HousePriceRegressionGridSearch.ipynb

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        "id": "w-ObRd98_Gvt"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        },
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      "outputs": [
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks/CSV_Files/\")"
      ],
      "metadata": {
        "id": "tj5oHbkh_Y6d"
      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "data = pd.read_csv('kc_house_data.csv')\n",
        "data.head()"
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              "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
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              "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
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              "\n",
              "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
              "0      5650     1.0           0     0  ...      7        1180              0   \n",
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              "4      8080     1.0           0     0  ...      8        1680              0   \n",
              "\n",
              "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
              "0      1955             0    98178  47.5112 -122.257           1340   \n",
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              "2      1933             0    98028  47.7379 -122.233           2720   \n",
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              "\n",
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              "2        8062  \n",
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              "id               0\n",
              "date             0\n",
              "price            0\n",
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              "sqft_lot         0\n",
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              "grade            0\n",
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            "RangeIndex: 21613 entries, 0 to 21612\n",
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              "21613"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "y8mxN_2gAwRh",
        "outputId": "700990c3-f34e-4810-96be-0a2e935efcdc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
              "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
              "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
              "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
              "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
              "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
              "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
              "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
              "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
              "\n",
              "           sqft_lot        floors    waterfront          view     condition  \\\n",
              "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
              "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
              "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
              "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
              "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
              "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
              "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
              "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
              "\n",
              "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
              "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
              "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
              "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
              "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
              "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
              "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
              "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
              "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
              "\n",
              "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
              "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
              "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
              "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
              "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
              "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
              "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
              "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
              "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d26878a6-5ad6-4cbc-8860-a16a675ccb37\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.580302e+09</td>\n",
              "      <td>5.400881e+05</td>\n",
              "      <td>3.370842</td>\n",
              "      <td>2.114757</td>\n",
              "      <td>2079.899736</td>\n",
              "      <td>1.510697e+04</td>\n",
              "      <td>1.494309</td>\n",
              "      <td>0.007542</td>\n",
              "      <td>0.234303</td>\n",
              "      <td>3.409430</td>\n",
              "      <td>7.656873</td>\n",
              "      <td>1788.390691</td>\n",
              "      <td>291.509045</td>\n",
              "      <td>1971.005136</td>\n",
              "      <td>84.402258</td>\n",
              "      <td>98077.939805</td>\n",
              "      <td>47.560053</td>\n",
              "      <td>-122.213896</td>\n",
              "      <td>1986.552492</td>\n",
              "      <td>12768.455652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.876566e+09</td>\n",
              "      <td>3.671272e+05</td>\n",
              "      <td>0.930062</td>\n",
              "      <td>0.770163</td>\n",
              "      <td>918.440897</td>\n",
              "      <td>4.142051e+04</td>\n",
              "      <td>0.539989</td>\n",
              "      <td>0.086517</td>\n",
              "      <td>0.766318</td>\n",
              "      <td>0.650743</td>\n",
              "      <td>1.175459</td>\n",
              "      <td>828.090978</td>\n",
              "      <td>442.575043</td>\n",
              "      <td>29.373411</td>\n",
              "      <td>401.679240</td>\n",
              "      <td>53.505026</td>\n",
              "      <td>0.138564</td>\n",
              "      <td>0.140828</td>\n",
              "      <td>685.391304</td>\n",
              "      <td>27304.179631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000102e+06</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98001.000000</td>\n",
              "      <td>47.155900</td>\n",
              "      <td>-122.519000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>651.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.123049e+09</td>\n",
              "      <td>3.219500e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1427.000000</td>\n",
              "      <td>5.040000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98033.000000</td>\n",
              "      <td>47.471000</td>\n",
              "      <td>-122.328000</td>\n",
              "      <td>1490.000000</td>\n",
              "      <td>5100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.904930e+09</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1910.000000</td>\n",
              "      <td>7.618000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1560.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98065.000000</td>\n",
              "      <td>47.571800</td>\n",
              "      <td>-122.230000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>7620.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.308900e+09</td>\n",
              "      <td>6.450000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>1.068800e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2210.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98118.000000</td>\n",
              "      <td>47.678000</td>\n",
              "      <td>-122.125000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>10083.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.900000e+09</td>\n",
              "      <td>7.700000e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.651359e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>98199.000000</td>\n",
              "      <td>47.777600</td>\n",
              "      <td>-121.315000</td>\n",
              "      <td>6210.000000</td>\n",
              "      <td>871200.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d26878a6-5ad6-4cbc-8860-a16a675ccb37')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f4f6c33-b5be-4ad3-a1c4-2832ea0623c5')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7f4f6c33-b5be-4ad3-a1c4-2832ea0623c5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-d26878a6-5ad6-4cbc-8860-a16a675ccb37 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-d26878a6-5ad6-4cbc-8860-a16a675ccb37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.hist(figsize=(20, 12))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wtmi-krLA3ko",
        "outputId": "276eb3bb-6313-4c21-d287-e217177c925c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'id'}>,\n",
              "        <Axes: title={'center': 'price'}>,\n",
              "        <Axes: title={'center': 'bedrooms'}>,\n",
              "        <Axes: title={'center': 'bathrooms'}>],\n",
              "       [<Axes: title={'center': 'sqft_living'}>,\n",
              "        <Axes: title={'center': 'sqft_lot'}>,\n",
              "        <Axes: title={'center': 'floors'}>,\n",
              "        <Axes: title={'center': 'waterfront'}>],\n",
              "       [<Axes: title={'center': 'view'}>,\n",
              "        <Axes: title={'center': 'condition'}>,\n",
              "        <Axes: title={'center': 'grade'}>,\n",
              "        <Axes: title={'center': 'sqft_above'}>],\n",
              "       [<Axes: title={'center': 'sqft_basement'}>,\n",
              "        <Axes: title={'center': 'yr_built'}>,\n",
              "        <Axes: title={'center': 'yr_renovated'}>,\n",
              "        <Axes: title={'center': 'zipcode'}>],\n",
              "       [<Axes: title={'center': 'lat'}>,\n",
              "        <Axes: title={'center': 'long'}>,\n",
              "        <Axes: title={'center': 'sqft_living15'}>,\n",
              "        <Axes: title={'center': 'sqft_lot15'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 20 Axes>"
            ],
            "image/png": 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9dOnSBSNHjkRKSgocHBwsnSKR2XB/WnXcn97DfWnV3Lp1C7/++iuaNGli6VTICln7vtra9xdPPvkk0tLSjKb9/PPP8Pf3t1BGVXP79m3Y2xufqnBwcEBxcbGFMqq+gIAA+Pj4GJ1HyMvLw8mTJ63mPIJh8OWXX37BwYMH0ahRI0unRLWArZwjq6i/CgoKgpOTk1GdaWlpyMjIkOpUKpU4f/680Qlpw0Cl4SS/Uqk0WochxpJtVVFf16VLF5utvaJ+sjL7bqVSidzcXCQnJ0sxhw4dQnFxMYKDg6WYo0ePQq/XSzFqtRotW7a02C24Kupjbbl2ixJUaVu3bhVyuVxs3LhRXLx4UUyYMEG4u7sLrVZr6dRqhZs3b4qzZ8+Ks2fPCgBi2bJl4uzZs+L333+3dGoWN2nSJOHm5iaSkpJEZmam9HP79m1Lp1YrvPnmm+LIkSMiPT1dnDt3Trz55pvCzs5OHDhwwNKp1VpPPfWUmDJliqXToBrAfWn5uD8tH/enVcN96f+8/vrrIikpSaSnp4vvv/9ehISEiMaNG4vs7GxLp0ZWyBb31da0vzh16pRwdHQU7777rvjll1/E5s2bhaurq/j8888tnVqlREREiEceeUTs3r1bpKeniy+//FI0btxYTJ8+3dKplaqiz24LFy4U7u7u4quvvhLnzp0TgwcPFgEBAeLOnTsWzvye8vIvKCgQzzzzjGjatKlISUkx+nvW6XSWTp0szBbOkVWmv5o4caJo1qyZOHTokDhz5oxQKpVCqVRK8wsLC0Xbtm1FaGioSElJEfv27ROenp5i5syZUsxvv/0mXF1dxbRp08RPP/0k4uPjhYODg9i3b59Z663Ig32drdZemX6yMvvuAQMGiE6dOomTJ0+KY8eOiRYtWogRI0ZI83Nzc4W3t7cYNWqUSE1NFVu3bhWurq7io48+Mmu996tMH2urtVsSB2CqaOXKlaJZs2ZCJpOJrl27ihMnTlg6pVrj8OHDAkCJn4iICEunZnGltQsAsWHDBkunViuMHTtW+Pv7C5lMJjw9PUW/fv14srAC1nQSgKqG+9LycX9aPu5Pq4b70v8ZNmyYaNKkiZDJZOKRRx4Rw4YNE5cvX7Z0WmSlbHFfbW37i2+++Ua0bdtWyOVy0apVK7F27VpLp1RpeXl5YsqUKaJZs2bC2dlZPPbYY+Ltt9+utSf8K/rsVlxcLGbPni28vb2FXC4X/fr1E2lpaZZN+j7l5Z+enl7m3/Phw4ctnTrVAtZ+jqwy/dWdO3fEq6++Kho2bChcXV3Fs88+KzIzM43Wc+XKFTFw4EDh4uIiGjduLF5//XWh1+uNYg4fPiw6duwoZDKZeOyxx2pln/hgX2fLtVfUT1Zm333t2jUxYsQIUb9+faFQKMRLL70kbt68aRTz448/ih49egi5XC4eeeQRsXDhwhqvrTyV6WNttXZLshNCCFNeUUNERERERERERERERFTX8RkwZHOysrLwn//8B40aNYKdnR0++OCDh1pf79690bZtW9MkR0REVAM2btwIOzs7nDlzxmTrTEpKgp2dHZKSkqRpY8aMwaOPPmqybRAREdUWpfV7RERUebX9WMFwzHTlyhVLp0J1DAdgyOZMnToV+/fvx8yZM/HZZ59hwIAB2Lt3L2JjYy2dWp1w+/ZtxMbG8sCFiKgO4D6fiIjM7b333sOuXbssnQYRERFRpXAAhmzOoUOHMHjwYLzxxht48cUX0apVK+zduxfz5s2zdGp1wu3btzFv3jyejCMisnK9evXCnTt30KtXrzJjuM8nIiJz4wAMEVHt9PHHHyMtLc3SaRDVOhyAIZuTnZ0Nd3d3S6dBRET00PLz8y22bXt7ezg7O8Penh8XiYgs6WH6Akv2I0REVLc4OTlBLpdbOg2iWodH1FQr3Lx5E9HR0Xj00Uchl8vh5eWF/v3744cffpBi1q5di8cffxwuLi7o2rUrvvvuO/Tu3Ru9e/cG8L97OQohEB8fDzs7O9jZ2WHMmDGIj48HAGmanZ1dlXNMTk5G9+7d4eLigoCAACQkJBjNLygowJw5cxAUFAQ3NzfUq1cPPXv2xOHDh0usa+vWrQgKCkKDBg2gUCjQrl07rFixwigmNzcX0dHR8PPzg1wuR/PmzbFo0SIUFxdLMVeuXIGdnR3ef/99xMfH47HHHoOrqytCQ0Pxxx9/QAiBBQsWoGnTpnBxccHgwYORk5NTIp9vv/0WPXv2RL169dCgQQOEhYXhwoULRjFjxoxB/fr18ddff2HIkCGoX78+PD098cYbb6CoqEjKx9PTEwAwb948qa15+zciqssOHz4MOzs77Ny5s8S8LVu2wM7ODhqNRtrP/vrrrxg0aBAaNGiAkSNHVmlbt2/fxiuvvIJGjRpBoVBg9OjRuH79ulFMWfvlRx99FGPGjJFeV3QvfO7ziYgqzxx9geF46MiRI3j11Vfh5eWFpk2bSvNN9ZnfID8/H6+//rp0vNKyZUu8//77EEJIMW3btkWfPn1K5FpcXIxHHnkE//nPf6Rp77//Prp3745GjRrBxcUFQUFB+OKLL4yWs7OzQ35+PjZt2mR0vGfw119/YezYsfD29oZcLkebNm2wfv36Etv/888/MWTIENSrVw9eXl6YOnUqdDpdpdqZiKguMpx/KusHKPkMmPvPWS1fvhz+/v5wcXHBU089hdTU1BLbuHTpEp5//nl4enrCxcUFLVu2xNtvv20Uc/bsWQwcOBAKhQL169dHv379cOLEiRLrunDhAvr27QsXFxc0bdoU77zzjtH5tPtVpn8kehiOlk6ACAAmTpyIL774AlFRUQgMDMS1a9dw7Ngx/PTTT+jcuTPWrVuHV155Bd27d0d0dDR+++03PPPMM/Dw8ICfnx+Ae7dK+eyzzzBq1Cj0798fo0ePBgA8/vjjuHr1KtRqNT777LNq5Xf9+nUMGjQIzz//PEaMGIHt27dj0qRJkMlkGDt2LAAgLy8Pn3zyCUaMGIHx48fj5s2bWLduHVQqFU6dOoWOHTsCANRqNUaMGIF+/fph0aJFAICffvoJ33//PaZMmQLg3gm0p556Cn/99RdeeeUVNGvWDMePH8fMmTORmZmJDz74wCi/zZs3o6CgAJMnT0ZOTg4WL16M559/Hn379kVSUhJmzJiBy5cvY+XKlXjjjTeMDkI+++wzREREQKVSYdGiRbh9+zbWrFmDHj164OzZs0adZ1FREVQqFYKDg/H+++/j4MGDWLp0KR5//HFMmjQJnp6eWLNmDSZNmoRnn30WQ4cOBQC0b9++Wu1ORGQLevfuDT8/P2zevBnPPvus0bzNmzfj8ccfh1KpxEcffYTCwkKoVCr06NED77//PlxdXau0raioKLi7uyM2NhZpaWlYs2YNfv/9d2kwxZS4zyciqjxz9gWvvvoqPD09MWfOHOkKGFN+5gcAIQSeeeYZHD58GOPGjUPHjh2xf/9+TJs2DX/99ReWL18OABg2bBhiY2Oh1Wrh4+MjbePYsWO4evUqhg8fLk1bsWIFnnnmGYwcORIFBQXYunUrnnvuOezevRthYWFSHS+//DK6du2KCRMmALh3vAcAWVlZ6NatG+zs7BAVFQVPT098++23GDduHPLy8hAdHQ0AuHPnDvr164eMjAy89tpr8PX1xWeffYZDhw5VqZ2JiOoST0/PEufU9Ho9pk6dCplMVu6yn376KW7evInIyEjcvXsXK1asQN++fXH+/Hl4e3sDAM6dO4eePXvCyckJEyZMwKOPPopff/0V33zzDd59910A9wZVevbsCYVCgenTp8PJyQkfffQRevfujSNHjiA4OBgAoNVq0adPHxQWFuLNN99EvXr1sHbtWri4uJTIrSr9I1G1CaJawM3NTURGRpY6r6CgQHh5eYmOHTsKnU4nTV+7dq0AIJ566imjeAAl1hUZGSmq+3Z/6qmnBACxdOlSaZpOpxMdO3YUXl5eoqCgQAghRGFhoVF+Qghx/fp14e3tLcaOHStNmzJlilAoFKKwsLDMbS5YsEDUq1dP/Pzzz0bT33zzTeHg4CAyMjKEEEKkp6cLAMLT01Pk5uZKcTNnzhQARIcOHYRer5emjxgxQshkMnH37l0hhBA3b94U7u7uYvz48Ubb0Wq1ws3NzWh6RESEACDmz59vFNupUycRFBQkvf77778FADF37twy6yMiqmtmzpwp5HK50b46OztbODo6SvtLw372zTffrPL6N2zYIACIoKAgqV8SQojFixcLAOKrr76SppW1j/b39xcRERHS68OHDwsA4vDhw9K0iIgI4e/vL73mPp+IqPLM1Rf06NHD6FijJj7z79q1SwAQ77zzjlHcf/7zH2FnZycuX74shBAiLS1NABArV640inv11VdF/fr1xe3bt6Vp9/9fiHvHgW3bthV9+/Y1ml6vXj2j/spg3LhxokmTJuKff/4xmj58+HDh5uYmrf+DDz4QAMT27dulmPz8fNG8efMS/R4REZXt1VdfFQ4ODuLQoUNCiJLHCoZzVi4uLuLPP/+Upp88eVIAEFOnTpWm9erVSzRo0ED8/vvvRtsoLi6W/j9kyBAhk8nEr7/+Kk27evWqaNCggejVq5c0LTo6WgAQJ0+elKZlZ2cLNzc3AUCkp6cLIarWPxI9DN6CjGoFd3d3nDx5ElevXi0x78yZM8jOzsbEiRONRtXHjBkDNzc3s+Tn6OiIV155RXotk8nwyiuvIDs7G8nJyQAABwcHKb/i4mLk5OSgsLAQXbp0MbqVmru7O/Lz86FWq8vc3o4dO9CzZ080bNgQ//zzj/QTEhKCoqIiHD161Cj+ueeeM2oLw6j/iy++CEdHR6PpBQUF+OuvvwDcuxonNzcXI0aMMNqOg4MDgoODS7192sSJE41e9+zZE7/99lv5DUhEVMeNHj0aOp3O6FYq27ZtQ2FhIV588UWjWMO3i6tjwoQJcHJyMlqXo6Mj9u7dW+11EhGRaZirLxg/fjwcHByk1zXxmX/v3r1wcHDAa6+9ZhT3+uuvQwiBb7/9FgDwxBNPoGPHjti2bZsUU1RUhC+++AJPP/200beR7///9evXcePGDfTs2dPoWKosQgj83//9H55++mkIIYzqVKlUuHHjhrSevXv3okmTJka3P3N1dZWuqCEioop9+umnWL16NRYvXlzqrSbvN2TIEDzyyCPS665duyI4OFg6Rvn7779x9OhRjB07Fs2aNTNa1nAVf1FREQ4cOIAhQ4bgsccek+Y3adIEL7zwAo4dO4a8vDwA9/bz3bp1Q9euXaU4T0/PErf0rE7/SFQdvAUZ1QqLFy9GREQE/Pz8EBQUhEGDBmH06NF47LHH8PvvvwMAWrRoYbSMk5OT0U63Jvn6+qJevXpG05544gkA9+5p2a1bNwDApk2bsHTpUly6dAl6vV6KDQgIkP7/6quvYvv27Rg4cCAeeeQRhIaG4vnnn8eAAQOkmF9++QXnzp2T7q3/oOzsbKPXD3ZQhsEYw+3ZHpxueB7AL7/8AgDo27dvqdtRKBRGr52dnUvk1LBhwxLPFyAiImOtWrXCv/71L2zevBnjxo0DcO+WM926dUPz5s2lOEdHR6P79VfVg31l/fr10aRJE1y5cqXa6yQiItMwV19w/7EHUDOf+X///Xf4+vqiQYMGRnGtW7eW5hsMGzYMb731Fv766y888sgjSEpKQnZ2NoYNG2a07O7du/HOO+8gJSXF6HkslbmF5t9//43c3FysXbsWa9euLTXGcAz1+++/o3nz5iXW27Jlywq3Q0REQEpKCiZOnIgRI0YgJiamwvgHj1GAe+fUtm/fDgDSAH/btm3LXMfff/+N27dvl7qvbt26NYqLi/HHH3+gTZs2+P3336UvJt/vwWWr2j8SVRcHYKhWeP7559GzZ0/s3LkTBw4cwJIlS7Bo0SJ8+eWXlk6t0j7//HOMGTMGQ4YMwbRp0+Dl5QUHBwfExcXh119/leK8vLyQkpKC/fv349tvv8W3336LDRs2YPTo0di0aROAe1fQ9O/fH9OnTy91W4bBH4P7v+FWmeni/z8Y0/AAss8++8zonswG9189U976iIioYqNHj8aUKVPw559/QqfT4cSJE1i1apVRjFwuh729ZS5QfvDhykREZHrm6AsevMe9pT/zDxs2DDNnzsSOHTsQHR2N7du3w83NzegLaN999x2eeeYZ9OrVC6tXr0aTJk3g5OSEDRs2YMuWLRVuw1Djiy++iIiIiFJj+IwyIqKHd/36dYSHh+OJJ57AJ598Yul0HkpV+0ei6uI7iWqNJk2a4NVXX8Wrr76K7OxsdO7cGe+++y6WLFkC4N7I9P2j0nq9Hunp6ejQoUOF637YBw9fvXoV+fn5RlfB/PzzzwAgPZDriy++wGOPPYYvv/zSaHtz584tsT6ZTIann34aTz/9NIqLi/Hqq6/io48+wuzZs9G8eXM8/vjjuHXrFkJCQh4q74oYHljp5eVlsm2Z+iHPRES2Yvjw4YiJicF///tf3LlzB05OTiW+/fuwfvnlF6NbANy6dQuZmZkYNGiQNK1hw4bIzc01Wq6goACZmZlV3h73+UREVWOOvuBBNfGZ39/fHwcPHsTNmzeNroK5dOmSNN8gICAAXbt2xbZt2xAVFYUvv/wSQ4YMgVwul2L+7//+D87Ozti/f7/R9A0bNpTYdml9j6enJxo0aICioqIKa/T390dqaiqEEEbrSktLq0TlRER1V3FxMUaOHInc3FwcPHgQrq6ulVrOcKXJ/X7++WfpfJrh7japqallrsPT0xOurq6l7qsvXboEe3t76S4w/v7+pW7zwWVron8kKg2fAUMWV1RUhBs3bhhN8/Lygq+vL3Q6Hbp06QJPT08kJCSgoKBAitm4cWOJE0hlMQycVDb+QYWFhfjoo4+k1wUFBfjoo4/g6emJoKAgAP/7ppjh6hIAOHnyJDQajdG6rl27ZvTa3t5e+jaW4VL7559/HhqNBvv37y+RS25uLgoLC6tVx4NUKhUUCgXee+89o1umGfz9999VXqehA65uWxMR2arGjRtj4MCB+Pzzz7F582YMGDAAjRs3Nuk21q5da7Q/X7NmDQoLCzFw4EBp2uOPP17iWWJr166t1hUw3OcTEVWNOfqCB9XEZ/5BgwahqKioxNU7y5cvh52dnVG/A9y7CubEiRNYv349/vnnnxKDTg4ODrCzszPqi65cuYJdu3aV2Ha9evVK9DsODg4IDw/H//3f/5V6Au/+GgcNGoSrV68aPYvn9u3bZd66jIiI7pk3bx7279+P//73vyVud1meXbt2Sc8iBoBTp07h5MmTUl/h6emJXr16Yf369cjIyDBa1nCOzcHBAaGhofjqq6+Mbq+clZWFLVu2oEePHtItwwYNGoQTJ07g1KlTUtzff/+NzZs3G627JvpHotLwChiyuJs3b6Jp06b4z3/+gw4dOqB+/fo4ePAgTp8+jaVLl8LJyQnvvPMOXnnlFfTt2xfDhg1Deno6NmzYUOlnwBgGSV577TWoVCo4ODhg+PDhlc7R19cXixYtwpUrV/DEE09g27ZtSElJwdq1a6WHHf/73//Gl19+iWeffRZhYWFIT09HQkICAgMDcevWLWldL7/8MnJyctC3b180bdoUv//+O1auXImOHTtK90yeNm0avv76a/z73//GmDFjEBQUhPz8fJw/fx5ffPEFrly5YpIDNYVCgTVr1mDUqFHo3Lkzhg8fDk9PT2RkZGDPnj148sknSxxUVcTFxQWBgYHYtm0bnnjiCXh4eKBt27bl3suTiKiuGD16tPTQ3wULFph8/QUFBejXrx+ef/55pKWlYfXq1ejRoweeeeYZKebll1/GxIkTER4ejv79++PHH3/E/v37q9WvcJ9PRFR1Nd0XPKgmPvM//fTT6NOnD95++21cuXIFHTp0wIEDB/DVV18hOjpa+laxwfPPP4833ngDb7zxBjw8PEp80zgsLAzLli3DgAED8MILLyA7Oxvx8fFo3rw5zp07ZxQbFBSEgwcPYtmyZfD19UVAQACCg4OxcOFCHD58GMHBwRg/fjwCAwORk5ODH374AQcPHkROTg4AYPz48Vi1ahVGjx6N5ORkNGnSBJ999lmlv8lNRFQXnT9/HgsWLECvXr2QnZ2Nzz//3Gj+iy++WOayzZs3R48ePTBp0iTodDp88MEHaNSokdFt9z/88EP06NEDnTt3xoQJExAQEIArV65gz549SElJAQC88847UKvV6NGjB1599VU4Ojrio48+gk6nw+LFi6V1TZ8+HZ999hkGDBiAKVOmoF69eli7di38/f2N+pSa6B+JSiWILEyn04lp06aJDh06iAYNGoh69eqJDh06iNWrVxvFrV69WgQEBAi5XC66dOkijh49Kp566inx1FNPGcUBEJGRkUbTCgsLxeTJk4Wnp6ews7MTVXnrP/XUU6JNmzbizJkzQqlUCmdnZ+Hv7y9WrVplFFdcXCzee+894e/vL+RyuejUqZPYvXu3iIiIEP7+/lLcF198IUJDQ4WXl5eQyWSiWbNm4pVXXhGZmZlG67t586aYOXOmaN68uZDJZKJx48aie/fu4v333xcFBQVCCCHS09MFALFkyRKjZQ8fPiwAiB07dhhN37BhgwAgTp8+XSJepVIJNzc34ezsLB5//HExZswYcebMGSkmIiJC1KtXr0T7zJ07t0R7Hj9+XAQFBQmZTCYAiLlz55bfyEREdYROpxMNGzYUbm5u4s6dO0bzytrPVoZh/37kyBExYcIE0bBhQ1G/fn0xcuRIce3aNaPYoqIiMWPGDNG4cWPh6uoqVCqVuHz5svD39xcRERFSnKEvOXz4sFGO9/dpQnCfT0RUVTXdFzz4Wd/A1J/5b968KaZOnSp8fX2Fk5OTaNGihViyZIkoLi4udftPPvmkACBefvnlUuevW7dOtGjRQsjlctGqVSuxYcOGUrd76dIl0atXL+Hi4iIAGPVdWVlZIjIyUvj5+QknJyfh4+Mj+vXrJ9auXWu0jt9//10888wzwtXVVTRu3FhMmTJF7Nu3r0S/R0RE9xiODcr6EaLkscL956yWLl0q/Pz8hFwuFz179hQ//vhjiW2kpqaKZ599Vri7uwtnZ2fRsmVLMXv2bKOYH374QahUKlG/fn3h6uoq+vTpI44fP15iXefOnRNPPfWUcHZ2Fo888ohYsGCBWLdunQAg0tPTS9RWUf9I9DDshLjvfklEVqZ3794AgKSkJIvmQUREVBmFhYXw9fXF008/jXXr1lk6HSIisgD2BUREVBdcuXIFAQEBWLJkCd544w1Lp0NkMXwGDBEREZGZ7Nq1C3///TdGjx5t6VSIiMhC2BcQERER1R18BgzVWTk5OSgoKChzvoODAzw9Pc2YERER2aqTJ0/i3LlzWLBgATp16oSnnnqqUsvduXMHN27cKDfGw8MDMpnMFGkSEVENYl9AREREVPdwAIbqrKFDh+LIkSNlzvf398eVK1fMlxAREdmsNWvW4PPPP0fHjh2xcePGSi+3bds2vPTSS+XGHD58WLolJxER1V7sC4iIiIjqHj4Dhuqs5ORkXL9+vcz5Li4uePLJJ82YERERkbHMzExcuHCh3JigoCA0bNjQTBkREZG5sS8gIiIisl4cgCEiIiIiIiIiIiIiIjIxe0snQEREREREREREREREZGs4AENERERERERERERERGRijpZOwJKKi4tx9epVNGjQAHZ2dpZOh4ioxgghcPPmTfj6+sLenmPv5sS+hojqCvY1lsO+hojqCvY1lsO+hojqElP2N3V6AObq1avw8/OzdBpERGbzxx9/oGnTppZOo05hX0NEdQ37GvNjX0NEdQ37GvNjX0NEdZEp+ps6PQDToEEDAPcaUqFQVHo5vV6PAwcOIDQ0FE5OTjWVXq1Rl+qtS7UCrNeWPVhrXl4e/Pz8pP0emU9d72tYR+3COmoXW6tDqVQiICCAfY0F1PW+pqawfcrGtikb26Z8D9s+PK6xnOr0Nfx7qBjbqHxsn4qxjSpWnTYyZX9TpwdgDJdMKhSKKh+ouLq6QqFQ1Ik3dl2qty7VCrBeW1ZWrbxU3Pzqel/DOmoX1lG72FodhoMT9jXmV9f7mprC9ikb26ZsbJvymap92NeYX3X6Gv49VIxtVD62T8XYRhV7mDYyRX/DG2YSERERERERERERERGZGAdgiIiIiIiIiIiIiIiITIwDMERERERERERERERERCbGARgiIiIiIiIiIiIiIiITc6zqAkePHsWSJUuQnJyMzMxM7Ny5E0OGDJHmjxkzBps2bTJaRqVSYd++fdLrnJwcTJ48Gd988w3s7e0RHh6OFStWoH79+lLMuXPnEBkZidOnT8PT0xOTJ0/G9OnTjda7Y8cOzJ49G1euXEGLFi2waNEiDBo0qKolVVvb2P3QFZnvwW9XFoaZbVtEREREVDs8+uYes2+zLnzujIuLw5dffolLly7BxcUF3bt3x6JFi9CyZUsp5u7du3j99dexdetW6HQ6qFQqrF69Gt7e3lJMRkYGJk2ahMOHD6N+/fqIiIhAXFwcHB3/d6iVlJSEmJgYXLhwAX5+fpg1axbGjBljlE98fDyWLFkCrVaLDh06YOXKlejatWuNtwPA4xoiIrI9/PxERLVFla+Ayc/PR4cOHRAfH19mzIABA5CZmSn9/Pe//zWaP3LkSFy4cAFqtRq7d+/G0aNHMWHCBGl+Xl4eQkND4e/vj+TkZCxZsgSxsbFYu3atFHP8+HGMGDEC48aNw9mzZzFkyBAMGTIEqampVS2JiIiIiIjqmCNHjiAyMhInTpyAWq2GXq9HaGgo8vPzpZipU6fim2++wY4dO3DkyBFcvXoVQ4cOleYXFRUhLCwMBQUFOH78ODZt2oSNGzdizpw5Ukx6ejrCwsLQp08fpKSkIDo6Gi+//DL2798vxWzbtg0xMTGYO3cufvjhB3To0AEqlQrZ2dnmaQwiIiIiIqoRVb4CZuDAgRg4cGC5MXK5HD4+PqXO++mnn7Bv3z6cPn0aXbp0AQCsXLkSgwYNwvvvvw9fX19s3rwZBQUFWL9+PWQyGdq0aYOUlBQsW7ZMGqhZsWIFBgwYgGnTpgEAFixYALVajVWrViEhIaGqZRERERERUR1y/xX6ALBx40Z4eXkhOTkZvXr1wo0bN7Bu3Tps2bIFffv2BQBs2LABrVu3xokTJ9CtWzccOHAAFy9exMGDB+Ht7Y2OHTtiwYIFmDFjBmJjYyGTyZCQkICAgAAsXboUANC6dWscO3YMy5cvh0qlAgAsW7YM48ePx0svvQQASEhIwJ49e7B+/Xq8+eabZmwVIiIiIiIypSoPwFRGUlISvLy80LBhQ/Tt2xfvvPMOGjVqBADQaDRwd3eXBl8AICQkBPb29jh58iSeffZZaDQa9OrVCzKZTIpRqVRYtGgRrl+/joYNG0Kj0SAmJsZouyqVCrt27aqJkoiIiIiIyIbduHEDAODh4QEASE5Ohl6vR0hIiBTTqlUrNGvWDBqNBt26dYNGo0G7du2MbkmmUqkwadIkXLhwAZ06dYJGozFahyEmOjoaAFBQUIDk5GTMnDlTmm9vb4+QkBBoNJpSc9XpdNDpdNLrvLw8AIBer4der690zYZYub2o9DKmUJUcLcmQp7Xka05sm7Kxbcr3sO3DdiUiImtj8gGYAQMGYOjQoQgICMCvv/6Kt956CwMHDoRGo4GDgwO0Wi28vLyMk3B0hIeHB7RaLQBAq9UiICDAKMZwUKPVatGwYUNotVqjAx1DjGEdpeGBysNtty580KlLtQKs15Y9WGtdqJmIiKi6iouLER0djSeffBJt27YFcO+4QyaTwd3d3Sj2/mOOso5JDPPKi8nLy8OdO3dw/fp1FBUVlRpz6dKlUvONi4vDvHnzSkw/cOAAXF1dK1n1/yzoUlzlZR7G3r17zbq9h6VWqy2dQq3Ftikb26Z81W2f27dvmzgTIrIGfF4dWTOTD8AMHz5c+n+7du3Qvn17PP7440hKSkK/fv1Mvbkq4YHKw6lLHyDrUq0A67Vlhlp5oEJERFS2yMhIpKam4tixY5ZOpVJmzpxpdDeAvLw8+Pn5ITQ0FAqFotLr0ev1UKvVmH3GHrpi853USI1VmW1bD8PQPv3794eTk5Ol06lV2DZlY9uU72Hbx/BFWiIiImtRI7cgu99jjz2Gxo0b4/Lly+jXrx98fHxKPEyysLAQOTk50nNjfHx8kJWVZRRjeF1RTFnPngF4oFJddekDZF2qFWC9tuzBWnmgQkREVLqoqCjs3r0bR48eRdOmTaXpPj4+KCgoQG5urtFVMPcfc/j4+ODUqVNG66vscYtCoYCLiwscHBzg4OBQpWMbuVwOuVxeYrqTk1O1PuPoiu3M+q1Sa/scVt12rQvYNmVj25Svuu3DNiUiImtT4wMwf/75J65du4YmTZoAAJRKJXJzc5GcnIygoCAAwKFDh1BcXIzg4GAp5u2334Zer5c6V7VajZYtW6Jhw4ZSTGJionTvZEOMUqksMxceqDz89i2dg7nUpVoB1mvLDLXWlXqJiIgqSwiByZMnY+fOnUhKSipxC+SgoCA4OTkhMTER4eHhAIC0tDRkZGRIxxxKpRLvvvsusrOzpdssq9VqKBQKBAYGSjEPXsl+/3GLTCZDUFAQEhMTMWTIEAD3bomWmJiIqKioGqufiIiIiIhqnn1VF7h16xZSUlKQkpICAEhPT0dKSgoyMjJw69YtTJs2DSdOnMCVK1eQmJiIwYMHo3nz5lCp7l290bp1awwYMADjx4/HqVOn8P333yMqKgrDhw+Hr68vAOCFF16ATCbDuHHjcOHCBWzbtg0rVqwwunplypQp2LdvH5YuXYpLly4hNjYWZ86c4UEKERERERFVKDIyEp9//jm2bNmCBg0aQKvVQqvV4s6dOwAANzc3jBs3DjExMTh8+DCSk5Px0ksvQalUolu3bgCA0NBQBAYGYtSoUfjxxx+xf/9+zJo1C5GRkdIXvyZOnIjffvsN06dPx6VLl7B69Wps374dU6dOlXKJiYnBxx9/jE2bNuGnn37CpEmTkJ+fj5deesn8DUNERBazcOFC2NnZGX3Z+O7du4iMjESjRo1Qv359hIeHl7hqMiMjA2FhYXB1dYWXlxemTZuGwsJCo5ikpCR07twZcrkczZs3x8aNG81QERERVXkA5syZM+jUqRM6deoE4N7BQqdOnTBnzhw4ODjg3LlzeOaZZ/DEE09g3LhxCAoKwnfffWd05cnmzZvRqlUr9OvXD4MGDUKPHj2wdu1aab6bmxsOHDiA9PR0BAUF4fXXX8ecOXMwYcIEKaZ79+7YsmUL1q5diw4dOuCLL77Arl27pIdmEhERERERlWXNmjW4ceMGevfujSZNmkg/27Ztk2KWL1+Of//73wgPD0evXr3g4+ODL7/8Uprv4OCA3bt3w8HBAUqlEi+++CJGjx6N+fPnSzEBAQHYs2cP1Go1OnTogKVLl+KTTz6RvqAGAMOGDcP777+POXPmoGPHjkhJScG+ffvg7e1tnsYgIiKLO336ND766CO0b9/eaPrUqVPxzTffYMeOHThy5AiuXr2KoUOHSvOLiooQFhaGgoICHD9+HJs2bcLGjRsxZ84cKSY9PR1hYWHo06cPUlJSEB0djZdffhn79+83W31ERHVVlW9B1rt3bwghypxfmZ23h4cHtmzZUm5M+/bt8d1335Ub89xzz+G5556rcHtERERERET3K++YxsDZ2Rnx8fGIj48vM8bf37/ELcYe1Lt3b5w9e7bcmKioKF7NT0RUR926dQsjR47Exx9/jHfeeUeafuPGDaxbtw5btmxB3759AQAbNmxA69atceLECXTr1g0HDhzAxYsXcfDgQXh7e6Njx45YsGABZsyYgdjYWMhkMiQkJCAgIABLly4FcO/uNMeOHcPy5cuNvhBARESmV+PPgCEiIiIiIiIiIqLSRUZGIiwsDCEhIUYDMMnJydDr9QgJCZGmtWrVCs2aNYNGo0G3bt2g0WjQrl07o6smVSoVJk2ahAsXLqBTp07QaDRG6zDE3H+rswfpdDrodDrpdV5eHgBAr9dDr9dXqi5DXGXjTUnuUPEXLUytOnVaso2sgaFd5Pbm/X1a0++D76GKVaeNTNmeHIAhIiIiIiIiIiKygK1bt+KHH37A6dOnS8zTarWQyWRwd3c3mu7t7Q2tVivFPHjLSsPrimLy8vJw584duLi4lNh2XFwc5s2bV2L6gQMH4OrqWvkCAajV6irFm8LirmbfZIVXxJbHEm1kTRZ0KTbr9h7md2kpfA9VrCptdPv2bZNtlwMwREREREREREREZvbHH39gypQpUKvVcHZ2tnQ6RmbOnImYmBjpdV5eHvz8/BAaGgqFQlGpdej1eqjVavTv3x9OTk41lWqp2saa//k2qbFVv52bJdvIGhjaZ/YZe+iK7cy23er8Li2F76GKVaeNDFf9mQIHYIiIiIiIiIiIiMwsOTkZ2dnZ6Ny5szStqKgIR48exapVq7B//34UFBQgNzfX6CqYrKws+Pj4AAB8fHxw6tQpo/VmZWVJ8wz/GqbdH6NQKEq9+gUA5HI55HJ5ielOTk5VPslbnWUelq7IfCfrDR6mRku0kTXRFduZ9Xdqjb8LvocqVpU2MmVb2ptsTURERERERERERFQp/fr1w/nz55GSkiL9dOnSBSNHjpT+7+TkhMTERGmZtLQ0ZGRkQKlUAgCUSiXOnz+P7OxsKUatVkOhUCAwMFCKuX8dhhjDOoiIqObwChgiIiIiIiIiIiIza9CgAdq2bWs0rV69emjUqJE0fdy4cYiJiYGHhwcUCgUmT54MpVKJbt26AQBCQ0MRGBiIUaNGYfHixdBqtZg1axYiIyOlK1gmTpyIVatWYfr06Rg7diwOHTqE7du3Y8+ePeYtmIioDuIADBERERERERERUS20fPly2NvbIzw8HDqdDiqVCqtXr5bmOzg4YPfu3Zg0aRKUSiXq1auHiIgIzJ8/X4oJCAjAnj17MHXqVKxYsQJNmzbFJ598ApXKep5zQURkrTgAQ0REREREREREVAskJSUZvXZ2dkZ8fDzi4+PLXMbf3x979+4td729e/fG2bNnTZEiERFVAZ8BQ0REVmfNmjVo3749FAoFFAoFlEolvv32W2n+3bt3ERkZiUaNGqF+/foIDw8v8dDJjIwMhIWFwdXVFV5eXpg2bRoKCwuNYpKSktC5c2fI5XI0b94cGzduNEd5RERERERERERkAzgAQ0REVqdp06ZYuHAhkpOTcebMGfTt2xeDBw/GhQsXAABTp07FN998gx07duDIkSO4evUqhg4dKi1fVFSEsLAwFBQU4Pjx49i0aRM2btyIOXPmSDHp6ekICwtDnz59kJKSgujoaLz88svYv3+/2eslIiIiIiIiIiLrw1uQERGR1Xn66aeNXr/77rtYs2YNTpw4gaZNm2LdunXYsmUL+vbtCwDYsGEDWrdujRMnTqBbt244cOAALl68iIMHD8Lb2xsdO3bEggULMGPGDMTGxkImkyEhIQEBAQFYunQpAKB169Y4duwYli9fznslExERERERERFRhTgAQ0REVq2oqAg7duxAfn4+lEolkpOTodfrERISIsW0atUKzZo1g0ajQbdu3aDRaNCuXTt4e3tLMSqVCpMmTcKFCxfQqVMnaDQao3UYYqKjo8vNR6fTQafTSa/z8vIAAHq9Hnq9vtJ1GWKrskxtxDpqF9ZRu1S2DrmDMEc6Ruri/oqIiIiIiMjUOABDRERW6fz581Aqlbh79y7q16+PnTt3IjAwECkpKZDJZHB3dzeK9/b2hlarBQBotVqjwRfDfMO88mLy8vJw584duLi4lJpXXFwc5s2bV2L6gQMH4OrqWuU61Wp1lZepjVhH7cI6apeK6ljc1UyJ3KeiB/mW5vDhwzWQCRERERERkfXiAAwREVmlli1bIiUlBTdu3MAXX3yBiIgIHDlyxNJpYebMmYiJiZFe5+Xlwc/PD6GhoVAoFJVej16vh1qtRv/+/eHk5FQTqZoF66hdWEftUtk62saa/9lTqbGVv9WioY4+ffrUYEZERERERETWhwMwRERklWQyGZo3bw4ACAoKwunTp7FixQoMGzYMBQUFyM3NNboKJisrCz4+PgAAHx8fnDp1ymh9WVlZ0jzDv4Zp98coFIoyr34BALlcDrlcXmK6k5NTtU4UV3e52oZ1mN+jb+4pMU3uILC4K9Dp3UPQFdmZfJtXFoaZfJ3lsabfR3kqqqMmflcVqe7+ioiIiIiIiP7H3tIJEBERmUJxcTF0Oh2CgoLg5OSExMREaV5aWhoyMjKgVCoBAEqlEufPn0d2drYUo1aroVAoEBgYKMXcvw5DjGEdRERERERERERE5eEVMEREZHVmzpyJgQMHolmzZrh58ya2bNmCpKQk7N+/H25ubhg3bhxiYmLg4eEBhUKByZMnQ6lUolu3bgCA0NBQBAYGYtSoUVi8eDG0Wi1mzZqFyMhI6eqViRMnYtWqVZg+fTrGjh2LQ4cOYfv27dizp+RVBURERERERERERA/iAAwREVmd7OxsjB49GpmZmXBzc0P79u2xf/9+9O/fHwCwfPly2NvbIzw8HDqdDiqVCqtXr5aWd3BwwO7duzFp0iQolUrUq1cPERERmD9/vhQTEBCAPXv2YOrUqVixYgWaNm2KTz75BCpV5Z+LQES2p7Rbu1WX4ZZwbWP3W+Q2Y0RERERERFSzOABDRERWZ926deXOd3Z2Rnx8POLj48uM8ff3x969e8tdT+/evXH27Nlq5UhERERERERERHUbnwFDRERERERERERERERkYrwChoiIiIhMwpS35yqP4dZdRERERERERLUZr4AhIiIiIiIiIiIiIiIyMV4BQ0RERERERERERPQQqnM1uOHK7rax+6Ersqvy8lcWhlV5GSIyLw7AEBEREdUwc92ai4gq7+jRo1iyZAmSk5ORmZmJnTt3YsiQIdL8MWPGYNOmTUbLqFQq7Nu3T3qdk5ODyZMn45tvvoG9vT3Cw8OxYsUK1K9fX4o5d+4cIiMjcfr0aXh6emLy5MmYPn260Xp37NiB2bNn48qVK2jRogUWLVqEQYMG1UzhRERERERkNrwFGRERERER1Tn5+fno0KED4uPjy4wZMGAAMjMzpZ///ve/RvNHjhyJCxcuQK1WY/fu3Th69CgmTJggzc/Ly0NoaCj8/f2RnJyMJUuWIDY2FmvXrpVijh8/jhEjRmDcuHE4e/YshgwZgiFDhiA1NdX0RRMRERERkVnxChgiIiIiIqpzBg4ciIEDB5YbI5fL4ePjU+q8n376Cfv27cPp06fRpUsXAMDKlSsxaNAgvP/++/D19cXmzZtRUFCA9evXQyaToU2bNkhJScGyZcukgZoVK1ZgwIABmDZtGgBgwYIFUKvVWLVqFRISEkxYMRERERERmRuvgCEiIiIiIipFUlISvLy80LJlS0yaNAnXrl2T5mk0Gri7u0uDLwAQEhICe3t7nDx5Uorp1asXZDKZFKNSqZCWlobr169LMSEhIUbbValU0Gg0NVkaERERERGZAa+AISIiIiIiesCAAQMwdOhQBAQE4Ndff8Vbb72FgQMHQqPRwMHBAVqtFl5eXkbLODo6wsPDA1qtFgCg1WoREBBgFOPt7S3Na9iwIbRarTTt/hjDOkqj0+mg0+mk13l5eQAAvV4PvV5f6RoNsXJ7UellTKEqOVqSIU9rydec2DZlY9uU72Hbh+1KRETWhgMwRERERGSV2sbuh67IztJpkI0aPny49P927dqhffv2ePzxx5GUlIR+/fpZMDMgLi4O8+bNKzH9wIEDcHV1rfL6FnQpNkValbZ3716zbu9hqdVqS6dQa7Ftysa2KV912+f27dsmzoSIiKhmcQCGiIiIiIioAo899hgaN26My5cvo1+/fvDx8UF2drZRTGFhIXJycqTnxvj4+CArK8soxvC6opiynj0DADNnzkRMTIz0Oi8vD35+fggNDYVCoah0TXq9Hmq1GrPP2ENXbL7BzNRYldm29TAM7dO/f384OTlZOp1ahW1TNrZN+R62fQxX/NmSNWvWYM2aNbhy5QoAoE2bNpgzZ470nLK7d+/i9ddfx9atW6HT6aBSqbB69WqjqyczMjIwadIkHD58GPXr10dERATi4uLg6Pi/035JSUmIiYnBhQsX4Ofnh1mzZmHMmDHmLJWIqE7iAAwREREREVEF/vzzT1y7dg1NmjQBACiVSuTm5iI5ORlBQUEAgEOHDqG4uBjBwcFSzNtvvw29Xi+daFSr1WjZsiUaNmwoxSQmJiI6OlrallqthlKpLDMXuVwOuVxeYrqTk1O1Tmjqiu3MejWZtZ2Urm671gVsm7KxbcpX3faxxTZt2rQpFi5ciBYtWkAIgU2bNmHw4ME4e/Ys2rRpg6lTp2LPnj3YsWMH3NzcEBUVhaFDh+L7778HABQVFSEsLAw+Pj44fvw4MjMzMXr0aDg5OeG9994DAKSnpyMsLAwTJ07E5s2bkZiYiJdffhlNmjSBSmUdg+JERNbK3tIJEBERERERmdutW7eQkpKClJQUAPdOTqWkpCAjIwO3bt3CtGnTcOLECVy5cgWJiYkYPHgwmjdvLp2oat26NQYMGIDx48fj1KlT+P777xEVFYXhw4fD19cXAPDCCy9AJpNh3LhxuHDhArZt24YVK1YYXb0yZcoU7Nu3D0uXLsWlS5cQGxuLM2fOICoqyuxtQkRE5vf0009j0KBBaNGiBZ544gm8++67qF+/Pk6cOIEbN25g3bp1WLZsGfr27YugoCBs2LABx48fx4kTJwDcu/3kxYsX8fnnn6Njx44YOHAgFixYgPj4eBQUFAAAEhISEBAQgKVLl6J169aIiorCf/7zHyxfvtySpRMR1Qm8AoaIiIiIiOqcM2fOoE+fPtJrw6BIREQE1qxZg3PnzmHTpk3Izc2Fr68vQkNDsWDBAqMrTzZv3oyoqCj069cP9vb2CA8Px4cffijNd3Nzw4EDBxAZGYmgoCA0btwYc+bMwYQJE6SY7t27Y8uWLZg1axbeeusttGjRArt27ULbtm3N0ApERFSbFBUVYceOHcjPz4dSqURycjL0ej1CQkKkmFatWqFZs2bQaDTo1q0bNBoN2rVrZ3RLMpVKhUmTJuHChQvo1KkTNBqN0ToMMfdfffkgnU4HnU4nvTbc/k2v10Ov11eqHkNcZeNNSe4gzL7N6pDbC6N/q8oSbWtOhvqq2z4Pu11rYMm/M2tRnTYyZXtyAIaIiIiIiOqc3r17Q4iyD+b3799f4To8PDywZcuWcmPat2+P7777rtyY5557Ds8991yF2yMiItt0/vx5KJVK3L17F/Xr18fOnTsRGBiIlJQUyGQyuLu7G8V7e3tDq9UCALRardHgi2G+YV55MXl5ebhz5w5cXFxK5BQXF4d58+aVmH7gwAG4urpWqT61Wl2leFNY3NXsm3woC7oUV2u5vXv3mjiT2qm67VNd1tiulvg7szZVaaPbt2+bbLscgCEiIiIiIiIiIrKQli1bIiUlBTdu3MAXX3yBiIgIHDlyxKI5zZw50+iWmXl5efDz80NoaCgUCkWl1qHX66FWq9G/f3+zP7+nbWzFX6SoDeT2Agu6FGP2GXvoiqv+PLbUWNt+ho/hPVTd9qkua2pXS/6dWYvqtJHhqj9TqPIAzNGjR7FkyRIkJycjMzMTO3fuxJAhQ6T5QgjMnTsXH3/8MXJzc/Hkk09izZo1aNGihRSTk5ODyZMn45tvvpEu1V+xYgXq168vxZw7dw6RkZE4ffo0PD09MXnyZEyfPt0olx07dmD27Nm4cuUKWrRogUWLFmHQoEHVaAYiIiIiIiIiIiLzk8lkaN68OQAgKCgIp0+fxooVKzBs2DAUFBQgNzfX6CqYrKws+Pj4AAB8fHxw6tQpo/VlZWVJ8wz/GqbdH6NQKEq9+gUA5HK50W03DZycnKp8krc6yzwsXZH5Ttabgq7Yrlo515UT7tVtn+qyxna1xN+ZtalKG5myLe2rukB+fj46dOiA+Pj4UucvXrwYH374IRISEnDy5EnUq1cPKpUKd+/elWJGjhyJCxcuQK1WY/fu3Th69KjRfZDz8vIQGhoKf39/JCcnY8mSJYiNjcXatWulmOPHj2PEiBEYN24czp49iyFDhmDIkCFITU2taklERERERERERES1QnFxMXQ6HYKCguDk5ITExERpXlpaGjIyMqBUKgEASqUS58+fR3Z2thSjVquhUCgQGBgoxdy/DkOMYR1ERFRzqnwFzMCBAzFw4MBS5wkh8MEHH2DWrFkYPHgwAODTTz+Ft7c3du3aheHDh+Onn37Cvn37cPr0aXTp0gUAsHLlSgwaNAjvv/8+fH19sXnzZhQUFGD9+vWQyWRo06YNUlJSsGzZMmmgZsWKFRgwYACmTZsGAFiwYAHUajVWrVqFhISEajUGERERERERERGRucycORMDBw5Es2bNcPPmTWzZsgVJSUnYv38/3NzcMG7cOMTExMDDwwMKhQKTJ0+GUqlEt27dAAChoaEIDAzEqFGjsHjxYmi1WsyaNQuRkZHSFSwTJ07EqlWrMH36dIwdOxaHDh3C9u3bsWfPHkuWTkRUJ5j0GTDp6enQarUICQmRprm5uSE4OBgajQbDhw+HRqOBu7u7NPgCACEhIbC3t8fJkyfx7LPPQqPRoFevXpDJZFKMSqXCokWLcP36dTRs2BAajcboXpSGmF27dpWZn06ng06nk14b7uWm1+uh1+srXachVm5f9kM7a0JVcqyJ7Vpq++ZUl2oFWK8te7DWulAzERERERGRtcnOzsbo0aORmZkJNzc3tG/fHvv370f//v0BAMuXL5du36/T6aBSqbB69WppeQcHB+zevRuTJk2CUqlEvXr1EBERgfnz50sxAQEB2LNnD6ZOnYoVK1agadOm+OSTT6BSme85F4++ycEeIqqbTDoAo9VqAQDe3t5G0729vaV5Wq0WXl5exkk4OsLDw8MoJiAgoMQ6DPMaNmwIrVZb7nZKExcXh3nz5pWYfuDAAbi6ulamRCMLuhRXeZmHsXfvXrNu70Fqtdqi2zenulQrwHptmaHW27dvWzgTIiIiIiIietC6devKne/s7Iz4+PgyHwUAAP7+/hWeM+rduzfOnj1brRyJiKj6TDoAU9vNnDnT6KqZvLw8+Pn5ITQ0FAqFotLr0ev1UKvVmH3GHrpi8z0AKjXWfN9MuJ+h3v79+9v8w5zqUq0A67VlD9ZquOKPiIiIiIiIiIiIzMOkAzA+Pj4AgKysLDRp0kSanpWVhY4dO0ox9z8YDAAKCwuRk5MjLe/j44OsrCyjGMPrimIM80sjl8ul+1/ez8nJqVonY3XFdtAVmW8AxtInjKvbTtaoLtUKsF5bZqi1rtRLRERERERERERUW9ibcmUBAQHw8fFBYmKiNC0vLw8nT56EUqkEACiVSuTm5iI5OVmKOXToEIqLixEcHCzFHD161OiZBWq1Gi1btkTDhg2lmPu3Y4gxbIeIiIiIiIiIiIiIiMhSqjwAc+vWLaSkpCAlJQUAkJ6ejpSUFGRkZMDOzg7R0dF455138PXXX+P8+fMYPXo0fH19MWTIEABA69atMWDAAIwfPx6nTp3C999/j6ioKAwfPhy+vr4AgBdeeAEymQzjxo3DhQsXsG3bNqxYscLo9mFTpkzBvn37sHTpUly6dAmxsbE4c+YMoqKiHr5ViIiIiIiIiIiIiIiIHkKVb0F25swZ9OnTR3ptGBSJiIjAxo0bMX36dOTn52PChAnIzc1Fjx49sG/fPjg7O0vLbN68GVFRUejXrx/s7e0RHh6ODz/8UJrv5uaGAwcOIDIyEkFBQWjcuDHmzJmDCRMmSDHdu3fHli1bMGvWLLz11lto0aIFdu3ahbZt21arIYiIiIiIiIiIiIiIiEylygMwvXv3hhCizPl2dnaYP38+5s+fX2aMh4cHtmzZUu522rdvj++++67cmOeeew7PPfdc+QkTERERERERERERERGZmUmfAUNEREREREREREREREQcgCEiIisUFxeHf/3rX2jQoAG8vLwwZMgQpKWlGcXcvXsXkZGRaNSoEerXr4/w8HBkZWUZxWRkZCAsLAyurq7w8vLCtGnTUFhYaBSTlJSEzp07Qy6Xo3nz5ti4cWNNl0dERERERERERDaAAzBERGR1jhw5gsjISJw4cQJqtRp6vR6hoaHIz8+XYqZOnYpvvvkGO3bswJEjR3D16lUMHTpUml9UVISwsDAUFBTg+PHj2LRpEzZu3Ig5c+ZIMenp6QgLC0OfPn2QkpKC6OhovPzyy9i/f79Z6yUiIiIiIiIiIutT5WfAEBERWdq+ffuMXm/cuBFeXl5ITk5Gr169cOPGDaxbtw5btmxB3759AQAbNmxA69atceLECXTr1g0HDhzAxYsXcfDgQXh7e6Njx45YsGABZsyYgdjYWMhkMiQkJCAgIABLly4FALRu3RrHjh3D8uXLoVKpzF43ERERERERERFZD14BQ0REVu/GjRsAAA8PDwBAcnIy9Ho9QkJCpJhWrVqhWbNm0Gg0AACNRoN27drB29tbilGpVMjLy8OFCxekmPvXYYgxrIOIiIiIiIiIiKgsvAKGiIisWnFxMaKjo/Hkk0+ibdu2AACtVguZTAZ3d3ejWG9vb2i1Winm/sEXw3zDvPJi8vLycOfOHbi4uJTIR6fTQafTSa/z8vIAAHq9Hnq9vtJ1GWKrskxtxDrukTsIU6ZTbXJ7YfSvtWIdNa8u7q+IiIiIiIhMjQMwRERk1SIjI5Gamopjx45ZOhUAQFxcHObNm1di+oEDB+Dq6lrl9anValOkZXF1vY7FXU2cyENa0KXY0imYBOuoOXv37q3yMocPH66BTIiIiIiIiKwXB2CIiMhqRUVFYffu3Th69CiaNm0qTffx8UFBQQFyc3ONroLJysqCj4+PFHPq1Cmj9WVlZUnzDP8apt0fo1AoSr36BQBmzpyJmJgY6XVeXh78/PwQGhoKhUJR6dr0ej3UajX69+8PJyenSi9X27COe9rG7q+BrKpObi+woEsxZp+xh67YztLpVBvrqHmpsZV/zpXh76NPnz41mBEREREREZH14QAMERFZHSEEJk+ejJ07dyIpKQkBAQFG84OCguDk5ITExESEh4cDANLS0pCRkQGlUgkAUCqVePfdd5GdnQ0vLy8A965uUCgUCAwMlGIe/Ba4Wq2W1lEauVwOuVxeYrqTk1O1TtxXd7napq7XoSuqXSfXdcV2tS6n6mAdNae6+ysiIiIiIiL6Hw7AEBGR1YmMjMSWLVvw1VdfoUGDBtIzW9zc3ODi4gI3NzeMGzcOMTEx8PDwgEKhwOTJk6FUKtGtWzcAQGhoKAIDAzFq1CgsXrwYWq0Ws2bNQmRkpDSAMnHiRKxatQrTp0/H2LFjcejQIWzfvh179uyxWO1ERERERERERGQd7C2dABERUVWtWbMGN27cQO/evdGkSRPpZ9u2bVLM8uXL8e9//xvh4eHo1asXfHx88OWXX0rzHRwcsHv3bjg4OECpVOLFF1/E6NGjMX/+fCkmICAAe/bsgVqtRocOHbB06VJ88sknUKkqf2seIiIiIiIiIiKqm3gFDBERWR0hRIUxzs7OiI+PR3x8fJkx/v7+FT5ounfv3jh79myVcyQiIiIiIiIiorqNV8AQEREREVGdc/ToUTz99NPw9fWFnZ0ddu3aZTRfCIE5c+agSZMmcHFxQUhICH755RejmJycHIwcORIKhQLu7u4YN24cbt26ZRRz7tw59OzZE87OzvDz88PixYtL5LJjxw60atUKzs7OaNeuXYVfDiAiIiIiIuvAARgiIiIiIqpz8vPz0aFDhzKvlFy8eDE+/PBDJCQk4OTJk6hXrx5UKhXu3r0rxYwcORIXLlyAWq3G7t27cfToUUyYMEGan5eXh9DQUPj7+yM5ORlLlixBbGws1q5dK8UcP34cI0aMwLhx43D27FkMGTIEQ4YMQWpqas0VT0REREREZsFbkBERERERUZ0zcOBADBw4sNR5Qgh88MEHmDVrFgYPHgwA+PTTT+Ht7Y1du3Zh+PDh+Omnn7Bv3z6cPn0aXbp0AQCsXLkSgwYNwvvvvw9fX19s3rwZBQUFWL9+PWQyGdq0aYOUlBQsW7ZMGqhZsWIFBgwYgGnTpgEAFixYALVajVWrViEhIcEMLUFERERERDWFAzBERERERET3SU9Ph1arRUhIiDTNzc0NwcHB0Gg0GD58ODQaDdzd3aXBFwAICQmBvb09Tp48iWeffRYajQa9evWCTCaTYlQqFRYtWoTr16+jYcOG0Gg0iImJMdq+SqUqcUu0++l0Ouh0Oul1Xl4eAECv10Ov11e6TkOs3L7iZ6uZUlVytCRDntaSrzmxbcrGtinfw7aPLbZrXFwcvvzyS1y6dAkuLi7o3r07Fi1ahJYtW0oxd+/exeuvv46tW7dCp9NBpVJh9erV8Pb2lmIyMjIwadIkHD58GPXr10dERATi4uLg6Pi/U39JSUmIiYnBhQsX4Ofnh1mzZmHMmDHmLJeIqM7hAAwREREREdF9tFotABid2DK8NszTarXw8vIymu/o6AgPDw+jmICAgBLrMMxr2LAhtFptudspTVxcHObNm1di+oEDB+Dq6lqZEo0s6FJc5WUehrU940atVls6hVqLbVM2tk35qts+t2/fNnEmlnfkyBFERkbiX//6FwoLC/HWW28hNDQUFy9eRL169QAAU6dOxZ49e7Bjxw64ubkhKioKQ4cOxffffw8AKCoqQlhYGHx8fHD8+HFkZmZi9OjRcHJywnvvvQfg3pcLwsLCMHHiRGzevBmJiYl4+eWX0aRJE6hUKovVT0Rk6zgAQ0REREREZEVmzpxpdNVMXl4e/Pz8EBoaCoVCUen16PV6qNVqzD5jD12xXU2kWqrUWOs40Wdon/79+8PJycnS6dQqbJuysW3K97DtY7jiz5bs27fP6PXGjRvh5eWF5ORk9OrVCzdu3MC6deuwZcsW9O3bFwCwYcMGtG7dGidOnEC3bt1w4MABXLx4EQcPHoS3tzc6duyIBQsWYMaMGYiNjYVMJkNCQgICAgKwdOlSAEDr1q1x7NgxLF++nAMwREQ1iAMwRERERERE9/Hx8QEAZGVloUmTJtL0rKwsdOzYUYrJzs42Wq6wsBA5OTnS8j4+PsjKyjKKMbyuKMYwvzRyuRxyubzEdCcnp2qd0NQV20FXZL4BGGs7KV3ddq0L2DZlY9uUr7rtUxfa9MaNGwAADw8PAEBycjL0er3RbTFbtWqFZs2aQaPRoFu3btBoNGjXrp3RFZUqlQqTJk3ChQsX0KlTJ2g0GqN1GGKio6NrvigiojqMAzBERERERET3CQgIgI+PDxITE6UBl7y8PJw8eRKTJk0CACiVSuTm5iI5ORlBQUEAgEOHDqG4uBjBwcFSzNtvvw29Xi+dNFSr1WjZsiUaNmwoxSQmJhqdAFOr1VAqlWaqloiIaovi4mJER0fjySefRNu2bQHcu2WlTCaDu7u7UeyDt8Us7XaWhnnlxeTl5eHOnTtwcXExmmeK543d/8wfuYN5nzdmLQzPYavu89hs8blI9+Pz6irGZ49VrDptZMr25AAMERERERHVObdu3cLly5el1+np6UhJSYGHhweaNWuG6OhovPPOO2jRogUCAgIwe/Zs+Pr6YsiQIQDu3bplwIABGD9+PBISEqDX6xEVFYXhw4fD19cXAPDCCy9g3rx5GDduHGbMmIHU1FSsWLECy5cvl7Y7ZcoUPPXUU1i6dCnCwsKwdetWnDlzBmvXrjVrexARkeVFRkYiNTUVx44ds3QqJn3emFqtxuKupsrMNlX3eWzW9ly16uLz6irGZ49VrCptZMpnjnEAhoiIiIiI6pwzZ86gT58+0mvDM1UiIiKwceNGTJ8+Hfn5+ZgwYQJyc3PRo0cP7Nu3D87OztIymzdvRlRUFPr16wd7e3uEh4fjww8/lOa7ubnhwIEDiIyMRFBQEBo3bow5c+ZgwoQJUkz37t2xZcsWzJo1C2+99RZatGiBXbt2Sd98JiKiuiEqKgq7d+/G0aNH0bRpU2m6j48PCgoKkJuba3QVzP23q/Tx8cGpU6eM1lfZW14qFIoSV78Apnne2P3P/On07qFKLVPXyO0FFnQprvbz2KzluWrVxefVVYzPHqtYddrIlM8c4wAMERERERHVOb1794YQZd/Ows7ODvPnz8f8+fPLjPHw8MCWLVvK3U779u3x3XfflRvz3HPP4bnnnis/YSIisklCCEyePBk7d+5EUlISAgICjOYHBQXByckJiYmJCA8PBwCkpaUhIyNDul2lUqnEu+++i+zsbHh5eQG4901vhUKBwMBAKebBb/WXd8tLUz5vzMnJyazPGrNG1X0eW1054c7n1VWMzx6rWFXayJRtyQEYIiIiIiIiIiIiC4iMjMSWLVvw1VdfoUGDBtIzW9zc3ODi4gI3NzeMGzcOMTEx8PDwgEKhwOTJk6FUKtGtWzcAQGhoKAIDAzFq1CgsXrwYWq0Ws2bNQmRkpDSIMnHiRKxatQrTp0/H2LFjcejQIWzfvh179uyxWO1ERHWBvaUTICIiIiIiIiIiqovWrFmDGzduoHfv3mjSpIn0s23bNilm+fLl+Pe//43w8HD06tULPj4++PLLL6X5Dg4O2L17NxwcHKBUKvHiiy9i9OjRRldxBgQEYM+ePVCr1ejQoQOWLl2KTz75BCqV9dxqiYjIGvEKGCIiIiIiIiIiIgso73aYBs7OzoiPj0d8fHyZMf7+/hU+OLx37944e/ZslXMkIqLq4wAMERERERERERERERGAR980/635riwMM/s2yTx4CzIiIiIiIiIiIiIiIiIT4wAMERERERERERERERGRiXEAhoiIiIiIiIiIiIiIyMT4DBgiIiIiIiIiIiIiK8NnlRDVfrwChoiIiIiIiIiIiIiIyMQ4AENERERERERERERERGRiHIAhIiIiIiIiIiIiIiIyMZMPwMTGxsLOzs7op1WrVtL8u3fvIjIyEo0aNUL9+vURHh6OrKwso3VkZGQgLCwMrq6u8PLywrRp01BYWGgUk5SUhM6dO0Mul6N58+bYuHGjqUshIiIiIiIiIiIiIiKqlhq5AqZNmzbIzMyUfo4dOybNmzp1Kr755hvs2LEDR44cwdWrVzF06FBpflFREcLCwlBQUIDjx49j06ZN2LhxI+bMmSPFpKenIywsDH369EFKSgqio6Px8ssvY//+/TVRDhERERERERERERERUZU41shKHR3h4+NTYvqNGzewbt06bNmyBX379gUAbNiwAa1bt8aJEyfQrVs3HDhwABcvXsTBgwfh7e2Njh07YsGCBZgxYwZiY2Mhk8mQkJCAgIAALF26FADQunVrHDt2DMuXL4dKpaqJkoiIiMhGPPrmniovI3cQWNwVaBu7H7oiuxrIioiIiIiIiIhsTY1cAfPLL7/A19cXjz32GEaOHImMjAwAQHJyMvR6PUJCQqTYVq1aoVmzZtBoNAAAjUaDdu3awdvbW4pRqVTIy8vDhQsXpJj712GIMayDiIiIiIiIiIiIiIjIkkx+BUxwcDA2btyIli1bIjMzE/PmzUPPnj2RmpoKrVYLmUwGd3d3o2W8vb2h1WoBAFqt1mjwxTDfMK+8mLy8PNy5cwcuLi6l5qbT6aDT6aTXeXl5AAC9Xg+9Xl/pGg2xcntR6WVMoSo51sR2LbV9c6pLtQKs15Y9WGtdqJmIiIiIiIiIiKg2MfkAzMCBA6X/t2/fHsHBwfD398f27dvLHBgxl7i4OMybN6/E9AMHDsDV1bXK61vQpdgUaVXa3r17zbq9B6nVaotu35zqUq0A67Vlhlpv375t4UyIyladW4IREREREREREdV2NfIMmPu5u7vjiSeewOXLl9G/f38UFBQgNzfX6CqYrKws6ZkxPj4+OHXqlNE6srKypHmGfw3T7o9RKBTlDvLMnDkTMTEx0uu8vDz4+fkhNDQUCoWi0jXp9Xqo1WrMPmMPXbH57gOfGmuZ59sY6u3fvz+cnJwskoO51KVaAdZryx6s1XDFHxEREREREREREZlHjQ/A3Lp1C7/++itGjRqFoKAgODk5ITExEeHh4QCAtLQ0ZGRkQKlUAgCUSiXeffddZGdnw8vLC8C9b3ArFAoEBgZKMQ9eDaJWq6V1lEUul0Mul5eY7uTkVK2TsbpiO7M+iNfSJ4yr207WqC7VCrBeW2ao1RbrPXr0KJYsWYLk5GRkZmZi586dGDJkiDRfCIG5c+fi448/Rm5uLp588kmsWbMGLVq0kGJycnIwefJkfPPNN7C3t0d4eDhWrFiB+vXrSzHnzp1DZGQkTp8+DU9PT0yePBnTp083Z6lERERERERERGSF7E29wjfeeANHjhzBlStXcPz4cTz77LNwcHDAiBEj4ObmhnHjxiEmJgaHDx9GcnIyXnrpJSiVSnTr1g0AEBoaisDAQIwaNQo//vgj9u/fj1mzZiEyMlIaPJk4cSJ+++03TJ8+HZcuXcLq1auxfft2TJ061dTlEBFRLZWfn48OHTogPj6+1PmLFy/Ghx9+iISEBJw8eRL16tWDSqXC3bt3pZiRI0fiwoULUKvV2L17N44ePYoJEyZI8/Py8hAaGgp/f38kJydjyZIliI2Nxdq1a2u8PiIiIiIiIiIism4mvwLmzz//xIgRI3Dt2jV4enqiR48eOHHiBDw9PQEAy5cvl75lrNPpoFKpsHr1aml5BwcH7N69G5MmTYJSqUS9evUQERGB+fPnSzEBAQHYs2cPpk6dihUrVqBp06b45JNPoFJZ5hZdRERkfgMHDjR67tj9hBD44IMPMGvWLAwePBgA8Omnn8Lb2xu7du3C8OHD8dNPP2Hfvn04ffo0unTpAgBYuXIlBg0ahPfffx++vr7YvHkzCgoKsH79eshkMrRp0wYpKSlYtmyZ0UANERERERERERHRg0w+ALN169Zy5zs7OyM+Pr7MbywDgL+/f4UPnO/duzfOnj1brRyJiMi2paenQ6vVIiQkRJrm5uaG4OBgaDQaDB8+HBqNBu7u7tLgCwCEhITA3t4eJ0+exLPPPguNRoNevXpBJpNJMSqVCosWLcL169fRsGHDEtvW6XTQ6XTSa8Pzd/R6PfR6faVrMMRWZZnaqDJ1yB2EudKpNrm9MPrXWrGO2qU211EX91dERERERESmVuPPgCEiIjI3rVYLAPD29jaa7u3tLc3TarXSs8YMHB0d4eHhYRQTEBBQYh2GeaUNwMTFxWHevHklph84cACurq5VrkWtVld5mdqovDoWdzVjIg9pQZdiS6dgEqyjdqmNdVT0ZajSHD58uAYyISIiIiIisl4cgCEiIjKhmTNnIiYmRnqdl5cHPz8/hIaGQqFQVHo9er0earUa/fv3h5OTU02kahaVqaNt7H4zZ1V1cnuBBV2KMfuMPXTFdpZOp9pYR+1Sm+tIja38rX0Nf+d9+vSpwYzMLzY2tsSAesuWLXHp0iUAwN27d/H6669j69atRrdWvn/wPyMjA5MmTcLhw4dRv359REREIC4uDo6O/zsMS0pKQkxMDC5cuAA/Pz/MmjULY8aMMUuNRERERERUszgAQ0RENsfHxwcAkJWVhSZNmkjTs7Ky0LFjRykmOzvbaLnCwkLk5ORIy/v4+CArK8soxvDaEPMguVwOuVxeYrqTk1O1BlKqu1xtU14duqLadeK5PLpiO6vKtyyso3apjXVUd39la9q0aYODBw9Kr+8fOJk6dSr27NmDHTt2wM3NDVFRURg6dCi+//57AEBRURHCwsLg4+OD48ePIzMzE6NHj4aTkxPee+89APdumRkWFoaJEydi8+bNSExMxMsvv4wmTZrw+ZZERERERDaAAzBERGRzAgIC4OPjg8TERGnAJS8vDydPnsSkSZMAAEqlErm5uUhOTkZQUBAA4NChQyguLkZwcLAU8/bbb0Ov10snFtVqNVq2bFnq7ceIiMi2ODo6ljrgfuPGDaxbtw5btmxB3759AQAbNmxA69atceLECXTr1g0HDhzAxYsXcfDgQXh7e6Njx45YsGABZsyYgdjYWMhkMiQkJCAgIABLly4FALRu3RrHjh3D8uXLOQBDRFRHHD16FEuWLEFycjIyMzOxc+dODBkyRJovhMDcuXPx8ccfIzc3F08++STWrFmDFi1aSDE5OTmYPHkyvvnmG9jb2yM8PBwrVqxA/fr1pZhz584hMjISp0+fhqenJyZPnozp06ebs1QiKsejb+6p1nJyB4HFXe/dWaKqX+q6sjCsWtukquEAjBWp7h9idfGPkIhqs1u3buHy5cvS6/T0dKSkpMDDwwPNmjVDdHQ03nnnHbRo0QIBAQGYPXs2fH19pYOZ1q1bY8CAARg/fjwSEhKg1+sRFRWF4cOHw9fXFwDwwgsvYN68eRg3bhxmzJiB1NRUrFixAsuXL7dEyUREZGa//PILfH194ezsDKVSibi4ODRr1gzJycnQ6/UICQmRYlu1aoVmzZpBo9GgW7du0Gg0aNeundEtyVQqFSZNmoQLFy6gU6dO0Gg0RuswxERHR5ebl06ng06nk17n5eUBuHc7OL1eX+n6DLFye1HpZUyhKjlakiFPa8nXnNg2ZWPblO9h28cW2zU/Px8dOnTA2LFjMXTo0BLzFy9ejA8//BCbNm2SjmtUKhUuXrwIZ2dnAMDIkSORmZkJtVoNvV6Pl156CRMmTMCWLVsA3OsnQkNDERISgoSEBJw/fx5jx46Fu7s7JkyYYNZ6iYjqGg7AEBGRVTpz5ozR8wYMz12JiIjAxo0bMX36dOTn52PChAnIzc1Fjx49sG/fPukgBQA2b96MqKgo9OvXT/qm2IcffijNd3Nzw4EDBxAZGYmgoCA0btwYc+bMsemDFFMP9j/Mt3GIiCwpODgYGzduRMuWLZGZmYl58+ahZ8+eSE1NhVarhUwmg7u7u9Ey3t7e0Gq1AACtVms0+GKYb5hXXkxeXh7u3LkDFxeXUnOLi4sr8XwaADhw4ABcXV2rXOuCLsVVXuZh7N2716zbe1hqtdrSKdRabJuysW3KV932uX37tokzsbyBAwdi4MCBpc4TQuCDDz7ArFmzMHjwYADAp59+Cm9vb+zatQvDhw/HTz/9hH379uH06dPo0qULAGDlypUYNGgQ3n//ffj6+mLz5s0oKCjA+vXrIZPJ0KZNG6SkpGDZsmU2fWxDRFQbcACGiIisUu/evSFE2d/YtbOzw/z58zF//vwyYzw8PKRvhZWlffv2+O6776qd58PiwAURkWXcfzKsffv2CA4Ohr+/P7Zv317mwIi5zJw5U/riAXDvm81+fn4IDQ2FQqGo9Hr0ej3UajVmn7GHrth8fU1qrHXcXs3QPv3797fJZxw9DLZN2dg25XvY9jFc8VdXpKenQ6vVGl0t6ebmhuDgYGg0GgwfPhwajQbu7u7S4AsAhISEwN7eHidPnsSzzz4LjUaDXr16QSaTSTEqlQqLFi3C9evXS729simutrz/iie5g3mvtrQWhqtQzX016sMw55Volrpa15o8zHuo5du7TZ1OuSz1GbA6V1+a8n3OARgiIiIiIqIKuLu744knnsDly5fRv39/FBQUIDc31+gqmKysLOmZMT4+Pjh16pTROrKysqR5hn8N0+6PUSgU5Q7yyOVyyOXyEtOdnJyqdUJTV2xn1sF+azspXd12rQvYNmVj25Svuu1T19rUcMVkaVdL3n81pZeXl9F8R0dHeHh4GMUEBASUWIdhXmkDMKa82lKtVmNx1yotUueY+2rUh2GJK1mtqX0sxRrayNJXQVfl6ktTXnHJARgiIiIiIqIK3Lp1C7/++itGjRqFoKAgODk5ITExEeHh4QCAtLQ0ZGRkQKlUAgCUSiXeffddZGdnSyfG1Go1FAoFAgMDpZgHD0TVarW0DiIiIksxxdWW91/x1OndQzWVqlWT2wss6FJs9qtRH4Y5r2Kw1NW61sSa3kOWvAKmqldfmvKKSw7AEBERERERPeCNN97A008/DX9/f1y9ehVz586Fg4MDRowYATc3N4wbNw4xMTHw8PCAQqHA5MmToVQq0a1bNwBAaGgoAgMDMWrUKCxevBharRazZs1CZGSkdPXKxIkTsWrVKkyfPh1jx47FoUOHsH37duzZY9rncRERkXUyXDGZlZWFJk2aSNOzsrLQsWNHKSY7O9toucLCQuTk5FR4xeX923iQKa+2dHJy4m2VK2Duq1EfhiWuRLOm9rEUa2gjS1/FWJX9lylztTfZmoiIiIiIiGzEn3/+iREjRqBly5Z4/vnn0ahRI5w4cQKenp4AgOXLl+Pf//43wsPD0atXL/j4+ODLL7+UlndwcMDu3bvh4OAApVKJF198EaNHjzZ6NllAQAD27NkDtVqNDh06YOnSpfjkk0+gUlnHM1KIiKhmBQQEwMfHB4mJidK0vLw8nDx50uiKy9zcXCQnJ0sxhw4dQnFxMYKDg6WYo0ePGj3TQK1Wo2XLlqXefoyIiEyHV8AQERERERE9YOvWreXOd3Z2Rnx8POLj48uM8ff3r/Be171798bZs2erlSMREVm/W7du4fLly9Lr9PR0pKSkwMPDA82aNUN0dDTeeecdtGjRAgEBAZg9ezZ8fX0xZMgQAEDr1q0xYMAAjB8/HgkJCdDr9YiKisLw4cPh6+sLAHjhhRcwb948jBs3DjNmzEBqaipWrFiB5cuXW6JkIqI6hQMwREREREREREREFnDmzBn06dNHem147kpERAQ2btyI6dOnIz8/HxMmTEBubi569OiBffv2wdnZWVpm8+bNiIqKQr9+/WBvb4/w8HB8+OGH0nw3NzccOHAAkZGRCAoKQuPGjTFnzhxMmDDBfIUSEdVRHIAhIiIiIiIiIiKygN69e0MIUeZ8Ozs7zJ8/3+gWlg/y8PDAli1byt1O+/bt8d1331U7TyIiqh4+A4aIiIiIiIiIiIiIiMjEOABDRERERERERERERERkYrwFGRERERERERERERER1ZhH39xj9m1eWRhm9m0+iFfAEBERERERERERERERmRgHYIiIiIiIiIiIiIiIiEyMAzBEREREREREREREREQmxgEYIiIiIiIiIiIiIiIiE+MADBERERERERERERERkYlxAIaIiIiIiIiIiIiIiMjEOABDRERERERERERERERkYo6WToBqr0ff3AMAkDsILO4KtI3dD12RXY1u88rCsBpdPxERERERERERERGROfAKGCIiIiIiIiIiIiIiIhPjAAwREREREREREREREZGJcQCGiIiIiIiIiIiIiIjIxPgMGCIiIiIiIiIiIiKqkOGZ0eZgeC41kTXjFTBEREREREREREREREQmxgEYIiIiIiIiIiIiIiIiE+MADBERERERERERERERkYlxAIaIiIiIiIiIiIiIiMjEHC2dABERERERERERWY65HqpteKB229j9SHv332bZJhERkSXxChgiIiIiIiIiIiIiIiITs/oBmPj4eDz66KNwdnZGcHAwTp06ZemUiIjIxrCvISKimsa+hoiIzIH9DRGReVn1Lci2bduGmJgYJCQkIDg4GB988AFUKhXS0tLg5eVl6fSoGsx12fP9riwMM/s2ich6sK8hIqKaxr6GiIjMgf0NEZH5WfUVMMuWLcP48ePx0ksvITAwEAkJCXB1dcX69estnRoREdkI9jVERFTT2NcQEZE5sL8hIjI/qx2AKSgoQHJyMkJCQqRp9vb2CAkJgUajsWBmRERkK9jXEBFRTWNfQ0RE5sD+hojIMqz2FmT//PMPioqK4O3tbTTd29sbly5dKnUZnU4HnU4nvb5x4wYAICcnB3q9vtLb1uv1uH37Nhz19igqtqtG9tbFsVjg9u1im623+Rvbpf/L7QVmdSpGx7e/hM7Gaj05s1+JaYb38rVr1+Dk5GSBrMyrLtX7YK03b94EAAghLJyZdWFf8/BspQ9hHbUL66h5165dq3SsYX+Vk5MDgH1NVdXFvqYq7y9LqkufHauKbVM2a20bx8J882znvr6vOvsCHtdUX1X7G1P0Nff/PZjrPWZtavPnwdqA7VMxtlH5rl27Vq2+2ZT9jdUOwFRHXFwc5s2bV2J6QECABbKxLi9YOgEzstVaGy+1dAZUG9y8eRNubm6WTsOmsa8pyVb2q6yjdmEdNethPjewr6l51t7X8HMpUd1m6PsaL6n+OtjX1Dxr72usSW39PFhbsH0qxjYq28N+7jRFf2O1AzCNGzeGg4MDsrKyjKZnZWXBx8en1GVmzpyJmJgY6XVxcTFycnLQqFEj2NlVfoQwLy8Pfn5++OOPP6BQKKpXgBWpS/XWpVoB1mvLHqxVCIGbN2/C19fX0qlZFfY1D4911C6so3axtToyMjJgZ2fHvqaK2NfUXmyfsrFtysa2Kd/Dtg+Pa6qvqv2NKfoa/j1UjG1UPrZPxdhGFatOG5myv7HaARiZTIagoCAkJiZiyJAhAO51BomJiYiKiip1GblcDrlcbjTN3d292jkoFIo69cauS/XWpVoB1mvL7q+V3xCrOvY1psM6ahfWUbvYSh1ubm42UYe5sa+p/dg+ZWPblI1tU76HaR8e11RPVfsbU/Y1/HuoGNuofGyfirGNKlbVNjJVf2O1AzAAEBMTg4iICHTp0gVdu3bFBx98gPz8fLz00kuWTo2IiGwE+xoiIqpp7GuIiMgc2N8QEZmfVQ/ADBs2DH///TfmzJkDrVaLjh07Yt++fSUeKEZERFRd7GuIiKimsa8hIiJzYH9DRGR+Vj0AAwBRUVFlXppfU+RyOebOnVviUkxbVZfqrUu1AqzXltWlWs2BfU31sY7ahXXULqyD7se+pvZh+5SNbVM2tk352D6WZ87+hr/virGNysf2qRjbqGKWbiM7IYSwyJaJiIiIiIiIiIiIiIhslL2lEyAiIiIiIiIiIiIiIrI1HIAhIiIiIiIiIiIiIiIyMQ7AEBERERERERERERERmRgHYKooPj4ejz76KJydnREcHIxTp05ZOqVKOXr0KJ5++mn4+vrCzs4Ou3btMpovhMCcOXPQpEkTuLi4ICQkBL/88otRTE5ODkaOHAmFQgF3d3eMGzcOt27dMoo5d+4cevbsCWdnZ/j5+WHx4sU1XVoJcXFx+Ne//oUGDRrAy8sLQ4YMQVpamlHM3bt3ERkZiUaNGqF+/foIDw9HVlaWUUxGRgbCwsLg6uoKLy8vTJs2DYWFhUYxSUlJ6Ny5M+RyOZo3b46NGzfWdHklrFmzBu3bt4dCoYBCoYBSqcS3334rzbelWh+0cOFC2NnZITo6WppmS/XGxsbCzs7O6KdVq1bSfFuq1VZVtO/NysrCmDFj4OvrC1dXVwwYMKDEvtdUv2dL1pGTk4PJkyejZcuWcHFxQbNmzfDaa6/hxo0bVlXH/YQQGDhwYKnrsZY6NBoN+vbti3r16kGhUKBXr164c+eONL8y/b6l69BqtRg1ahR8fHxQr149dO7cGf/3f/9nFFPTddjK5w5T1PHjjz9ixIgR8PPzg4uLC1q3bo0VK1aU2Bb7ndrDWo9tHoa5Pl9Zg7p0jFhVFbXNmDFjSryPBgwYYBRjq21jK/0ele3mzZuIjo6Gv78/XFxc0L17d5w+fVqaf+vWLURFRaFp06ZwcXFBYGAgEhISjNZh6+8Bc7XRg/sZOzs7bN261SimNraRKdpn7dq16N27NxQKBezs7JCbm1tiO9a8nzVXGz366KMl3kMLFy40irHVNjLleYiH/jsTVGlbt24VMplMrF+/Xly4cEGMHz9euLu7i6ysLEunVqG9e/eKt99+W3z55ZcCgNi5c6fR/IULFwo3Nzexa9cu8eOPP4pnnnlGBAQEiDt37kgxAwYMEB06dBAnTpwQ3333nWjevLkYMWKENP/GjRvC29tbjBw5UqSmpor//ve/wsXFRXz00UfmKlMIIYRKpRIbNmwQqampIiUlRQwaNEg0a9ZM3Lp1S4qZOHGi8PPzE4mJieLMmTOiW7duonv37tL8wsJC0bZtWxESEiLOnj0r9u7dKxo3bixmzpwpxfz222/C1dVVxMTEiIsXL4qVK1cKBwcHsW/fPrPW+/XXX4s9e/aIn3/+WaSlpYm33npLODk5idTUVJur9X6nTp0Sjz76qGjfvr2YMmWKNN2W6p07d65o06aNyMzMlH7+/vtvm6zVVpW37y0uLhbdunUTPXv2FKdOnRKXLl0SEyZMqJH9laXrOH/+vBg6dKj4+uuvxeXLl0ViYqJo0aKFCA8Pt6o67rds2TIxcODAEuuxljqOHz8uFAqFiIuLE6mpqeLSpUti27Zt4u7du1JMRf1+baijf//+4l//+pc4efKk+PXXX8WCBQuEvb29+OGHH8xWh6187jBFHevWrROvvfaaSEpKEr/++qv47LPPhIuLi1i5cqXZ6qDKs+Zjm4dhjs9X1qIuHSNWVUVtExERIQYMGGD0PsrJyTGKsdW2sZV+j8r2/PPPi8DAQHHkyBHxyy+/iLlz5wqFQiH+/PNPIYQQ48ePF48//rg4fPiwSE9PFx999JFwcHAQX331lbQOW38PmKONhBACgNiwYYPRvub+fXBtbSNTtM/y5ctFXFyciIuLEwDE9evXS2zHmvez5mojf39/MX/+fKP30P37a1tuI1OdhzDF3xkHYKqga9euIjIyUnpdVFQkfH19RVxcnAWzqrrSTnL4+PiIJUuWSNNyc3OFXC4X//3vf4UQQly8eFEAEKdPn5Zivv32W2FnZyf++usvIYQQq1evFg0bNhQ6nU6KmTFjhmjZsmUNV1S+7OxsAUAcOXJECHGvNicnJ7Fjxw4p5qeffhIAhEajEULc+8Btb28vtFqtFLNmzRqhUCik+qZPny7atGljtK1hw4YJlUpV0yVVqGHDhuKTTz6x2Vpv3rwpWrRoIdRqtXjqqaekARhbq3fu3LmiQ4cOpc6ztVrrggf3vWlpaQKANFgqxL1+xdPTU3z88cdCCNP9ni1dR2m2b98uZDKZ0Ov1VlfH2bNnxSOPPCIyMzNLrMda6ggODhazZs0qc72V6fdNqbp11KtXT3z66adG6/Lw8JBizF2HELbzuaM6dZTm1VdfFX369JFes9+pPWzl2KaqzPH5yhrVtWPEqihrAGbw4MFlLlNX2kYI2+n36J7bt28LBwcHsXv3bqPpnTt3Fm+//bYQQog2bdqI+fPnlznf1t8D5mojIUrf/9yvNraRKdrnfocPHy51cMGa97PmaiMh7g3ALF++vMxc6kobGVTnPIQp/s54C7JKKigoQHJyMkJCQqRp9vb2CAkJgUajsWBmDy89PR1ardaoNjc3NwQHB0u1aTQauLu7o0uXLlJMSEgI7O3tcfLkSSmmV69ekMlkUoxKpUJaWhquX79upmpKMlxa5uHhAQBITk6GXq83qrdVq1Zo1qyZUb3t2rWDt7e3FKNSqZCXl4cLFy5IMfevwxBjyfdDUVERtm7divz8fCiVSputNTIyEmFhYSVyssV6f/nlF/j6+uKxxx7DyJEjkZGRAcA2a61rdDodAMDZ2VmaZm9vD7lcjmPHjgEw3e/Z0nWU5saNG1AoFHB0dARgPXXcvn0bL7zwAuLj4+Hj41NiPdZQR3Z2Nk6ePAkvLy90794d3t7eeOqpp4zqrEy/b+k6AKB79+7Ytm0bcnJyUFxcjK1bt+Lu3bvo3bu3xeqwlc8d1amjrPUY1gGw36ktbPnYpjJq+vOVLbD1Y0RTSEpKgpeXF1q2bIlJkybh2rVr0ry61Da20u/RPYWFhSgqKjL6DAYALi4u0mew7t274+uvv8Zff/0FIQQOHz6Mn3/+GaGhoQBs/z1grjYyiIyMROPGjdG1a1esX78eQghpXm1sI1O0T2VY837WXG1ksHDhQjRq1AidOnXCkiVLjG6vVdfaqDrnIUzxd8YBmEr6559/UFRUZPQLAQBvb29otVoLZWUahvzLq02r1cLLy8tovqOjIzw8PIxiSlvH/dswt+LiYkRHR+PJJ59E27ZtpVxkMhnc3d2NYh+st6JayorJy8szuoe+OZw/fx7169eHXC7HxIkTsXPnTgQGBtpkrVu3bsUPP/yAuLi4EvNsrd7g4GBs3LgR+/btw5o1a5Ceno6ePXvi5s2bNldrXWT4gD1z5kxcv34dBQUFWLRoEf78809kZmYCMN172tJ1POiff/7BggULMGHCBGmatdQxdepUdO/eHYMHDy51PdZQx2+//Qbg3nMQxo8fj3379qFz587o16+fdG//yvT7lq4DALZv3w69Xo9GjRpBLpfjlVdewc6dO9G8eXOL1GErnzuqW8eDjh8/jm3btlXqb539jnnZ8rFNRczx+coW2PIxoikMGDAAn376KRITE7Fo0SIcOXIEAwcORFFREYC60za20u/R/zRo0ABKpRILFizA1atXUVRUhM8//xwajUb6DLZy5UoEBgaiadOmkMlkGDBgAOLj49GrVy8Atv8eMFcbAcD8+fOxfft2qNVqhIeH49VXX8XKlSul+bWxjUzRPpVhzftZc7URALz22mvYunUrDh8+jFdeeQXvvfcepk+fLs2vS21U3fMQpvg7c6xc2UTWKTIyEqmpqeV+C9sWtGzZEikpKbhx4wa++OILRERE4MiRI5ZOy+T++OMPTJkyBWq1usQouC0aOHCg9P/27dsjODgY/v7+2L59O1xcXCyYGZmCk5MTvvzyS4wbNw4eHh5wcHBASEgIBg4caPStptquqnXk5eUhLCwMgYGBiI2NNX/CZahMHV9//TUOHTqEs2fPWjjbslWmjuLiYgDAK6+8gpdeegkA0KlTJyQmJmL9+vWlDnCbW2XfV7Nnz0Zubi4OHjyIxo0bY9euXXj++efx3XffoV27dmbP21Y+d5iijtTUVAwePBhz586t1jf1iGoKP1+RKQwfPlz6f7t27dC+fXs8/vjjSEpKQr9+/SyYmXnZSr9Hxj777DOMHTsWjzzyCBwcHNC5c2eMGDECycnJAO6d9Dxx4gS+/vpr+Pv74+jRo4iMjISvr2+Jb4nbKnO10ezZs6X/d+rUCfn5+ViyZAlee+01k9dkSnwPVcxcbRQTEyP9v3379pDJZHjllVcQFxcHuVxu8rpMyZRtZOnzELwCppIaN24MBwcHZGVlGU3Pysoq9RYk1sSQf3m1+fj4IDs722h+YWEhcnJyjGJKW8f92zCnqKgo7N69G4cPH0bTpk2l6T4+PigoKEBubq5R/IP1VlRLWTEKhcLsB28ymQzNmzdHUFAQ4uLi0KFDB6xYscLmak1OTkZ2djY6d+4MR0dHODo64siRI/jwww/h6OgIb29vm6r3Qe7u7njiiSdw+fJlm/vd1lVBQUFISUlBbm4uMjMzsW/fPly7dg2PPfYYANPtr2paRXUY3Lx5EwMGDECDBg2wc+dOODk5SfOsoY5Dhw7h119/hbu7u7QPAoDw8HDpllfWUEeTJk0AAIGBgUbLtW7dWroNT2X6/ZpWUR2//vorVq1ahfXr16Nfv37o0KED5s6diy5duiA+Pt7sddjK546HqcPg4sWL6NevHyZMmIBZs2YZzWO/UzvY8rFNVdXE5ytbYKvHiDXlscceQ+PGjXH58mUAdaNtbKXfo5Ief/xxHDlyBLdu3cIff/yBU6dOQa/X47HHHsOdO3fw1ltvYdmyZXj66afRvn17REVFYdiwYXj//fcB1I33gDnaqDTBwcH4888/pdv11tY2etj2qQxr38+ao41KExwcjMLCQly5cgVA3Wijhz0PYYq/Mw7AVJJMJkNQUBASExOlacXFxUhMTIRSqbRgZg8vICAAPj4+RrXl5eXh5MmTUm1KpRK5ubnSKCNw70RUcXExgoODpZijR49Cr9dLMWq1Gi1btkTDhg3NVA0ghEBUVBR27tyJQ4cOISAgwGh+UFAQnJycjOpNS0tDRkaGUb3nz5832pmr1WooFArphJVSqTRahyGmNrwfiouLodPpbK7Wfv364fz580hJSZF+unTpgpEjR0r/t6V6H3Tr1i38+uuvaNKkic39bus6Nzc3eHp64pdffsGZM2ek21uZ6vds6TqAe/1KaGgoZDIZvv766xJXsVlDHW+++SbOnTtntA8CgOXLl2PDhg1WU8ejjz4KX19fpKWlGcX//PPP8Pf3B1C5ft9cyqrj9u3bAO49t+J+Dg4O0lU+5qjDVj53mKIOALhw4QL69OmDiIgIvPvuuyW2w36ndrDlY5uqqonPV7bA1o4Ra9qff/6Ja9euSV9ysOW2sZV+jypWr149NGnSBNevX8f+/fsxePBg6PV66PX6cj9/1aX3QE22UWlSUlLQsGFD6cqF2t5G1W2fyrCV/WxNtlFpUlJSYG9vL92+zdbbyBTnIUzydyao0rZu3SrkcrnYuHGjuHjxopgwYYJwd3cXWq3W0qlV6ObNm+Ls2bPi7NmzAoBYtmyZOHv2rPj999+FEEIsXLhQuLu7i6+++kqcO3dODB48WAQEBIg7d+5I6xgwYIDo1KmTOHnypDh27Jho0aKFGDFihDQ/NzdXeHt7i1GjRonU1FSxdetW4erqKj766COz1jpp0iTh5uYmkpKSRGZmpvRz+/ZtKWbixImiWbNm4tChQ+LMmTNCqVQKpVIpzS8sLBRt27YVoaGhIiUlRezbt094enr+v/buPC6qqvEf+GdAhlVAlDWRMM0FFxQTyXJF0LA0rdS0cEkfCUzEx4XCfUHNDRWXTMVKHpeeNFNjcUFTUZEkwS1NzJ4U+D4q4AojnN8f/OY+jqAsXmaBz/v18qVz75l7P+c4zGHm3HuOCA8Pl8pcvXpVWFhYiEmTJokLFy6I6OhoYWxsLOLi4rRa36lTp4rDhw+LzMxMcfbsWTF16lShUChEQkJCjatrWbp27SrGjx8vPa5J9Z04caJISkoSmZmZ4tixY8LX11c0aNBA5OTk1Li61lTlvfdu375dHDp0SPzxxx9i165dws3NTQwYMEDjGHL8P+u6Hnl5ecLb21u0bt1aXLlyReO9+fHjxwZTj7IAEDt37pQeG0o9li1bJqytrcWOHTvE5cuXRUREhDAzMxNXrlyRypTX7+u6HoWFhaJJkybizTffFCdPnhRXrlwRixcvFgqFQuzdu1dr9agpv3fIUY/09HRhb28vhg0bpnEMdb+ljXpQxRnyZ5sXoY3frwxFbfqMWFnPa5u7d++Kf/7znyI5OVlkZmaK/fv3i/bt24umTZuKR48eSceoqW1TU/o9era4uDjx888/i6tXr4qEhATRtm1b4e3tLQoLC4UQJZ/BPTw8xKFDh8TVq1fFpk2bhJmZmVi9erV0jJr+GtBGG+3evVusX79epKeni8uXL4vVq1cLCwsLMX36dKmMvraRHO1z8+ZNcebMGbF+/XoBQBw5ckScOXNG3Lp1SypjyO+z2mij48ePi2XLlom0tDTxxx9/iO+++07Y29uLjz/+WDpGTW4jub6HkOPnjAMwlbRy5UrRqFEjoVQqRceOHcWJEyd0HalCDh06JACU+hMYGCiEEKK4uFhMmzZNODo6ClNTU9GzZ09x6dIljWPcunVLDBkyRFhZWQlra2sxYsQIcffuXY0yv/32m3jjjTeEqampeOmll8SCBQu0VUVJWfUEIDZt2iSVefjwofj0009FvXr1hIWFhXj33XfFzZs3NY5z7do10adPH2Fubi4aNGggJk6cKFQqlUaZQ4cOCU9PT6FUKkXjxo01zqEtI0eOFG5ubkKpVAp7e3vRs2dPafBFiJpV17I8PQBTk+o7aNAg4ezsLJRKpXjppZfEoEGDNL4crUl1ranKe++NiooSDRs2FCYmJqJRo0YiIiJCFBQUaBxDrv9nXdbjWc8HIDIzMw2mHmV5egDGkOoRGRkpGjZsKCwsLISPj4/45ZdfNPZXpN/XdT1+//13MWDAAOHg4CAsLCxEmzZtxDfffKPVetSU3zvkqMeMGTPKPIabm5vW6kGVY6ifbV6Etn6/MgS16TNiZT2vbR48eCD8/PyEvb29MDExEW5ubmL06NGlBi9ratvUlH6Pnm3btm2icePGQqlUCicnJxEcHCxyc3Ol/Tdv3hTDhw8XLi4uwszMTDRr1kwsWbJEFBcXS2Vq+mtAG230888/C09PT2FlZSUsLS1F27Ztxdq1a0VRUZFGFn1sIzna51m/Vz5ZP0N+n9VGG6Wmpgpvb29hY2MjzMzMRIsWLcT8+fM1LhYQoua2kZzfQ7zoz5lCCANa6ZeIiIiIiIiIiIiIiMgAcA0YIiIiIiIiIiIiIiIimXEAhkhmMTExUCgUuHbtmq6jEBGRAWN/QkRE5cnOzsZ7772H+vXrQ6FQYPny5S90vOHDh+Pll1/W2KZQKDBz5sxKH2vmzJlQKBQvlIeIiAyPNvomIkPCARgiPREbG/vCnRIREREREdUeEyZMQHx8PMLDw/Htt9+id+/e2LdvX5UGTAxVQkICRo0ahVatWsHY2PiZX9Jdu3YNCoWizD9bt27VbmgiohpMn/qm+fPnY9euXaW237t3DzNmzEDv3r1hZ2cHhUKBmJiYMo8xfPjwMvuO5s2bV294qjHq6DoAEZWIjY1FRkYGQkNDdR2FiIiIiIgMwMGDB9GvXz/885//lLatWrUK0dHRsn3R9fDhQ9SpU/mvDiIiIjB16lRZMjxPbGwstm3bhvbt28PFxaXc8kOGDMFbb72lsc3Hx6e64hER1Tra6Jsqav78+XjvvffQv39/je3//e9/MXv2bDRq1Aht27ZFUlLSc49jamqKr7/+WmObjY2NzGmppuIADBEREREREZEBysnJga2tbbWew8zMrErPq1OnTpUGbipr/vz5WL9+PUxMTNC3b19kZGQ8t3z79u0xbNiwas9FRFRbaaNvelHOzs64efMmnJyccPr0abz22mvPLV+nTh32HVRlnIKMqJr9+OOPCAgIgIuLC0xNTfHKK69gzpw5KCoqksp069YNe/fuxZ9//indysj5LYmI6GmrV6+Gh4cHTE1N4eLiguDgYOTm5mqU6datG1q1aoXz58+je/fusLCwwEsvvYRFixaVOt6ff/6Jd955B5aWlnBwcJCmC1AoFOVeBUZERFVz9+5dhIaG4uWXX4apqSkcHBzQq1cv/Prrr1KZr776Cq+88grMzc3RsWNH/PLLL+jWrRu6desG4H/rhAkhEB0dLX2GGD58OKKjowFAY5qUF/HkGjDff/89FAoFDh8+XKrcunXroFAopAGQstaAUSgUCAkJwa5du9CqVSuYmprCw8MDcXFxpY6XlJSEDh06wMzMDK+88grWrVtX5jFdXFxgYmJSqTrdv38fhYWFlXoOEVFNZmh90/379zFx4kS4urrC1NQUzZo1w+LFiyGEkMooFArcv38fmzdv1sgClNzR4uTkVKlzFhUVIT8//4VyU+3EO2CIqllMTAysrKwQFhYGKysrHDx4ENOnT0d+fj6+/PJLAMAXX3yBvLw8/Oc//8GyZcsAAFZWVrqMTUREembmzJmYNWsWfH19ERQUhEuXLmHNmjVISUnBsWPHNL58unPnDnr37o0BAwbggw8+wPfff48pU6agdevW6NOnD4CSDy09evTAzZs3MX78eDg5OSE2NhaHDh3SVRWJiGqFsWPH4vvvv0dISAhatmyJW7du4ejRo7hw4QLat2+PDRs24B//+Adef/11hIaG4urVq3jnnXdgZ2cHV1dXAECXLl3w7bff4qOPPkKvXr3w8ccfAwBeeeUV3LhxA4mJifj2229lzx4QEAArKyts374dXbt21di3bds2eHh4oFWrVs89xtGjR/HDDz/g008/Rd26dbFixQoMHDgQ169fR/369QEAZ86cQe/eveHs7IxZs2ahqKgIs2fPhr29/QvXYdasWZg0aRIUCgW8vLwwb948+Pn5vfBxiYgMmSH1TUIIvPPOOzh06BBGjRoFT09PxMfHY9KkSfj777+l79W+/fZbfPLJJ+jYsSPGjBkjZamKBw8ewNraGg8ePEC9evUwZMgQLFy4kN/dUcUIIpLVpk2bBACRmZkphBDiwYMHpcr84x//EBYWFuLRo0fStoCAAOHm5qallEREpO+e7E9ycnKEUqkUfn5+oqioSCqzatUqAUBs3LhR2ta1a1cBQHzzzTfStoKCAuHk5CQGDhwobVuyZIkAIHbt2iVte/jwoWjevLkAIA4dOlS9FSQiqqVsbGxEcHBwmfsKCwuFg4OD8PT0FAUFBdL2r776SgAQXbt21SgPoNSxgoODRVU/6gcGBpb6TAJAzJgxQ3o8ZMgQ4eDgIB4/fixtu3nzpjAyMhKzZ8+Wts2YMaNUDgBCqVSKK1euSNt+++03AUCsXLlS2vb2228LCwsL8ffff0vbLl++LOrUqfPcuj3vM9Wff/4p/Pz8xJo1a8Tu3bvF8uXLRaNGjYSRkZHYs2fPM49JRFQbGFLftGvXLgFAzJ07V6Pce++9JxQKhUYfY2lpKQIDA597/JSUFAFAbNq0qcz9U6dOFVOmTBHbtm0T//rXv0RgYKAAIDp37ixUKlWV6kS1C6cgI6pm5ubm0r/v3r2L//73v3jzzTfx4MEDXLx4UYfJiIjIUOzfvx+FhYUIDQ2FkdH/fn0bPXo0rK2tsXfvXo3yVlZWGnMUK5VKdOzYEVevXpW2xcXF4aWXXsI777wjbTMzM8Po0aOrsSZERGRra4uTJ0/ixo0bpfadPn0aOTk5GDt2LJRKpbR9+PDherPY76BBg5CTk6MxVeX333+P4uJiDBo0qNzn+/r6alyB3KZNG1hbW0t9VFFREfbv34/+/fvDxcVFKtekSRPpLs6qaNSoEeLj4zF27Fi8/fbbGD9+PM6cOQN7e3tMnDixysclIqoJDKlv2rdvH4yNjfHZZ59pbJ84cSKEEPj5559lPV9kZCQWLFiADz74AIMHD0ZMTAzmzZuHY8eO4fvvv5f1XFQzcQCGqJqdO3cO7777LmxsbGBtbQ17e3vpS7G8vDwdpyMiIkPw559/AgCaNWumsV2pVKJx48bSfrWGDRuWmle5Xr16uHPnjsYxX3nllVLlmjRpImd0IiJ6yqJFi5CRkQFXV1d07NgRM2fOlAYf1O/nTZs21XiOiYkJGjdurPWsZenduzdsbGywbds2adu2bdvg6emJV199tdznN2rUqNS2J/uonJwcPHz4sMz+SO4+ys7ODiNGjMClS5fwn//8R9ZjExEZEkPqm/7880+4uLigbt26GttbtGgh7a9uEyZMgJGREfbv31/t5yLDxwEYomqUm5uLrl274rfffsPs2bPx008/ITExEQsXLgQAFBcX6zghERHVRMbGxmVuF08sSklERLrxwQcf4OrVq1i5ciVcXFzw5ZdfwsPDQ/YrdquLqakp+vfvj507d+Lx48f4+++/cezYsQrd/QLoXx+lXrvg9u3bOjk/EZE+MPS+SdvMzc1Rv3599h1UIRyAIapGSUlJuHXrFmJiYjB+/Hj07dsXvr6+qFevXqmyT1+BTEREpObm5gYAuHTpksb2wsJCZGZmSvsre8w//vij1BdeV65cqXpQIiKqEGdnZ3z66afYtWsXMjMzUb9+fcybN096P798+bJGeZVKhczMzAodWxufKwYNGoT//ve/OHDgAHbs2AEhRIUHYMrj4OAAMzOzMvuj6uij1Fd429vby35sIiJDYih9k5ubG27cuIG7d+9qbFdP8//kZ6Pq6hPVSwyw76CK4AAMUTVSX9315JdbhYWFWL16damylpaWnJKMiIjK5OvrC6VSiRUrVmj0KRs2bEBeXh4CAgIqfUx/f3/8/fff2L17t7Tt0aNHWL9+vSyZiYiotKKiolK/8zs4OMDFxQUFBQXo0KED7O3tsXbtWhQWFkplYmJikJubW6FzWFpaAkCFy1eFr68v7OzssG3bNmzbtg0dO3aEu7u7LMc2NjaGr68vdu3apbEWwZUrV17oSuz/+7//K7Xt77//xsaNG9GmTRs4OztX+dhERIbM0Pqmt956C0VFRVi1apXG9mXLlkGhUGisF2ZpaflC53z06FGpgR4AmDNnDoQQ6N27d5WPTbVHHV0HIKrJXn/9ddSrVw+BgYH47LPPoFAo8O2335Z5e72Xlxe2bduGsLAwvPbaa7CyssLbb7+tg9RERKRv7O3tER4ejlmzZqF379545513cOnSJaxevRqvvfaatLZYZfzjH//AqlWrMGTIEIwfPx7Ozs7YsmULzMzMAPDOTCKi6nD37l00bNgQ7733Htq2bQsrKyvs378fKSkpWLJkCUxMTDB37lz84x//QI8ePTBo0CBkZmZi06ZNFZ5n38vLCwDw2Wefwd/fH8bGxhg8eLCs9TAxMcGAAQOwdetW3L9/H4sXL5b1+DNnzkRCQgI6d+6MoKAg6Yu2Vq1aIS0tTaPs2bNnpYsJrly5gry8PMydOxcA0LZtW+kz1eTJk/HHH3+gZ8+ecHFxwbVr17Bu3Trcv38fUVFRsuYnIjIkhtY3vf322+jevTu++OILXLt2DW3btkVCQgJ+/PFHhIaG4pVXXtE47/79+7F06VK4uLjA3d0d3t7eAIBVq1YhNzdXGuz/6aefpPXAxo0bBxsbG2RlZaFdu3YYMmQImjdvDgCIj4/Hvn370Lt3b/Tr169KdaBaRhCRrDZt2iQAiMzMTCGEEMeOHROdOnUS5ubmwsXFRUyePFnEx8cLAOLQoUPS8+7duyc+/PBDYWtrKwAINzc3neQnIiL98HR/IoQQq1atEs2bNxcmJibC0dFRBAUFiTt37mg8r2vXrsLDw6PU8QIDA0v1LVevXhUBAQHC3Nxc2Nvbi4kTJ4p///vfAoA4ceJENdSKiKh2KygoEJMmTRJt27YVdevWFZaWlqJt27Zi9erVGuVWr14t3N3dhampqejQoYM4cuSI6Nq1q+jatatGOQAiODhYY9vjx4/FuHHjhL29vVAoFKIyH/vL6isAiBkzZpQqm5iYKAAIhUIh/vrrr1L7Z8yYUercZeUVQgg3NzcRGBiose3AgQOiXbt2QqlUildeeUV8/fXXYuLEicLMzEyjnLq/LOvPk8eMjY0VXbp0Efb29qJOnTqiQYMG4t133xWpqanPbxQiohrOEPumu3fvigkTJggXFxdhYmIimjZtKr788ktRXFysUe7ixYuiS5cuwtzcvFS/4Obm9sz+Q/0Z7M6dO2LYsGGiSZMmwsLCQpiamgoPDw8xf/58UVhYWOE6UO2mEIKrsRIRERFRieXLl2PChAn4z3/+g5deeknXcYiI6P/r1q0bgJJ1Jmur/v3749y5c6XWISAiIt1g30RUPq4BQ0RERFRLPXz4UOPxo0ePsG7dOjRt2pSDL0REpFNP91GXL1/Gvn37pC/7iIiIiAwB14AhIiIiqqUGDBiARo0awdPTE3l5efjuu+9w8eJFbNmyRdfRiIhIRrdv39ZYOPlpxsbGsLe312Ki8jVu3BjDhw9H48aN8eeff2LNmjVQKpWYPHmyrqMREZEMDLFvIqoKDsAQERER1VL+/v74+uuvsWXLFhQVFaFly5bYunUrBg0apOtoREQkowEDBuDw4cPP3O/m5oZr165pL1AF9O7dG//617+QlZUFU1NT+Pj4YP78+WjatKmuoxERkQwMsW8iqgquAUNERERERERUg6WmpuLOnTvP3G9ubo7OnTtrMREREdV27JuotuAADBERERERERERERERkcyMdB2AiIiIiIiIiIiIiIiopqnVa8AUFxfjxo0bqFu3LhQKha7jEBFVGyEE7t69CxcXFxgZcexdm9jXEFFtwb5Gd9jXEFFtwb5Gd9jXEFFtImd/U6sHYG7cuAFXV1ddxyAi0pq//voLDRs21HWMWoV9DRHVNuxrtI99DRHVNuxrtI99DRHVRnL0N7V6AKZu3boAShrS2tpaZzlUKhUSEhLg5+cHExMTneWoCkPNztzaZai5AcPN/nTu/Px8uLq6Su97pD360tdUhKG+3gHDzg4wv64Zcn59ys6+RncMqa8B9Ot1+yJqQj1qQh2AmlGPmlAHoPrrwb5Gd6ra19SU17Yc2BYl2A4l2A4l9LUd5OxvavUAjPqWSWtra50PwFhYWMDa2lqvXmgVYajZmVu7DDU3YLjZn5Wbt4prn770NRVhqK93wLCzA8yva4acXx+zs6/RPkPqawD9fN1WRU2oR02oA1Az6lET6gBorx7sa7Svqn1NTXlty4FtUYLtUILtUELf20GO/oYTZhIREREREREREREREcms2gdgFixYAIVCgdDQUGnbo0ePEBwcjPr168PKygoDBw5Edna2xvOuX7+OgIAAWFhYwMHBAZMmTcLjx481yiQlJaF9+/YwNTVFkyZNEBMTU93VISIiIiIiIiIiIiIiKle1DsCkpKRg3bp1aNOmjcb2CRMm4KeffsKOHTtw+PBh3LhxAwMGDJD2FxUVISAgAIWFhTh+/Dg2b96MmJgYTJ8+XSqTmZmJgIAAdO/eHWlpaQgNDcUnn3yC+Pj46qwSERERERERERERERFRuaptAObevXsYOnQo1q9fj3r16knb8/LysGHDBixduhQ9evSAl5cXNm3ahOPHj+PEiRMAgISEBJw/fx7fffcdPD090adPH8yZMwfR0dEoLCwEAKxduxbu7u5YsmQJWrRogZCQELz33ntYtmxZdVWJiIiIiIiIiIiIiIioQupU14GDg4MREBAAX19fzJ07V9qempoKlUoFX19faVvz5s3RqFEjJCcno1OnTkhOTkbr1q3h6OgolfH390dQUBDOnTuHdu3aITk5WeMY6jJPTnX2tIKCAhQUFEiP8/PzAZQs9qNSqV60ylWmPrcuM1SVoWZnbu3SVu5WM+W/A87USGBOB8BrdhwKiksvvJUx01/2c8rh6TY3tNcM6VarmfEoKNLewqbXFgRo7VxERFQ7vTx1r+zHNDUWWNTx2f0m+zciov/hZwwiqq2qZQBm69at+PXXX5GSklJqX1ZWFpRKJWxtbTW2Ozo6IisrSyrz5OCLer963/PK5Ofn4+HDhzA3Ny917sjISMyaNavU9oSEBFhYWFS8gtUkMTFR1xGqzFCzM7d2VXfuRR2r79hzOhSXuX3fvn3Vd1IZqNv8wYMHOk5CRERERERERERUu8g+APPXX39h/PjxSExMhJmZmdyHfyHh4eEICwuTHufn58PV1RV+fn6wtrbWWS6VSoXExET06tULJiYmOstRFYaanbm1S1u5q+8OmGJMO21kcHfAPNnm6jv+iIiIiIiIiIiISDtkH4BJTU1FTk4O2rdvL20rKirCkSNHsGrVKsTHx6OwsBC5ubkad8FkZ2fDyckJAODk5IRTp05pHDc7O1vap/5bve3JMtbW1mXe/QIApqamMDU1LbXdxMREL77M1pccVWGo2Zlbu6o7d3XezlxQrCjz+Pr+/6Buc33PSUREREREREREVNMYyX3Anj17Ij09HWlpadKfDh06YOjQodK/TUxMcODAAek5ly5dwvXr1+Hj4wMA8PHxQXp6OnJycqQyiYmJsLa2RsuWLaUyTx5DXUZ9DCIiIiIiIiIiIl2JjIzEa6+9hrp168LBwQH9+/fHpUuXNMo8evQIwcHBqF+/PqysrDBw4MBSFxxfv34dAQEBsLCwgIODAyZNmoTHjx9rlElKSkL79u1hamqKJk2aICYmplSe6OhovPzyyzAzM4O3t3epi5+JiEh+sg/A1K1bF61atdL4Y2lpifr166NVq1awsbHBqFGjEBYWhkOHDiE1NRUjRoyAj48POnXqBADw8/NDy5Yt8dFHH+G3335DfHw8IiIiEBwcLN3BMnbsWFy9ehWTJ0/GxYsXsXr1amzfvh0TJkyQu0pERERERERERESVcvjwYQQHB+PEiRNITEyESqWCn58f7t+/L5WZMGECfvrpJ+zYsQOHDx/GjRs3MGDAAGl/UVERAgICUFhYiOPHj2Pz5s2IiYnB9OnTpTKZmZkICAhA9+7dkZaWhtDQUHzyySeIj//fNN3btm1DWFgYZsyYgV9//RVt27aFv7+/xsXPREQkP9mnIKuIZcuWwcjICAMHDkRBQQH8/f2xevVqab+xsTH27NmDoKAg+Pj4wNLSEoGBgZg9e7ZUxt3dHXv37sWECRMQFRWFhg0b4uuvv4a/v36ux0Ckay9P3avV811bEKDV8xERERERERHpk7i4OI3HMTExcHBwQGpqKrp06YK8vDxs2LABsbGx6NGjBwBg06ZNaNGiBU6cOIFOnTohISEB58+fx/79++Ho6AhPT0/MmTMHU6ZMwcyZM6FUKrF27Vq4u7tjyZIlAIAWLVrg6NGjWLZsmfQ92dKlSzF69GiMGDECALB27Vrs3bsXGzduxNSpU7XYKkREtYtWBmCSkpI0HpuZmSE6OhrR0dHPfI6bmxv27dv33ON269YNZ86ckSMiERERERERERFRtcnLywMA2NnZAShZR1mlUsHX11cq07x5czRq1AjJycno1KkTkpOT0bp1azg6Okpl/P39ERQUhHPnzqFdu3ZITk7WOIa6TGhoKACgsLAQqampCA8Pl/YbGRnB19cXycnJZWYtKChAQUGB9Dg/Px8AoFKpoFKpKlxndVlTI1Hh58ihMhm1RZ1JH7NpE9uhBNuhhL62g5x5dHIHDBERERERERERUW1RXFyM0NBQdO7cGa1atQIAZGVlQalUwtbWVqOso6MjsrKypDJPDr6o96v3Pa9Mfn4+Hj58iDt37qCoqKjMMhcvXiwzb2RkJGbNmlVqe0JCAiwsLCpY6/+Z06G40s95EeVd1K1LiYmJuo6gF9gOJdgOJfStHR48eCDbsTgAQ0REBqeoqAgzZ87Ed999h6ysLLi4uGD48OGIiIiAQqEAAAghMGPGDKxfvx65ubno3Lkz1qxZg6ZNm0rHuX37NsaNG4effvpJmhozKioKVlZWUpmzZ88iODgYKSkpsLe3x7hx4zB58mSt15mIiIiIiAxXcHAwMjIycPToUV1HqZDw8HCEhYVJj/Pz8+Hq6go/Pz9YW1tX+DgqlQqJiYmYdtoIBcWK6ohapoyZ+rdEgbotevXqBRMTE13H0Rm2Qwm2Qwl9bQf1XX9y4AAMEREZnIULF2LNmjXYvHkzPDw8cPr0aYwYMQI2Njb47LPPAACLFi3CihUrsHnzZri7u2PatGnw9/fH+fPnYWZmBgAYOnQobt68KS2IOWLECIwZMwaxsbEASjpcPz8/+Pr6Yu3atUhPT8fIkSNha2uLMWPG6Kz+RERERERkOEJCQrBnzx4cOXIEDRs2lLY7OTmhsLAQubm5GnfBZGdnw8nJSSpz6tQpjeNlZ2dL+9R/q7c9Wcba2hrm5uYwNjaGsbFxmWXUx3iaqakpTE1NS203MTGp0pekBcUKFBRpbwBGn77IfVpV27CmYTuUYDuU0Ld2kDOLkWxHIiIi0pLjx4+jX79+CAgIwMsvv4z33nsPfn5+0gcTIQSWL1+OiIgI9OvXD23atME333yDGzduYNeuXQCACxcuIC4uDl9//TW8vb3xxhtvYOXKldi6dStu3LgBANiyZQsKCwuxceNGeHh4YPDgwfjss8+wdOlSXVWdiIiIiIgMhBACISEh2LlzJw4ePAh3d3eN/V5eXjAxMcGBAwekbZcuXcL169fh4+MDAPDx8UF6ejpycnKkMomJibC2tkbLli2lMk8eQ11GfQylUgkvLy+NMsXFxThw4IBUhoiIqgcHYIiIyOC8/vrrOHDgAH7//XcAwG+//YajR4+iT58+AIDMzExkZWVpLERpY2MDb29vaZHJ5ORk2NraokOHDlIZX19fGBkZ4eTJk1KZLl26QKlUSmX8/f1x6dIl3Llzp9rrSUREREREhis4OBjfffcdYmNjUbduXWRlZSErKwsPHz4EUPIZZdSoUQgLC8OhQ4eQmpqKESNGwMfHB506dQIA+Pn5oWXLlvjoo4/w22+/IT4+HhEREQgODpbuUBk7diyuXr2KyZMn4+LFi1i9ejW2b9+OCRMmSFnCwsKwfv16bN68GRcuXEBQUBDu37+PESNGaL9hiIhqEU5BRkREBmfq1KnIz89H8+bNYWxsjKKiIsybNw9Dhw4F8L/FKMtaZPLJhSodHBw09tepUwd2dnYaZZ6+Su3JBS/r1atXKltBQQEKCgqkx+p5Q1UqFVQqVZXrrA3qfKZGQifnleMY+t7Gz8L8umXI+fUpuz5kICIi0idr1qwBAHTr1k1j+6ZNmzB8+HAAwLJly6T1KAsKCuDv74/Vq1dLZY2NjbFnzx4EBQXBx8cHlpaWCAwMxOzZs6Uy7u7u2Lt3LyZMmICoqCg0bNgQX3/9Nfz9/7cOyqBBg/B///d/mD59OrKysuDp6Ym4uLhSn5mIiEheHIAhIiKDs337dmzZsgWxsbHw8PBAWloaQkND4eLigsDAQJ1mi4yMxKxZs0ptT0hIgIWFhQ4SVd6cDsVaPd++fftkO1ZiYqJsx9IF5tctQ86vD9kfPHig6whERER6RYjyL2wyMzNDdHQ0oqOjn1nGzc2t3N+Zu3XrhjNnzjy3TEhICEJCQsrNRERE8uEADBERGZxJkyZh6tSpGDx4MACgdevW+PPPPxEZGYnAwEBpIcns7Gw4OztLz8vOzoanpyeAkoUqn5xHGQAeP36M27dvl7uYpXpfWcLDwxEWFiY9zs/Ph6urK/z8/GBtbf0Cta5+KpUKiYmJmHbaCAXF2lsgM2Omf/mFyqHO3qtXL71auK+imF+3DDm/PmVX3/FHREREREREJTgAQ0REBufBgwcwMtJcxszY2BjFxSV3bri7u8PJyQkHDhyQBlzy8/Nx8uRJBAUFAShZqDI3Nxepqanw8vICABw8eBDFxcXw9vaWynzxxRdQqVTSF5uJiYlo1qxZmdOPAYCpqak0F/OTTExMdP7laEUVFCtQUKS9ARg528WQ2rkszK9bhpxfH7Lr+vxERERERET6xqj8IkRERPrl7bffxrx587B3715cu3YNO3fuxNKlS/Huu+8CABQKBUJDQzF37lzs3r0b6enp+Pjjj+Hi4oL+/fsDAFq0aIHevXtj9OjROHXqFI4dO4aQkBAMHjwYLi4uAIAPP/wQSqUSo0aNwrlz57Bt2zZERUVp3OFCRERERERERERUFt4BQ0REBmflypWYNm0aPv30U+Tk5MDFxQX/+Mc/MH36dKnM5MmTcf/+fYwZMwa5ubl44403EBcXBzMzM6nMli1bEBISgp49e0oLX65YsULab2Njg4SEBAQHB8PLywsNGjTA9OnTMWbMGK3Wl4iIiIiIiIiIDA8HYIiIyODUrVsXy5cvx/Lly59ZRqFQYPbs2Zg9e/Yzy9jZ2SE2Nva552rTpg1++eWXqkYlIiIiIiIiIqJailOQERERERERERERERERyYwDMERERERERERERERERDLjFGRERERERESkNa1mxmNRx5K/C4oUuo5DRERERFRteAcMERERERERERERERGRzDgAQ0REREREREREREREJDMOwBAREREREREREREREcmMAzBERERERERl+PvvvzFs2DDUr18f5ubmaN26NU6fPi3tF0Jg+vTpcHZ2hrm5OXx9fXH58mWNY9y+fRtDhw6FtbU1bG1tMWrUKNy7d0+jzNmzZ/Hmm2/CzMwMrq6uWLRokVbqR0RERERE1YsDMERERERERE+5c+cOOnfuDBMTE/z88884f/48lixZgnr16kllFi1ahBUrVmDt2rU4efIkLC0t4e/vj0ePHkllhg4dinPnziExMRF79uzBkSNHMGbMGGl/fn4+/Pz84ObmhtTUVHz55ZeYOXMmvvrqK63Wl4iIiIiI5FdH1wGIiIiIiIj0zcKFC+Hq6opNmzZJ29zd3aV/CyGwfPlyREREoF+/fgCAb775Bo6Ojti1axcGDx6MCxcuIC4uDikpKejQoQMAYOXKlXjrrbewePFiuLi4YMuWLSgsLMTGjRuhVCrh4eGBtLQ0LF26VGOghoiIiIiIDA8HYIiIiIiIiJ6ye/du+Pv74/3338fhw4fx0ksv4dNPP8Xo0aMBAJmZmcjKyoKvr6/0HBsbG3h7eyM5ORmDBw9GcnIybG1tpcEXAPD19YWRkRFOnjyJd999F8nJyejSpQuUSqVUxt/fHwsXLsSdO3c07rhRKygoQEFBgfQ4Pz8fAKBSqaBSqWRvC7mZGgmNvw1VefUwhP8LdUZDyPo8NaEeNaEOQPXXw9Dbh4iIah8OwBARERERET3l6tWrWLNmDcLCwvD5558jJSUFn332GZRKJQIDA5GVlQUAcHR01Hieo6OjtC8rKwsODg4a++vUqQM7OzuNMk/eWfPkMbOyssocgImMjMSsWbNKbU9ISICFhUUVa6w9czqo/y7WbRCZPKse+/bt03KSqktMTNR1BFnUhHrUhDoA1VePBw8eVMtxiYiIqgsHYIiIiIiIiJ5SXFyMDh06YP78+QCAdu3aISMjA2vXrkVgYKBOs4WHhyMsLEx6nJ+fD1dXV/j5+cHa2lqHySrGa3Yc5nQoxrTTRigoVug6TpWZGonn1iNjpr8OUlWOSqVCYmIievXqBRMTE13HqbKaUI+aUAeg+uuhvuOPiIjIUMg+ALNmzRqsWbMG165dAwB4eHhg+vTp6NOnDwDg0aNHmDhxIrZu3YqCggL4+/tj9erVGleOXb9+HUFBQTh06BCsrKwQGBiIyMhI1Knzv7hJSUkICwvDuXPn4OrqioiICAwfPlzu6hARERERUS3k7OyMli1bamxr0aIF/v3vfwMAnJycAADZ2dlwdnaWymRnZ8PT01Mqk5OTo3GMx48f4/bt29LznZyckJ2drVFG/Vhd5mmmpqYwNTUttd3ExMQgvrhVD1YUFCtQUGS4AzBqz6qHIfxfqBnKa6c8NaEeNaEOQPXVoya0DRER1S5Gch+wYcOGWLBgAVJTU3H69Gn06NED/fr1w7lz5wAAEyZMwE8//YQdO3bg8OHDuHHjBgYMGCA9v6ioCAEBASgsLMTx48exefNmxMTEYPr06VKZzMxMBAQEoHv37khLS0NoaCg++eQTxMfHy10dIiIiIiKqhTp37oxLly5pbPv999/h5uYGAHB3d4eTkxMOHDgg7c/Pz8fJkyfh4+MDAPDx8UFubi5SU1OlMgcPHkRxcTG8vb2lMkeOHNFY1yAxMRHNmjUrc/oxIiIiIiIyHLIPwLz99tt466230LRpU7z66quYN28erKyscOLECeTl5WHDhg1YunQpevToAS8vL2zatAnHjx/HiRMnAJTMW3z+/Hl899138PT0RJ8+fTBnzhxER0ejsLAQALB27Vq4u7tjyZIlaNGiBUJCQvDee+9h2bJlcleHiIiIiIhqoQkTJuDEiROYP38+rly5gtjYWHz11VcIDg4GACgUCoSGhmLu3LnYvXs30tPT8fHHH8PFxQX9+/cHUHLHTO/evTF69GicOnUKx44dQ0hICAYPHgwXFxcAwIcffgilUolRo0bh3Llz2LZtG6KiojSmGCMiIiIiIsNUrWvAFBUVYceOHbh//z58fHyQmpoKlUoFX19fqUzz5s3RqFEjJCcno1OnTkhOTkbr1q01piTz9/dHUFAQzp07h3bt2iE5OVnjGOoyoaGhz81TUFCAgoIC6bF67lCVSqVxxZm2qc+tywxVZajZa2NuU2Mhd5znejKjttq7OupoaiQ0/n6avr6Gnm5zfc1JRESkr1577TXs3LkT4eHhmD17Ntzd3bF8+XIMHTpUKjN58mTcv38fY8aMQW5uLt544w3ExcXBzMxMKrNlyxaEhISgZ8+eMDIywsCBA7FixQppv42NDRISEhAcHAwvLy80aNAA06dPx5gxY7RaXyIiIiIikl+1DMCkp6fDx8cHjx49gpWVFXbu3ImWLVsiLS0NSqUStra2GuUdHR2RlZUFAMjKytIYfFHvV+97Xpn8/Hw8fPgQ5ubmZeaKjIzErFmzSm1PSEiAhYVFleoqp8TERF1HqDJDzV6bci/qWA1BnmPfvn2ltlV3e1dnHed0KC5ze1n11CfqNn/w4IGOkxARERmevn37om/fvs/cr1AoMHv2bMyePfuZZezs7BAbG/vc87Rp0wa//PJLlXMSEREREZF+qpYBmGbNmiEtLQ15eXn4/vvvERgYiMOHD1fHqSolPDxc41b+/Px8uLq6ws/PD9bW1jrLpVKpkJiYiF69ehncgnKGmr025m41U3drJJkaCczpUIxpp42kRVcNRXnZM2b66yBV+Z5+rajv+CMiIiIiIiLtOHLkCL788kukpqbi5s2b2LlzpzRNJQAMHz4cmzdv1niOv78/4uLipMe3b9/GuHHj8NNPP0l3UkZFRcHKykoqc/bsWQQHByMlJQX29vYYN24cJk+erHHcHTt2YNq0abh27RqaNm2KhQsX4q233qqeihMRkaRaBmCUSiWaNGkCAPDy8kJKSgqioqIwaNAgFBYWIjc3V+MumOzsbDg5OQEAnJyccOrUKY3jZWdnS/vUf6u3PVnG2tr6mXe/AICpqSlMTU1LbTcxMdGLL+H1JUdVGGr22pS7oEj3Ax8FxQq9yFEVz8qu768f9WtF33MSERERERHVNPfv30fbtm0xcuRIDBgwoMwyvXv3xqZNm6THT39vNXToUNy8eROJiYlQqVQYMWIExowZI91dmZ+fDz8/P/j6+mLt2rVIT0/HyJEjYWtrK01nefz4cQwZMgSRkZHo27cvYmNj0b9/f/z6669o1apVNdWeiIgAwEgbJykuLkZBQQG8vLxgYmKCAwcOSPsuXbqE69evw8fHBwDg4+OD9PR05OTkSGUSExNhbW2Nli1bSmWePIa6jPoYREREREREREREutSnTx/MnTsX77777jPLmJqawsnJSfpTr149ad+FCxcQFxeHr7/+Gt7e3njjjTewcuVKbN26FTdu3ABQstZYYWEhNm7cCA8PDwwePBifffYZli5dKh0nKioKvXv3xqRJk9CiRQvMmTMH7du3x6pVq6qv8kREBKAaBmDCw8Nx5MgRXLt2Denp6QgPD0dSUhKGDh0KGxsbjBo1CmFhYTh06BBSU1MxYsQI+Pj4oFOnTgAAPz8/tGzZEh999BF+++03xMfHIyIiAsHBwdJVAGPHjsXVq1cxefJkXLx4EatXr8b27dsxYcIEuatDRERERERERERULZKSkuDg4IBmzZohKCgIt27dkvYlJyfD1tYWHTp0kLb5+vrCyMgIJ0+elMp06dIFSqVSKuPv749Lly7hzp07UhlfX1+N8/r7+yM5Obk6q0ZERKiGKchycnLw8ccf4+bNm7CxsUGbNm0QHx+PXr16AQCWLVsmzVlZUFAAf39/rF69Wnq+sbEx9uzZg6CgIPj4+MDS0hKBgYEaC1u6u7tj7969mDBhAqKiotCwYUN8/fXX8PfXz7UYiIhIfn///TemTJmCn3/+GQ8ePECTJk2wadMm6cOJEAIzZszA+vXrkZubi86dO2PNmjVo2rSpdAy55lMmIiIiIiKqrN69e2PAgAFwd3fHH3/8gc8//xx9+vRBcnIyjI2NkZWVBQcHB43n1KlTB3Z2dsjKygIAZGVlwd3dXaOMo6OjtK9evXrIysqStj1ZRn2MshQUFKCgoEB6rF5XVKVSQaVSVbiO6rKmRqLCz5FDZTJqizqTPmbTJrZDCbZDCX1tBznzyD4As2HDhufuNzMzQ3R0NKKjo59Zxs3NDfv27Xvucbp164YzZ85UKSMRkVxenrpX6+e8tiBA6+fUN3fu3EHnzp3RvXt3/Pzzz7C3t8fly5c1btdftGgRVqxYgc2bN8Pd3R3Tpk2Dv78/zp8/DzMzMwDyzKdMRERERERUFYMHD5b+3bp1a7Rp0wavvPIKkpKS0LNnTx0mAyIjIzFr1qxS2xMSEmBhYVHp483pUCxHrAor73tFXUpMTNR1BL3AdijBdiihb+3w4MED2Y4l+wAMERFRdVu4cCFcXV01Fqt88qovIQSWL1+OiIgI9OvXDwDwzTffwNHREbt27cLgwYOl+ZRTUlKku2ZWrlyJt956C4sXL4aLi4vGfMpKpRIeHh5IS0vD0qVLOQBDRERERESyaty4MRo0aIArV66gZ8+ecHJy0lgjGQAeP36M27dvw8nJCQDg5OSE7OxsjTLqx+WVUe8vS3h4OMLCwqTH+fn5cHV1hZ+fH6ytrStcJ5VKhcTEREw7bYSCYkWFn/eiMmbq3yw56rbo1asXTExMdB1HZ9gOJdgOJfS1HdR3/cmBAzBERGRwdu/eDX9/f7z//vs4fPgwXnrpJXz66acYPXo0ACAzMxNZWVka8xzb2NjA29sbycnJGDx4cLnzKb/77rvPnE954cKFuHPnjsYdN0RERERERC/iP//5D27dugVnZ2cAgI+PD3Jzc5GamgovLy8AwMGDB1FcXAxvb2+pzBdffAGVSiV9eZmYmIhmzZpJn1d8fHxw4MABhIaGSudKTEyEj4/PM7OYmppKazE/ycTEpEpfkhYUK1BQpL0BGH36IvdpVW3DmobtUILtUELf2kHOLByAISIig3P16lWsWbMGYWFh+Pzzz5GSkoLPPvsMSqUSgYGB0lzGz5vnWK75lJ8m11zJumDI8zPr67yxFcX8umXI+fUpuz5kICIi0if37t3DlStXpMeZmZlIS0uDnZ0d7OzsMGvWLAwcOBBOTk74448/MHnyZDRp0kRa47hFixbo3bs3Ro8ejbVr10KlUiEkJASDBw+Gi4sLAODDDz/ErFmzMGrUKEyZMgUZGRmIiorCsmXLpPOOHz8eXbt2xZIlSxAQEICtW7fi9OnT+Oqrr7TbIEREtRAHYIiIyOAUFxejQ4cOmD9/PgCgXbt2yMjIwNq1axEYGKjTbHLPlawLhjw/s77NG1tZzK9bhpxfH7LLOU8yERFRTXD69Gl0795deqye0iswMBBr1qzB2bNnsXnzZuTm5sLFxQV+fn6YM2eOxp0nW7ZsQUhICHr27AkjIyMMHDgQK1askPbb2NggISEBwcHB8PLyQoMGDTB9+nSNKZNff/11xMbGIiIiAp9//jmaNm2KXbt2oVWrVlpoBSKi2o0DMEREZHCcnZ3RsmVLjW0tWrTAv//9bwD/m+s4Oztbun1f/djT01MqI8d8yk+Ta65kXTDk+Zn1dd7YimJ+3TLk/PqUXc55komIiGqCbt26QYhn310eHx9f7jHs7OwQGxv73DJt2rTBL7/88twy77//Pt5///1yz0dERPLiAAwRERmczp0749KlSxrbfv/9d7i5uQEA3N3d4eTkhAMHDkgDLvn5+Th58iSCgoIAyDef8tPknitZFwx5fmZDaueyML9uGXJ+fciu6/MTERERERHpGyNdByAiIqqsCRMm4MSJE5g/fz6uXLmC2NhYfPXVVwgODgYAKBQKhIaGYu7cudi9ezfS09Px8ccfw8XFBf379wegOZ/yqVOncOzYsTLnU1YqlRg1ahTOnTuHbdu2ISoqSuMOFyIiIiIiIiIiorLwDhgiIjI4r732Gnbu3Inw8HDMnj0b7u7uWL58OYYOHSqVmTx5Mu7fv48xY8YgNzcXb7zxBuLi4mBmZiaVkWM+ZSIiIiIiIiIiorJwAIZIB16eurdKzzM1FljUEWg1M16r0wMR6aO+ffuib9++z9yvUCgwe/ZszJ49+5ll5JpPmYiIiIiIiIiI6GkcgCEiIiKdquqg9JMqM0B9bUHAC5+PiIiIiIiIiKg8XAOGiIiIiIiIiIiIiIhIZhyAISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGTGARgiIiIiIiIiIiIiIiKZcQCGiIiIiIiIiIiIiIhIZhyAISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGTGARgiIiIiIiIiIiIiIiKZcQCGiIiIiIiIiIiIiIhIZhyAISIiIiIiIiIiIiIikhkHYIiIiIiIiMqxYMECKBQKhIaGStsePXqE4OBg1K9fH1ZWVhg4cCCys7M1nnf9+nUEBATAwsICDg4OmDRpEh4/fqxRJikpCe3bt4epqSmaNGmCmJgYLdSIiIiIiIiqWx1dByAiIiIiItJnKSkpWLduHdq0aaOxfcKECdi7dy927NgBGxsbhISEYMCAATh27BgAoKioCAEBAXBycsLx48dx8+ZNfPzxxzAxMcH8+fMBAJmZmQgICMDYsWOxZcsWHDhwAJ988gmcnZ3h7++v9bqSfF6euler57u2IECr5yMiIiKi8vEOGCIiIiIiome4d+8ehg4divXr16NevXrS9ry8PGzYsAFLly5Fjx494OXlhU2bNuH48eM4ceIEACAhIQHnz5/Hd999B09PT/Tp0wdz5sxBdHQ0CgsLAQBr166Fu7s7lixZghYtWiAkJATvvfceli1bppP6EhERERGRfHgHDBERERER0TMEBwcjICAAvr6+mDt3rrQ9NTUVKpUKvr6+0rbmzZujUaNGSE5ORqdOnZCcnIzWrVvD0dFRKuPv74+goCCcO3cO7dq1Q3JyssYx1GWenOrsaQUFBSgoKJAe5+fnAwBUKhVUKtWLVrnamRoJjb8Nlb7Voyr/9+rnGMLr5nlqQj1qQh2A6q+HobcPERHVPrIPwERGRuKHH37AxYsXYW5ujtdffx0LFy5Es2bNpDKPHj3CxIkTsXXrVhQUFMDf3x+rV6/W+GBy/fp1BAUF4dChQ7CyskJgYCAiIyNRp87/IiclJSEsLAznzp2Dq6srIiIiMHz4cLmrREREREREtdDWrVvx66+/IiUlpdS+rKwsKJVK2Nraamx3dHREVlaWVObJzzjq/ep9zyuTn5+Phw8fwtzcvNS5IyMjMWvWrFLbExISYGFhUfEK6sicDuq/i3UbRCb6Uo99+/ZV+bmJiYkyJtGdmlCPmlAHoPrq8eDBg2o5LhERUXWRfQDm8OHDCA4OxmuvvYbHjx/j888/h5+fH86fPw9LS0sAnCuZiIiIiIj0219//YXx48cjMTERZmZmuo6jITw8HGFhYdLj/Px8uLq6ws/PD9bW1jpMVjFes+Mwp0Mxpp02QkGxQtdxqszUSOhVPTJmVv5zsEqlQmJiInr16gUTE5NqSKUdNaEeNaEOQPXXQ33HHxERkaGQfQAmLi5O43FMTAwcHByQmpqKLl26SHMlx8bGokePHgCATZs2oUWLFjhx4gQ6deokzZW8f/9+ODo6wtPTE3PmzMGUKVMwc+ZMKJVKjbmSAaBFixY4evQoli1bxgEYIiIiIiJ6IampqcjJyUH79u2lbUVFRThy5AhWrVqF+Ph4FBYWIjc3V+MumOzsbDg5OQEAnJyccOrUKY3jZmdnS/vUf6u3PVnG2tq6zLtfAMDU1BSmpqaltpuYmBjEF7fqwYqCYgUKinQ/cPGi9KUeL/J/byivnfLUhHrUhDoA1VePmtA2RERUuxhV9wny8vIAAHZ2dgDKnysZwDPnSs7Pz8e5c+ekMmXNlaw+BhERERERUVX17NkT6enpSEtLk/506NABQ4cOlf5tYmKCAwcOSM+5dOkSrl+/Dh8fHwCAj48P0tPTkZOTI5VJTEyEtbU1WrZsKZV58hjqMupjEBERERGR4ZL9DpgnFRcXIzQ0FJ07d0arVq0A6HauZH1drNKQF9sz1Oy6zm1qXLWFOvVtoc+KMtTcgH5mr8jr9unXuKH9jBIREela3bp1pc8wapaWlqhfv760fdSoUQgLC4OdnR2sra0xbtw4+Pj4oFOnTgAAPz8/tGzZEh999BEWLVqErKwsREREIDg4WLqDZezYsVi1ahUmT56MkSNH4uDBg9i+fTv27t2r3QoTEZHsjhw5gi+//BKpqam4efMmdu7cif79+0v7hRCYMWMG1q9fj9zcXHTu3Blr1qxB06ZNpTK3b9/GuHHj8NNPP8HIyAgDBw5EVFQUrKyspDJnz55FcHAwUlJSYG9vj3HjxmHy5MkaWXbs2IFp06bh2rVraNq0KRYuXIi33nqr2tuAiKi2q9YBmODgYGRkZODo0aPVeZoK0/fFKg15sT1Dza6r3Is6vtjz9WWhz8oy1NyAfmWvzAKr6tc4F6skIiKS37Jly6QvwwoKCuDv74/Vq1dL+42NjbFnzx4EBQXBx8cHlpaWCAwMxOzZs6Uy7u7u2Lt3LyZMmICoqCg0bNgQX3/9NadVJiKqAe7fv4+2bdti5MiRGDBgQKn9ixYtwooVK7B582a4u7tj2rRp8Pf3x/nz56X1x4YOHYqbN28iMTERKpUKI0aMwJgxYxAbGwug5OJiPz8/+Pr6Yu3atUhPT8fIkSNha2uLMWPGAACOHz+OIUOGIDIyEn379kVsbCz69++PX3/9tdTFBkREJK9qG4AJCQnBnj17cOTIETRs2FDa7uTkpLO5kvV1sUpDXmzPULPrOnermfFVep6+LfRZUYaaG9DP7BVZYPXp1zgXqyQiInpxSUlJGo/NzMwQHR2N6OjoZz7Hzc2t3IsnunXrhjNnzsgRkYiI9EifPn3Qp0+fMvcJIbB8+XJERESgX79+AIBvvvkGjo6O2LVrFwYPHowLFy4gLi4OKSkp6NChAwBg5cqVeOutt7B48WK4uLhgy5YtKCwsxMaNG6FUKuHh4YG0tDQsXbpUGoCJiopC7969MWnSJADAnDlzkJiYiFWrVmHt2rVaaAkiotpL9gEYIQTGjRuHnTt3IikpCe7u7hr7vby8pLmSBw4cCKDsuZLnzZuHnJwcODg4ACh7ruSnP8iUN1eyvi9WqS85qkKu7C9P1c5UC6bGAos6Au3mHcSleX21cs4nveginfqy0GdlGWpuQL+yV+ZnTf2zaajvLRW1YMEChIeHY/z48Vi+fDkA4NGjR5g4cSK2bt2qcVXyk9NXXr9+HUFBQTh06BCsrKwQGBiIyMhI1Knzv+4xKSkJYWFhOHfuHFxdXREREYHhw4druYZERERERFSTZGZmIisrS2N9YxsbG3h7eyM5ORmDBw9GcnIybG1tpcEXAPD19YWRkRFOnjyJd999F8nJyejSpQuUSqVUxt/fHwsXLsSdO3dQr149JCcna1yQrC6za9euZ+aTaxp/dVltT+utj9Nwc4rwEmyHEmyHEvraDnLmkX0AJjg4GLGxsfjxxx9Rt25dac0WGxsbmJubw8bGhnMlExGRbFJSUrBu3Tq0adNGY/uECROwd+9e7NixAzY2NggJCcGAAQNw7NgxAEBRURECAgLg5OSE48eP4+bNm/j4449hYmKC+fPnAyj5UBQQEICxY8diy5YtOHDgAD755BM4OztzahgiIiIiIqoy9fdlZa1v/OT6x+oLk9Xq1KkDOzs7jTJPX/z85DrK9erVe+Y6yupjlEXuafy1Pa13Zabu1jZDncZfbmyHEmyHEvrWDnJO5S/7AMyaNWsAlNxG/6RNmzZJVwxzrmQiIpLDvXv3MHToUKxfvx5z586Vtufl5WHDhg2IjY1Fjx49AJT0Qy1atMCJEyfQqVMnJCQk4Pz589i/fz8cHR3h6emJOXPmYMqUKZg5cyaUSiXWrl0Ld3d3LFmyBADQokULHD16FMuWLWN/Q0RERERENZZc0/irp8fW9rTeFZm6W9t0PR2+vmA7lGA7lNDXdpBzKv9qmYKsPJwrmYiI5BAcHIyAgAD4+vpqDMCkpqZCpVJp3M7fvHlzNGrUCMnJyejUqROSk5PRunVrjSvB/P39ERQUhHPnzqFdu3ZITk7WOIa6TGho6DMzyXWrvi7oanoAOagzVyS7Pv4/6Ott1xXF/LqjT9n1IQMREZGhUK9xnJ2dDWdnZ2l7dnY2PD09pTI5OTkaz3v8+DFu375d7hrJT57jWWXU+8si9zT+2p7WW5++yH1abZgmvCLYDiXYDiX0rR3kzCL7AAwREZE2bN26Fb/++itSUlJK7cvKyoJSqYStra3G9qdv5y/rNnz1vueVyc/Px8OHD2Fubl7q3HLfqq8L2p4eQE4Vyc7pCKoP8+uOPmSX8zZ9IiKims7d3R1OTk44cOCANOCSn5+PkydPIigoCEDJ+se5ublITU2Fl5cXAODgwYMoLi6Gt7e3VOaLL76ASqWSvjBMTExEs2bNUK9ePanMgQMHNC4kK28dZSIikgcHYIiIyOD89ddfGD9+PBITE2FmZqbrOBrkulVfF3Q1PYAcTI0E5nQo1tvs5U2BoK+3XVcU8+uOPmWX8zZ9IiKimuDevXu4cuWK9DgzMxNpaWmws7NDo0aNEBoairlz56Jp06Zwd3fHtGnT4OLigv79+wMomQK5d+/eGD16NNauXQuVSoWQkBAMHjwYLi4uAIAPP/wQs2bNwqhRozBlyhRkZGQgKioKy5Ytk847fvx4dO3aFUuWLEFAQAC2bt2K06dP46uvvtJqexAR1UYcgCEiIoOTmpqKnJwctG/fXtpWVFSEI0eOYNWqVYiPj0dhYSFyc3M17oJ58jZ7JycnnDp1SuO4Fb1V39rausy7XwD5b9XXBW1PDyAnfc1e0f97Q3qdlIX5dUcfsuv6/ERERPrm9OnT6N69u/RYfaFWYGAgYmJiMHnyZNy/fx9jxoxBbm4u3njjDcTFxWlcZLZlyxaEhISgZ8+e0nrKK1askPbb2NggISEBwcHB8PLyQoMGDTB9+nSMGTNGKvP6668jNjYWERER+Pzzz9G0aVPs2rULrVq10kIrEBHVbhyAISIig9OzZ0+kp6drbBsxYgSaN2+OKVOmwNXVFSYmJjhw4AAGDhwIALh06RKuX78u3Wbv4+ODefPmIScnBw4ODgBKbsO3trZGy5YtpTJPT1fFW/WJiIiIiKgiunXr9ty1khUKBWbPno3Zs2c/s4ydnR1iY2Ofe542bdrgl19+eW6Z999/H++///7zAxMRkew4AENERAanbt26pa7WsrS0RP369aXto0aNQlhYGOzs7GBtbY1x48bBx8cHnTp1AgD4+fmhZcuW+Oijj7Bo0SJkZWUhIiICwcHB0h0sY8eOxapVqzB58mSMHDkSBw8exPbt27F3717tVpiIiIiIiIiIiAwOB2CIiKhGWrZsmXSLfkFBAfz9/bF69Wppv7GxMfbs2YOgoCD4+PjA0tISgYGBGlefubu7Y+/evZgwYQKioqLQsGFDfP311/D3f/56HkRERERERERERByAISKiGiEpKUnjsZmZGaKjoxEdHf3M57i5uZWaYuxp3bp1w5kzZ+SISEREREREREREtYiRrgMQERERERERERERERHVNByAISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGTGARgiIiIiIiIiIiIiIiKZcQCGiIiIiIiIiIiIiIhIZhyAISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGRWR9cBSH+9PHVvuWVMjQUWdQRazYxHQZFCC6nkV5F6EhERERERERERERFVBu+AISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGTGARgiIiIiIiIiIiIiIiKZcQCGiIiIiIiIiIiIiIhIZhyAISIiIiIiIiIiIiIikhkHYIiIiIiIiIiIiIiIiGTGARgiIiIiIiIiIiIiIiKZcQCGiIiIiIjoKZGRkXjttddQt25dODg4oH///rh06ZJGmUePHiE4OBj169eHlZUVBg4ciOzsbI0y169fR0BAACwsLODg4IBJkybh8ePHGmWSkpLQvn17mJqaokmTJoiJianu6hERERERkRZwAIaIiIiIiOgphw8fRnBwME6cOIHExESoVCr4+fnh/v37UpkJEybgp59+wo4dO3D48GHcuHEDAwYMkPYXFRUhICAAhYWFOH78ODZv3oyYmBhMnz5dKpOZmYmAgAB0794daWlpCA0NxSeffIL4+Hit1peIiIiIiORXR9cBiIiIiIiI9E1cXJzG45iYGDg4OCA1NRVdunRBXl4eNmzYgNjYWPTo0QMAsGnTJrRo0QInTpxAp06dkJCQgPPnz2P//v1wdHSEp6cn5syZgylTpmDmzJlQKpVYu3Yt3N3dsWTJEgBAixYtcPToUSxbtgz+/v5arzcREREREclH9jtgjhw5grfffhsuLi5QKBTYtWuXxn4hBKZPnw5nZ2eYm5vD19cXly9f1ihz+/ZtDB06FNbW1rC1tcWoUaNw7949jTJnz57Fm2++CTMzM7i6umLRokVyV4WIiIiIiAgAkJeXBwCws7MDAKSmpkKlUsHX11cq07x5czRq1AjJyckAgOTkZLRu3RqOjo5SGX9/f+Tn5+PcuXNSmSePoS6jPgYRERERERku2e+AuX//Ptq2bYuRI0dq3H6vtmjRIqxYsQKbN2+Gu7s7pk2bBn9/f5w/fx5mZmYAgKFDh+LmzZvSrf4jRozAmDFjEBsbCwDIz8+Hn58ffH19sXbtWqSnp2PkyJGwtbXFmDFj5K4SERERERHVYsXFxQgNDUXnzp3RqlUrAEBWVhaUSiVsbW01yjo6OiIrK0sq8+Tgi3q/et/zyuTn5+Phw4cwNzcvlaegoAAFBQXS4/z8fACASqWCSqV6gZpqh6mR0PjbUOlbParyf69+jiG8bp6nJtSjJtQBqP56GHr7lGXmzJmYNWuWxrZmzZrh4sWLAErWG5s4cSK2bt2KgoIC+Pv7Y/Xq1Rp9x/Xr1xEUFIRDhw7BysoKgYGBiIyMRJ06//vaLykpCWFhYTh37hxcXV0RERGB4cOHa6WORES1mewDMH369EGfPn3K3CeEwPLlyxEREYF+/foBAL755hs4Ojpi165dGDx4MC5cuIC4uDikpKSgQ4cOAICVK1firbfewuLFi+Hi4oItW7agsLAQGzduhFKphIeHB9LS0rB06VIOwBARERERkayCg4ORkZGBo0eP6joKACAyMrLUl3UAkJCQAAsLCx0kqpw5HdR/F+s2iEz0pR779u2r8nMTExNlTKI7NaEeNaEOQPXV48GDB9VyXF3z8PDA/v37pcdPDpxMmDABe/fuxY4dO2BjY4OQkBAMGDAAx44dA/C/9cacnJxw/Phx3Lx5Ex9//DFMTEwwf/58AP9bb2zs2LHYsmULDhw4gE8++QTOzs6c7pKIqJppdQ2YzMxMZGVladxib2NjA29vbyQnJ2Pw4MFITk6Gra2tNPgCAL6+vjAyMsLJkyfx7rvvIjk5GV26dIFSqZTK+Pv7Y+HChbhz5w7q1atX5vn19Uoxfb3SxdS4/Cu59O2qr4pibu0y1NyAfmavyHvF0+8r+vb+8qIiIyPxww8/4OLFizA3N8frr7+OhQsXolmzZlIZXilGRERyCAkJwZ49e3DkyBE0bNhQ2u7k5ITCwkLk5uZq3AWTnZ0NJycnqcypU6c0jpednS3tU/+t3vZkGWtr6zLvfgGA8PBwhIWFSY/z8/Ph6uoKPz8/WFtbV72yWuI1Ow5zOhRj2mkjFBQrdB2nykyNhMHX40XrkDFTP764ValUSExMRK9evWBiYqLrOFVSE+oAVH891N/j1DR16tSR+oUncb0xIiLDp9UBGPVt9mXdYv/kLfgODg4a++vUqQM7OzuNMu7u7qWOod73rAEYfb9STN+udFnUseJl9eWqr8pibu0y1NyAfmWvzNWN6veVmnal2OHDhxEcHIzXXnsNjx8/xueffw4/Pz+cP38elpaWAHilGBERvRghBMaNG4edO3ciKSmp1OcPLy8vmJiY4MCBAxg4cCAA4NKlS7h+/Tp8fHwAAD4+Ppg3bx5ycnKkzziJiYmwtrZGy5YtpTJP9+2JiYnSMcpiamoKU1PTUttNTEwM4otb9Rf9BcUKFBQZ5sDFk2pCPapaB317vRnKz8Dz1IQ6ANVXj5rQNmW5fPkyXFxcYGZmBh8fH0RGRqJRo0blrjfWqVOnZ643FhQUhHPnzqFdu3bPXG8sNDRUW1UkIqq1tDoAo2v6eqWYvl7p0mpmfLllDPWqL+bWLkPNDehn9opcafj0+0pNu1IsLi5O43FMTAwcHByQmpqKLl268EoxIiJ6YcHBwYiNjcWPP/6IunXrSheD2djYwNzcHDY2Nhg1ahTCwsJgZ2cHa2trjBs3Dj4+PujUqRMAwM/PDy1btsRHH32ERYsWISsrCxEREQgODpYGUMaOHYtVq1Zh8uTJGDlyJA4ePIjt27dj7969Oqs7ERFpj7e3N2JiYtCsWTPcvHkTs2bNwptvvomMjIwasd6Yuqy2Z5XQx1kgauoMFZXFdijBdiihr+0gZx6tDsCob6fMzs6Gs7OztD07Oxuenp5SmZycHI3nPX78GLdv3y73Nv0nz1EWfb9STF9yqFXmCihDveqLubXLUHMD+pW9Mu8T6vcVfXpvqQ55eXkAADs7OwDQ6ZVi+jrdZUXo6sORHPRxusAnNftiz3P3lwz2lkzLI9dgrzanhdHXX5orypDz61N2fcggpzVr1gAAunXrprF906ZN0lSUy5Ytg5GREQYOHKgx3aWasbEx9uzZg6CgIPj4+MDS0hKBgYGYPXu2VMbd3R179+7FhAkTEBUVhYYNG+Lrr7/mQD8RUS3x5DrKbdq0gbe3N9zc3LB9+/ZnTkWpDXLPIqPtWSVeZF2s6qZvM+DoCtuhBNuhhL61g5wzyWh1AMbd3R1OTk44cOCANOCSn5+PkydPIigoCEDJLfi5ublITU2Fl5cXAODgwYMoLi6Gt7e3VOaLL76ASqWSvlRMTExEs2bNnjn9GBER1UzFxcUIDQ1F586d0apVKwDQ6ZVi+j7dZUXo05R7lWXI2QF58+viQ6e+/dJcWYacXx+y17TpLoUof0DXzMwM0dHRiI6OfmYZNze3cn8eu3XrhjNnzlQ6IxER1Ty2trZ49dVXceXKFfTq1cvg1xtTz86g7Vkl9GWNqifp6ww42sZ2KMF2KKGv7SDnTDKyD8Dcu3cPV65ckR5nZmYiLS0NdnZ2aNSoEUJDQzF37lw0bdoU7u7umDZtGlxcXNC/f38AJdO79O7dG6NHj8batWuhUqkQEhKCwYMHw8XFBQDw4YcfYtasWRg1ahSmTJmCjIwMREVFYdmyZXJXh4iI9FxwcDAyMjJw9OhRXUcBoL/TXVaErj4cyUEfpwusjOrIr+07YPTxl+aKMuT8+pS9pk13SUREpAv37t3DH3/8gY8++qhGrTem7VkldP170fPUhlkqKoLtUILtUELf2kHOLLIPwJw+fRrdu3eXHqu/hAoMDERMTAwmT56M+/fvY8yYMcjNzcUbb7yBuLg4mJmZSc/ZsmULQkJC0LNnT+mW/hUrVkj7bWxskJCQgODgYHh5eaFBgwaYPn06xowZI3d1iIhIj4WEhGDPnj04cuQIGjZsKG13cnLS2ZVi+j7dZUXo05R7lWXI2QF58+vi9WZIr/OyGHJ+fciu6/MTEREZon/+8594++234ebmhhs3bmDGjBkwNjbGkCFDuN4YEVENIPsATLdu3Z57u75CocDs2bM15j1+mp2dHWJjY597njZt2uCXX36pck4iIjJcQgiMGzcOO3fuRFJSEtzd3TX26/JKMSIiIiIioor6z3/+gyFDhuDWrVuwt7fHG2+8gRMnTsDe3h4A1xsjIjJ0Wl0DhoiISA7BwcGIjY3Fjz/+iLp160prttjY2MDc3JxXihERERERkUHYunXrc/dzvTEiIsNmpOsARERElbVmzRrk5eWhW7ducHZ2lv5s27ZNKrNs2TL07dsXAwcORJcuXeDk5IQffvhB2q++UszY2Bg+Pj4YNmwYPv744zKvFEtMTETbtm2xZMkSXilGREREREREREQVwjtgiIjI4Dxvqks1XilGRERERERERES6xDtgiIiIiIiIiIiIiIiIZMYBGCIiIiIiIiIiIiIiIplxAIaIiIiIiIiIiIiIiEhmHIAhIiIiIiIiIiIiIiKSGQdgiIiIiIiIiIiIiIiIZMYBGCIiIiIiIiIiIiIiIplxAIaIiIiIiIiIiIiIiEhmHIAhIiIiIiIiIiIiIiKSGQdgiIiIiIiIiIiIiIiIZMYBGCIiIiIiIiIiIiIiIplxAIaIiIiIiIiIiIiIiEhmHIAhIiIiIiIiIiIiIiKSWR1dByAiIiIiIiIiw/Py1L1aP+e1BQFaPycRERFRVfEOGCIiIiIiIiIiIiIiIpnxDhgDoouri4iIiIiIiIiIiIiIqPJ4BwwREREREREREREREZHMOABDREREREREREREREQkM05BRkRERFQDaXPqUlNjgUUdtXY6IiIiIiIiIoPAO2CIiIiIiIiIiIiIiIhkxjtgqkjOq0rVV422mhmPgiKFbMclIiLDps07GADexUBEVBtpu68BAFNjrZ+SiIiIiEgneAcMERERERERERERERGRzAz+Dpjo6Gh8+eWXyMrKQtu2bbFy5Up07MjLd4mISD7sa4iIqLqxryGqmLLu2qruWSWuLQiQ/ZhEusL+hohIuwx6AGbbtm0ICwvD2rVr4e3tjeXLl8Pf3x+XLl2Cg4ODruMREVENwL6GqOK0PZ0qvxCjmoJ9DRERaQP7GyIi7TPoKciWLl2K0aNHY8SIEWjZsiXWrl0LCwsLbNy4UdfRiIiohmBfQ0RE1Y19DRERaQP7GyIi7TPYO2AKCwuRmpqK8PBwaZuRkRF8fX2RnJxc5nMKCgpQUFAgPc7LywMA3L59GyqVqlLnr/P4fhVSP+NYxQIPHhSjjsoIRcXau2pUDoaanbm1y1BzA/qZ/datW+WWUalUePDgAW7dugUTExPcvXsXACCEqO54NUpN6msqdD49fL1XlCFnB5i/qiryflgRT79nGhJ9ys6+pmpqW18DGP57nlpNqEdNqANQ/fVo8s/tsh/zaaZGAhHtiuH5xQ84MsW32s9XXaq7X2JfU3WV7W/k6mvUrwltv89o4+e2sp78OS+QqS1OhveU5TjapE+/v+oS26GEvraDnP2NwQ7A/Pe//0VRUREcHR01tjs6OuLixYtlPicyMhKzZs0qtd3d3b1aMlbGh7oO8AIMNTtza5eh5gb0L3uDJVV/7t27d2FjYyNfmBqupvU1FaFvr/fKMOTsAPNXxYu8H1L1YV9TObWxrwEM/z1PrSbUoybUAagZ9VDXocGXOo1hENjXVF5l+5ua0NfoI7nfq/j7MFH1kqO/MdgBmKoIDw9HWFiY9Li4uBi3b99G/fr1oVDo7mqf/Px8uLq64q+//oK1tbXOclSFoWZnbu0y1NyA4WZ/OrcQAnfv3oWLi4uuo9V4+trXVIShvt4Bw84OML+uGXJ+fcrOvkZ7DLmvAfTrdfsiakI9akIdgJpRj5pQB6D668G+Rnvk6mtqymtbDmyLEmyHEmyHEvraDnL2NwY7ANOgQQMYGxsjOztbY3t2djacnJzKfI6pqSlMTU01ttna2lZXxEqztrbWqxdaZRhqdubWLkPNDRhu9idz8wqxyquJfU1FGOrrHTDs7ADz65oh59eX7OxrKq+29jWA/rxuX1RNqEdNqANQM+pRE+oAVG892NdUTWX7G7n7mpry2pYD26IE26EE26GEPraDXP2NkSxH0QGlUgkvLy8cOHBA2lZcXIwDBw7Ax8dHh8mIiKimYF9DRETVjX0NERFpA/sbIiLdMNg7YAAgLCwMgYGB6NChAzp27Ijly5fj/v37GDFihK6jERFRDcG+hoiIqhv7GiIi0gb2N0RE2mfQAzCDBg3C//3f/2H69OnIysqCp6cn4uLiSi0opu9MTU0xY8aMUrd2GgJDzc7c2mWouQHDzW6oufVRTelrKsKQXzeGnB1gfl0z5PyGnJ3+pzb1NUDNed3WhHrUhDoANaMeNaEOQM2pR02li/6Gr4n/YVuUYDuUYDuUqA3toBBCCF2HICIiIiIiIiIiIiIiqkkMdg0YIiIiIiIiIiIiIiIifcUBGCIiIiIiIiIiIiIiIplxAIaIiIiIiIiIiIiIiEhmHIAhIiIiIiIiIiIiIiKSGQdgqtmCBQugUCgQGhoKALh27RoUCkWZf3bs2FHmMVQqFaZMmYLWrVvD0tISLi4u+Pjjj3Hjxg29zg0AM2fORPPmzWFpaYl69erB19cXJ0+e1PvcTxo7diwUCgWWL19ebbkB+bIPHz68VPnevXvrfW4AuHDhAt555x3Y2NjA0tISr732Gq5fv67XuZ/1nC+//FKvc9+7dw8hISFo2LAhzM3N0bJlS6xdu7ZaMpPuzJs3D6+//josLCxga2tbav9vv/2GIUOGwNXVFebm5mjRogWioqI0yvzwww/o1asX7O3tYW1tDR8fH8THx1c4w5UrV1C3bt0yz6+P2ZOSktCvXz84OzvD0tISnp6e2LJlS6Wy6zI/AJw9exZvvvkmzMzM4OrqikWLFukk/82bN/Hhhx/i1VdfhZGRkfS+VZ6UlBT07NkTtra2qFevHvz9/fHbb78ZRHYAiImJQZs2bWBmZgYHBwcEBwdX+Ln6kB8Abt26hYYNG0KhUCA3N7dSz6Xa48iRI3j77bfh4uIChUKBXbt2aewXQmD69OlwdnaGubk5fH19cfnyZY0yt2/fxtChQ2FtbQ1bW1uMGjUK9+7d0ygjx3vas0RGRuK1115D3bp14eDggP79++PSpUsaZR49eoTg4GDUr18fVlZWGDhwILKzszXKXL9+HQEBAbCwsICDgwMmTZqEx48fa5RJSkpC+/btYWpqiiZNmiAmJka2eqxZswZt2rSBtbW11F/8/PPPBlWHpz39O6+h1GPmzJmlfidv3ry5QdUBAP7++28MGzYM9evXh7m5OVq3bo3Tp09L+w3h55v0R3R0NF5++WWYmZnB29sbp06d0nWkMulbn1Beu1Ukixx0/X6sy3bQp/fCHTt2oHnz5jAzM0Pr1q2xb98+jf0VyVIVRUVFmDZtGtzd3WFubo5XXnkFc+bMgRCiVrXDCxNUbU6dOiVefvll0aZNGzF+/HghhBCPHz8WN2/e1Pgza9YsYWVlJe7evVvmcXJzc4Wvr6/Ytm2buHjxokhOThYdO3YUXl5eep1bCCG2bNkiEhMTxR9//CEyMjLEqFGjhLW1tcjJydHr3Go//PCDaNu2rXBxcRHLli2TPXN1ZA8MDBS9e/fWeN7t27f1PveVK1eEnZ2dmDRpkvj111/FlStXxI8//iiys7P1OvfTz9m4caNQKBTijz/+0Ovco0ePFq+88oo4dOiQyMzMFOvWrRPGxsbixx9/lD036c706dPF0qVLRVhYmLCxsSm1f8OGDeKzzz4TSUlJ4o8//hDffvutMDc3FytXrpTKjB8/XixcuFCcOnVK/P777yI8PFyYmJiIX3/9tdzzFxYWig4dOog+ffqUeX59zD5v3jwREREhjh07Jq5cuSKWL18ujIyMxE8//WQQ+fPy8oSjo6MYOnSoyMjIEP/617+Eubm5WLdundbzZ2Zmis8++0xs3rxZeHp6Su9bz3P37l1hZ2cnhg8fLi5evCgyMjLEwIEDhaOjoygsLNTr7EIIsWTJEuHi4iK2bNkirly5In777bdKv6/qMr9av379RJ8+fQQAcefOnUo9l2qPffv2iS+++EL88MMPAoDYuXOnxv4FCxYIGxsbsWvXLvHbb7+Jd955R7i7u4uHDx9KZXr37i3atm0rTpw4IX755RfRpEkTMWTIEGm/XO9pz+Lv7y82bdokMjIyRFpamnjrrbdEo0aNxL1796QyY8eOFa6uruLAgQPi9OnTolOnTuL111+X9j9+/Fi0atVK+Pr6ijNnzoh9+/aJBg0aiPDwcKnM1atXhYWFhQgLCxPnz58XK1euFMbGxiIuLk6WeuzevVvs3btX/P777+LSpUvi888/FyYmJiIjI8Ng6vCksn7nNZR6zJgxQ3h4eGj8bv5///d/BlWH27dvCzc3NzF8+HBx8uRJcfXqVREfHy+uXLkilTGEn2/SD1u3bhVKpVJs3LhRnDt3TowePVrY2tpWy+f8F6VPfUJF2q28LHLQ9fuxLttBn94Ljx07JoyNjcWiRYvE+fPnRUREhDAxMRHp6emVylIV8+bNE/Xr1xd79uwRmZmZYseOHcLKykpERUXVqnZ4URyAqSZ3794VTZs2FYmJiaJr167P/eDr6ekpRo4cWanjnzp1SgAQf/755wsm1VTdufPy8gQAsX///hdMqqk6cv/nP/8RL730ksjIyBBubm7VNgAjd/bAwEDRr18/eUOWQe7cgwYNEsOGDZM5ZWnV/Rrv16+f6NGjxwumLE3u3B4eHmL27Nka29q3by+++OILOeKSntm0aVOFB0A+/fRT0b179+eWadmypZg1a1a5x5o8ebIYNmxYpc7/NF1lf9Jbb70lRowYUannqGk7/+rVq0W9evVEQUGBtG3KlCmiWbNmFcrwNLnyl/e+pZaSkiIAiOvXr0vbzp49KwCIy5cvVyiHmraz3759W5ibm8v2O46286utXr1adO3aVRw4cIADMFRhTw/AFBcXCycnJ/Hll19K23Jzc4Wpqan417/+JYQQ4vz58wKASElJkcr8/PPPQqFQiL///lsIIf97WnlycnIEAHH48GEps4mJidixY4dU5sKFCwKASE5OFkKUDEQZGRmJrKwsqcyaNWuEtbW1lHvy5MnCw8ND41yDBg0S/v7+1VIPIYSoV6+e+Prrrw2uDs/6nddQ6jFjxgzRtm3bMvcZSh2mTJki3njjjWfuN9Sfb9KNjh07iuDgYOlxUVGRcHFxEZGRkTpMVTG67BPKa7eKZHlR+vB+rMt20Kf3wg8++EAEBARonN/b21v84x//qHCWqgoICCj1/dKAAQPE0KFDa1U7vChOQVZNgoODERAQAF9f3+eWS01NRVpaGkaNGlWp4+fl5UGhUFR6SpfyVGfuwsJCfPXVV7CxsUHbtm1fNKoGuXMXFxfjo48+wqRJk+Dh4SFn1FKqo82TkpLg4OCAZs2aISgoCLdu3ZIrrkTO3MXFxdi7dy9effVV+Pv7w8HBAd7e3qWmspBDdb7Gs7OzsXfv3kr/PFeE3Llff/117N69G3///TeEEDh06BB+//13+Pn5yRmbDFBeXh7s7Oyeub+4uBh37959bhkAOHjwIHbs2IHo6Gi5Iz6TXNkre1y5yJE/OTkZXbp0gVKplLb5+/vj0qVLuHPnjqx5nyZHOzVr1gz169fHhg0bUFhYiIcPH2LDhg1o0aIFXn75ZXmClkGO7ImJiSguLsbff/+NFi1aoGHDhvjggw/w119/yZTy2eR6jZ4/fx6zZ8/GN998AyMjfkygqsvMzERWVpbG7y02Njbw9vZGcnIygJL3K1tbW3To0EEq4+vrCyMjI2nKYm2/p+Xl5QGA9POUmpoKlUqlUY/mzZujUaNGGvVo3bo1HB0dNTLm5+fj3LlzUpmnf4fz9/eXjiGnoqIibN26Fffv34ePj4/B1eFZv/MaUj0uX74MFxcXNG7cGEOHDpWmVDaUOuzevRsdOnTA+++/DwcHB7Rr1w7r16+X9hvqzzdpX2FhIVJTUzVeK0ZGRvD19a2W9z+56apPqEi7VSTLi9L1+7Gu20Gf3gvLa6uKZKmq119/HQcOHMDvv/8OoGQ65KNHj6JPnz61qh1eVB2dnr2G2rp1K3799VekpKSUW1b9pcLrr79e4eM/evQIU6ZMwZAhQ2Btbf0iUTVUV+49e/Zg8ODBePDgAZydnZGYmIgGDRrIERlA9eReuHAh6tSpg88++0yumGWqjuy9e/fGgAED4O7ujj/++AOff/45+vTpg+TkZBgbG+tl7pycHNy7dw8LFizA3LlzsXDhQsTFxWHAgAE4dOgQunbtqpe5n7Z582bUrVsXAwYMeJGYpVRH7pUrV2LMmDFo2LAh6tSpAyMjI6xfvx5dunSRKzYZoOPHj2Pbtm3Yu3fvM8ssXrwY9+7dwwcffPDMMrdu3cLw4cPx3XffydpPPY9c2Z+2fft2pKSkYN26dXLEfCa58mdlZcHd3V1jm/qDT1ZWFurVqydP4KdUJH9F1K1bF0lJSejfvz/mzJkDAGjatCni4+NRp071/NoqV/arV6+iuLgY8+fPR1RUFGxsbBAREYFevXrh7NmzGh8m5CRX/oKCAgwZMgRffvklGjVqhKtXr8qUkGqjrKwsAND44kX9WL0vKysLDg4OGvvr1KkDOzs7jTLaek8rLi5GaGgoOnfujFatWknnUCqVpS56e7oeZdVTve95ZfLz8/Hw4UOYm5u/cP709HT4+Pjg0aNHsLKyws6dO9GyZUukpaUZTB2e9zuvofxfeHt7IyYmBs2aNcPNmzcxa9YsvPnmm8jIyDCYOly9ehVr1qxBWFgYPv/8c6SkpOCzzz6DUqlEYGCgQf58k27897//RVFRUZmvlYsXL+ooVcXosk+4c+dOue1WkSwvQh/ej3XdDvr0XvistnryGOVlqaqpU6ciPz8fzZs3h7GxMYqKijBv3jwMHTq0wueuCe3wonhpm8z++usvjB8/Hlu2bIGZmdlzyz58+BCxsbGVulpepVLhgw8+gBACa9asedG4kurM3b17d6SlpeH48ePo3bs3PvjgA+Tk5MgRu1pyp6amIioqCjExMVAoFLLkLEt1tfngwYPxzjvvoHXr1ujfvz/27NmDlJQUJCUl6W3u4uJiAEC/fv0wYcIEeHp6YurUqejbt69sC8NX988mAGzcuBFDhw4t9/iVUV25V65ciRMnTmD37t1ITU3FkiVLEBwcjP3798sVnarJ1KlTSy3u+vSfqnygycjIQL9+/TBjxoxn3gkVGxuLWbNmYfv27aV+gXrS6NGj8eGHH5Ya0DOE7E86dOgQRowYgfXr18PDw8Pg8j9t6dKlOstfUQ8fPsSoUaPQuXNnnDhxAseOHUOrVq3QoUMHvc9eXFwMlUqFFStWwN/fH506dcK//vUvXL58GR999JHe5w8PD0eLFi0wbNiwFzoOkaEKDg5GRkYGtm7dqusoVdKsWTOkpaXh5MmTCAoKQmBgIM6fP6/rWBVWmd959VmfPn3w/vvvo02bNvD398e+ffuQm5uL7du36zpahRUXF6N9+/aYP38+2rVrhzFjxmD06NGyfS4jMgSG3ie8iJryfvyi+F5YYvv27diyZQtiY2Px66+/YvPmzVi8eDE2b96s62gGhXfAyCw1NRU5OTlo3769tK2oqAhHjhzBqlWrUFBQIN2F8P333+PBgwf4+OOPK3Rs9eDLn3/+iYMHD8p6VXF15ra0tESTJk3QpEkTdOrUCU2bNsWGDRsQHh6ul7l/+eUX5OTkoFGjRhrHnDhxIpYvX45r1669cO7qyl6Wxo0bo0GDBrhy5Qp69uypl7kbNGiAOnXqoGXLlhrbW7RogaNHj75w5urK/aRffvkFly5dwrZt22TJW525Hz58iM8//xw7d+5EQEAAAKBNmzZIS0vD4sWLy53mjHRr4sSJGD58+HPLNG7cuFLHPH/+PHr27IkxY8YgIiKizDJbt27FJ598gh07dpT7Gjl48CB2796NxYsXAwCEECguLsaXX36JuXPnYuDAgXqbXe3w4cN4++23sWzZMulnyhDa3snJCdnZ2Rrb1I8nT56MiRMnPvf51ZG/MmJjY3Ht2jUkJydLU2DFxsbC1tYWixcvlt6zyqLr7M7OzgCg0ZfZ29ujQYMG8Pb2xqxZs577fF3nP3jwINLT0/H9998DKPm5BUr66C+++KLc/ERPcnJyAlDy/qP+2VA/9vT0lMo8fVHW48ePcfv2ben5z3tPU5eRQ0hICPbs2YMjR46gYcOGGvUoLCxEbm6uxtW12dnZGhlPnTr13IzPqoe1tbUsd44AgFKpRJMmTQAAXl5eSElJQVRUFAYNGmQQdSjvd974+HiDqMfTbG1t8eqrr+LKlSvo1auXQdTB2dm5zM9l//73vzVyGMrPN+lOgwYNYGxsXOb/sz7/H+u6TzA2Ni633SqSpar05f1Y1+2gT++Fzyrz5P7yslTVpEmTMHXqVAwePBgA0Lp1a/z555+IjIxEYGBgrWmHF6bTFWhqoPz8fJGenq7xp0OHDmLYsGEiPT1do2zXrl3FwIEDK3TcwsJC0b9/f+Hh4SFycnIMJndZGjduLGbMmPGCiUtUR+7//ve/pY7p4uIipkyZIi5evChL7urKXpa//vpLKBQK8eOPP8oRu9py+/j4iGHDhmls69+/vxgyZIhe51YLDAwUXl5esmR9UnXkzsvLEwDEvn37NLaPGTNG9OrVS9b8pB+et5h3RkaGcHBwEJMmTXrm82NjY4WZmZnYtWtXhc53/vx5jdfs3LlzRd26dUV6erq4ffu2XmcXQohDhw4JS0tLsWrVqkplLYu286sXLywsLJS2hYeHV3lB2xfNr1bRheBXrFghnJycRHFxsbRNpVIJS0tLsWXLlorGFkJoP/ulS5cEALF//35p261bt4SRkZGIj4+vaGyJtvNfuXJF4+d248aNAoA4fvy4yM7OrmR6qm0AiJ07d0qP1YugLl68WNqWl5dX5oKsp0+flsrEx8eXuSCrXO9pTysuLhbBwcHCxcVF/P7776X2qxf3/f7776VtFy9eLHOh4Sd/TtatWyesra3Fo0ePhBAlCw23atVK49hDhgyRfQH7J3Xv3l0EBgYaTB3K+53XUOrxtLt374p69eqJqKgog6nDkCFDSi08HRoaKnx8fIQQhvPzTfqhY8eOIiQkRHpcVFQkXnrpJWkRdX2iT31Cee1WkSxVpU/vx7psB316L/zggw9E3759NbL4+PiUWnz+eVmqys7OTqxevVpj2/z580XTpk1rVTu8KA7AaEFZH3wvX74sFAqF+Pnnn8t8TrNmzcQPP/wghCgZfHnnnXdEw4YNRVpamrh586b0p6CgQG9z37t3T4SHh4vk5GRx7do1cfr0aTFixAhhamoqMjIy9DZ3Wdzc3MSyZctkTFm2F81+9+5d8c9//lMkJyeLzMxMsX//ftG+fXvRtGlTqZPTx9xCCPHDDz8IExMT8dVXX4nLly+LlStXCmNjY/HLL7/odW4hSt7QLSwsxJo1a6orqgY5cnft2lV4eHiIQ4cOiatXr4pNmzYJMzOzUh0rGbY///xTnDlzRsyaNUtYWVmJM2fOiDNnzoi7d+8KIYRIT08X9vb2YtiwYRp9y5MD/Vu2bBF16tQR0dHRGmVyc3OlMitXrhQ9evR4Zo7nfZGsb9kPHjwoLCwsRHh4uMZzbt26ZRD5c3NzhaOjo/joo49ERkaG2Lp1q7CwsBDr1q3Ten4hhPQ8Ly8v8eGHH4ozZ86Ic+fOSft/+OEHjV+qL1y4IExNTUVQUJA4f/68yMjIEMOGDRM2Njbixo0bep1dCCH69esnPDw8xLFjx0R6erro27evaNmypcYHCX3O/6RDhw4JAOLOnTsVzk61y927d6XXGQCxdOlScebMGfHnn38KIYRYsGCBsLW1FT/++KM4e/as6Nevn3B3dxcPHz6UjtG7d2/Rrl07cfLkSXH06FHRtGlTjYtv5HpPe5agoCBhY2MjkpKSNH6eHjx4IJUZO3asaNSokTh48KA4ffq08PHxkb58EUKIx48fi1atWgk/Pz+RlpYm4uLihL29vQgPD5fKXL16VVhYWIhJkyaJCxcuiOjoaGFsbCzi4uJkqcfUqVPF4cOHRWZmpjh79qyYOnWqUCgUIiEhwWDqUJanf+c1hHpMnDhRJCUliczMTHHs2DHh6+srGjRoIL1HG0IdTp06JerUqSPmzZsnLl++LLZs2SIsLCzEd999J5UxhJ9v0g9bt24VpqamIiYmRpw/f16MGTNG2NraiqysLF1HK0Wf+oSKtFt5WeSkq/djXbaDPr0XHjt2TNSpU0csXrxYXLhwQcyYMUOYmJhoXJRbkSxVERgYKF566SWxZ88ekZmZKX744QfRoEEDMXny5FrVDi+KAzBaUNaXpeHh4cLV1VUUFRWV+RwAYtOmTUIIITIzMwWAMv8cOnRIb3M/fPhQvPvuu8LFxUUolUrh7Ows3nnnHXHq1KlqyyxH7rLocgCmMtkfPHgg/Pz8hL29vTAxMRFubm5i9OjR1f7LjVxtvmHDBtGkSRNhZmYm2rZtW6kr1qtCrtzr1q0T5ubmGl+KVic5ct+8eVMMHz5cuLi4CDMzM9GsWTOxZMkSjavOyfAFBgY+t++YMWNGmfvd3NykY3Tt2rXMMoGBgVKZGTNmaDznaVUZgILkY4MAAAJ/SURBVNFV9medt2vXrgaRXwghfvvtN/HGG28IU1NT8dJLL4kFCxZUKrtc+YUQ5ZbZtGmTePqG7ISEBNG5c2dhY2Mj6tWrJ3r06FGpK9h0mT0vL0+MHDlS2NraCjs7O/Huu++K69evVzi7rvM/iQMwVB71a+RZ71HFxcVi2rRpwtHRUZiamoqePXuKS5cuaRzj1q1bYsiQIcLKykpYW1uLESNGSIONanK8pz3Lsz5jPfk708OHD8Wnn34q6tWrJywsLMS7774rbt68qXGca9euiT59+ghzc3PRoEEDMXHiRKFSqUq1l6enp1AqlaJx48bP/exRWSNHjhRubm5CqVQKe3t70bNnT2nwxVDqUJanf+c1hHoMGjRIODs7C6VSKV566SUxaNAgceXKFYOqgxBC/PTTT6JVq1bC1NRUNG/eXHz11Vca+w3h55v0x8qVK0WjRo2EUqkUHTt2FCdOnNB1pDLpW59QXrtVJItcdPl+rMt20Kf3wu3bt4tXX31VKJVK4eHhIfbu3VvpLFWRn58vxo8fLxo1aiTMzMxE48aNxRdffKFxQ0BtaIcXpRDi/0/wTERERERERERERERERLIw0nUAIiIiIiIiIiIiIiKimoYDMERERERERERERERERDLjAAwREREREREREREREZHMOABDREREREREREREREQkMw7AEBERERERERERERERyYwDMERERERERERERERERDLjAAwREREREREREREREZHMOABDREREREREREREREQkMw7AEBERERERERERERERyYwDMERERERERERERERERDLjAAwREREREREREREREZHMOABDREREREREREREREQks/8HuaaqqCca1N8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Ry91h6d5A9ma"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, 3:].values# independent variable, id, date and price are at the first place of data\n",
        "y = data['price'].values#dependent variable"
      ],
      "metadata": {
        "id": "5N1znXPlBE63"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBRz53WhBGmt",
        "outputId": "08ffbd86-9db8-4a47-caf6-1ee8cccc5b86"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 3.00000e+00  1.00000e+00  1.18000e+03 ... -1.22257e+02  1.34000e+03\n",
            "   5.65000e+03]\n",
            " [ 3.00000e+00  2.25000e+00  2.57000e+03 ... -1.22319e+02  1.69000e+03\n",
            "   7.63900e+03]\n",
            " [ 2.00000e+00  1.00000e+00  7.70000e+02 ... -1.22233e+02  2.72000e+03\n",
            "   8.06200e+03]\n",
            " ...\n",
            " [ 2.00000e+00  7.50000e-01  1.02000e+03 ... -1.22299e+02  1.02000e+03\n",
            "   2.00700e+03]\n",
            " [ 3.00000e+00  2.50000e+00  1.60000e+03 ... -1.22069e+02  1.41000e+03\n",
            "   1.28700e+03]\n",
            " [ 2.00000e+00  7.50000e-01  1.02000e+03 ... -1.22299e+02  1.02000e+03\n",
            "   1.35700e+03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "id": "kywGMYamBMZe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b9889f3-0144-4e70-92ac-24ec5c7b5cea"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21613,)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_matrix = xgb.DMatrix(data=X, label=y)"
      ],
      "metadata": {
        "id": "NtsFNkaABQsc"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(data_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qR5bb_gNBUb2",
        "outputId": "64613696-3dc4-44c5-c9c1-11069d6069fd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "xgboost.core.DMatrix"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)"
      ],
      "metadata": {
        "id": "ve6RNRLgBZ01"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xg_reg = xgb.XGBRegressor(colsample_bytree=0.8, learning_rate=0.1, max_depth=6, n_estimators=1000, verbosity=3)"
      ],
      "metadata": {
        "id": "5ns4pR0lBb7b"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xg_reg.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "n10hbxQeBoyB",
        "outputId": "47c4429c-f395-49ea-c265-fe5168bec86f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:22:45] DEBUG: ../src/gbm/gbtree.cc:157: Using tree method: 2\n",
            "[14:22:46] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:46] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:47] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:47] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:47] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:47] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:48] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 62 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 52 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 52 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:22:49] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 58 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 56 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:50] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:22:51] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
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            "[14:23:10] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 66 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:10] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 66 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] INFO: ../src/tree/updater_prune.cc:98: tree pruning end, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
            "[14:23:11] ======== Monitor (0): Learner ========\n",
            "[14:23:11] Configure: 0.015502s, 1 calls @ 15502us\n",
            "\n",
            "[14:23:11] EvalOneIter: 0.01039s, 1000 calls @ 10390us\n",
            "\n",
            "[14:23:11] GetGradient: 0.178536s, 1000 calls @ 178536us\n",
            "\n",
            "[14:23:11] PredictRaw: 1.08349s, 1000 calls @ 1083485us\n",
            "\n",
            "[14:23:11] UpdateOneIter: 25.4713s, 1000 calls @ 25471250us\n",
            "\n",
            "[14:23:11] ======== Monitor (0): GBTree ========\n",
            "[14:23:11] BoostNewTrees: 24.1723s, 1000 calls @ 24172288us\n",
            "\n",
            "[14:23:11] CommitModel: 0.000888s, 1000 calls @ 888us\n",
            "\n",
            "[14:23:11] ======== Monitor (0): TreePruner ========\n",
            "[14:23:11] PrunerUpdate: 0.849703s, 1000 calls @ 849703us\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</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\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xg_reg.predict(X_test)"
      ],
      "metadata": {
        "id": "ZbjZbcw5CJU6"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "Z4-d7yWGCPsi"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics.mean_absolute_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQdgekosCTBY",
        "outputId": "2a61fbfa-bee4-4058-f7f5-d40508eb2178"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64945.955041982794"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics.r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oepNysN2CXQx",
        "outputId": "fa949878-bd6a-4520-ebf4-0cf81cc453cd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8973787460363895"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['price'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YW8n6ATMCa4U",
        "outputId": "9c88f96a-eb7c-4c5c-ed38-91f3a19a0349"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2.161300e+04\n",
              "mean     5.400881e+05\n",
              "std      3.671272e+05\n",
              "min      7.500000e+04\n",
              "25%      3.219500e+05\n",
              "50%      4.500000e+05\n",
              "75%      6.450000e+05\n",
              "max      7.700000e+06\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['price'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Al9pBuinCehd",
        "outputId": "c217fc8b-4ff7-4f24-ea51-bfda5a952fc3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "367127.19648269983"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb.plot_importance(xg_reg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "9ns4krtpChc4",
        "outputId": "8dc6735a-3550-40da-dd38-f552d2c68747"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "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": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kD1dwUb0dxPy",
        "outputId": "cd94f276-b9a9-4395-d473-7a7502091343"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
              "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
              "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
              "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_specefic_value_for_new_data():\n",
        "  bedrooms=float(input(\"please insert a valid bedrooms:\"))\n",
        "  bathrooms=float(input(\"please insert a valid bathrooms:\"))\n",
        "  sqft_living=float(input(\"please insert a valid sqft_living:\"))\n",
        "  sqft_lot=float(input(\"please insert a valid sqft_lot:\"))\n",
        "  floors=float(input(\"please insert a valid  floors:\"))\n",
        "  waterfront=float(input(\"please insert a valid waterfront:\"))\n",
        "  view=float(input(\"please insert a valid view:\"))\n",
        "  condition=float(input(\"please insert a valid condition:\"))\n",
        "  grade=float(input(\"please insert a valid grade:\"))\n",
        "  sqft_above=float(input(\"please insert a valid sqft_above:\"))\n",
        "  sqft_basement=float(input(\"please insert a valid sqft_basement:\"))\n",
        "  yr_built=float(input(\"please insert a valid yr_built:\"))\n",
        "  zipcode=float(input(\"please insert a valid zipcode:\"))\n",
        "  lat=float(input(\"please insert a valid lat:\"))\n",
        "  long=float(input(\"please insert a valid long:\"))\n",
        "  sqft_living15=float(input(\"please insert a sqft_living15:\"))\n",
        "  sqft_lot15=float(input(\"please insert a sqft_lot15:\"))\n",
        "  new_data=dict()\n",
        "  new_data['bedrooms']=bedrooms\n",
        "  new_data['bathrooms']=bathrooms\n",
        "  new_data['sqft_living']=sqft_living\n",
        "  new_data['sqft_lot']=sqft_lot\n",
        "  new_data['floors']=floors\n",
        "  new_data['waterfront']=waterfront\n",
        "  new_data['condition']=condition\n",
        "  new_data['grade']=grade\n",
        "  new_data['sqft_above']=sqft_above\n",
        "  new_data['yr_built']=yr_built\n",
        "  new_data['sqft_basement']=sqft_basement\n",
        "  new_data['zipcode']=zipcode\n",
        "  new_data['lat']=lat\n",
        "  new_data['long']=long\n",
        "  new_data['sqft_living15']=sqft_living15\n",
        "  new_data['sqft_lot15']=sqft_lot15\n"
      ],
      "metadata": {
        "id": "L350Tz4JdvXS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data_fram=get_specefic_value_for_new_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eed22797-581a-40b4-bad3-027f534f6827",
        "id": "lEeGeRCmfzSd"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "please insert a valid age:41\n",
            "please insert a valid sex:1\n",
            "please insert a valid cp:2\n",
            "please insert a valid trestbps:125\n",
            "please insert a valid  chol:190\n",
            "please insert a valid fbs:0\n",
            "please insert a valid restecg:160\n",
            "please insert a valid thalach:0\n",
            "please insert a valid exang:1.5\n",
            "please insert a valid oldpeak:1.5\n",
            "please insert a valid slope:2\n",
            "please insert a valid ca:0\n",
            "please insert a valid thal:1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=pd.DataFrame.from_dict([new_data_fram])"
      ],
      "metadata": {
        "id": "-dFxarsTf0rf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_W2kNkJUf54N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data_fram=get_specefic_value_for_new_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feaxcz0-PNAk",
        "outputId": "eed22797-581a-40b4-bad3-027f534f6827"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "please insert a valid age:41\n",
            "please insert a valid sex:1\n",
            "please insert a valid cp:2\n",
            "please insert a valid trestbps:125\n",
            "please insert a valid  chol:190\n",
            "please insert a valid fbs:0\n",
            "please insert a valid restecg:160\n",
            "please insert a valid thalach:0\n",
            "please insert a valid exang:1.5\n",
            "please insert a valid oldpeak:1.5\n",
            "please insert a valid slope:2\n",
            "please insert a valid ca:0\n",
            "please insert a valid thal:1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[:, 14]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVq6J3_fCmUz",
        "outputId": "1204e459-8e38-4756-a1ee-4e03ef943e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1955\n",
              "1        1951\n",
              "2        1933\n",
              "3        1965\n",
              "4        1987\n",
              "         ... \n",
              "21608    2009\n",
              "21609    2014\n",
              "21610    2009\n",
              "21611    2004\n",
              "21612    2008\n",
              "Name: yr_built, Length: 21613, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "z1EqYZheCqbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        "    'colsample_bytree': [0, .5],\n",
        "    'learning_rate': [.05,.1],\n",
        "    'max_depth': [3,5],\n",
        "    'n_estimators':[500,1000],\n",
        "    'verbosity':[3,5]\n",
        "}\n",
        "gsh = GridSearchCV(xgb.XGBRegressor(), param_grid=params, scoring='accuracy', cv=4, n_jobs=-1)"
      ],
      "metadata": {
        "id": "Vkg-cdVmCuGw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gsh.fit(X, y)"
      ],
      "metadata": {
        "id": "5j3LszWqS8ZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68414a78-5b46-4bf5-96f8-19540051f551"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "64 fits failed out of a total of 128.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "64 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/xgboost/sklearn.py\", line 396, in fit\n",
            "    callbacks=callbacks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/xgboost/training.py\", line 216, in train\n",
            "    xgb_model=xgb_model, callbacks=callbacks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/xgboost/training.py\", line 74, in _train_internal\n",
            "    bst.update(dtrain, i, obj)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/xgboost/core.py\", line 1109, in update\n",
            "    dtrain.handle))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/xgboost/core.py\", line 176, in _check_call\n",
            "    raise XGBoostError(py_str(_LIB.XGBGetLastError()))\n",
            "xgboost.core.XGBoostError: value 5 for Parameter verbosity exceed bound [0,3]\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
            " nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[04:40:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
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            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
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            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
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            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
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            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
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            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[04:40:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, estimator=XGBRegressor(), n_jobs=-1,\n",
              "             param_grid={'colsample_bytree': [0, 0.5],\n",
              "                         'learning_rate': [0.05, 0.1], 'max_depth': [3, 5],\n",
              "                         'n_estimators': [500, 1000], 'verbosity': [3, 5]},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gsh.best_estimator_"
      ],
      "metadata": {
        "id": "4I7qO6fGVHFr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a765c7a-5a4c-4b8c-cb20-2e9e16f19f08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(colsample_bytree=0, learning_rate=0.05, n_estimators=500,\n",
              "             verbosity=3)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gsh.best_score_"
      ],
      "metadata": {
        "id": "AgTa_56rVK1j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98b009f0-6007-44f8-ad94-cb4d7293417d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nan"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gsh.refit()\n",
        "gsh.predict()"
      ],
      "metadata": {
        "id": "WEYsXwX1VQ9_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "outputId": "f50abeea-931b-4079-e07f-fb7ccd716ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-47b578644bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgsh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgsh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gsh.best_params_"
      ],
      "metadata": {
        "id": "HCm08PXtVTv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86ec4010-5de3-40ea-8434-64f452eb400c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0,\n",
              " 'learning_rate': 0.05,\n",
              " 'max_depth': 3,\n",
              " 'n_estimators': 500,\n",
              " 'verbosity': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}