/xyjj/++FiHU2f16NGDmTNncvPNN3PyySfHOhwRETkIGZZlWbEOQkRERERERERE6h4NtRMRERERERERkf1CiScREREREREREdkvlHgSERERqWUilhnrEGQ/qOx9tSJ6r+savacicqjRHE8iIiIitdD9Of9jXXh7rMOQfaSFoz73p55b6bac+78kvC7vAEck+4OjRTKp9/ePdRgiIgeUVrUTERERqYXWhbezMpQV6zDkAAivyyO0ckeswxAREdkjGmonIiIiIiIiIiL7hSqeRERERGS/Ca/aSej7LDyDD6+yTeFz3+O55AhsKXF7dIzI1kIKH/kWKxTB0aE+Cdd1q9Am9EMWRa8tBSDu3Ha4T2ixR8eSyq0KZ/N9aB2DPcdU2ea5ws+4xNObFFv8Hh1jaySfRwo/IWRF6OBoxHUJx1do80NoHa8VfQPAuXFHcYK76n93IiJyYCjxJCIiIiL7jaN1PRyt61XbJuHG7nt1jKKXF+MZ0hFntwwKHpxLaHEWzq4ZJdutiEnhCz+Q9MSJGHF28m74FFevJhjxzr06rpRq7UintSO92jY3Jpy4V8d4uehLhnh60c3ZnAcLprA4tJ6uzuYl2yOWyQuFc3gi6WLiDAc35L1NL1cr4g33Xh1XRET2jhJPIiIiIrLXLNPC9/ACzCwf9laphBZsJvX9swktySYwey2Jo3vie3gBOGyYO4sxtxfhvbcP9qZJ5I2aTeJdx2JvsGeVMOHfc3B2iyaaXH2aEVqcXS7xZG72YctIxJYcTUA4OtYn/OuOkn2k5kzL4mHfdLLMPFrZG7IgtIr3U69jSWgDswMrGJ14Kg/7puPAzk7Tx3bTx73eM2lqr8eovHe5K/F0GtiT9ujYv4ez6fZnoqmPqw2LQxvKJZ42mzlk2JJJtnkA6Ohowq/hrJJ9REQkNpR4EhEREZG9Fpy3CcNlI/mZk4lsLiAwfVWl7eyZSSTedgz+mavxf7KahKu7Vtoukl2I76H5FR539cvEc0678g+WWaTZ8Low8wPlNpv5AWxeV8nfbZW0kZqZF/wdl2HnmeQhbI7kMD2wtNJ2mfZ63JY4kJn+5XziX8bVCf0rbZcdyech37QKj/dzteMcz1HlHrMofZ+9Rhz5ZnG57flmMV5b6XBNr61iGxEROfCUeBIRERGRvWZuysfRoT4A9ibekuqiv3O0T4u2yUgk/NO2KvuzpyeQ/NRJNTu4YZT80fIFsSWVP7YtyY1ZECyN1RfEmaThV3tik7mTDo7GADSxp5ZUF/1de0cjADLsyfwU3lRlf+n2JJ5K/keNjm1Q+j77rABJfzt2ks1DgekvbWP6SXJWHp+IiBw4SjyJiIiIyF6zNfESWvQHDGpN5A8fZl4VFUVGmT9blTeB3at4crRJJbQkG2eXdILzNxE3sNXfYkvEzPJhFgQw3A7CK7YTf1XllVZSvSa2VBaF1jKIzvwRySWvioqiskmiat7m3ap4auNIZ0loA12cmcwPrmJgXKcKsWWZeRSYftyGgxXhzVwV36/mT05ERPYLJZ5EREREZK+5jm1K8JuN5I38FHvLFGxpe1dpsjsVT/FXdcH36Lfw2lIc7dJwdI1Ocl12tbz467pRcPuXAHgu7IAtQROL74ljXW34JriSkXnv0NLegDRb4l71tzsVT1fF9+NR3wxe4xvaOTLo6sgEyq+Wd138AG4v+ACACz1Hk2BTZZuISKwZlmVV9yOEiIiIiByEhm6bwMpQVqzDKMcKmxgOG5HNBRT8cy4pE06NdUi1RltnBhMbXF3ptm1DJxNaueMAR1S1sBXBYdjZHMnhnwWTmZAyNNYh1RrOtmk0mHhWrMMQETmgVPEkIiIiIvuE74G5mHkBrOIwCdd3i3U4sp884JtCnllMsRXk+oTjYx2OiIgc5JR4EhEREZF9wvvAcbEOQQ6AB7znxDoEERGpRWyxDkBEREREpKycIZMPyHH8U34n97qZ5N30KQUPzsUKRg7IcaXUkJyXD8hxloc2cUXuq5y8YzzbIvkH5JgiIhKlxJOIiIiIHJKcR2WQ/PwpJD99MrYG8QRmr411SLKftLQ34PnkSznc0TjWoYiIHHI01E5EREREaiSS5cM3bh447WBZeB88jsiaXIomLoeIieF14b23D4bbQd6o2Tja1COyLg8rFCHu9NYEZq3FzPHjva8P9mZJ5I2ajb1FMuamAizTwntPH2ypcSXHs8ImhU8tJLLFByGT+Gu74uzYgKK3lhOcvxnD48DVswmeCzvs0fOxN/GW/Nlw2cFu7PVrVFdkRfIY55uKEzsWFg96z2VNZBsTi+YSwcRrxHGv90zchpNRee/SxpHOush2QlaE0+O6MCuwnByziPu8Z9HMXo9Ree/Swl6fTeZOTAvu8Z5Bqi2h5HhhK8JThZ+yJZJLiAjXxg+go7MJbxXNZ35wFR7DSU9XKy70HL1HzydRq9uJiMSMEk8iIiIiUiOhH7Nxdm9E/NAjAbAsC0fbNJKfPBGAwpcXE/hiA3EDDwPA2bkhCTcche+xbwn/tpOkx44nMHst/hmrSbi6a7RNh/q4Rx2N/5PVFE/6mYRrSyclD3yyCntjL4m39cTM9VNw11ckP38Kwc/WkfzSqRgeB5ZZcYHmgnu/xswPlHvM3sRL4uielT6v8Po8gt9tIfmpk/b+Raojfgytp7uzBUPj+wDR97qtI50nky8G4OXCL/ki8CsD4zoB0NnZjBsSTuAx3wx+C//BY0kXMTuwghn+ZVyd0B+ADs7GjHKfzCf+ZUwqXsi1CQNKjvdJYBmN7anclngquWYRdxX8H88nX8pnwRW8lHw5HsOFWcli3PcWfES+WVzusSb2VEYnakVFEZGDhRJPIiIiIlIj7gGZFL+9goJx87CnJ+AZ2onwulyKX1uKFTQxc/y4E5wl7e1t6wFgaxCPvXFiyZ+tpdklbRxHNADAeUR9gnM3ljteeE0u4RXbCS7cAoDlCwKQMLIHhU8vwoqYxJ3ZBmenhuX2251JziNZPnwPL8B7Xx8Mjy6N/zLA3Z63ixcwrmAq6fYkhnr6sC68ndeKvyFoRcgxC0lwu0rat7VnANDA5qWxPaXkz0utDSVtjnA0if7f2YS5wZXljrcmvI0V4c0sDK4BwGf5ARiZcBJPF84mYpmcGdeVTs6m5fbTROciIgc/nV1FREREpGYMg/irugDge2QBoUV/4J++Gs/QI3F2bEDhSz9C2aoUw6j8z2WahH/ejr2Jl9DP27E3Syp3OEeLFOxNvHguiA6ls0LRyb8dHevjPCqDSHYhBfd8TcqE8tUtNa14MncWU3D/NySO7om9sRcpZWBwVXw/AB7xfcKi0Fqm+5cy1NOHjs4mvFT4Rdm3EeNv+/6lbJufw1toYk/l59AWmtnrlTteC0d9mthTucDTA4CQFX2vOzqacJSzBdmRfO4p+B8TUoaW208VTyIiBz8lnkRERESkRoILNuP/6DewGRhOO45ODXAXhyl87FvsmckY8U6MMhVPNRFeuZPArDXROZ7uPrbcNvfprSl8ehF5N38GgKN1Kgk3HEXBPV9HV6ALRog7u22FPmta8VQ0YQlWjp/CZ7+PHu+EFsSd0Wa34q+rFgRX8ZH/R2wYOA07nRxNKXYHeaxwBpn2NOINFwnG7s2btDKcxazAckzL4m7vmeW2ne7uzNOFs7k57z0AWjsackPCCdxT8D+CVoQgYc6O61ahz5pWPK0Pb+eZos9YHdnKg76pDHC15xzPUbsVv4iI7BnDsioZLC0iIiIiB7Wh2yawMpQV6zD2St6o2STedSz2BvGxDiXm2jozmNjg6kq3bRs6mdDKHQc4on1rVN673JV4Og3sSbtuXIc526bRYOJZsQ5DROSAssU6ABERERERERERqZs01E5EREREYkKryB06nkr+R6xDEBGRGFHFk4iIiIiIiIiI7BeqeBIRERE5hESyfBQ+9h1Jj59wQI6XN2o2hEwS7+wdXb3uhyyKXlsKQNy57XCf0KLKfS3LouiFHwn/tgPDaSfh9l7VzgflG/8dkTU5ALj6NMPzj47VxhaYvRb/xysBiL+yM86uGVXHEozgG/8dZpYPI8lN4u29sCW6qmwfXp1D4VOLwLJwHZeJ58Loynz5d3xBaMlW0mZcVG1s+0JWJI/HCmfweNLg/X4siM7jFCLCnYmn08Seyg+hdbxW9A0A58YdxQnuw6vc17IsXiiaw2/hLJyGndsTTqt2PqitkXweKfyEkBWhg6MR1yUcX21sswMr+Nj/IwBXxh9HV2fzKtsGrTDjfTPJMvNIMjzcnjiIRFvVE6n/p+grPg/8TBN7aslrXWwFuSP/Q7aZBbyTek21sYmI1HWqeBIRERGR/Srx/r7Ym3ixIiaFL/yA99/9SXriBIrf+xmrKFTlfqHvszDzAiQ/czJxgw+n+JUl1R7Hc1EHkl8YSNJzpxCct4nI5oIq25q+IMWTfiHpyRPx/rsfhc//iGVWveZOYNYa7BkJJD9zMq5jGuP/4NdqYyl8ehGJY3uS9MzJBOdtLIkl6aEB2OrFVbtvbXZ/4lk0sacSsUxeKJzDv73n80TSYN4r/o4iK1Dlft+H1pFnFvNM8hAGxx3DK8XfVHucl4u+ZIinF88kD2G76WNxaH2VbX2mn0nFC3ky6WL+7T2P5wvnYFazvtKswE9k2JN5JnkIx7gO4wP/wmpjOSeuG08kXVzuMY/h0vBCEZE/KfEkIiIiUssVvvgjga82AGBFTHKHT8cKmxT9Zwl5t3xG7tWf4J+8ssJ+vocXEFq+FYDQkmx8j30LQHhtLvm3fk7eLZ9RcP83WP7wPonT3OzDlpGILdmN4Xbg6Fif8K9Vr9YWWpKFq28zAJzdM6ptC2BvFq2QMWwG2P/8rwrhn7fj7NwQw2XHlhyHrb4HM8tXTSzZJbG4+jQjtKTqFQWtYASrMIS9aRKGzcDVswmhpVurjb2mXiycw1eB3wCIWCbDc18jbEX4T9FX3JL3HlfnTmSyf3GF/R72TWd5aBMAS0IbeMw3A4C14W3cmv8+t+S9x/0FH+O3qk4E7o7NZg4ZtmSSbR7chpOOjib8Gq76NVsS2kBfV1sAujtb8Gt4S7X9/x7OptufVUt9XG1YHNpQZdufw1vo7GyGy3CQbIunvi2RLDOvRrH0cbVhSTV9A9S3ebFR9b81EZFDnYbaiYiIiNRy7oGHUfTKEtz9Mgkt/ANn13QMhw3PJUcQ73FgBSPkDp+Oe1DrGvVX+NRCEu88Fnt6Av6PfsM/bRWe89uXbLdCEfJHz6mwn7NTA+KHd6myXzM/gM1bOjzN5nVh5lddBWPlBUvaG4ZRbUVSWYHZa7E1iMeekVh13/kBjDKxGIkurPwgNN51e8PrwsoL1rxvrxurmue5Owa6O/FK0df0c7djYWgtXZ2ZOAw7l3h64Yl3EbTCDM99jUHuI2vU31OFn3Jn4hmk25P4yP8j0/xLON/To2R7yIowOn9Shf06OZsyPP64KvvNN4vx2koru7y2OPLN4irb51ml7Q3DqLYiCcCidLvXqL7vfMuP1yiNJdGII98qpjEpVbQvLmnvNeLIs6ruW0REdk2JJxEREZFaztEyBSs3gJnjJzBrDZ4h0bmN/FNWEpy7CWwGVo4fM8dffseyRRplbvQj6/LwPTQ/+peQiaNLw/K7Oe17tCKdLcmNWVCasDF9QZxJVc+dYyS7StpbloVRTQXTX4LfbSbw6Rq8/+pfbTsjyY1VUFpBZfmCGElVz9kUbR+EjD/bJu+ira+0cijad9XPc3e0dDQg1yoixyxkVmA5Qzy9AJjiX8zc4O/YsJFjFZFjFpWPqcyfyyZt1kW285BvGgAhInRxZJbbz2nY92jIWJLNQ4FZ+u/NZ/pJcnqqbJ9slLa3LAu7Uf3ADKPMM/JZAZJsVfedZMRRYJWJxfKTZFTX3kOB5SeDZHxWgORq2oqIyK4p8SQiIiJSB7hObIH/45VEsgtxtKmHWRAgMGMNya+eBhGL3MumVNjH8LowswuhE+WGsdlbpOC9pw+2tOgNtxWKlNtvTyuebE0SMbN8mAUBDLeD8IrtxF/VFYBIdiH29ITy/XVOJ/jFetx9mxH+MRt727To8QNhrOIwtpTycyWFlmZT/NZPeB8egOGylzxu5vgxEpzlHnMcXp+iV5ZEh8X5w5jbi7H9WSFVZSzzN+FoU4/gvE04O6dHYykKYUVMbN7SxJLhsmPEO4hsKcDWKJHgd1tIvO2YKl+X3XWi63A+9v9IdiSfNo50Ckw/MwLLeTX5CiKYXJb7H6B8xZDX8JBt5tMJyg15a2FvwD3eM0mzRZ97yCr/Xu9pxVMTWypZZh4Fph+34WBFeDNXxfcDIDuST/rfJg7v7GzGF8Ff6Otuy4/h9bS1Ryd6D1ghiq0QKbbyk8q3caSzJLSBLs5M5gdXMTCuEwA5ZiEJhhuXUXqbc7ijMa8UfU3QCuO3Qmw3fWTYkquJJZP5wd9p40hnXvB3OjujybgiK0DEsspVcomIyK4p8SQiIiJSB7hPaEHORR8RP6wzEB06Zm+RTP7I2dibJ1daceMe1BrfuHkEPl+HvVHpsLSEUT3wPbwAK2IC4Bl8OK6jS8eg7WnFk2G3EX9dNwpu/zLa74UdsCU4ASgYM4eUN84o197ZoxGhhVvIG/lpdFW7sT0BCC3fRmj+ZhJGdi/X3vfQAowEJwV3fwVA/DVdcXaoT+HzP+C5sAOOtvVK2toSXXjOa0/+zZ9Fn/N13TBsBlbYpODOL0l5dVD512rgYfjGf0veTZ9iJLpJvCNaaRSYsx6rKFSyal3JaziiO76HFkRXtevTDHsT726/XlU5wX04F+W8yLD4PgAkGm5a2OszMv8dmtvTKq3mGeQ+knG+qXwe+JlG9uSSx0clnMTDvulErOh7PdhzDEe7DivZvqcVT3bDxnXxA7i94AMALvQcTcKfK8ONKZjEGylXlWvfw9mShaE1jLtwFacAAA0+SURBVMx7B6dhZ2zCaQAsD21ifmg1IxNOLNf+qvh+POqbwWt8QztHBl3/rNR6vnAOF3p60NZRukJhoi2O8zzduTn/PQCuSxiAzTAIWxHuLPiQV1OuKNf3QPcRjPfN5Ka8d0g04rgjMfpvYU7gF4qsIBd6ji7X/sPi7/kq+CsbIju4Nf99RiWcTDN7PUREJMqwrF0MoBYRERGRg87QbRNYGap6suaDRcEDczG3F5E4tleVyZfItiL8H/xCwvVH1ajP4nd+wtmzCY5WqTWOwXtvnxq1DS3fSvj3HDzntqtR+8Jnv8dzyRHYUmtWBZN/xxdENvtIfbN8kq2tM4OJDa6udJ9tQycTWln9xOoHgwcKJrPd9DE28TSa2Ct/b7ZFCvjAv4jrE46vUZ/vFC+gp7MVrRwNd934zxju9Z5Vo7bLQ5v4PZzNuZ6a/bt7tvAzLvH0ItWWsMu2xVaQO/I/BCiXuHO2TaPBxJrFJyJSVyjxJCIiIlIL1ZbEk9RMXUg8ya4p8SQih6LqZ+0TERERERERERHZQ0o8iYiIiBxCIlk+8m/9PGbHzxkyucZtQ0uyCa/O2et+anos32PfVng8MHstudfOoOjVJTXuK5LlIzhv0z6Mbv/JiuRxa/77sQ5jn5hYNJfZgRW7bLcwuIZPAz9VuX1U3rtsi+Tvy9BERA5pmlxcRERERA5KoSXZ2Jt4azyXU1lWxMSw7/1vrIFP1+K9r2+5ydd3xcwqJDh/E65jm+718WXfKzt5uoiI7H9KPImIiIjUYUWvLCG0OBtcduLOboujXelqW4E56/BPWwWBCPbMJBLG9ISIhe+BuZi5fnDYiL/0CGyNEvGNmwdOO1gW3gePw+atuEpejURMfE8uJLImF1tGAt67jsWyLAqfXkRkdS5ETDxDj8TRPo3AzDUYbjv+6atIeqg/hsdZ2k/YxPfIAiKbCrA3SyJh9DGY2YUU3P8N9sxkiJjEX9OVwicXYgUiGE4biWN7Yavnie6XVYhVGMRzaSfcfZuVf80mLsPyh7E39hL+ZTu+cfOIO6ctZm6A4PxNWIUhXEc3In54F0xfEN9930RXADQMEkb1oPiDXwj/uoO8UbOJH94ZZ6eaTYx9ILxS9DWLQ+tx4eDsuG60K7P625zAL0zzLyHw/+3dbYwc5WHA8f/M7Mvt3t6tX8EG29hgqCF2DK6jwjkpBIpJ6gQrfVMj18VJk34oUFDkNlRRGkGtCGjUNinKBxAqbtNUFKkJsUKxrSBh4jipk4AhjgshhmJsfNhg3+3e3u7dzkw/rLP2YYsa6uHF/H8fb3ZnnplnP5z+emaGNnOiqfxl70eJSbit/h0OJw1yhKwuDzAznMS6+gbyRKSk/E3f79AXntzD1Y9VT5p8qf5t4jQhIODm3qsJgoB/qG8iJiUk4It91zIl7OX2+nfJEfFqUudgUuev+65lVjSFx1rPsH50K2eG/aSknB1N5qnxF/le6+fcXFnO7fXvUiDH5yrXcEf9IT7Rs4Td7QMcSGqsLg/wxPgL3NvYQkTIrGgyaysfBeDfm9v5n/gVRtIWX+77Xaph+ZTNgSS91xieJEmSTlNjP9pLvK9O/13LCYKANE5IDjS62wuXzaJ45VwAarc+RnvHy4RnlElqY1S/thyANElpPbyb/NKZlNe8v/O317ybZmzri4w+sOu4458ouiSvNjsxa1qZoZs3E78wRPzCMDRjqv+4nGSoydANm5j0zx+n+JFzic7uo3j1vOP2nbwySmn1QqKz+qh9+QeM/3Af0bwqyf4R+r9yFWGlQO2271NavYj8RdMY27aX0W/upPeGpfT++QcISjmSoRbDN20+Gp7aCfU7f0g0f3L3XFuPPE/lC8uIppdJR9uUfm8BaZoyfMMm4hV1kn11wullKrdc1r1epd+/kNbm56j8xaVvYtay86OxX7IvPsRd/X9EEATEacKBpNbdflnhPK4sXgjArbUH2dHewxlhP7WkydeqqwBI0pSHW0+xND+XNeXOmwJf+3vYOvYLHhjdftzx/6T8myzKH10F9kw8yPSwj1sqK7r7HqfNV/r/kDAIeLD5OBuaj3PdkePMiaawtvIRHm4+xUPNJ/lM+XLuaTzK16t/TDko8LnhfwPgwtxM7hrp3E56KBnhV6N7tj3I/OhMdrcPdMf9d/WN/H31k0wNK8Rp0h3botwsru+9ivsa3+fRsae5tueSN3fRJUmGJ0mSpNNVvPsw+SUzCIIA4Lhbz8afHGT0/l0QpySDIyQDDfIXn0nxijnU1m0l6MlRum4RxQ/PYfQbO6mt20p0Zi+lNYs6q5+OKCybddK3lYXTy4TTOqtHohkVkqEW8Z5hcoumd7ZXewgrBdLa2OvvZ3IP0Vl9AOQXTiPeM0w0r0o0bxJhpdA9/8bdj3e+kKSEM3pJk5TG+idp7zwIUUD88sjR6/H4IOGsPnrX/sYJjzm25QWaDz0LBMQv1UkONMgtPoP2roPU1m0lrBYpfer9J3Ud3g6744MsyZ/T/T1EwcTfw5Pje7h/9L+ISRlMhhhI5nNxfg5XFBewrraBniDPdaUBPlxcwDdGt7GutoEzo37WlD5InqO/h2WF81lWOP//HM/i3Gx2tfexrraBaljiU6UPcjht8PWRRxhJxxhJWyw4ZkXWgtxMAGZEVX7WfpHDaYNJYZlK2Fl9d1HuLAByQcTksMxPxp9ndjSFsTTmJ+PPMzOaRHjk3AEOpw36wxJTw8px16N7rLDKwWPinCTpjTM8SZIknaaieZNobX6O4orzuiuejtW4+wn677yScGqJ2q2PAZCOxRQ/Np+elRfQ2rib5gO7KH96MeXPXgxA/Y5tjG9/icLA0dD0RlY8nXCcs/sZe2wPrJhPMtQkqY8R9BUgFx435l9JDjWJ99eJZlRo//wVCpd3Vi0F4dGwEM2tUlr1PnLnd24vTMdj4l8eIt59uLu66vCq73Q/n//ATHLzJ1O//QdUPn/ZcaGu8U87mLT+45CPGL5xU+eP7YTSqoWd7euforXpOXIXTIF44iqgd4J50TQ2t3ayori4u+LpWHc3HuXO/j9galjh1lrn4e1jaZuPFRezsucSNrZ+xgPNH/Pp8of4bPlyAO6oP8T28ecYKMzv7udkVzy1iVlV6qwUW9/YyqbWTvYmh7iqeBFXFS/i282f8ov2YPfzAUfnNgWqQYlDyQgjSYtyUOC/2y8xL9cJmJfk53BvYwurSwO00jb3NrZwdXHhhPFMCsoMJ6O8mowwJewlTpNufAqO+dw7byYl6d3F8CRJknSaKlx6NuM7Xmb4+o3Qk6Nn5cRnPBWvOZfhtd8jOqcKR26XSgZHOm93CwNoJ/TeuJSxbXtpfutpCAOCfNRdndQ9zhtY8XQi+WWzGNv+EkM3bIQ4pff6XycIAgpLZzJy148Z37aXyl8NEPQc/dc1nFZi9F93Ej8/RDSrj/ylZ5MMjkzYb/nPljDy1e2ko+3O+S4/l+IVc0jbCUM3bSY3fzJBJT/hOz2f+DXoyVFft5XKF5ZNPM8PzWHoxk1Es/sJSp2xtJ9+lcY9T0AuhCShcssAYbVIY1+N2pe2UPrk+8gtmPqmr82pdGnhPHaM7+H64X+hhzwrX/OMp2uKC1k7fD/nRFNJj+SWwWSYv63/JyEBbWJu7P0tto09y7eaPyUkIB9ELMpNnPuTXfH0dHs/9zQeJUdEQsItlRXsT4b46shmHmntYlrY97rfj4KQz5Qv56bhb3JG2DfhOVNL8nO5p7GFxfnZjKcJt9Uf5PO9vz3h+0EQcHNlOV+s/Qd5ognPeJIknTpB+tqbsiVJkvSOt+bA3Twzvv/tHoZOkQvyM7hv+p+ecNuBNQ8y/swrb/GIlIX8BVOZft/Kt3sYkvSW+v+/Y1aSJEmSJEk6AcOTJEmSJEmSMmF4kiRJkiRJUiYMT5IkSZIkScqEb7WTJEl6F5qbm/Z2D0Gn0OvNZ25u9S0cibLkXEp6L/KtdpIkSe8ycZoQBS5cP92caF7TOCGInOvTiXMq6b3G8CRJkiRJkqRMmNolSZIkSZKUCcOTJEmSJEmSMmF4kiRJkiRJUiYMT5IkSZIkScqE4UmSJEmSJEmZMDxJkiRJkiQpE4YnSZIkSZIkZcLwJEmSJEmSpEwYniRJkiRJkpQJw5MkSZIkSZIyYXiSJEmSJElSJgxPkiRJkiRJyoThSZIkSZIkSZkwPEmSJEmSJCkThidJkiRJkiRlwvAkSZIkSZKkTBieJEmSJEmSlAnDkyRJkiRJkjJheJIkSZIkSVImDE+SJEmSJEnKhOFJkiRJkiRJmTA8SZIkSZIkKROGJ0mSJEmSJGXC8CRJkiRJkqRMGJ4kSZIkSZKUCcOTJEmSJEmSMmF4kiRJkiRJUiYMT5IkSZIkScqE4UmSJEmSJEmZMDxJkiRJkiQpE4YnSZIkSZIkZcLwJEmSJEmSpEwYniRJkiRJkpQJw5MkSZIkSZIyYXiSJEmSJElSJgxPkiRJkiRJyoThSZIkSZIkSZkwPEmSJEmSJCkThidJkiRJkiRl4n8BYv8UQLdZyZkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}