Warehouse_stock_prediction_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parita2003/Warehouse_Stock_Prediction/blob/main/Copy_of_prediction_model_using_GBR_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "wBLUjzuUONPQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as stats\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ll6WF9ikONPS",
        "outputId": "c17fa981-a1f7-4b74-d2e2-8b3145351b44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Item_ID        Item_Name    Category        Date  Last_Month_Sales  \\\n",
              "0          13            Peach      Fruits  12-21-2019                82   \n",
              "1          15            Lemon      Fruits   9-29-2023                71   \n",
              "2          11           Cherry      Fruits  11-04-2022                71   \n",
              "3          27           Potato  Vegetables  02-07-2021                82   \n",
              "4          40       Green Bean  Vegetables  09-11-2021                82   \n",
              "...       ...              ...         ...         ...               ...   \n",
              "2994       32      Bell Pepper  Vegetables  11-02-2022                82   \n",
              "2995       43           Radish  Vegetables  12-06-2019                90   \n",
              "2996       40       Green Bean  Vegetables   7-26-2019                71   \n",
              "2997       45  Brussels Sprout  Vegetables  01-01-2023                23   \n",
              "2998       12        Blueberry      Fruits  12-14-2021                82   \n",
              "\n",
              "      Capacity  Last_Year_Sales  Temperature Weather  Predicted_sales  \n",
              "0          109               77           33  Autumn               81  \n",
              "1           99               68           32  Winter               68  \n",
              "2           99               68           38   Sunny               68  \n",
              "3          109               77           48  Autumn               78  \n",
              "4          109               77           36  Autumn               78  \n",
              "...        ...              ...          ...     ...              ...  \n",
              "2994        99               68           46  Autumn               69  \n",
              "2995        99               68           40   Sunny               70  \n",
              "2996        99               68           33   Rainy               70  \n",
              "2997        99               68           50  Autumn               68  \n",
              "2998       109               77           36  Autumn               81  \n",
              "\n",
              "[2999 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1658aaf1-8db4-41c9-8274-788ecfef7692\" 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>Item_ID</th>\n",
              "      <th>Item_Name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Date</th>\n",
              "      <th>Last_Month_Sales</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>Last_Year_Sales</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Weather</th>\n",
              "      <th>Predicted_sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>Peach</td>\n",
              "      <td>Fruits</td>\n",
              "      <td>12-21-2019</td>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>33</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>Lemon</td>\n",
              "      <td>Fruits</td>\n",
              "      <td>9-29-2023</td>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>32</td>\n",
              "      <td>Winter</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>Cherry</td>\n",
              "      <td>Fruits</td>\n",
              "      <td>11-04-2022</td>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>38</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>Potato</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>02-07-2021</td>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>48</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40</td>\n",
              "      <td>Green Bean</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>09-11-2021</td>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>36</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2994</th>\n",
              "      <td>32</td>\n",
              "      <td>Bell Pepper</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>11-02-2022</td>\n",
              "      <td>82</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>46</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>43</td>\n",
              "      <td>Radish</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>12-06-2019</td>\n",
              "      <td>90</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>40</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>40</td>\n",
              "      <td>Green Bean</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>7-26-2019</td>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>Rainy</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>45</td>\n",
              "      <td>Brussels Sprout</td>\n",
              "      <td>Vegetables</td>\n",
              "      <td>01-01-2023</td>\n",
              "      <td>23</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>50</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>12</td>\n",
              "      <td>Blueberry</td>\n",
              "      <td>Fruits</td>\n",
              "      <td>12-14-2021</td>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>36</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2999 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1658aaf1-8db4-41c9-8274-788ecfef7692')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1658aaf1-8db4-41c9-8274-788ecfef7692 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-1658aaf1-8db4-41c9-8274-788ecfef7692');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e02a2bd9-1e00-45c3-be8e-264083d598fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e02a2bd9-1e00-45c3-be8e-264083d598fc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e02a2bd9-1e00-45c3-be8e-264083d598fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_22f91484-89ee-4d6c-a5ef-4b2aa6852c44\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_22f91484-89ee-4d6c-a5ef-4b2aa6852c44 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2999,\n  \"fields\": [\n    {\n      \"column\": \"Item_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36,\n          35,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Item_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Cauliflower\",\n          \"Cabbage\",\n          \"Pear\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Vegetables\",\n          \"Fruits\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1218,\n        \"samples\": [\n          \"10-02-2022\",\n          \"1-19-2019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last_Month_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 12,\n        \"max\": 90,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          22,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Capacity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 40,\n        \"max\": 116,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          75,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last_Year_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 9,\n        \"max\": 83,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          58,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 32,\n        \"max\": 50,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          33,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Winter\",\n          \"Rainy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted_sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 9,\n        \"max\": 89,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          84,\n          87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('test3 (3).csv')\n",
        "df_original = df.copy()\n",
        "df1=pd.read_csv(\"current.csv\")\n",
        "num_distinct_values = df[\"Category\"].nunique()\n",
        "Capacity_sum = sum(df[\"Capacity\"])/len(df[\"Capacity\"])\n",
        "Amt_avai_sum = sum(df1[\"Amt_avai\"])/len(df1[\"Amt_avai\"])\n",
        "percent = Amt_avai_sum/Capacity_sum\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "giNHDMsBONPT"
      },
      "outputs": [],
      "source": [
        "# Keeping the Item_ID for later use\n",
        "item_ids = df['Item_ID'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "3NGE3eASONPT"
      },
      "outputs": [],
      "source": [
        "# One-hot encoding\n",
        "categorical_cols = ['Item_Name', 'Category', 'Weather']\n",
        "df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "pdIjbrXsONPT"
      },
      "outputs": [],
      "source": [
        "# The target variable is the actual sales values\n",
        "y = df['Predicted_sales'].values\n",
        "df.drop(columns=['Item_ID', 'Date', 'Predicted_sales'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "QccGFYkVONPT"
      },
      "outputs": [],
      "source": [
        "X = df.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_opTu36SONPT"
      },
      "outputs": [],
      "source": [
        "# Scaling the features\n",
        "scaler_X = MinMaxScaler()\n",
        "X = scaler_X.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "fB9St-ciONPT"
      },
      "outputs": [],
      "source": [
        "# Scaling the target\n",
        "scaler_y = MinMaxScaler()\n",
        "y = scaler_y.fit_transform(y.reshape(-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "pZVPiRd9ONPT"
      },
      "outputs": [],
      "source": [
        "# Train-validation split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "kTUuD2dlONPT",
        "outputId": "d9c0f6b2-573a-4f54-cd78-815c352f5e90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=0.05, max_depth=4, n_estimators=500,\n",
              "                          random_state=0)"
            ],
            "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>GradientBoostingRegressor(learning_rate=0.05, max_depth=4, n_estimators=500,\n",
              "                          random_state=0)</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\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.05, max_depth=4, n_estimators=500,\n",
              "                          random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Gradient Boosting Regressor\n",
        "gbr = GradientBoostingRegressor(n_estimators=500, learning_rate=0.05, max_depth=4, random_state=0)\n",
        "gbr.fit(X_train, y_train.ravel())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEVId19sONPU",
        "outputId": "a46212c5-d04c-41ea-b2bb-92768880c098"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['scaler_y.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "joblib.dump(gbr, \"gbr_model.pkl\")\n",
        "joblib.dump(scaler_X, \"scaler_X.pkl\")\n",
        "joblib.dump(scaler_y, \"scaler_y.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "JuQ1vnylONPU"
      },
      "outputs": [],
      "source": [
        "# Predict on validation set for Gradient Boosting Regressor\n",
        "y_val_pred_gbr = gbr.predict(X_val)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "CyYn1aJFONPU"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics for Gradient Boosting Regressor\n",
        "mae_gbr = mean_absolute_error(y_val, y_val_pred_gbr)\n",
        "mse_gbr = mean_squared_error(y_val, y_val_pred_gbr)\n",
        "r2_gbr = r2_score(y_val, y_val_pred_gbr)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu8-rArgONPU",
        "outputId": "c392b5db-48fe-43d4-ca2b-4f0381253d9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Regressor Metrics:\n",
            "Mean Absolute Error (MAE): 0.012119512272567447\n",
            "Mean Squared Error (MSE): 0.00022764658363523348\n",
            "R^2 Score: 0.9806707072976087\n"
          ]
        }
      ],
      "source": [
        "print(\"Gradient Boosting Regressor Metrics:\")\n",
        "print(\"Mean Absolute Error (MAE):\", mae_gbr)\n",
        "print(\"Mean Squared Error (MSE):\", mse_gbr)\n",
        "print(\"R^2 Score:\", r2_gbr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAsFf9oEONPU",
        "outputId": "2c78a540-0d9f-42e0-884c-657d75edf385"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy-like Metric (within 5% threshold): 0.9883333333333333\n"
          ]
        }
      ],
      "source": [
        "# Define a threshold (e.g., 5%)\n",
        "threshold = 0.05\n",
        "\n",
        "# Calculate the number of predictions within the threshold\n",
        "within_threshold = np.sum(np.abs(y_val - y_val_pred_gbr.reshape(-1, 1)) <= threshold * y_val)\n",
        "\n",
        "# Calculate the accuracy-like metric\n",
        "accuracy_like_metric = within_threshold / len(y_val)\n",
        "\n",
        "print(\"Accuracy-like Metric (within 5% threshold):\", accuracy_like_metric)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vmRX4WUDONPU"
      },
      "outputs": [],
      "source": [
        "def plot_predictions_vs_actual(y_true, y_pred):\n",
        "    plt.figure(figsize=(12, 5))\n",
        "\n",
        "    # Actual vs Predicted Sales\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.scatter(y_true, y_pred, alpha=0.5)\n",
        "    plt.plot([0, 1], [0, 1], '--k', transform=plt.gca().transAxes)\n",
        "    plt.xlabel('Actual Sales')\n",
        "    plt.ylabel('Predicted Sales')\n",
        "    plt.title('Actual vs. Predicted Sales')\n",
        "\n",
        "    # Residuals vs Predicted Values\n",
        "    residuals = y_true.ravel() - y_pred\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.scatter(y_pred, residuals, alpha=0.5)\n",
        "    plt.hlines(0, min(y_pred), max(y_pred), colors='k', linestyles='--')\n",
        "    plt.xlabel('Predicted Sales')\n",
        "    plt.ylabel('Residuals')\n",
        "    plt.title('Residuals vs. Predicted Sales')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "YbWKDniuONPV"
      },
      "outputs": [],
      "source": [
        "def plot_accuracy_over_thresholds(y_true, y_pred):\n",
        "    thresholds = np.linspace(0, 0.5, 50)  # Varying thresholds from 0 to 50%\n",
        "    accuracies = []\n",
        "\n",
        "    for threshold in thresholds:\n",
        "        within_threshold = np.sum(np.abs(y_true - y_pred.reshape(-1, 1)) <= threshold * y_true)\n",
        "        accuracy = within_threshold / len(y_true)\n",
        "        accuracies.append(accuracy)\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.plot(thresholds, accuracies, marker='o')\n",
        "    plt.xlabel('Threshold')\n",
        "    plt.ylabel('Accuracy-like Metric')\n",
        "    plt.title('Model Accuracy-like Metric over Varying Thresholds')\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hh5hlTKuONPV",
        "outputId": "d6cb01a1-9c21-4b4d-e1ee-05e39b68123f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAHqCAYAAADVi/1VAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADiqklEQVR4nOzdeXhU5dn48e85s08m+04IhE2RzQUEARUUMC5VwR1eFXErVati3atSa5W3tm6tCy4VbcVXEdEfigURtW4IAqINsoWdhGwkmSSzz5zz+yNmJGRhAkkmE+7PdeWCOfOcmftMJnOeuc/z3I+i67qOEEIIIYQQQgghhBCdSI12AEIIIYQQQgghhBDi6CNJKSGEEEIIIYQQQgjR6SQpJYQQQgghhBBCCCE6nSSlhBBCCCGEEEIIIUSnk6SUEEIIIYQQQgghhOh0kpQSQgghhBBCCCGEEJ1OklJCCCGEEEIIIYQQotNJUkoIIYQQQgghhBBCdDpJSgkhhBBCCCGEEEKITidJKSEEiqLwhz/8IdphdEl/+MMfUBSl0ba8vDyuueaa6ATUjOZi7Czy3hFCCNEVteXc2BnnsvHjxzN+/PgOfY7uTPpjrZP+mIhlkpQSop09//zzKIrCqFGjDvsxiouL+cMf/sD69evbL7AYpShK+EdVVXr06MFZZ53F559/Hu3Q2qSr/E4/+OADxo0bR0ZGBna7nb59+3LZZZexdOnSqMYlhBCie3rttdcancuNRiM5OTlcc801FBUVRTs8ESHpj7Uv6Y8J8QtjtAMQoruZP38+eXl5rF69msLCQvr379/mxyguLubhhx8mLy+PE044of2DjDGTJk3i6quvRtd1duzYwfPPP8+ZZ57JkiVLOOecczo9ns2bN6Oqbcvpd4Xf6V//+lfuuusuxo0bx3333YfdbqewsJBPPvmEt956i7PPPjsqcQkhhOj+/vjHP9KnTx+8Xi/ffvstr732Gl999RUFBQVYrdZ2f74HHniAe++9t90f92gm/bH2If0xIRqTpJQQ7WjHjh188803LFq0iF//+tfMnz+f2bNnRzusmHfMMcdw5ZVXhm9PmTKFYcOG8fTTT7fYCfJ6vZjN5jZ3ViJhsVja/TE7WjAY5JFHHmHSpEl8/PHHTe4vKyuLQlRCCCGOFueccw4jRowA4PrrryctLY0///nPLF68mMsuu6zdn89oNGI0yled9iT9sSMn/TEhmpLpe0K0o/nz55OcnMx5553HJZdcwvz585ttV11dzaxZs8jLy8NisdCzZ0+uvvpqKioq+Pzzzzn55JMBmDFjRnio9GuvvQa0PH/+4FoFfr+fhx56iOHDh5OYmEhcXBynnXYan332WZuPq7S0FKPRyMMPP9zkvs2bN6MoCs8++ywAgUCAhx9+mAEDBmC1WklNTeXUU09l+fLlbX7elgwdOpS0tDR27NgBwOeff46iKLz11ls88MAD5OTkYLfbqampAWDVqlWcffbZJCYmYrfbGTduHF9//XWTx/3qq684+eSTsVqt9OvXjxdffLHZ52/ud3Akv9OOiPFgFRUV1NTUMHbs2Gbvz8jICP//SN87RUVFXHvttWRmZmKxWBg8eDCvvvpqk3Z///vfGTx4MHa7neTkZEaMGMGbb74Z0XMIIYSIbaeddhoA27Zta7R906ZNXHLJJaSkpGC1WhkxYgSLFy9u1CaSvkZz9X18Ph+zZs0iPT2d+Ph4LrjgAvbu3dsktmuuuYa8vLwm25t7zHnz5nHmmWeSkZGBxWJh0KBBvPDCCxG9Bm09D0p/rDHpj7VO+mMiVsjlAyHa0fz587noooswm81MnTqVF154ge+++y58AgSoq6vjtNNOY+PGjVx77bWcdNJJVFRUsHjxYvbu3ctxxx3HH//4Rx566CFuvPHGcKdtzJgxbYqlpqaGV155halTp3LDDTdQW1vLP/7xD/Lz81m9enWbhixnZmYybtw4FixY0GTk19tvv43BYODSSy8F6jtsc+bM4frrr2fkyJHU1NSwZs0a1q1bx6RJk9p0DC2pqqqiqqqqydTIRx55BLPZzJ133onP58NsNvPpp59yzjnnMHz4cGbPno2qquEO5JdffsnIkSMB+O9//8tZZ51Feno6f/jDHwgGg8yePZvMzMxDxnOkv9POiDEjIwObzcYHH3zAb3/7W1JSUlpseyTvndLSUk455RQUReGWW24hPT2df//731x33XXU1NRw++23A/Dyyy9z6623cskll3Dbbbfh9Xr58ccfWbVqFdOmTTvk8QghhIhtO3fuBCA5OTm8bcOGDYwdO5acnBzuvfde4uLiWLBgAZMnT+bdd99lypQpwOH3Na6//nreeOMNpk2bxpgxY/j0008577zzjug4XnjhBQYPHswFF1yA0Wjkgw8+4KabbkLTNG6++eYW9zuc86D0x1on/bFfSH9MxBRdCNEu1qxZowP68uXLdV3XdU3T9J49e+q33XZbo3YPPfSQDuiLFi1q8hiapum6ruvfffedDujz5s1r0qZ379769OnTm2wfN26cPm7cuPDtYDCo+3y+Rm2qqqr0zMxM/dprr220HdBnz57d6vG9+OKLOqD/97//bbR90KBB+plnnhm+ffzxx+vnnXdeq4/VFoB+3XXX6eXl5XpZWZm+atUqfcKECTqgP/HEE7qu6/pnn32mA3rfvn11t9sd3lfTNH3AgAF6fn5++LXVdV13u916nz599EmTJoW3TZ48WbdarfquXbvC23766SfdYDDoB39UHvw7OJLfaUfF2JyGOOPi4vRzzjlHf/TRR/W1a9c2aXck753rrrtOz87O1isqKhq1u+KKK/TExMTw7+fCCy/UBw8efMiYhRBCxLZ58+bpgP7JJ5/o5eXl+p49e/SFCxfq6enpusVi0ffs2RNuO2HCBH3o0KG61+sNb9M0TR8zZow+YMCA8LZI+hqzZ89udG5cv369Dug33XRTo3bTpk1rci6bPn263rt370M+pq7rjfodDfLz8/W+ffs22nZwP+1wz4PSH/uF9Md+If0xEctk+p4Q7WT+/PlkZmZyxhlnAPWrlFx++eW89dZbhEKhcLt3332X448/Pny170DtuYyswWDAbDYDoGkalZWVBINBRowYwbp169r8eBdddBFGo5G33347vK2goICffvqJyy+/PLwtKSmJDRs2sHXr1iM/iJ/94x//ID09nYyMDEaNGsXXX3/NHXfcEb7K02D69OnYbLbw7fXr17N161amTZvG/v37qaiooKKiApfLxYQJE/jiiy/QNI1QKMSyZcuYPHkyvXr1Cu9/3HHHkZ+ff8j4juR32lkxAjz88MO8+eabnHjiiSxbtozf//73DB8+nJNOOomNGzeG2x3ue0fXdd59913OP/98dF0PH0tFRQX5+fk4nc7w/klJSezdu5fvvvsuotiFEELEtokTJ5Kenk5ubi6XXHIJcXFxLF68mJ49ewJQWVnJp59+ymWXXUZtbW34/LF//37y8/PZunVreLW+w+lrfPTRRwDceuutjbYf3JdoqwP7HU6nk4qKCsaNG8f27dtxOp0t7ne450Hpj7VM+mP1pD8mYo0kpYRoB6FQiLfeeoszzjiDHTt2UFhYSGFhIaNGjaK0tJQVK1aE227bto0hQ4Z0Slyvv/46w4YNC9cSSE9PZ8mSJa12klqSlpbGhAkTWLBgQXjb22+/jdFo5KKLLgpv++Mf/0h1dTXHHHMMQ4cO5a677uLHH388ouO48MILWb58OZ988gmrVq2ioqKCJ554oknRzD59+jS63dARmz59Ounp6Y1+XnnlFXw+H06nk/LycjweDwMGDGjy3Mcee+wh4zuS32lnxdhg6tSpfPnll1RVVfHxxx8zbdo0vv/+e84//3y8Xm+43eG8d8rLy6muruall15qciwzZswAfingec899+BwOBg5ciQDBgzg5ptvbrZmgxBCiO7hueeeY/ny5SxcuJBzzz2XioqKRoWqCwsL0XWdBx98sMk5pGGqWsM55HD6Grt27UJVVfr169doe1vOoc35+uuvmThxInFxcSQlJZGens79998P0Oo583DPg9Ifa5n0x+pJf0zEGqkpJUQ7+PTTT9m3bx9vvfUWb731VpP758+fz1lnndUuz9XSlZ5QKITBYAjffuONN7jmmmuYPHkyd911FxkZGRgMBubMmdOkqGikrrjiCmbMmMH69es54YQTWLBgARMmTCAtLS3c5vTTT2fbtm38v//3//j444955ZVXeOqpp5g7dy7XX3/9YT1vz549mThx4iHbHXhVDuqvKgH85S9/aXHevcPhwOfzHVZc7SFaMSYkJDBp0iQmTZqEyWTi9ddfZ9WqVYwbN+6w3zsNx3LllVcyffr0ZtsMGzYMqL+iuHnzZj788EOWLl3Ku+++y/PPP89DDz3UbAFXIYQQsW3kyJHh1fcmT57MqaeeyrRp09i8eTMOhyN8DrnzzjtbHHHSULuoI/oaB2qtr3Wgbdu2MWHCBAYOHMiTTz5Jbm4uZrOZjz76iKeeeip8TM05kvOg9Mfan/THpD8mokeSUkK0g/nz55ORkcFzzz3X5L5Fixbx3nvvMXfuXGw2G/369aOgoKDVx2ttiHFycjLV1dVNtu/atYu+ffuGby9cuJC+ffuyaNGiRo93cGHMtpg8eTK//vWvw0PGt2zZwn333dekXUpKCjNmzGDGjBnU1dVx+umn84c//KFdOopt0XA1NCEhodVOVHp6Ojabrdkh7ps3b47oeQ73d9pZMbZmxIgRvP766+zbtw84/PdOw2pGoVAook5rXFwcl19+OZdffjl+v5+LLrqIRx99lPvuuw+r1XpExySEEKLravhifcYZZ/Dss89y7733hvswJpMponNIW/savXv3RtM0tm3b1mhES3Pn0Nb6Wgf64IMP8Pl8LF68uNFUrkhXRzvc86D0x1p+HumPSX9MxB6ZvifEEfJ4PCxatIhf/epXXHLJJU1+brnlFmpra8PLGV988cX88MMPvPfee00eS9d1oP7kADTbIerXrx/ffvstfr8/vO3DDz9kz549jdo1jJpqeEyoX+Z25cqVh32sSUlJ5Ofns2DBAt566y3MZjOTJ09u1Gb//v2NbjscDvr379/oypLT6WTTpk2HNY2wLYYPH06/fv3461//Sl1dXZP7y8vLgfrXKj8/n/fff5/du3eH79+4cSPLli075PMcye+0s2J0u90t/u7//e9/A78MOz/c947BYODiiy/m3XffbbZT2HAs0PR9YjabGTRoELquEwgEDnk8QgghYtv48eMZOXIkTz/9NF6vl4yMDMaPH8+LL74Y/lJ+oNbOIc31NQ52zjnnAPC3v/2t0fann366Sdt+/frhdDobTXfbt29fk/N8c+dLp9PJvHnzWoyjpWNoy3lQ+mPNk/4Y4f2kPyZiiYyUEuIILV68mNraWi644IJm7z/llFNIT09n/vz5XH755dx1110sXLiQSy+9lGuvvZbhw4dTWVnJ4sWLmTt3Lscffzz9+vUjKSmJuXPnEh8fT1xcHKNGjaJPnz5cf/31LFy4kLPPPpvLLruMbdu28cYbbzSpkfCrX/2KRYsWMWXKFM477zx27NjB3LlzGTRoULMn20hdfvnlXHnllTz//PPk5+eTlJTU6P5BgwYxfvx4hg8fTkpKCmvWrGHhwoXccsst4TbvvfceM2bMYN68eVxzzTWHHcuhqKrKK6+8wjnnnMPgwYOZMWMGOTk5FBUV8dlnn5GQkMAHH3wA1BedXLp0Kaeddho33XQTwWCQv//97wwePPiQNRiO9HfaGTG63W7GjBnDKaecwtlnn01ubi7V1dW8//77fPnll0yePJkTTzwROLL3zv/+7//y2WefMWrUKG644QYGDRpEZWUl69at45NPPqGyshKAs846i6ysLMaOHUtmZiYbN27k2Wef5bzzziM+Pj6i368QQojYdtddd3HppZfy2muvMXPmTJ577jlOPfVUhg4dyg033EDfvn0pLS1l5cqV7N27lx9++AGIrK9xsBNOOIGpU6fy/PPP43Q6GTNmDCtWrKCwsLBJ2yuuuIJ77rmHKVOmcOutt+J2u3nhhRc45phjGhWYPuusszCbzZx//vn8+te/pq6ujpdffpmMjIxmE2sHOtLzoPTHmpL+2C+kPyZiShRW/BOiWzn//PN1q9Wqu1yuFttcc801uslkCi/Lun//fv2WW27Rc3JydLPZrPfs2VOfPn16o2Vb/9//+3/6oEGDdKPR2GTp2ieeeELPycnRLRaLPnbsWH3NmjVNlhrWNE1/7LHH9N69e+sWi0U/8cQT9Q8//LDZZY45aBnZ1tTU1Og2m00H9DfeeKPJ/X/605/0kSNH6klJSbrNZtMHDhyoP/roo7rf7w+3aVge+uDleJsD6DfffHOrbRqWIH7nnXeavf/777/XL7roIj01NVW3WCx679699csuu0xfsWJFo3b/+c9/9OHDh+tms1nv27evPnfu3GaXfz54CWJdP/LfaXvHeLBAIKC//PLL+uTJk8PvCbvdrp944on6X/7yl0ZLDh/pe6e0tFS/+eab9dzcXN1kMulZWVn6hAkT9Jdeeinc5sUXX9RPP/308PH269dPv+uuu3Sn09nqcQghhIgtDef87777rsl9oVBI79evn96vXz89GAzquq7r27Zt06+++mo9KytLN5lMek5Ojv6rX/1KX7hwYXi/SPoazZ0bPR6Pfuutt+qpqal6XFycfv755+t79uxp9lz28ccf60OGDNHNZrN+7LHH6m+88Uazj7l48WJ92LBhutVq1fPy8vQ///nP+quvvqoD+o4dO8LtDu6nHel5UPpj0h87kPTHRCxTdP2A8YBCCCGEEEIIIYQQQnQCqSklhBBCCCGEEEIIITqdJKWEEEIIIYQQQgghRKeTpJQQQgghhBBCCCGE6HRRTUp98cUXnH/++fTo0QNFUXj//fcPuc/nn3/OSSedhMVioX///rz22msdHqcQQgghhBBCCCGEaF9RTUq5XC6OP/54nnvuuYja79ixg/POO48zzjiD9evXc/vtt3P99dezbNmyDo5UCCGEEEIIIYQQQrSnLrP6nqIovPfee0yePLnFNvfccw9LliyhoKAgvO2KK66gurqapUuXdkKUQgghhBBCCCGEEKI9GKMdQFusXLmSiRMnNtqWn5/P7bffHvFjaJpGcXEx8fHxKIrSzhEKIYQQoivRdZ3a2lp69OiBqkopzfYm/SohhBDi6NER/aqYSkqVlJSQmZnZaFtmZiY1NTV4PB5sNluTfXw+Hz6fL3y7qKiIQYMGdXisQgghhOg69uzZQ8+ePaMdRrdTXFxMbm5utMMQQgghRCdqz35VTCWlDsecOXN4+OGHm2zfs2cPCQkJUYhICCGEEB1p+/btvPLKK/zpT3+irq6O3Nxc4uPjox1Wt9Twukq/SgghhOj+ampq2r1fFVNJqaysLEpLSxttKy0tJSEhodlRUgD33Xcfd9xxR/h2w4uYkJAgnSchhBCim/noo4/4n//5H6qrq+nVqxczZ84EkKllHaThdZV+lRBCCHH0aM9+VUwlpUaPHs1HH33UaNvy5csZPXp0i/tYLBYsFktHhyaEEEKIKNI0jUcffZTZs2ej6zqjR4/myiuvjHZYQgghhBCiFVGt+FlXV8f69etZv349ADt27GD9+vXs3r0bqB/ldPXVV4fbz5w5k+3bt3P33XezadMmnn/+eRYsWMCsWbOiEb4QQgghugCn08mUKVN46KGH0HWdmTNn8vnnn9OjR49ohyaEEEIIIVoR1ZFSa9as4YwzzgjfbphmN336dF577TX27dsXTlAB9OnThyVLljBr1iyeeeYZevbsySuvvEJ+fn6nxy6EEEKI6Nu4cSOTJ09my5YtmM1mXnjhBa699tpohyWEEEIIISIQ1aTU+PHj0XW9xftfe+21Zvf5/vvvOzAqIYQQQsSKuro6du3aRc+ePVm0aBEnn3xytEMSQgghhBARiqmaUkIIIYQQBzr55JNZtGgRI0aMICMjI9rhCCGEEEKINohqTSkhhBBCiLaorKxkypQprFu3Lrzt3HPPlYSUEEIIIUQMkpFSQgghhIgJP/zwAxdddBHbt29n8+bN/Pe//8VgMEQ7LCGEEEIIcZhkpJQQQgghurz/+7//Y/To0Wzfvp0+ffrwf//3f5KQEkIIIYSIcZKUEkIIIUSXFQwGueOOO5g2bRoej4f8/HzWrFnD8ccfH+3QouK5554jLy8Pq9XKqFGjWL16davt33nnHQYOHIjVamXo0KF89NFHLbadOXMmiqLw9NNPt3PUQgghhBDNk6SUEEIIIbqkmpoaJk2axFNPPQXA/fffz5IlS0hJSYlyZNHx9ttvc8cddzB79mzWrVvH8ccfT35+PmVlZc22/+abb5g6dSrXXXcd33//PZMnT2by5MkUFBQ0afvee+/x7bff0qNHj44+DCGEEEKIMElKCSGEEKJLcjgc2O12HA4HixYt4tFHHz2qp+w9+eST3HDDDcyYMYNBgwYxd+5c7HY7r776arPtn3nmGc4++2zuuusujjvuOB555BFOOukknn322UbtioqK+O1vf8v8+fMxmUydcShCCCGEEIAkpYQQQgjRjjRNZ0+lm00lNeypdKNpepvbh0IhAFRV5Y033mD16tVMmTKlM8Lvsvx+P2vXrmXixInhbaqqMnHiRFauXNnsPitXrmzUHiA/P79Re03TuOqqq7jrrrsYPHhwxwQvhBBCiCPW1j5WrJDV94QQQgjRLgrLallWUMq28jq8wRBWo4F+6Q7yh2TSPyP+kO2Neoh1bz9FlsPIO2/+E0VRSE5OJjk5OQpH07VUVFQQCoXIzMxstD0zM5NNmzY1u09JSUmz7UtKSsK3//znP2M0Grn11lsjisPn8+Hz+cK3a2pqIj0EIYQQQhymtvaxYokkpYQQQghxxArLapn39U4qXX6yE63YzTbc/iAFxU6KnR5mjM1r1Gk6uH2gtobXHrmVvZt/AEXh/117A5MnnR7FI+r+1q5dyzPPPMO6detQFCWifebMmcPDDz/cwZEJIYQQokFb+1ixRqbvCSGEEOKIaJrOsoJSKl1++qfHoetQ5faj69A/PY5Kl5+PN5SGh5kf2H5AhoPywh945paL2bv5B2yOBM793dMUG3t0m2Hp7SEtLQ2DwUBpaWmj7aWlpWRlZTW7T1ZWVqvtv/zyS8rKyujVqxdGoxGj0ciuXbv43e9+R15eXrOPed999+F0OsM/e/bsOfKDE0IIIUSzDu4zxVtNGFSFeKuJARmOJn2sWCRJKSGEEEIckaJqD9vK67CZVNbuqmbl9v2s2rGfldv3s3ZXNTaTSmFZHUXVnkbtsxIsfP3Bmzx/13RqqyrI7nMMt/99IaNOn9CovQCz2czw4cNZsWJFeJumaaxYsYLRo0c3u8/o0aMbtQdYvnx5uP1VV13Fjz/+yPr168M/PXr04K677mLZsmXNPqbFYiEhIaHRjxBCCCE6RkOfKTvR2mRUs6IoZCdaY77PJNP3hBBCCHFEXP4gFXU+9rt8+AIaDqsRk8FIIKRRVuvF6fWTGmfB5Q+G23uDIda89jRfvvc6ACeMO5fL73gUi81OUNMorfGG24t6d9xxB9OnT2fEiBGMHDmSp59+GpfLxYwZMwC4+uqrycnJYc6cOQDcdtttjBs3jieeeILzzjuPt956izVr1vDSSy8BkJqaSmpqaqPnMJlMZGVlceyxx3buwQkhhBBdiKbpFFV7cPmDxJmN5CTZUNXIprq3p4Y+k91sa/Z+m9kQ830mSUoJIYQQ4ojYTQYq6ny4fUEyEn65kmcxGjDHqZTWeEGvbwcQZzZiNRroc8JYvvnwTc6bcQfjLp4R3s/jD2ExGogzSzflQJdffjnl5eU89NBDlJSUcMIJJ7B06dJwMfPdu3ejqr8Mgh8zZgxvvvkmDzzwAPfffz8DBgzg/fffZ8iQIdE6BCGEEKLL60pFxRv6TG5/kHirqcn93aHPFLuRCyGEEKJLqK9ioKDT0hXE+vt0wOl0kpOUQL90B57ASdz36scYE9LZ7/JjNqg4LAb2Ob0MzUkkJ6n5q4JHs1tuuYVbbrml2fs+//zzJtsuvfRSLr300ogff+fOnYcZmRBCCBH7ulpR8ZwkG/3SHRQUO3FYjI2m8Om63i36TFJTSgghhBBHxBMIkeYw47AaqXT58QVDaLqOLxii0uXHYTWSGmfi2WeepF+/fmzbVkj+kEwMqsKqMoUvtpbz7fYKvthazrINpRhUhbMGZ0ZlmLwQQgghjk5dsai4qirkD8kkJc7M1rI6ar0BgppGrTfA1rI6UuLMMd9nkqSUEEIIIY5InNlImsPCsZkO0uOteAMaVW4/3oBGRoKVvokGVv9jNn995CH279/Pm2+++cvOykH/id0+lRBCCCFi2MFFxXVdp8YToKLOR603SFaCJSpFxftnxDNjbB5DeiRS7Q6ws8JFtTvA0JzETh+51RFk+p4QQgghjsiBQ8tH9E6izhfCH9IwG1S8+/fy0gM3U7l3G0ajkWeeeYZf/3omc/+znZCmkz8os1F7h8VAYbmLjzeU0jfNEdNX/oQQQggROw4sKl7p8lNYVkeV208wpGE0qCTajFiMhqgUFe+fEU/f8Y4uUXy9vUlSSgghhBAtimT1mYah5cVOD4XlLrITrSTZTfzwzWe889d78LtrSc/I5L1F7zJ27Fj2VLrDVyJVVSXB1njg9oHLG+em2DvzcIUQQghxlGooKl5c7WZrmQuPP4jDasJkNRII6ZQ4fagKlNf6GJjV+fGpqtIt+0WSlBJCCCFEs9qy+kzD0PKG9qu/XMGSv94Ous6JI0by4f97jx49egBHx/LGQgghhIgtOUk2+qbFsfjHYkKaTmqcGQB/UCOo6ei6htFg4Mc9Tsb2S+sWo5S6AklKCSGEEKKJw1l95sCh5VWn5FDy+ZuMOOkE/va3v2E2m8PtjobljYUQQggRW1RV4YReSby7bm99PSlvgFpvELc/hD+oYTIo9Egy8f2eKhnN3Y6ktyeEEEKIRg5efaZh+eF4qwmHxcjWsrpmaz5t376dXr16kZtiJzfFzpf/+QybreloqKNheWMhhBBCxJ60eAu5KXac7gC7Kt0ENA2TqpJkNxFvNeILaGwprWVjSY0kpdqJrL4nhBBCiEYOXn3mQIqiNKr51GDRokUcf/zx/P73vw9vay4hBUfH8sZCCCGEiD1xZiOpcWYMBoVEm4m8FDvZiVaS7WasRgMOi4FgSGfNzko0TY92uN2CJKWEEEII0cgvNZ+aH1BtMxvwBUO4/EFCoRD3338/F198MXV1dXz33XcEAoFDPkd3X95YCCGEELEnJ8lGeryVEqcXm1nF6QlSVuujqNrDnio3e6o9JNtNlNf4Gl2cE4dPpu8JIYQQopFIaz4FXDWcd81lLFu2DIBZs2bx+OOPYzRG1r3ozssbCyGEECL2qKrCiLxkFv9QRFGVB1VRMBtVNE3HHdBQFPAEQux3+WVBlnYiSSkhhBBCNBJJzadkbzEXTrqIHTt2YLPZeOWVV5g2bVqbn6u7Lm8shBBCiNg0MCueOIsRrz9EUNepdPlpmKlnUKGoykMgpGM3GaIbaDchSSkhhBBCNNJQ86nY6WFrWX1tKZvZgMcfYp/Ti8MQZO69M6jav58+ffrw3nvvcfzxx0c7bCGEEEKII6YDFqMBk1HF5QmEE1IKoGkQQqfE6eXrbRX0So2LZqjdgtSUEkIIIUQTrdV8+vWEQTz/7LOcffbZrFmzRhJSQgghhOg2PIEQKXYTdb4gwZCOQv0IKVWFhsHjQV3nX9/sIhjUohprdyAjpYQQQgjRrANrPu0u3kdNRSn5px+Pqir0v+IKLr/88iar8wkhhBBCxLI4sxFFgUBIQ6E+GaXr9Qkpg6pgUhSCIY09VW7W7K7klL5p0Q45pslIKSGEEEK0SFUV9hUWcMU547l26sWUlpaE75OElBBCCCG6m5wkGxaTAU0Di1HBYlSxmFQsRhWzoT6FYjaqBEIaW0vrohxt7JOklBBCCCFa9I9//IPTTjuNvXv3kpiYSF2ddL6EEEII0X2pqsKJuUkoCgQ1HVBQFQVQCGo6qqpgMqooioJVip0fMUlKCSGEEKIJn8/HzJkzuf766/H7/Vx44YWsXr2aAQMGRDs0IYQQQogOdd6wbBJsJkAhpGkEQzqarmMyqsSZVfxBjXirkZPzkqMdasyTpJQQQghxFNE0nT2VbjaV1LCn0o3WsKTMAYqLixk/fjwvvvgiiqLwyCOPsGjRIhISEqIQsRBCCCFE5+qVEscZx6ZjDo+IUnFYDFgMKm6/hqoonNo/jV4psvrekZJC50IIIUQXo2k6RdUeXP4gcWYjOUk2VFVpdjvQbFuvN8jb63ZTVOUlJ9nK5Sf1Ym+Nh3//uI/vdlZR6w8QbzZxcl4y5wzLpn9GfPj5//jHP/Ltt9+SlJTEm2++yTnnnBOtl0IIIYQQotOpqsJNZ/Rnv8tPQVEN3kAQX7B+Kp/FaGBYzwRuOqM/qir1NY+UJKWEEEKILqSwrJal/y3hv0VOXIEgcSYjQ3MSOa5HApv21bKtvA5vMITVaCDJZkIHip0e3P4gdnN9231OLx+sL6bOF0Sjflj04//eQqrDjMsfwhcMhVeR2bCvhjW7q3jwV4PCiam//OUvVFVV8dhjj9GvX79ovhxCCCGEEFHRPyOeB381iDdW7uKzTaVUugMAJNqM9E13RDm67kOSUkIIIUQXUVhWy9OfbGVzSQ2+oIamg6rAxn01oCj0SrGRnWglwWqiyuXjm20VuP0hrEb152WKVVZvr6TaE+DASXka4AqEcFV5MKkKiXYTJlUhoOl4/CFWby1h5v2LWfbiIxgMKvHx8bz99tvRehmEEEIIIbqMHfvdOL0hgpqOpumU1fp4//tidlS4efBXxzUabS7aTpJSQgghRBegaTpvfrubNTsr8QbqRzOF9PpRTrquo6Hg9nj5qVjFF6wvuBnUdEI61LTheQKaTp0vQEjTMaoKZncFm998hE0lhTyYa+ex2b/vqEMUQgghhIgZmqbz3GeFrNlVSSik/bwCHwRCGr6AxjfbKvjff2/ipatGyDS+IyBJKSGEEKIL2FPl5j9bytnv8hMINS0+DjoVHp36cU9Hxhuof/yabd9TsfhxNE8NBnsCpsz+R/zYQgghhBDdwe79Lr7aWkEopKED3kB9H6yhlxYI6XyxpZwvtpYx/tjMqMUZ62T1PSGEEKIL2FZex+4qVwsJqfal6zrOVe9StuAhNE8N5qz+ZE9/mv7Hj+rw5xZCCCGEiAVrdlVR5wui6+AP6mjUXxrU+SUx5Q/p/OPL7c2uZiwiIyOlhBBCiC5gn9NDINTxz6P5vez/9zO4N30JQNyQiaSc9RtUk4WcBEvHByCEEEIIEQO8wRAhTcd/iAuG/y2uZU+Vm96pcZ0UWfciI6WEEEKILuD7nZWd8jyBil24t3wDqoGUs24i9dzbUE31yaj3v9/bKTEIIYQQQnR1/TMcGCLImDjdAQrLajs+oG5KRkoJIYQQXcDKbeWd8jyWHseSmn8LxpQcrD0HNbrvm+37OyUGIYQQQoiubkSvFJJsJjwBf6vtdGDTvhomHJfVOYF1MzJSSgghhOgCimqCHfK4uq7hXLkAf/nO8DbHsElNElIAle5OmD8ohBBCCBEDjEaVvumOiNru3O/u4Gi6LxkpJYQQQnRTms9FxYdP4ilcRd1/l5M949nwVL1m23dibEKIjqVpOkXVHlz+IHFmIzlJNlmyXAgh2ig9PrJ6m7srXR0cSfclSSkhhBCiG/JX7Kb8vUcJVhaBwUTiKZe2mpASQnQfhWW1LCsoZVt5Hd5gCKvRQL90B/lDMumfER/t8IQQImZkRpiU2rvfTTCoYTTKZLS2kqSUEEII0QUYgPaaPOfa/DX7P3oa3e/BEJ9O+pT7sGQfc8j9TDKIQoiYV1hWy7yvd1Lp8pOdaMVutuH2BykodlLs9DBjbJ4kpoQQIkK90yJbUa/GF2LdnipG9knt4Ii6H0lKCSGEEF1An2QjhVVHVldK10JUf/kGNd++A4Cl11DSL7gHQ1xSRPunxhmO6PmFENGlaTrLCkqpdPkZkOFAUeozzfFWEw6Lka1ldXy8oZS+aQ6ZyifalUwXFd1VdqItoguHgZBGeZ2vM0LqdiQpJYQQQnQBqYkOCquqj/hx/Ps2AxB/8mSSx89AUSNPNA3KTjji5xdCRE9RtYdt5XVkJ1rDCakGiqKQnWilsKyOomoPuSn2KEUpuhuZLiq6s37pDlLjzZTVtr4CXyCko+t6J0XVvciERyGEEKIL6JMe2fDw1iiqgbQL7iHtwntJOfP6NiWkAEb0STviGIQQ0ePyB/EGQ9jNzV93tpkN+IIhXP6OWe1THH0aposWFDtJspvom+YgyW6ioNjJvK93UlhWG+0QhTgiPZPtnNwr+ZDtQjr0cFg7IaLuR5JSQgghRBcwNCfpsPZz/fQ5VZ/+I3zbYE8kbuCpbX4ckwr5Q7MPKwYhRNcQZzZiNRpwt5B08vhDWIwG4lpIWgnRFgdPF423mjCoCvFWEwMyHFS6/Hy8oRRNk9EjInapqkJqvDmitq+t2tXB0XRPckYSQgghuoAeSTZMKgS0yNrroSBVn71K7drFAFjzTsDRbzgK9Vfr2vIVQFFg/LHp9El1tDluIUTXkZNko1+6g4JiJw6LsdEUPl3X2ef0MjQnkZwkWxSjFN2FTBc9Oki9MLAYIxt5/uWWclmB7zBIUkoIIYToAvqmO+iVGsfu/a4WE1MGBeLMBoLuanYueAzP7v8CkDj6crKPG0GC3UqPRAs1/hC17gBOTwB/SMdsUOib7qDOF2BPlQd/UEcHFMBiVBiSk8g95xx31HUyhehuVFUhf0gmxU4PW8vqkwU2swGPP8Q+p5eUODNnDc6Uv3XRLn6ZLtp8ktNmNlBa45XpojFM6oXV65kSWSLf6Q2yetd+xvRL7+CIuhdJSgkhhBBdQG6ynXED0vnIH8L7c0df03RQIBAEVYHUOBPW6h18948H8VaXo1rsZJ9/B6mDT2XsgDTsZiO13iD9Ms1cdUpvarwB9rv8pMaZOSk3mZ2VLj76oZivCvdT6wsQbzFx2oBUzhnW46jqXArRnfXPiGfG2LzwF8nSGi8Wo4GhOYmcNfjo+iIpOtaB00XjraYm98t00djWUC+s0uUnO9GK3WzD7Q9SUOyk2Olhxti8o+bz5PKTevHoko34D7EEn6bDdzsrJSnVRvIJIYQQQnQBqqow7ZRelNX52FxSiy8YQtfrp9Y5PQH8QQ3nfz9j7aIn0IIBHJm9OOZ/HiYYn016vAVdh5CmM6xny188+2fEc8uEY5gy/Ogehi9Ed9c/I56+4x1H/ZQb0bFkumj3dXC9sIbfbbzVhMNiZGtZHR9vKKVvmuOo+FyxWo0cl53AD3trDtm2xiMjA9tKklJCCCFEF9E/I57bJw5g6X9L+G+RE3cgiN1kpEeSle0VLn4ojkcLBkgbciqDrrgXi93BgAwHU0f2Ii3eEtEXT1VVpLaHEEcB+VsXHU2mi3ZfUi/sFw01tc48NiOipFSSremoQdE6SUoJIYQQXUj/jHhuOuOXEQ42o0puShzbK+pY2juFjzPTSOgzhDiLmWE9E8kfknXUDJ8XQgjRtch00e5J6oXVO7CmVkWdFxVobT0aBXC6/WiaLsnYNpCklBBCCNHFNIxw+Oqrr/jNb37Dhx9+SP/evbnpDAcXnpgj03GEEEJE1cErsv369L7s+zlJIeen2Cf1wprW1IqzGFizswp3K8skGxT4aV/tUTGCrD1133eREEIIEaN0Xee5555j1qxZBINBHnzwQf75z3/KdBwhhBBR19qKbAOzEqIdnmgHR3u9sOZqalXU+UiwmXAHfE3aK4BRBaOqUlztodYX6PygY5gkpYQQQoguxOPxMHPmTP75z38CcMUVV/DCCy9EOSohhBBCVmQ7Whzt9cIOrKkFUOMJUOcLoioKRhVCGugHtNepXyXZoCr4ghp13u49rbG9SVJKCCGE6CJ27drFRRddxLp161BVlb/85S/MmjWrSZFRIYQQorPJimxHl6O5XlhDTS1vQGXjvhrKanz4QyHqfIEmCakGvhAEQyFSHWYcluilWQ6eWhsLU2klKSWEEEJ0AT/++CNnnnkm+/fvJy0tjbfffpszzzwz2mEJIYQQgKzIdjTqnxFP3/H1i6/UuAPsqKxDURQqXX6CQQ2jUY12iB0izmzEH9T4Ztt+arwBdA00XScQ0ptNSDUIAS5fkLgoJaVam1rblZOI3fNdJIQQQsSYAQMG0KtXL4YPH87atWslISWa9dxzz5GXl4fVamXUqFGsXr261fbvvPMOAwcOxGq1MnToUD766KPwfYFAgHvuuYehQ4cSFxdHjx49uPrqqykuLu7owxBCxKBfVmRr/gu3zWzAFwx1+xXZjjaqqrCltJY5/97Iox9u5KH3C7jt/77nmnmrWbGxNNrhdYjsBCtVLj+lNV4CQQ1/SMMX1AiGWlt7r16VJ8D28tpOiLKxhqm1BcVOkuwm+qY5SLKbKCh2Mu/rnRSWdX5MkZKklBBCCBElLpcLTavv4NhsNpYsWcKXX35Jr169ohyZ6Irefvtt7rjjDmbPns26des4/vjjyc/Pp6ysrNn233zzDVOnTuW6667j+++/Z/LkyUyePJmCggIA3G4369at48EHH2TdunUsWrSIzZs3c8EFF3TmYQkhYsSBK7I152hYke1otGJjKQ9/8BPf763G5Q8R0nRc/hDf763m4Q9+6paJqSKnh2p3AEVRDkhG6c3P2zuIpsGC7/aiaRE0bicHT62Nt5owqArxVhMDMhxUuvx8vKG0U2NqC0lKCSGEEFFQWFjIqFGjeOSRR8LbsrOzsdm650o24sg9+eST3HDDDcyYMYNBgwYxd+5c7HY7r776arPtn3nmGc4++2zuuusujjvuOB555BFOOukknn32WQASExNZvnw5l112GcceeyynnHIKzz77LGvXrmX37t2deWhCiBjQsCLbPqcXXW/85bZhRbb+GY5uuyLb0SgY1Hj+s0LKa72YVBW72RD+Makq5bVeXvi8kGDw0COIYsmOCheeQAi7SUUBQrqOP6gTiCCnowPbK1wUVXs6Osywtkyt7YokKSWEEEJ0siVLljBixAg2bNjASy+9RE1NTbRDEl2c3+9n7dq1TJw4MbxNVVUmTpzIypUrm91n5cqVjdoD5Ofnt9gewOl0oigKSUlJ7RK3EKL7aFiRLSXOzNayOmq9AYKaRq03wNayum6/ItvRaM3uSgrL6jCqCnaziqoohH6ur2Q2KBhVha2ldazZXRntUNtdSNPxBUPo6LR1gJHT7afWF+iYwJoR61NrJSklhBBCdBJN0/jjH//I+eefj9PpZMyYMXz33XckJCREOzTRxVVUVBAKhcjMzGy0PTMzk5KSkmb3KSkpaVN7r9fLPffcw9SpU1t8T/p8Pmpqahr9CCGOHg0rsg3pkUi1O8DOChfV7gBDcxKZMTavSxdTFm1XWFaHL6hhMxsIalDrDVDjDVDjCVLrCxLSdTyBEIVlddEOtV31TYsjzmLA7dcIhkABVAUMEe5f5fZT6+m8pFSsT63tmlEJIYQQ3YzT6eTqq69m8eLFAPzmN7/h6aefxmw2RzkyIeqLnl922WXous4LL7zQYrs5c+bw8MMPd2JkQoiu5sAV2WJp2XnRdlajAUUBfyCEX6uvXaQo9QkaHQgENTQdqt2dl4DpDD2T7RybFc+OCjc69ckoTYdIJyn6QrC5pIZRfdM6MMpfNEytLSh24rAYG03ha5haOzQnsctOrZWRUkIIIUQHC4VCnH766SxevBiLxcKrr77K888/LwkpEbG0tDQMBgOlpY0LypaWlpKVldXsPllZWRG1b0hI7dq1i+XLl7c6cu++++7D6XSGf/bs2XOYRySEiGWqqpCbYmdgVgK5KXZJSHVTI3on47AYqfOHCIY0/EENT0DDHaj/N6CBqihUuwNdtoj24VBVhbH90zD8nC0JtSEh1WDFpvJOe01ifWpt1JNSbV3a+Omnn+bYY4/FZrORm5vLrFmz8Hq9nRStEEII0XYGg4Hbb7+d3NxcvvrqK2bMmBHtkESMMZvNDB8+nBUrVoS3aZrGihUrGD16dLP7jB49ulF7gOXLlzdq35CQ2rp1K5988gmpqamtxmGxWEhISGj0I4QQonvqlRrHSb2T0XXwh/RmEzM6Ojsqum4R7cPVM9lOvMWI4TDzOCVOb6e+JrE8tTaq0/caljaeO3cuo0aN4umnnyY/P5/NmzeTkZHRpP2bb77Jvffey6uvvsqYMWPYsmUL11xzDYqi8OSTT0bhCIQQQojmhUIhioqK6NWrFwAzZszg0ksvxeFwRDkyEavuuOMOpk+fzogRIxg5ciRPP/00LpcrnOS8+uqrycnJYc6cOQDcdtttjBs3jieeeILzzjuPt956izVr1vDSSy8B9QmpSy65hHXr1vHhhx8SCoXC9aZSUlJkJJ8QQhzlVFVhyok5fPJTaYttghqs2VlFrbd7TeHrmxZHSpyZGm8QBVAUoIURUwfmrRrGRrn9oU5/TWJ1am1Uk1IHLm0MMHfuXJYsWcKrr77Kvffe26T9N998w9ixY5k2bRoAeXl5TJ06lVWrVnVq3EIIIURr9u/fz7Rp09i0aRNr164lLa2+poAkpMSRuPzyyykvL+ehhx6ipKSEE044gaVLl4aLme/evRtV/WUQ/JgxY3jzzTd54IEHuP/++xkwYADvv/8+Q4YMAaCoqChc4+yEE05o9FyfffYZ48eP75TjEkII0XXZTCqhQ8xCc3qDlNd6gcROiakz9Ey20y/DwY797vrRUgotrsKn198d/tdiUkDXqfN1/mp3DVNrY0nUpu8dztLGY8aMYe3ateEpftu3b+ejjz7i3HPP7ZSYhRBCiENZv349I0aM4OOPP6a8vJz169dHOyTRjdxyyy3s2rULn8/HqlWrGDVqVPi+zz//nNdee61R+0svvZTNmzfj8/koKCho1GfKy8tD1/VmfyQhJYQQAmDZhpZHSR1owZq9HRxJ51JVhfHHZmBUFUJ6/Yiw1upKNazQZ1IhyWbGbjHisMq6cpGI2qvU2tLGmzZtanafadOmUVFRwamnnoqu6wSDQWbOnMn999/f4vP4fD58Pl/4tixdLIQQoqPMnz+fG264AY/HQ58+fXjvvfc4/vjjox2WEEIIIcRhqXT5I2pXUORE0/QuP1WsLUb3S8VmMhAI/TyFj59HQx00akqlPiFlNRuIt5hIsJvITbYRbzFFJ/AYE/VC523x+eef89hjj/H888+zbt06Fi1axJIlS3jkkUda3GfOnDkkJiaGf3JzczsxYiGEEEeDQCDA7bffzpVXXonH4yE/P581a9ZIQkoIIYQQMe3YrMhKD1S5/OytcndwNJ1LoX7ElKqCQa0fKaXTOCGlAGnxZjITreQk2chLs5PhsHBSr2RykmzRCTzGRC0pdThLGz/44INcddVVXH/99QwdOpQpU6bw2GOPMWfOHDSt+cF0snSxEEKIjvbwww/zzDPPAPD73/+eJUuWkJKSEuWohBBCCCGOzK/H9sMQQTtvIMTWstoOj6czaJrOnko3X26tQFXAbKifvtccHUiNs3DagDSG9Uwk0WaiV6qdswZndqtRYx0pakmpw1na2O12NyrgCfXLbAPoevNVx2TpYiGEEB3td7/7HSeeeCKLFi3iT3/6U/jcJIQQQoijT0NSY1NJDXsq3WgtVciOAXa7iaG5h/4OHdCgsMzVCRF1rMKyWl74fBtPLd/C4h+KcftD+A6xiN6O8jqcniAhDYb1TGLG2Dz6Z8R3TsDdQFQrb7V1aePzzz+fJ598khNPPJFRo0ZRWFjIgw8+yPnnny9fAIQQQnSq//znP5x++ukoikJycjJr1qxpcuFECCGEEEeXwrJalhWUsq28Dm8whNVooF+6g/whmTGbqJh8Yk/W7/mp1TY6UOONrP5UV1VYVsu8r3dS6fKTnWglzhLPxn3OVgucA3hDOqcfk8ap/dPJSbLJCKk2impSqq1LGz/wwAMoisIDDzxAUVER6enpnH/++Tz66KPROgQhhBBHGZ/Px2233caLL77Ic889x0033QQgCSkhhBDiKHdwUsNutuH2BykodlLs9MTsCJqUODMGBUKHGPC1oagmZouda5rOsoJSKl1+BmQ4UBQFXdexGA3U+Q6VloKtpbVMHdm7EyLtfqK+RuEtt9zCLbfc0ux9n3/+eaPbRqOR2bNnM3v27E6ITAghhGisqKiISy65hG+//RZFUait7R61E4QQnUvTdIqqPbj8QeLMRrmyLkQ30FxSAyDeasJhMbK1rI6PN5TSN80Rc3/vfdPiiDMbqPGFWm33/e4q9la56ZUa10mRtZ+iag/byuvITrSGf3eKohBvMbLfdYj5e0S+SqFoKupJKSGEECIWfPnll1x66aWUlpaSlJTEm2++yTnnnBPtsIQQMaY7Tu0RQjSf1GigKArZiVYKy+ooqvaQm2KPUpSHJ9FmJjPeQo2v9dX1nN4QG0ucMZmUcvmDeIMh7ObGK+alxJnZWek55P4mGTF/2OSVE0IIIVqh6zp///vfOfPMMyktLWXo0KGsWbNGElJCiDZrmNpTUOwkyW6ib5qDJLuJgmIn877eSWE3WblKiKPRL0mN5sd92MwGfMEQLn+wkyM7cjlJNqymyFIHn28q7eBoOkac2YjVaMB90O/HaoqsdnVJjTemC9pHk4yUEkIIIVpRUFDA7bffjqZpXHHFFbzyyivExcXeFUAhRHR156k9QojGSY14q6nJ/R5/CIvRQFwLSauuTFUVAhEmXL7dXtXB0XSMnCQb/dIdFBQ7cViM4c9oszGyz+Pd+12s3rkfb6C+/lSftDhyk+3yeR6B2PuLEEIIcdSJZv2VoUOH8vjjj6MoCrNmzWoyJF8IISLRnaf2CCFaTmpA/ajrfU4vQ3MSyUmytfIoXVcgeOhi3wB7Kz0EgxpGY2xNylJVhfwhmRQ7PWwtq/+stpkN2MwGFOpXF2xNsdPLfe/+iD+ooyuQZDNzSp8Upp3SS6ZmH4IkpYQQQnRph1N/5UiTWCtWrKBXr14MGDAAgN/97nftcixCiKNXS/VKGtjMBkprvDE5tUcI0XJSw+MPsc/pJSXOzFmDM2N25Eyk0/cCOny3q5LR/dI6OKL21z8jnhlj88L9ztIaLxajkUSbkWpP65/NAQ1qfSF6JFlRUKh2B1i+sZSyOh+3TxwgialWSFJKCCFEl3WopZWvOqU3Nd4A+11+UuPMnJSbzM5KV4tJrJ4JNt5et5uiKi85yVYuP6kXVusvp0Jd1/nrX//Kvffey8CBA1m1ahUOhyOKr4AQorvozlN7hBD1mk9qGBiak8hZg2N7MYN+6Q42lLgiavvNtvKYTEpB/e+w73hHo4ubGfFmXvpy5yH3TbQasZnqP8MzE1T2u/xsKa1lWUEJfcfL1OyWyFlPCCFEl3So+itfb6vghtdXU+0JEghpmAwqfdPiSIqzYERnU1kNbp+G3aLidPn4d8E+dpTX4gr8MgD7iaWbmX5qX3531rHU1dVx3XXXsWDBAgBGjRqF0SinSSFE++juU3uEEPWaS2p0ZtmBjnLmoCwW/zeyIuZrdlZ0cDQdS1WVRtOoe0T4uezxh8L/VxSFeKuRWm+QH/c6ZWp2K6S3LYQQoks6sP4KQI0ngD+kYTaoVNT5+HGvk0DolwSTNxjih6KaJo9T5YGi6uY7RzV+jb9/WkhF0S4+fPJ3FBQUYDKZeOaZZ5g5c6bUjxJCtJvuPrVHCPGLg5Ma3cG5g7O5nR8ialuwu2l/LKYpRFRXyuUPout6uP9oMtRPeXQHgjI1uxWSlBJCCNElNdRf8QZUNu2rpdLtJ6hpGIDtFS4CkdXbPCT3tu94/Om/ovlcZGdns3DhQsaMGdM+Dy6EEAfozlN7hBDdm9lsiLhtbTfLv8SZTRhVDtn3DGo6/qCGxVT/WgVC9TvYTUaZmt0KeWWEEEJ0SXFmI/6gxrrdVQRDOg6rEZPBSEWNp90SUrquU/PtO2g+F/2GnMSXH39IdnZ2+zy4EEI0o7tO7RFCiAO53QHs9qb182LRiLxkHFYTVe7AIduG9PrxVLquU+sNYlAVhvWUqdmtia11GoUQQhw1shOs+AIaVe4AyXYTFqMBVVHYV+ltt+dQFIW0C+4h4ZRLSbjgQUlICSE6RcPUnoFZCeSm2CUhJYSICeY2tH3ui60dFkdn650Sx8m9kznUJ7U3oOEPhnC6A+yt8uD2hzgmI578IVnyOd8KSUoJIYTokvbVeLGYVJJs9VemfMEQmq5zpCkpf8Vuar57P3zbGJ9K8rjpVIa6x9U8IYQQQoiO0DvNEnHbFZvKOzCSzqNpOkXVHk4/Ng1DC9mThomNOrC3ysuuShe13gB2k4GM+Mhfs6OVTN8TQgjRJbn8QcxGleG9U9hR4aLK7afOd2RFClybv2b/R0+j+z0YEzKwHyu1o4QQ0dPwZUem8QkhurKGz6pxx2SxtWJXRPt4vf4OjqrjFZbVhmsA7q5yoSoKKjoHV5EIAapSP+KnR5KVvNQ4shKsGA0K+2q8zPt6JzPG5kndwBZIUkoIIUSXFGc2YjUasJpUTs5LptYbxB/SKCxztfmxdC1E9Zf/oubbhQBYeg3F0nNQe4cshBARO/DLjjcYwmo00C/dQf4QKXguhOg6DvyscvkOXVOpgaYfaq26rq2wrJZ5X++k0uUnO9GKP2Rjo6EWVQnhC9aPilJ+/lEVCOn1yalku4lBPRLDjxNvNbG1rI6PN5TSN80hFx6aIUkpIYQQXVJOko1+6Q4Kip0MyHCQYDu86XUhTw0Vi/+Cd+f3AMSfPJnk8TNQ1MhXkRFCiPZ08Jcdu9mG2x+koNhJsdMjV9SFEF3CgZ9VWQlWHJbI0wfldZEnsLoaTdNZVlBKpcvPgAwHiqKgaToWo4LTU9/GoIDZ+Mt8vmBAQwH2VnnQNA1Vrb9PURSyE60UltVRVO0hN8UehSPq2iQpJYQQoktSVYX8IZkUOz1sLasjO9GKrQ3LEQP4S7dT9t6jhJylKEYLqefcStygcR0UsRBCHFpzX3ag/mq6w2KUK+pCiC7hwM+q1DgTm0tqqXRHPiXPE4JgUMNojL0y1kXVHraV1/c9Gz6jE2wmku0WKt31pSQ0HfxBLfx/AJMB6rxB9jm95CT/knyymQ2U1nhx+Y+sDEV3FXvvECGEEEeN/hnxzBibx5AeiVS7A+ysaNvUvUBlESFnKcakLLKu+qskpIQQUdfcl50GB19RF0KIaGn4rLKZVH7Y66Ss1ovVpGJuQwbhu52VHRdgB3L5g3iDIezmX8bwKIpCTrKNhmsFOqDr9f820HUIaDruQKjR43n8ISxGA3FmGRPUHHlVhBBCdGn9M+LpO94RLga8bV8Vmyp8Ee0bd9xp6AEvtmNGY7A6Wm2bkyCnRCFEx/vly46t2fvliroQoitw+YN4AiGqXH48/hApcWYURSEzwcye6shGTK3eUcHo/mkdHGn7a6hr6vYHibf+Uj4i3WHBoCjoP9fL0qBRViqggY6GzfjLyH5d19nn9DI0J5GcpOY/9492MlJKCCFEl6eqCrkpdgZmJXDuCbkttgu5qih//38J1u4Pb3MMm3TIhBTAxSf3apdYhRCiNQd+2WmOXFEXQnQFcWYjmq5TXufDYa3/PPIFQtjMkdf43F5e21HhdaiGuqb7nN5wAgrAYTFgMtQPlWoodG5QwKTW/x8gqME+p5ugplHrDbC1rI6UODNnDc6UKdktkLOdEEKImDK4ZyIKjYdLA/iKN1P+3mOE6vaj+d1kXvbHiB9TASYP69meYQohRLMOXMTBYTE2msInV9SFEF1FTpKN7EQrP+ypxmpUqaj14wmE2rSq3o97qjsuwA7UUl1TpzeIyaDiCdTXklKV+il7oZ9fEoNSX19qU0kdiXYLNpOBoTmJnDVYVlVtjSSlhBBCxJT+6fH0TLayt8obTkzVrl9K5SdzIRTEmNKTlAk3tOkxJxyXQV76oUdTCSHEkWrpy47HH2Kf0ytX1IWIYZqmh8sNxJmN5CTZYvZvWVUVJhyXyfKfStld6UZVFCxGlV/GBB3afnfsrsDXUNd0WUEpW0trKKn14vGHsJpUXL76i6Ohg/NzOliNCgZVYcLADEb1TY3p90BnkaSUEEKImJKbbGfScVks/rGYmjo3+5a+QN0PywBwHDOanMm/w2i24wlqaHr9VawMhxmTyUiZ04P3gB6E1aAwNDeJe88ZKB0GIUSnOfDLzrbyOkprvFiMckVdiFhWWFYb/pv2BkNYjQb6pTvIHxK7f9Oj+6SSlWCl2hNAVaDGGySkRT5Syh+MvG1X1D8jnp1Zbj7fXMqeKg9BTcflC3LgYamA8vMIKQ3whXQsQY04q5HcFHtLDy0OIEkpIYQQMUVVFaad0osde4tY9PJ91O3eCIpC7sTpHHPWVQzskcRN4/uxvaKOEqePrEQLZw3MYne1myU/7OPrbRXUeoPEW42c2j+Nc4dlx2xnUQgRuw5exCHWR1UIcTQrLKtl3tc7qXT5yU60YjfbcPuDFBQ7KXZ6mDE2Lyb7GvtqvCTHmUmPM1Na5yOkabRh9h4hDfz+EGaz4dCNu4CDR7ptLqnlf5duotYbIDXOjM1sYF+1F4/TC9QnpAwHVOlWFAVN0/EEQyRZI6+9dbSTpJQQQoiY0z8jntvPGcqyvwTx2uMZe8PDDDjpNIb1TCR/SBb9M+IZ1COxyT6/neDgouE95QugEKJLaFjEoeGL0JayWvlsEiLGaJrOsoJSKl1+BmQ4wnXi4q0mHBYjW8vq+HhDKX3THDH3d+3y19dQshgNhII6Ia1pTc/WBHRYurGEC47P6bAY20thWS1L/1vCf4ucuAJBbAaVLaV11PlD9E2zo6r12acEm5FiZ/0+GqBr9aPy63909J8Ln+pKbI8S60ySlBJCCBEzGlZAURSF4/tk8/myJZTW+kjP6RXRF7mGL4BCCNFVdMcpP0IcTYqqPWwrr68Pd+DCBVDfX8lOtFJYVkdRtSfm+iBxZiMhTaOkxktQ19EO4zE27avp8kmpwrJanv5kK1tKa8PTEz3+IGW1PmxmA76ghtWk4A9quP0hVAi/Fg21pQ4sdm42GnB6ml9hVTQlSSkhhBAxwePxMHPmTIYMGcJdd90FwDHHDOCYKMclhBCHq2HKz/46PwlWIwlWE5qm89+i2J7yI8TRxOUP4g2GsJubXzHTZjZQWuPF5Y+9JEVOko14i5FabzCchGlIu0U6Dkg7nExWJ9I0nTe/3c0Pe6oxG1XirSZMBoXykI6ug9cforjag9VkwBPQ8AdD6NDsStANr01Q0/D6Q517IDFMklJCCCG6vF27dnHRRRexbt06zGYz06ZNIyena191E0KI1jRM+dld6SYY1Ni530VQ0zCqKsk2Ey5/MGan/AhxNIkzG7EaDbj9QeKbqSPk8YewGA3EmWPvq7eqKvROi2uUkGrrpLTcZGs7R9W+9lS5+XZHJaqikBpnDo92s5kNGNT6GlFVngBxmo7dZMBsMOINhAj8/KIYfp66pyj1NaYCWn3R8x/3VHPhCTny+R0B9dBNhBBCiOj55JNPGD58OOvWrSMtLY2lS5dKQkoIEfOKqj18v6eK8lov5XU+rCaVZLsZq0mlvM5HWY2XdburKKr2RDtUIUQrcpJs9Et3sM/pDZcZaKDrOvucXvpnOMhJan4kVVdnMf6SMjicKkn/2VLefsF0gB0VLqo9fpLspkbTLx0WAxaDQkivH+1lVMCgKqiqgs30S+H2kF6fhApq4AvVv0ZxFgOrdlaxp8odhSOKPZKUEkII0SXpus5f/vIX8vPz2b9/PyNGjGDt2rWcccYZ0Q5NCCGOWK03wO79bgJBjZQ4MxajAVVRsBgNpMSZCYY09lS6qfUGoh2qEKIVqqqQPySTlDgzW8vqqPUGCGoatd4AW8vqSIkzc9bgzJgdMWMzGziS0L/bWYW/i09lU3TQD0q5qapKWrw1PDosqIGm6/iCGr5Q4zmJIR10vX4lPqtRRdehos7LjgpX5x1EDJOklBBCiC5H13Wuuuoq7r77bjRNY8aMGXz55Zf06tUr2qEJIUS7qPMF8QRCWEyGZosjW0wG3P4Qdb7Yq0MjxNGmf0Y8M8bmMaRHItXuADsrXFS7AwzNSYz52nDHZMZjMR5+VsrtD/HxppJ2jKh99U2LI9FuosYdaDLSLc5ixGhQMCr1U65rPEH8QY0EqxGjAiYVjCqYDQpGg4KqKIR0HZc3gMsXQtNlBb5IxN7EViGEEN2eoiiMHj2aBQsW8MwzzzBz5swmX9qEECIWaZpOUbWH8jofRoOCL6DhsOiNPuN0XccX0LCbDTis0l0XIhb0z4in73gHRdUeXP5gRKsCx4IRvVLITrSyveLwphKHdJ0Sp6+do2o/PZPtnNI3leU/lbK/zke8zYTJoBIIadR4/BhVhd7pcQzukYjLF8QdCOENBFnnriakAQpNklkhHbRgiCqXPzoHFWPkLCeEEKLL8Hg82Gz1NRduuukmJk2axDHHyPp6QojuobCslmUFpWwrr6PS7cPjDxHUdAJa/RS+hi9Cdd4gRqNKbrKNeEvTwslCiK5JVRVyU+zRDqNdGY0qpw1IZ3vF7sPaP6SBpnfdJfhUVWHaqF6U1frYUlJLrTcIP6+vZzEayE2xkxZvxRcMUVDkZL/LTyCkhQudo9cXgDeo9VP4tJ9v6zp8saWcKSf27NDEZMOFjlhOhEpSSgghRNRpmsYjjzzCm2++yapVq0hKSkJRFElICSG6jcKyWuZ9vZNKl5/sRCvZiVZqPUF27nfhC4RwegIYVAWjqpIeb8GoqpzUKzlmiyMLIbqPHkf4OVRS7UPT9C6bLOmfEc/tEwewtKCE/xY5cftD2M0GhuUkMTA7nn+u3Mnyn8rwhTSam5HXUHMK6hNSqlq/Kt9P+2rZW+WmV2pch8R94IUObzCE1WigX7qD/CGZMTVlVJJSQgghosrpdHLVVVfxwQcfALBgwQJuvPHGKEclhBDtR9N0lhWUUunyMyDDEZ6qN6hHIr6gRlmtlziLgcHZiWi6Tq03SKrDEtPFkYUQ3YfNbMCg1I8CatCWaknr9tSvJNqVR5H1z4jnpmamX2qazmNLfsIX1Fo9ZgVQlfofXa8fgeV0+9le4eqQpNTBFzrsZhtuf5CCYifFTk9M1TKTpJQQQoio+emnn5gyZQpbtmzBYrEwd+5crrnmmmiHJYQQ7aqo2sO28jqyE62NakelxJk5sVcyPxU7Kav1UVbrI9luZljPJM4aHFtXuoUQ3dcxmfHYzAY8/hChCLNRCvVFwEM6lNf6YmIl0eamX67asZ/dVR506leJU5T6hJx20OtgMoBBVdE0HYNBQVUVPIFQk1X92kNLFzrirSYcFiNby+r4eEMpfdMcMXFhQ5JSQgghomLhwoVcc801uFwucnNzWbRoESNGjIh2WEII0e5c/iDeYAi7uekUmJQ4M6P6prBxXw2XnZzLkB6JMVkTRAjRfY3olcIxGQ7W73W22k4BTD+vQmdUFQIhDfXnletidSXR1TsqCYR0FOoTUi0l5UJafV0ps8mA1ajiDYRQFQWbydDuMbV0oQPqFwvKTrRSWFbX5UenNVCjHYAQQoijz7x587j00ktxuVyceeaZrF27VhJSQohuK85sxGo04PY3/6XMG9BItlsY0iOR3BS7JKSEEF2CpunsqXRTWFHHmcdmNBkddDAdMKoKBlUhqOlouo7FbCDBZorZlURrDhjh1dooMUWBeIsRu8lAUKtfUTU5zkyCtf0Xq/jlQkfzr6nNbMAXDOFq4ZzT1cTmO0MIIURM+9WvfkWvXr247LLLmDNnDkajnI6EEN1XTpKNfukOCoqdOCz1n3e13iD+kIZJVSip8TKsZ5IUNRdCdBkHF9HeVeGKaD9PQMNsUECpHzWUajeTm2KP2ZVEj8lwAIeuoRXUwBcMoaoqCvWjYPtnOIjvgKTUgRc6mnt8jz+ExWggroWkVVcTG1EKIYSIeXv37qVnz54ApKen8+OPP5KYmBjlqIQQouOpqkL+kEyKnR6+312N2x+k1hfEH9TwBzXS4y1cOiJeRkgJIbqE5opoby+tiWhfBTAaVGxmAz2TbMRbTTG9kuiovqlYjeCNYNCRruuYDQqpcWZSHJYOO+6DL3QcOIVP13X2Ob0MzUmMmddcpu8JIYTocG+88QYDBgxg3rx54W2SkBJCHE36Z8Rz5sAMarwB9jm9+IMhLEaV7CQrCTYTn24qo7CsNtphCiGOcgcX0Y63mjCoCkZDZKkDVYEzBqYz/ph0shKt9Eq1x/RKor1S4siLcPU8l0+j2hNgx343hWUu4q3GDjnuhgsdKXFmtpbVUesNENQ0ar0BtpbVkRJnjqnXXJJSQgghOkwgEOC2227jqquuwuv18sEHH6Dr7b8KiRBHi+eee468vDysViujRo1i9erVrbZ/5513GDhwIFarlaFDh/LRRx81ul/XdR566CGys7Ox2WxMnDiRrVu3duQhHLU0TWfTvlqyE22cNSiTsf3TGdMvjbH90jgxN4lKl5+PN5SiHapoixBCdKCWimjHRTj9LqhDjTuIpsOwnknMGJsX0yuJqqrCcdkRXkhV6qfWxVuNBEIhXl+5ixUbSw/reRvqeW0qqWFPpbvJuaF/RjwzxuYxpEci1e4AOytcVLsDDM1JjLnXXKbvCSGE6BClpaVcdtllfPHFFwA88MAD/OEPf2iySogQIjJvv/02d9xxB3PnzmXUqFE8/fTT5Ofns3nzZjIyMpq0/+abb5g6dSpz5szhV7/6FW+++SaTJ09m3bp1DBkyBIDHH3+cv/3tb7z++uv06dOHBx98kPz8fH766SesVmtnH2K31vBFr0eStdkaILG2WpIQontqabXQ+DYUKreadGZNOqbbrCSaao/s2OPNKr1S7JiNKrqus7vKw+vf7GTcgHSMxsjHAx1cz8tqNNAv3UH+kMxGyab+GfH0He+gqNqDyx8kzmyMyddcklJCCCHa3erVq7nooosoKioiPj6ef/7zn0yePDnaYQkR05588kluuOEGZsyYAcDcuXNZsmQJr776Kvfee2+T9s888wxnn302d911FwCPPPIIy5cv59lnn2Xu3Lnous7TTz/NAw88wIUXXgjAP//5TzIzM3n//fe54oorOu/gWuFytVxc12AwNEqetdZWVVVsNtthtXW73S2O8lQUBbvdfsi25dU11Lpc9EhKA+pHqVU66/AFg5gNKjazSl2dh/JqJymW+v3j4n6ZMuLxeNA0rcWYD2zr9XoJhULt0tZut4cvJvh8PoLBlgurtKWtzWZDVeu/pPn9fgKBQLu0tVqtGAyGNrcNBAL4/f4W21oslvCiHG1pGwwG8fl8LbY1m82YTKY2tw2FQni93hbbmkwmzGZzm9tqmobH42mXtkajEYvFAtS/391ud7u0bcvffSx9RjTXti1/9+31GaEEfI2KaAf8PrRQiHSrjuZv+j5STJbw370eDKBrITbtqSDFouPxNP49xupnRFG1Bz0URA+1HK9iNBH4+eV3eX0ooSCJBo3C4gq+3ryXEXmp4batfUZsK6vlX9/uosoVICvRQmaSDZ+mUFDsZG9lLVOHZ9PvoFFQKRZIsRgwm00xl5ACQD/KOJ1OHdCdTme0QxFCiG5p7969utVq1QF94MCB+saNG6MdkjiKdZfzvs/n0w0Gg/7ee+812n711VfrF1xwQbP75Obm6k899VSjbQ899JA+bNgwXdd1fdu2bTqgf//9943anH766fqtt97a7GN6vV7d6XSGf/bs2dPhry/1ix41+3Puuec2amu321tsO27cuEZt09LSWmw7YsSIRm179+7dYttBgwY1ajto0KAW2yakZesPLy7QH3jvv/oVL67UE3KPbbFtWlpao8cdN25ci23tdnujtueee26rr9uBLrnkklbb1tXVhdtOnz691bZlZWXhtjfddFOrbXfs2BFue+edd7batqCgINx29uzZrbZdvXp1uO3jjz/eatvPPvss3PbZZ59tte2HH34Ybjtv3rxW2y5YsCDcdsGCBa22nTdvXrjthx9+2GrbZ599Ntz2s88+a7Xt448/Hm67evXqVtvOnj073LagoKDVtnfeeWe47Y4dO1pte9NNN4XblpWVtdp2+vTp4bZ1dXWttr3kkksavYdbaxtrnxG9e/du1HbEiBEttu3Iz4hnV2zVZ/5rjf7Esk368aflt9o2d9ZCvfc9H+q97/lQjxsyodW2sfoZMeMf3+opk2a22jb9ktl63j0f6sNmL9V7XnhHq23b8hlxxZ1z9Cc/3qw/sWyTfs7vnm7993bAZ0RH6Yh+ldSUEkII0a5ycnK4++67mTJlCqtWrWLgwIHRDkmImFdRUUEoFCIzM7PR9szMTEpKSprdp6SkpNX2Df+25THnzJlDYmJi+Cc3N/ewjudoZTSobC2t4/vdVZTXelFlOrMQogs6sIh2IKRHO5yoOyYrsvpMOuD2Bwl2wGumKAop9sjqesUaRdeProqzNTU1JCYm4nQ6SUhIiHY4QgjRLRQVFREKhejVqxdAePh4w1BqIaKlu5z3i4uLycnJ4ZtvvmH06NHh7XfffTf/+c9/WLVqVZN9zGYzr7/+OlOnTg1ve/7553n44YcpLS3lm2++YezYsRQXF5OdnR1uc9lll6EoCm+//XaTx/T5fI2mGdTU1JCbm9uhr293mppTWFbLQ0sKKanxkpVgwaAF8QeDuLxBLCaV5DgLI/NSuP60vqiqItP3DqOtTN+rJ9P32t62K3xGdIXpew1tG+oabS7aj9cfwGI0sPiHYg6OvLnpewA/PDgJs9nQqG2sfUaYzRZKav1sLHZyw+urDzl9T1ENmAxgVnRCgQC+oIbRoHDZ8BzuPXdweGpdS58Rm0treOGzbfRJiwtftDCaTBiM9X/3voCf7fuqmXlGP47NbHrOPfAzoqN0RL9KakoJIYQ4Il9++SWXXnopOTk5fPXVV406BkKI9pGWlobBYKC0tPEqPqWlpWRlZTW7T1ZWVqvtG/4tLS1tlJQqLS3lhBNOaPYxLRZL+ItjZznwi1S02h74JfFI2qYkKqTHWzCqCi5/iJCmYFAtZKfH0y89DpNBZU9tgOqA2qTY+YFfgA+lLUXq29K2Lb//trQ1m83hREe02ppMpoi/zLWlrdFoDH/5bM+2BoMh4vdwW9qqqtohbRVF6ZC20L0+Iw7Wlr/79v6M+KWIdg9qfQHqvEFW766h1HWIxAz1fxuf7qjm4pN6tdi2q39GFJbVsmzVTraV1+EJhLBbTXgCh/771EKgmQyEjCoWk0K81cj3JX4q/Qq9U5u+pw78u09PUoh3OAiqpmYXxPCHFBwOB+lJicTFdZ8FMeRbgxBCiMOi6zp/+9vfOPPMMyktLSUQCFBZWRntsITolsxmM8OHD2fFihXhbZqmsWLFikYjpw40evToRu0Bli9fHm7fp08fsrKyGrWpqalh1apVLT6mODIufxCzUeWUvqmM7pvKyD71/47onUxKnAWb2YAvGMLlb/lLnxBCdBZVVfAFQ3y2sZy3Vu8hzhb5KJx//1DcgZF1rMKyWuZ9vZOCYidJdhP90h3kJEWW9AsBIU3HZjLQK8VOdpINp8fPjoqWR941yEmy0S/dwT6nt8loOl3X2ef00j8j8lhihYyUEkII0WZut5uZM2fyr3/9C4CpU6fy8ssvt+mqohCibe644w6mT5/OiBEjGDlyJE8//TQulyu8Gt/VV19NTk4Oc+bMAeC2225j3LhxPPHEE5x33nm89dZbrFmzhpdeegmoH41w++2386c//YkBAwbQp08fHnzwQXr06CGrZXaQOLMRq9GAJxAioZkvdx5/CIvRQJxZuuhCiOhrSM5UuvxkJ1qZNDCLF7/aEeG+NR0cXcfQNJ1lBaVUuvwMyHCEpxumOiwUlrc8rbRBglklJyUOh8WAqqp4ApFfZFBVhfwhmRQ7PWwtqyM70YrNbMDjD7HP6SUlzsxZgzNjc4W9VsgZTwghRJvs3LmTKVOmsH79egwGA3/5y1+4/fbbwydtIUTHuPzyyykvL+ehhx6ipKSEE044gaVLl4YLle/evbvR1NkxY8bw5ptv8sADD3D//fczYMAA3n//fYYMGRJuc/fdd+Nyubjxxhuprq7m1FNPZenSpW2a0iUi13AVvKDYicNibPS52XAVfGhOYre7Ci6EiD0tJWcUaFJXqjkef8v1rbqyomoP28rrE0IHfkYnNDOdrjkhIN5a//mu6zpOd4Akm4m+aZFduO2fEc+MsXksKyhlW3kdpTVeLEYDQ3MSOWtwJv0zIiu6Hkuk0LkQQog2Oeuss1i+fDlpaWksWLCAM844I9ohCdEiOe93LHl92+7gkQcHXwWfMTavW37pEELElj2Vbp5avoUke+P6Rs9/tpVABPkmhwkKHjmvAyPsGJtKavjbiq30TXNgOGBEUrXbz/+t3o3/ECvrqcDgnHgMqkqtJ4A/pDNpUCYPnDeoTSOcNE2nqNqDyx8kzmwkJ8nWJUZIdcR5X2pKCSGEaJOXX36Zs88+m7Vr10pCSggh2qjhKviQHolUuwPsrHBR7Q4wNCdRElJCiC7D5Q/iDYawHzSd2BBhBsEdqE+sxJqGadbug2r7JdpMpMYduoC6BpQ4vdR6gxhUleNzk5g2qlebE0qqqpCbYmdgVgK5KfYukZDqKDJ9TwghRKvq6upYunQpl1xyCQC9e/fm3//+d5SjEkKI2PXLqlZd7yq4EEJA4+RMvNWEruvUeoPEW4x4g4euk6QB28tr6Z8ZW6NoW5pmrSgKOUkW9tX4DvkYDouR/hkOhuUkkT+kc6fcddURVq2RpJQQQogWbd26lcmTJ/PTTz+xePFizj///GiHJIQQ3ULDVXAhhOiKDkzO+IMhtpe7qXT7MaiRT7aav2onsy8Y1oFRtr/Wio27Ipm3CBybFc/vzxvU6QmhwrLacC0qbzCE1WigX7qj0xNjbSXT94QQQjShaTrz/u9dho84mZ9++ons7GxSU1OjHZYQQgghhOgEDckZg6rwny0VFFW7MRsV0uMjXwjj043lHRhhx2lpmnX/9HgMEeSY1u2qIjvB2ukJqXlf76Sg2EmS3UTfNAdJdhMFxU7mfb2TwrLaToulrWSklBBCiEa2lDiZde9DfPT63wDocewJ3PzIc2T0HxrlyIQQQgghRGfpm+Ygw2HBZjagAm5fiLYsk1ZS7e2w2DragdOsa70B6nxBdla4+GxTGXX+UKv7ltcF+HZnBaf2z+iUWFtaKTHeasJhMbK1rI6PN5TSN83RJafySVJKCCGOcgfOPd9RXM6tv76OwjX/AWDM+dOYcM1d7HGHmPf1TinCK4QQQghxlCiq9lDtCTC2XyrV7gDbyuuocgci3t+nQzCoYTTG5gQtVVXwBUN8tqmcbeV1uP0BghFk5XTg803lnZaUKqr2sK28fqphQ0KqgaIoZCdaKSyro6ja0yWnjUtSSgghjmIHzj33BIJ8+8kSCtf8B4PJzGW3/ZGTz5oCQJJD7/JXWYQQQgghRPtpWIHPGlQpKHayv85PW9fTW71rP2P6pXdIfB2tYUpcpctPdqKVHkk2/rvXSZHz0MXO/cHI6k+1h19WSrQ1e7/NbKC0xovLf+gC9dEQmylLIYQQR+zguefpDispQ8fRe9J0Rt/2HP3Gnhtue/BVFiGEEEII0b3FmY1YDCrr91RTWuND0yEY0tqURFi9o7LD4utIB0+Ji7eaMKgKQ3OSItp/RO+Ujg3wAAeulNgcjz+ExWggztw1xyRJUkoIIY5CDSfailoPuz+dj+KtJaTrKMCIKTdgyerPtnIX+gFDlG1mA75gqMteZRFCCCGEEO0nJ8lGmsPMnko3vkCIak+AWl+ItowBqmnDdL9o0zSdPZVuNpXUsGZXJYVlteEpcbquU+MJkGA1RPRYvVKbH7XUERpWStzn9DbquwPous4+p5f+GQ5ykjovprbomqkyIYQQHaqo2kPB9r2seO5+tq1fydbvVzJ19ksYDSpBDRxWI5UuP7XeIAk2E9D1r7IIIYQQQoj2o6oKfTIceAIhDnc2milG6kkdWNLCGwzhC4TYU+VhRO8UAiFfeLaAM8Ik29MrtvLq9JGdUvKiYaXEYqeHrWX1taVsZgMef4h9Ti8pcWbOGpzZZctvyDcLIYQ4Cq1Zt4759/8PteXFmC02TjnnMhLtZpLtZsprvSTZTYQ0DX+ovgfScJVlaE5il73KIoQQQggh2leazXLYCSmAfc6uX/bh4NpRdrONshovG/fVsnJbBb6gRpXbT1DTCYYiq6q1cV8te6rc9E6N6+Do6/XPiGfG2LxwYq20xovFaGBoTiJnDc7s0gsVSVJKCCGOMvPnz+f6G27A6/GQnNWT6/7wHD36DgSgf4aDOl+Q8lo/RoOCQVGo9QZi4iqLEEIIIYRoXxtKqo5o/x/3Orv0CnwH145qWL0uK9FKrxQb6/c48f2clTMZwKASUZLOH9TYUeHqtKQU1Cem+o53hFfVjjMbyUmydfm+e9d8ZwghhGh3gUCA22+/nSuvvBKvx8OgkeOY/Id/kt3n2HCblDgzx/dMxGJSsRpVKup8VLsDDM1JZMbYvC59lUUIIYQQQrQvlzd0RPtXuvys23Nkia2OVFTtYVt5Xbh2VANFUchKsOIPauEVB/2hyBJSAEqb1ylsH6qqkJtiZ2BWArkp9i6fkAIZKSWEEEcNt9vNkiVLAHjggQe48qbf8c9v9zSZe77f5WdE72TOHZpNWrwlZq6yCCGEEEKI9uWwHVnKwBvQKK/1tVM07c/lD+INhrCbm5ancPl/Kere1hSTL6jTS0peRKTNI6Vef/318JcagLvvvpukpCTGjBnDrl272jU4IYQQ7ScxMZH33nuP9957j0ceeYRjs5OYMTaPIT0SqXYH2FnhCo+KuvbUPpw6ID2mrrIIIYQQQoj2dcYxmRzJzLuQrqPp0Rk1FIk4sxGr0YC7mdWl/aG2jxIzKGBSFRQFylxdNxnXlbQ57fnYY4/xwgsvALBy5Uqee+45nnrqKT788ENmzZrFokWL2j1IIYQQh+fll18mFAoxc+ZMAIYMGcKQIUPC98fq3HMhhBBCCNHxUhwW+qbZ2VLmbvO+CmBQFGxmQ/sH1k5ykmz0S3dQUOzEYTE2msKnt6HAu1EBg6qgqgpWkwGDAlURrtR3tGtzUmrPnj30798fgPfff5+LL76YG2+8kbFjxzJ+/Pj2jk8IIcRh8Pl8/Pa3v+Xll1/GaDRy6qmnNkpGHahh7rkQQgghhBAHykmycWJucpuTUgpgMig4LEaMatctZa2qCvlDMil2esIlLawmlfJaH3urI1s50KCA3WLEoCjEWQxYjCr+kE5qnLmDo+8e2vzucDgc7N+/H4CPP/6YSZMmAWC1WvF4uv5yj0II0d3t3buXcePG8fLLL6MoCg8//DCDBg2KdlhCCCGEECLGqKqC2dS2tIGqgN2skmgzkZVoo09a561Adzj6Z8SHS1rsrnSzYmMZ326vxOVrOqWvOboOmQkWshKtJFiNOL1B8lLjOCk3uYMj7x7anJSaNGkS119/Pddffz1btmzh3HPPBWDDhg3k5eW1OYDnnnuOvLw8rFYro0aNYvXq1a22r66u5uabbyY7OxuLxcIxxxzDRx991ObnFUKI7uiLL75g+PDhrFq1iuTkZD766CPuv/9+1C58hUoIIYQQQnRdvgiXnDMboFeKjT6pcWTEW7CaDIzum0Juctcfkd8/I55JgzKJMxtJj7cwum8qE4/LxBxBF1oDKmp9FFd72FbhwhfQSHeY2Vnp6vC4u4M2f0t57rnnGD16NOXl5bz77rukpqYCsHbtWqZOndqmx3r77be54447mD17NuvWreP4448nPz+fsrKyZtv7/X4mTZrEzp07WbhwIZs3b+bll18mJyenrYchhBDdzrPPPsuECRMoKytj2LBhrFmzhrPPPjvaYQkhhBBCiBjWI9EaUTt/CIyqAgqYjAaOz01i6qheMVGrVNN0lv9Uij+kcVKvZLKTbKQ6LCTaTRHt7/QECek6GQ4ro/om4wlqzPt6J4VltR0ceexrc02ppKQknn322SbbH3744TY/+ZNPPskNN9zAjBkzAJg7dy5Llizh1Vdf5d57723S/tVXX6WyspJvvvkGk6n+zXE4o7OEEKI78ng8BINBpk6dyssvv0xcXNceKi2EEEIIIbq+kX1TUVZsI5I19KxGhdwUB8N6JpI/JIv+GfEdHl97KKr2sK28vqZUQ7FzRVHokWijvO7QBcv7ptk5oVcy2YlWVFVF13W2ltXx8YZS+qY5YiIxFy2HNZ/jyy+/5Morr2TMmDEUFRUB8K9//Yuvvvoq4sfw+/2sXbuWiRMn/hKMqjJx4kRWrlzZ7D6LFy9m9OjR3HzzzWRmZjJkyBAee+wxQq0s1ejz+aipqWn0I4QQ3YV+wBK7d955J4sXL2b+/PmSkBJCCCGEEO1iZO9UEiyRraCXk2zjutP6cMbADCxGA5oWSSor+lz+IN5gCLu58bid9HhLRPvbLUZyku3hkhmKopCdaKWwrI6iCAumH63anJR69913yc/Px2azsW7dOnw+HwBOp5PHHnss4sepqKggFAqRmZnZaHtmZiYlJSXN7rN9+3YWLlxIKBTio48+4sEHH+SJJ57gT3/6U4vPM2fOHBITE8M/ubm5EccohBBd2fLlyxk/fjy1tfXDghVF4fzzz2+0lK0QQgghhBBHwmhU6ZUaWV2owlIXb63ew7OfFfLU8i288Pm2mJjCFmc2YjUacPsbFzfPSbZjNR66b11c7UHTGtfespkN+IIhXP7ICqYfrdqclPrTn/7E3Llzefnll8NT6ADGjh3LunXr2jW4g2maRkZGBi+99BLDhw/n8ssv5/e//z1z585tcZ/77rsPp9MZ/tmzZ0+HxiiEEB1N13Uef/xxzj77bL744gv+93//N9ohCSGEEEKIbmxwVkJE7Vz+IEl2E33THCTZTRQUO2OitlJOko1+6Q72Ob2NZiIk2kyHHC2lAE53gK2ltVTU+ajxBNB1HY8/hMVoIM7c5qpJR5U2vzqbN2/m9NNPb7I9MTGR6urqiB8nLS0Ng8FAaWlpo+2lpaVkZWU1u092djYmkwmD4Zehg8cddxwlJSX4/X7MZnOTfSwWCxZLZEPuhBCiq6urq+Paa6/lnXfeAeDaa6/lwQcfjHJUQgghhBCiO0uIi6zgd603gMNiRFEU4q0mHBZjTNRWUlWF/CGZFDs9bC2rIyvBQkiDGm8A9RCzEHQgoMP3e6qJt5owGlSS7SaMqsrofqnkJNk65yBiVJtHSmVlZVFYWNhk+1dffUXfvn0jfhyz2czw4cNZsWJFeJumaaxYsYLRo0c3u8/YsWMpLCxsNCxuy5YtZGdnN5uQEkKI7mTr1q2MGjWKd955B5PJxAsvvMArr7yC1RrZiihCCCGEEEIcDqMhstSBJwhVLn/4dizVVuqfEc+MsXlkJ1pZtaOSFZtKWb2zkkBIO/TO1Ce2Eu0mVAW2V7jYV+Pl2Kz4LpuI6yranJS64YYbuO2221i1ahWKolBcXMz8+fO58847+c1vftOmx7rjjjt4+eWXef3119m4cSO/+c1vcLlc4dX4rr76au67775w+9/85jdUVlZy2223sWXLFpYsWcJjjz3GzTff3NbDEEIcRTRNZ0+lm00lNeypdMdMwcUDffHFF5x88sn89NNPZGdn8/nnnzNz5kypHyWEEEIIITrcwOzIV9H7aZ+z0e1Yq63kDYRIj7dwcl4KEwdmMjjLEdF+WkijxhNA06FvWhzZiVY2l9TG5HePztTm6Xv33nsvmqYxYcIE3G43p59+OhaLhTvvvJPf/va3bXqsyy+/nPLych566CFKSko44YQTWLp0abj4+e7du8PV6wFyc3NZtmwZs2bNYtiwYeTk5HDbbbdxzz33tPUwhBBHicKyWpYVlLKtvA5vMITVaKBfuoP8IZkxs0QtQP/+/bHZbAwePJiFCxeSnZ0d7ZCEEEIIIcRR4uzjsjHwAy2ve/+LoipXo9uxUltJ03SWFZRS5Q5wfM+k8MXfwrLIRkrpisLJeSlYjAbirUbqfMHwCLHclMgKxR+NFP3AKl5t4Pf7KSwspK6ujkGDBuFwRJY9jLaamhoSExNxOp0kJERWrE0IEZsKy2qZ9/VOKl1+shOt2M1G3P4g+5xeUuLMzBib16UTUz6fr1FNvC1btpCXlyfTlYVoAznvdyx5fYUQ4ugx+IEluCIY7GQ3wQ2nDwDqF+jZWlbH0JxEZo7r16Wnsu2pdPPU8i0k2U3EW3+pofXZplJ+LKo55P5Wo8IVJ/ci0V7fVw9qGjsrXPx2wgAGRlgovqvriPN+m6fvNTCbzQwaNIiRI0fGTEJKCHH0aLjSUenyMyDDQbzVhEGtL7g4IMNBpcvPxxtKu+xw2g0bNjBs2DDefvvt8LZjjjlGElJCCCGEECIqtMgGDOEO1Cdkar0BtpbVkRJn5qzBmV06IQX1Kwd6gyHsP4/o0nWdGk+ASMMOhHSq3IHw7VgZIRZtEb06F110UcQPuGjRosMORggh2ktRtYdt5XVkJ1qb1F06uOBiVxtOu3DhQq655hpcLhcPP/wwF198MUajnMyEEEIIIUT06AYgwsTUln01xNvMDM1J5KzBsVE2I85sxGo04PYHCYR0CsvqqHL7cfsCh94ZCOkQ+vkF0nWdfU4vQ3MSZfW9Q4joW05iYmJHxyGEEO3qlysdzZ8EbGYDpTXeLlVwMRQK8fvf/54///nPAJx55pm89dZbkpASQgghhBBR1zPBQuF+X0RtXf4AD10whJwkW5cfIdUgJ8lGv3QH327fT5XbjycQwmJQcVhNGBUvwQgmWDjr/NQ6AuFyIbEwQizaIvqmM2/evI6OQwgh2tWBVzoOnBPeoKsNp92/fz9Tp05l+fLlANx5553MmTNHElJCCCGEEKJLuP70/tz73oaI2m4tq+tysxEORVUVJg3O4OOfStjn9GJSoUrTCWl6/cyLCMpxl7v8pLsDMTVCLNrk244QoltquNJRUOzEYTE2msLX1YbT1tTUcPLJJ7Njxw7sdjuvvvoql19+ebTDEkIIIYQQIuyiE3MjTkp5u9BshLawmYzYzQYUoM4fwqAoKApYTSoBX+trDyrA6QPSuGp0n5gaIRZth5WUWrhwIQsWLGD37t34/f5G961bt65dAhNCiCOhqgr5QzIpdnrYWlZfW8pmNuDxh7rccNqEhAQuueQSFi1axHvvvcfQoUOjHZIQQgghhBCNmM0GEq0KTu+hRwzZzU1nKsSCWm+A/XV+EmxGUg1mqt0B/EENYwRLxKkK+IK6JKTaqM2r7/3tb39jxowZZGZm8v333zNy5EhSU1PZvn0755xzTkfEKIQQh6V/RjwzxuYxpEci1e4AOytcVP88nHbG2LyoDqcNBALs378/fPuxxx5jzZo1kpASIkZVV1dHOwQhhBCiw6U7Iptl4PQEuuwq162p8wXxBEIoClS5/fhDGmaTis1sJM7cOH1ycNrJoCp8VVjB3ip35wXcDbQ5KfX888/z0ksv8fe//x2z2czdd9/N8uXLufXWW3E6nR0RoxBCHLb+GfH8Znw/Zk06ht9OGMCsSccwc1y/qCakSktLmThxIueff354tKnRaCQpKSlqMQkhIvfnP/+Zt99+O3z7sssuIzU1lZycHH744YcoRiaEEEJ0rMwEc0TtvCHYWV7XwdG0P4fViM1soMpVP0LKZjKADkFNw6g2Tp80pNwMCiRajViNKuU1XrbF4HFHU5uTUrt372bMmDEA2Gw2amtrAbjqqqv4v//7v/aNTggh2oGqKuSm2BmYlUBuij2qw2lXrVrF8OHD+eKLLygoKGDDhsjm5Qshuo65c+eSm5sLwPLly1m+fDn//ve/Oeecc7jrrruiHJ0QQgjRccb0S4u47bKf9nVgJB0j3mIiPd5C8OcC505PAKfHj9MdoNb7S50sowoOs4FEq5GUODNWkwGTUSWg6VTU+Vt5BnGwNielsrKyqKysBKBXr158++23AOzYsQM9gmr0QghxtHr55Zc5/fTTKSoq4rjjjuO7777jxBNPjHZYQog2KikpCSelPvzwQy677DLOOuss7r77br777rsoRyeEEEJ0nLOH9Yi47bpd1R0XSAfJSbIxIMOBqiiENB1/SEOn/iL3gXWldB10dHxBDacnQLXbT50viKpAqiOy0WSiXpuTUmeeeSaLFy8GYMaMGcyaNYtJkyZx+eWXM2XKlHYPUAghYp3P5+PXv/41N954I36/n4suuohVq1Zx7LHHRjs0IcRhSE5OZs+ePQAsXbqUiRMnAvUre4ZCra/MI4QQQsSyPqkOUm2RrpcWe4NWVFXhjIEZKApoOiRaTcRbjFiN9SOhGuZbhHTw+DX8QY1gSCeg6fiDOqBgkCLnbdLm1fdeeuklNE0D4OabbyY1NZVvvvmGCy64gF//+tftHqAQQsS6mTNn8tprr6EoCo8++ij33nsviiInKyFi1UUXXcS0adMYMGAA+/fvDy/08v3339O/f/8oRyeEEEJ0HFVVGNk3hX9vKDtk21SHtRMian99UuNIsZvYr+kENQ1vQEPTdRQFDCoE69Mh9SOoDurSqwp8vqmc0/qnywp8EWpzUkpVVdQDCnxdccUVXHHFFe0alBBCdCf3338///nPf3jhhRfIz8+PdjhCiCP01FNPkZeXx549e3j88cdxOBwA7Nu3j5tuuinK0QkhhBAdK80RWRqhxOnq4Eg6hjsQIifZDkBpjQ+jqmAxGVAVhTpvgODPqwoq1CehGpJTdrMRu9nA6h372VvlpldqXPQOIoZEnJSqqKjA5XLRu3fv8LYNGzbw17/+FZfLxeTJk5k2bVqHBCmEELFE13W+//57TjrpJAAGDBjA5s2bMZlMUY5MCNEeTCYTd955Z5Pts2bNikI0QgghROfa74psqvqaXU6CQQ2jsc1Vg6IqzmwkNc5MRa0Ps0kFXScQ0lEUHYvJgC8URNNB+Tkhpfw8Zc9iMuCwGqn2BNhe4ZKkVIQiTkr99re/pUePHjzxxBMAlJWVcdppp9GjRw/69evHNddcQygU4qqrruqwYIUQoqtzu93MnDmT+fPns3TpUiZNmgQgCSkhYlxDPc1IXHDBBR0YiRBCCBFdKREW8nb5Nb7bVcnoNqzY1xXkJNlIj7ewakcl6XFmnN4gAV99IkrXdTQdDAqoikKc2QBKfeFzty9IIKhhNMi0vbaIOCn17bff8tprr4Vv//Of/yQlJYX169djNBr561//ynPPPSdJKSHEUWvnzp1MmTKF9evXYzAY2LZtWzgpJYSIbZMnT46onaIoUuxcCCFEtzYyL5U3vt1zyHY6sHpH7CWlVFVhRJ8UPvixmL3VXoKaBrqOpkHg59rtIR1Ax+UPEdL0X0q6KyESLEZ6p9qjE3wMingcXUlJCXl5eeHbn376KRdddBFGY31e64ILLmDr1q3tHqAQQsSC5cuXM3z4cNavX096ejrLly9n5syZ0Q5LCNFONE2L6EcSUkIIIbq7swdlYTVFNhqoxhvo4Gg6xrGZ8RhVhUCofnW9oNaQiPpFSIeApqNRn4DTqR8xVecPsmr7/ihEHZsiTkolJCRQXV0dvr169WpGjRoVvq0oCj6fr12DE0KIrk7Xdf785z9z9tlnU1lZyYgRI1i7di1nnHFGtEMTQnQjlZWV/M///A8JCQkkJSVx3XXXUVdX1+o+Xq83vFKyw+Hg4osvprS0NHz/Dz/8wNSpU8nNzcVms3HcccfxzDPPdPShCCGEiHFms4HT+kc2+klBP3SjLkjXdLwBDQUwNqyip9QXN2+Owi/3aRr8c+Uugg3L9IlWRTx975RTTuFvf/sbL7/8MosWLaK2tpYzzzwzfP+WLVvIzc3tkCCFEKKrWrp0Kffeey8AM2bM4Pnnn8dqjc3lb4UQkXO5XPznP/9h9+7d+P3+Rvfdeuut7f58//M//8O+fftYvnw5gUCAGTNmcOONN/Lmm2+2uM+sWbNYsmQJ77zzDomJidxyyy1cdNFFfP311wCsXbuWjIwM3njjDXJzc/nmm2+48cYbMRgM3HLLLe1+DEIIIbqPK0b24pON5YdMORUUOdE0HVWNrTpLOyvdQP3op/oi52BQFDT0JiOm1J+TVQZVQVUgqOns3u9mze5KTukbW1MXo0HRdT2i1OWPP/7IhAkTqKmpIRgMcv/99/PII4+E77/qqquIi4tj7ty5HRZse6ipqSExMRGn00lCQkK0wxFCxDhd15k5cyYnnHACM2fORFFi64QrRHfXEef977//nnPPPRe3243L5SIlJYWKigrsdjsZGRls3769XZ6nwcaNGxk0aBDfffcdI0aMAOoT4ueeey579+6lR48eTfZxOp2kp6fz5ptvcskllwCwadMmjjvuOFauXMkpp5zS7HPdfPPNbNy4kU8//TSi2KRfJYQQR6dd+11c8eI37Kvxt9ouxWZk0c1jyUtzdFJk7ePzzWXcvfAHqt0BQlrjlImm0ygZZzWpGBQFfk5YqQqEQjoP/GoQV43O68ywO1xHnPcjnr43bNgwNm7cyIIFC/jmm28aJaQArrjiCu655552CUoIIbqyZcuWUVVVBdRPXX7xxRf5zW9+IwkpIY4Ss2bN4vzzz6eqqgqbzca3337Lrl27GD58OH/961/b/flWrlxJUlJSOCEFMHHiRFRVZdWqVc3us3btWgKBABMnTgxvGzhwIL169WLlypUtPpfT6SQlJaX9ghdCCNEt5Sbb6ZcR3+L9CmAxKniCGt/trOq8wNpJn7Q4rEaVkKZjNihYTQYsJhWryYDx4C6/Xl9RKqTXj6YyGVRUtX4fcWgRJ6UA0tLSuPDCCxvVkmpw3nnn0adPn3YLTAghuhpN0/jDH/7A2WefzZVXXommyTxxIY5G69ev53e/+x2qqmIwGPD5fOTm5vL4449z//33t/vzlZSUkJGR0Wib0WgkJSWFkpKSFvcxm80kJSU12p6ZmdniPt988w1vv/02N954Y4ux+Hw+ampqGv0IIYQ4+qiqwkm9ksO1lA78UQGLScVuNqDr4A3E3iIgucl2js1KQKd+ZBToKICm6+gHJaVCen0bs6pgM6n4gxrxViMn5yV3fuAxqE1JKSGEOFpVV1dz4YUX8vDDDwPQp08fWWVLiKOUyWRCVeu7UBkZGezevRuAxMRE9uw59BLZDe69914URWn1Z9OmTR1yDAcrKCjgwgsvZPbs2Zx11lkttpszZw6JiYnhH6knKoQQR68x/VOJM9ePHDIbFUwGBbNRwW42kGAxEgzpmI0qAzJja+oe1CfdrhrdmzizEU3XCYQ0vIH6n9BB16VtRhWHxYjJqOIJaKiKwqn90+iVEhed4GNMxIXOhRDiaLVhwwamTJnC1q1bsVgsvPjii0yfPj3aYQkhouTEE0/ku+++Y8CAAYwbN46HHnqIiooK/vWvfzFkyJCIH+d3v/sd11xzTatt+vbtS1ZWFmVlZY22B4NBKisrycrKana/rKws/H4/1dXVjUZLlZaWNtnnp59+YsKECdx444088MADrcZz3333cccdd4Rv19TUSGJKCCGOUiN6pXBsVjw/7avBqNYnpRRFQdd1PIEQgZDGkJxERvSKzWnhp/ZPZ9KgDJZtKMXjD3FgLkoFtJ//DWo6IX8QRamfsjckJ4Gbzugfc8Xdo0WSUkII0Yp33nmHGTNm4HK56NWrF4sWLWL48OHRDksIEUWPPfYYtbW1ADz66KNcffXV/OY3v2HAgAG8+uqrET9Oeno66enph2w3evRoqqurWbt2bfjz59NPP0XTtGZLKgAMHz4ck8nEihUruPjiiwHYvHkzu3fvZvTo0eF2GzZs4Mwzz2T69Ok8+uijh4zFYrFgsVgiOTwhhBDdnNGoctMZ/Xn4g5+oqPXiD2r109uoLwTuMJu48IQcjMbYnKClqgozx/Vn3e5qdle6G1U31wADYDYpJNtNZCRYibeaGJmXwtlDs+jfSr0t0ZgkpYQQogUej4e77roLl8vFmWeeyVtvvRXRF0ghRPd2YMHxjIwMli5d2qHPd9xxx3H22Wdzww03MHfuXAKBALfccgtXXHFFeOW9oqIiJkyYwD//+U9GjhxJYmIi1113HXfccQcpKSkkJCTw29/+ltGjR4dX3isoKODMM88kPz+fO+64I1xrymAwyGedEEKIiEw4LpPiag/PfVZIlduPSv1CQIk2Ez2T7WwqqaWwrDZmkzQWk0pI0+prmR8kBAQCOgaDym8nDGBARjw5STYZIdVGESWl2lLEUpYDFkJ0FzabjUWLFrFgwQL+9Kc/YTRKHl8IER3z58/nlltuYcKECaiqysUXX8zf/va38P2BQIDNmzfjdrvD25566qlwW5/PR35+Ps8//3z4/oULF1JeXs4bb7zBG2+8Ed7eu3dvdu7c2SnHJYQQIrZpmk6NJ0j/DAdVLh/V7iAo4LAYMRtUdle6+f/t3XlcVOX+B/DPLMwCA4PIjggqKu4mpKLXLEUxl1xwTRPNJUvLK5VL3jSt1MrtVpZZLlmWZS5XzTQlNRdcwiVXUkwxkUWRndmf3x9e53cJVEDgsHzer9e8Xs6Z55z5nIM6D995nuf8fC4F9d11VbJYcyUtB8mZBhRRkwIAWAAkZeRDBsDfzbECk1UfMiGKqvkVJJfLi32r88q+8G9WVhb0ej0yMzNZQCOiQk6ePIlLly5h8ODBUkchojJQHp/79erVe2C/6MqVK2XyPlUB+1VERDXb9fQ8zNl2Dn/dyYPFKqDTKOGgkMNstSHHYIFCLoO/myNm92lWJYs2645cxcwt5x7a7p9dGuCf3YMrIJG0yuNzv1hf++/du9f+56tXr2L69OkYNWqUfU2C2NhYfPnll5g/f36ZhCIiksLXX3+NcePGQQiBBg0aFFo7ymYTuJGRj1yTBU4qJYfnEtVQ//znPws8N5vNOHnyJHbu3InXX39dmlBEREQSyDaYkXg7D1abDbV1avuXNmqlAionOW7nGHE9PQ/ZBrPESUvnzF8ZxWq340wyXglvzN8NSqFYRanOnTvb/zx37lwsXrwYw4YNs2975pln0KJFC6xYsYJ3pCKiKsdsNuO1116zT4Xp2bMnGjRoUKDN5dRs7DqbgoS0HBgsVmiUCjTw0CGiuVeVnSNPRKUzefLkIrcvW7YMv/32WwWnISIikk6O0YJ8sxXOGmWhUcQymQxqBwWyDRbkGC0SJXw0127nPbwRgMQ7efjrTh7q1nYq50TVT4mXwY+NjS2wwOc9oaGhOHbsWJmEIiKqKCkpKQgPD7cXpGbNmoVt27YVuIX65dRsrD50FWeTMuHq6ID67jq4OjrgbFImVh+6isup2RKlJ6LK5Omnn8bGjRuljkFERFRhdBoltCoFjGYb/r4ykBACRrMNjioFdJqquTarTutQrHYGi8DltJxyTlM9lbgo5e/vj88//7zQ9i+++AL+/v5lEoqIqCIcPXoUISEh+PXXX+Hs7IwtW7Zgzpw5AGS4np6Hi8lZuHY7FzvPJiM914SGnjo4axygkMvgrHFAQ08d0nNN+PlcCmy2hy7PR0TV3A8//AA3NzepYxAREVUYZ7UD6ro5QqmQIT3XBKPFCpsQMFqsSM81QamUw9/NEc7q4hV3KpuezbyL3TY5M78ck1RfJS5XLlmyBJGRkfjpp5/Qrl07AMCxY8dw6dIlfjtIRFXKrl27cOPGDQQHB2PLli1o3LhxoWl6VqvA9Tt5CPZ2LnJIso9eg8upObiRkV8lF28kopJ77LHHCvx/IIRAcnIy0tLSCtzdjoiIqLrzc9XiMf9aMJptsNhsuJNnRo7RAqVcDg9nNZRyOdrUrQU/V63UUUulVwtfTN34O8y2h7dNvJ2H6+l5XH+2hEpclOrZsyf++OMPfPrpp7h48SIAoE+fPpgwYQJHShFRlfKvf/0LGo0GEyZMgIuLi32aXnquCT56DRxVWvx1Jw+3c02IT8mGk1oJNyd1gWNoVQqkZBmQa6qa8+SJqOT69etX4LlcLoeHhweefPJJBAdX/zvvEBER3SOXyxDR3AtJmfm4nWNEnVpaKOQyWG0C2QYLauvU6N7Mq8oWZ1QqBdrVd8PBy+kPbXvo8i2kZpu4/mwJycTfJ35Wc7x1MVHN9ddff2HOnDn48MMPodUW/LbGZhP4dF8CziZloqGnzj4KIivfjNiEWzCYrfCt5YjQgFoFRkhkG8zIyDNjSrdGHClFVAnxc798lef15R1PiYiqjv+dbWC0WKFWKhDkqUP3ZlW/KLPopwv4aP+Vh7ZzVskxMLQunNRK5JksuJlpgJuTCqM7Blb5a3BPeXzul2q1sQMHDuCzzz7DlStXsGHDBvj5+eGrr75CvXr18I9//KNMghERlaVff/0VgwYNQmpqKlQqFZYtW1bg9RsZ+UhIy4GPXlOg6OSsUaKWkxpJGXe//ck2WODy3wUPhRC4mWlACz99lR2STETFk5WVVey2LH49Ot7xlIioagnydEb9J3XV8suEbHPxZkTkme4u9n5v/VmdWolLqTn4+VwK6rvrqsW1KA8lXuh848aNiIiIgFarxYkTJ2A0GgEAmZmZmDdvXpkHJCJ6FEIIfPjhh+jatStSU1PRqlUrvPrqq4Xa5ZosMFiscFQVrNXLZDIEeeqg0yiRmW/GnTwTLDYbsg1mXErNgZuTqkoPSSai4nF1dUWtWrWK9aBHwzueEhFVTXK5DP5ujgj2doG/m2O16R/birGeFABYAaTnmezP/77+LBWtxEWpd955B8uXL8fnn38OB4f/X0G/Y8eOOHHiRJmGIyJ6FHl5eRg5ciQmT54Mi8WCZ599FocPH0b9+vULtXVSKaFRKpBXxNpQbk4qNPbSwc1JjXyzFVdv5SIjz4wWfvpqNRyXiO5v7969+OWXX/DLL79g1apV8PT0xNSpU7F582Zs3rwZU6dOhZeXF1atWiV11CrNZhPYdTaFdzwlIqJKo01g8b9wyjWYCzzXqhQwWqxcf/YBSjx9Lz4+Hk888USh7Xq9HhkZGWWRiYjokV27dg39+vXDqVOnoFAosHDhQkyePLnQHfTu8XPVooGHDmeTMqFTKwvdWSvfbEOvFt7o08oXeWZrtRqSTEQP17lzZ/uf586di8WLF2PYsGH2bc888wxatGiBFStWICoqSoqI1cL9plIDvOMpERFJo2dTH7zhcAZ5xbgFn8FSsE2+6e76Wk6qUq2cVCOUeKSUt7c3Ll++XGj7wYMHixx9QEQklb/++gseHh7Ys2cP/vnPf963IAX8/51D3JxUuJSag2yDudA0vYjm3qhb26naDUkmopKJjY1FaGhooe2hoaE4duyYBImqj/tNpb6H3zgTEVFFU6kU6BLsWay2DvKCX2zfzDQgyFPH9WcfoMRFqXHjxmHy5Mk4evQoZDIZkpKSsG7dOrz22mt48cUXyyMjEVGJBQQEYOvWrYiLi8OTTz5ZrH2CPJ0xumMgmvvqkZFn5jQ9IiqSv78/Pv/880Lbv/jiC/j7+0uQqPp40FRqgN84ExGRNLo18ypWu+t38rn+bAmV+BN9+vTpsNls6Nq1K/Ly8vDEE09ArVbjtddew8svv1weGYmIHio7OxtjxozBiBEj8MwzzwAAwsLCSnyc6nznECIqG0uWLEFkZCR++ukntGvXDgBw7NgxXLp0CRs3bpQ4XdX2sKnUvOMpERFJQadRQiEDrA9Z0jDxdh4SUrLhqHZACz89ujfjXWMfpsRFKZlMhpkzZ+L111/H5cuXkZOTg6ZNm0Kn05VHPiKih/rjjz/Qv39/nD9/Hnv37sXVq1fh5ORU6uPdu3MIEVFRevbsiT/++AOffvopLl68CADo06cPJkyYwJFSj+jeVOqkzHxcSr27tpRWpUC+yYqbmQZ+40xERJJQyORQK+UPXVfKCqB13Vro1dKXX2wXU4mLUs8//zz+/e9/w9nZGU2bNrVvz83Nxcsvv8y7zhBRhdq2bRtGjBiBrKws+Pj4YOPGjY9UkCIiKg5/f3/MmzdP6hjV0r2p1LvOpiAhLQcpWQaolQp+40xERJKp7+4ErUrx0KKUTQDZRgu/4C6BEhelvvzySyxYsADOzgU7BPn5+Vi7di2LUkRUIWw2G+bOnYs5c+YAADp27IgNGzbAx8dH4mREVB39/vvvaN68OeRyOX7//fcHtm3ZsmUFpaq+OJWaiKhqsNlEjfi/uk4tR9Sr7YTbuRkPbeus5rqHJVHsq5WVlQUhBIQQyM7Ohkajsb9mtVqxY8cOeHoWb0V6IqJHYTabMWDAAGzfvh0AMGnSJCxatAgqlUriZERUXbVu3RrJycnw9PRE69atIZPJIEThhSVkMhmsVqsECasfTqUmIqrcLqdm20e1GixWaJQKNPDQIaJ59RvVKpfLMPjxOvgtMeOhbd2cHMo/UDVS7KKUq6srZDIZZDIZGjVqVOh1mUxmH7FARFSeHBwcEBAQAI1Gg+XLlyMqKkrqSERUzf3555/w8PCw/5mIiKgmu5yajdWHriI91wQfvQaOKi3yTBacTcpEUmZ+tbxzdXM/V7g5OiA9z3zfNnIAhy/fxsCQutVyxFh5KHZRau/evRBCoEuXLti4cSPc3Nzsr6lUKgQEBMDX17dcQhIRAXdHSDk43P3mYfHixZgwYQKaN28ucSoiqgkCAgKK/DMREVFNY7MJ7DqbgvRcExp66ux3SnXWOECnVuJSag5+PpeC+u66alWYcdY4INjHBUcSbqOolaWUMsBBKcfZpGxcv5OHgNpc57Y4il2U6ty5M4C73w7WrVu3wC16iYjKk9VqxcyZM3H8+HHs2rULSqUSKpWKBSkiksSXX34Jd3d39OrVCwAwdepUrFixAk2bNsW3337LohUREVVrNzLykZB29w6pf68LyGQy+Og1uJyagxsZ+dVqGrafqxY+eg0cFDLIZYDFJmATgEwGOMhlcFAq7i6GbjLjz1u5LEoVk7ykO/zyyy/44YcfCm3fsGEDvvzyyzIJRUR0z+3bt9GjRw+89957+OWXX7Bz506pIxFRDTdv3jxotVoAQGxsLD7++GO8//77cHd3x5QpUyROR0REVL5yTRYYLFY4qooe46JVKWC0WJFrslRwsvIll8sQElALCoUcCoUcSoUcchkgE4DZJmC22qBWyjiAp4RKXJSaP38+3N3dC2339PTkrZGJqEydPHkSISEh2LNnDxwdHbF+/Xr07t1b6lhEVMNdv34dQUFBAIAtW7Zg4MCBGD9+PObPn48DBw5InI6IiKh8OamU0CgVyLtP0SnfZIVaqYDTfYpWVdk/gtxR21EFk8UGs9UGqw2wCMBmA0wWG25mGKCQyVDfnaOkiqvERanExETUq1ev0PaAgAAkJiaWSSgioq+++godOnTAtWvX0KBBAxw5cgRDhgyROhYREXQ6HW7fvg0A+Pnnn9GtWzcAgEajQX5+vpTRiIiIyp2fqxYNPHS4mWkodCdaIQRuZhoQ5KmDn6tWooTlx1evhaeLGoAABADZ3el7kAE2AZhtQJbRgjwj78RbXCUuSnl6euL3338vtP306dOoXbt2mYQiopptwYIFGDlyJAwGA3r27Injx4+jRYsWUsciIgIAdOvWDWPHjsXYsWPxxx9/oGfPngCAc+fOITAwUNpwRERE5UwulyGiuRfcnFS4lJqDbIMZFpsN2QYzLqXmwM1Jhe7NvKrVIuf33MwywNXRAWqlEjYAQuBuceq/a0sp5YDBbMXKw1dgs4mHHI2AUhSlhg0bhldeeQV79+6F1WqF1WrFL7/8gsmTJ2Po0KHlkZGIapg+ffpAp9PhzTffxLZt21CrVi2pIxER2S1btgxhYWFIS0vDxo0b7V/KxcXFYdiwYRKnIyIiKn9Bns4Y3TEQzX31yMgz4+qtXGTkmdHCT4/RHQMR5OksdcRykWuywGKzQSGXwUEhg4McUMhlUCpk0DrI4ap1gBzAiat3cP1OntRxq4QST/J8++23cfXqVXTt2hVK5d3dbTYbRo4cyTWliKjUbt26ZV+vrlmzZrh8+TK8vLwkTkVEVJirqys+/vjjQtvnzJkjQRoiIiJpBHk6o/6TOtzIyEeuyQInlRJ+rtpqOULqHieVEmYLYLba4KRSwmixwWKzAeLu9L18y92CVY7RyjvwFVOJR0qpVCp89913uHjxItatW4dNmzYhISEBq1atgkqlKo+MRFTNrVixAoGBgQUWCGZBiogqswMHDmDEiBHo0KEDbty4AeDuWngHDx6UOBkREVHFkctl8HdzRLC3C/zdHKt1QQq4u56Wr6sWZqsNeSYLrDYBpVwOB6UccpkMJvPdBdD/vtYW3V+Ji1L3NGrUCIMGDULv3r0REBBQlpmIqIYwGo0YN24cXnjhBeTm5uKbb76ROhIR0UNt3LgRERER0Gq1OHHiBIxGIwAgMzOTo8aJiIiqMblcht6tvO8WoCwC8v8udC4E7haoFDIIAJABgW6OUsetEoo1fS86Ohpvv/02nJycEB0d/cC2ixcvLpNgRFS9/fXXX4iMjMSxY8cgk8kwb948TJs2TepYREQP9c4772D58uUYOXIk1q9fb9/esWNHvPPOOxImIyIiovJWz10HD2cVUrKMMFltUAgZZLK760rJAAjI4KxxgKyajxorK8UqSp08eRJms9n+5/uRyXjRiejh9u/fj8GDByM1NRW1atXC+vXr0b17d6ljEREVS3x8PJ544olC2/V6PTIyMio+EBEREVWYfLMV9dx1UCsVuJVjgs1mA2SAXCaDXC6Dm5MKvnot8s1WqaNWCcUqSu3du7fIPxMRldSJEyfQtWtXWK1WtGrVCps3b0a9evWkjkVEVGze3t64fPkyAgMDC2w/ePAg6tevL00oIiIiqhBOKiXcdWq461S4mWlAapYRZpsNDnI5PF3U8NFrAMjgpCrxfeVqJF4lIqpQjz32GAYOHAilUokVK1bA0ZFzrYmoahk3bhwmT56MVatWQSaTISkpCbGxsXj11Vcxa9YsqeMRERFROfJz1aKBhw5nkzIRGlALOUYrTFYbVAo5dGoFLqflooWfHn6uWqmjVgnFKkoNGDCg2AfctGlTqcMQUfV09epV1K5dG87OzpDJZFi7di0cHBw45ZeIqqTp06fDZrOha9euyMvLwxNPPAG1Wo3XX38dY8eOlToeERERlSO5XIaI5l5IyszH5bRc+Og1cHV0QL7JistpuXBzUqF7M69qfyfCslKsu+/p9Xr7w8XFBTExMfjtt9/sr8fFxSEmJgZ6vb7cghJR1bR7926EhITg+eeft98aVaVSsSBFRFWWTCbDzJkzkZ6ejrNnz+LIkSNIS0uDXq/ndGQiIqIaIMjTGaM7BqK5rx4ZeWZcvZWLjDwzWvjpMbpjIII8naWOWGUUa6TU6tWr7X+eNm0aBg8ejOXLl0OhUAAArFYrXnrpJbi4uJRPSiKqcoQQeP/99/HGG2/AZrMhMTERWVlZLF4TUZVlNBrx1ltvYffu3faRUf369cPq1avRv39/KBQKTJkyReqYREREVAGCPJ1R/0kdbmTkI9tgRo7RAp1GCbVSAZtNcKRUMcnEvaELxeTh4YGDBw+icePGBbbHx8ejQ4cOuH37dpkGLGv3finOzMxkEY2onGRnZ+P555/HDz/8AAAYO3YsPvroI2g0GomTEVFNU5af+9OmTcNnn32G8PBwHD58GGlpaRg9ejSOHDmCN954A4MGDbJ/YVdTsF9FREQ13eXUbOw6m4KEtBwYLFZolAo08NAhorlXtRsxVR6f+yVe6NxiseDixYuFilIXL168eytEIqrR/vjjD/Tv3x/nz5+Hg4MDPv74Y4wfP17qWEREj2zDhg1Yu3YtnnnmGZw9exYtW7aExWLB6dOnOSWZiIioBrqcmo3Vh64iPdcEH70Gjiot8kwWnE3KRFJmPqfyFUOJi1KjR4/GmDFjkJCQgLZt2wIAjh49igULFmD06NFlHpCIqg6bzYZ+/frhwoUL8PX1xQ8//ICwsDCpYxERlYm//voLISEhAIDmzZtDrVZjypQpLEgRERHVQDabwK6zKUjPNaGhpw4AkG2wwGS1wctZjeQsI34+l4L67jpO5XuAEhelFi5cCG9vbyxatAg3b94EAPj4+OD111/Hq6++WuYBiajqkMvl+OKLL/Dmm29i3bp18Pb2ljoSEVGZsVqtUKlU9udKpRI6nU7CRERERCSVGxn5SEjLgY9egzt5JiSk5iI9zwSLzQalXA4nlQInEm24kZEPfzdHqeNWWiUuSsnlckydOhVTp05FVlYWADzyXMJly5bhgw8+QHJyMlq1aoWPPvrIPgrrQdavX49hw4ahb9++2LJlyyNlIKLSycjIwKlTp/Dkk08CADp06IA9e/Zw5AARVTtCCIwaNQpqtRoAYDAYMGHCBDg5ORVot2nTJiniERERUQXKNVlgsFhhMMtx5kYm8k1W6DRKOCiUMFttyMgzIS3HiAs3s1iUegB5aXayWCzYs2cPvv32W/svnklJScjJySnxsb777jtER0dj9uzZOHHiBFq1aoWIiAikpqY+cL+rV6/itddeQ6dOnUpzCkRUBs6ePYvHH38cvXr1wu+//27fzoIUEVVHUVFR8PT0hF6vh16vx4gRI+Dr62t/fu9BRERE1Z+TSgm1Qo745Gzkm6xwc1JBrVRALpNBrVRAp3GAxSoQd+0ObLYS3V+uRinxSKlr166hR48eSExMhNFoRLdu3eDs7Iz33nsPRqMRy5cvL9HxFi9ejHHjxtnXo1q+fDl+/PFHrFq1CtOnTy9yH6vViuHDh2POnDk4cOAAMjIySnoaRPSIvv/+ezz//PPIzc1F3bp1YbVapY5ERFSuVq9eLXUEIiKiSs9mE7iRkY9ckwVOKiX8XLXVck0lP1ctPJzVOPpnOjycVQW+mBdCINdogY+rBqlZBk7he4ASj5SaPHkyQkNDcefOHWi1Wvv2/v37IyYmpkTHMplMiIuLQ3h4+P8HkssRHh6O2NjY++43d+5ceHp6YsyYMQ99D6PRiKysrAIPIio9i8WCadOmYciQIcjNzUXXrl0RFxeHxx57TOpoREREREQkocup2fh0XwKW7P4DH8ZcwpLdf+DTfQm4nJotdbQyJ5fLEFrPDUqFDDkGK4wWK2xCwGixIj3XBK1KiUZezjBZbcg1WaSOW2mVeKTUgQMHcPjw4QILfQJAYGAgbty4UaJj3bp1C1arFV5eXgW2e3l54eLFi0Xuc/DgQaxcuRKnTp0q1nvMnz8fc+bMKVEuIirarVu3MGzYMOzZswcA8Nprr2H+/PlQKkv8XwkREREREVUjl1OzsfrQVaTnmuCj18BRpUWeyYKzSZlIyszH6I6BCPJ0ljpmmWri7YJGXs64k2tCrsmKHKMFSrkcni4aNPDQwUEhg9Fsg5OKvy/dT4mvjM1mK3Kazl9//QVn5/L9C5adnY3nnnsOn3/+Odzd3Yu1z4wZMxAdHW1/npWVBX9///KKSFStrVixAnv27IGjoyNWrVqFIUOGSB2JiIiIiIgkZrMJ7DqbgvRcExp66uxT2Zw1DtCplbiUmoOfz6WgvruuWk3l83PV4jH/WjhzIwMtXDQw2wRUCjmcNXdLLZdSc9DCTw8/V+1DjlRzlXj6Xvfu3bF06VL7c5lMhpycHMyePRs9e/Ys0bHc3d2hUCiQkpJSYHtKSkqRt5JPSEjA1atX0adPHyiVSiiVSqxduxZbt26FUqlEQkJCoX3UajVcXFwKPIiodKZOnYpx48bhyJEjLEgREVWg9PR0DB8+HC4uLnB1dcWYMWMeeoMZg8GAiRMnonbt2tDpdIiMjCzU57rn9u3bqFOnDmQyGdfqrARsNoHr6Xm4mJyF6+l5XCCXiCq9Gxn5SEjLgY9eU+imRzKZDD56DS6n5uBGRr5ECcuHXC5DRHMv1NapkZJthFoph6NagRyjBZdSc+DmpEL3Zl7VqhBX1kpclFq4cCEOHTqEpk2bwmAw4Nlnn7VP3XvvvfdKdCyVSoWQkJACa1HZbDbExMQgLCysUPvg4GCcOXMGp06dsj+eeeYZPPXUUzh16hRHQBGVMbPZjMWLF8NoNAIAlEolVqxYgRYtWkicjIioZhk+fDjOnTuH3bt3Y/v27fj1118xfvz4B+4zZcoUbNu2DRs2bMD+/fuRlJSEAQMGFNl2zJgxaNmyZXlEpxKqSeuxEFH1kWuywGCxwvE+09S0KgWMFmu1XFspyNMZozsGormvHhl5Zly9lYuMPDNa+Omr5ZTFslbi6Xv+/v44ffo0vvvuO5w+fRo5OTkYM2YMhg8fXmDh8+KKjo5GVFQUQkND0bZtWyxduhS5ubn2u/GNHDkSfn5+mD9/PjQaDZo3b15gf1dXVwAotJ2IHk1ycjIGDx6MAwcO4NKlS/j000+ljkREVCNduHABO3fuxPHjxxEaGgoA+Oijj9CzZ08sXLgQvr6+hfbJzMzEypUr8c0336BLly4A7t49sEmTJjhy5Ajat29vb/vpp58iIyMDs2bNwk8//VQxJ0VFqonrsRBR9eCkUkKjVCDPZIGzxqHQ6/kmK9RKRbVdWynI0xn1n9TViLsOlrUS/Y0wm80IDg7G9u3bMXz4cAwfPvyRAwwZMgRpaWmYNWsWkpOT0bp1a+zcudO++HliYiLk8hIP6CKiR3DkyBFERkYiKSkJLi4uePrpp6WORERUY8XGxsLV1dVekAKA8PBwyOVyHD16FP379y+0T1xcHMxmc4E7HAcHB6Nu3bqIjY21F6XOnz+PuXPn4ujRo7hy5Ur5nwzdV01dj4WIqgc/Vy0aeOhwNikTOrWywBQ+IQRuZhqq/dpKcrkM/m6OUseockpUlHJwcIDBYCjzEJMmTcKkSZOKfG3fvn0P3HfNmjVlnoeoJluxYgUmTZoEs9mMJk2aYPPmzWjcuLHUsYiIaqzk5GR4enoW2KZUKuHm5obk5OT77qNSqewjyu/x8vKy72M0GjFs2DB88MEHqFu3brGKUkaj0T6lG7h7AxkqGyVZj4W/9BBRZXNvbaWkzHxcSr37f5lWpUC+yYqbmQaurUT3VeIhSBMnTsR7770Hi6X6zQUlqsmMRiPGjRuHF154AWazGZGRkTh69CgLUkRE5WT69OmQyWQPfFy8eLHc3n/GjBlo0qQJRowYUex95s+fD71eb39wPc+yU5PXYyGi6oFrK1FplHhC5/HjxxETE4Off/4ZLVq0gJOTU4HXN23aVGbhiKji3LhxAxs2bIBMJsO8efMwbdq0Qt/UEhFR2Xn11VcxatSoB7apX78+vL29kZqaWmC7xWJBenp6kXcrBgBvb2+YTCZkZGQUGC31v3c4/uWXX3DmzBn88MMPAO5OrwDu3h155syZmDNnTqHjzpgxA9HR0fbnWVlZLEyVkZq+HgsRVQ9cW4lKqsSfaq6uroiMjCyPLEQkofr162P9+vWQy+Xo3r271HGIiKo9Dw8PeHh4PLRdWFgYMjIyEBcXh5CQEAB3C0o2mw3t2rUrcp+QkBA4ODggJibG3m+Lj49HYmKi/Q7HGzduRH7+/9+a+/jx43j++edx4MABNGjQoMjjqtVqqNXqEp0nFQ/XYyGi6oJrK1FJlLgotXr16vLIQUQVTAiBjz76CE2aNEG3bt0AAD169JA4FRER/V2TJk3Qo0cPjBs3DsuXL4fZbMakSZMwdOhQ+533bty4ga5du2Lt2rVo27Yt9Ho9xowZg+joaLi5ucHFxQUvv/wywsLC7Iuc/73wdOvWLfv7/X0tKip/XI+FiIhqomIXpWw2Gz744ANs3boVJpMJXbt2xezZs6HV8tsaoqomLy8P48ePx7p16+Dm5obz58/b73hJRESVz7p16zBp0iR07doVcrkckZGR+PDDD+2vm81mxMfHIy8vz75tyZIl9rZGoxERERH45JNPpIhPxXRvPZZdZ1OQkJaDlCwD1EoFWvjp0b2ZF9djISKiakcm7i0g8BBvv/023nrrLYSHh0Or1WLXrl0YNmwYVq1aVd4Zy1RWVhb0ej0yMzPh4uIidRyiCvfnn39iwIABOHXqFBQKBRYuXIjJkydz/Sgiqpb4uV++eH3Lh80muB4LERFVOuXxuV/skVJr167FJ598ghdeeAEAsGfPHvTq1QtffPEF5PIS38SPiCTw888/Y9iwYUhPT4eHhwe+//57PPnkk1LHIiIiov/B9ViIiKimKHY1KTExET179rQ/Dw8Ph0wmQ1JSUrkEI6KyI4TAe++9h6effhrp6elo27YtTpw4wYIUERERERERSabYRSmLxQKNRlNgm4ODA8xmc5mHIqKyFx8fD5vNhrFjx2L//v2oU6eO1JGIiIiIiIioBiv29D0hBEaNGlXgNsAGgwETJkyAk5OTfdumTZvKNiERPTKZTIZPPvkEPXr0wODBg6WOQ0RERERERFT8olRUVFShbSNGjCjTMERUdrZu3Ypvv/0WX3/9NRQKBTQaDQtSREREREREVGkUuyi1evXq8sxBRGXEZrNhzpw5mDt3LgDgqaeewvjx4yVORURERERERFRQsYtSRFT5ZWRkYPjw4dixYwcAYNKkSRg1apS0oYiIiIiIiIiKwKIUUTVx9uxZ9O/fH5cvX4ZGo8Fnn32GkSNHSh2LiIiIiIiIqEgsShFVA9u2bcOwYcOQm5uLgIAAbNq0CW3atJE6FhEREREREdF9yaUOQESPzs/PD1arFeHh4fjtt99YkCIiIiIiIqJKjyOliKooq9UKhUIBAGjTpg0OHDiA1q1bQ6nkP2siIiIiIiKq/DhSiqgKOnHiBJo1a4bffvvNvi00NJQFKSIiIiIiIqoyWJQiqmLWrl2Ljh07Ij4+HlOnTpU6DhEREREREVGpsChFVEWYzWa8/PLLiIqKgsFgQM+ePbFp0yapYxERERERERGVCotSRFVAcnIyunTpgo8//hgAMGvWLGzbtg2urq7SBiMiIiIiIiIqJS5AQ1TJJSYmIiwsDElJSXB2dsZXX32Fvn37Sh2LiIiIiIiI6JGwKEVUydWpUwchISHQ6/XYvHkzGjduLHUkIiIiIiIiokfGohRRJWQ0GmGz2aDVaiGXy/HVV19BLpfD2dlZ6mhEREREREREZYJrShFVMtevX8cTTzyBF154AUIIAIBer2dBioiIiIiIiKoVjpQiqkT279+PQYMGIS0tDZcuXcL169dRt25dqWMRERERERERlTmOlCKqBIQQWLp0Kbp27Yq0tDS0atUKcXFxLEgRERERERFRtcWiFJHE8vLyMGLECEyZMgVWqxXDhw/H4cOHUa9ePamjEREREREREZUbTt8jkljfvn2xZ88eKBQKLFq0CK+88gpkMpnUsYiIiIiIiIjKFYtSRBKbMWMGzp07h2+//RadO3eWOg4RERERERFRhWBRiqiCCSFw6dIlNGrUCADQpUsXJCQkQKvVSpyMiIiIiIiIqOJwTSmiCpSdnY2BAwciNDQU8fHx9u0sSBEREREREVFNw6IUUQWJj49Hu3btsGnTJhgMBpw+fVrqSERERERERESSYVGKqAJs3boVbdu2xYULF+Dr64tff/0VgwcPljoWERERERERkWRYlCIqRzabDbNnz0bfvn2RlZWFTp06IS4uDu3bt5c6GhEREREREZGkWJQiKkeff/455s6dCwB4+eWXERMTA29vb4lTEREREREREUmPd98jKkejR4/Gxo0b8dxzz+G5556TOg4RERERERFRpcGiFFEZ27NnD5588kkolUqoVCrs2rULMplM6lhERERERERElQqn7xGVEYvFgqlTp6Jbt26YNm2afTsLUkRERERERESFcaQUURm4desWhg4dipiYGACAQqGAEIIFKSIiIiIiIqL7YFGK6BGdOHECAwYMwLVr1+Dk5IRVq1Zh8ODBUsciIiIiIiIiqtQ4fY/oEaxduxYdO3bEtWvXEBQUhCNHjrAgRURERERERFQMLEoRlVJycjJeeuklGAwG9OrVC8ePH0fz5s2ljkVERERERERUJXD6HlEpeXt7Y82aNTh79ixmzZoFuZw1XiIiIiIiIqLiYlGKqARiY2Nhs9nQsWNHAMDAgQMxcOBAiVMRERERERERVT0c2kFUDEIIfPbZZ+jcuTMGDBiAGzduSB2JiIiIiIiIqErjSCmihzAYDJg0aRJWrlwJAOjUqRNcXFwkTkVERERERERUtbEoRfQA169fR2RkJI4fPw65XI53330X06ZNg0wmkzoaERERERERUZXGohTRfezbtw+DBw9GWloa3Nzc8O2336J79+5SxyIiIiIiIiKqFliUIrqPL774AmlpaWjdujU2bdqEevXqSR2JiIiIiIiIqNpgUYroPj777DMEBARg5syZcHR0lDoOERERERERUbXCu+8R/deff/6JN954A0IIAICTkxPeffddFqSIiIiIiIiIygFHShEB+PnnnzF06FDcuXMHHh4emDJlitSRiIiIiIiIiKo1jpSiGk0IgQULFqBHjx64c+cO2rZti0GDBkkdi4iIqID09HQMHz4cLi4ucHV1xZgxY5CTk/PAfQwGAyZOnIjatWtDp9MhMjISKSkphdqtWbMGLVu2hEajgaenJyZOnFhep0FERERUAItSVGNlZ2dj0KBBmDFjBoQQGDt2LPbv3486depIHY2IiKiA4cOH49y5c9i9eze2b9+OX3/9FePHj3/gPlOmTMG2bduwYcMG7N+/H0lJSRgwYECBNosXL8bMmTMxffp0nDt3Dnv27EFERER5ngoRERGRnUzcW0CnhsjKyoJer0dmZiZcXFykjkMS+eOPP9C/f3+cP38eDg4O+Pjjjx/auScioqqnOnzuX7hwAU2bNsXx48cRGhoKANi5cyd69uyJv/76C76+voX2yczMhIeHB7755hsMHDgQAHDx4kU0adIEsbGxaN++Pe7cuQM/Pz9s27YNXbt2LVW26nB9iYiIqHjK43OfI6WoRrp9+zYuXboEX1/fYn3bTEREJJXY2Fi4urraC1IAEB4eDrlcjqNHjxa5T1xcHMxmM8LDw+3bgoODUbduXcTGxgIAdu/eDZvNhhs3bqBJkyaoU6cOBg8ejOvXr983i9FoRFZWVoEHERERUWmxKEU1UlhYGDZs2IC4uDi0b99e6jhERET3lZycDE9PzwLblEol3NzckJycfN99VCoVXF1dC2z38vKy73PlyhXYbDbMmzcPS5cuxQ8//ID09HR069YNJpOpyOPOnz8fer3e/vD393/0EyQiIqIai0UpqhEyMjIwePBg/P777/Ztffv2hbe3t4SpiIioJps+fTpkMtkDHxcvXiy397fZbDCbzfjwww8RERGB9u3b49tvv8WlS5ewd+/eIveZMWMGMjMz7Y8HjaoiIiIiehil1AGIytvZs2fRr18/JCQk4Pz58/j9998hl7MeS0RE0nr11VcxatSoB7apX78+vL29kZqaWmC7xWJBenr6fb9c8fb2hslkQkZGRoHRUikpKfZ9fHx8AABNmza1v+7h4QF3d3ckJiYWeVy1Wg21Wv2wUyMiIiIqFhalqFr7/vvvMXr0aOTl5SEgIABr165lQYqIiCoFDw8PeHh4PLRdWFgYMjIyEBcXh5CQEADAL7/8ApvNhnbt2hW5T0hICBwcHBATE4PIyEgAQHx8PBITExEWFgYA6Nixo337vTvPpqen49atWwgICHjk8yMiIiJ6GP52TtWSxWLB66+/jiFDhiAvLw/h4eH47bff0KZNG6mjERERlUiTJk3Qo0cPjBs3DseOHcOhQ4cwadIkDB061H7nvRs3biA4OBjHjh0DAOj1eowZMwbR0dHYu3cv4uLiMHr0aISFhdnXUmzUqBH69u2LyZMn4/Dhwzh79iyioqIQHByMp556SrLzJSIiopqDRSmqdrKystCjRw8sXLgQADB16lT89NNPcHd3lzgZERFR6axbtw7BwcHo2rUrevbsiX/84x9YsWKF/XWz2Yz4+Hjk5eXZty1ZsgS9e/dGZGQknnjiCXh7e2PTpk0Fjrt27Vq0a9cOvXr1QufOneHg4ICdO3fCwcGhws6NiIiIai6ZEEJIHaIiZWVlQa/XIzMzEy4uLlLHoXJgtVrRo0cPxMbGYvXq1Rg0aJDUkYiISCL83C9fvL5EREQ1R3l87nNNKao2bDYb5HI5FAoF1q9fj+TkZDRr1kzqWERERERERERUBBalqMozmUyIjo6GxWLB8uXLAQC1a9dG7dq1JU5GRERERERERPfDohRVacnJyRg0aBAOHjwIABg/fjwXMyciIiIiIiKqArjQOVVZsbGxaNOmDQ4ePAgXFxds3bqVBSkiIiIiIiKiKqJSFKWWLVuGwMBAaDQatGvXzn4746J8/vnn6NSpE2rVqoVatWohPDz8ge2p+hFC4LPPPkPnzp1x8+ZNNG3aFMePH0efPn2kjkZERERERERExSR5Ueq7775DdHQ0Zs+ejRMnTqBVq1aIiIhAampqke337duHYcOGYe/evYiNjYW/vz+6d++OGzduVHByksprr72GCRMmwGw2Y+DAgThy5AgaNWokdSwiIiIiIiIiKgHJi1KLFy/GuHHjMHr0aDRt2hTLly+Ho6MjVq1aVWT7devW4aWXXkLr1q0RHByML774AjabDTExMRWcnKTSpUsXKJVKLFiwAN9//z2cnZ2ljkREREREREREJSTpQucmkwlxcXGYMWOGfZtcLkd4eDhiY2OLdYy8vDyYzWa4ubmVV0yqBLKzs+3Fp169euHSpUsIDAyUNhQRERERERERlZqkI6Vu3boFq9UKLy+vAtu9vLyQnJxcrGNMmzYNvr6+CA8PL/J1o9GIrKysAg+qOoQQWLJkCYKCgvDnn3/at7MgRURERERERFS1ST5971EsWLAA69evx+bNm6HRaIpsM3/+fOj1evvD39+/glNSaeXl5WHEiBGIjo5GamoqvvzyyxLtb7MJXE/Pw8XkLFxPz4PNJsopKRERERERERGVlKTT99zd3aFQKJCSklJge0pKCry9vR+478KFC7FgwQLs2bMHLVu2vG+7GTNmIDo62v48KyuLhakq4MqVKxgwYABOnz4NhUKBRYsW4ZVXXin2/pdTs7HrbAoS0nJgsFihUSrQwEOHiOZeCPLkGlREREREREREUpN0pJRKpUJISEiBRcrvLVoeFhZ23/3ef/99vP3229i5cydCQ0Mf+B5qtRouLi4FHlS57dq1C6GhoTh9+jQ8PT0RExODyZMnQyaTFWv/y6nZWH3oKs4mZcLV0QH13XVwdXTA2aRMrD50FZdTs8v5DIiIiIiIiIjoYSQdKQUA0dHRiIqKQmhoKNq2bYulS5ciNzcXo0ePBgCMHDkSfn5+mD9/PgDgvffew6xZs/DNN98gMDDQvvaUTqeDTqeT7DyobOzYsQO9e/eGEAJt27bFxo0bUadOnWLvb7MJ7DqbgvRcExp66uyFLGeNA3RqJS6l5uDncymo766DXF68IhcRERERERERlT3Ji1JDhgxBWloaZs2aheTkZLRu3Ro7d+60L36emJgIufz/B3R9+umnMJlMGDhwYIHjzJ49G2+99VZFRqdy0KVLF4SEhKB169b4+OOPoVarS7T/jYx8JKTlwEevKTSySiaTwUevweXUHNzIyIe/m2NZRiciIiIiIiKiEpC8KAUAkyZNwqRJk4p8bd++fQWeX716tfwDUYW6evUq/P39oVAooNFosHfv3lKPess1WWCwWOGo0hb5ulalQEqWAbkmy6NEJiIiIiIiIqJHVKXvvkdV39atW9GqVasCo9weZRqmk0oJjVKBvPsUnfJNVqiVCjipKkU9loiIiIiIiKjGYlGKJGGz2TBr1iz07dsXWVlZ+PXXX2E2mx/5uH6uWjTw0OFmpgFCiAKvCSFwM9OAIE8d/FyLHklFRERERERERBWDRSmqcHfu3EGfPn3w9ttvAwBeeeUV7NmzBw4ODo98bLlchojmXnBzUuFSag6yDWZYbDZkG8y4lJoDNycVujfz4iLnRERERERERBLjHCaqUGfOnEH//v2RkJAAjUaDzz//HCNGjCjT9wjydMbojoHYdTYFCWk5SMkyQK1UoIWfHt2beSHI07lM34+IiIiIiIiISo5FKaowubm56NKlC27duoWAgABs2rQJbdq0KZf3CvJ0Rv0ndbiRkY9ckwVOKiX8XLUcIUVERERERERUSbAoRRXGyckJixcvxldffYVvv/0WtWvXLtf3k8tl8HdzLNf3ICIiIiIiIqLS4ZpSVK5u3bqFM2fO2J8/99xz2LlzZ7kXpIiIiIiIiIiocuNIKXogm02UegrciRMnMGDAAFitVsTFxcHT0xMAIJezFkpERERERERU07EoRfd1OTXbvli4wWKFRqlAAw8dIpo/fLHwtWvX4oUXXoDBYEBQUBAyMjLsRSkiIiIiIiIiIg5ZoSJdTs3G6kNXcTYpE66ODqjvroOrowPOJmVi9aGruJyaXeR+JpMJkyZNQlRUFAwGA3r37o3jx4+jUaNGFXwGRERERERERFSZcaQUFWKzCew6m4L0XBMaeuogk92druescYBOrcSl1Bz8fC4F9d11BabyJScnY9CgQTh48CAAYPbs2Zg1axbkcvkjTQMkIiIiIiIiouqHRSkq5EZGPhLScuCj19gLUvfIZDL46DW4nJqDGxn5Be5u9+abb+LgwYNwcXHB119/jT59+gC4O+pq55lknLmRiVyzBU4OSrTw06NHC++HTgMkIiIiIiIiouqJRSkqJNdkgcFihaNKW+TrWpUCKVkG5JosBbYvXLgQt27dwnvvvWefrnc5NRtL91xCfHIWjBYbhABkMuDKrRxcTMnGP8MbsjBFREREREREVANxTSkqxEmlhEapQN7fik735JusUCsVUNgsWLlyJYQQAAC9Xo+NGzdB614HF5OzkHg7F+tir+G3q3dwO9eErHwLsg1mZOVbcDvXhN+u3sG3RxNhs4mKPD0iIiIiIiIiqgQ4UooK8XPVooGHDmeTMqFTKwtM4RNC4GamAXUc8jC8Xw8cP34cubm5eOWVV+5O0zt7d5revYLW2RtZMJqtcFDI4KBUQAYZBACzxYociw37/kjDyA55CKjtJNHZEhEREREREZEUWJSiQuRyGSKaeyEpMx+XUu+uLaVVKZBvsuJmpgHZf57G4n9PQ/rtW3Bzc0NwcPD/TNPLhslihU0IGMw2ZOSZIZcBkMlhMlrs0/eUchlgA1KzDLhyK4dFKSIiIiIiIqIahkUpKlKQpzNGdwzErrMpSEjLQUqWASqFHGmHN2HLivdgtVrRunVrbNq0CQEBgXjnx/P47Wo6jGbr3aoTBIxmGwQAqwCMZhvUDnIoZLg7UsomIP774u1sk7QnS0REREREREQVjkUpuq8gT2fUf1KHGxn5uJWRhbnTJmPLD98DAEaMGIHPPvsMjo6OSLydi/1/pCHHaIGDXAaVUg6FTAabzQyY7x7L+t9lo2QyGe5OBhQwWmyQy2Wo5eQgxekRERERERERkYS40Dk9kFwug7+bI/JuJmDb5o1QKpX48MMPsXbtWjg6OgIAEtJykJplgByAVqWEUi6DTAY4KAr+9bLaBAQAmwAsNgHIAJVSDic1a6NERERERERENQ2rAVQsHTt2xCeffILg4GA88cQTBV67lWOCxSbgqFLgf9ZEh0wmg0L2/6OkrDYBGWwQkEEmk0GllMFdp4aLhiOliIiIiIiIiGoajpSiIgkh8MEHHyA+Pt6+bfz48YUKUgBQW6eCUi777zpRwr5dfm9B8/9yUMihUiqgVsrh6KCAp7MGQZ46OLMoRURERERERFTjsChFhWRnZyMyMhJTp05Fv379kJ+f/8D2DTx08HTWwGYD8s02WP5bnBIAZPK7o6XUChk8dGq4OjrAQ6dGYy8d/N0c0aZuLfi5aivmxIiIiIiIiIio0uD0PSogPj4e/fr1w8WLF6FSqRAdHQ2t9sFFI/9ajujcyAM7zt6E0WKDyWKzv6ZWyCED4OmsRtt6blDI5bDaBLINFtTWqdC9mRfk/zOaioiIiIiIiIhqBhalyO4///kPnnvuOWRnZ8PPzw8//PAD2rdv/9D95HIZnm1fF6k5RsQnZ8FoscEm7k7fUyvl8HXVooGHDhl5ZuSZzFArFWhZR4/uzbwQ5OlcAWdGRERERERERJUNi1IEm82Gt956C2+//TYAoFOnTtiwYQO8vLyKfYwgT2f8M7whdp5JxpkbmcgzW+DooETLOnpENPdGfXcdbmTkI9dkgZNKCT9XLUdIEREREREREdVgLEoRrFYr9u7dCwB45ZVXsHDhQjg4lHzx8SBPZ7z01P2LT/5ujmWam4iIiIiIiIiqLhalCA4ODtiwYQP27t2LYcOGPdKx5HIZi09ERERERERE9FC8+14N9f3332PmzJn2597e3o9ckCIiIiIiIiIiKi6OlKphLBYLZsyYgYULFwIAnnzySXTr1k3iVERERERERERU07AoVYPcunULQ4cORUxMDABg2rRp6NKli8SpiIiIiIiIiKgmYlGqhoiLi8OAAQOQmJgIJycnrFmzBgMHDpQ6FhERERERERHVUFxTqgZYt24dOnbsiMTERDRs2BBHjx5lQYqIiKgKSU9Px/Dhw+Hi4gJXV1eMGTMGOTk5D9zHYDBg4sSJqF27NnQ6HSIjI5GSklKgzfHjx9G1a1e4urqiVq1aiIiIwOnTp8vzVIiIiIjsWJSqATQaDYxGI3r37o1jx46hWbNmUkciIiKiEhg+fDjOnTuH3bt3Y/v27fj1118xfvz4B+4zZcoUbNu2DRs2bMD+/fuRlJSEAQMG2F/PyclBjx49ULduXRw9ehQHDx6Es7MzIiIiYDaby/uUiIiIiCATQgipQ1SkrKws6PV6ZGZmwsXFReo45UYIAZlMZn++Z88edOnSBXI565BERFRzVIfP/QsXLqBp06Y4fvw4QkNDAQA7d+5Ez5498ddff8HX17fQPpmZmfDw8MA333xjHx198eJFNGnSBLGxsWjfvj1+++03PP7440hMTIS/vz8A4MyZM2jZsiUuXbqEoKCgh2arDteXiIiIiqc8PvdZoaiGYmNjERISgr/++su+LTw8nAUpIiKiKig2Nhaurq72ghTw/5/rR48eLXKfuLg4mM1mhIeH27cFBwejbt26iI2NBQA0btwYtWvXxsqVK2EymZCfn4+VK1eiSZMmCAwMLNdzIiIiIgJYlKpWhBBYvnw5OnfujJMnT2LmzJlSRyIiIqJHlJycDE9PzwLblEol3NzckJycfN99VCoVXF1dC2z38vKy7+Ps7Ix9+/bh66+/hlarhU6nw86dO/HTTz9BqSz6XjhGoxFZWVkFHkRERESlxaJUNWEwGDB27Fi8+OKLMJvNGDhwIJYtWyZ1LCIiIrqP6dOnQyaTPfBx8eLFcnv//Px8jBkzBh07dsSRI0dw6NAhNG/eHL169UJ+fn6R+8yfPx96vd7+uDftj4iIiKg0iv4ajKqU69evIzIyEsePH4dcLsf8+fPx+uuvF1hTioiIiCqXV199FaNGjXpgm/r168Pb2xupqakFtlssFqSnp8Pb27vI/by9vWEymZCRkVFgtFRKSop9n2+++QZXr15FbGysfYr/N998g1q1auE///kPhg4dWui4M2bMQHR0tP15VlYWC1NERERUaixKVXG///47wsPDkZaWBjc3N6xfvx7dunWTOhYRERE9hIeHBzw8PB7aLiwsDBkZGYiLi0NISAgA4JdffoHNZkO7du2K3CckJAQODg6IiYlBZGQkACA+Ph6JiYkICwsDAOTl5UEulxf4Euvec5vNVuRx1Wo11Gp1ic6TiIiI6H44fa+Ka9CgAby9vfHYY48hLi6OBSkiIqJqpkmTJujRowfGjRuHY8eO4dChQ5g0aRKGDh1qv/PejRs3EBwcjGPHjgEA9Ho9xowZg+joaOzduxdxcXEYPXo0wsLC0L59ewBAt27dcOfOHUycOBEXLlzAuXPnMHr0aCiVSjz11FOSnS8RERHVHBwpVQXl5+dDo9FAJpPByckJO3bsQO3ataHVaqWORkREROVg3bp1mDRpErp27Qq5XI7IyEh8+OGH9tfNZjPi4+ORl5dn37ZkyRJ7W6PRiIiICHzyySf214ODg7Ft2zbMmTMHYWFhkMvleOyxx7Bz5074+PhU6PkRERFRzSQTQgipQ1SkrKws6PV6ZGZmwsXFReo4JXblyhX0798fQ4cOxYwZM6SOQ0REVKlV9c/9yo7Xl4iIqOYoj899Tt+rQnbu3InQ0FD8/vvv+Oijj5CdnS11JCIiIiIiIiKiUmFRqgoQQmDevHno2bMn7ty5g7Zt2+LYsWNwdnaWOhoRERERERERUalwTalKLjs7G1FRUdi8eTMAYOzYsfj444955xsiIiIiIiIiqtJYlKrELBYLOnXqhNOnT0OlUuHjjz/GuHHjpI5FRERERERERPTIOH2vElMqlXjxxRfh5+eH/fv3syBFRERERERERNUGi1KVjM1mw40bN+zPx48fj3PnzqF9+/YSpiIiIiIiIiIiKlssSlUid+7cQZ8+fdCpUyekp6cDAGQyGfR6vcTJiIiIiIiIiIjKFotSlcSZM2fw+OOPY8eOHbh58yZOnDghdSQiIiIiIiIionLDolQlsH79erRv3x4JCQkIDAzE4cOHER4eLnUsIiIiIiIiIqJyw6KUhCwWC1577TUMGzYMeXl56NatG3777Tc89thjUkcjIiIiIiIiIipXLEpJaPbs2Vi0aBEAYNq0afjpp59Qu3ZtiVMREREREREREZU/FqUkFB0djebNm2PDhg1YsGABFAqF1JGIiIiIiIiIiCqEUuoANU1sbCzCwsIAALVr18apU6dYjCIiIiIiIiKiGocjpSqIyWTCpEmT0KFDB3zxxRf27SxIEREREREREVFNxJFSFSA5ORmDBg3CwYMHAQCpqakSJyIiIiIiIiIikhaLUmXEZhO4kZGPXJMFTiol/Fy1kMtliI2NRWRkJG7evAm9Xo+vv/4avXv3ljouERERERFRtXe/39OIqHJgUaoMXE7Nxq6zKUhIy4HBYoVGqUB9dyekn/gR78ycCrPZjKZNm2LLli1o2LCh1HGJiIiIiIiqvaJ+T2vgoUNEcy8EeTpLHY+IwKLUI7ucmo3Vh64iPdcEH70Gjiot8kwW/Ho0Dhv+FQ0IgYEDB2L16tXQ6XRSxyUiIiIiIqr27vd72tmkTCRl5mN0x0AWpogqARalHoHNJrDrbArSc01o6KmDTHZ3GKizxgHtQx/DtYEvwreWE9Z/sgAKBdeUJyIiIiIiKm8P+j1Np1biUmoOfj6XgvruOk7lI5IYKyWP4EZGPhLScuCj10Amk+Hy6aO4ffM6AEAmk6HXcy+hXpdnkZRpkDgpERERERFRzfD339P+l0wmg49eg8upObiRkS9RQiK6h0WpR5BrssBgsULroMD+jWuwfNporJ4zCSbD3f/ctCoFjBYrck0WiZMSERERERHVDPd+T3NUFT0xiL+nEVUenL73CJxUSigsJnw1fxbO/LoDAOBbvzHw32p8vskKtVIBp/v8Z0hERERERERly0mlhEapQJ7JAmeNQ6HX+XsaUeXBf4WPwJh+E5vnPo8bVy5CrlCi7wvT8Y++IyCTySCEwM1MA1r46eHnqpU6KhERERERUY3g56pFAw8dziZlQqdWFpjCx9/TiCoXFqVKaefOnXj22Wdx584dOOrdED5pAVq36wCrEMg3WnAz0wA3JxW6N/Pi4nlEREREREQVRC6XIaK5F5Iy83Ep9e7aUlqVAvkmK39PI6pkWJQqBZvNhjlz5uDOnTto164dFn72JU7fViAhLQcpWQaolQq08NOjezMv3maUiIiIiIioggV5OmN0x0DsOpvC39OIKjEWpUpBLpfj+++/x7///W+8++67UKvV6GATuJGRj1yTBU4qJfxctay8ExERERERSSTI0xn1n9Tx9zSiSqxS3H1v2bJlCAwMhEajQbt27XDs2LEHtt+wYQOCg4Oh0WjQokUL7Nixo9wzXrx4ER9//LH9ub+/PxYuXAi1Wg3g7hBRfzdHBHu7wN/Nkf/RERERERERSYy/pxFVbpIXpb777jtER0dj9uzZOHHiBFq1aoWIiAikpqYW2f7w4cMYNmwYxowZg5MnT6Jfv37o168fzp49W24Zt2zZgrZt2+Lll1/G1q1by+19iIiIiIiIiIhqCpkQQkgZoF27dnj88cfto5BsNhv8/f3x8ssvY/r06YXaDxkyBLm5udi+fbt9W/v27dG6dWssX778oe+XlZUFvV6PzMxMuLi4PLCt1WrFW2+9hXfeeQcA8MQTT+D777+Hl5dXSU6RiIiIJFKSz30qOV5fIiKimqM8PvclHSllMpkQFxeH8PBw+za5XI7w8HDExsYWuU9sbGyB9gAQERFx3/aldefOHfTp08dekJo8eTL27NnDghQRERERERERURmQdKHzW7duwWq1Fir0eHl54eLFi0Xuk5ycXGT75OTkItsbjUYYjUb786ysrIfmOnPmDPr374+EhARotVqsWLECI0aMeOh+RERERERERERUPJKvKVXe5s+fD71eb3/4+/s/dJ/Tp08jISEBgYGBOHz4MAtSRERERERERERlTNKRUu7u7lAoFEhJSSmwPSUlBd7e3kXu4+3tXaL2M2bMQHR0tP15VlbWQwtTI0aMQF5eHiIjI1G7du3inAoREREREREREZWApCOlVCoVQkJCEBMTY99ms9kQExODsLCwIvcJCwsr0B4Adu/efd/2arUaLi4uBR5/l5aWhhEjRhQodo0fP54FKSIiIiIiIiKiciLpSCkAiI6ORlRUFEJDQ9G2bVssXboUubm5GD16NABg5MiR8PPzw/z58wHcXXC8c+fOWLRoEXr16oX169fjt99+w4oVK0r1/nFxcRgwYAASExORkZFR4K5+RERERERERERUPiQvSg0ZMgRpaWmYNWsWkpOT0bp1a+zcudO+mHliYiLk8v8f0NWhQwd88803+Ne//oU33ngDDRs2xJYtW9C8efMSv/eaNWswYcIEGI1GNGzYEO+9916ZnRcREREREREREd2fTAghpA5RkbKysqDX6zFu3Dh8/vnnAIDevXvjq6++gqurq7ThiIiIqEzd+9zPzMwscgo/PRpeXyIiopqjPD73q/3d9+7nXkHqrbfewn/+8x8WpIiIiIiIiIiIKpDk0/cq2r2BYTqdDitXrkSPHj2Qk5MjcSoiIiIqD1lZWQD+//Ofyta963rvOhMREVH1VR79qhpXlMrOzgYA5OTkYMiQIRKnISIiooqQnZ0NvV4vdYxq516/yt/fX+IkREREVFFu375dZv2qGremlM1mQ1JSEpydnSGTycr02FlZWfD398f169e5roIEeP2lw2svHV576fDaS6ck114IgezsbPj6+ha4cQqVjZL0q/hvpvLgz6Jy4c+jcuHPo3Lhz6NyyczMRN26dXHnzp0yWwKpxo2UksvlqFOnTrm+h4uLC//BSIjXXzq89tLhtZcOr710invtOUKq/JSmX8V/M5UHfxaVC38elQt/HpULfx6VS1l+0cevDImIiIiIiIiIqMKxKEVERERERERERBWORakypFarMXv2bKjVaqmj1Ei8/tLhtZcOr710eO2lw2tfNfHnVnnwZ1G58OdRufDnUbnw51G5lMfPo8YtdE5ERERERERERNLjSCkiIiIiIiIiIqpwLEoREREREREREVGFY1GKiIiIiIiIiIgqHItSJbRs2TIEBgZCo9GgXbt2OHbs2APbb9iwAcHBwdBoNGjRogV27NhRQUmrn5Jc+88//xydOnVCrVq1UKtWLYSHhz/0Z0UPVtK/+/esX78eMpkM/fr1K9+A1VhJr31GRgYmTpwIHx8fqNVqNGrUiP/3lFJJr/3SpUvRuHFjaLVa+Pv7Y8qUKTAYDBWUtnr49ddf0adPH/j6+kImk2HLli0P3Wffvn1o06YN1Go1goKCsGbNmnLPSUVjP6nyYL+pcmE/qnJh36pyYX+rcpCsDyao2NavXy9UKpVYtWqVOHfunBg3bpxwdXUVKSkpRbY/dOiQUCgU4v333xfnz58X//rXv4SDg4M4c+ZMBSev+kp67Z999lmxbNkycfLkSXHhwgUxatQoodfrxV9//VXByauHkl7/e/7880/h5+cnOnXqJPr27VsxYauZkl57o9EoQkNDRc+ePcXBgwfFn3/+Kfbt2ydOnTpVwcmrvpJe+3Xr1gm1Wi3WrVsn/vzzT7Fr1y7h4+MjpkyZUsHJq7YdO3aImTNnik2bNgkAYvPmzQ9sf+XKFeHo6Ciio6PF+fPnxUcffSQUCoXYuXNnxQQmO/aTKg/2myoX9qMqF/atKhf2tyoPqfpgLEqVQNu2bcXEiRPtz61Wq/D19RXz588vsv3gwYNFr169Cmxr166deOGFF8o1Z3VU0mv/dxaLRTg7O4svv/yyvCJWa6W5/haLRXTo0EF88cUXIioqip2pUirptf/0009F/fr1hclkqqiI1VZJr/3EiRNFly5dCmyLjo4WHTt2LNec1VlxOkRTp04VzZo1K7BtyJAhIiIiohyTUVHYT6o82G+qXNiPqlzYt6pc2N+qnCqyD8bpe8VkMpkQFxeH8PBw+za5XI7w8HDExsYWuU9sbGyB9gAQERFx3/ZUtNJc+7/Ly8uD2WyGm5tbecWstkp7/efOnQtPT0+MGTOmImJWS6W59lu3bkVYWBgmTpwILy8vNG/eHPPmzYPVaq2o2NVCaa59hw4dEBcXZx9yfuXKFezYsQM9e/askMw1FT9rKwf2kyoP9psqF/ajKhf2rSoX9reqtrL6HFeWZajq7NatW7BarfDy8iqw3cvLCxcvXixyn+Tk5CLbJycnl1vO6qg01/7vpk2bBl9f30L/aOjhSnP9Dx48iJUrV+LUqVMVkLD6Ks21v3LlCn755RcMHz4cO3bswOXLl/HSSy/BbDZj9uzZFRG7WijNtX/22Wdx69Yt/OMf/4AQAhaLBRMmTMAbb7xREZFrrPt91mZlZSE/Px9arVaiZDUL+0mVB/tNlQv7UZUL+1aVC/tbVVtZ9cE4UoqqvQULFmD9+vXYvHkzNBqN1HGqvezsbDz33HP4/PPP4e7uLnWcGsdms8HT0xMrVqxASEgIhgwZgpkzZ2L58uVSR6v29u3bh3nz5uGTTz7BiRMnsGnTJvz44494++23pY5GRFRs7DdJi/2oyod9q8qF/a3qhyOlisnd3R0KhQIpKSkFtqekpMDb27vIfby9vUvUnopWmmt/z8KFC7FgwQLs2bMHLVu2LM+Y1VZJr39CQgKuXr2KPn362LfZbDYAgFKpRHx8PBo0aFC+oauJ0vzd9/HxgYODAxQKhX1bkyZNkJycDJPJBJVKVa6Zq4vSXPs333wTzz33HMaOHQsAaNGiBXJzczF+/HjMnDkTcjm/ByoP9/usdXFx4SipCsR+UuXBflPlwn5U5cK+VeXC/lbVVlZ9MP7EikmlUiEkJAQxMTH2bTabDTExMQgLCytyn7CwsALtAWD37t33bU9FK821B4D3338fb7/9Nnbu3InQ0NCKiFotlfT6BwcH48yZMzh16pT98cwzz+Cpp57CqVOn4O/vX5Hxq7TS/N3v2LEjLl++bO/AAsAff/wBHx8fdppKoDTXPi8vr1BH6F4H9u56kVQe+FlbObCfVHmw31S5sB9VubBvVbmwv1W1ldnneImWRa/h1q9fL9RqtVizZo04f/68GD9+vHB1dRXJyclCCCGee+45MX36dHv7Q4cOCaVSKRYuXCguXLggZs+ezVsdl1JJr/2CBQuESqUSP/zwg7h586b9kZ2dLdUpVGklvf5/x7vGlF5Jr31iYqJwdnYWkyZNEvHx8WL79u3C09NTvPPOO1KdQpVV0ms/e/Zs4ezsLL799ltx5coV8fPPP4sGDRqIwYMHS3UKVVJ2drY4efKkOHnypAAgFi9eLE6ePCmuXbsmhBBi+vTp4rnnnrO3v3c74tdff11cuHBBLFu2rFS3I6ZHx35S5cF+U+XCflTlwr5V5cL+VuUhVR+MRakS+uijj0TdunWFSqUSbdu2FUeOHLG/1rlzZxEVFVWg/ffffy8aNWokVCqVaNasmfjxxx8rOHH1UZJrHxAQIAAUesyePbvig1cTJf27/7/YmXo0Jb32hw8fFu3atRNqtVrUr19fvPvuu8JisVRw6uqhJNfebDaLt956SzRo0EBoNBrh7+8vXnrpJXHnzp2KD16F7d27t8j/v+9d66ioKNG5c+dC+7Ru3VqoVCpRv359sXr16grPTXexn1R5sN9UubAfVbmwb1W5sL9VOUjVB5MJwTFuRERERERERERUsbimFBERERERERERVTgWpYiIiIiIiIiIqMKxKEVERERERERERBWORSkiIiIiIiIiIqpwLEoREREREREREVGFY1GKiIiIiIiIiIgqHItSRERERERERERU4ViUIiIiIiIiIiKiCseiFBHVCDKZDFu2bCnX91izZg1cXV3L9T2IiIiIHmbUqFHo16+f/fmTTz6Jf/7znxWeY9++fZDJZMjIyCjX9/n7+RJR1cGiFBGVqdjYWCgUCvTq1avE+wYGBmLp0qVlH6oY0tLS8OKLL6Ju3bpQq9Xw9vZGREQEDh06JEkeIiIiql5GjRoFmUwGmUwGlUqFoKAgzJ07FxaLpdzfe9OmTXj77beL1baiCkn3nD59Gs888ww8PT2h0WgQGBiIIUOGIDU1tULen4ikpZQ6ABFVLytXrsTLL7+MlStXIikpCb6+vlJHKpbIyEiYTCZ8+eWXqF+/PlJSUhATE4Pbt29LHY2IiIiqiR49emD16tUwGo3YsWMHJk6cCAcHB8yYMaNQW5PJBJVKVSbv6+bmVibHKWtpaWno2rUrevfujV27dsHV1RVXr17F1q1bkZubK3U8IqoAHClFRGUmJycH3333HV588UX06tULa9asKdRm27ZtePzxx6HRaODu7o7+/fsDuDus/Nq1a5gyZYr9W0QAeOutt9C6desCx1i6dCkCAwPtz48fP45u3brB3d0der0enTt3xokTJ4qdOyMjAwcOHMB7772Hp556CgEBAWjbti1mzJiBZ555xt5u8eLFaNGiBZycnODv74+XXnoJOTk5Dzz2f/7zH7Rp0wYajQb169fHnDlz7N+ICiHw1ltv2Udn+fr64pVXXil2biIiIqpa7o3GDggIwIsvvojw8HBs3boVwP9PQXv33Xfh6+uLxo0bAwCuX7+OwYMHw9XVFW5ubujbty+uXr1qP6bVakV0dDRcXV1Ru3ZtTJ06FUKIAu/79+l7RqMR06ZNg7+/P9RqNYKCgrBy5UpcvXoVTz31FACgVq1akMlkGDVqFADAZrNh/vz5qFevHrRaLVq1aoUffvihwPvs2LEDjRo1glarxVNPPVUgZ1EOHTqEzMxMfPHFF3jsscdQr149PPXUU1iyZAnq1atnP78xY8bY37dx48b497///cDjPizrnTt3MHz4cHh4eECr1aJhw4ZYvXr1A49JROWDRSkiKjPff/89goOD0bhxY4wYMQKrVq0q0Cn68ccf0b9/f/Ts2RMnT55ETEwM2rZtC+DusPI6depg7ty5uHnzJm7evFns983OzkZUVBQOHjyII0eOoGHDhujZsyeys7OLtb9Op4NOp8OWLVtgNBrv204ul+PDDz/EuXPn8OWXX+KXX37B1KlT79v+wIEDGDlyJCZPnozz58/js88+w5o1a/Duu+8CADZu3IglS5bgs88+w6VLl7Blyxa0aNGi2OdNREREVZtWq4XJZLI/j4mJQXx8PHbv3o3t27fDbDYjIiICzs7OOHDgAA4dOgSdTocePXrY91u0aBHWrFmDVatW4eDBg0hPT8fmzZsf+L4jR47Et99+iw8//BAXLlzAZ599Bp1OB39/f2zcuBEAEB8fj5s3b9oLQPPnz8fatWuxfPlynDt3DlOmTMGIESOwf/9+AHeLZwMGDECfPn1w6tQpjB07FtOnT39gDm9vb1gsFmzevLlQIe0em82GOnXqYMOGDTh//jxmzZqFN954A99///19j/uwrG+++SbOnz+Pn376CRcuXMCnn34Kd3f3B2YlonIiiIjKSIcOHcTSpUuFEEKYzWbh7u4u9u7da389LCxMDB8+/L77BwQEiCVLlhTYNnv2bNGqVasC25YsWSICAgLuexyr1SqcnZ3Ftm3b7NsAiM2bN993nx9++EHUqlVLaDQa0aFDBzFjxgxx+vTp+7YXQogNGzaI2rVr25+vXr1a6PV6+/OuXbuKefPmFdjnq6++Ej4+PkIIIRYtWiQaNWokTCbTA9+HiIiIqr6oqCjRt29fIYQQNptN7N69W6jVavHaa6/ZX/fy8hJGo9G+z1dffSUaN24sbDabfZvRaBRarVbs2rVLCCGEj4+PeP/99+2vm81mUadOHft7CSFE586dxeTJk4UQQsTHxwsAYvfu3UXm3Lt3rwAg7ty5Y99mMBiEo6OjOHz4cIG2Y8aMEcOGDRNCCDFjxgzRtGnTAq9Pmzat0LH+7o033hBKpVK4ubmJHj16iPfff18kJyfft70QQkycOFFERkban//vtS1O1j59+ojRo0c/8D2IqGJwpBQRlYn4+HgcO3YMw4YNAwAolUoMGTIEK1eutLc5deoUunbtWubvnZKSgnHjxqFhw4bQ6/VwcXFBTk4OEhMTi32MyMhIJCUlYevWrejRowf27duHNm3aFJiCuGfPHnTt2hV+fn5wdnbGc889h9u3byMvL6/IY54+fRpz5861j8TS6XQYN24cbt68iby8PAwaNAj5+fmoX78+xo0bh82bN1fIYqdEREQkje3bt0On00Gj0eDpp5/GkCFD8NZbb9lfb9GiRYF1pE6fPo3Lly/D2dnZ3pdwc3ODwWBAQkICMjMzcfPmTbRr186+j1KpRGho6H0znDp1CgqFAp07dy527suXLyMvLw/dunUr0K9Zu3YtEhISAAAXLlwokAMAwsLCHnrsd999F8nJyVi+fDmaNWuG5cuXIzg4GGfOnLG3WbZsGUJCQuDh4QGdTocVK1bct59XnKwvvvgi1q9fj9atW2Pq1Kk4fPhwsa8FEZUtLnRORGVi5cqVsFgsBRY2F0JArVbj448/hl6vh1arLfFx5XJ5oeHcZrO5wPOoqCjcvn0b//73vxEQEAC1Wo2wsLACw+GLQ6PRoFu3bujWrRvefPNNjB07FrNnz8aoUaNw9epV9O7dGy+++CLeffdduLm54eDBgxgzZgxMJhMcHR0LHS8nJwdz5szBgAEDinwvf39/xMfHY8+ePdi9ezdeeuklfPDBB9i/fz8cHBxKlJ2IiIgqv6eeegqffvopVCoVfH19oVQW/HXMycmpwPOcnByEhIRg3bp1hY7l4eFRqgyl6Y/dW0Pzxx9/hJ+fX4HX1Gp1qXL8r9q1a2PQoEEYNGgQ5s2bh8ceewwLFy7El19+ifXr1+O1117DokWLEBYWBmdnZ3zwwQc4evRoqbM+/fTTuHbtGnbs2IHdu3eja9eumDhxIhYuXPjI50JEJcOiFBE9MovFgrVr12LRokXo3r17gdf69euHb7/9FhMmTEDLli0RExOD0aNHF3kclUoFq9VaYJuHhweSk5MhhLAvfn7q1KkCbQ4dOoRPPvkEPXv2BHB3TYNbt2498nk1bdoUW7ZsAQDExcXBZrNh0aJFkMvvDjJ90FoGANCmTRvEx8cjKCjovm20Wi369OmDPn36YOLEifZvBtu0afPI+YmIiKhycXJyemC/4O/atGmD7777Dp6ennBxcSmyjY+PD44ePYonnngCwN1+WVxc3H37Ei1atIDNZsP+/fsRHh5e6PV7I7X+t0/WtGlTqNVqJCYm3neEVZMmTeyLtt9z5MiRh59kEe/foEED+933Dh06hA4dOuCll16yt7k34qkoxckK3O1jRkVFISoqCp06dcLrr7/OohSRBFiUIqJHtn37dty5cwdjxoyBXq8v8FpkZCRWrlyJCRMmYPbs2ejatSsaNGiAoUOHwmKxYMeOHZg2bRoAIDAwEL/++iuGDh0KtVoNd3d3PPnkk0hLS8P777+PgQMHYufOnfjpp58KdMwaNmyIr776CqGhocjKysLrr79eom8Bb9++jUGDBuH5559Hy5Yt4ezsjN9++w3vv/8++vbtCwAICgqC2WzGRx99hD59+uDQoUNYvnz5A487a9Ys9O7dG3Xr1sXAgQMhl8tx+vRpnD17Fu+88w7WrFkDq9WKdu3awdHREV9//TW0Wi0CAgKKnZ2IiIiqr+HDh+ODDz5A3759MXfuXNSpUwfXrl3Dpk2bMHXqVNSpUweTJ0/GggUL0LBhQwQHB2Px4sXIyMi47zEDAwMRFRWF559/Hh9++CFatWqFa9euITU1FYMHD0ZAQABkMhm2b9+Onj17QqvVwtnZGa+99hqmTJkCm82Gf/zjH8jMzMShQ4fg4uKCqKgoTJgwAYsWLcLrr7+OsWPHIi4ursg7Mf+v7du3Y/369Rg6dCgaNWoEIQS2bduGHTt22O+G17BhQ6xduxa7du1CvXr18NVXX+H48eP2u/P9XXGyzpo1CyEhIWjWrBmMRiO2b9+OJk2alPbHRESPQtolrYioOujdu7fo2bNnka8dPXpUALAvGr5x40bRunVroVKphLu7uxgwYIC9bWxsrGjZsqVQq9Xif/97+vTTT4W/v79wcnISI0eOFO+++26Bhc5PnDghQkNDhUajEQ0bNhQbNmwotGg6HrDQucFgENOnTxdt2rQRer1eODo6isaNG4t//etfIi8vz95u8eLFwsfHR2i1WhERESHWrl1bYPHOvy90LoQQO3fuFB06dBBarVa4uLiItm3bihUrVgghhNi8ebNo166dcHFxEU5OTqJ9+/Ziz549D7vcREREVAX972LcJXn95s2bYuTIkcLd3V2o1WpRv359MW7cOJGZmSmEuLuw+eTJk4WLi4twdXUV0dHRYuTIkfdd6FwIIfLz88WUKVOEj4+PUKlUIigoSKxatcr++ty5c4W3t7eQyWQiKipKCHF3cfalS5eKxo0bCwcHB+Hh4SEiIiLE/v377ftt27ZNBAUFCbVaLTp16iRWrVr1wIXOExISxLhx40SjRo2EVqsVrq6u4vHHHxerV6+2tzEYDGLUqFFCr9cLV1dX8eKLL4rp06cXuBHO36/dw7K+/fbbokmTJkKr1Qo3NzfRt29fceXKlfv+bIio/MiEuM+9N4mIiIiIiIiIiMoJ775HREREREREREQVjkUpIiIiIiIiIiKqcCxKERERERERERFRhWNRioiIiIiIiIiIKhyLUkREREREREREVOFYlCIiIiIiIiIiogrHohQREREREREREVU4FqWIiIiIiIiIiKjCsShFREREREREREQVjkUpIiIiIiIiIiKqcCxKERERERERERFRhWNRioiIiIiIiIiIKtz/AYPyzfZuuIDFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIjCAYAAAAQgZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABn/klEQVR4nO3dd3zT1f7H8Xe6KdAyCrRApSwFBAFBEBEQZaoooky1iID3Ciha5Sc4KIgXcG9FUcArUxRxIRuuC0RZyhSQIauM0kFbuvL9/VESCElL0qZJ076ejwcP7Tcn3+8nPU15c3K+55gMwzAEAAAA+CA/bxcAAAAAFBZhFgAAAD6LMAsAAACfRZgFAACAzyLMAgAAwGcRZgEAAOCzCLMAAADwWYRZAAAA+CzCLAAAAHwWYRZljslk0oQJE1x+3oEDB2QymTRr1iy31wR7N910k2666Sbr146+/w888IAqVKjg+eJKgJiYGD3wwAPeLqPMmzBhgkwmk7fLKJClxlOnTnm7FEnFU8+lvy/ys3btWplMJq1du9Zt14b3EWbhFbNmzZLJZJLJZNJPP/1k97hhGIqOjpbJZNLtt9/uhQrdY8mSJTKZTKpZs6bMZrO3y0EhxMTEyGQyqUuXLg4fnz59uvVn+ffff3f5/Dt27NCECRN04MCBIlZaul1zzTW64oorVNAO7O3bt1eNGjWUk5Pjwco8zxLInPkDlAUB3i4AZVtISIjmzp2rG2+80eb4//73Px0+fFjBwcFeqsw95syZo5iYGB04cECrV6/ONxDh8urUqaOMjAwFBgZ6/NohISFas2aNjh8/rsjISJvH5syZo5CQEJ07d65Q596xY4cmTpyom266STExMU4/b/fu3fLzKzvjEffee6/Gjh2rH3/8UR07drR7/MCBA1q3bp1GjRqlgADP/dX27LPPauzYsR67niQ1btxYn376qc2xcePGqUKFCnrmmWc8WgtQEpSd34QokW699VYtXLjQbiRl7ty5atWqlV1w8CVpaWn66quvFBcXp5YtW2rOnDneLilfaWlp3i7hskwmk0JCQuTv7+/xa7dv314VKlTQggULbI4fPnxYP/74o2677TaP1GEYhjIyMiRJwcHBXgn2xS09Pd3h8UGDBslkMmnu3LkOH583b54Mw9C9995bpOu7+l4ICAhQSEhIka7pqho1aui+++6z+RMeHu7weFGZzeZC/0MN8BTCLLxq4MCBOn36tFasWGE9lpWVpc8//1yDBg1y+Jy0tDQ98cQTio6OVnBwsK666iq98sordh8/ZmZm6vHHH1e1atVUsWJF3XHHHTp8+LDDcx45ckQPPvigatSooeDgYF199dWaMWNGkV7bl19+qYyMDPXt21cDBgzQokWLHP6lcO7cOU2YMEFXXnmlQkJCFBUVpT59+mjfvn3WNmazWW+++aaaNWumkJAQVatWTT169LB+rF3QfN5L5whb5qvt2LFDgwYNUuXKla0j43/88YceeOAB1atXTyEhIYqMjNSDDz6o06dPO/yeDR06VDVr1lRwcLDq1q2rhx9+WFlZWfr7779lMpn0+uuv2z3vl19+kclk0rx581z6fjo7Z3nLli2qVq2abrrpJp09e9Zaa1H6NyQkRH369LELUvPmzVPlypXVvXt3h8/btWuX7rnnHlWpUkUhISFq3bq1vv76a+vjs2bNUt++fSVJnTt3tn40bJnPFxMTo9tvv13Lli1T69atVa5cOX3wwQfWxy6dM5uUlKTHH39cMTExCg4OVu3atRUbG3vZuYk5OTmaNGmS6tevr+DgYMXExOjpp59WZmamtc3tt9+uevXqOXx+u3bt1Lp1a5tjs2fPVqtWrVSuXDlVqVJFAwYM0D///GPT5qabblLTpk21ceNGdezYUaGhoXr66acdXiM6OlodO3bU559/ruzsbLvH586dq/r166tt27Y6ePCgRowYoauuukrlypVT1apV1bdvX7upHJbpTv/73/80YsQIVa9eXbVr19aaNWtkMpn05ZdfOryOyWTSunXrJDmeM2symTRq1CgtXrxYTZs2tf7MLV261O58a9euVevWrRUSEqL69evrgw8+KLZ5uElJSXrggQdUqVIlhYeHa8iQIXb/eLDUPmfOHF199dUKDg621u3s++jtt9/W1VdfrdDQUFWuXFmtW7d2+I8QZ+px5mczP4cPH1bv3r1Vvnx5Va9eXY8//rjD5+3Zs0d33323IiMjFRISotq1a2vAgAFKTk6+7DVQMjDNAF4VExOjdu3aad68eerZs6ck6fvvv1dycrIGDBigt956y6a9YRi64447tGbNGg0dOlQtWrTQsmXLNGbMGB05csQmPA0bNkyzZ8/WoEGDdMMNN2j16tUOR9ASEhJ0/fXXW3+JV6tWTd9//72GDh2qlJQUPfbYY4V6bXPmzFHnzp0VGRmpAQMGaOzYsfrmm2+s4UWScnNzdfvtt2vVqlUaMGCARo8erdTUVK1YsULbtm1T/fr1JUlDhw7VrFmz1LNnTw0bNkw5OTn68ccftX79ersQ4ay+ffuqYcOGmjx5svUfAitWrNDff/+tIUOGKDIyUtu3b9eHH36o7du3a/369da/YI8ePao2bdooKSlJDz30kBo1aqQjR47o888/V3p6uurVq6f27dtrzpw5evzxx+2+LxUrVtSdd95ZqLoL8ttvv6l79+5q3bq1vvrqK5UrV85t/Tto0CB169ZN+/bts/bL3Llzdc899zgcId2+fbvat2+vWrVqaezYsSpfvrw+++wz9e7dW1988YXuuusudezYUY8++qjeeustPf3002rcuLEkWf8r5U0nGDhwoP71r39p+PDhuuqqqxzWd/bsWXXo0EE7d+7Ugw8+qGuvvVanTp3S119/rcOHDysiIiLf1zZs2DB98sknuueee/TEE0/o119/1ZQpU7Rz505roOvfv79iY2P122+/6brrrrM+9+DBg1q/fr1efvll67H//Oc/eu6559SvXz8NGzZMJ0+e1Ntvv62OHTtq8+bNqlSpkrXt6dOn1bNnTw0YMED33XefatSokW+d9957rx566CEtW7bMZi79n3/+qW3btmn8+PGS8n4OfvnlFw0YMEC1a9fWgQMH9P777+umm27Sjh07FBoaanPeESNGqFq1aho/frzS0tJ00003KTo6WnPmzNFdd91l03bOnDmqX7++2rVrl2+dkvTTTz9p0aJFGjFihCpWrKi33npLd999tw4dOqSqVatKkjZv3qwePXooKipKEydOVG5urp5//nlVq1atwHMXVr9+/VS3bl1NmTJFmzZt0kcffaTq1avrxRdftGm3evVqffbZZxo1apQiIiIUExPj9Pto+vTpevTRR3XPPfdo9OjROnfunP744w/9+uuvdgMUztTjzM+mIxkZGbrlllt06NAhPfroo6pZs6Y+/fRTrV692qZdVlaWunfvrszMTD3yyCOKjIzUkSNH9O233yopKUnh4eFF/K7DIwzAC2bOnGlIMn777TfjnXfeMSpWrGikp6cbhmEYffv2NTp37mwYhmHUqVPHuO2226zPW7x4sSHJeOGFF2zOd8899xgmk8nYu3evYRiGsWXLFkOSMWLECJt2gwYNMiQZ8fHx1mNDhw41oqKijFOnTtm0HTBggBEeHm6ta//+/YYkY+bMmZd9fQkJCUZAQIAxffp067EbbrjBuPPOO23azZgxw5BkvPbaa3bnMJvNhmEYxurVqw1JxqOPPppvm4Jqu/T1xsfHG5KMgQMH2rW1vNaLzZs3z5Bk/PDDD9ZjsbGxhp+fn/Hbb7/lW9MHH3xgSDJ27txpfSwrK8uIiIgwBg8ebPe8S3Xq1Mno1KmT9WtHr3Hw4MFG+fLlDcMwjJ9++skICwszbrvtNuPcuXPWNs72b34sP4M5OTlGZGSkMWnSJMMwDGPHjh2GJON///ufzc+zxS233GI0a9bMphaz2WzccMMNRsOGDa3HFi5caEgy1qxZ4/DakoylS5c6fOzi7+P48eMNScaiRYvs2lr6xBHLe2XYsGE2x5988klDkrF69WrDMAwjOTnZCA4ONp544gmbdi+99JJhMpmMgwcPGoZhGAcOHDD8/f2N//znPzbt/vzzTyMgIMDmeKdOnQxJxrRp0/Kt72KJiYlGcHCw3c/u2LFjDUnG7t27DcNw/HO8bt06Q5Lx3//+13rM0m833nijkZOTY9N+3LhxRnBwsJGUlGQ9duLECSMgIMDh++likoygoCDr7yPDMIytW7cakoy3337beqxXr15GaGioceTIEeuxPXv2GAEBAXbnvJyrr77a5v1yMUuNDz74oM3xu+66y6hatapd7X5+fsb27dttjjv7PrrzzjuNq6++usBana3H2Z9Nw7D/ffHGG28YkozPPvvMeiwtLc1o0KCBzftt8+bNhiRj4cKFBdaMko1pBvC6fv36KSMjQ99++61SU1P17bff5jvFYMmSJfL399ejjz5qc/yJJ56QYRj6/vvvre0k2bW7dBTOMAx98cUX6tWrlwzD0KlTp6x/unfvruTkZG3atMnl1zR//nz5+fnp7rvvth4bOHCgvv/+e505c8Z67IsvvlBERIQeeeQRu3NYRkG/+OILmUwmxcfH59umMP7973/bHStXrpz1/8+dO6dTp07p+uuvlyTr98FsNmvx4sXq1auXw1FhS039+vVTSEiIzVzhZcuW6dSpU26Zy3exNWvWqHv37rrlllu0aNEi642D7uxff39/9evXzzo9Ys6cOYqOjlaHDh3s2iYmJmr16tXq16+fUlNTrdc8ffq0unfvrj179ujIkSNOXbdu3br5TmO42BdffKHmzZvbjSRKBf+cWN4rcXFxNsefeOIJSdJ3330nSQoLC1PPnj312Wef2UzpWbBgga6//npdccUVkqRFixbJbDarX79+Nt/vyMhINWzYUGvWrLG5TnBwsIYMGXLZ1ydJlStX1q233qqvv/7aOrfVMAzNnz9frVu31pVXXinJ9uc4Oztbp0+fVoMGDVSpUiWH/T18+HC7udixsbHKzMzU559/bvNac3JynPr57dKli3UEX8pbjSEsLEx///23pLxPZVauXKnevXurZs2a1nYNGjSwfkrlbpe+5zt06KDTp08rJSXF5ninTp3UpEkT69euvI8qVaqkw4cP67fffityPc7+bDqyZMkSRUVF6Z577rEeCw0N1UMPPWTTzjLyumzZsnzna6PkI8zC66pVq6YuXbpo7ty5WrRokXJzc21+AV3s4MGDqlmzpipWrGhz3PKx7MGDB63/9fPzs/nLRJLdR7QnT55UUlKSPvzwQ1WrVs3mj+Uv2BMnTrj8mmbPnq02bdro9OnT2rt3r/bu3auWLVsqKytLCxcutLbbt2+frrrqqgLvvt63b59q1qypKlWquFxHQerWrWt3LDExUaNHj1aNGjVUrlw5VatWzdrOMn/s5MmTSklJUdOmTQs8f6VKldSrVy+buXJz5sxRrVq1dPPNN0vK+2j8+PHj1j8nT550+XWcO3dOt912m1q2bKnPPvtMQUFB1sfc3b+DBg3Sjh07tHXrVs2dO1cDBgxwGBT37t0rwzD03HPP2V3X8o8SZ6/rqJ8c2bdv32X7xBHLe6VBgwY2xyMjI1WpUiXre0rKm2rwzz//WOeL7tu3Txs3blT//v2tbfbs2SPDMNSwYUO7175z5067112rVi2bPruce++913pzpZQ3B/vAgQM2N35lZGRo/Pjx1nn1ERERqlatmpKSkhzOg3T0PW7UqJGuu+46m3+MzZkzR9dff73d98oRS7i/WOXKla3/mD1x4oQyMjIcnsuZ8xfGpTVVrlxZkmz+gS3Zfz9ceR899dRTqlChgtq0aaOGDRtq5MiR+vnnnwtVjys/m5c6ePCgGjRoYPf+vPTvgLp16youLk4fffSRIiIi1L17d7377rvMl/UxzJlFiTBo0CANHz5cx48fV8+ePW3m1BUny9qv9913nwYPHuywzTXXXOPSOffs2WMdlWjYsKHd43PmzLEbHSiq/EbecnNz833OxaNXFv369dMvv/yiMWPGqEWLFqpQoYLMZrN69OhRqHVyY2NjtXDhQv3yyy9q1qyZvv76a40YMcK6pNQrr7yiiRMnWtvXqVPH5fVWg4ODdeutt+qrr77S0qVLbeZSurt/27Ztq/r16+uxxx7T/v378/0EwXLdJ598Mt9RVWcDi6N+Kg7OjPL36tVLoaGh+uyzz3TDDTfos88+k5+fn808cLPZLJPJpO+//97hyhOXbnLh6uu7/fbbFR4errlz52rQoEGaO3eu/P39NWDAAGubRx55RDNnztRjjz2mdu3aKTw8XCaTSQMGDHD4c5xfDbGxsRo9erQOHz6szMxMrV+/Xu+8845Tdea36oZxyY2qnuRsTZd+P1x5HzVu3Fi7d+/Wt99+q6VLl+qLL77Qe++9p/Hjx9u8112pp7jXy3311Vf1wAMP6KuvvtLy5cv16KOPasqUKVq/fr1q165drNeGexBmUSLcdddd+te//qX169fbLX90sTp16mjlypVKTU21GZ3dtWuX9XHLf81ms3Xk02L37t0257OsdJCbm+u2NWDnzJmjwMBAffrpp3a/rH/66Se99dZbOnTokK644grVr19fv/76q7Kzs/NdZql+/fpatmyZEhMT8x2dtYxoJCUl2RwvaOTiUmfOnNGqVas0ceJE6400Ul44v1i1atUUFhambdu2XfacPXr0ULVq1TRnzhy1bdtW6enpuv/++62Px8bG2qwxXJjgZjKZNGfOHN15553q27evvv/+e+tOQMXRvwMHDtQLL7ygxo0bq0WLFg7bWO76DwwMvOx13fUXdf369Z3qk0tZ3it79uyxufEsISFBSUlJ1veUJJUvX1633367Fi5cqNdee00LFixQhw4dbD4mr1+/vgzDUN26da0f+7tTcHCw7rnnHv33v/9VQkKCFi5cqJtvvtlmGb/PP/9cgwcP1quvvmo9du7cObv3x+UMGDBAcXFxmjdvnnWN44tHoYuievXqCgkJ0d69e+0ec3TMm1x9H5UvX179+/dX//79lZWVpT59+ug///mPxo0b59IyZq78bDp67rZt22QYhs177NK/AyyaNWumZs2a6dlnn9Uvv/yi9u3ba9q0aXrhhRecrhfewzQDlAgVKlTQ+++/rwkTJqhXr175trv11luVm5trNzry+uuvy2QyWeeaWf576WoIb7zxhs3X/v7+uvvuu/XFF184DAKF+dh7zpw56tChg/r376977rnH5s+YMWMkyTrv8u6779apU6ccjvZYRifuvvtuGYZhN6pxcZuwsDBFRETohx9+sHn8vffec7puS/C+dFTk0u+Zn5+fevfurW+++cbhjlcXPz8gIEADBw7UZ599plmzZqlZs2Y2I6H16tVTly5drH/at2/vdL0XCwoK0qJFi3TdddepV69e2rBhg/U1ubt/hw0bpvj4eJugdKnq1avrpptu0gcffKBjx44VeN3y5ctLsv+HiKvuvvtubd261eEd3gWNBt56662S7Pv5tddekyS7FUD69++vo0eP6qOPPtLWrVvtwl2fPn3k7++viRMn2l3XMAyHy7y56t5771V2drb+9a9/6eTJk3Zry/r7+9td++233y7wkwpHIiIi1LNnT82ePVtz5sxRjx49ClwVwhX+/v7q0qWLFi9erKNHj1qP79271zr3v6Rw5X10af8GBQWpSZMmMgzD4ZJqBXH1Z/PS5x49etRmznN6ero+/PBDm3YpKSl265w3a9ZMfn5+Ti3/hZKBkVmUGPl9fHWxXr16qXPnznrmmWd04MABNW/eXMuXL9dXX32lxx57zDpHtkWLFho4cKDee+89JScn64YbbtCqVascjnhMnTpVa9asUdu2bTV8+HA1adJEiYmJ2rRpk1auXKnExESnX8Ovv/6qvXv3atSoUQ4fr1Wrlq699lrNmTNHTz31lGJjY/Xf//5XcXFx2rBhgzp06KC0tDStXLlSI0aM0J133qnOnTvr/vvv11tvvaU9e/ZYP/L/8ccf1blzZ+u1hg0bpqlTp2rYsGFq3bq1fvjhB/31119O1x4WFqaOHTvqpZdeUnZ2tmrVqqXly5dr//79dm0nT56s5cuXq1OnTnrooYfUuHFjHTt2TAsXLtRPP/1kM00kNjZWb731ltasWWO3BJA7lStXTt9++61uvvlm9ezZU//73//UtGlTt/avlDfic/G6vfl59913deONN6pZs2YaPny46tWrp4SEBK1bt06HDx/W1q1bJeX9rPr7++vFF19UcnKygoODdfPNN6t69eou1TVmzBh9/vnn6tu3rx588EG1atVKiYmJ+vrrrzVt2jQ1b97c4fOaN2+uwYMH68MPP1RSUpI6deqkDRs26JNPPlHv3r3VuXNnm/a33nqrKlasqCeffNIaci5Wv359vfDCCxo3bpwOHDig3r17q2LFitq/f7++/PJLPfTQQ3ryySddem2X6tSpk2rXrm1dfq1Pnz42j99+++369NNPFR4eriZNmmjdunVauXKldUksV8TGxlrn8E+aNKlIdV9qwoQJWr58udq3b6+HH37Y+g/1pk2basuWLW69VlE5+z7q1q2bIiMjrVsL79y5U++8845uu+02u3sdLsfVn82LDR8+XO+8845iY2O1ceNGRUVF6dNPP7Vblm316tUaNWqU+vbtqyuvvFI5OTnWT9Uu/dlGCeapZROAizlaysiRS5fmMgzDSE1NNR5//HGjZs2aRmBgoNGwYUPj5Zdftlt+KCMjw3j00UeNqlWrGuXLlzd69epl/PPPP3ZLVRlG3lJaI0eONKKjo43AwEAjMjLSuOWWW4wPP/zQ2saZpbkeeeQRQ5Kxb9++fNtMmDDBkGRs3brVMIy8ZYSeeeYZo27dutZr33PPPTbnyMnJMV5++WWjUaNGRlBQkFGtWjWjZ8+exsaNG61t0tPTjaFDhxrh4eFGxYoVjX79+hknTpzId2mukydP2tV2+PBh46677jIqVapkhIeHG3379jWOHj3q8Ht28OBBIzY21qhWrZoRHBxs1KtXzxg5cqSRmZlpd96rr77a8PPzMw4fPpzv9+VSri7NZXHq1CmjSZMmRmRkpLFnzx7DMJzr3/w4+hm8VH4/z/v27TNiY2ONyMhIIzAw0KhVq5Zx++23G59//rlNu+nTpxv16tUz/P39bZYNKujaly7NZRiGcfr0aWPUqFFGrVq1jKCgIKN27drG4MGD7ZZTulR2drYxceJE689gdHS0MW7cOJtlxS527733GpKMLl265HvOL774wrjxxhuN8uXLG+XLlzcaNWpkjBw50rp8lmHk9fHllnHKz5gxYwxJRr9+/eweO3PmjDFkyBAjIiLCqFChgtG9e3dj165ddt8zZ34PZWZmGpUrVzbCw8ONjIwMu8fzW5pr5MiRdm0d9dmqVauMli1bGkFBQUb9+vWNjz76yHjiiSeMkJCQy3wHbDmzNNel73nL69+/f/9lazcM595HH3zwgdGxY0ejatWqRnBwsFG/fn1jzJgxRnJycqHqcfZn89LfF4aR9zvqjjvuMEJDQ42IiAhj9OjRxtKlS23eY3///bfx4IMPGvXr1zdCQkKMKlWqGJ07dzZWrlzp8HuAkslkGF6cjQ6gTGjZsqWqVKmiVatWebsUwCU5OTmqWbOmevXqpY8//tgj1+zdu7e2b99uN18dgGPMmQVQrH7//Xdt2bJFsbGx3i4FcNnixYt18uTJYvv5zcjIsPl6z549WrJkifUmRgCXx8gsgGKxbds2bdy4Ua+++qpOnTqlv//+26U7mQFv+vXXX/XHH39o0qRJioiIKNTmKc6IiorSAw88oHr16ungwYN6//33lZmZqc2bNztc2g+APW4AA1AsPv/8cz3//PO66qqrNG/ePIIsfMr777+v2bNnq0WLFpo1a1axXadHjx6aN2+ejh8/ruDgYLVr106TJ08myAIuYGQWAAAAPos5swAAAPBZhFkAAAD4rDI3Z9ZsNuvo0aOqWLFise/3DAAAANcZhqHU1FTVrFlTfn4Fj72WuTB79OhRRUdHe7sMAAAAXMY///yj2rVrF9imzIVZy3Z6//zzj8LCwor9etnZ2Vq+fLm6deumwMDAYr8e3I8+9H30oe+jD30b/ef7PN2HKSkpio6Odmob5DIXZi1TC8LCwjwWZkNDQxUWFsYb2EfRh76PPvR99KFvo/98n7f60JkpodwABgAAAJ9FmAUAAIDPIswCAADAZxFmAQAA4LMIswAAAPBZhFkAAAD4LMIsAAAAfBZhFgAAAD6LMAsAAACfRZgFAACAzyLMAgAAwGcRZgEAAOCzCLMAAADwWQHevPgPP/ygl19+WRs3btSxY8f05Zdfqnfv3gU+Z+3atYqLi9P27dsVHR2tZ599Vg888IBH6gXgOblmQxv2J+pE6jlVrxiiNnWryN/PVOS2nJtzc27Pn7tqaIDMRr6nLbF1c+4LbX/dn6iNp0yquj9R7RpUL/DcnubVMJuWlqbmzZvrwQcfVJ8+fS7bfv/+/brtttv073//W3PmzNGqVas0bNgwRUVFqXv37h6oGPC+kvLLrThrWbrtmCZ+s0PHks9Zj0WFhyi+VxP1aBpV6Lac23fPfctVET5ZN+e+0LZSkL8CYxJ0e4vaPlU35764rb/+u+f3As/tDSbDMC7zbyXPMJlMlx2Zfeqpp/Tdd99p27Zt1mMDBgxQUlKSli5d6tR1UlJSFB4eruTkZIWFhRW17MvKzs7WkiVLdOuttyowMLDYrwf3y87O1rffLVG1JtfrdHoOIa+Ya1m67Zgenr1Jl/5isnxH3r/vWutznG1reR/612mlR+Zvdeu5i7Nuzn2h7dsDmiv34Ebr71JfqZtzX8yQSSafq5tzO39ud3Ilr3l1ZNZV69atU5cuXWyOde/eXY899li+z8nMzFRmZqb165SUFEl5ASU7O7tY6ryY5RqeuJYvyDUb+v3gGZ1IzVT1isFqXafyZUOhs+2L69xL/jiqiZv8lbT+d+uxyLBgPXtrI3W/uoZd+2XbE/TCkl06npJ52fautnUUxI4nn9PDszfp7QHNrc9xpW1xt3elba7Z0ISvtzv4S1AylPcLdOI323VTw6qS5HRbc26OzIY0eckut5/b389UbHVz7gttX1iyS081yftd6kt1F+bcnRpUUXpWrp5bvC3ftpI0dtGfysrKkZ+fSWazofHf7HSqvSSn27rz3JJJhg/Wzbkdt73059vdXMlNPjUye+WVV2rIkCEaN26c9diSJUt02223KT09XeXKlbN7zoQJEzRx4kS743PnzlVoaKhbai/rzIa0L8WklGwpLFCqH2bI0c/11tMmLTrgp6SsCw9WCjLUJ8as5lXtfwxdaV9c59562qQZf1nuk7z4ReW1efDKwrd3pa3ZUF6gzrq07YXnVAqS4q/NleR8Wz+Ta+d2tb0ztVQMlO5rYFZmrnTgrLT6qL+DdrYah5slSTuTL38Pa+Nws8KCpJQs59tzbs7tzXObZMhw+H4BSp5RTXLVMNz9UTI9PV2DBg1yamS21IdZRyOz0dHROnXqlMemGaxYsUJdu3YtldMMnB1ZzG907uKPEAvbvrjOnWs2dNOrP9i8tktVLR+kdwc2t466jJy3VafTsi7bXtJl21YMDtCA62orLStHf59M0/r9Z/Jta1EhOC8Ins3MdaptoL+fsnPNxdbe2VoAFE7dqqGqUj5IiWlZ2n863an2kpxuy7k59+W81reZel3j/qkGKSkpioiIKH3TDCIjI5WQkGBzLCEhQWFhYQ6DrCQFBwcrODjY7nhgYKBHw6Wnr+cJS7cdcxgKE1Iy9cj8rda5NLlmQ//5fneBH1VMWrJLTWtXVlpWjpLTs/Xc1zsK/hjky+3acfysJOm/6w441dbPZJLZMC7b/vGFf6jm8hAlpmcr9VxOgd+D02lZGvDRbwW2KWz71MwcTf/pgNPnllwLjnlti7u9cyLDglWzUjnlGoa2/pN82fYDr4uWJM377R+n2taqFKKft+7SuhOXH/V19dxXVC2vQ6fTnG7PuQt/7nbVc9W+eSMdSTrnU3W7eu53B12r8sH+emDm5X9XTO5zjdrVr6p1+05r4PT1TrWX5HRbzs25LyeqUvliyTeunNOnwmy7du20ZMkSm2MrVqxQu3btvFRR6eTMDUm5ZkMTvyk4cD71xZ86cDpNu46l2tz446h9QkqmbnplrdM1ns3M0Xtr97m9rSRl5xo6mJjhdPsq5QNVPjhAaZk5Sky7/ByfKuXz3qDOtO10ZYSaR1dWYlqmZq8/dNn2L9+T9wtozOd/ONW2eXQlbf0nqdjaO1vL6/1bql39qso1G7rxxdU6nnzO4c+WSVJkeIheuKuZJGntXyedamvOzVGt1J3afy5YCSmZbj23ZVTe2facu7DnDla/emm6vWNd+fkH+FDdrp+7R9NISXk3SV6ubZu6VSRJbepWcak95+bc7j63N3l104SzZ89qy5Yt2rJli6S8pbe2bNmiQ4fy/tIeN26cYmNjre3//e9/6++//9b//d//adeuXXrvvff02Wef6fHHH/dG+aXS0m3HdOOLqzVw+nqNnr9FA6ev140vrtbSbcds2m3Yn1hgQJWk5IxsTf1+txZvOerUtQP8TIqoEKzqFe1H0h3p2DBCHRvaL9eTX9sHbohxuv3oWxrqpbuvcartu4Na6cf/u1nvDmrldHtn2/67UwPFdb1SE+9oqqjwkHxn0ZmU90unz7W11efa2k63vbJGxWJt72xbyy9Dfz+T4ns1sT52aVtJiu/VRP5+JpfaSpKfSXr21kbFcu7irJtz53mmZyPrXHxfqptzX8zwybo5t3Pn9iavhtnff/9dLVu2VMuWLSVJcXFxatmypcaPHy9JOnbsmDXYSlLdunX13XffacWKFWrevLleffVVffTRR6wx6yaWJTguDamWu86///OYth1J1hsr/9L/fb7VqXO2rlNZ3ZvY3/HvyKdD2+r3Z7vozQEtnWr/8E0N9PBNDZxuO+GOq51uf329qrq7lWtBzPIvXmfau9JWKjm/3Iq7Fknq0TRK7993rSLDQ2zaR4aH2C0D40pbSep+dY1iO3dx1s25r7Vb3cNX6ubcF1QKyrsnwdfq5tzOn9tbSswNYJ7COrOOWT7eLWi01XInuyvmDb9ebepWceqj45+eutn6UZyz7SUV27n9/UwXrbFnmd17oZ2U//p9kmzO76i9K20vPn9pX2fWwp1r9V76PiwpG09wbufb5ve7tKTXzbkv7AB2csd63X5b/n8XlsS6OfeFtuv2ntDyH39Vtw5tPbIDmCt5jTBbzHwlzDo7KTzI3083XVVNNzeurteW/6WTqQXPP7w0FErOBbfiDIWutv92y2E9u2iLzTJepSXkebK9q+d2J195HyJ/9KFvo/98n6f7sNRumoDicyK14PmvFpP7NNU9rfLu4q1ULlAPz94kkxyHwos/PrZ8VHFpcIvMJ7i50r44zy3lfTSdfSDX6R3AejSNUtcmkU4FN1faWvj7mdSuftV8Hy9s2+Ju7+q5AQBwBmEWkqTqFUMu30hSrUoXNpooTIh0JbgVZyh0tb2fSWpbt4rT/xol5AEA4BmEWUhyfbkOC1dDYUka+SNEAgDg+wizkHThDvV/n59LerHLLcFBKAQAAN7i1aW5ULL0aBplt/yNVPKW4AAAALBgZBZWZzNz9Mu+05KkJ7tdqegqoR6/6xwAAMAVhFlYzd9wSKnnclSvWnmNuKmB/AiwAACghGOaASRJ2blmzfz5gCRpeId6BFkAAOATCLOQJC3585iOJGUookKw7mpZy9vlAAAAOIUwCxmGoQ/+97ck6YEb6igk0N/LFQEAADiHMAv9su+0dhxLUblAf93bto63ywEAAHAaYRb64Ie8Udn+10WrcvkgL1cDAADgPMJsGbfzWIp++Ouk/EzS0BvrerscAAAAlxBmy7jpP+aNyvZsFqXoKqFergYAAMA1hNky7Fhyhr7eclSS9K+O9bxcDQAAgOsIs2XYzJ8PKMdsqG3dKrqmdiVvlwMAAOAywmwZlXIuW3N/PSRJ+lcnRmUBAIBvIsyWUfM3HNLZzBw1rF5BN11Z3dvlAAAAFAphtgzKyjFrxk8HJLF1LQAA8G2E2TLo2z+O6njKOVWvGKw7W9b0djkAAACFRpgtYwzD0IfnN0l4oH2MggPYuhYAAPiuAG8XAM/INRvasD9R//vrpHYdT1W5QD/d24atawEAgG8jzJYBS7cd08RvduhY8jnrMZPJpHV/n1KPplFerAwAAKBomGZQyi3ddkwPz95kE2QlKT0rVw/P3qSl2455qTIAAICiI8yWYrlmQxO/2SGjgDYTv9mhXHNBLQAAAEouwmwptmF/ot2I7MUMSceSz2nD/kTPFQUAAOBGhNlS7ERq/kG2MO0AAABKGsJsKVa9Yohb2wEAAJQ0hNlSrE3dKooKD1F++3uZJEWFh6hN3SqeLAsAAMBtCLOlmL+fSfG9mjh8zBJw43s1kT/b2QIAAB9FmC3lejSN0vv3XatAf9vAGhkeovfvu5Z1ZgEAgE9j04QyoPvVkQoJ8FN2bq7G9mik5tGV1KZuFUZkAQCAzyPMlgFn0rOVmpkrSXqgfYxCAv29XBEAAIB7MM2gDNh/Kk2SVDM8hCALAABKFcJsGXDgfJiNiSjv5UoAAADcizBbBuwnzAIAgFKKMFsG7D+dF2brViXMAgCA0oUwWwZYphnUZWQWAACUMoTZUs4wDObMAgCAUoswW8qdPJuptKxc+ZmkK6qEerscAAAAtyLMlnIHTqVLkmpVLqegALobAACULqSbUm7/qbOSpBhu/gIAAKUQYbaU239+ZJabvwAAQGlEmC3lWMkAAACUZoTZUu7AaVYyAAAApRdhthQzmw1rmGXDBAAAUBoRZkuxhNRzOpdtVoCfSbUrl/N2OQAAAG5HmC3F9p/MG5WNrhKqAH+6GgAAlD4knFJsv2W+bFU2SwAAAKUTYbYUu7CSQQUvVwIAAFA8CLOl2IU1ZhmZBQAApRNhthRjWS4AAFDaEWZLqVyzoUOn80Zm2coWAACUVoTZUupoUoaycs0K8vdTzUosywUAAEonwmwptf/8zV9XVA2Vv5/Jy9UAAAAUD8JsKWXd+Yv5sgAAoBQjzJZS+08RZgEAQOlHmC2lLGvMcvMXAAAozQizpdQBy0oGrDELAABKMcJsKZSda9Y/iZYNExiZBQAApRdhthQ6fCZDOWZDIYF+qlExxNvlAAAAFBvCbCl08XxZP5blAgAApRhhthRiJQMAAFBWEGZLIcsaszGEWQAAUMoRZksh68gsy3IBAIBSjjBbCjEyCwAAygrCbCmTmZOrI2cyJLHGLAAAKP0Is6XMP4npMhtSheAAVasQ7O1yAAAAihVhtpTZf+rCzl8mE8tyAQCA0o0wW8pcvMYsAABAaUeYLWX2n2aNWQAAUHYQZksZRmYBAEBZ4vUw++677yomJkYhISFq27atNmzYUGD7N954Q1dddZXKlSun6OhoPf744zp37pyHqi35LGvMsiwXAAAoC7waZhcsWKC4uDjFx8dr06ZNat68ubp3764TJ044bD937lyNHTtW8fHx2rlzpz7++GMtWLBATz/9tIcrL5kysnJ1LDkv2NcjzAIAgDLAq2H2tdde0/DhwzVkyBA1adJE06ZNU2hoqGbMmOGw/S+//KL27dtr0KBBiomJUbdu3TRw4MDLjuaWFQcT80Zlw8sFqnL5IC9XAwAAUPwCvHXhrKwsbdy4UePGjbMe8/PzU5cuXbRu3TqHz7nhhhs0e/ZsbdiwQW3atNHff/+tJUuW6P7778/3OpmZmcrMzLR+nZKSIknKzs5Wdna2m15N/izX8MS19h7Pe211qpbzyPXKCk/2IYoHfej76EPfRv/5Pk/3oSvX8VqYPXXqlHJzc1WjRg2b4zVq1NCuXbscPmfQoEE6deqUbrzxRhmGoZycHP373/8ucJrBlClTNHHiRLvjy5cvV2io53bIWrFiRbFfY+URkyR/BZ5L0pIlS4r9emWNJ/oQxYs+9H30oW+j/3yfp/owPT3d6bZeC7OFsXbtWk2ePFnvvfee2rZtq71792r06NGaNGmSnnvuOYfPGTdunOLi4qxfp6SkKDo6Wt26dVNYWFix15ydna0VK1aoa9euCgwMLNZr/bR4u3ToiNo3a6hbb65frNcqSzzZhyge9KHvow99G/3n+zzdh5ZP0p3htTAbEREhf39/JSQk2BxPSEhQZGSkw+c899xzuv/++zVs2DBJUrNmzZSWlqaHHnpIzzzzjPz87KcABwcHKzjYflvXwMBAj76hPHG9g6czJEn1a1Tkl0Ux8PTPDNyPPvR99KFvo/98n6f60JVreO0GsKCgILVq1UqrVq2yHjObzVq1apXatWvn8Dnp6el2gdXf31+SZBhG8RXrI9gwAQAAlDVenWYQFxenwYMHq3Xr1mrTpo3eeOMNpaWlaciQIZKk2NhY1apVS1OmTJEk9erVS6+99ppatmxpnWbw3HPPqVevXtZQW1adzczRydS8G91YYxYAAJQVXg2z/fv318mTJzV+/HgdP35cLVq00NKlS603hR06dMhmJPbZZ5+VyWTSs88+qyNHjqhatWrq1auX/vOf/3jrJZQYlp2/qpYPUlgIH+EAAICywes3gI0aNUqjRo1y+NjatWttvg4ICFB8fLzi4+M9UJlvOXCanb8AAEDZ4/XtbOEelpHZmKqEWQAAUHYQZkuJv09Zbv7y3Nq5AAAA3kaYLSUOWMNsBS9XAgAA4DmE2VLiwOm8nTJiGJkFAABlCGG2FEjOyFZiWpYk5swCAICyhTBbClimGFSvGKzywV5foAIAAMBjCLOlAMtyAQCAsoowWwr8ffL8zV9MMQAAAGUMYbYUsIzM1q1GmAUAAGULYbYUYMMEAABQVhFmfZxhGNpvXWOWMAsAAMoWwqyPO5OerZRzOZKkOlVZYxYAAJQthFkfZxmVrRkeopBAfy9XAwAA4FmEWR9nCbMsywUAAMoiwqyPO8B8WQAAUIYRZn3c/tOEWQAAUHYRZn0cy3IBAICyLMDbBaBwcs2GNuw/rb0JqZKk6CqsZAAAAMoeRmZ90NJtx3Tji6s1cPqvysw1JEmDZ2zQ0m3HvFwZAACAZxFmfczSbcf08OxNOpZ8zuZ4Qso5PTx7E4EWAACUKYRZH5JrNjTxmx0yHDxmOTbxmx3KNTtqAQAAUPoQZn3Ihv2JdiOyFzMkHUs+pw37Ez1XFAAAgBcRZn3IidT8g2xh2gEAAPg6wqwPqV4xxK3tAAAAfB1h1oe0qVtFUeEhMuXzuElSVHiI2tSt4smyAAAAvIYw60P8/UyK79VEkuwCreXr+F5N5O+XX9wFAAAoXQizPqZH0yi9f9+1igy3nUoQGR6i9++7Vj2aRnmpMgAAAM9jBzAf1KNplLo0rqGrnvteuWbp3UEt1aNpFCOyAACgzGFk1kdl5piVa877/5sb1SDIAgCAMokw66POpGdJkoIC/BQSSDcCAICyiRTko5LSsyVJlUMDZTIxKgsAAMomwqyPSs7IC7OVygV5uRIAAADvIcz6KMvIbHhooJcrAQAA8B7CrI9KysibM1upHGEWAACUXYRZH2UZma3EyCwAACjDCLM+Kun8agaVQpkzCwAAyi7CrI9iZBYAAIAw67OSWM0AAACAMOurkhmZBQAAIMz6KssOYKxmAAAAyjLCrI+yTDNgnVkAAFCWEWZ9kGEY1mkGlVnNAAAAlGGEWR+UkZ2rrFyzJObMAgCAso0w64Msy3IF+fupXKC/l6sBAADwHsKsD7KE2fDQQJlMJi9XAwAA4D2EWR+UxEoGAAAAkgizPsm6YQLzZQEAQBlHmPVBF7ayZSUDAABQthFmfVBSBtMMAAAAJMKsT2IrWwAAgDyEWR9k3cqWaQYAAKCMI8z6IOvSXEwzAAAAZZzLYXbKlCmaMWOG3fEZM2boxRdfdEtRKJhlNQO2sgUAAGWdy2H2gw8+UKNGjeyOX3311Zo2bZpbikLBmDMLAACQx+Uwe/z4cUVFRdkdr1atmo4dO+aWolAwy2oGTDMAAABlncthNjo6Wj///LPd8Z9//lk1a9Z0S1EoWBIjswAAAJKkAFefMHz4cD322GPKzs7WzTffLElatWqV/u///k9PPPGE2wuErYysXGXmmCWxmgEAAIDLYXbMmDE6ffq0RowYoaysvI+7Q0JC9NRTT2ncuHFuLxC2LFMMAvxMKh/k7+VqAAAAvMvlMGsymfTiiy/queee086dO1WuXDk1bNhQwcHBxVEfLnHxVrYmk8nL1QAAAHiXy2HWokKFCrruuuvcWQucwHxZAACAC5wKs3369NGsWbMUFhamPn36FNh20aJFbikMjiWfn2ZQiZUMAAAAnAuz4eHh1o+0w8PDi7UgFOwMI7MAAABWToXZmTNnSpIMw9DEiRNVrVo1lStXrlgLg2MXtrJlJQMAAACX1pk1DEMNGjTQ4cOHi6seXIZlNYPKjMwCAAC4Fmb9/PzUsGFDnT59urjqwWWwlS0AAMAFLu8ANnXqVI0ZM0bbtm0rjnpwGdZpBmyYAAAA4PrSXLGxsUpPT1fz5s0VFBRkN3c2MTHRbcXB3pl0VjMAAACwcDnMvv766yzW70XJGUwzAAAAsHA5zD7wwAPFUAacZd00gdUMAAAAXJ8z6+/vrxMnTtgdP336tPz9/d1SFPJnWc2AkVkAAIBChFnDMBwez8zMVFAQo4XF6Vx2rs5lmyURZgEAACQXphm89dZbkiSTyaSPPvpIFSpUsD6Wm5urH374QY0aNXJ/hbCyzJf19zOpQrDLM0QAAABKHacT0euvvy4pb2R22rRpNlMKgoKCFBMTo2nTprm/QlhdvJIBN+EBAAC4MM1g//792r9/vzp16qStW7dav96/f792796tZcuWqW3bti4X8O677yomJkYhISFq27atNmzYUGD7pKQkjRw5UlFRUQoODtaVV16pJUuWuHxdX3RhjVmmGAAAAEiFWM1gzZo1kqSsrCzt379f9evXV0BA4T7yXrBggeLi4jRt2jS1bdtWb7zxhrp3767du3erevXqdu2zsrLUtWtXVa9eXZ9//rlq1aqlgwcPqlKlSoW6vq+5sJIBYRYAAEAqxA1gGRkZGjp0qEJDQ3X11Vfr0KFDkqRHHnlEU6dOdelcr732moYPH64hQ4aoSZMmmjZtmkJDQzVjxgyH7WfMmKHExEQtXrxY7du3V0xMjDp16qTmzZu7+jJ8UvL5lQwqs/sXAACApEKMzI4dO1Zbt27V2rVr1aNHD+vxLl26aMKECRo7dqxT58nKytLGjRs1btw46zE/Pz916dJF69atc/icr7/+Wu3atdPIkSP11VdfqVq1aho0aJCeeuqpfJcFy8zMVGZmpvXrlJQUSVJ2drays7OdqrUoLNdwx7VOnz0nSQoL8fdI7cjjzj6Ed9CHvo8+9G30n+/zdB+6ch2Xw+zixYu1YMECXX/99TY3IV199dXat2+f0+c5deqUcnNzVaNGDZvjNWrU0K5duxw+5++//9bq1at17733asmSJdq7d69GjBih7OxsxcfHO3zOlClTNHHiRLvjy5cvV2hoqNP1FtWKFSuKfI6NB/0k+Skx4YiWLPmn6EXBJe7oQ3gXfej76EPfRv/5Pk/1YXp6utNtXQ6zJ0+edDifNS0trdjvsDebzapevbo+/PBD+fv7q1WrVjpy5IhefvnlfMPsuHHjFBcXZ/06JSVF0dHR6tatm8LCwoq1XinvXxYrVqxQ165dFRhYtLmuv3y1XTp6RC0aN9Stneu7qUJcjjv7EN5BH/o++tC30X++z9N9aPkk3Rkuh9nWrVvru+++0yOPPCJJ1gD70UcfqV27dk6fJyIiQv7+/kpISLA5npCQoMjISIfPiYqKUmBgoM2UgsaNG+v48ePKyspyuGlDcHCwgoOD7Y4HBgZ69A3ljuulnMuVJFWtGMIvAy/w9M8M3I8+9H30oW+j/3yfp/rQlWu4HGYnT56snj17aseOHcrJydGbb76pHTt26JdfftH//vc/p88TFBSkVq1aadWqVerdu7ekvJHXVatWadSoUQ6f0759e82dO1dms1l+fnn3rv3111+KiooqE7uPWVcz4AYwAAAASYVYzeDGG2/Uli1blJOTo2bNmmn58uWqXr261q1bp1atWrl0rri4OE2fPl2ffPKJdu7cqYcfflhpaWkaMmSIJCk2NtbmBrGHH35YiYmJGj16tP766y999913mjx5skaOHOnqy/BJSRkszQUAAHCxQi0QW79+fU2fPr3IF+/fv79Onjyp8ePH6/jx42rRooWWLl1qvSns0KFD1hFYSYqOjtayZcv0+OOP65prrlGtWrU0evRoPfXUU0WuxRckW3YAY9MEAAAASYUMs+40atSofKcVrF271u5Yu3bttH79+mKuqmQ6Y900gWkGAAAAkgthNr91XC+Vm5tb6GKQv3PZucrIzvvesp0tAABAHqfDrGEYqlOnjgYPHqyWLVsWZ01wIOX8fFk/k1Qx2OsD6gAAACWC06low4YN+vjjj/Xmm2+qbt26evDBB3XvvfeqcuXKxVkfzrPe/BUaJD+/4l3PFwAAwFc4vZpB69at9f777+vYsWOKi4vTl19+qdq1a2vAgAHs6OEB1mW5WMkAAADAyuWluUJCQnTfffdp1apV2rZtm06cOKEePXooMTGxOOrDeWfOr2TAfFkAAIALCjX58vDhw5o1a5ZmzZql9PR0jRkzxiNbw5ZlyYzMAgAA2HE6zGZlZenLL7/Uxx9/rB9//FE9e/bUG2+8oZ49ezq90gEKLynDssYsy3IBAABYOB1mo6KiVLFiRQ0ePFjvvfeeqlevLklKS0uzaccIbfG4sJUtI7MAAAAWTofZM2fO6MyZM5o0aZJeeOEFu8cNw5DJZGKd2WJyYStbRmYBAAAsnA6za9asKc46cBnJjMwCAADYcTrMdurUqTjrwGVYVjMgzAIAAFzg8tJc8A7LnNlwVjMAAACwIsz6iOSLdgADAABAHsKsj0g6P82gMtMMAAAArAizPiArx6y0rLxVIljNAAAA4IJCh9m9e/dq2bJlysjIkJS3NBeKh2XDBJNJqhhSqE3bAAAASiWXw+zp06fVpUsXXXnllbr11lt17NgxSdLQoUP1xBNPuL1AXFiWK7xcoPz8TF6uBgAAoORwOcw+/vjjCggI0KFDhxQaGmo93r9/fy1dutStxSHPhQ0TmC8LAABwMZc/s16+fLmWLVum2rVr2xxv2LChDh486LbCcMGFrWyZLwsAAHAxl0dm09LSbEZkLRITExUcHOyWomAriQ0TAAAAHHI5zHbo0EH//e9/rV+bTCaZzWa99NJL6ty5s1uLQx7ryCzTDAAAAGy4PM3gpZde0i233KLff/9dWVlZ+r//+z9t375diYmJ+vnnn4ujxjLPspoB0wwAAABsuTwy27RpU/3111+68cYbdeeddyotLU19+vTR5s2bVb9+/eKoscxjK1sAAADHXB6ZXbNmjTp37qxnnnnG7rF3331XI0eOdEthuMC6mgFzZgEAAGy4PDLbp08fbdy40e74m2++qXHjxrmlKNiyrDNbmWkGAAAANlwOsy+//LJ69uypXbt2WY+9+uqrGj9+vL777ju3Foc8ljmz4YzMAgAA2HB5msGwYcOUmJioLl266KefftKCBQs0efJkLVmyRO3bty+OGsu8M2msZgAAAOCIy2FWkv7v//5Pp0+fVuvWrZWbm6tly5bp+uuvd3dtOC85g00TAAAAHHEqzL711lt2x2rVqqXQ0FB17NhRGzZs0IYNGyRJjz76qHsrLOOyc806m5kjiZFZAACASzkVZl9//XWHx/39/fXzzz9b15c1mUyEWTezjMqaTFIYYRYAAMCGU2F2//79xV0H8mFZYzYsJFD+fiYvVwMAAFCyuLyaATwrKd2y+xejsgAAAJdyamQ2Li5OkyZNUvny5RUXF1dg29dee80thSGPZWSW+bIAAAD2nAqzmzdvVnZ2tvX/82My8TG4u1l2/wpnJQMAAAA7ToXZNWvWOPx/FD/rNANGZgEAAOwwZ7aEs6xmUJk5swAAAHacGpnt06eP0ydctGhRoYuBPcucWaYZAAAA2HMqzIaHhxd3HcjHGaYZAAAA5MupMDtz5szirgP5uLCVLWEWAADgUkWaMzt16lQlJSW5qRQ4Yl2aizALAABgp0hhdvLkyUpMTHRXLXAgKSNvmkF4OebMAgAAXKpIYdYwDHfVgXxYRmZZzQAAAMAeS3OVYDm5ZqWey5EkVWI1AwAAADtO3QCWnx07dqhmzZruqgWXsNz8JUlhIUXqKgAAgFLJ5ZHZmJgYPf/88zp06JCio6Pl7+9fHHVBF7ayrRgSoAB/BtEBAAAu5XJCeuyxx7Ro0SLVq1dPXbt21fz585WZmVkctZV5rGQAAABQsEKF2S1btmjDhg1q3LixHnnkEUVFRWnUqFHatGlTcdRYZiWfX8mgMvNlAQAAHCr0Z9fXXnut3nrrLR09elTx8fH66KOPdN1116lFixaaMWMGKx24wZm081vZsvsXAACAQ4W+qyg7O1tffvmlZs6cqRUrVuj666/X0KFDdfjwYT399NNauXKl5s6d685ay5wk6+5fjMwCAAA44nKY3bRpk2bOnKl58+bJz89PsbGxev3119WoUSNrm7vuukvXXXedWwsti5LT86YZVGJkFgAAwCGXw+x1112nrl276v3331fv3r0VGGgftOrWrasBAwa4pcCy7MLILGEWAADAEZfD7N9//606deoU2KZ8+fKaOXNmoYtCHstqBsyZBQAAcMzlG8BOnDihX3/91e74r7/+qt9//90tRSGPZWSW1QwAAAAccznMjhw5Uv/884/d8SNHjmjkyJFuKQp5kixzZplmAAAA4JDLYXbHjh269tpr7Y63bNlSO3bscEtRyMOmCQAAAAVzOcwGBwcrISHB7vixY8cUEFDolb7ggGVkNrwc0wwAAAAccTnMduvWTePGjVNycrL1WFJSkp5++ml17drVrcWVZblmQynnciQxMgsAAJAfl4dSX3nlFXXs2FF16tRRy5YtJUlbtmxRjRo19Omnn7q9wLIq5fzNXxLrzAIAAOTH5TBbq1Yt/fHHH5ozZ462bt2qcuXKaciQIRo4cKDDNWdROGfOTzGoGBygAP9C7zoMAABQqhVqkmv58uX10EMPubsWXMSyLFc4UwwAAADyVeg7tnbs2KFDhw4pKyvL5vgdd9xR5KIgJbOSAQAAwGUVagewu+66S3/++adMJpMMw5AkmUwmSVJubq57KyyjkjLOrzHLSgYAAAD5cnky5ujRo1W3bl2dOHFCoaGh2r59u3744Qe1bt1aa9euLYYSyybrVraMzAIAAOTL5ZHZdevWafXq1YqIiJCfn5/8/Px04403asqUKXr00Ue1efPm4qizzLGE2cqEWQAAgHy5PDKbm5urihUrSpIiIiJ09OhRSVKdOnW0e/du91ZXhlm3smWaAQAAQL5cHplt2rSptm7dqrp166pt27Z66aWXFBQUpA8//FD16tUrjhrLJMtqBtwABgAAkD+Xw+yzzz6rtLQ0SdLzzz+v22+/XR06dFDVqlW1YMECtxdYVlnnzLJhAgAAQL5cDrPdu3e3/n+DBg20a9cuJSYmqnLlytYVDVB0F0ZmmWYAAACQH5fmzGZnZysgIEDbtm2zOV6lShWCrJsln58zyw1gAAAA+XMpzAYGBuqKK65gLVkPOMOmCQAAAJfl8moGzzzzjJ5++mklJiYWRz2QlGs2lHLOMmeWaQYAAAD5cXnO7DvvvKO9e/eqZs2aqlOnjsqXL2/z+KZNm9xWXFmVei5b5zdW4wYwAACAArgcZnv37l0MZeBilpUMygf5KyjA5cFzAACAMsPlMBsfH18cdeAirGQAAADgnBIx7Pfuu+8qJiZGISEhatu2rTZs2ODU8+bPny+TyVTqRovPWHb/4uYvAACAArkcZv38/OTv75/vH1ctWLBAcXFxio+P16ZNm9S8eXN1795dJ06cKPB5Bw4c0JNPPqkOHTq4fM2SLpmVDAAAAJzi8jSDL7/80ubr7Oxsbd68WZ988okmTpzocgGvvfaahg8friFDhkiSpk2bpu+++04zZszQ2LFjHT4nNzdX9957ryZOnKgff/xRSUlJ+Z4/MzNTmZmZ1q9TUlKsdWdnZ7tcr6ss13DlWqfPnpMkhQUHeKRGFKwwfYiShT70ffShb6P/fJ+n+9CV65gMw3LffNHMnTtXCxYs0FdffeX0c7KyshQaGqrPP//cZqrA4MGDlZSUlO+54uPj9ccff+jLL7/UAw88oKSkJC1evNhh2wkTJjgM2XPnzlVoaKjTtXrS9/+YtPSwv26oYVb/emZvlwMAAOBR6enpGjRokJKTkxUWFlZgW5dHZvNz/fXX66GHHnLpOadOnVJubq5q1Khhc7xGjRratWuXw+f89NNP+vjjj7VlyxanrjFu3DjFxcVZv05JSVF0dLS6det22W+OO2RnZ2vFihXq2rWrAgOdmzaw8btd0uFDuuaq+rq1a8NirhCXU5g+RMlCH/o++tC30X++z9N9aPkk3RluCbMZGRl66623VKtWLXecLl+pqam6//77NX36dEVERDj1nODgYAUHB9sdDwwM9OgbypXrpWbm7bBWtUIIb/oSxNM/M3A/+tD30Ye+jf7zfZ7qQ1eu4XKYrVy5skwmk/VrwzCUmpqq0NBQzZ4926VzRUREyN/fXwkJCTbHExISFBkZadd+3759OnDggHr16mU9ZjbnfQwfEBCg3bt3q379+i7VUBJZVjMI5wYwAACAArkcZl9//XWbMOvn56dq1aqpbdu2qly5skvnCgoKUqtWrbRq1SrrnFmz2axVq1Zp1KhRdu0bNWqkP//80+bYs88+q9TUVL355puKjo529eWUSJZNEyqx+xcAAECBXA6zDzzwgFsLiIuL0+DBg9W6dWu1adNGb7zxhtLS0qyrG8TGxqpWrVqaMmWKQkJC1LRpU5vnV6pUSZLsjvuyZDZNAAAAcIrLYXbmzJmqUKGC+vbta3N84cKFSk9P1+DBg106X//+/XXy5EmNHz9ex48fV4sWLbR06VLrTWGHDh2Sn1+J2NvBY5LYNAEAAMApLofZKVOm6IMPPrA7Xr16dT300EMuh1lJGjVqlMNpBZK0du3aAp87a9Ysl69XkpnNxoWRWaYZAAAAFMjlIc9Dhw6pbt26dsfr1KmjQ4cOuaWosiz1XI7M51f+5QYwAACAgrkcZqtXr64//vjD7vjWrVtVtWpVtxRVliVl5E0xCA3yV3CA69sDAwAAlCUuh9mBAwfq0Ucf1Zo1a5Sbm6vc3FytXr1ao0eP1oABA4qjxjKFlQwAAACc5/Kc2UmTJunAgQO65ZZbFBCQ93Sz2azY2FhNnjzZ7QWWNUnn58uGs5IBAADAZbkcZoOCgrRgwQK98MIL2rJli8qVK6dmzZqpTp06xVFfmWNdyYCRWQAAgMsq9Ha2DRs2VMOGDd1ZC3TxGrOEWQAAgMtxec7s3XffrRdffNHu+EsvvWS39ixcdyaNDRMAAACc5XKY/eGHH3TrrbfaHe/Zs6d++OEHtxRVlllWM2BkFgAA4PJcDrNnz55VUJD9qGFgYKBSUlLcUlRZlsxqBgAAAE5zOcw2a9ZMCxYssDs+f/58NWnSxC1FlWVJzJkFAABwmss3gD333HPq06eP9u3bp5tvvlmStGrVKs2bN08LFy50e4FlSa7Z0D+JaZKk48mZyjUb8vczebkqAACAksvlkdlevXpp8eLF2rt3r0aMGKEnnnhChw8f1sqVK9W7d+9iKLFsWLrtmG58cbX2nMgLs6+v/Es3vrhaS7cd83JlAAAAJVehlua67bbbdNttt9kd37Ztm5o2bVrkosqapduO6eHZm2Rccvx48jk9PHuT3r/vWvVoGuWV2gAAAEoyl0dmL5WamqoPP/xQbdq0UfPmzd1RU5mSazY08ZsddkFWkvXYxG92KNfsqAUAAEDZVugw+8MPPyg2NlZRUVF65ZVXdPPNN2v9+vXurK1M2LA/UceSz+X7uCHpWPI5bdif6LmiAAAAfIRL0wyOHz+uWbNm6eOPP1ZKSor69eunzMxMLV68mJUMCulEav5BtjDtAAAAyhKnR2Z79eqlq666Sn/88YfeeOMNHT16VG+//XZx1lYmVK8Y4tZ2AAAAZYnTI7Pff/+9Hn30UT388MNq2LBhcdZUprSpW0VR4SE6nnzO4bxZk6TI8BC1qVvF06UBAACUeE6PzP70009KTU1Vq1at1LZtW73zzjs6depUcdZWJvj7mRTfy/EUDcsKs/G9mrDeLAAAgANOh9nrr79e06dP17Fjx/Svf/1L8+fPV82aNWU2m7VixQqlpqYWZ52lWo+mUXr/vmtVMcR2oDwyPIRluQAAAArg8moG5cuX14MPPqiffvpJf/75p5544glNnTpV1atX1x133FEcNZYJPZpGaWCbKyRJna6M0Lzh1+unp24myAIAABSgSOvMXnXVVXrppZd0+PBhzZs3z101lVnJ6dmSpNZ1qqhd/apMLQAAALiMIm+aIEn+/v7q3bu3vv76a3ecrsxKTM+SJFWpEOTlSgAAAHyDW8Is3ONM2vkwG0qYBQAAcAZhtgSxjMxWLk+YBQAAcAZhtgRJtIzMEmYBAACcQpgtIXJyzUrOyLsBrDLTDAAAAJxCmC0hkjOyZZzfAqxyaKB3iwEAAPARhNkS4sz5+bLh5QIV4E+3AAAAOIPUVEIkpuVNMWC+LAAAgPMIsyVEYlqmJKYYAAAAuIIwW0IwMgsAAOA6wmwJYZkzS5gFAABwHmG2hLCsMcuGCQAAAM4jzJYQbGULAADgOsJsCcFWtgAAAK4jzJYQiYzMAgAAuIwwW0IwZxYAAMB1hNkSwjJntiphFgAAwGmE2RLgXHau0rJyJTEyCwAA4ArCbAmQlJ63YYK/n0lhIQFergYAAMB3EGZLgNPWrWyDZDKZvFwNAACA7yDMlgBnrFvZBnq5EgAAAN9CmC0BEtnKFgAAoFAIsyWAdfcvwiwAAIBLCLMlgHWNWTZMAAAAcAlhtgQ4wzQDAACAQiHMlgCnGZkFAAAoFMJsCcCcWQAAgMIhzJYAiYRZAACAQiHMlgDMmQUAACgcwqyXGYZh3TShMmEWAADAJYRZLzubmaOsXLMkqQo3gAEAALiEMOtlllHZkEA/lQvy93I1AAAAvoUw62WWrWyrlg/2ciUAAAC+hzDrZZZluSqXD/RyJQAAAL6HMOtlbGULAABQeIRZL2ONWQAAgMIjzHqZZc4sI7MAAACuI8x6mWXObFVGZgEAAFxGmPUy65xZwiwAAIDLCLNexla2AAAAhUeY9TJWMwAAACg8wqyXsZoBAABA4RFmvSjXbCgpI287WzZNAAAAcB1h1ouSM7JlGHn/zzQDAAAA1xFmvcgyxSAsJECB/nQFAACAq0hQXsRKBgAAAEVDmPWi02dZYxYAAKAoCLNeZB2ZZb4sAABAoRBmvYhluQAAAIqGMOtFZwizAAAARUKY9aLEdObMAgAAFEWJCLPvvvuuYmJiFBISorZt22rDhg35tp0+fbo6dOigypUrq3LlyurSpUuB7Usy6zQD5swCAAAUitfD7IIFCxQXF6f4+Hht2rRJzZs3V/fu3XXixAmH7deuXauBAwdqzZo1WrdunaKjo9WtWzcdOXLEw5UXnWWaASOzAAAAheP1MPvaa69p+PDhGjJkiJo0aaJp06YpNDRUM2bMcNh+zpw5GjFihFq0aKFGjRrpo48+ktls1qpVqzxcedElss4sAABAkQR48+JZWVnauHGjxo0bZz3m5+enLl26aN26dU6dIz09XdnZ2apSpYrDxzMzM5WZmWn9OiUlRZKUnZ2t7OzsIlTvHMs1HF3LugNYsMkjtaBwCupD+Ab60PfRh76N/vN9nu5DV67j1TB76tQp5ebmqkaNGjbHa9SooV27djl1jqeeeko1a9ZUly5dHD4+ZcoUTZw40e748uXLFRoa6nrRhbRixQqbr3PMUlpm3rf/95//px1e7Qk449I+hO+hD30ffejb6D/f56k+TE9Pd7qtT0eoqVOnav78+Vq7dq1CQkIcthk3bpzi4uKsX6ekpFjn2YaFhRV7jdnZ2VqxYoW6du2qwMBA6/GElHPSrz/I38+kPrf3lJ+fqdhrQeHk14fwHfSh76MPfRv95/s83YeWT9Kd4dUwGxERIX9/fyUkJNgcT0hIUGRkZIHPfeWVVzR16lStXLlS11xzTb7tgoODFRwcbHc8MDDQo2+oS6+XkpkhSaocGqjgYObM+gJP/8zA/ehD30cf+jb6z/d5qg9duYZXbwALCgpSq1atbG7estzM1a5du3yf99JLL2nSpElaunSpWrdu7YlS3c6ylW1lluUCAAAoNK9PM4iLi9PgwYPVunVrtWnTRm+88YbS0tI0ZMgQSVJsbKxq1aqlKVOmSJJefPFFjR8/XnPnzlVMTIyOHz8uSapQoYIqVKjgtdfhKrayBQAAKDqvh9n+/fvr5MmTGj9+vI4fP64WLVpo6dKl1pvCDh06JD+/CwPI77//vrKysnTPPffYnCc+Pl4TJkzwZOlFcoZluQAAAIrM62FWkkaNGqVRo0Y5fGzt2rU2Xx84cKD4C/KARDZMAAAAKDKvb5pQVrGVLQAAQNERZr2EkVkAAICiI8x6iWXObFXCLAAAQKERZr0kMS1vmzZGZgEAAAqPMOslZ5gzCwAAUGSEWS8wDOOiObPshAIAAFBYhFkvSMvKVVauWRLrzAIAABQFYdYLLFMMggP8VC7Q38vVAAAA+C7CrBdYphhULR8kk8nk5WoAAAB8F2HWCxLTWWMWAADAHQizXmBdyYAwCwAAUCSEWS+wrmTAslwAAABFQpj1gkRGZgEAANyCMOsFlq1sCbMAAABFQ5j1ggsbJhBmAQAAioIw6wVn0rIlsZUtAABAURFmveB0WqYktrIFAAAoKsKsF5xJPz8yyzQDAACAIiHMeliu2VASN4ABAAC4BWHWw1IysmU28v6fdWYBAACKhjDrYZatbCuGBCjQn28/AABAUZCmPIwNEwAAANyHMOthbGULAADgPoRZDzvDyCwAAIDbEGY9LJGVDAAAANyGMOthjMwCAAC4D2HWwxLPb2XLnFkAAICiI8x6WOL5rWyrsJUtAABAkRFmPSwxnZFZAAAAdyHMephlzmzVCoRZAACAoiLMetgZ1pkFAABwG8KsB2XlmJWamSOJ1QwAAADcgTDrQWfOrzHrZ5LCQrgBDAAAoKgIsx508Va2fn4mL1cDAADg+wizHmSdL8sUAwAAALcgzHoQW9kCAAC4F2HWg6xb2bKSAQAAgFsQZj3IupUtI7MAAABuQZj1ILayBQAAcC/CrAexlS0AAIB7EWY9iK1sAQAA3Isw60GJbGULAADgVoRZDzrD0lwAAABuRZj1EMMwdJqRWQAAALcizHpIelausnLMkhiZBQAAcBfCrIecOb+SQXCAn0KD/L1cDQAAQOlAmPWQi+fLmkwmL1cDAABQOhBmPeQMa8wCAAC4HWHWQyzLcjFfFgAAwH0Isx5iHZklzAIAALgNYdZDLLt/VQkN9HIlAAAApQdh1kMSz4/MVikf7OVKAAAASg/CrIdcWM2AkVkAAAB3Icx6CHNmAQAA3I8w6yHW1QxYmgsAAMBtCLMeYplmwMgsAACA+xBmPcBsSEnnpxlUJcwCAAC4DWHWAzJy8gKtJFVimgEAAIDbEGY9IC0n778VgwMUFMC3HAAAwF1IVh5wNm+GAfNlAQAA3Iww6wFpOSZJhFkAAAB3I8x6gGVklq1sAQAA3Isw6wGWObNsZQsAAOBehFkPSMvOm2bAVrYAAADuRZj1gLPnR2aZMwsAAOBehFkPuDBnljALAADgToRZD2A1AwAAgOJBmPWAtPMjs2xlCwAA4F6EWQ9IY84sAABAsSDMFrPsXLMycs+vZsCcWQAAALcizBazM+l5cwz8TFJYOZbmAgAAcCfCbDE7k5YlSQovFyh/P5OXqwEAAChdCLPFKNds6Jd9pyVJ5QL9lWs2vFwRAABA6VIiwuy7776rmJgYhYSEqG3bttqwYUOB7RcuXKhGjRopJCREzZo105IlSzxUqfOWbjumG19crclL/5IkHU0+pxtfXK2l2455uTIAAIDSw+thdsGCBYqLi1N8fLw2bdqk5s2bq3v37jpx4oTD9r/88osGDhyooUOHavPmzerdu7d69+6tbdu2ebjy/C3ddkwPz96kY8nnbI4fTz6nh2dvItACAAC4idfD7Guvvabhw4dryJAhatKkiaZNm6bQ0FDNmDHDYfs333xTPXr00JgxY9S4cWNNmjRJ1157rd555x0PV+5YrtnQxG92yNGEAsuxid/sYMoBAACAGwR48+JZWVnauHGjxo0bZz3m5+enLl26aN26dQ6fs27dOsXFxdkc6969uxYvXuywfWZmpjIzM61fp6SkSJKys7OVnZ1dxFdg79f9iXYjshczJB1LPqd1e0+obd0qbr8+3M/yc1IcPy/wDPrQ99GHvo3+832e7kNXruPVMHvq1Cnl5uaqRo0aNsdr1KihXbt2OXzO8ePHHbY/fvy4w/ZTpkzRxIkT7Y4vX75coaGhhaw8fxtPmST5X7bd8h9/1emdjM76khUrVni7BBQRfej76EPfRv/5Pk/1YXp6utNtvRpmPWHcuHE2I7kpKSmKjo5Wt27dFBYW5vbrVd2fqP/u+f2y7bp1aMvIrI/Izs7WihUr1LVrVwUGslawL6IPfR996NvoP9/n6T60fJLuDK+G2YiICPn7+yshIcHmeEJCgiIjIx0+JzIy0qX2wcHBCg4OtjseGBhYLJ3RrkF1RYWH6HjyOYfzZk2SIsND1K5Bddad9THF9TMDz6EPfR996NvoP9/nqT505RpevQEsKChIrVq10qpVq6zHzGazVq1apXbt2jl8Trt27WzaS3lD3vm19zR/P5PiezWRlBdcL2b5Or5XE4IsAACAG3h9NYO4uDhNnz5dn3zyiXbu3KmHH35YaWlpGjJkiCQpNjbW5gax0aNHa+nSpXr11Ve1a9cuTZgwQb///rtGjRrlrZdgp0fTKL1/37WKDA+xOR4ZHqL377tWPZpGeakyAACA0sXrc2b79++vkydPavz48Tp+/LhatGihpUuXWm/yOnTokPz8LmTuG264QXPnztWzzz6rp59+Wg0bNtTixYvVtGlTb70Eh3o0jVLXJpFat/eElv/4q7p1aMvUAgAAADfzepiVpFGjRuU7srp27Vq7Y3379lXfvn2Luaqi8/czqW3dKjq901DbulUIsgAAAG7m9WkGAAAAQGERZgEAAOCzCLMAAADwWYRZAAAA+CzCLAAAAHwWYRYAAAA+izALAAAAn0WYBQAAgM8izAIAAMBnEWYBAADgswizAAAA8FmEWQAAAPgswiwAAAB8VoC3C/A0wzAkSSkpKR65XnZ2ttLT05WSkqLAwECPXBPuRR/6PvrQ99GHvo3+832e7kNLTrPktoKUuTCbmpoqSYqOjvZyJQAAAChIamqqwsPDC2xjMpyJvKWI2WzW0aNHVbFiRZlMpmK/XkpKiqKjo/XPP/8oLCys2K8H96MPfR996PvoQ99G//k+T/ehYRhKTU1VzZo15edX8KzYMjcy6+fnp9q1a3v8umFhYbyBfRx96PvoQ99HH/o2+s/3ebIPLzcia8ENYAAAAPBZhFkAAAD4LMJsMQsODlZ8fLyCg4O9XQoKiT70ffSh76MPfRv95/tKch+WuRvAAAAAUHowMgsAAACfRZgFAACAzyLMAgAAwGcRZgEAAOCzCLNu8O677yomJkYhISFq27atNmzYUGD7hQsXqlGjRgoJCVGzZs20ZMkSD1WK/LjSh9u3b9fdd9+tmJgYmUwmvfHGG54rFPlypQ+nT5+uDh06qHLlyqpcubK6dOly2fctip8rfbho0SK1bt1alSpVUvny5dWiRQt9+umnHqwWl3L170KL+fPny2QyqXfv3sVbIC7LlT6cNWuWTCaTzZ+QkBAPVnsBYbaIFixYoLi4OMXHx2vTpk1q3ry5unfvrhMnTjhs/8svv2jgwIEaOnSoNm/erN69e6t3797atm2bhyuHhat9mJ6ernr16mnq1KmKjIz0cLVwxNU+XLt2rQYOHKg1a9Zo3bp1io6OVrdu3XTkyBEPVw4LV/uwSpUqeuaZZ7Ru3Tr98ccfGjJkiIYMGaJly5Z5uHJIrvefxYEDB/Tkk0+qQ4cOHqoU+SlMH4aFhenYsWPWPwcPHvRgxRcxUCRt2rQxRo4caf06NzfXqFmzpjFlyhSH7fv162fcdtttNsfatm1r/Otf/yrWOpE/V/vwYnXq1DFef/31YqwOzihKHxqGYeTk5BgVK1Y0Pvnkk+IqEZdR1D40DMNo2bKl8eyzzxZHebiMwvRfTk6OccMNNxgfffSRMXjwYOPOO+/0QKXIj6t9OHPmTCM8PNxD1RWMkdkiyMrK0saNG9WlSxfrMT8/P3Xp0kXr1q1z+Jx169bZtJek7t2759sexaswfYiSxR19mJ6eruzsbFWpUqW4ykQBitqHhmFo1apV2r17tzp27FicpcKBwvbf888/r+rVq2vo0KGeKBMFKGwfnj17VnXq1FF0dLTuvPNObd++3RPl2iHMFsGpU6eUm5urGjVq2ByvUaOGjh8/7vA5x48fd6k9ildh+hAlizv68KmnnlLNmjXt/qEJzyhsHyYnJ6tChQoKCgrSbbfdprfffltdu3Yt7nJxicL0308//aSPP/5Y06dP90SJuIzC9OFVV12lGTNm6KuvvtLs2bNlNpt1ww036PDhw54o2UaAx68IACXI1KlTNX/+fK1du9ZrNy+gcCpWrKgtW7bo7NmzWrVqleLi4lSvXj3ddNNN3i4NBUhNTdX999+v6dOnKyIiwtvloJDatWundu3aWb++4YYb1LhxY33wwQeaNGmSR2shzBZBRESE/P39lZCQYHM8ISEh3xuDIiMjXWqP4lWYPkTJUpQ+fOWVVzR16lStXLlS11xzTXGWiQIUtg/9/PzUoEEDSVKLFi20c+dOTZkyhTDrYa723759+3TgwAH16tXLesxsNkuSAgICtHv3btWvX794i4YNd/xdGBgYqJYtW2rv3r3FUWKBmGZQBEFBQWrVqpVWrVplPWY2m7Vq1Sqbf61crF27djbtJWnFihX5tkfxKkwfomQpbB++9NJLmjRpkpYuXarWrVt7olTkw13vQ7PZrMzMzOIoEQVwtf8aNWqkP//8U1u2bLH+ueOOO9S5c2dt2bJF0dHRniwfcs97MDc3V3/++aeioqKKq8z8efsONF83f/58Izg42Jg1a5axY8cO46GHHjIqVapkHD9+3DAMw7j//vuNsWPHWtv//PPPRkBAgPHKK68YO3fuNOLj443AwEDjzz//9NZLKPNc7cPMzExj8+bNxubNm42oqCjjySefNDZv3mzs2bPHWy+hzHO1D6dOnWoEBQUZn3/+uXHs2DHrn9TUVG+9hDLP1T6cPHmysXz5cmPfvn3Gjh07jFdeecUICAgwpk+f7q2XUKa52n+XYjUD73O1DydOnGgsW7bM2Ldvn7Fx40ZjwIABRkhIiLF9+3aP106YdYO3337buOKKK4ygoCCjTZs2xvr1662PderUyRg8eLBN+88++8y48sorjaCgIOPqq682vvvuOw9XjEu50of79+83JNn96dSpk+cLh5UrfVinTh2HfRgfH+/5wmHlSh8+88wzRoMGDYyQkBCjcuXKRrt27Yz58+d7oWpYuPp34cUIsyWDK3342GOPWdvWqFHDuPXWW41NmzZ5oWrDMBmGYXh+PBgAAAAoOubMAgAAwGcRZgEAAOCzCLMAAADwWYRZAAAA+CzCLAAAAHwWYRYAAAA+izALAAAAn0WYBQAAgM8izAJAMVu7dq1MJpOSkpI8et1Zs2apUqVKRTrHgQMHZDKZtGXLlnzbeOv1AYBEmAWAIjGZTAX+mTBhgrdLBIBSLcDbBQCALzt27Jj1/xcsWKDx48dr9+7d1mMVKlTQ77//7vJ5s7KyFBQU5JYaAaA0Y2QWAIogMjLS+ic8PFwmk8nmWIUKFaxtN27cqNatWys0NFQ33HCDTeidMGGCWrRooY8++kh169ZVSEiIJCkpKUnDhg1TtWrVFBYWpptvvllbt261Pm/r1q3q3LmzKlasqLCwMLVq1couPC9btkyNGzdWhQoV1KNHD5sAbjab9fzzz6t27doKDg5WixYttHTp0gJf85IlS3TllVeqXLly6ty5sw4cOFCUbyEAFAlhFgA85JlnntGrr76q33//XQEBAXrwwQdtHt+7d6+++OILLVq0yDpHtW/fvjpx4oS+//57bdy4Uddee61uueUWJSYmSpLuvfde1a5dW7/99ps2btyosWPHKjAw0HrO9PR0vfLKK/r000/1ww8/6NChQ3ryySetj7/55pt69dVX9corr+iPP/5Q9+7ddccdd2jPnj0OX8M///yjPn36qFevXtqyZYuGDRumsWPHuvk7BQAuMAAAbjFz5kwjPDzc7viaNWsMScbKlSutx7777jtDkpGRkWEYhmHEx8cbgYGBxokTJ6xtfvzxRyMsLMw4d+6czfnq169vfPDBB4ZhGEbFihWNWbNm5VuPJGPv3r3WY++++65Ro0YN69c1a9Y0/vOf/9g877rrrjNGjBhhGIZh7N+/35BkbN682TAMwxg3bpzRpEkTm/ZPPfWUIck4c+aMwzoAoDgxMgsAHnLNNddY/z8qKkqSdOLECeuxOnXqqFq1atavt27dqrNnz6pq1aqqUKGC9c/+/fu1b98+SVJcXJyGDRumLl26aOrUqdbjFqGhoapfv77NdS3XTElJ0dGjR9W+fXub57Rv3147d+50+Bp27typtm3b2hxr166d098DAHA3bgADAA+5+ON/k8kkKW/OqkX58uVt2p89e1ZRUVFau3at3bksS25NmDBBgwYN0nfffafvv/9e8fHxmj9/vu666y67a1quaxiGO14OAJQIjMwCQAl17bXX6vjx4woICFCDBg1s/kRERFjbXXnllXr88ce1fPly9enTRzNnznTq/GFhYapZs6Z+/vlnm+M///yzmjRp4vA5jRs31oYNG2yOrV+/3sVXBgDuQ5gFgBKqS5cuateunXr37q3ly5frwIED+uWXX/TMM8/o999/V0ZGhkaNGqW1a9fq4MGD+vnnn/Xbb7+pcePGTl9jzJgxevHFF7VgwQLt3r1bY8eO1ZYtWzR69GiH7f/9739rz549GjNmjHbv3q25c+dq1qxZbnrFAOA6phkAQAllMpm0ZMkSPfPMMxoyZIhOnjypyMhIdezYUTVq1JC/v79Onz6t2NhYJSQkKCIiQn369NHEiROdvsajjz6q5ORkPfHEEzpx4oSaNGmir7/+Wg0bNnTY/oorrtAXX3yhxx9/XG+//bbatGmjyZMn263MAACeYjKYPAUAAAAfxTQDAAAA+CzCLAAAAHwWYRYAAAA+izALAAAAn0WYBQAAgM8izAIAAMBnEWYBAADgswizAAAA8FmEWQAAAPgswiwAAAB8FmEWAAAAPuv/AUUNzjyYatE/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_predictions_vs_actual(y_val, y_val_pred_gbr)\n",
        "plot_accuracy_over_thresholds(y_val, y_val_pred_gbr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "68pLqGtqONPV",
        "outputId": "b5916d44-fe38-4e68-bfb1-98365716f03a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate training loss (Mean Squared Error) over each boosting iteration\n",
        "train_loss = [mean_squared_error(y_train, y_pred) for y_pred in gbr.staged_predict(X_train)]\n",
        "iterations = np.arange(len(train_loss)) + 1\n",
        "\n",
        "# Plot the training loss\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(iterations, train_loss, '-b', label='Training Loss (MSE)')\n",
        "plt.xlabel('Boosting Iterations')\n",
        "plt.ylabel('Mean Squared Error (MSE)')\n",
        "plt.title('Training Loss over Boosting Iterations')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "2gD-IBvzONPV",
        "outputId": "9c5f289f-4d13-4fe8-ccd0-16b2b0fd922c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Actual vs. Predicted Sales')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 1. Scatter Plot (Actual vs. Predicted)\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(y_val, y_val_pred_gbr, alpha=0.5)\n",
        "plt.plot([0, 1], [0, 1], '--k', transform=plt.gca().transAxes)\n",
        "plt.xlabel('Actual Sales')\n",
        "plt.ylabel('Predicted Sales')\n",
        "plt.title('Actual vs. Predicted Sales')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zff1LjBXONPV",
        "outputId": "7baa16ef-5793-47fb-d4e9-7402d82dd5b1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2. Residuals Plot\n",
        "residuals = y_val.ravel() - y_val_pred_gbr\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(y_val_pred_gbr, residuals, alpha=0.5)\n",
        "plt.hlines(0, min(y_val_pred_gbr), max(y_val_pred_gbr), colors='k', linestyles='--')\n",
        "plt.xlabel('Predicted Sales')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residuals vs. Predicted Sales')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "kVCfBf6uONPV",
        "outputId": "28464136-f294-45c8-bc58-2d5ff5823f5f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+QAAAHWCAYAAAD+cEOeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKsUlEQVR4nO3deVhWdf7/8dcNyA2igCiLJKip5ZZWNippmUqRtmgyLU4WmmZNtrjV5ExWtkjaoi0u06RomTnZOi1a5jZToaWtLqm5AIrgnQg3guyf3x/9vL/eiqZ43/dBfD6u61zT/TnnPu/3wTPUy3PO59iMMUYAAAAAAMCn/KxuAAAAAACAsxGBHAAAAAAACxDIAQAAAACwAIEcAAAAAAALEMgBAAAAALAAgRwAAAAAAAsQyAEAAAAAsACBHAAAAAAACxDIAQAAAACwAIEcAFAntWjRQkOHDrW6jTrv2Wef1bnnnit/f39deOGFVrdzjCuuuEJXXHHFH263atUq2Ww2rVq1yqv92Gw2Pf74416tAQA4cxDIAQC13rx582Sz2bRu3bpq119xxRXq2LHjadf59NNPCUun4PPPP9dDDz2kHj16KC0tTZMnTz7utkOHDpXNZnMtdrtd5513nh599FGVlJT4sGsAAGqPAKsbAADAG7Zs2SI/v1P7e+dPP/1UM2bMIJSfpBUrVsjPz09z5sxRYGDgH25vt9v12muvSZIKCgr04Ycf6sknn9T27dv15ptveqXHzz//3Cv7BQDAEwjkAIA6yW63W93CKSsqKlJISIjVbZy0ffv2KTg4+KTCuCQFBARoyJAhrs/33HOPLr30Ur311lt64YUXFB0d7fEeT7Y3AACswC3rAIA66ehnyMvLyzVp0iS1adNGQUFBaty4sXr27Klly5ZJ+v2W6hkzZkiS263VhxUVFWncuHGKi4uT3W7X+eefr+eee07GGLe6hw4d0v33368mTZqoYcOGuv7667Vnz55jnh1+/PHHZbPZtGnTJv3lL39Ro0aN1LNnT0nSTz/9pKFDh+rcc89VUFCQYmJidMcdd2j//v1utQ7vY+vWrRoyZIjCwsIUGRmpiRMnyhijrKwsDRgwQKGhoYqJidHzzz9/Uj+7iooKPfnkk2rVqpXsdrtatGihv//97yotLXVtY7PZlJaWpqKiItfPat68eSe1/yP30bNnTxljtGPHDrd1S5Ys0WWXXaaQkBA1bNhQ11xzjTZu3Oi2TU5OjoYNG6ZmzZrJbreradOmGjBggHbt2uXaprpnyHfv3q2BAwcqJCREUVFRGjNmjNuxHXa8eQiO3mdZWZkeffRRdenSRWFhYQoJCdFll12mlStX/uHPoLCwUKNHj1aLFi1kt9sVFRWlK6+8Ut99990ffhcAcObjCjkA4IxRUFCg33777Zjx8vLyP/zu448/rtTUVI0YMUJdu3aV0+nUunXr9N133+nKK6/UXXfdpezsbC1btkxvvPGG23eNMbr++uu1cuVKDR8+XBdeeKE+++wzPfjgg9qzZ4+mTZvm2nbo0KF6++23ddttt6l79+5avXq1rrnmmuP2deONN6pNmzaaPHmyK9wvW7ZMO3bs0LBhwxQTE6ONGzfq1Vdf1caNG7VmzRq3vyiQpJtvvlnt2rXTM888o08++URPPfWUIiIi9M9//lN9+vTRlClT9Oabb2r8+PH605/+pMsvv/yEP6sRI0Zo/vz5+vOf/6xx48Zp7dq1Sk1N1ebNm/X+++9Lkt544w29+uqr+uabb1y3oV966aV/+OdwtMPhuVGjRq6xN954QykpKUpKStKUKVNUXFysWbNmqWfPnvr+++/VokULSVJycrI2btyo++67Ty1atNC+ffu0bNkyZWZmurY52qFDh9S3b19lZmbq/vvvV2xsrN544w2tWLHilHs/zOl06rXXXtPgwYN15513qrCwUHPmzFFSUpK++eabE052d/fdd+udd97Rvffeq/bt22v//v368ssvtXnzZl188cU17gkAcIYwAADUcmlpaUbSCZcOHTq4fad58+YmJSXF9blz587mmmuuOWGdUaNGmer+1fjBBx8YSeapp55yG//zn/9sbDab+fXXX40xxqxfv95IMqNHj3bbbujQoUaSeeyxx1xjjz32mJFkBg8efEy94uLiY8beeustI8n897//PWYfI0eOdI1VVFSYZs2aGZvNZp555hnX+IEDB0xwcLDbz6Q6P/zwg5FkRowY4TY+fvx4I8msWLHCNZaSkmJCQkJOuL+jt3U4HMbhcJhff/3VPPfcc8Zms5mOHTuaqqoqY4wxhYWFJjw83Nx5551u38/JyTFhYWGu8QMHDhhJ5tlnnz1h3V69eplevXq5Pk+fPt1IMm+//bZrrKioyLRu3dpIMitXrnSNH30OHW+fFRUVprS01G2bAwcOmOjoaHPHHXe4jR99HoSFhZlRo0ad8BgAAHUXt6wDAM4YM2bM0LJly45ZOnXq9IffDQ8P18aNG7Vt27ZTrvvpp5/K399f999/v9v4uHHjZIzRkiVLJElLly6V9Puz0Ue67777jrvvu++++5ix4OBg1z+XlJTot99+U/fu3SWp2luZR4wY4fpnf39/XXLJJTLGaPjw4a7x8PBwnX/++cfcGn60Tz/9VJI0duxYt/Fx48ZJkj755JMTfv9EioqKFBkZqcjISLVu3Vrjx49Xjx499OGHH7qu+i9btkz5+fkaPHiwfvvtN9fi7++vbt26uW4DP/zs+qpVq3TgwIGT7uHTTz9V06ZN9ec//9k1Vr9+fY0cObLGx+Xv7+96Vr2qqkp5eXmqqKjQJZdc8oe3noeHh2vt2rXKzs6ucX0AwJmLW9YBAGeMrl276pJLLjlmvFGjRtXeyn6kJ554QgMGDNB5552njh076uqrr9Ztt912UmE+IyNDsbGxatiwodt4u3btXOsP/6+fn59atmzptl3r1q2Pu++jt5WkvLw8TZo0SYsWLdK+ffvc1hUUFByzfXx8vNvnsLAwBQUFqUmTJseMH/0c+tEOH8PRPcfExCg8PNx1rDURFBSkjz76SNLvz3FPnTrVNTHcYYf/wqRPnz7V7iM0NFTS75P2TZkyRePGjVN0dLS6d++ua6+9VrfffrtiYmJOeHytW7c+5rb/888/v8bHJUnz58/X888/r19++cXtEYrq/nyPNHXqVKWkpCguLk5dunRR//79dfvtt+vcc889rX4AAGcGrpADAM4Kl19+ubZv3665c+eqY8eOeu2113TxxRe7nn+2ypFh9LCbbrpJ//rXv3T33Xfrvffe0+eff+66+l5VVXXM9v7+/ic1JumYSeiO5+jA6gn+/v5KTExUYmKihg4dquXLlysnJ0d33XWXa5vDx/fGG29UezfEhx9+6Np29OjR2rp1q1JTUxUUFKSJEyeqXbt2+v777z3S7/F+BpWVlW6fFyxYoKFDh6pVq1aaM2eOli5dqmXLlqlPnz7V/nkd6aabbtKOHTv08ssvKzY2Vs8++6w6dOjguusCAFC3EcgBAGeNiIgIDRs2TG+99ZaysrLUqVMnt5nPjxfAmjdvruzsbBUWFrqN//LLL671h/+3qqpKO3fudNvu119/PekeDxw4oOXLl+vhhx/WpEmTdMMNN+jKK6/02RXTw8dw9K39ubm5ys/Pdx2rJzRt2lRjxozRRx99pDVr1kiSWrVqJUmKiopyhfcjl6NnTG/VqpXGjRunzz//XBs2bFBZWdkJZ5Nv3ry5tm/ffsxfTGzZsuWYbRs1aqT8/Pxjxo++S+Cdd97Rueeeq/fee0+33XabkpKSlJiYqJKSkpP5Mahp06a655579MEHH2jnzp1q3Lixnn766ZP6LgDgzEYgBwCcFY6+VbtBgwZq3bq12+uuDr8D/OgQ1r9/f1VWVuqVV15xG582bZpsNpv69esnSUpKSpIkzZw50227l19++aT7PHxl++jAOH369JPex+no379/tfVeeOEFSTrhjPE1cd9996l+/fp65plnJP3+MwwNDdXkyZOrnT3f4XBIkoqLi48JvK1atVLDhg2rfYXZYf3791d2drbeeecd11hxcbFeffXVY7Zt1aqV1qxZo7KyMtfYxx9/rKysLLftqvszW7t2rdLT04/bh/T7lfajH0GIiopSbGzsCY8BAFB38Aw5AOCs0L59e11xxRXq0qWLIiIitG7dOtfrpg7r0qWLJOn+++9XUlKS/P39dcstt+i6665T79699Y9//EO7du1S586d9fnnn+vDDz/U6NGjXVd1u3TpouTkZE2fPl379+93vfZs69atkk7uNvDQ0FBdfvnlmjp1qsrLy3XOOefo888/P+aqu7d07txZKSkpevXVV5Wfn69evXrpm2++0fz58zVw4ED17t3bo/UaN26sYcOGaebMmdq8ebPatWunWbNm6bbbbtPFF1+sW265RZGRkcrMzNQnn3yiHj166JVXXtHWrVvVt29f3XTTTWrfvr0CAgL0/vvvKzc3V7fccstx691555165ZVXdPvtt2v9+vVq2rSp3njjDdWvX/+YbUeMGKF33nlHV199tW666SZt375dCxYscP15H3bttdfqvffe0w033KBrrrlGO3fu1OzZs9W+fXsdPHjwuL0UFhaqWbNm+vOf/6zOnTurQYMG+uKLL/Ttt9+e9DvjAQBnOCuneAcA4GQcfu3Zt99+W+36Xr16/eFrz5566inTtWtXEx4eboKDg03btm3N008/bcrKylzbVFRUmPvuu89ERkYam83m9gq0wsJCM2bMGBMbG2vq1atn2rRpY5599lnX67oOKyoqMqNGjTIRERGmQYMGZuDAgWbLli1GkttryA6/sszhcBxzPLt37zY33HCDCQ8PN2FhYebGG2802dnZx3112tH7ON7ryKr7OVWnvLzcTJo0ybRs2dLUq1fPxMXFmQkTJpiSkpKTqlOdE227fft24+/v7/bntXLlSpOUlGTCwsJMUFCQadWqlRk6dKhZt26dMcaY3377zYwaNcq0bdvWhISEmLCwMNOtWze315kdPuYjX1FmjDEZGRnm+uuvN/Xr1zdNmjQxDzzwgFm6dOkxrz0zxpjnn3/enHPOOcZut5sePXqYdevWHbPPqqoqM3nyZNO8eXNjt9vNRRddZD7++GOTkpJimjdv7ra/I/8MS0tLzYMPPmg6d+5sGjZsaEJCQkznzp3NzJkzT+pnCgA489mMOcnZXQAAQI388MMPuuiii7RgwQLdeuutVrcDAABqCZ4hBwDAgw4dOnTM2PTp0+Xn56fLL7/cgo4AAEBtxTPkAAB40NSpU7V+/Xr17t1bAQEBWrJkiZYsWaKRI0cqLi7O6vYAAEAtwi3rAAB40LJlyzRp0iRt2rRJBw8eVHx8vG677Tb94x//UEAAfw8OAAD+D4EcAAAAAAAL8Aw5AAAAAAAWIJADAAAAAGCBOv8wW1VVlbKzs9WwYUPZbDar2wEAAAAA1HHGGBUWFio2NlZ+fse/Dl7nA3l2djaz2gIAAAAAfC4rK0vNmjU77vo6H8gbNmwo6fcfRGhoqMXdAAAAAADqOqfTqbi4OFcePZ46H8gP36YeGhpKIAcAAAAA+MwfPTbNpG4AAAAAAFiAQA4AAAAAgAUsDeSVlZWaOHGiWrZsqeDgYLVq1UpPPvmkjDGubYwxevTRR9W0aVMFBwcrMTFR27Zts7BrAAAAAABOn6WBfMqUKZo1a5ZeeeUVbd68WVOmTNHUqVP18ssvu7aZOnWqXnrpJc2ePVtr165VSEiIkpKSVFJSYmHnAAAAAACcHps58nK0j1177bWKjo7WnDlzXGPJyckKDg7WggULZIxRbGysxo0bp/Hjx0uSCgoKFB0drXnz5umWW275wxpOp1NhYWEqKChgUjcAAAAAgNedbA619Ar5pZdequXLl2vr1q2SpB9//FFffvml+vXrJ0nauXOncnJylJiY6PpOWFiYunXrpvT09Gr3WVpaKqfT6bYAAAAAAFDbWPras4cfflhOp1Nt27aVv7+/Kisr9fTTT+vWW2+VJOXk5EiSoqOj3b4XHR3tWne01NRUTZo0ybuNAwAAAABwmiy9Qv7222/rzTff1MKFC/Xdd99p/vz5eu655zR//vwa73PChAkqKChwLVlZWR7sGAAAAAAAz7D0CvmDDz6ohx9+2PUs+AUXXKCMjAylpqYqJSVFMTExkqTc3Fw1bdrU9b3c3FxdeOGF1e7TbrfLbrd7vXcAAAAAAE6HpVfIi4uL5efn3oK/v7+qqqokSS1btlRMTIyWL1/uWu90OrV27VolJCT4tFcAAAAAADzJ0ivk1113nZ5++mnFx8erQ4cO+v777/XCCy/ojjvukCTZbDaNHj1aTz31lNq0aaOWLVtq4sSJio2N1cCBA61sHQAAAACA02JpIH/55Zc1ceJE3XPPPdq3b59iY2N111136dFHH3Vt89BDD6moqEgjR45Ufn6+evbsqaVLlyooKMjCzgEAAAAAOD2WvofcF3gPOQAAAADAl86I95ADAAAAAHC2svSWdQAAAADH53A45HQ6fVozNDRUkZGRPq0JnK0I5AAAAEAt5HA4NGTYCOUVFvu0bkTD+lqQ9hqhHPABAjkAAABQCzmdTuUVFisyIVkhEdE+qVmUlytH+rtyOp0EcsAHCOQAAABALRYSEa3QqGY+q+fwWSUATOoGAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYIMDqBgAAAFB3OBwOOZ1On9ULDQ1VZGSkz+oBgCcRyAEAAOARDodDQ4aNUF5hsc9qRjSsrwVprxHKAZyRCOQAAADwCKfTqbzCYkUmJCskItrr9YrycuVIf1dOp5NADuCMRCAHAACAR4VERCs0qplPajl8UgUAvINJ3QAAAAAAsACBHAAAAAAACxDIAQAAAACwgKWBvEWLFrLZbMcso0aNkiSVlJRo1KhRaty4sRo0aKDk5GTl5uZa2TIAAAAAAB5haSD/9ttvtXfvXteybNkySdKNN94oSRozZow++ugjLV68WKtXr1Z2drYGDRpkZcsAAAAAAHiEpbOsH/16imeeeUatWrVSr169VFBQoDlz5mjhwoXq06ePJCktLU3t2rXTmjVr1L17dytaBgAAAADAI2rNM+RlZWVasGCB7rjjDtlsNq1fv17l5eVKTEx0bdO2bVvFx8crPT39uPspLS2V0+l0WwAAAAAAqG1qTSD/4IMPlJ+fr6FDh0qScnJyFBgYqPDwcLftoqOjlZOTc9z9pKamKiwszLXExcV5sWsAAAAAAGqm1gTyOXPmqF+/foqNjT2t/UyYMEEFBQWuJSsry0MdAgAAAADgOZY+Q35YRkaGvvjiC7333nuusZiYGJWVlSk/P9/tKnlubq5iYmKOuy+73S673e7NdgEAAAAAOG214gp5WlqaoqKidM0117jGunTponr16mn58uWusS1btigzM1MJCQlWtAkAAAAAgMdYfoW8qqpKaWlpSklJUUDA/7UTFham4cOHa+zYsYqIiFBoaKjuu+8+JSQkMMM6AAAAAOCMZ3kg/+KLL5SZmak77rjjmHXTpk2Tn5+fkpOTVVpaqqSkJM2cOdOCLgEAAAAA8CzLA/lVV10lY0y164KCgjRjxgzNmDHDx10BAAAAAOBdteIZcgAAAAAAzjYEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALBBgdQMAAABWcTgccjqdPqsXGhqqyMhIn9UDANRuBHIAAHBWcjgcGjJshPIKi31WM6JhfS1Ie41QDgCQRCAHAABnKafTqbzCYkUmJCskItrr9YrycuVIf1dOp5NADgCQRCAHAABnuZCIaIVGNfNJLYdPqgAAzhRM6gYAAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFjA8kC+Z88eDRkyRI0bN1ZwcLAuuOACrVu3zrXeGKNHH31UTZs2VXBwsBITE7Vt2zYLOwYAAAAA4PRZGsgPHDigHj16qF69elqyZIk2bdqk559/Xo0aNXJtM3XqVL300kuaPXu21q5dq5CQECUlJamkpMTCzgEAAAAAOD0BVhafMmWK4uLilJaW5hpr2bKl65+NMZo+fboeeeQRDRgwQJL0+uuvKzo6Wh988IFuueUWn/cMAAAAAIAnWHqF/D//+Y8uueQS3XjjjYqKitJFF12kf/3rX671O3fuVE5OjhITE11jYWFh6tatm9LT06vdZ2lpqZxOp9sCAAAAAEBtY2kg37Fjh2bNmqU2bdros88+01//+lfdf//9mj9/viQpJydHkhQdHe32vejoaNe6o6WmpiosLMy1xMXFefcgAAAAAACoAUsDeVVVlS6++GJNnjxZF110kUaOHKk777xTs2fPrvE+J0yYoIKCAteSlZXlwY4BAAAAAPAMSwN506ZN1b59e7exdu3aKTMzU5IUExMjScrNzXXbJjc317XuaHa7XaGhoW4LAAAAAAC1jaWBvEePHtqyZYvb2NatW9W8eXNJv0/wFhMTo+XLl7vWO51OrV27VgkJCT7tFQAAAAAAT7J0lvUxY8bo0ksv1eTJk3XTTTfpm2++0auvvqpXX31VkmSz2TR69Gg99dRTatOmjVq2bKmJEycqNjZWAwcOtLJ1AAAAAABOi6WB/E9/+pPef/99TZgwQU888YRatmyp6dOn69Zbb3Vt89BDD6moqEgjR45Ufn6+evbsqaVLlyooKMjCzgEAAAAAOD2WBnJJuvbaa3Xttdced73NZtMTTzyhJ554woddAQAAAADgXZY+Qw4AAAAAwNmKQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWCDA6gYAAADgPQ6HQ06n0ye1MjIyVFFe4ZNaAFAXEMgBAADqKIfDoSHDRiivsNgn9UoOFWv3nr2KLy/3ST0AONMRyAEAAOoop9OpvMJiRSYkKyQi2uv19m3foIysuaqsIJADwMkgkAMAANRxIRHRCo1q5vU6B/fneL0GANQlTOoGAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWCDA6gYAAACAM4XD4ZDT6fRJrYyMDFWUV/ikFgBrEMgBAACAk+BwODRk2AjlFRb7pF7JoWLt3rNX8eXlPqkHwPcI5AAAAMBJcDqdyissVmRCskIior1eb9/2DcrImqvKCgI5UFcRyAEAAIBTEBIRrdCoZl6vc3B/jtdrALAWk7oBAAAAAGABAjkAAAAAABYgkAMAAAAAYAFLA/njjz8um83mtrRt29a1vqSkRKNGjVLjxo3VoEEDJScnKzc318KOAQAAAADwDMuvkHfo0EF79+51LV9++aVr3ZgxY/TRRx9p8eLFWr16tbKzszVo0CALuwUAAAAAwDMsn2U9ICBAMTExx4wXFBRozpw5Wrhwofr06SNJSktLU7t27bRmzRp1797d160CAAAAAOAxlgfybdu2KTY2VkFBQUpISFBqaqri4+O1fv16lZeXKzEx0bVt27ZtFR8fr/T09OMG8tLSUpWWlro+O51Orx8DAAAArFFeVqaMjAyf1MrIyFBFeYVPagE4O1gayLt166Z58+bp/PPP1969ezVp0iRddtll2rBhg3JychQYGKjw8HC370RHRysn5/jvZExNTdWkSZO83DkAAACsVnqwQLt27tDovz8uu93u9Xolh4q1e89exZeXe70WgLODpYG8X79+rn/u1KmTunXrpubNm+vtt99WcHBwjfY5YcIEjR071vXZ6XQqLi7utHsFAABA7VJeekhVtgA16T5IjWObe73evu0blJE1V5UVBHIAnmH5LetHCg8P13nnnadff/1VV155pcrKypSfn+92lTw3N7faZ84Ps9vtPvkbUgAAANQO9RtFKjSqmdfrHNx//Ls0AaAmLJ9l/UgHDx7U9u3b1bRpU3Xp0kX16tXT8uXLXeu3bNmizMxMJSQkWNglAAAAAACnz9Ir5OPHj9d1112n5s2bKzs7W4899pj8/f01ePBghYWFafjw4Ro7dqwiIiIUGhqq++67TwkJCcywDgAAAAA441kayHfv3q3Bgwdr//79ioyMVM+ePbVmzRpFRkZKkqZNmyY/Pz8lJyertLRUSUlJmjlzppUtAwAAAADgEZYG8kWLFp1wfVBQkGbMmKEZM2b4qCMAAAAAAHyjVj1DDgAAAADA2YJADgAAAACABQjkAAAAAABYoEaBfMeOHZ7uAwAAAACAs0qNAnnr1q3Vu3dvLViwQCUlJZ7uCQAAAACAOq9Ggfy7775Tp06dNHbsWMXExOiuu+7SN9984+neAAAAAACos2oUyC+88EK9+OKLys7O1ty5c7V371717NlTHTt21AsvvCCHw+HpPgEAAAAAqFNOa1K3gIAADRo0SIsXL9aUKVP066+/avz48YqLi9Ptt9+uvXv3eqpPAAAAAADqlNMK5OvWrdM999yjpk2b6oUXXtD48eO1fft2LVu2TNnZ2RowYICn+gQAAAAAoE4JqMmXXnjhBaWlpWnLli3q37+/Xn/9dfXv319+fr/n+5YtW2revHlq0aKFJ3sFAAAAAKDOqFEgnzVrlu644w4NHTpUTZs2rXabqKgozZkz57SaAwAAAACgrqpRIN+2bdsfbhMYGKiUlJSa7B4AAAAAgDqvRs+Qp6WlafHixceML168WPPnzz/tpgAAAAAAqOtqFMhTU1PVpEmTY8ajoqI0efLk024KAAAAAIC6rkaBPDMzUy1btjxmvHnz5srMzDztpgAAAAAAqOtqFMijoqL0008/HTP+448/qnHjxqfdFAAAAAAAdV2NAvngwYN1//33a+XKlaqsrFRlZaVWrFihBx54QLfccounewQAAAAAoM6p0SzrTz75pHbt2qW+ffsqIOD3XVRVVen222/nGXIAAAAAAE5CjQJ5YGCg/v3vf+vJJ5/Ujz/+qODgYF1wwQVq3ry5p/sDAAAAAKBOqlEgP+y8887Teeed56leAAAAAAA4a9QokFdWVmrevHlavny59u3bp6qqKrf1K1as8EhzAAAAAADUVTUK5A888IDmzZuna665Rh07dpTNZvN0XwAAAAAA1Gk1CuSLFi3S22+/rf79+3u6HwAAAAAAzgo1eu1ZYGCgWrdu7eleAAAAAAA4a9QokI8bN04vvviijDGe7gcAAAAAgLNCjW5Z//LLL7Vy5UotWbJEHTp0UL169dzWv/feex5pDgAAoC4pLytTRkaGz+plZGSoorzCZ/UAAKemRoE8PDxcN9xwg6d7AQAAqLNKDxZo184dGv33x2W3231Ss+RQsXbv2av48nKf1AMAnJoaBfK0tDRP9wEAAFCnlZceUpUtQE26D1Lj2OY+qblv+wZlZM1VZQWBHABqoxoFckmqqKjQqlWrtH37dv3lL39Rw4YNlZ2drdDQUDVo0MCTPQIAANQZ9RtFKjSqmU9qHdyf45M6AICaqVEgz8jI0NVXX63MzEyVlpbqyiuvVMOGDTVlyhSVlpZq9uzZnu4TAAAAgA/4eq6D0NBQRUZG+qweUJvUKJA/8MADuuSSS/Tjjz+qcePGrvEbbrhBd955p8eaAwAAAOA7Vsx1ENGwvhakvUYox1mpRoH8f//7n77++msFBga6jbdo0UJ79uzxSGMAAAAAfMvXcx0U5eXKkf6unE4ngRxnpRoF8qqqKlVWVh4zvnv3bjVs2PC0mwIAAABgHV/OdeDwSRWgdvKryZeuuuoqTZ8+3fXZZrPp4MGDeuyxx9S/f39P9QYAAAAAQJ1Voyvkzz//vJKSktS+fXuVlJToL3/5i7Zt26YmTZrorbfe8nSPAAAAAADUOTUK5M2aNdOPP/6oRYsW6aefftLBgwc1fPhw3XrrrQoODvZ0jwAAAAAA1Dk1umVdkgICAjRkyBBNnTpVM2fO1IgRI04rjD/zzDOy2WwaPXq0a6ykpESjRo1S48aN1aBBAyUnJys3N7fGNQAAAAAAqC1qdIX89ddfP+H622+//ZT29+233+qf//ynOnXq5DY+ZswYffLJJ1q8eLHCwsJ07733atCgQfrqq69OuWcAAAAAAGqTGr+H/Ejl5eUqLi5WYGCg6tevf0qB/ODBg7r11lv1r3/9S0899ZRrvKCgQHPmzNHChQvVp08fSVJaWpratWunNWvWqHv37jVpHQAAAACAWqFGt6wfOHDAbTl48KC2bNminj17nvKkbqNGjdI111yjxMREt/H169ervLzcbbxt27aKj49Xenr6cfdXWloqp9PptgAAAAAAUNvU+Bnyo7Vp00bPPPPMMVfPT2TRokX67rvvlJqaesy6nJwcBQYGKjw83G08OjpaOTk5x91namqqwsLCXEtcXNxJ9wMAAAAAgK94LJBLv0/0lp2dfVLbZmVl6YEHHtCbb76poKAgj/UwYcIEFRQUuJasrCyP7RsAAAAAAE+p0TPk//nPf9w+G2O0d+9evfLKK+rRo8dJ7WP9+vXat2+fLr74YtdYZWWl/vvf/+qVV17RZ599prKyMuXn57tdJc/NzVVMTMxx92u322W320/tgAAAAAAA8LEaBfKBAwe6fbbZbIqMjFSfPn30/PPPn9Q++vbtq59//tltbNiwYWrbtq3+9re/KS4uTvXq1dPy5cuVnJwsSdqyZYsyMzOVkJBQk7YBAAAAAKg1ahTIq6qqTrtww4YN1bFjR7exkJAQNW7c2DU+fPhwjR07VhEREQoNDdV9992nhIQEZlgHAAAAAJzxahTIfWXatGny8/NTcnKySktLlZSUpJkzZ1rdFgAAAAAAp61GgXzs2LEnve0LL7xw0tuuWrXK7XNQUJBmzJihGTNmnPQ+AAAAAAA4E9QokH///ff6/vvvVV5ervPPP1+StHXrVvn7+7tN0maz2TzTJQAAAAAAdUyNAvl1112nhg0bav78+WrUqJEk6cCBAxo2bJguu+wyjRs3zqNNAgAAAABQ19ToPeTPP/+8UlNTXWFckho1aqSnnnrqpGdZBwAAAADgbFajQO50OuVwOI4ZdzgcKiwsPO2mAAAAAACo62oUyG+44QYNGzZM7733nnbv3q3du3fr3Xff1fDhwzVo0CBP9wgAAAAAQJ1To2fIZ8+erfHjx+svf/mLysvLf99RQICGDx+uZ5991qMNAgAAAABQF9UokNevX18zZ87Us88+q+3bt0uSWrVqpZCQEI82BwAAAABAXVWjW9YP27t3r/bu3as2bdooJCRExhhP9QUAAAAAQJ1Wo0C+f/9+9e3bV+edd5769++vvXv3SpKGDx/OK88AAAAAADgJNQrkY8aMUb169ZSZman69eu7xm+++WYtXbrUY80BAAAAAFBX1egZ8s8//1yfffaZmjVr5jbepk0bZWRkeKQxAAAAAADqshpdIS8qKnK7Mn5YXl6e7Hb7aTcFAAAAAEBdV6NAftlll+n11193fbbZbKqqqtLUqVPVu3dvjzUHAAAAAEBdVaNb1qdOnaq+fftq3bp1Kisr00MPPaSNGzcqLy9PX331lad7BAAAAACgzqnRFfKOHTtq69at6tmzpwYMGKCioiINGjRI33//vVq1auXpHgEAAAAAqHNO+Qp5eXm5rr76as2ePVv/+Mc/vNETAAAAAAB13ilfIa9Xr55++uknb/QCAAAAAMBZo0a3rA8ZMkRz5szxdC8AAAAAAJw1ajSpW0VFhebOnasvvvhCXbp0UUhIiNv6F154wSPNAQAAAABQV51SIN+xY4datGihDRs26OKLL5Ykbd261W0bm83mue4AAAAAAKijTimQt2nTRnv37tXKlSslSTfffLNeeuklRUdHe6U5AAAAAADqqlN6htwY4/Z5yZIlKioq8mhDAAAAAACcDWo0qdthRwd0AAAAAABwck4pkNtstmOeEeeZcQAAAAAATt0pPUNujNHQoUNlt9slSSUlJbr77ruPmWX9vffe81yHAAAAAADUQacUyFNSUtw+DxkyxKPNAAAAAABwtjilQJ6WluatPgAAAAAAOKuc1qRuAAAAAACgZgjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFLA3ks2bNUqdOnRQaGqrQ0FAlJCRoyZIlrvUlJSUaNWqUGjdurAYNGig5OVm5ubkWdgwAAAAAgGdYGsibNWumZ555RuvXr9e6devUp08fDRgwQBs3bpQkjRkzRh999JEWL16s1atXKzs7W4MGDbKyZQAAAAAAPCLAyuLXXXed2+enn35as2bN0po1a9SsWTPNmTNHCxcuVJ8+fSRJaWlpateundasWaPu3btb0TIAAAAAAB5Ra54hr6ys1KJFi1RUVKSEhAStX79e5eXlSkxMdG3Ttm1bxcfHKz09/bj7KS0tldPpdFsAAAAAAKhtLA/kP//8sxo0aCC73a67775b77//vtq3b6+cnBwFBgYqPDzcbfvo6Gjl5OQcd3+pqakKCwtzLXFxcV4+AgAAAAAATp3lgfz888/XDz/8oLVr1+qvf/2rUlJStGnTphrvb8KECSooKHAtWVlZHuwWAAAAAADPsPQZckkKDAxU69atJUldunTRt99+qxdffFE333yzysrKlJ+f73aVPDc3VzExMcfdn91ul91u93bbAAAAAACcFssD+dGqqqpUWlqqLl26qF69elq+fLmSk5MlSVu2bFFmZqYSEhIs7hIAAACAJ5SXlSkjI8Nn9UJDQxUZGemzesCJWBrIJ0yYoH79+ik+Pl6FhYVauHChVq1apc8++0xhYWEaPny4xo4dq4iICIWGhuq+++5TQkICM6wDAAAAdUDpwQLt2rlDo//+uM/uco1oWF8L0l4jlKNWsDSQ79u3T7fffrv27t2rsLAwderUSZ999pmuvPJKSdK0adPk5+en5ORklZaWKikpSTNnzrSyZQAAAAAeUl56SFW2ADXpPkiNY5t7vV5RXq4c6e/K6XQSyFErWBrI58yZc8L1QUFBmjFjhmbMmOGjjgAAAAD4Wv1GkQqNauaTWg6fVAFOjuWzrAMAAAAAcDYikAMAAAAAYAECOQAAAAAAFiCQAwAAAABgAQI5AAAAAAAWIJADAAAAAGABAjkAAAAAABYgkAMAAAAAYAECOQAAAAAAFiCQAwAAAABgAQI5AAAAAAAWCLC6AQAAgMMcDoecTqdPamVkZKiivMIntQAAqA6BHAAA1AoOh0NDho1QXmGxT+qVHCrW7j17FV9e7pN6AAAcjUAOAABqBafTqbzCYkUmJCskItrr9fZt36CMrLmqrCCQAwCsQSAHAAC1SkhEtEKjmnm9zsH9OV6vAQDAiTCpGwAAAAAAFiCQAwAAAABgAQI5AAAAAAAWIJADAAAAAGABAjkAAAAAABZglnUAAHBcDodDTqfTJ7UyMjJUUV7hk1oAANQGBHIAAFAth8OhIcNGKK+w2Cf1Sg4Va/eevYov573gAICzA4EcAABUy+l0Kq+wWJEJyQqJiPZ6vX3bNygja64qKwjkAICzA4EcAACcUEhEtEKjmnm9zsH9OV6vAQBAbcKkbgAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUsDeSpqan605/+pIYNGyoqKkoDBw7Uli1b3LYpKSnRqFGj1LhxYzVo0EDJycnKzc21qGMAAAAAADzD0kC+evVqjRo1SmvWrNGyZctUXl6uq666SkVFRa5txowZo48++kiLFy/W6tWrlZ2drUGDBlnYNQAAAAAApy/AyuJLly51+zxv3jxFRUVp/fr1uvzyy1VQUKA5c+Zo4cKF6tOnjyQpLS1N7dq105o1a9S9e3cr2gYAAAAA4LTVqmfICwoKJEkRERGSpPXr16u8vFyJiYmubdq2bav4+Hilp6dXu4/S0lI5nU63BQAAAACA2qbWBPKqqiqNHj1aPXr0UMeOHSVJOTk5CgwMVHh4uNu20dHRysnJqXY/qampCgsLcy1xcXHebh0AAAAAgFNWawL5qFGjtGHDBi1atOi09jNhwgQVFBS4lqysLA91CAAAAACA51j6DPlh9957rz7++GP997//VbNmzVzjMTExKisrU35+vttV8tzcXMXExFS7L7vdLrvd7u2WAQAAAAA4LZZeITfG6N5779X777+vFStWqGXLlm7ru3Tponr16mn58uWusS1btigzM1MJCQm+bhcAAAAAAI+x9Ar5qFGjtHDhQn344Ydq2LCh67nwsLAwBQcHKywsTMOHD9fYsWMVERGh0NBQ3XfffUpISGCGdQAAAADAGc3SQD5r1ixJ0hVXXOE2npaWpqFDh0qSpk2bJj8/PyUnJ6u0tFRJSUmaOXOmjzsFAAAAAMCzLA3kxpg/3CYoKEgzZszQjBkzfNARAAAAAAC+UWtmWQcAAAAA4GxCIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMAClr72DAAAAADqMofDIafT6bN6oaGhioyM9Fk9nB4COQAAAAB4gcPh0JBhI5RXWOyzmhEN62tB2muE8jMEgRwAAAAAvMDpdCqvsFiRCckKiYj2er2ivFw50t+V0+kkkJ8hCOQAAAAA4EUhEdEKjWrmk1oOn1SBpzCpGwAAAAAAFiCQAwAAAABgAQI5AAAAAAAWIJADAAAAAGABAjkAAAAAABZglnUAAM4gDodDTqfTJ7UyMjJUUV7hk1oA4CvlZWXKyMjwSS1+j+KPEMgBADhDOBwODRk2QnmFxT6pV3KoWLv37FV8eblP6gGAt5UeLNCunTs0+u+Py263e70ev0fxRwjkAACcIZxOp/IKixWZkKyQiGiv19u3fYMysuaqsoL/kARQN5SXHlKVLUBNug9S49jmXq/H71H8EQI5AABnmJCIaIVGNfN6nYP7c7xeAwCsUL9RJL9HUSswqRsAAAAAABYgkAMAAAAAYAECOQAAAAAAFiCQAwAAAABgAQI5AAAAAAAWIJADAAAAAGABAjkAAAAAABYgkAMAAAAAYAECOQAAAAAAFiCQAwAAAABgAQI5AAAAAAAWIJADAAAAAGABAjkAAAAAABYgkAMAAAAAYAECOQAAAAAAFiCQAwAAAABgAQI5AAAAAAAWsDSQ//e//9V1112n2NhY2Ww2ffDBB27rjTF69NFH1bRpUwUHBysxMVHbtm2zplkAAAAAADzI0kBeVFSkzp07a8aMGdWunzp1ql566SXNnj1ba9euVUhIiJKSklRSUuLjTgEAAAAA8KwAK4v369dP/fr1q3adMUbTp0/XI488ogEDBkiSXn/9dUVHR+uDDz7QLbfc4stWAQAAAADwqFr7DPnOnTuVk5OjxMRE11hYWJi6deum9PT0436vtLRUTqfTbQEAAAAAoLaptYE8JydHkhQdHe02Hh0d7VpXndTUVIWFhbmWuLg4r/YJAAAAAEBN1NpAXlMTJkxQQUGBa8nKyrK6JQAAAAAAjlFrA3lMTIwkKTc31208NzfXta46drtdoaGhbgsAAAAAALVNrQ3kLVu2VExMjJYvX+4aczqdWrt2rRISEizsDAAAAACA02fpLOsHDx7Ur7/+6vq8c+dO/fDDD4qIiFB8fLxGjx6tp556Sm3atFHLli01ceJExcbGauDAgdY1DQAAAACAB1gayNetW6fevXu7Po8dO1aSlJKSonnz5umhhx5SUVGRRo4cqfz8fPXs2VNLly5VUFCQVS0DAAAAAOARlgbyK664QsaY46632Wx64okn9MQTT/iwKwAAAAAAvK/WPkMOAAAAAEBdRiAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACwQYHUDAACcyRwOh5xOp09qZWRkqKK8wie1AABnpvKyMmVkZPisXmhoqCIjI31Wr64hkAMAUEMOh0NDho1QXmGxT+qVHCrW7j17FV9e7pN6AIAzS+nBAu3auUOj//647Ha7T2pGNKyvBWmvEcpriEAOAEANOZ1O5RUWKzIhWSER0V6vt2/7BmVkzVVlBYEcAHCs8tJDqrIFqEn3QWoc29zr9YrycuVIf1dOp5NAXkMEcgAATlNIRLRCo5p5vc7B/TlerwEAOPPVbxTpk38vSZLDJ1XqLiZ1AwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALMBrzwAAAAAANVJeVqaMjAyf1gwNDa0z7z0nkAMAAAAATlnpwQLt2rlDo//+uOx2u8/qRjSsrwVpr9WJUE4gBwAAAACcsvLSQ6qyBahJ90FqHNvcJzWL8nLlSH9XTqeTQA4AAAAAOLvVbxSp0KhmPqvn8Fkl7yOQAwC8xuFwyOl0+qxeWVmZAgMDfVYvIyNDFeUVPqsHAADqFgI5AMArHA6HhgwbobzCYp/UKy8r057MDDVr3lIB9Xzzr7eSQ8XavWev4svLfVIPAADULQRyAIBXOJ1O5RUWKzIhWSER0V6vt2/7Bu3YNVeNug7w2XNs+7ZvUEbWXFVWEMgBAMCpI5ADALwqJCLaJ8+VHdyfI8m3z7EdrgkAAFATflY3AAAAAADA2Ygr5LWIryc/Cg0NrROvCgAAAACAMxGBvJbw9eRHkhTRsL4WpL1GKAcAAAAAC5wRgXzGjBl69tlnlZOTo86dO+vll19W165drW7Lo3w9+VFRXq4c6e/K6XQSyAEAAADAArU+kP/73//W2LFjNXv2bHXr1k3Tp09XUlKStmzZoqioKKvb8zhfTX4kSQ6fVAEAAAAAVKfWT+r2wgsv6M4779SwYcPUvn17zZ49W/Xr19fcuXOtbg0AAAAAgBqr1VfIy8rKtH79ek2YMME15ufnp8TERKWnp1f7ndLSUpWWlro+FxQUSJJPJ0uricLCQlVWVCh/7y6Vl3j/OfKiA/tUeuiQNm3apMLCQq/XA3D2ycrKUllJic9+rzn37ZapqpIzJ0sBNq+Xs6Qm9ahX22tS78yuZ0VN6lHvVBUd2KfKigoVFhbW6ox3uDdjzAm3s5k/2sJC2dnZOuecc/T1118rISHBNf7QQw9p9erVWrt27THfefzxxzVp0iRftgkAAAAAwDGysrLUrNnxH0mu1VfIa2LChAkaO3as63NVVZXy8vLUuHFj2Ww++msbnBSn06m4uDhlZWUpNDTU6nZgMc4HHInzAUfifMCROB9wJM4HHKk2nQ/GGBUWFio2NvaE29XqQN6kSRP5+/srNzfXbTw3N1cxMTHVfsdut8tut7uNhYeHe6tFeEBoaKjl/4dB7cH5gCNxPuBInA84EucDjsT5gCPVlvMhLCzsD7ep1ZO6BQYGqkuXLlq+fLlrrKqqSsuXL3e7hR0AAAAAgDNNrb5CLkljx45VSkqKLrnkEnXt2lXTp09XUVGRhg0bZnVrAAAAAADUWK0P5DfffLMcDoceffRR5eTk6MILL9TSpUsVHR1tdWs4TXa7XY899tgxjxjg7MT5gCNxPuBInA84EucDjsT5gCOdiedDrZ5lHQAAAACAuqpWP0MOAAAAAEBdRSAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcnhVXl6ebr31VoWGhio8PFzDhw/XwYMHT/idkpISjRo1So0bN1aDBg2UnJys3Nxct22+/fZb9e3bV+Hh4WrUqJGSkpL0448/evNQ4AHeOh8kad68eerUqZOCgoIUFRWlUaNGeesw4CHePB8kaf/+/WrWrJlsNpvy8/O9cATwFG+cCz/++KMGDx6suLg4BQcHq127dnrxxRe9fSiooRkzZqhFixYKCgpSt27d9M0335xw+8WLF6tt27YKCgrSBRdcoE8//dRtvTFGjz76qJo2barg4GAlJiZq27Zt3jwEeIgnz4Xy8nL97W9/0wUXXKCQkBDFxsbq9ttvV3Z2trcPAx7i6d8NR7r77rtls9k0ffp0D3d9igzgRVdffbXp3LmzWbNmjfnf//5nWrdubQYPHnzC79x9990mLi7OLF++3Kxbt850797dXHrppa71hYWFJiIiwgwdOtT88ssvZsOGDSY5OdlER0ebsrIybx8SToM3zgdjjHn++edNbGysefPNN82vv/5qfvzxR/Phhx9681DgAd46Hw4bMGCA6devn5FkDhw44IUjgKd441yYM2eOuf/++82qVavM9u3bzRtvvGGCg4PNyy+/7O3DwSlatGiRCQwMNHPnzjUbN240d955pwkPDze5ubnVbv/VV18Zf39/M3XqVLNp0ybzyCOPmHr16pmff/7Ztc0zzzxjwsLCzAcffGB+/PFHc/3115uWLVuaQ4cO+eqwUAOePhfy8/NNYmKi+fe//21++eUXk56ebrp27Wq6dOniy8NCDXnjd8Nh7733nuncubOJjY0106ZN8/KRnBiBHF6zadMmI8l8++23rrElS5YYm81m9uzZU+138vPzTb169czixYtdY5s3bzaSTHp6ujHGmG+//dZIMpmZma5tfvrpJyPJbNu2zUtHg9PlrfMhLy/PBAcHmy+++MK7BwCP8tb5cNjMmTNNr169zPLlywnktZy3z4Uj3XPPPaZ3796eax4e0bVrVzNq1CjX58rKShMbG2tSU1Or3f6mm24y11xzjdtYt27dzF133WWMMaaqqsrExMSYZ5991rU+Pz/f2O1289Zbb3nhCOApnj4XqvPNN98YSSYjI8MzTcNrvHU+7N6925xzzjlmw4YNpnnz5pYHcm5Zh9ekp6crPDxcl1xyiWssMTFRfn5+Wrt2bbXfWb9+vcrLy5WYmOgaa9u2reLj45Weni5JOv/889W4cWPNmTNHZWVlOnTokObMmaN27dqpRYsWXj0m1Jy3zodly5apqqpKe/bsUbt27dSsWTPddNNNysrK8u4B4bR463yQpE2bNumJJ57Q66+/Lj8//jVX23nzXDhaQUGBIiIiPNc8TltZWZnWr1/v9mfp5+enxMTE4/5Zpqenu20vSUlJSa7td+7cqZycHLdtwsLC1K1btxOeH7CWN86F6hQUFMhmsyk8PNwjfcM7vHU+VFVV6bbbbtODDz6oDh06eKf5U8R/qcBrcnJyFBUV5TYWEBCgiIgI5eTkHPc7gYGBx/ySjI6Odn2nYcOGWrVqlRYsWKDg4GA1aNBAS5cu1ZIlSxQQEOCVY8Hp89b5sGPHDlVVVWny5MmaPn263nnnHeXl5enKK69UWVmZV44Fp89b50NpaakGDx6sZ599VvHx8V7pHZ7lrXPhaF9//bX+/e9/a+TIkR7pG57x22+/qbKyUtHR0W7jJ/qzzMnJOeH2h//3VPYJ63njXDhaSUmJ/va3v2nw4MEKDQ31TOPwCm+dD1OmTFFAQIDuv/9+zzddQwRynLKHH35YNpvthMsvv/zitfqHDh3S8OHD1aNHD61Zs0ZfffWVOnbsqGuuuUaHDh3yWl1Uz+rzoaqqSuXl5XrppZeUlJSk7t2766233tK2bdu0cuVKr9VF9aw+HyZMmKB27dppyJAhXquBk2P1uXCkDRs2aMCAAXrsscd01VVX+aQmgNqlvLxcN910k4wxmjVrltXtwALr16/Xiy++qHnz5slms1ndjguXE3HKxo0bp6FDh55wm3PPPVcxMTHat2+f23hFRYXy8vIUExNT7fdiYmJUVlam/Px8tysfubm5ru8sXLhQu3btUnp6uut21IULF6pRo0b68MMPdcstt9T84HDKrD4fmjZtKklq3769a31kZKSaNGmizMzMGhwRTofV58OKFSv0888/65133pH0+0zLktSkSRP94x//0KRJk2p4ZDhVVp8Lh23atEl9+/bVyJEj9cgjj9ToWOA9TZo0kb+//zFvS6juz/KwmJiYE25/+H9zc3Nd/444/PnCCy/0YPfwJG+cC4cdDuMZGRlasWIFV8fPAN44H/73v/9p3759bnfQVVZWaty4cZo+fbp27drl2YM4WZY+wY467fBEPevWrXONffbZZyc1Uc8777zjGvvll1/cJup56aWXTExMjKmqqnJtU15ebkJCQsybb77ppaPB6fLW+bBlyxYjyW1St/379xs/Pz/z2WefeelocLq8dT78+uuv5ueff3Ytc+fONZLM119/fdxZWWEtb50LxhizYcMGExUVZR588EHvHQBOW9euXc29997r+lxZWWnOOeecE07cdO2117qNJSQkHDOp23PPPedaX1BQwKRuZwBPnwvGGFNWVmYGDhxoOnToYPbt2+edxuEVnj4ffvvtN7f/Rvj5559NbGys+dvf/mZ++eUX7x3IHyCQw6uuvvpqc9FFF5m1a9eaL7/80rRp08btVTa7d+82559/vlm7dq1r7O677zbx8fFmxYoVZt26dSYhIcEkJCS41m/evNnY7Xbz17/+1WzatMls2LDBDBkyxISFhZns7GyfHh9OjTfOB2N+f71Vhw4dzFdffWV+/vlnc+2115r27dvzGrxazlvnw5FWrlzJLOtnAG+cCz///LOJjIw0Q4YMMXv37nUt/Ad57bNo0SJjt9vNvHnzzKZNm8zIkSNNeHi4ycnJMcYYc9ttt5mHH37Ytf1XX31lAgICzHPPPWc2b95sHnvssWpfexYeHm4+/PBD89NPP5kBAwbw2rMzgKfPhbKyMnP99debZs2amR9++MHtd0Fpaaklx4iT543fDUerDbOsE8jhVfv37zeDBw82DRo0MKGhoWbYsGGmsLDQtX7nzp1Gklm5cqVr7NChQ+aee+4xjRo1MvXr1zc33HCD2bt3r9t+P//8c9OjRw8TFhZmGjVqZPr06XPCV92gdvDW+VBQUGDuuOMOEx4ebiIiIswNN9zg9lo81E7eOh+ORCA/M3jjXHjssceMpGOW5s2b+/DIcLJefvllEx8fbwIDA03Xrl3NmjVrXOt69eplUlJS3LZ/++23zXnnnWcCAwNNhw4dzCeffOK2vqqqykycONFER0cbu91u+vbta7Zs2eKLQ8Fp8uS5cPh3R3XLkb9PUHt5+nfD0WpDILcZ8/8fsAMAAAAAAD7DLOsAAAAAAFiAQA4AAAAAgAUI5AAAAAAAWIBADgAAAACABQjkAAAAAABYgEAOAAAAAIAFCOQAAAAAAFiAQA4AAAAAgAUI5AAAQJK0a9cu2Ww2/fDDD8fdZtWqVbLZbMrPz/dobZvNpg8++MCj+wQAoLYjkAMAcAYZOnSobDabbDab6tWrp5YtW+qhhx5SSUnJae87Li5Oe/fuVceOHT3QKQAA+CMBVjcAAABOzdVXX620tDSVl5dr/fr1SklJkc1m05QpU05rv/7+/oqJifFQlwAA4I9whRwAgDOM3W5XTEyM4uLiNHDgQCUmJmrZsmWSpKqqKqWmpqply5YKDg5W586d9c4777i+e+DAAd16662KjIxUcHCw2rRpo7S0NEnV37L+6aef6rzzzlNwcLB69+6tXbt2ufXy+OOP68ILL3Qbmz59ulq0aOH6/O233+rKK69UkyZNFBYWpl69eum7777z6M8EAIAzEYEcAIAz2IYNG/T1118rMDBQkpSamqrXX39ds2fP1saNGzVmzBgNGTJEq1evliRNnDhRmzZt0pIlS7R582bNmjVLTZo0qXbfWVlZGjRokK677jr98MMPGjFihB5++OFT7rGwsFApKSn68ssvtWbNGrVp00b9+/dXYWFhzQ8cAIA6gFvWAQA4w3z88cdq0KCBKioqVFpaKj8/P73yyisqLS3V5MmT9cUXXyghIUGSdO655+rLL7/UP//5T/Xq1UuZmZm66KKLdMkll0iS25Xso82aNUutWrXS888/L0k6//zz9fPPP5/yrfF9+vRx+/zqq68qPDxcq1ev1rXXXntK+wIAoC4hkAMAcIbp3bu3Zs2apaKiIk2bNk0BAQFKTk7Wxo0bVVxcrCuvvNJt+7KyMl100UWSpL/+9a9KTk7Wd999p6uuukoDBw7UpZdeWm2dzZs3q1u3bm5jh4P+qcjNzdUjjzyiVatWad++faqsrFRxcbEyMzNPeV8AANQlBHIAAM4wISEhat26tSRp7ty56ty5s+bMmeOaHf2TTz7ROeec4/Ydu90uSerXr58yMjL06aefatmyZerbt69GjRql5557rka9+Pn5yRjjNlZeXu72OSUlRfv379eLL76o5s2by263KyEhQWVlZTWqCQBAXUEgBwDgDObn56e///3vGjt2rLZu3Sq73a7MzEz16tXruN+JjIxUSkqKUlJSdNlll+nBBx+sNpC3a9dO//nPf9zG1qxZc8y+cnJyZIyRzWaTpGPeY/7VV19p5syZ6t+/v6Tfn03/7bffanK4AADUKUzqBgDAGe7GG2+Uv7+//vnPf2r8+PEaM2aM5s+fr+3bt+u7777Tyy+/rPnz50uSHn30UX344Yf69ddftXHjRn388cdq165dtfu9++67tW3bNj344IPasmWLFi5cqHnz5rltc8UVV8jhcGjq1Knavn27ZsyYoSVLlrht06ZNG73xxhvavHmz1q5dq1tvvVXBwcFe+VkAAHAmIZADAHCGCwgI0L333qupU6dqwoQJmjhxolJTU9WuXTtdffXV+uSTT9SyZUtJUmBgoCZMmKBOnTrp8ssvl7+/vxYtWlTtfuPj4/Xuu+/qgw8+UOfOnTV79mxNnjzZbZt27dpp5syZmjFjhjp37qxvvvlG48ePd9tmzpw5OnDggC6++GLddtttuv/++xUVFeWdHwYAAGcQmzn6wS8AAAAAAOB1XCEHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAgRyAAAAAAAsQCAHAAAAAMACBHIAAAAAACxAIAcAAAAAwAIEcgAAAAAALEAgBwAAAADAAv8P2T+znWtOj4MAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 3. Histogram of Residuals\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.hist(residuals, bins=30, edgecolor='k', alpha=0.7)\n",
        "plt.xlabel('Residual')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Histogram of Residuals')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "E0lawjqQONPV",
        "outputId": "23d9a286-207d-4bb1-e28f-d298d81cdcc8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "stats.probplot(residuals, dist=\"norm\", plot=plt)\n",
        "plt.title('Q-Q Plot of Residuals')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "d-9PbUCFONPW"
      },
      "outputs": [],
      "source": [
        "# # print(predict_sales_for_item_gbr(int(input(\"Enter the item ID\")),input(\"enter the available amount\")))\n",
        "# for i in range len(df1['Item_ID']:\n",
        "#   item = int(input(\"Enter the item ID\"))\n",
        "#   Amount = float(input(\"enter the available amount\"))\n",
        "#   print(predict_sales_for_item_gbr(item, Amount))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "8HWf1Qt3QQyq",
        "outputId": "79ff324d-d696-4fe2-c632-2f74e8ffd171"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Last_Month_Sales  Capacity  Last_Year_Sales  Temperature  \\\n",
              "0                   82       109               77           33   \n",
              "1                   71        99               68           32   \n",
              "2                   71        99               68           38   \n",
              "3                   82       109               77           48   \n",
              "4                   82       109               77           36   \n",
              "...                ...       ...              ...          ...   \n",
              "2994                82        99               68           46   \n",
              "2995                90        99               68           40   \n",
              "2996                71        99               68           33   \n",
              "2997                23        99               68           50   \n",
              "2998                82       109               77           36   \n",
              "\n",
              "      Item_Name_Artichoke  Item_Name_Asparagus  Item_Name_Avocado  \\\n",
              "0                   False                False              False   \n",
              "1                   False                False              False   \n",
              "2                   False                False              False   \n",
              "3                   False                False              False   \n",
              "4                   False                False              False   \n",
              "...                   ...                  ...                ...   \n",
              "2994                False                False              False   \n",
              "2995                False                False              False   \n",
              "2996                False                False              False   \n",
              "2997                False                False              False   \n",
              "2998                False                False              False   \n",
              "\n",
              "      Item_Name_Banana  Item_Name_Beets  Item_Name_Bell Pepper  ...  \\\n",
              "0                False            False                  False  ...   \n",
              "1                False            False                  False  ...   \n",
              "2                False            False                  False  ...   \n",
              "3                False            False                  False  ...   \n",
              "4                False            False                  False  ...   \n",
              "...                ...              ...                    ...  ...   \n",
              "2994             False            False                   True  ...   \n",
              "2995             False            False                  False  ...   \n",
              "2996             False            False                  False  ...   \n",
              "2997             False            False                  False  ...   \n",
              "2998             False            False                  False  ...   \n",
              "\n",
              "      Item_Name_Spinach  Item_Name_Strawberry  Item_Name_Sweet Potato  \\\n",
              "0                 False                 False                   False   \n",
              "1                 False                 False                   False   \n",
              "2                 False                 False                   False   \n",
              "3                 False                 False                   False   \n",
              "4                 False                 False                   False   \n",
              "...                 ...                   ...                     ...   \n",
              "2994              False                 False                   False   \n",
              "2995              False                 False                   False   \n",
              "2996              False                 False                   False   \n",
              "2997              False                 False                   False   \n",
              "2998              False                 False                   False   \n",
              "\n",
              "      Item_Name_Tomato  Item_Name_Watermelon  Item_Name_Zucchini  \\\n",
              "0                False                 False               False   \n",
              "1                False                 False               False   \n",
              "2                False                 False               False   \n",
              "3                False                 False               False   \n",
              "4                False                 False               False   \n",
              "...                ...                   ...                 ...   \n",
              "2994             False                 False               False   \n",
              "2995             False                 False               False   \n",
              "2996             False                 False               False   \n",
              "2997             False                 False               False   \n",
              "2998             False                 False               False   \n",
              "\n",
              "      Category_Vegetables  Weather_Rainy  Weather_Sunny  Weather_Winter  \n",
              "0                   False          False          False           False  \n",
              "1                   False          False          False            True  \n",
              "2                   False          False           True           False  \n",
              "3                    True          False          False           False  \n",
              "4                    True          False          False           False  \n",
              "...                   ...            ...            ...             ...  \n",
              "2994                 True          False          False           False  \n",
              "2995                 True          False           True           False  \n",
              "2996                 True           True          False           False  \n",
              "2997                 True          False          False           False  \n",
              "2998                False          False          False           False  \n",
              "\n",
              "[2999 rows x 57 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22049d5f-9c7e-4abe-bbbb-92cb908d1932\" 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>Last_Month_Sales</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>Last_Year_Sales</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Item_Name_Artichoke</th>\n",
              "      <th>Item_Name_Asparagus</th>\n",
              "      <th>Item_Name_Avocado</th>\n",
              "      <th>Item_Name_Banana</th>\n",
              "      <th>Item_Name_Beets</th>\n",
              "      <th>Item_Name_Bell Pepper</th>\n",
              "      <th>...</th>\n",
              "      <th>Item_Name_Spinach</th>\n",
              "      <th>Item_Name_Strawberry</th>\n",
              "      <th>Item_Name_Sweet Potato</th>\n",
              "      <th>Item_Name_Tomato</th>\n",
              "      <th>Item_Name_Watermelon</th>\n",
              "      <th>Item_Name_Zucchini</th>\n",
              "      <th>Category_Vegetables</th>\n",
              "      <th>Weather_Rainy</th>\n",
              "      <th>Weather_Sunny</th>\n",
              "      <th>Weather_Winter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>33</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>32</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>38</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>48</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>36</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2994</th>\n",
              "      <td>82</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>46</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>90</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>40</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>71</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>23</td>\n",
              "      <td>99</td>\n",
              "      <td>68</td>\n",
              "      <td>50</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>82</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>36</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2999 rows × 57 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22049d5f-9c7e-4abe-bbbb-92cb908d1932')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-22049d5f-9c7e-4abe-bbbb-92cb908d1932 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-22049d5f-9c7e-4abe-bbbb-92cb908d1932');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-098a2c9c-5f24-4160-b011-a0dcbbeac22a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-098a2c9c-5f24-4160-b011-a0dcbbeac22a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-098a2c9c-5f24-4160-b011-a0dcbbeac22a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7418a2e1-18ad-4e51-896b-e634dd70306b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7418a2e1-18ad-4e51-896b-e634dd70306b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "1jFMKyo7hcXM"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_csv(\"current.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xA4sKZgdqbUG",
        "outputId": "54ffd0e7-47b7-4b0d-e227-f5b2c97fe954"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Item_ID        Item_Name  Amt_avai\n",
              "0        13            Peach        13\n",
              "1        15            Lemon        15\n",
              "2        11           Cherry        11\n",
              "3        27           Potato        27\n",
              "4        40       Green Bean        40\n",
              "5        42           Celery        42\n",
              "6         5            Grape         5\n",
              "7         4            Mango         4\n",
              "8        44        Asparagus        44\n",
              "9        14             Plum        14\n",
              "10       38         Eggplant        38\n",
              "11       24          Avocado        24\n",
              "12        3           Orange         3\n",
              "13       36      Cauliflower        36\n",
              "14       26           Tomato        26\n",
              "15        7             Kiwi         7\n",
              "16        6        Pineapple         6\n",
              "17       39          Pumpkin        39\n",
              "18       30          Spinach        30\n",
              "19       34          Lettuce        34\n",
              "20       25       Cantaloupe        25\n",
              "21       45  Brussels Sprout        45\n",
              "22       41     Sweet Potato        41\n",
              "23       43           Radish        43\n",
              "24       37         Zucchini        37\n",
              "25       31         Cucumber        31\n",
              "26       50            Beets        50\n",
              "27       17           Papaya        17\n",
              "28       20        Cranberry        20\n",
              "29        8       Strawberry         8\n",
              "30        9             Pear         9\n",
              "31       18        Raspberry        18\n",
              "32       28           Carrot        28\n",
              "33       33            Onion        33\n",
              "34        1            Apple         1\n",
              "35       21            Guava        21\n",
              "36        2           Banana         2\n",
              "37       16             Lime        16\n",
              "38       49             Okra        49\n",
              "39       35          Cabbage        35\n",
              "40       47             Kale        47\n",
              "41       23              Fig        23\n",
              "42       29         Broccoli        29\n",
              "43       12        Blueberry        12\n",
              "44       48             Peas        48\n",
              "45       32      Bell Pepper        32\n",
              "46       19       Blackberry        19\n",
              "47       22      Pomegranate        22\n",
              "48       46        Artichoke        46\n",
              "49       10       Watermelon        10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b762046e-1a3e-41bc-ab9f-6c4591865d4e\" 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>Item_ID</th>\n",
              "      <th>Item_Name</th>\n",
              "      <th>Amt_avai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>Peach</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>Lemon</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>Cherry</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>Potato</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40</td>\n",
              "      <td>Green Bean</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>42</td>\n",
              "      <td>Celery</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Grape</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>Mango</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>44</td>\n",
              "      <td>Asparagus</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14</td>\n",
              "      <td>Plum</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>38</td>\n",
              "      <td>Eggplant</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>24</td>\n",
              "      <td>Avocado</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>Orange</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>36</td>\n",
              "      <td>Cauliflower</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>26</td>\n",
              "      <td>Tomato</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>7</td>\n",
              "      <td>Kiwi</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>6</td>\n",
              "      <td>Pineapple</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>39</td>\n",
              "      <td>Pumpkin</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>30</td>\n",
              "      <td>Spinach</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>34</td>\n",
              "      <td>Lettuce</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>25</td>\n",
              "      <td>Cantaloupe</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>45</td>\n",
              "      <td>Brussels Sprout</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>41</td>\n",
              "      <td>Sweet Potato</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>43</td>\n",
              "      <td>Radish</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>37</td>\n",
              "      <td>Zucchini</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>31</td>\n",
              "      <td>Cucumber</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>50</td>\n",
              "      <td>Beets</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>17</td>\n",
              "      <td>Papaya</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>20</td>\n",
              "      <td>Cranberry</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>Strawberry</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>9</td>\n",
              "      <td>Pear</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>18</td>\n",
              "      <td>Raspberry</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>28</td>\n",
              "      <td>Carrot</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>33</td>\n",
              "      <td>Onion</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1</td>\n",
              "      <td>Apple</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>21</td>\n",
              "      <td>Guava</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2</td>\n",
              "      <td>Banana</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>16</td>\n",
              "      <td>Lime</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>49</td>\n",
              "      <td>Okra</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>35</td>\n",
              "      <td>Cabbage</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>47</td>\n",
              "      <td>Kale</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>23</td>\n",
              "      <td>Fig</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>29</td>\n",
              "      <td>Broccoli</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>12</td>\n",
              "      <td>Blueberry</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>48</td>\n",
              "      <td>Peas</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>32</td>\n",
              "      <td>Bell Pepper</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>19</td>\n",
              "      <td>Blackberry</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>22</td>\n",
              "      <td>Pomegranate</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>46</td>\n",
              "      <td>Artichoke</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>10</td>\n",
              "      <td>Watermelon</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b762046e-1a3e-41bc-ab9f-6c4591865d4e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b762046e-1a3e-41bc-ab9f-6c4591865d4e 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-b762046e-1a3e-41bc-ab9f-6c4591865d4e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-60cba7b4-96c6-42bd-b0c6-5c421ad53ca2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60cba7b4-96c6-42bd-b0c6-5c421ad53ca2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60cba7b4-96c6-42bd-b0c6-5c421ad53ca2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e018526d-f536-4c58-9de6-f62e011792c3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e018526d-f536-4c58-9de6-f62e011792c3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"Item_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36,\n          35,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Item_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Cauliflower\",\n          \"Cabbage\",\n          \"Pear\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amt_avai\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 1,\n        \"max\": 50,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36,\n          35,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uvca4GQQtcXK",
        "outputId": "04a47c6d-5937-4f51-945c-4b0a34a1d15b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50\n"
          ]
        }
      ],
      "source": [
        "No_of_item = len(df1['Item_ID'])\n",
        "print(No_of_item)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkGGAtKZ2yeS",
        "outputId": "790ffaa3-f990-465e-9db6-008c22bb90ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of distinct values in column 'Category': 2\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "# Print the number of distinct values\n",
        "print(\"Number of distinct values in column '{}': {}\".format(\"Category\", num_distinct_values))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HEMisEL21Pl",
        "outputId": "0e7c21cd-938d-48e4-c0a3-aadce5ef6c6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102.16372124041347\n"
          ]
        }
      ],
      "source": [
        "print(Capacity_sum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2VDG6pJ3CIj",
        "outputId": "fc0bcbdb-2207-4022-9dc1-a314e6a714f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102.16372124041347\n",
            "25.5\n",
            "The available capacity in the warehouse:  75.04006344875306\n"
          ]
        }
      ],
      "source": [
        "print(Capacity_sum)\n",
        "print(Amt_avai_sum)\n",
        "avai_perc = 100-percent*100\n",
        "print((\"The available capacity in the warehouse: \"),avai_perc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6A5MYQ831NU",
        "outputId": "3874ccee-9ed9-4a48-dbff-cf5444e78b72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index of the row where 1 exists in 'column1': 34\n",
            "Apple\n"
          ]
        }
      ],
      "source": [
        "Amt_abt_to_finish = min(df1['Amt_avai'])\n",
        "\n",
        "\n",
        "if Amt_abt_to_finish in df1['Amt_avai'].values:\n",
        "    # Get the index of the row where the value exists\n",
        "    row_index = df1[df1['Amt_avai'] == Amt_abt_to_finish].index[0]\n",
        "    print(\"Index of the row where {} exists in 'column1': {}\".format(Amt_abt_to_finish, row_index))\n",
        "    value_at_index = df1['Item_Name'].iloc[row_index]\n",
        "    print(value_at_index)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "H_tgedmhUT5H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3141c3a0-a315-4b1b-c9da-eb711b999af3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Item_ID        Item_Name  Amt_avai  Sentiment\n",
            "0        13            Peach        13   0.053206\n",
            "1        15            Lemon        15   0.213590\n",
            "2        11           Cherry        11   0.225485\n",
            "3        27           Potato        27   0.161879\n",
            "4        40       Green Bean        40   0.049747\n",
            "5        42           Celery        42   0.241794\n",
            "6         5            Grape         5   0.082657\n",
            "7         4            Mango         4   0.149444\n",
            "8        44        Asparagus        44   0.196151\n",
            "9        14             Plum        14   0.205000\n",
            "10       38         Eggplant        38   0.232557\n",
            "11       24          Avocado        24   0.458333\n",
            "12        3           Orange         3   0.260688\n",
            "13       36      Cauliflower        36   0.067121\n",
            "14       26           Tomato        26   0.215333\n",
            "15        7             Kiwi         7  -0.008182\n",
            "16        6        Pineapple         6   0.340714\n",
            "17       39          Pumpkin        39   0.142121\n",
            "18       30          Spinach        30   0.183333\n",
            "19       34          Lettuce        34   0.160747\n",
            "20       25       Cantaloupe        25   0.281131\n",
            "21       45  Brussels Sprout        45   0.027292\n",
            "22       41     Sweet Potato        41   0.202256\n",
            "23       43           Radish        43   0.230758\n",
            "24       37         Zucchini        37   0.312778\n",
            "25       31         Cucumber        31   0.331905\n",
            "26       50            Beets        50   0.265879\n",
            "27       17           Papaya        17   0.081667\n",
            "28       20        Cranberry        20   0.351766\n",
            "29        8       Strawberry         8   0.226923\n",
            "30        9             Pear         9  -0.042576\n",
            "31       18        Raspberry        18   0.215370\n",
            "32       28           Carrot        28  -0.012500\n",
            "33       33            Onion        33   0.413745\n",
            "34        1            Apple         1   0.017758\n",
            "35       21            Guava        21   0.373298\n",
            "36        2           Banana         2   0.131850\n",
            "37       16             Lime        16   0.277491\n",
            "38       49             Okra        49   0.080043\n",
            "39       35          Cabbage        35   0.161585\n",
            "40       47             Kale        47   0.050246\n",
            "41       23              Fig        23   0.011111\n",
            "42       29         Broccoli        29   0.107136\n",
            "43       12        Blueberry        12   0.329167\n",
            "44       48             Peas        48   0.139205\n",
            "45       32      Bell Pepper        32   0.311035\n",
            "46       19       Blackberry        19  -0.243500\n",
            "47       22      Pomegranate        22   0.027639\n",
            "48       46        Artichoke        46   0.301136\n",
            "49       10       Watermelon        10   0.301136\n"
          ]
        }
      ],
      "source": [
        "# Function to analyze sentiment\n",
        "import requests\n",
        "from textblob import TextBlob\n",
        "\n",
        "def analyze_sentiment(item_name):\n",
        "    # News API endpoint\n",
        "    url = ('https://newsapi.org/v2/everything?'\n",
        "           'q={}&'\n",
        "           'sortBy=popularity&'\n",
        "           'apiKey=0d87bdd74dab48b6b8025a26cf249b36')\n",
        "\n",
        "    # Make a request to the News API\n",
        "    response = requests.get(url.format(item_name))\n",
        "\n",
        "    # Parse the JSON response\n",
        "    data = response.json()\n",
        "\n",
        "    # Check if 'articles' key exists in the response\n",
        "    if 'articles' not in data:\n",
        "        return 0  # Return 0 if no articles found\n",
        "\n",
        "    # Initialize variables for sentiment analysis\n",
        "    total_sentiment = 0\n",
        "    num_articles = 0\n",
        "\n",
        "    for article in data['articles']:\n",
        "        title = article.get('title')\n",
        "        description = article.get('description')\n",
        "        text = title + '. ' + description if description else title\n",
        "        sentiment = TextBlob(text).sentiment.polarity\n",
        "        total_sentiment += sentiment\n",
        "        num_articles += 1\n",
        "        if num_articles == 5:\n",
        "            break\n",
        "\n",
        "    # Calculate average sentiment\n",
        "    if num_articles > 0:\n",
        "        average_sentiment = total_sentiment / num_articles\n",
        "    else:\n",
        "        average_sentiment = 0\n",
        "\n",
        "    return average_sentiment\n",
        "\n",
        "# Apply sentiment analysis to each row in the DataFrame\n",
        "df1['Sentiment'] = df1['Item_Name'].apply(analyze_sentiment)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(df1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "a25m0FDO4ClR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9c767d0-c383-4aff-88a1-69fd823b7aa0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Item_ID        Item_Name  Amt_avai  Sentiment  Predicted_Sales  \\\n",
            "0        13            Peach        13   0.053206        87.359478   \n",
            "1        15            Lemon        15   0.213590        75.117523   \n",
            "2        11           Cherry        11   0.225485        75.369140   \n",
            "3        27           Potato        27   0.161879        86.859594   \n",
            "4        40       Green Bean        40   0.049747        86.891809   \n",
            "5        42           Celery        42   0.241794        88.289521   \n",
            "6         5            Grape         5   0.082657        76.242578   \n",
            "7         4            Mango         4   0.149444        75.886032   \n",
            "8        44        Asparagus        44   0.196151        76.760305   \n",
            "9        14             Plum        14   0.205000        22.597759   \n",
            "10       38         Eggplant        38   0.232557        76.242085   \n",
            "11       24          Avocado        24   0.458333        75.816382   \n",
            "12        3           Orange         3   0.260688        87.002723   \n",
            "13       36      Cauliflower        36   0.067121        86.605790   \n",
            "14       26           Tomato        26   0.215333        87.469332   \n",
            "15        7             Kiwi         7  -0.008182        65.448303   \n",
            "16        6        Pineapple         6   0.340714        76.298384   \n",
            "17       39          Pumpkin        39   0.142121        76.313958   \n",
            "18       30          Spinach        30   0.183333        76.522630   \n",
            "19       34          Lettuce        34   0.160747        76.835945   \n",
            "20       25       Cantaloupe        25   0.281131        76.375239   \n",
            "21       45  Brussels Sprout        45   0.027292        86.633754   \n",
            "22       41     Sweet Potato        41   0.202256        87.259332   \n",
            "23       43           Radish        43   0.230758        75.384455   \n",
            "24       37         Zucchini        37   0.312778        75.800795   \n",
            "25       31         Cucumber        31   0.331905        86.459485   \n",
            "26       50            Beets        50   0.265879        76.055671   \n",
            "27       17           Papaya        17   0.081667        87.425276   \n",
            "28       20        Cranberry        20   0.351766        75.889275   \n",
            "29        8       Strawberry         8   0.226923        75.639826   \n",
            "30        9             Pear         9  -0.042576        45.859129   \n",
            "31       18        Raspberry        18   0.215370        75.823944   \n",
            "32       28           Carrot        28  -0.012500        75.753625   \n",
            "33       33            Onion        33   0.413745        87.119146   \n",
            "34        1            Apple         1   0.017758        76.036522   \n",
            "35       21            Guava        21   0.373298        76.055698   \n",
            "36        2           Banana         2   0.131850        75.761790   \n",
            "37       16             Lime        16   0.277491        76.513755   \n",
            "38       49             Okra        49   0.080043        86.503108   \n",
            "39       35          Cabbage        35   0.161585        74.913980   \n",
            "40       47             Kale        47   0.050246        75.316773   \n",
            "41       23              Fig        23   0.011111        86.471574   \n",
            "42       29         Broccoli        29   0.107136        86.636010   \n",
            "43       12        Blueberry        12   0.329167        76.246121   \n",
            "44       48             Peas        48   0.139205        86.593141   \n",
            "45       32      Bell Pepper        32   0.311035        86.760708   \n",
            "46       19       Blackberry        19  -0.243500        17.770490   \n",
            "47       22      Pomegranate        22   0.027639        76.007754   \n",
            "48       46        Artichoke        46   0.301136        22.777632   \n",
            "49       10       Watermelon        10   0.301136        86.779397   \n",
            "\n",
            "    Sales_Difference  \n",
            "0          74.359478  \n",
            "1          60.117523  \n",
            "2          64.369140  \n",
            "3          59.859594  \n",
            "4          46.891809  \n",
            "5          46.289521  \n",
            "6          71.242578  \n",
            "7          71.886032  \n",
            "8          32.760305  \n",
            "9           8.597759  \n",
            "10         38.242085  \n",
            "11         51.816382  \n",
            "12         84.002723  \n",
            "13         50.605790  \n",
            "14         61.469332  \n",
            "15         58.448303  \n",
            "16         70.298384  \n",
            "17         37.313958  \n",
            "18         46.522630  \n",
            "19         42.835945  \n",
            "20         51.375239  \n",
            "21         41.633754  \n",
            "22         46.259332  \n",
            "23         32.384455  \n",
            "24         38.800795  \n",
            "25         55.459485  \n",
            "26         26.055671  \n",
            "27         70.425276  \n",
            "28         55.889275  \n",
            "29         67.639826  \n",
            "30         36.859129  \n",
            "31         57.823944  \n",
            "32         47.753625  \n",
            "33         54.119146  \n",
            "34         75.036522  \n",
            "35         55.055698  \n",
            "36         73.761790  \n",
            "37         60.513755  \n",
            "38         37.503108  \n",
            "39         39.913980  \n",
            "40         28.316773  \n",
            "41         63.471574  \n",
            "42         57.636010  \n",
            "43         64.246121  \n",
            "44         38.593141  \n",
            "45         54.760708  \n",
            "46         -1.229510  \n",
            "47         54.007754  \n",
            "48        -23.222368  \n",
            "49         76.779397  \n",
            "Maximum Predicted Sales: 80.26320062733944\n",
            "Product with Maximum Predicted Sales: Celery\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "\n",
        "# Initialize maximum predicted sales and associated product name\n",
        "m = 0\n",
        "demand = \"\"\n",
        "\n",
        "def predict_sales_for_item_gbr(item_no, avail, df):\n",
        "    global m, demand\n",
        "\n",
        "    # Load the saved model and scalers\n",
        "    loaded_gbr = joblib.load(\"gbr_model.pkl\")\n",
        "    loaded_scaler_X = joblib.load(\"scaler_X.pkl\")\n",
        "    loaded_scaler_y = joblib.load(\"scaler_y.pkl\")\n",
        "\n",
        "    # Check if item_no exists in the dataset\n",
        "    if item_no not in df['Item_ID'].values:\n",
        "        return f\"Item number {item_no} does not exist.\"\n",
        "\n",
        "    item_name = df[df['Item_ID'] == item_no]['Item_Name'].iloc[0]\n",
        "    item_idx = df[df['Item_ID'] == item_no].index[0]\n",
        "    latest_data = X[item_idx].reshape(1, -1)\n",
        "    predicted_sales_scaled = loaded_gbr.predict(latest_data)\n",
        "    predicted_sales = loaded_scaler_y.inverse_transform(predicted_sales_scaled.reshape(-1, 1))[0][0]\n",
        "    sales_diff = predicted_sales - avail\n",
        "\n",
        "    # Update maximum predicted sales and associated product name\n",
        "    if predicted_sales > m:\n",
        "        m = predicted_sales\n",
        "        demand = item_name\n",
        "\n",
        "    # Apply strategies to increase predicted sales and sales difference based on sentiment analysis\n",
        "    sentiment = df[df['Item_ID'] == item_no]['Sentiment'].iloc[0]\n",
        "    if sentiment > 0:  # Positive sentiment\n",
        "        # Example strategy: Increase predicted sales by 10% for positive sentiment\n",
        "        predicted_sales *= 1.10\n",
        "    elif sentiment < 0:  # Negative sentiment\n",
        "        # Example strategy: Decrease predicted sales by 5% for negative sentiment\n",
        "        predicted_sales *= 0.95\n",
        "\n",
        "    # Update sales difference based on the updated predicted sales\n",
        "    sales_diff = predicted_sales - avail\n",
        "\n",
        "    # Add predicted sales and sales difference to the dataset\n",
        "    df.loc[df['Item_ID'] == item_no, 'Predicted_Sales'] = predicted_sales\n",
        "    df.loc[df['Item_ID'] == item_no, 'Sales_Difference'] = sales_diff\n",
        "\n",
        "    return df\n",
        "\n",
        "# Iterate over all items in df1 and update the dataset\n",
        "for i in range(len(df1['Item_ID'])):\n",
        "    item = df1[\"Item_ID\"].iloc[i]\n",
        "    amount = df1[\"Amt_avai\"].iloc[i]\n",
        "    df1 = predict_sales_for_item_gbr(item, amount, df1)\n",
        "\n",
        "df1.sort_values('Item_ID', ascending=True)\n",
        "# Display the updated dataset\n",
        "print(df1)\n",
        "\n",
        "# Print maximum predicted sales and its associated product name\n",
        "print(\"Maximum Predicted Sales:\", m)\n",
        "print(\"Product with Maximum Predicted Sales:\", demand)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}