car price prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "applied ds ex1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWe1FsHv07im"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGtLST95PQ_e",
        "outputId": "3036dbda-ecc9-40c5-fd06-a5c55a03fc7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "car_data=pd.read_csv(\"/car data.csv\")\n",
        "car_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car_Name  Year  Selling_Price  ...  Seller_Type  Transmission Owner\n",
              "0     ritz  2014           3.35  ...       Dealer        Manual     0\n",
              "1      sx4  2013           4.75  ...       Dealer        Manual     0\n",
              "2     ciaz  2017           7.25  ...       Dealer        Manual     0\n",
              "3  wagon r  2011           2.85  ...       Dealer        Manual     0\n",
              "4    swift  2014           4.60  ...       Dealer        Manual     0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miAHLk3ZMAp6",
        "outputId": "8956bcd3-492d-4357-f8ef-d56aa1c1cb80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "print(car_data.shape)\n",
        "print((car_data==None).sum())\n",
        "print((car_data!=car_data).sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(301, 9)\n",
            "Car_Name         0\n",
            "Year             0\n",
            "Selling_Price    0\n",
            "Present_Price    0\n",
            "Kms_Driven       0\n",
            "Fuel_Type        0\n",
            "Seller_Type      0\n",
            "Transmission     0\n",
            "Owner            0\n",
            "dtype: int64\n",
            "Car_Name         0\n",
            "Year             0\n",
            "Selling_Price    0\n",
            "Present_Price    0\n",
            "Kms_Driven       0\n",
            "Fuel_Type        0\n",
            "Seller_Type      0\n",
            "Transmission     0\n",
            "Owner            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIztLKPUNbeP"
      },
      "source": [
        "so, no empty values so handling missing values is not required."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIR6mnRk2AVk",
        "outputId": "3c4b15ea-8290-4cd1-cc63-b5244f109f36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "car_data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Car_Name          object\n",
              "Year               int64\n",
              "Selling_Price    float64\n",
              "Present_Price    float64\n",
              "Kms_Driven         int64\n",
              "Fuel_Type         object\n",
              "Seller_Type       object\n",
              "Transmission      object\n",
              "Owner              int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aw9x4lTm2v1L"
      },
      "source": [
        "are in expected datatyped so no conversions required."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VlSiyLa10IP",
        "outputId": "a003df27-5ef0-48df-8f83-69a3f8e402d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        }
      },
      "source": [
        "sns.pairplot(car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fa5a39aada0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l5p07uT7WL2K1BKGOODsQNi8SkEl2+uixXsCKVVlJpuHHOtvYtamDZEqbX6jNti/0cOZqyOzvzNWQ6dTl2vzx357jejQxaYwedm3qMN8/eaKPD//EOp480ceuTR3ml6T8+ge3Li/5+szlEBeGo1wYjpoXjdxxB587xZnLoZJ9HnzuFBeGo1X7LMuNX80xFjMXhqOmUwcT6261WE2Nfvgn1hVp8InjvaaWdm3qKNJzfn1O33evbytam6nWT9b2nUdvfwirstA3NGo6dZBZ+6deydipx4718vA9a4tsyyNHC+3bI0d76O0PlR1LECrh3EC0xPW6h3MDtW+HxIbWL+LYCUIdMRiOm4Y2RyxpMBSJMRSJEU2kGIrEUAqi8VTJtoPhmPl+IBwr2caYFCA0ljRQqvD9eCJllpfqI9d+8mtDw1AkxuAUY5frcygSo1qUG7+aYyxmyn1+1yITGh0vo8GcHqbTTu79UCRWtDZTrZ+s7TuP/lCMG9Ekhi6vqVjSmFaTufcDIdGKcGuUu14PhuMLNKPKERtav4hjJwh1RJvfaSY7zeGyW2j1uWj1ufA4bLT6Mrd4eFy2km3b/BO3gLT7XSXbTL7TwmW3kJ8NwmW30OCwFSReLdd+8muLglafi7Zpxi53ntWi3PjVHGMxU+7za/FNaLTBWVqDk7U0XX2rz1W0NlOtn6ztO4/2gJulHjtWVV5TLrulYk0GA6IV4dYod71u8zsXaEaVIza0fhHHThDqiI1BP4f2dBc4VIf2dAFp0jqN3Qr/t2+Q46evYLcqHtvdVdj2gW42LQtM9LcswKcfKOzv196zgWaPY9IY3bx45or5/sCOTv7H37/BgR2dHD99hf3bOwvaH9jRyfOvXS75etPyAKubPKxu8nBk35aC447s28Km5YGSfR7Zt8UMrlINyo1fzTEWM6ubPHz2A5uL1j1tpE2N/o+/f6NIg4/t7jK1dPz0lSI959fn9P1P5weL1maq9ZO1fefR1e4nrQ3Wt3r5jZ++rWDt92/v5MUzV3hiTxdPv/pmkW05vLfQvh3e201Xe6DsWIJQCRuCnhLX6242BGvfDokNrV8kQXkeklxycbFYE5QXRMX0OQk0WDGARDYq5ts3YrT6iqNiBv0uNk0RFfN6JE6gwU4smcbtsGJBmdE07wj6zaiYDQ4rHoeNsUQav7t8VMwrI+O0B1x4nTYu3hijYYqomEORzJzzo2LeiMaxz1NUzPzx38Ebw2ecEDoXFfPS8DhOuwWfw0ZLYCIq5lAkzoq8qJhtPhd2a/momAG3nZZJUTGXNFixThMVs9T6ydrWPTPWY7momPFUGrvVgsuWi4qZiZIZiaVo8jiwWQwSZlRMF13tAQmcIkxmVtfsxRAVU2xoTVJ2ISQqpiDUGW63nbvWNJWt72j0mq9vb2+ctj+Hw8oPrVo6bbv1bT7Wt/kqmyTQ3TEx9trW0sdZLIq1LV7WtngLykuVzQXlxhcqw2JR3B4McHuw+NeNVeUlOi1rZzB+ufWTtX3nYbNZuC04vc0ThPmi0e3irjX14chNRmxofSL/khIEQRAEQRAEQahz5u0XO6XUCuAZoA3QwFe01k8qpZYCzwKrgQvAPq31iFLqduCrwFbgd7TWn83r6wIQAdJASmu9rcR4CngS+A/AGPDLWuvX5uwEBWEeMAzNG9dGeet6FJfdgsdhw2m3oJQmntS0+B10BDK3S+QnF21w2Eik0wQDTgZuxs3b3BocVsKxBDaLlWuROC0+J+OJFFaLhcYGO+OJNNdG43QE3MRTBkOROO0BFy1+OwOhBEPhOM0+B0Gfk+VLPFwaGStKZmoYmreuR7k4HMXlsOC0WlnqcaBUJpJdrSc+TaUMevtDDIZjNHmcGGiaPM6anvN8YRiavqEwF/ISlC9psJBIg1VZiaXSWC0QGc8kK2/zO0mm07hsVlx2K+FY5jbhZq8Dr9PGuqUezt8Y5erNWEF/K5ZUP2F0bl37Q7GSSaklOW/9kkikOXM1xM3xBH6XnRvRBE0eB+F4Eo/DhlUpbowl8DpseJxWxpJpwrEUrV4HHqeVcFavLT4nfqeVm+Mp0YAwKxbDrZhiA+uL+bwVMwV8Qmv9mlLKB3xfKfUy8MvACa31Z5RSnwI+BXwSuAHsB/aW6e+ntNbXpxjvvUBn9vEjwBezz4JQlxiG5ls9A3ziaxMJQw/s6GRZowu/ywZoLo+M0d84ztblTXz79cGC5KJ/+P6NvHUtyqPHJpJFH97bjcOq+M2vny1K5vsrP7meL333PImU5uEfXcWTJzK5n7atCrBv26qipNJLh0Y58FeFyUzvv6OtaB4HdnTiye7z++J332RkLFGziU9TKYOjp6/wyNGeos/nkzvvqMk5zxflEpSvbW5gLGHwvTeG2LOlg6s34wWae2xXF19/7RIf2LaS//c7E4nIP/PgRs4PjfKp588W9NfR6OLGWJLNHU1Vc+5KrWt+UmpJzlu/JBJpjp65yp/8XR8PbVtZkIj8t3beTkrrguTlOXv0xe++SUejs6Rt+8t/vsi5oVHRgDAjJEG5sBDM262YWuv+3C9mWusI8DrQATwAPJ1t9jRZR05rPaS1/hcgOcshHwCe0Rn+CWhUSrXfyjkIixiLDaXUtI+OFSsXbIoXhqOmUwcTCaHfuBbFarHQ4HBw/lqUdFrR2x8qSi7a6HaYX7BzZY8c7eH8tWjJZL65RNEPbl1uOnUAD9+ztmRS6WRKFyUzLTWPJ0/0cT2a4Ho0YSYur9XEp739IfPLPxR+PrU65/miXILyZBp+6xtn2bt1ZTZZ+aQE5S9mkkQ/dqwwEfmb16OmU5ffX0bTlqomjC61rvlJqSU5b/1y5mqIR1/IJByfnIh8eCxRlLw8Z48e3Lq8rG378E+sEw0IM0YSlAsLwYIET1FKrQbeBfwz0Ka17s9WDZC5VXM6NPBtpZQGvqy1/kqJNh3A23nvL2fL+vMbKaU+AnwEYOXKhfvSLiwwRqqiCJrPfvSeOZ3GVHqcKqH3yFgSi8ok570+mokmObntjWhyRsnIJycWz1EuwW80kSoq6w9NnQA9v/+hSKzmNmmXm3/u86nFOVeT2egxmtXH9UjcLJvcJj+5fY5yiaUNDYORGA6rhc0rqnNe5dZ1IBRj84qpk/Mu5vWudSq5Xg9k1y73N5rPVBpTqrxtG8/aNtGAMJmpbeTiTFAu+q9t5j14ilLKC3wd+LjWOpxfpzPx5iuJOf/jWuutZG63/JhS6idmOx+t9Ve01tu01ttaWlpm240gVIWp9DhVQu8lDXZafS4sCpq9TtoD7qK2Sz32GSUjn5xYPEe5BL8eh62orD1Qfs4WRUH/tZj4tNTnmP/51OKcq8ls9Ohx2cxE5fnJyvPbuLPJ7fMzjJRLLG1R0OZzVTVhdLl1zY0hyXlrk0qu1+15azd5DafSmNblbZs7a9tEA8JkpraRkqBcmH/m1bFTStnJOHV/rrV+Pls8mLtFMvs8NF0/Wusr2ech4BvAXSWaXQHy/7+7PFsmCHXJ6iYPn/tAYcLQAzs6WdfiIW0YjCUSrG/xYLVqutr9RclFb44nOLSnMFn04b3drG/xlEzmm0sU/fXvX+bAjomEvk+/+mbJpNJ2mypKZtrVHiiax4EdnTR7HDR7HGbi8lpNfNrV7ufw3u6Sn0+tznm+KJeg3G6B33/fRr7x2qVssvJJCcp3dfHMq2/yxJ7CRORrmj185sGNRf1lNG1UNWF0qXXNT0otyXnrl43LAhx6oJvjp68UJSJf2uAoSl6es0fPv3a5rG37079/QzQgzBhJUC4sBPOWoDwbpfJp4IbW+uN55X8EDOcFT1mqtf7NvPrHgdFcVEyllAewaK0j2dcvA4e01i9NGu9ngF8lExXzR4CntNalHEATSVC+uJhpgvJKb8Ws0t/MrJKdTo6K2eCw4bJZwKJJJDQtgXJRMa0k0wZt2aiYg+E4rT4nDc5JUTG9TsaTxVExlwUySaaHRuME/S5ac1ExI3GaPA7a/RNRMScnM81Fxbx0I4rDZsFlm4iKORCu/cSnE1Ex4yz1ONCLNyrmrBKU9w2FuZiXoHyJx0I8DTZlJZ5OY1GZqJhDo3FavU6SRhqnzYo7GxXzRjRBk9eB12FjXVMmKmb/zXhBf3MZFXMgFCNYIim1JOddcGasxxyTo2KORBMs9TiIxJM0ZKNijowlaXBY8Tgy0VvD42mavXa8rkxUzKFInGavE79LomIKgCQoF/3XFjWRoPzHgF8EziqlTmXLfhv4DPCcUupDwEVgH4BSKgicBPyAoZT6OHAn0Ax8I+MnYgP+IufUKaV+BUBr/SXgm2ScuvNk0h38x3k4R0GYUywWRWebj84KEoWXSy66Ykl17o9fWSKneanxLBbFulYv61qLx13dXPv36ttsFjavWLLQ06hJLBbFbcEAt5VIUD5bNnYsYWNH1borS25dy+3bk+S89YvDYWXb6hIGShDmGUlQLsw38+bYaa3/kfIe5o4S7QfI3D45mTCwucwYX8p7rYGPzXymgiAIgiAIgiAI9cW8B08RBEEQBEEQBEEQqsuCpDsQBGH2xGIpzvaHGMjes+9zWYkn08RTmShcK5d6MAxNb3+I/lCM9oCbJR4b1yMJxhJpIrEULT4HhtaMjCXxuWy4bFZuRBM0OK14HTbCsRRjiTSNDXba/E6SKc2lkTE8DhvBgJNUGoYisYr2neTv9ZvvfSqlxgYWbD6Lkeh4nN6BUXMPSZPHymhMMzwWx2WzEmiw43PBQChttml0WxlPGETiaaKJFMsCbrxOGwPhGMsaXdyMJukPZ7Tb2GDj6s35X6vcHrzc31BXe/X3+Qlzg2FoLt2IciOaIJk2QEEypYnEUiz12FnqsTEyltlH1+pzMp5I4bBn7Ggmkq+VgVCcaDxFs89J2jBIG5h79YzFu89WqDL1vMdObGB9Io6dINQRsViKY2f7zaSnmShbXSxrdHLs1BXWtPhZ3+ollkzzG//nDLGkwaomN5+4/zaujIybicZz0eCe+d5FRsYS5muHTfErP7meJ473mu0e293Fl757novD46xqchfVH9m3hZ1dwZJfcAxD81LvgJnodLr21aTc2A6b4lf/4l/nfT6Lkeh4nL/uGSrS46omJ28Pj/M/X32L/7b3TnqvJIvarGxy8eFnvm+WHbxvA987f537u9uL9PeX/3yRc0Oj87ZWqZTB0dNXzCTmuaiZezd3yBebGscwNK/8YJCBUAzD0FgsikgsZdq+VU1uPnZvZ4Ee92/v5NmTl3ho20q+8EofP/tDK8vawPz2n9x5h9gOoSw3x2N8u+faJNvXzf3dLTXv3IkNrF9kdQShjjjbHzIvEpBJGProsV6sFit7t67kyRN9nL0Som9o1Gyza1MH54dGzS82ueOePNHHg1uXF7zetanD/EKTa/fE8V52beow+5pcf/C5U1wYjpac74XhqOlYVdK+mpQb+8zl0ILMZzHSOzBaUo8KG8NjCXZt6sButZdpYy0oO/LyOX75x9eU1N+Hf2LdvK5Vb3/I/EKTm8cjR3vo7Q/N+djCrXFhOMqZyyGGInGuRzORe/Nt365NHUV6fOqVPnZt6uCpV/p4+J61U9rA/PZiO4SpODcQLWH7ejg3UPuaERtYv4hjJwh1xEA4bhraHLGkwfXROMOjmTpDg5GXkUGpzPtSxylV+FqpqduVqx+KxErOdzAcm1H7alJubGNStor5ms9iZLCMHgcjMQyd0ctQpDINxJIGI9FkybbjiVTZ4+aC/lDpOQ+ERCe1zmA4ZtrA3CN/LaeycbGkwXg8NaUNnNxebIdQjrL2MRxfoBlVjtjA+kUcO0GoI4J+p5kwNIfLbqHZ66TJm6mzKJh8Z5BVUfK4XEq+ya/LtStX3+orfVtJm981o/bVpNzYkz+b+ZrPYqStjB7bfC4sCrSGVl9lGnDZLSz12Eu2dTtsZY+bC9oD7pLzCAZEJ7VOm9+FVVHwKLWWk99rnXlucNoqsoG59mI7hHKUtY9+5wLNqHLEBtYv4tgJQh2xsT3AoT3dpsHN7VdKG2mOvnaJAzs62dgRoLPVa7Y5fvoK61q9HNjRWXDcgR2dPP/a5YLXx09f4bHdXQXtHtvdxYtnrph9Ta4/sm+LGZRkMtV3hl0AACAASURBVKubPBzZt6Xi9tWk3NiblgcWZD6Lka6gt6QeNSmaGhy8eOYKyXSyTJt0QdnB+zbw1X98q6T+/vTv35jXtepq93N4b+GcD+/tpqu9evn6hLlhdZOHjcsDtPicNHkctPicBbbv+OkrRXrcv72TF89cYf/2Tp5+9c0pbWB+e7EdwlRsCHpK2L5uNgRrXzNiA+sXpbWevtU7hG3btumTJ08u9DSEKqGU4qEvv1pR22c/ek9FbZ/96D1U6W9m2t325fSYHxWz1efE77aSSKaJlYiKORCKEfS7WOK1MxxJEE2kGY2naPZmomLeHEvhdVpx2a2MjCVw2a34nKWjYr49MkZDXlTMa6MxWn2VR8UcilTWvpqUGhtYsPnUOFN+COX0WBAV0+ekyZuJinljLIHDZqHRbcfnzouK6XPS2DARFXMskSYYcOJz2hmMxGgPZKJiDoQz2m302OkPzf9a5SLCDYRiBAMuutoDEjRgfpmVHqF8VMzReIolDRNRMa9F4rTkomLarMRThVExxxIpmjwO0lqbUTGXeBxoiYr5TmRW1+zFEBVTbGBNUlaPEhVTEOoMl8vGD69pmrKNxaLYvGIJm1dMlK1cemvjrm/zFbxf1+qt6DiLRbG2xcvalsraV5NyYy/UfBYjHreTu9ZMf2tRJfpbk12TVU2wOa98VdP8r5XNZin6GxLqA4tFsbrZy+rm2fexEJoTFh+Nbhd3rakPR24yYgPrE3G9BUEQBEEQBEEQ6hxx7ARBEARBEARBEOocuRVTEOoIw9CcH4rw1nAUt92K12nD47RwfTSB3WolGHCyvDGzj+yt61Eu3ojicdhob3QyEk0yGI7jd9uIJVME3A4A4imDZFoznkjT5HXgslkYDMfxOG20eh1Ek2mu3hynyePEWOC9Jbk9c4PhGG1+2R+30KRSBueGwoTGUwxHE7Rl93w6rFasVsVgKE4knqTBbiMcS+Jz2Qj6XLT5nPRdjxJNpLkRTbC80U33son9G+XWOZFIc+ZqiIFwjHa/i43LAjgc1lnNXbS0eMntsRuKxAmNZ3SXTKexW60oNEpZzP11hpHGYbMxlkiDMnBYbFwbjdPkdRBw24glDSxKMZZI35JORG/vTOp5j51otj4Rx04Q6gTD0Hyrp59PfO00saRhRrPsaHTR6nfwp/9wnh13tLNi6RhjCYNf/Yt/JZY02LYqwEM/vIrffaHHPG7/9k5e+fcBfuHu1QyEYmYCXzM64f+9wMhYggM7OvE4rHzxu28yMpZg//ZOnj15iU/uvIOdXcF5NfKGoXmpd8BMOp6Lkjjf8xAypFIGJ34wyI1o0kzo7LJbOPRAN2ubXVwYjvH5V/p4aNtKnnplQl+P/MwddDS6GAgnCo779APdvG9LBxaLKrnO2ztbONbTz6N5Oj70QDd7Ny2bsXMnWlq8GIbmlR8M8ua1KEdePmeu72O7uvi7H/TzU7e188SLE7p7fHcXKcPg6L9e5me3ruSJF1+bOGZ3FwG3jT946d+5ODw+a52I3t6Z3ByP8e2ea2aS8lxUzPu7W2reuRPN1i9yK6Yg1AkXhqOmUweZZKFPnujj/LUoCiu/cPcaHjvWSyoNZy6HzHYP37PWdOpyxz31Sh8P37OWt65HTacuV3fk5XM8uHW52f/1aMJ8/9Qrfeza1MHB505xYTg67+efu8jk5roQ8xAy9PaHSKa06ZxBZk0efaEHw7DwyNEedm3qMJ26XP3hv34dq8VSdNzvvtBDb3+o7Dqf7Q+ZTl3+WGeuhmY8d9HS4uXCcJQzl0OmUweZ9X3ixV5+4e41plOXK3/8eC9DkTgP37O2qO6J470YBuza1GGWzUYnord3JucGoqZTB1mbdayHcwO1v+6i2fpFHDtBqBMGwzHTyOaIJQ0MDUORGDfHksSSBiNjSYy8jAzj8VTJ48YTKQxNyTqlCvvPf69U5nkoEqv6OU5FufOf73kIGfpDMaJltDUYiRVoZXL9SDRZsnwgFCu7zgPheOmxwjNff9HS4mUwHCtr126W0Z2hy9vJaCJl2r9c2Ux1Inp7ZzJY1mbFF2hGlSOarV/mxbFTSq1QSv2dUurflFK9SqkD2fKlSqmXlVJ92ecl2fLblVLfU0rFlVK/Pl0/Jca7VykVUkqdyj4enY/zFIS5pM3vMpOF5nDZLVgUtPpcNDbYcdktLGmwk3+nRIPTVvK4BocNq6JkXS5VX67//PdaZ55bffN7K0m585/veQgZ2gNuPK7S2spfq1L1Szz2kuXBgKvsOgf9zrJjzRTR0uKlze8qa9cay+jOosrbSY/DRn7q0tnoRPT2zqStrM2aPj3MQiOarV/m6xe7FPAJrfWdwN3Ax5RSdwKfAk5orTuBE9n3ADeA/cBnK+ynFP+gtd6SfRyq8vkIwryzusnD5z6wueAL84Ednaxv8aBJ8+f/9BZP7OnCZoVNywNmu6dffZNPP9BdcNz+7Z08/eqbrG72cGBHZ0Hdwfs28Pxrl83+mz0O8/3+7Z28eOYKR/ZtMZN9z+f5H9m3pWCuCzEPIUNXux+7VfHY7q6CNTn0QDcWZXB4bzfHT19h//ZCfT3yM3eQNoyi4z79QDdd7YGy67yxPcChSTo+9EA3m5YFZjx30dLiZXWTh43LAxy8b0PB+j62q4s//6e3eGxXoe4e391Fq8/J06++WVT32O4uLBZ48cwVs2w2OhG9vTPZEPRwaM8km7Wnmw3B2l930Wz9onT+v6Lma1ClXgC+kH3cq7XuV0q1A9/RWt+W1+5xYFRrPdnBK+hHa/3ypPJ7gV/XWu+ayby2bdumT548OaNzEWoXpRQPffnVito++9F7Kmr77EfvoUp/M9PuPi6lx1xUzAvDYzjtFnwOGx5XNiqmxUqwsTAq5qUbURomRcX0uWzEU2n8LjtKQSJlkMhFxfQ4cNktDEXiNDhstHgdjCXTXL0ZY6nHga6RqJhDkRitPonSVWWm/CBL6bHSqJhuu41ILIXXaSXodxHMi4o5Ek3QUSYq5uR1zkXFzEVp21SFqJiipZplxnrMUSoqZiqdxjY5KqbXiSaN3ZqJiqnQ2K1Wro3GWepx0Oi2EU8ZqCpGxRS91S2zumYvhqiYotmapOxCzHtUTKXUauBdwD8DbVrr/mzVANA2y35K8aNKqdPAVTJOXu/sZiwItYPFotgQ9LMh6C8ov61E23WtXta1es33K5dWPk7XpPfdHZUfO5dYLIq1LV7WtninbyzMOTabhTuXNZatX7m0/DptXuEoW1dunR0OK9tWz0DIUyBaWrxYLIrVzV5WN9fO2ore3pk0ul3ctaY+HLnJiGbrk3kNnqKU8gJfBz6utQ7n1+nMzyAV/RQyVT9ZXgNWaa03A58Hjk7R10eUUieVUievXbtW4ZkIwtwgehRqCdGjUEuIHoVaQzQp1Brz5tgppexknLE/11o/ny0ezN6CSfZ5aJb9FKC1DmutR7OvvwnYlVLNZdp+RWu9TWu9raWlZcbnJQjVRPQo1BKiR6GWED0KtYZoUqg15uVWTKWUAv4MeF1rfSSv6hjwS8Bnss8vzLKfye2CwKDWWiul7iLjwA7f2lkIwsJjGJo3ro3y1vWoGbHNabdgtcBoPE2L10nayKQ/aPO7aPc66RkIMxCOsyyQiZw5FInT6nUynkozFInR4nUxlkgzPBqnze8iaaRRZCJ3rVy6sHvpBsMxGhw2Eun0gu7tE0pjGJq3ro3SH44RiaVo8TlY0mAlHDOwKkVoPInTZiUST+Jz2QnHkrR4XaxvdtM7MGruO9kY9ON2280+c2s/eU/TVHUzJZUy6O0P0R+K0R5w09XuN/f4CfVNTicDN2PYbYqb40nafE7iKYNQLEmLx0kkniISS9Hqc3Jnm4/BaJzBcJzro3FafE7GEymavE6cNitvj4zhcdho8ztZ3tjApZGxAg0CVdOlsLhYDHvsRNf1xXztsfsx4BeBs0qpU9my3ybj0D2nlPoQcBHYB6ZjdhLwA4ZS6uPAncCmUv1orb+plPoVAK31l4D3A/9ZKZUCxoEP6oWIEiMIVcQwNN/qGeATX8skDc1FrVzW6MLjtDEWS/Kvl26aiXlXNbn52L2dZoLUXPtnvneRkbEEB+/bQNDnYDCcMJNF5yLBff37l9hxR5DONi/bb2ubV2NuGJqXegfM5Ki5aJzPnrzEJ3fewc6uoFxcagDD0Jz490HOD42aSe5ddgt/+P5NJNMGT53o46FtK80E5bl1/PTJf+Nj93byJ9/p4+LwuBkpbnd3EKfTVrT2R/ZtYWdXEKBs3Uz1kEoZHD19hUeOTvxtHN7bzd7NHeLc1Tml7Meh3XdyfTTBl757nv90zxr6Bgs1+0fv38R4Is2jxybs4OO7u3h7ZJzDf/16gf1cvsTNZ7/9A1O7R/ZtwWFT/Opf/Ost61JYXNwcj/HtnmsF1+BDe7q5v7ul5p27Un9Houv6YF6uYFrrf9RaK631prwUBN/UWg9rrXdorTu11u/RWt/Ith/QWi/XWvu11o3Z1+Fy/WSP+VLWqUNr/QWtdZfWerPW+m6tdWWhEYWap2PFSpRSFT0WGxeGo6ZTB5lkoU+e6OONa1EcVguNHqfp1AHs2tRhXlDy2z+4dTmxpMGRl8/R6HGaTl2uzRPHe3n4nrU8eaKPM5dDXBiOzvt55i4muTk99UofuzZ1cPC5U/M+H6E0F4ajnL0SMr8gQ2atzg+N8jvf6GHXpg7TqcvV5dbx0WOZ+lz5o8d6ODsQLrn2uTWfqm6m9PaHTKcu19cjR3vo7Q/d8uciLCyldNLgtPPE8V52bepgeCxRpNm+oVHTqcuVPX68l6FIvMh+9g2NFmj34HOnOHM5VBVdCouLcwPRomvwo8d6ODdQ+9qopr0V5pd5j4opCLfC1ctvzyiFwWJiMBwzjWyOWNLA0DAylkRrCuqVomT7nM8bSxqMRJMl24wnUmbfQ5HYvEbFKneeufOZ7/kIpRkMxzB0scZyZVPpL1+HufLBcJy0oUseMxSJFek7v26meugPldbYQCjG5hUz6kqoMUrZj2g8ZWpuKs3mk7N/pcoma7dUO7FTwmA4XlJXg+H4As2ocspdh0XXtY/ccyIIdUKb32UmC83hsluwKFjSYGepx16yfvL73E3JLruFJWWOcTtsZt+tvvm9ZaTceWqdeZ7v+QilafO7sKpijeWXTbWO+TfHu+yZPZ3l1r7V55qybqa0B9wl+woGRFv1TimdeFw2s2w6zebI2b9SZZO1W6qd2Cmhze8sqas2v3OBZlQ51bS3wvwijp0g1Amrmzx87gNbCr40H9jRyboWD4m0wc1onIP3bTDrj5++wqE93UXtn3/tMi67hYP3beBmNM5ju7sK2jy2u4tnXn2TAzs62bQ8YAYHmM/zPLKv8Dz3b+/kxTNXOLJvy7zPRyjN6iYP3R0BDuzoLFirda1e/tv7ujl++gr7t3eWXMdDe7p58cwVs/zQnm42Bv0l1z635lPVzZSudj+H9xb+bRze201Xe+CWPxdhYSmlk7FYksd2d3H89BWWNjiKNLu+1cuhPYV28PHdXbT6nEX2s7PVW6DdI/u2sGl5oCq6FBYXG4KeomvwoT3dbAjWvjaqaW+F+UVJTJEJtm3bpk+ePLnQ0xCmQCk1o1sxq9322Y/eQ5X+ZqbdBFhKjyWjYtosWK0QjadpzkbFvDYao9U3ERVzMBwnGHCxpMHOtdE4zR4nsVSaa5EYzV4X44k016NxWn0u0kYaaioqppVk2mCpRMWca6b8YMvpMT8qZrPXwVKPlUjMwKIU4VgSh3VSVEyPk/UtDdNGxRyKZDRcKipmqbqZkouKORCKEQy46GoPSOCU2mLGesxhRsUMxbBbp46K2eJz0jU5KqbXyXgqRVODE6fdyuWRMRomRcXM1yBQNV0KNcusrtmLISqm6LomKbsQssdOEGaCxVZxYJZly1dw5e1L1R3eouhs89HZ5puy3brWiXvgf3hN06S6qY+tBSwWxdoWr9zLX+NYLIp1bT7WTaPHUty1pvTtSFOtfTV1YbNZ2LxiieypW4TMRier3XZWN5duP9nelupb7JVQika3i7vW1IcjNxm5DtcnM3LslFJWoFdrffsczUcQahsj9Y4N3iIIgiAIgiDULjNy7LTWaaXUD5RSK7XW1f0pQhCEaZl8K6bXacPjtBCOTSTwBnh7JMq1SILro3EaHFbcDitum5X1zV4uh8a5MBzF47KRSKZZ3tjAmhav3GIhzIrx8SRns7f7tvmdLFvipLXBzQ+uhbl6M4bbbsXrtOGwWgiPp3DaLYRiCVp9Llw2K9dG47T5XaxcMpH4WZLSC7dCIpHmzNUQA+EY7X4XXUE/g9EYg6E4N8YSNHkcROJJXDYrAbedG9EEDpsFu8XCWDJNNJGiPeDm9lYfl0PjBZpc2uDEaslEVpWkzcJ0LIZbMSVBeX0xm1sxlwC9Sqn/DzATWmit91RtVoIgFDFVgnKlNb/+tdN8cucdNDZYuXwjVpBs98COTlY1NfDvg5GCpMz7t3fy6LFefvOn7+C93ZJ4VJgZ4+NJjvcMFCTg/cOf3UjauMlvfeNsgf48Ditf/O6bjIwl+K2dt3N+KGrmXcwFL/n8KxNJyyUpvTAbEok0R89c5dEXJjT55ENbGBlL8vjx3gLb9+zJS3zwh1fS7HUQTxlEYikzx92qJjcf+6nOgn7yj3nmexcZGUtI0mahLJKgXFgIZrNT/HeBXcAh4HN5D0EQ5pCpEpQ3epxmAu9UmqJku0+e6MMwKErKnEsY/YmvSeJRYeacHQgXJeA9fy1qOnW5sidP9HE9muDBrcuJJQ2GxxKmU5dr88jRwqTlkpRemA1nroZMZwwyWkqmtenU5cpy+nryRB9uu42hSLwgcfmuTR1F/eQfk9Oy6FMohyQoFxaCGTt2WuvvlnrMxeSE2qJjxUqUUhU9OlasXOjpLjqmS1CeS/x8o0zS8VyS3snl+Ym/BWEmlErAO1Wy51zcoXJtJid+Fm0KM2VgigTl+eTrK5pIFWkyV1fumJxWRZ9CORZrgnKhtpnxrZhKqbuBzwN3AA7ACkS11v4qz02oMa5eflsChywguYSh+cY2P0F5LvFzLlH55Ha5JL2TyyXxtzBbcgl48zWVS/ZcSqdpY+o2kxM/izaFmdJewk5OZ/s8DltZTZY7JqdV0adQjlL2sd4SlE+eu2i99pnNrZhfAH4O6APcwIeBP6nmpARBKGaqBOU3o3EzgbfNSlGy3QM7OrEoipIy5xJGf+4DknhUmDkbg/6iBLzrWjz8/vs2Fumv2ePg+dcuZ/750ODg4H0bihKE5yd+lqT0wmzYuCzAoQcKNWm3KB7f3VXS9h3Y0cl4MpPPLj9x+fHTV4r6yT8mp2XRp1AOSVAuLASzymOntT6vlLJqrdPAV5VS/wr8VnWnJrxT6FixkquX317oadQ8Fovivd1BNrT9+ERUTIcNj8tCJJbmq79810RUzMYo//tDP8LwaDwTEdNhxWWz0tnsZcvyxkxUTKeNRCrNnz38w6yVqJjCLHC77ezuDrK6uSET9c3nZNnSTFTM9W0e+m/GTZ06bBY+94HNOGwWIrEELT4X22/7ca5H47T6MlExt65cUpCUfmd3UCKxCTPC4bCyd9My1jZ7zGh+3dmomM/8x7sYGUuw1ONgNJ7i9963kYDbzkheVMw/fXgbY4k0Qb+T29v8bFtVqMmf7gpitcC7VjZK0mZhShrdLu7vbmF18111FxXTYlHs7Apy+/53S4LyOmM2jt2YUsoBnFJK/SHQz+x++RMEQG7xnAmVJihf1eRlVVPpuvUuH+tnkVBaEErhdtu5a02x2DZ2LGFjx/THr2dCi5IMV6gGDoeVbauXFpStcnlZ1TRzbZXTZLlk5oKQjyQoF+ab2Thkv5g97lfJpDtYAfxsNSclCIIgCIIgCIIgVM6Mf7HTWl9USrmBdq31E3MwJ0EQBEEQBEEQBGEGzCYq5m7gs2QiYq5RSm0BDk2XoFwptQJ4BmgDNPAVrfWTSqmlwLPAauACsE9rPaKUuh34KrAV+B2t9Wfz+toJPEkmIuefaq0/U2I8Z3a8HwKGgYe01hdmer6CUEsYhuaNa6MTe+ycNpp9DpY3Zu59NwzNpRtRBsNxwrEkAbedYMDJyGiS/nCM9oCbrnY/NpsFw9BcGI4yHI3jsFoYS6Rp88/8Pvpq9SPMHbk1yu05qtbaGIbmjaFR3hoexWW34nPauKPNj8NhLRhv5ZIGLt8cK9Blm99JKg1DkcrnlH8eyxpd3IxO6PqONh+XQ+NVP0eh+syVHisZr8Fhw9AGdquF0Xia0ViSxgYH4VgCu8VCi89BysikkIkl06xt8rCqycPFG2NcvBHF47DR5neycmlt6Gu+P0thZtwcj3FuIFp3e+xAtFWvzGaP3ePAXcB3ALTWp5RSayo4LgV8Qmv9mlLKB3xfKfUy8MvACa31Z5RSnwI+BXwSuAHsB/bmd6KUspKJwnkfcBn4F6XUMa31v00a70PAiNZ6vVLqg8AfAA/N4nwFoSYwDM23egbMJOW5aIPLGl1cHhnjrlXNfKdviL7BUTPR7qomN//l3vU8lk1Ynos+uGfjMv72B0P8wUuv89C2lTz1Sp9Zf2TfFnZ2BSsy4Iaheal34Jb7EeaO3Brlks1Wa20yeuznE187XaDHt0fGCTY6ePjPTpoa/PX7b+PyyHiBLn/lJ9fzxPHeiueUfx4bWr383I+sKjj+0APd/Mnf9XFxeFz0V8PMlR4rHW9Vk5uP79jAQDhm6tFlt/Br79nAy//Wzy/cvZqBUGHd4b3dfP6VCW0d2NFJZ5uX7be1Lai+5vuzFGbGzfEY3+65ZiYpz0XFvL+7peadO9FW/TKbPXZJrXVoUpku2TK/gdb9WuvXsq8jwOtAB/AA8HS22dNkHTmt9ZDW+l+A5KSu7gLOa63f1FongL/K9jGZ/H7/D7BDKSVqFOqWC8NR06mDTLLQJ0/08ca1KKk09PaHOHM5ZH4hAdi1qcN06nLHPHK0hzNXQxx87hS7NnWYzliu/uBzp7gwHK14TtXoR5g7cmtU7bXJ6PF0kR7PXxslnVYFGuwbGi3SZc4pq3RO+efx4Z9YV3T8oy/0sGtTR1XPUag+c6XHSsfbtamDt4ajBXqMJQ3++G/P8fA9a3nrenHdI0cLtfXkiT7OXA4tuL7m+7MUZsa5gajp1EHWTh3r4dxA7a+PaKt+qdixU0p9M/vLXK9S6ucBq1KqUyn1eaCykIYTfa0G3gX8M9Cmte7PVg2QuVVzKjqA/Nj4l7NlZdtprVNACCgK3aaU+ohS6qRS6uS1a9dmcBaCUH2m0uNgOFaQLBQyxtbQmduG+kMxDE1BG6UoecxAtq9y9UORWEXzHaxSP8LcUU43lazNbPV4fTSe1wcV63KqOeWPNx5PlTw+/193or/aZK70WOl4pfSYm8N4IlW2brK2DM2C6+tWPkuhOkxtI+Ml12cwHKfWEW3VLzP5xe6rwN+Q2QfXDcSBvyDjMB2otBOllBf4OvBxrXU4v05rrang179qorX+itZ6m9Z6W0tLy3wOLQhFTKXHNr/LTBaaw2W3YFGwpMFOe8CFVVGyzeT3wby+StW3+iq7TaStSv0Ic0c53VSyNrPVY7PXWVBeqS6nmlP+eA1OW8njtS58L/qrPeZKjzMZr5weGxy2snWTtWVRLLi+buWzFKrD1DbSWXJ92vyF9rEWEW3VLxU7dlrrr5EJZOIFfoZMwJO/AkaAj1XSh1LKTsap+3Ot9fPZ4kGlVHu2vh0YmqabK2RSLORYni0r204pZQMCZIKoCEJdsrrJw+c+sKXAkTqwo5N1LR5sVuhqD7BxeYADOzrNNsdPX+GJPV0Fxxze282mZQGO7NvC8dNX2L+9s6D+yL4tZqLzSuZUjX6EuSO3RtVem4weNxfpcX2LF6tVF2hwfau3SJeP7e6a0Zzyz+N//P0bRccfeqCbF89cqeo5CtVnrvRY6XjHT19hdZOnQI+5PXZPv/omq5uL6w7vLdTWgR2dbFoeWHB9zfdnKcyMDUEPh/Z0F9qpPd1sCNb++oi26peZBk9JkMld5yTj4FX861p2f9ufAa9rrY/kVR0Dfgn4TPb5hWm6+hegM3tb6BXgg8DPl2iX6/d7wPuBV7K/CApCXWKxKN7bHWRD249PRMV02Gj2T0TF3H5bG+tbvGxduYRwLEmj205bwMmz/8/dDIRjBAMuutoD2GwWdnYFuT3o40Y0zrMfuXtW0SwtFlWVfoS5w1yj/e9mKBKj1VedtcnosZ0NrT7eGs7oMT8q5jfzxstFxczXZavfyV//13dzbbSyOU0+j/aAq0DXd7T52bZqSVXPUag+c6XHSsbLRMW0orWmsy1jJyPZqJiRWII72jfQ4nPQ2eqluyNALJlmTZOH1U0e3rViCZduRGmooaiY8/1ZCjOj0e3i/u4WVjffVXdRMUVb9UvFjl02xcARMg7TVq312AzH+jEyyc3PKqVOZct+m4xD95xS6kPARWBfdrwgcBLwA4ZS6uPAnVrrsFLqV8ncFmoF/qfWujd7zCHgpNb6GBkn8n8ppc6TibD5wRnOVxBqDotF0dnmo7PNV7Z+dbOX1c3egvKVS2FzibZrW7ysbfFyK1SrH2HumKs1slgUnUEfncFiPU4er5QuAda1Vj6nyeexqqlQ16LD+mC+bUY1xlvX6p2RVucLsb+1TaPbxV1rat+RK4Voqz6ZyS92vwN8IOdEzRSt9T8C5Vz9HSXaD5C5zbJUX98Evlmi/NG81zHgA7OZqyAIgiAIgiAIQj1RsWOntX73XE5EEARBEARBEARBmB2zSVAuCEINYBiaC8NRBsOxgj1t+eXtARdaw1Akbu5tiqXSLAs0sKZZ7pcXqkM5LU7VZuWSBi6NjDEcjeOwWor2ZpbqE5h2HEGolOls6HA0jkJxIxqnyePEYoElDQ7SRibVgWhQAjF4YgAAIABJREFUmI6x8QQ9AxFzj1130EeD27HQ0xIWMeLYCUIdYhial3oHzASiuYhV99/RxrdfH+Tgc6dY0uDgP//kWqKJtJlwNxf97ZGjPXxy5x3s7ArKlxLhliinxXxtTW6zqsnNf93eyedf6eOhbSvN5PaldJwr/8LPv4tESk85jiBUynQ29A9eer1Im7//vo28NnaTP/qbH4gGhWkZG0/wYs+gmaQ8FxVzV3ebOHfCnDGTPHaCINQIF4aj5hcSyCQOPfjcKXr7Q2b5g1uXcz2aMJ26XLs//ttz7NrUwcHnTnFhOLqQpyEsAsppMV9bk9vs2tTBI0d72LWpw/zinH9svo5z5WcuF5eJhoXZMp0NLaXNt4ajplOXf4xoUChFz0DEdOogo5dHj/XQMxBZ4JkJixlx7AShDhkMx8yLRY5Y0qA/NFGuFBiaku2UyjwPRWLzNmdhcVJOi/namtwmp7/c8+Rj83Wco5yWRcPCbJjOhpbSpmhQmAmD4XhJvQyG4ws0I+GdgDh2glCHtPldZuLQHC67hfZAYblVUbKd1pnnVl99hmEWaodyWszXVrk2+c/55ZN1DOW1LBoWZkMlNlQ0KNwKbX5nSb20+Z0LNCPhnYA4doJQh6xu8nBk35aCLyBH9m2hqz1gln/9+5dp8jg4sKOzoN2vvWcDL565wpF9W8yAFIIwW8ppMV9bk9scP32Fw3u7OX76Cvu3d06p41z5xuXFZaJhYbZMZ0NLaXN1k4ff+OnbRINCRXQHfRza012gl0N7uukukfdTEKqFBE8RhDrEYlHs7Apy+/53MxSJ0eqbiM6WXx70Z6Jibl25hEgsScBtJ55K8z9/6S6JiilUham0OFWblUsa2LpyCTeicZ79yN1FUTFL9QlMOY4gVMq0NjToYzga539/6Ee4EU2w1OPAZoF3NTRy74ZWro2KBoWpaXA72NXdxurmBomKKcwb4tgJQp1isSjWtnhZ2+KdtnzNpDaCUE3KaXG6NlMdU67P6cYRhEqZiQ2dzLpW0aAwPQ1uB3etaVroaQjvIORWTEEQBEEQBEEQhDpHHDtBEARBEARBEIQ6R27FFIQ6IpFIc+ZqiP5QjFa/k0a3lXXNfmy2zP9oDENzYTjKYDhWsF8px3T1gjBTxseTnB0Im3tINgb9uN32onZzrT3RtgBT66BcXX55q9fJeCrN5ZFx2gNuuton7Ot0/Qv1yVyu6dh4gp6BSF3usROt1yfi2Alzg8WGUmIAqkkikebo6atmwlOX3cITe7q4Gorx7nWtWCyKl3oHzKS7uYhtO7uC5peXqeoFYaaMjyc53jNQoMlDe7rZ3R0scO7mWnuibQGm1gFQsu7+O9r49uuDBeUHdnTyzPcuMjKW4PDebvZu7sBms4jOFiFzuaZj4wle7Bksso+7uttq3rkTrdcvciumMDcYKR768qsVPYTKOHM1ZF4gIJPo9LFjvViVhd7+EBeGo6YRztUffO4UF4ajANPWC8JMOTsQLtLko8d6ODsQLmg319oTbQswtQ7K1fX2h4rKnzzRx4NblxNLGjxytIfe/tC0/Qv1yVyuac9ApKR97BmI3HLfc41ovX4Rx04Q6oSBcMw0sjliSYORsSQDoRiDZeqHIjGAaesFYaYMhuMlNTUYjk9qN7faE20LMLUOytX1h0qX5244iSUNBkJiQxcrc7mmldrHWkS0Xr/Mm2OnlFqhlPo7pdS/KaV6lVIHsuVLlVIvK6X6ss9LsuVKKfWUUuq8UuqMUmprtvynlFKn8h4xpdTeEuP9slLqWl67D8/XuQrCXNDud5mJTnO47BaWNNgJBly0lalv9bkApq0XhJnS5neW1FSb3zmp3dxqT7QtwNQ6KFfXHnCXLNd64nUwIDZ0sTKXa1qpfaxFROv1y3z+YpcCPqG1vhO4G/iYUupO4FPACa11J3Ai+x7gvUBn9vER4IsAWuu/01pv0VpvAbYDY8C3y4z5bK6t1vpP5+rEBGE+2LgswKE93aaxze2xS2uDrvYAq5s8HNm3paD+yL4tZmLn6eoFYaZsDPqLNHloTzcbg/6CdnOtPdG2AFProFxdV7u/qPzAjk6ef+0yLruFw3u76WoPTNu/UJ/M5Zp2B30l7WN30HfLfc81ovX6Zd6Cp2it+4H+7OuIUup1oAN4ALg32+xp4DvAJ7Plz2itNfBPSqlGpVR7tp8c7we+pbUem5+zEISFw+GwsnfzMta2eBgIxWjxOWlsKIyKubMryO373/3/s3fn8W3Ud/74X29JluVTcXzncBwTc9k5SF1KKbAlod2UDRAKhNLd7QXN9/v9FRJKl9JSjpKmB6VNS6DbbugFbI+kpISjbFo2oYUuR9fQkMQESAh2cOIjURLZli3Z8nx+f+iwZI9s2ZY0M/br+XjoEWs0I30Uvecz8575HOjs9qOsIH4UK5tNRn2daLxycrJwWX0FqktyRx0VM92xx9gmYOw4SPRa7PKSvGz4g4OoKclDhduFukp3tH5lnE096fxNc3OcWFlfHlc/WmVUTMa6dRkyKqaIVAM4B8ArAMpjkrV2AOXhv2cDeC9ms9bwstjE7hMANo7yUVeJyEUA3gbwRaXUe6OsS2R6TqcdDdUzE75uswlqSvNRU5o/odeJxisnJwvnzi8ec710xx5jm4DR4yDRa3rL62fPGPf7kzWl8zfNzXEmVT+aEWPdmjI+eIqI5APYBuBmpVTc0Gnhu3MqyfepBLAQwB8TrPIUgGql1CIAzyJ0N1DvfdaISKOINB47dizJb0GUHoxHMhPGI5kJ45HMhjFJZpPRxE5EshBK6n6llPp9eHFHOEmLJGud4eVHAMyN2XxOeFnEagCPK6UG9D5LKeVRSkWGHvopgPclWG+zUqpBKdVQWlo6ka9FlDKMRzITxiOZCeORzIYxSWaTsaaYEpqt+mcA9iulYptPPgng0wC+E/73iZjlN4rIbwF8AIB3WP+66wB8dZTPi+2PdzmA/Sn5IkQGCgY1NLV50eb1o9Kdg7rKof51REZgTJLVaZrC4RM+dHQF4OsPYt7MPMwvYX8imjxNU2j2+NDR5Ud5obX6qVm57NNZJvvYfQjAvwLYKyK7w8tuRyih2yoi1wNoQehOHAA8A+BSAAcRGvnys5E3CvfRmwvgL7EfICLrATQqpZ4EsFZELkdoNM4TAD6Tji9FlCnBoIbtrx/BHdtDE55GRmxbtXg2T6TJEIxJsjpNU9j1VgcOdPTg/p0HonG8cfUSrKir4IksTZimKexoao9O9G2luLJy2ae7jB15lVJ/VUqJUmpRzBQEz4SbTC5XStUqpS5RSp0Ir6+UUl9QSp2mlFqolGqMea9mpdRspZQ27DPuCid1UEp9VSlVp5RarJS6WCn1Zqa+K1E6NLV5oyfQQGiy0Du270NTm9fgktF0xZgkq2v2+LCn1RtN6oBQHN+ydTeaPT6DS0dW1uzxRRMjwFpxZeWyT3e8pEpkEW1ef7SSjfAPaGj3+g0qEU13jEmyuo4uPzQF3Tju7GYc08R1dOnXj1aIKyuXfbpjYkdkEZXunOhkoRGuLBsq3C6DSkTTHWOSrK680AW7QDeOywoYxzRx5YUuy8aVlcs+3TGxI7KIuspCbFhVH61sI/2Z6irdBpeMpivGJFlddXEeFs5xY93y2rg43rh6CaqL8wwuHVlZdXEeNq5eYsm4snLZpztDJignovFzOGxYtXg2asvy0e71o8LtQl2lm4NUkGEYk2R1Nptg2RnlWFCaj6VVRejtD6KKo2JSCthsghV1FThz7YXo7PajrMA6I0tauezTHRM7IgtxOGxYPLcIi+eOvS5RJjAmyepsNkF1ST6qS/KNLgpNMTaboKY0HzWl1ostK5d9OuNlVSIiIiIiIovjHTsiCxk+YWhVUS4On+zVfZ7rdKB/cBDFedlsQkFpozeJLYCklqU6JjmhLk3UWHVrbCxNNM4Yn9NPf/8g9hz1or3Lj8pCFxbOcsPptBtdLDKxydYTTOyILEJvwtANq+rxwK4DaPH0YV5xDm5aVhs3WfTaZbXY0ngYt604ixOLUsolmsTW6RDc+Ou/R5c9+Mlz0B9UaZ3slhPq0kSNVbfGxhKACcUZ43P66e8fxPY9R3HXE0PH5PVX1GPVollM7khXKuoJNsUksgi9CUPv2L4PKxfNBgCsXDR7xGTRm3YdwMpFszmxKKVFokls97R645btafWmfbJbTqhLEzVW3RobSxONM8bn9LPnqDea1AGh3/yuJ/Zhz1GvwSUjs0pFPcHEjsgiEk0YKuGLOCL6k+xGlnNiUUq1RDGpqfj1MjEBNCfUpYkaq26NPO/s9k84zhif0097gt+8o4u/OelLRT3BxI7IIhJNGKpU/HO91zmxKKVDopgc3mIkExNAc0Jdmqhk69ayAteE44zxOf1UJvjNywv5m5O+VNQTTOyILEJvwtANq+rx9J4jAICnXj8yYrLotctq8fSeI5xYlNIi0SS2i+a445YtnONO+2S3nFCXJmqsujU2liYaZ4zP6WfhLDfWXxF/TF5/RT0WzXIbXDIyq1TUExw8hcgi9CYMrSrKxdKqohHPQ6Ni2jEwqGFFfQVHX6O0SDSJLQA8o7MsnZPdckJdmqhk6tbYWJpInDE+px+n045Vi2ahpiQvOsLhIo6KSaNIRT3BxI7IQvQmDB3rOVE6JZrENtllmSgL0ViSqVtHW3ein0FTm9NpR0P1TKOLQRYy2XqCTTGJiIiIiIgsLmOJnYjMFZHnROQNEWkSkXXh5TNF5FkRORD+tyi8XERkk4gcFJE9IrI05r0GRWR3+PFkgs/LFpEt4e1fEZHqTHxPIiIiIiKiTMtkU8wggC8ppV4TkQIAr4rIswA+A2CnUuo7IvIVAF8BcBuAjwGoDT8+AODH4X8BoE8ptWSMz7sewEml1AIR+QSAewFcm+ovRZRJmqbQ7PFF2+uP1vY6dt1ZM1w45RtAW5cfle4c1FUWwuHgDXuavPHE5FjbAcDhEz50dAXg6w9i3sw8zC9hPyTKjESxPHx5VVEuDp/sHXfMp6t8ZF5+fxB727xo7wqgojAbCyvdcLnYC4rSJ2PRpZRqA9AW/rtbRPYDmA3gCgAfDq/2MIA/I5TYXQHgEaWUAvCyiMwQkcrw+yTjCgBfD//9GIAHRUTC70dkOZqmsKOpPTp5ZWS0pBV1FSMO7rHrnl6Wj+s+MA/3PNUU3W7DqnqsWjybyR1NynhicqztHvzkOQCAAx09uH/ngXG9H9FkJYrlj55Vjj/t74hbvmFVPR7YdQAtnr6MxehE9zUyjt8fxJN723DXk/uiv9n6y+tx+cJKJneUNoac1YWbRZ4D4BUA5THJWjuA8vDfswG8F7NZa3gZALhEpFFEXhaRVQk+Jrq9UioIwAugOFXfgSjTmj2+6EEdCE1aecvW3Wj2+EZd94aLTosmdZHt7ti+D01t3oyWn6ae8cTkWNvtafViT6s3mtSN5/2IJitRLDe1eUcsv2P7PqxcNDtuvXTH6ET3NTLO3jZvNKkDQr/ZXU/uw14eeymNMp7YiUg+gG0AblZKdcW+Fr6blswdtXlKqQYAnwTwQxE5bRLlWRNOEhuPHTs20bchSonR4rGjyx89QET4BzR0dvtHvE/sun2BoO527d6R2xHFGqt+HE9MjrWdpkKPibwfTQ/pPF4niuU2r/5ykfjn6Y7Rie5rlF6jxWR7V0D3N+voCmSyiDTNZDSxE5EshJK6Xymlfh9e3CEileHXKwF0hpcfATA3ZvM54WVQSkX+PYRQ081zdD4uur2IOAC4AXiGr6SU2qyUalBKNZSWlk7q+xFN1mjxWF7oik5aGeHKsqGswDXifWLXzc126G5X4R65HVGsserH8cTkWNvZJfSYyPvR9JDO43WiWK506y+P7dSRiRid6L5G6TVaTFYUZuv+ZuWF2ZksIk0zmRwVUwD8DMB+pdTGmJeeBPDp8N+fBvBEzPJPhUfHPA+AVynVJiJFIpIdfs8SAB8C8IbOR8a+79UAdrF/HVlZdXEeNq5eEj1QRPpYRAadSLTuQ8+/g7svq4vbbsOqetRVujNafpp6xhOTY223cI4bC+e4sW557bjfj2iyEsVyXaV7xPINq+rx9J4jceulO0Ynuq+RcRZWurH+8vq432z95fVYyGMvpVEme29+CMC/AtgrIrvDy24H8B0AW0XkegAtAFaHX3sGwKUADgLoBfDZ8PKzAPyHiGgIJabfUUq9AQAish5Ao1LqSYSSyEdF5CCAEwA+kebvR5RWNptgRV0Fzlx7ITq7/SgrSDwq2vB1K90ubPn8eWjv8qPC7UJdpZsDp9CkjScmk9kOABaU5mNpVRF6+4Oo4qiYlCGjxfLw5VVFuVhaVTSumE9n+cicXC4HLl9YifkluejoCqCco2JSBmRyVMy/AkhUAy3XWV8B+ILO8hcBLEzwGXfF/O0HcM2ECktkUjaboKY0HzWl+eNed14xsDjdBaRpZzwxmcx21SX5qC4Z33sRpUKimNRbPpGYT1f5yLxcLgfeP5/j9lHm8JI9ERERERGRxTGxIyIiIiIisjg29CWaAjRNodnjQ0eXH+WF7HtB1sMYpqmM8T098XenTGNiR2Rxmqawo6k9OnltZLS0FXUVPICQJTCGaSpjfE9P/N3JCGyKOQXNnlsFEUnqMXtuldHFpUlq9viiBw4gNAHqLVt3o9njM7hkRMlhDNNUxvienvi7kxF4x24KOtr6Hq79jxeTWnfL/zk/zaWhdOvo8kcPHBH+AQ2d3X6OnkaWwBimqYzxPT3xdycj8I4dkcWVF7qiE6BGuLJsKCtwGVQiovFhDNNUxvienvi7kxGY2E13NkfSzTbJnKqL87Bx9ZLoASTSjj8y4TOR2TGGaSpjfE9P/N3JCGyKOd1pQTbbtDibTbCirgJnrr0Qnd1+lBVw5C2yFsYwTWWM7+mJvzsZgYkd0RRgswlqSvPZbp8sizFMUxnje3ri706ZxqaYREREREREFsfEjoiIiIiIyOLYFDMNZs+twtHW95Ja156VjcGBwJjrzZozF0feOzzZotEUoGkKzR4fOrr8KC8c2WZ/rNeJ0iGZuGNskhWkKk4Z7xQMamhq86LN60elOwd1lYVwOHhPhdKHiV0ajHceuWTW5cAlBIROFHY0tUcnPY2MsrWirgI2m4z5OlE6JBN3jE2yglTFKeOdgkEN218/gju274vGwIZV9Vi1eDaTO0obRlaSZs+t4rQAZLhmjy96ogCEJju9ZetuNHt8Sb1OlA7JxB1jk6wgVXHKeKemNm80qQNCMXDH9n1oavMaXDKaynjHLknjvQtHlA4dXf7oQSLCP6Chs9uPmtL8MV8nSodk4o6xSVaQqjhlvFObVz8G2r1+LJ5rUKFoysvYHTsRmSsiz4nIGyLSJCLrwstnisizInIg/G9ReLmIyCYROSgie0RkaXj5EhF5Kfwee0Tk2gSf9xkROSYiu8OPGzL1XYnSpbzQFZ3sNMKVZUNZgSup14nSIZm4Y2ySFaQqThnvVOnO0Y2BCjdjgNInk00xgwC+pJQ6G8B5AL4gImcD+AqAnUqpWgA7w88B4GMAasOPNQB+HF7eC+BTSqk6ACsA/FBEZiT4zC1KqSXhx0/T8q0yxeZgU1BCdXEeNq5eEj1YRPptVBfnJfU6UTokE3eMTbKCVMUp453qKguxYVV9XAxsWFWPukq3wSWjqSxjTTGVUm0A2sJ/d4vIfgCzAVwB4MPh1R4G8GcAt4WXP6KUUgBeFpEZIlKplHo75j2PikgngFIApzL1XQyhBdkUlGCzCVbUVeDMtReis9uPsoL4kdbGep0oHZKJO8YmWUGq4pTxTg6HDasWz0ZtWT7avX5UuF2oq3Rz4BRKK0P62IlINYBzALwCoDyc9AFAO4Dy8N+zAcTOGdAaXhZZFyJyLgAngHcSfNRVInIRgLcBfFEpNWIOAhFZg9AdQVRVVU3sCxGlSDLxaLMJakrzE/bTGOt1omSNp35MJu4YmzQZmTpepypOGe9T31gx6XDYsHhuEfvUUcZk/LKBiOQD2AbgZqVUV+xr4btzKsn3qQTwKIDPKqU0nVWeAlCtlFoE4FmE7gaOoJTarJRqUEo1lJaWjuObEKUe45HMhPFIZsJ4JLNhTJLZZDSxE5EshJK6Xymlfh9e3BFO0iLJWmd4+REAsdc45oSXQUQKAfwBwNeUUi/rfZZSyqOUisz8/VMA70vldyEiIiIiIjILCd0ky8AHhUb1eBjACaXUzTHL7wPgUUp9R0S+AmCmUurLIvJPAG4EcCmADwDYpJQ6V0ScAP4LwFNKqR+O8nmVkSaeInIlgNuUUueNUcZjAFom902TUgLgeAY+Jx1Y9tQ4rpRaMdoKY8Sjmb6LHrOXDzB/GTNdvlFj0qLxyHKNn1nKNhXjMZX4HTNrqh+zR8OyG2O0sieMx0wmdhcAeAHAXgCRppO3I9TPbiuAKoR2iNVKqRPhRPBBhEa+7EWoyWWjiPwLgF8AaIp5+88opXaLyHoAjUqpJ0Xk2wAuR2g0zhMA/p9S6s20f9EkiEijUqrB6HJMBMtuDmb/LmYvH2D+Mpq9fLHMWlaWa/zMXLZkTYXvMBZ+R2ux8ndh2Y0x0bJnclTMvwJINBzUcp31FYAv6Cz/TwD/meAz7or5+6sAvjqhwhIREREREVkIx1wlIiIiIiKyOCZ2xthsdAEmgWU3B7N/F7OXDzB/Gc1evlhmLSvLNX5mLluypsJ3GAu/o7VY+buw7MaYUNkz1seOiIiIiIiI0oN37IiIiIiIiCyOiR0REREREZHFMbEjIiIiIiKyOCZ2MVasWKEA8MFHJh5jYjzykeHHqBiPfGT4MSrGIx8ZfoyJMclHBh8JMbGLcfy4VSenp6mI8UhmwngkM2E8ktkwJskMmNgRERERERFZnOUTOxFxicjfROR1EWkSkXvCy38pIu+KyO7wY4nRZSUiIiIiIkoHh9EFSIEAgGVKqR4RyQLwVxH5r/BrtyqlHjOwbFOGpik0e3zo6PKjvNCF6uI82GxidLGIiMhirHY8sVp5yTwYO5Rplk/sVGiG9Z7w06zwY9SOhTQ+mqawo6kdt2zdDf+ABleWDRtXL8GKugpWUERElDSrHU+sVl4yD8YOGcHyTTEBQETsIrIbQCeAZ5VSr4Rf+qaI7BGRH4hItoFFtLRmjy9aMQGAf0DDLVt3o9njM7hkRERkJVY7nlitvGQejB0yguXv2AGAUmoQwBIRmQHgcRGpB/BVAO0AnAA2A7gNwPrh24rIGgBrAKCqqipjZbaSji5/tGKK8A9o6Oz2o6Y036BSTU3JxuPnb/oSjh4/NWL5rJIZeOiB76etfDS9sH6kVJvM8cSIeOTxj0YzWkwydsgIU+KOXYRS6hSA5wCsUEq1qZAAgF8AODfBNpuVUg1KqYbS0tJMFtcyygtdcGXFh4ory4ayApdBJZq6ko3Ho8dPoeDiG0Y89JI9ooli/UipNpnjiRHxyOMfjWa0mGTskBEsn9iJSGn4Th1EJAfARwC8KSKV4WUCYBWAfcaV0tqqi/OwcfWSaAUVaSdeXZxncMmIiMhKrHY8sVp5yTwYO2SEqdAUsxLAwyJiRyhR3aqUelpEdolIKQABsBvA/zWykFZmswlW1FXgzLUXorPbj7ICjuxERETjZ7XjidXKS+bB2CEjWD6xU0rtAXCOzvJlBhRnyrLZBDWl+WwXTkREk2K144nVykvmwdihTLN8YkeUSZyThmh64L5uXfztyCwYi5RpTOyIksQ5aYimB+7r1sXfjsyCsUhGsPzgKUSZwjlpiKYH7uvWxd+OzIKxSEZgYkeUpNHmpCGiqYP7unXxtyOzYCySEZjYESWJc9IQTQ/c162Lvx2ZBWORjMDEjihJnJOGaHrgvm5d/O3ILBiLZAQOnkKUJM5JQzQ9cF+3Lv52ZBaMRTICEzuiceCcNETTA/d16+JvR2bBWKRMY1NMIiIiIiIii2NiR0REREREZHFM7IiIiIiIiCyOiR0REREREZHFWT6xExGXiPxNRF4XkSYRuSe8fL6IvCIiB0Vki4g4jS4rERERERFROlg+sQMQALBMKbUYwBIAK0TkPAD3AviBUmoBgJMArjewjERERERERGlj+cROhfSEn2aFHwrAMgCPhZc/DGCVAcUjIiIiIiJKO8sndgAgInYR2Q2gE8CzAN4BcEopFQyv0gpgtlHlIyIiIiIiSqcpkdgppQaVUksAzAFwLoAzk91WRNaISKOINB47dixtZSRKBuORzITxSGbCeCSzYUyS2UyJxC5CKXUKwHMAPghghog4wi/NAXAkwTablVINSqmG0tLSDJWUSB/jkcyE8Uhmwngks2FMktlYPrETkVIRmRH+OwfARwDsRyjBuzq82qcBPGFMCYmIiIiIiNLLMfYqplcJ4GERsSOUqG5VSj0tIm8A+K2IbADwdwA/M7KQRERERERE6WL5xE4ptQfAOTrLDyHU346IiIiIiGhKs3xTTCIiIiIioumOiR0REREREZHFMbEjIiIiIiKyOCZ2REREREREFsfEjoiIiIiIyOKY2BEREREREVkcEzsiIiIiIiKLY2JHRERERERkcUzsiIiIiIiILI6JHRERERERkcUxsSMiIiIiIrI4JnZEREREREQWZ/nETkTmishzIvKGiDSJyLrw8q+LyBER2R1+XGp0WYmIiIiIiNLBYXQBUiAI4EtKqddEpADAqyLybPi1Hyilvmdg2YiIiIiIiNLO8omdUqoNQFv4724R2Q9gtrGlIiIiIiIiyhzLN8WMJSLVAM4B8Ep40Y0iskdEfi4iRYYVjIiIiIiIKI2mTGInIvkAtgG4WSnVBeDHAE4DsAShO3rfT7DdGhFpFJHGY8eOZay8RHoYj2QmjEcyE8YjmQ1jksxmSiR2IpKFUFL3K6XU7wFAKdWhlBpUSmkAHgJwrt62SqnNSqkGpVRDaWlp5gpNpIMi7TyRAAAgAElEQVTxSGbCeCQzYTyS2TAmyWxMldiJSK6I3CkiD4Wf14rIyjG2EQA/A7BfKbUxZnllzGpXAtiXjjITEREREREZzWyDp/wCwKsAPhh+fgTA7wA8Pco2HwLwrwD2isju8LLbAVwnIksAKADNAP5POgpM5qdpCs0eHzq6/CgvdKG6OA82mxhdLCIyMdYb0wd/a0oXxhZlmtkSu9OUUteKyHUAoJTqDd+RS0gp9VcAeus8k44CkrVomsKOpnbcsnU3/AMaXFk2bFy9BCvqKli5EpEu1hvTB39rShfGFhnBbIldv4jkIHSXDSJyGoCAsUUiK2v2+HDvjv24/oIaRC4R3LtjP86sKEBNaf64349X34isYax9NRjU0NTmRZvXj0p3DuoqC+FwhHonNHt80ZMxAPAPaLhl626cufbCCdUbZIxkYmB36ym82d6FGy6swbZXW9Hm9fO3ppRo9vjw87++g+9evRh9gSBysx34+V/fmfD5B1EyzJbY3Q1gB4C5IvIrhJpZfsbQEpGleXwBXNtQhU27DkSvmK1dVguPLzDuipVX34isYax9NRjUsP31I7hj+77o6xtW1WPV4tlwOGzo6PJHk7oI/4CGzm4/T8gsYiIxsHZZLR59uQVtXj9/a5o0b18/rlpahS8/9no0xu5eWQdvX7/RRaMpzFSDpyilngXwcYSSud8AaFBK/dnIMpG1CSSa1AGhk7NNuw5AdFvvji7RVfxmjy+lZSaiyRlrX21q80ZP6COv37F9H5ravACA8kIXXFnxh0dXlg1lBa4MfguajInEwKZdB/DxpXP4W1NKBAcV7nm6KS7G7nm6CcFBZXDJaCozVWInIlcCCCql/qCUehpAUERWGV0usq4TvoDulfcTvvFfMRvtKj4RmcdY+2qbV//1dm/o9eriPGxcvSSa3EXu9lQX52Wg9JQKE40Buw38rSklOrv1zz86u9nDiNLHdE0xlVKPR54opU6JyN0AthtYJrKw8sIcuLJscZWrK8uG8sLsCbyXS/e9eGWXyFzG2lcr3fr1QoU79LrNJlhRV4Ez116Izm4/ygrYn9ZqJhoDF9WWYmlVEX9rmrSKBDFYXshzBkofU92xg355zJZ8koXUVRZiw6r6uCvvG1bVo67SPe734lV8ImsYa19Npl6w2QQ1pfk4r6YENaX5PNG3mInGwJI5M/hbU0osnOXG+iviY2z9FfVYNGv85x9EyTJb0tQoIhsB/Cj8/AsIzWtHNCEOhw2rFs9GbVk+2r1+VLhdqKt0R0e/Gw9exSeyhrH21VTWC2ROjAEymtNpx6pFs1BTkhcdmXXRLDecTrvRRaMpzGyJ3U0A7gSwJfz8WYSSO6IJczhsWDy3CIvnTv69IlfxOVoakbmNta+msl4gc2IMkNGcTjsaqmcaXQyaRkyV2CmlfAC+YnQ5iIiIiIiIrMQUiZ2I/FApdbOIPIXw5OSxlFKXG1AsIiIiIiIiSzBFYgfg0fC/3zO0FDRtaZpCs8cXbQfPvnNE1sZ9moZjTFCmMeYo00yR2CmlXhURO4A1Sql/Nro8NL1omsKOpvboZLaR0dNW1FWwAiayIO7TNBxjgjKNMUdGMM3wT0qpQQDzRMQ5nu1EZK6IPCcib4hIk4isCy+fKSLPisiB8L9FaSk4WV6zxxeteIHQBKK3bN2NZo/P4JIR0URwn6bhGBOUaYw5MoJpEruwQwD+R0TuFJFbIo8xtgkC+JJS6mwA5wH4goicjdAgLDuVUrUAdoKDskxJmqZw6FgPXnrnOA4d64GmjeiiOaaOLn/cBKJAqALu7PanqphElEGp2KdTUbeQeSSKiRaPj78xpQXPLcgIpmiKGeOd8MMGoCCZDZRSbQDawn93i8h+ALMBXAHgw+HVHgbwZwC3pba4ZKRUNXMoL3TBlWWLq4BdWTaUFbh0P5Pt5YnMJ3bfzHU6kt6nE70Xm1BZ2/C6uqxAv57/+3unsGnnQf7GlHKJYq40P7l6iGgiTHPHTkSWAGgCsFUpdU/sYxzvUQ3gHACvACgPJ30A0A6gPMVFJoOlqplDdXEeNq5eAldWaHeIHOCri/Pi1ouc7F266QVc99AruHTTC9jR1M6rvEQGG75vrv3ta9iwqn7MfToRNqGyNr26+l1Pz4h6ft3yWvyusRUAf2NKPYcduPuyuriYu/uyOjg4PzmlkSnu2InIXQD+BcCrAL4rIt9WSj00zvfIB7ANwM1KqS6RoStuSiklIrpn3yKyBsAaAKiqqprgNyAjjNbMYTwTiNtsghV1FThz7YXo7PajrED/Tlyik70z116YsgnLGY9kJlaJx+H7ZounDw/sOoAta85D38Bgwn06kVTVLZRaycajXl1946//jh3rLsQz4XpeILh5y260eYeaxfE3pvEaLSbbvQH85C8Hcf0FNRABlAJ+8peDqClZjHnFjDFKD1MkdgCuBbBEKdUrIsUAdgBIOrETkSyEkrpfKaV+H17cISKVSqk2EakE0Km3rVJqM4DNANDQ0MBbLxYyniaUY7HZBDWl+aMe0DNxssd4JDOxSjzq7Zstnj70DQzivJqScb9fKusWSp1k4zFRXd3e5cd5NSWoKc3HoWM9ONnbH7cOf2Mar9Fi0tcfRIunDz967mDcNr39wcwVkKYdsyR2AaVULwAopTwiknQTUQndmvsZgP1KqY0xLz0J4NMAvhP+94kUlpdMoLo4Dw9+8hzsafVCU4BdgIVz3Ek3txovnuwRmVOq981M1y2UWsnEQ6QJ/vB+lHq/MftW00TMm5mnG4dVM1mPUPqYJbGrEZEnw38LgNNinkMpdfko234IwL8C2Csiu8PLbkcoodsqItcDaAGwOvXFJqP1BxU2P38o7sCcLuM5ESCizEnHvpnJuoVSK5l4SLYJPgfSoYmaNzMXG1bV447t+6Kxs2FVPebNzDW6aDSFmSWxu2LY8+8lu6FS6q8IJYN6lk+4RGR6mejzFivZEwEiyqxU75uZrlsotZKNh2Sa4DMWaKIOn+zFA7sOxPWxe2DXASytKmLsUNqYIrFTSv0lmfVEZJtS6qp0l4eswcgBDpRpexsRTX2JmsaNdZKeLA6eYn02m0Tv0HV0hQZImUiyz1igiero8uv2sWPsUDqZIrEbhxqjC0Dmkek+b2ySQ2S8TOyH7E9rfUbMc0oUi/PYkRFMM49dknifhKKSnX8uVTi3FZHxMrEfZrpuodTL9DynRMPZbcC65bUj5k60W+3MmyzFanfsiKIy3eeNTXKIjJeJ/ZD9aa0v0/OcEg3X5vXjkZda4vrYPfJSC86pmoHqEp4zUHpYLbFjTUpxUtmvZizlhS7MK87BykWzIeFIfOr1I2ySQ5QhmqaQ67Rj7fIF0BSw7dVWtHn9aWkal8m6hVKv0u2KxgkQipWTvf1pm+eUaLjyQhecjqHTVhHA6RCeM1BaWS2xu83oAtD0VVWUi5uW1Y4YuriqiEMXE6WbXp+ptctqsaXxMG5bcRabxlGUpim80dYdN13FuuW1qC3PZ5xQxvCcgYxgqpa+IrJXRPYMe7wgIj8QkWKl1J+MLiNNX4dP9kYraCDUrOeO7ftw+GSvwSUjmvr0+kxt2nUAmz5xDgcwojh6sXL/zgOYX5zPOKGM4TkDGcFUiR2A/wLwBwD/HH48BaARQDuAXxpXLKLR+2wQUXol2v/6BgZ5sk5xEsXKsR7W1ZQ5PGcgI5itKeYlSqmlMc/3ishrSqmlIvIvhpWKCBz2mshI3P8oWYwVMgPGIRnBbHfs7CJybuSJiLwfgD38NGhMkcgImqZw6FgPXnrnOA4d64GmGT/TBYe9JjJOqvc/M9YxlBpmrKsZb9OPGeOQpj6z3bG7AcDPRSQfoREwuwDcICJ5AL5taMkoY8w6ETiHvSYyTir3P7PWMZQaZqurGW/Tl9MhWHNRDTQF2MKjYhKlk6kSO6XU/wJYKCLu8HNvzMtbjSkVZVqiiWXPXHuh4cNNc9hrIuOkav8zcx1DqWGmuprxNj01e3y48dd/H9EU8xn+7pRGpkrsRCQbwFUAqgE4JDxZmFJq/Sjb/BzASgCdSqn68LKvA/g8gGPh1W5XSj2TtoJTSnEi8Mz5/E1fwtHjp3Rfm1UyAw898P0Ml4go/VjHUCYx3qYn/u5kBFMldgCeAOAF8CqAQJLb/BLAgwAeGbb8B0qp76WuaJQqmqbQ7PGho8uP8sKRTWTY4Thzjh4/hYKLb9B/7bmfZrg0RJnBOoZSicc00sPfnYxgtsRujlJqxXg2UEo9LyLV6SkOpVoyfQ0iHY6Hr5PODsdjHZiJKLPSuU9WFeViw6p6Thw8jaQrnsx6TCPjVRXl4r6rF+FAZw80BdgFWFCWz3qG0spsid2LIrJQKbU3Be91o4h8CqF58L6klDqZgvekSWr2+HDvjv24/oIahFva4t4d+3FmRUG0aUKmO76zYzuRuaR7nzx8she//VsLvnv1YvQFgsjNduDhFw9haVURm0hNQamMp+EJok0wZv85sw3mQpnReqoXnd0BbH7+UDTubv3HM9B6qhfVJaxnKD3MlthdAOAzIvIuQk0xBYBSSi0a5/v8GMA3AKjwv98H8Dm9FUVkDYA1AFBVVTXBYlOyPL4Arm2owqZdB6IV3dpltTjhC8SdUGWy47uZOrYzHslMjIrHZPfJid6F8fgCWHZmBb782Ouj1kNkLuONx0h8HOsOpKSO10sQv3XlQhTlOtHmHZp0Wq8flZkGc6HUGS0mO7sDuO+Pb8XF3X1/fAuLZruZ2FHamG0eu48BqAXwUQCXITQoymXjfROlVIdSalAppQF4CMC5o6y7WSnVoJRqKC0tnWCxKVlOuy2a1AGhim7TrgPIshsXiok6OHd0+RNskT6MRzITo+JxtEEHIiIn2ZduegHXPfQKLt30AnY0tSc1P5gZ6yEa23jiMTY+Xjh4fMx4SobeBYfbH9+LaxrmxK3HflTTx2gx6e0b0I07b99AJotI04wpjmIiUhj+szvBY7zvVxnz9EoA+yZbRkqN3v5B3Yqut3/QoBIBuU5HdALRCFeWDblOu0ElIpreIoMOxBp+spzorl6zxzfm+5uxHqLUGh4fY8VTMhJdcDi9vICTUNMIeU67btzl8NyC0sgUiR2AX4f/fRWhPnGvxjwaR9tQRH4D4CUAZ4hIq4hcD+C7IrJXRPYAuBjAF9NWchqXRCds5YXGXd3sHxzE2mW1cQfmtctqMTCojbElEaVDZLCJ0U6Wk7mrl4gZ6yFKrdj42PZq64g6fiLJV6K4OauiEM+svRC/XfMBPLP2QvbPJgBAtsOOdcvj427d8lq4HEzsKH1M0cdOKbUy/O/8CWx7nc7in026UJQWZhwdrDgvG1saD0cHdFEK2NJ4GCvqKwwrE9F0lsxgE5MZStyM9RClVmx8tHn9ePTlFqy5qAbnzJ2BecV5Exq8JFHczC/Ji/ahI4ooznciz2nHmotqoCnAJqG7eMX5TqOLRlOYKRI7EVk62utKqdcyVRZKLzOODlZdnIfbVpzFkzwiExlrsInJJGdmrIcotYbHx8nefpxZUYh/OL1swr8z44bGo2pmHmYV9eC4rz+6bFZRDqpm8tyC0scUiR1Co1YmogAsy1RBKP3MNjqYzSb46Fnl2LLmPLR5/ah056CuspAHayIDJDvS5WRPss1WD1FqjRYfk5nTjnFDybLZBB+uLUNpfjbPLShjTJHYKaUuNroMNHWNdRDXNIU/7e/gPHZEBgsGNfxhXxtu27YnqX2RJ9k0Gr340Juy4N6rFuGf6ivhcJhl2AGaCnhuQUYwRWInIh8f7XWl1O8zVRaaWpKZmNZM89ily+dv+hKOHj81Yvkbb76ND/CyCpmApim8eMgTTeqAqbkvkrH06vvbtu1BUa4TFywo4Qk3pcy7x/XPLc646UKcVsb6jNLDFIkdRp+rTgFgYkcTkkzSNtroelPlZPLo8VMouPiGEcv9ezlgLJlDs8eHxpYTU35fJGMlqu8bW05gTlEO44xSpuWETzfWDp/wMbGjtDFFYqeU+qzRZaCpqaPLj6JcJz6+dA4kfCF226utcSeKkxldj4hSo6PLD01Bd18szee+SKmRqL6vLSvAsZ4AB0OhlMkLz5E7PNZynaY49aYpylTRJSLlAL4FYJZS6mMicjaADyqlOH0BTUil24VPfXAe7t95INoUc93yWlTEzFc1ntH1JtPpnogSq3S7kOe0Y8Oqerx3ohdbG1txsrcf65bX4l1PT3RIeaJkJKqrq4vzcO9Vi+L6ca5bXotvPbMfJ3v72QeKUqbCnY1vf3wh3j3ug6YAuwDVJXmocGcbXTSawkyV2AH4JYBfAPha+PnbALaA89LRBA1qwG//d2iOOiD0/KNnD81Rl+zoesn01yOi8dM0hTfauuMuwKy/vA7uXCfePd6Dbzz9BmpKOEgKJWesAVL+qb4SRblOtHv70HqqD4+81II2b2hie/bppFTRNOCErx+bnz8UjcNb//EMaNrY2xJNlNmGgCpRSm0FoAGAUioIYNDYIpGZaZrCoWM9eOmd4zh0rAeapuJeP9EbwOfOnw97ONLtAnzu/Pk42RvQfT+ldBcDSNxfr9njS8l3IZqu9Patu55sQn9Qw2/+dhjXNlTh6KneEfs3kZ5EA6T89Z3j+N9mDw6f7MX5NcUoKcjGpp0Ho0ldZN0Wjw8vvXMczcd78E5n4uML0WiO9QRw3x/fiovD+/74Fo736J9/EKWC2e7Y+USkGKEBUyAi5wHwGlskMqtk7qDlZNnROzAYd8Vs3fJauLLsce+z660O7Gn1RptLLJzjxrIzyuPuxFlhkJV9+/bin667fsRyjn5JZpZo3+rrD+Jz58/Hz198F1Uzz8SOpnbeIacxJYqn1w6fRE6WHX0Dgzi9rABVxbmYV5yDFk9fdD1Xlg1/f+8UftfYOqIZP1to0Hh09Q3oxqG3b8CgEtF0YLbE7hYATwI4TUT+B0ApgKuNLRKZVTIjXvoCg9EDc2Sd+3cewNKqouj7HD7hw4GOnhHJ34LSfFSXDCVsVhhkpV/ZOPolWU6ifav1VB9ysuz4xPurcOi4D//+54NsJkdjShRPTrsNhTlZccnahlX1eGDXAbR4+qJ1/yMvteDjS+eMOHawmSaNR2FOlm4cFrqyDCwVTXWmaIopIu8XkQql1GsA/gHA7QACAP4EoNXQwpFpjXYHLcLXH9Rdp7c/GPM+Ad3kr6MrvrlEZJAVV1ZotxltkBUiSp7evrV2WS1+19iK+3cewNyiXASC2oj9m0hPZICU4fHktAu+8fQbcXX9Hdv3YdMnzsFv13wAD3/23Gh/OxGMeXwhGo2mqXALoaE4XLe8FhrYpJfSxyx37P4DwCXhv89HaPCUmwAsAbAZo9y1E5GfA1gJoFMpVR9eNhOhQVeqATQDWK2UOpmmspNBkrmDNm9mnu46VTOHkjFff1B3SoTY5A9IfpAVIhqfyL5V+KkGvHb4FKpL8nD0VC+uet8cbHu1FWIDfv9aq+nukJM52WyCj51dgbKCbLx3ohc5TgcefvEQrn3/vAQX+gZRXuhCs8eH1Q1zsLUxdD3Z7C00yNy6/EH81942fPfqxejrDyLX6cBDz7+D03jHl9LILImdXSl1Ivz3tQA2K6W2AdgmIrvH2PaXAB4E8EjMsq8A2KmU+o6IfCX8/LYUl5kMlsw0BfNL9NeZXxKzzsxc3SkR5s3MHfGZNpugppSj8xGlms0mqHS7kGUXfPmx1+P2xeDgYHQoet4hp7FomsJ/v9UZV+9/88qF8PkHdJO1gUGFSze9EBdz/7W3DeuW147oY8f4o2SdVpKHjy2sHFGfnVbCGKL0MU1iJyKO8CiYywGsiXlt1DIqpZ4Xkephi68A8OHw3w8D+DOY2E2KGedvS+YOms0m+OhZ5diy5jy0ef2odOegrrIwbp3uQFC3KeaHTivO+Hcims58CfbFRz93Lp5Ze2Hc/m3GOonMQa//9dce34uffboBP/mX9+HuJ/dF+9Tde9Ui3PnE3hEx9/Bnz0V5YTY+enYFjvWwhQaNn6+f5xaUeWZJ7H4D4C8ichxAH4AXAEBEFmBio2KWK6Xawn+3AyhPSSmnKTPP3zbWHTRNU/jT/o5Ry37U69dtitnm9WPR3Ex9E6LpKTZBG9QUinKdI4afP9k7gPfPL47bxqx1EhkvUf/rlw6dwE9fOIQfXrsExXlOdAeCyM92oD+oRqyrKRUdPOu0MrbQoPFr79I/t2jvCmCRsUWjKcwUiZ1S6psishNAJYA/KRWdTcyGUF+7yby3EpGEPVVFZA3Cdwirqqom81FTVjKjT5pVMmWfU5Sj2xRzdlHOiPdL5i7BZO4kMB7JTNIVj5qm8O5xH456e3Gsux+3P743bt+LnTDalWXDzDxn3PZWrpNo4pKJR01TyHU6dJtcKgUU5Trx7nEfbt6ye9SYs4vg0LEe3qWjUY0Wk6X52brnFqX5Tr23IkoJU4yKCQBKqZeVUo8rpXwxy94Oj5Q5Xh0iUgkA4X87R/nczUqpBqVUQ2lp6QQ+aupLdPWzo8v8o4MlM3JmtsOu21wi22GP2y4y39323UfwP+948MTuI9j1VkfcpLWROwmXbnoB1z30Ci7d9AJ2NLUnPbEt45HMJB3xGNmPnnj9CE71BqNJHTC0713TMAfA0GiGatgoclauk2jixorHSP279rev4d8+ekbcaIRfvOR0/P61Vt1pDPRi7rX3To67/qbpZ7SY7BvQn26pb2DQiKLSNGGaxC7FngTw6fDfnwbwhIFlsbzI1c9Yriwbcp32BFuYR2TkzFjDRzZrPdmre5LYerI3blnsfHcP7jqI/3j+EA509ODwiei1iIR3Epo9PhAR0OIZ2o/e6ujW3fdOLy/ALR85HddfUIMtjYfhtMfvw1aukyh9IvVvf1ChKC8Lay6qwY3LFmDNRTVwOULxkmgag9nuHNy4bEE05rr9g6y/aVJ8gUHdWPMFmNhR+piiKeZkiMhvEBoopUREWgHcDeA7ALaKyPUAWgCsNq6E1tc/OIi1y2qxaddQc4K1y2oxMKiNvbHBqopycf8nlmAgqOALBJHnciDLLqgqGhrxMi9bv9lOnjN+90g0393SqqJoX4zR7hCyiRhNd5qm8N7J3rj9SG/fy82yo35WIQ52duPm5adjqHV+iJXrJEqfSP378aVz8LXH942Iq+svqIFd9GMuL9uBba+2wukQ3LbiLBzs7MGNyxZg26utrL9pQgpz9M8tCnMsf+pNJmb56FJKXZfgpeUZLcgUVpyXjS2Nh3H9BTUQAZQCtjQexor6CqOLNmZ/ttaTPpzwDeCep5qiJ4B3X1aH1pM+VJcWAAD6+oO6J4l9A/Hz2CUz2Xkyc+sRTVfNHh/aYwYrys+2Y/3ldbjryaH9c93yWnxt+z6c7O3HuuW18A8EYbfF350zc51ExonUv4nuylXNzEFgYBB3X1YXd0y45/I6/PLFQ7j90rPQEwjGDcrzjSvqMWsG628aPwUN91xeh7ufjI81cIJySiPLJ3aUftXFebhtxVmjzhdnhGRGxmvrCkQP4EDo4H7PU034xWfeH03s8rIduieJ379mcdznJTPZeTJz6xFNV6GRL7W4AQVu/9gZWHNRDWa7c3DE2xc3iMX9Ow9gzUU1mFc8EPc+Zq2TyFiR+vet9i7duvrwiT4AwNN7jsTV9//+54NYuWg2DnR2Y/Pzh+KOF3c+sQ8//uelmFWYC4djqvZeoXSwwY5///PBEbH23asWj70x0QQxsaMxJTNfnBGSGRnP09Ove+XW4+uPPndl2fGJ91eNGLnKlRXfX2d+SR6+f80SfOl3QyeT378mfrJzs/5fEZlBpduFQU3hnkcao/vlL15swac+OA9HvH3YtPNg3PqhYeeBLn/83XPuZ6QnMm9p1cwczJ2Zizu27xsx8uVV75uDFk8ffvRcfKyJAJrSv9P39/dOYWaeE4vnFmXy65DFHesJ6Mba8Z6AQSWi6YCJHSVlrPniUi2ZKQOS6c9WXpidoGlkdvR5V28Qj7zUEndV7ZGXWrB4zowRZdKUhjUX1UBTgE0ATWnQNDViUvRM/l8RWYGmKbzR1o1u/wD8Axoq3a7o/E4iwPmnFcfdLQFC+6pNgPxsR/Q9htcL3M8oInbe0qJcJ9ZcVIPasgK0eHzIzbLjZG/ogl6iqRAcNv3XBjWg3evHYs5rSuMwM8+pG0/Dp28hSiW2KyDTSXbKgLIC/REvS/OH+kOUu51Yf3l93LDX6y+vR4V7qGLNzrLB6RhKzEQAp0PgHNbspqnNi1sf24NNOw/iwV0HsWnnQdz62B40tXlT9t2JpqrIHfYZuU7ctuIM3PKR0+GwhSbs/eF/H8Dxnn6sv7wubl9dt7wWJXlOzHK7Jj2VCE19sa042rx+bNp5EG2netE/qKFvQMND/9qAigIn7lx5dlycrV1Wi6f3HEFNaR42rKrXfa3CzX52ND4F2Q7cfVl8nXb3ZXXRC1VE6cDoItNp9vhw74790TtoAHDvjv04s6Ig7uq83Qbc8pHTsfHZt6PNbW75yOmIHRm9s2sAWxtb8N2rF6OvP4gcpwOPvHgINaV5qJoZWqd3YAD/96IFuOfpmAFWVtbBPxDfr6fNq3+HsI1XconG1NEVGjTlvRO9cc2e1y6rxaMvt+Dffvc6bv/YmfjRJ8+Bp6cfOU4H2k71onJGDqpL8pOuF2j6Gt6Ko9LtgoJE7wRH4m3HvtbQCJk2oGFeEbx9A/jWlQvh6QnALgo/+uRSvN56CoNaqL/1TctqUVfpNvCbkRUNaINw5zjwvasXw9cfRJ7TAZsNCGqc7oDSh4kdmY7HF8C1DVUjRqk84QvEncAd6wkg226LaxqZbbfheE8gOv1Au9ePxhYvGlv+HvcZ7TETGedkZeGep1+LH2Dl6Sb85/UfiNumNF+/WWdJfjaIaHTlhS5c0zAH3/vTW3H72qZdB3D9BTX40XMHcRDkyf0AACAASURBVKoviB//pQkfXzoHi2YX4pKzK6LNsJOtF2j6Gj4q8ceXzsEP/vvthPEGAJs+sQRHTvXh1sf2ROPqrpVn4ayKQnT5B3DfVYuxtKqIA6fQuPUENNyy9fUR5wwPfarBwFLRVMfEjkzHabNFT96AoYPxls+fF7+e3YZv73hzRKW5Zc3QeiUF+m3ci2PauPcHNd07ccPnxLLZgG9fuRDvenzQFGCX0ChsPN4Tja2qKBe1ZQW6+5qE5xarLslDm9ePn/31EJ6JGQQJSL5eoOmrujgP37pyIW5/fC/8AxrsNv3BUCJ3fEN9OGXE/KTrnw7dGY7EIZM6moi+BFMk9fXzjh2lDxM7Mp0TvfojWZ7o7Y9b1ts/mGBeuaFK053r0J1HZkbuUOiXF7owrzgHKxfNjh7wn3r9yIi554pynXit91Rcs55b//EMnJMbP8gKEY3UeqoXeU677r5mE+DulXU41RtIOHVBsvUCTV82m2Bp1QysuagGuU4H3jcvVDdHumFue7UVJ3v7oVQoqbtz5dkJB+Gy24B7r1rEKTRowmbkZunWd7HnH0Spxugi08l1OnQrw1xnfLiWFYydkJ3oGdCdR+ZbVy6MruOwA//3HxaMmMTcET/bAQY14L4/xjcju++Pb+HDp5el/j+BaIrp7A7g6KneEfvaPZfXITAwiJ88fxDfunIRfnPDB1CY48Qr73riRsTNdTp0774PrxdoequamYdFc9w41NmDFk9v3IW4dctrUVaYDU93AL/87Ptx+IQP8yoKsHb5AmgqlPi1ef1wZdmwoKwAs9zZnEKDJizbbsf/+4cF+HpMfff1y+qQbbePvTHRBPGISBkXDGpoavOizetHpTsHdZWFcU1dKtzZuPmS0/Hu8aEmjzdfcjoq3PF92ZJJyHoCQd15ZHoCQ/NitXv1JzF/+LPnYl7xUFOwzm79K7vHevw4rYx9fIgSCQY1tHv9cOdm49bHXo/b1+5+sgnXX1CDFk8f3j3eg0JXFr7801fiJh5fUVeB8sJs3cGSygvZx5WGaJqC027HrBm5+LdhsXb/zgO48eIF+N6f3sbmT70PgA03hOdUjPTZ3NJ4GGuXn472U71YNLsQmqZw+IQPHV0B+PqDmDczD/NLOGcija0rEIwmdUAoBr/+VBN+yj52lEZM7CijgkEN218/Ejdx7IZV9Vi1eHY0udPCcwYNv9KqxedU6OjST8ge+dxQQlZWkK17V680ZsCTyLxasfwDGrr98aNiRqZXGH7HIHZ6BSKKp2kKLzd70O7tw+yiXN19LdthC98lycdnfvG/cfv0LVt344ybLsS8mbmodLviBkuqdLswZ0auEV+LTEjTFP6wrw23bduDGy6s0Y210vxsrF2+AAXZDnzzD2+gKNcZnU8xEBzE1y49G/7gIH71t8OoKcvHwWM9ONDREzeSa+RiA5M7Gk2iPna97GNHacTEjjKqqc2LB8KjkkUSrQd2HUBtWT4Wzy0CEGqyNbwz+/07D2BpVRHmxwymcKpXPyE71TuUkNkgunf/7DJ0QC7MydJN2ApzsuLe22EH7r6sbswmm1PFvn178U/XXT9i+aySGXjoge8bUCKyGk1TeKvDiyybDeXuHCjoTwBdU5KHb165ED1+/ROhwyd8EEF05MLYbetmuTkqJgEITZVz27ahGNGLtRO9/RjUgJcOeXDHpWejvcuPbz6zP1qn37nybGx+/h2sXDQbe1pDc5RGLjICQxcbzhw2uA/RcInPLXjqTekzpaNLRJoBdAMYBBBUSvH+9wRpmkKzx4eOLn9cv5fx8vgC+Nz58+Hp7Y8mWp87fz5O+ALRdXyBYNxVVCDU96G3Pxj3XnlOe4I+N0OZln9wUPfu36wZQ3fZlFK4/WNn4rhvqEzFeU4oFT/xcbs3gG2vHo7OiZfrdODhFw+hpiQvrsnmVNGvbCi4+IYRy48+91MDSkNWo2kKLxzoxKm+AbxzLHRhJd9px7evXIivhkctjJxIZzlsyMu2wWHX36ftNkk4yEVntx81pfkpq6PIuiIxUul2weUItQZ570QvtjaGBk255SOnI9tui7v7tm55LYpyndF5Sr/x9Bu47+rFAICTvQEU5WbjhgtrAAz1wfMPaOjo8jOxo1HlOOz49scXxl1Yri7JQ45FrgazTrWmKZ3YhV2slDpudCGsTNMUdjS145atuyfdFGVmbjbeau8ZkWgV5Q41jaxwZ+OzH6oe0ZembFhfmgKXQ/cOWqFrKKwHgkr37l9sG/e+gUH0DWhxZfriJaejbyC+uUT/4CCWnVmBL4f7bUT6ZAwMslkF0XDvdPZgUFM4eir+wsotHzkd93/iHOw94sUZ5QVoO9WLji4/qovzcFalC+uW14448X6zrQsfqCnRTfrKClwpraPIuiIDag2f7/DOlWejND8b7xzrwbeffXPE8SB2Xjv/gIa3Orrx0xcO4RtX1ON7f3oTLZ6+aH3/6MstONnbj0BQQzCocSoESqh3IIhj3YER9V+FBfoFs061LtZINKZmjy+6cwNDTVGaPb5xv1fkQDr8wOoPDp2s9fZr0aQuss7GZ99GX3/81XpffxA/+UtoxMsbly3A9RfU4Cd/OQhfzJ29noB+067YwVNysuwjJrH9wX+/DVdW/FW1wuwsbGk8HP28Gy6swZbGw8jPjm+ySUTAu54e2GPujgBD+3J/UMNPXziEA53dmDMzD79rbMXtj++Fw2ZH1cxcrLkotI+tuagGuVl2/OLFFgwMDmLj6iVwZYUOW7HTIqSyjiLrikybMXy+w288/QYCQQ19A/pT5MS0zIcrywalQsvvfGIfrnnf3Oh6m3YdwDUNc/DFS07H3U/uw4uHPNC0+JYdRBFBDbrnMsOmyDWlZo8P9+7YH3e+c++O/axTLWCq37FTAP4kIgrAfyilNhtdICtK1ARqeFOU/v5B7DnqRXuXH5WFLiyc5YbTGZ8cdQfGHqjkeE9Ad53jPYG4ZT3+QfQHhw6qIkB/UKHHP3QHrTBHf4j0gpi7eicT9NXz9sYPntIdGBhxJXjtslr0BOLXIyLAlWVPeGElEByMDj3/kz8fRJvXDyB0MlFR6MJbHd0AQlOM/OT5QzjZ24+i3Gyc6huIGzzF6QidkY/VTJOmh3c9PvQmSN78A4M4Z+4M3eNB5AZE7F25yHaxA235BzTMLcrFxmffRpvXj8aWE5g9I4ejIpMuXxIXls3K4wvonu+c8AVYp5rcVE/sLlBKHRGRMgDPisibSqnnY1cQkTUA1gBAVVWVEWU0vcTzRw0lbf39g9i+5yjuemJotMv1V9Rj1aJZccmdO0FnYnfMQCX52fqfl58dH64z85341AfnjWi2VZznHCq7w647RHpuzN24soJs3c8rKYhvLuG020dcCd606wAe+ey5yf9njoHxSGYymXjMddrhytLvM1dakI2SfCe++vt90aTOlWVDYEDDjb/5+4j9euPqJbDbgBt//fcR7/XM2gtRXqg/Ym3snJZkfWPFY57TgRaPTzcW3jvZi43Pvj2iqe89l9ehojAb//7P5+DN9m48+nJLXEzmxhx3XFk2NHt6o3PdDWrA/vYuTn8wjY0Wk6X5Cc4t8s3fFNNpt+me72xZc57BJaOxTOnETil1JPxvp4g8DuBcAM8PW2czgM0A0NDQMKXaVKSq42v/4CDWLqsdceVmIKY9wZ6jXvzoufjRLn/03AHUlOShoXpmdL1su0030XLah1oFZ9lFt59Nlj2+7MFB/f5zD8ckWqf8AygvcOJ7Vy+GLxBEnsuBwcFBdMXcIayvdOMbV9Tjzpik9BtX1GNhpTvu83wJhi729afu6ttUjkeynsnEY77Ljs6uAL555UJ8LWawlH/76Bm464l9uHtlHa5pmBM3YNF7J0MnzY+81II1F9VgtjsHR7x9OLuyIDpoRazIXblzq4vx4CfPwZ5Wb/T9Fs5xo7o4L3X/GWS4seKxvDAbb7ULbv3HM3DfH9+KO3488lJLXGydVVGIwhwH7DbBeyf6MK84F3NmDMDpEHzh4gWw24BFs92wCXDjsgXRGH3s1VasXb4Ac4ty4ekJ4OjJXjR7fLyLMU2NFpMKakQs3vqPZyDUmMzcevv173xzqgbzm7KJnYjkAbAppbrDf38UwHqDi5UxyXZ8TSb5K87LjvYtEwGUArY0HsaK+oroOid6+3Vv25/s7Y97L09vADNcjrhEq9c/gJO9Q80snQ5BxbD5qircrhGJnccX0B09M3aEzaK8LBzr7sedv389LmmbkTt0h9DptOOKRbMwvyQv+v+wSKcZadXMPN2rb1UzefJIFEvTFPYf7UG3fwD5LkfcvlyUlwW3KwvdgWDcoALfuKIej73aCgBo8/qxaedB3LhsAR7cdRDnn1aM8kKX7pyUkbty/UEV934bVy8x6uuTQapm5mFWUQ8OdfbgvqsXoz84iPJCF259bE/0Llyb14/fNbbi3qsWwtPTD5tN8OhLzXi7swffvWoR/r8PL8DdTzbFJYXbXg2NqvmtKxfi0x+qxtceH7oI+MVLTmfzNNLlCwThtNvim4/bbfBZoClmonl72QrC/KZsYgegHMDjEjoDcAD4tVJqh5EFCgY1NLV50eb1o9Kdg7rKwrSNqJVoMIHYuXeSTf6qi/Nw24qzRqwXezW8KCdL97b9o5+Lb6ZYVuDCwU4f7nrqjbg7dmfPHro7FhwEfvVyMz51fg36AkHkZoemFbhtxVlx7zXL7dJtillZOFTxBAZU9E5cpFx3PrEPjwwrl9Npj7uzqGd+SR6+f80SfOl3Q/8P379mCeaXMLEjivXucR+++vhe/GD1Enwxph4CQicHP1i9BO+d8I3YL2+8eAE+trASc4py0dcfREFOFuYV5yAny4457hzctKwWd2wfOqnesKoeVUW5SdV3NPXZbIJlZ5SjpiQfJ3wBZNlt6BvQ4i4wVoaPG2sefXVoNOWVdfjN31pw8FjPiDnrYkfNvP3xvVhzUU3c6z/477cz1jyNw89bS57TEZ0jMcKVZcN/fi513TfSxW6DbsspO4dcNL0pm9gppQ4BWGx0OSKCQQ3bXz8y4qRk1eLZaUnukhlM4N3jQ6MeRa6A37tjP84oL4jrDG6zCS75/9m78/ioynt/4J9ntsySTPaNhCREwmIWKCKuWIXaF7YIuHe5atVerq0Wf6X91Vt/VutytVpr63bbUm2r9raitVfFa7l6Rau9Li1uQAwCAkmBJCQh60xmf35/TGYyJ3MmOQmTzJnk83698tIMZ+Y8k/Oc55zveZbv/CL87ppTFAujxF5Q/AGpuj9/UDnkwGoyqq4StWL+mdFtej1+1bQCvR7lIiXegPoKm0uuOjnm76C+EMvRPuVCLFplmIXi6VuGeeZdVJm4nEYTCkk0tfZFhymrnX9NbX1wWk247py5yLIaUZZjx4FOF+rKstHS5cL/jTn3f3h+Le54sREbVs6Ptp+Rz7n5uV1YUpHLxVMoymAQqMp3YHdbPzY+/SFy7RbFDeolS8vjrhu3vdiIn166GI1D9TZW7KqZHn8IdosR150zd0SO1ckfnsbl59NPp8unOqqo0+Ub/Y060NrrwZ93tiry9v7qjU/xmYocVBWwTdWzaRvY6U1ja6/qTUlNUSYWzc5N+v60LCZwpNeNryyrjC71HxlW0trrVgR2gUAIL+w8MmpQWpKjvr+SbGW3fceA+vDJTpcXc5EFAMi2auv96xzwJVg9c7jRLHGqT14uHpFHRsuKnge7XAkXb5hJN49MXE6jOdDpwqFud/g8S7AwUTAE5NotePKdZly2tEIRyI1MGP3DLY245sxqbG8+ljB44+IpFCu2Bzd2Xl1taTaCoZD6qpmBIDIt6ov9zC/OwvUr5uKdTzuQZTXjZ/8zYpRI9uTXM/ZKp59cu1l1VFGuXf8pkkqzrTivvlTxgP2GlTUocbJN1Tt2qk6RRBP/24bG/SdbVb4jYc6nCKtJPX9bhkkZ0CQKShtbe6PbzMqy4s51dYr93bmuDrNG3FgVZWXgitMq8dhf9+Phbfvw6Jv7ccVplYolpXsSpB/oGZF+oDTbGt1f9DuZDYqGpyQnA7evqVWU6/Y1tSjJGd5fZEXPf3rsXVz/+w/w1cfexXM7jsA34insaL0CRBTuVdjfOQCb2YibzluAPk8AP1h9ouL827CiBi/uOIzmY26sbiiLe4jzwKt7ceGS8uhnRnpMQhKq53tRllVTe0czQygk0dzlUrTVkTmbvmAQuQ6Laj060OnCibOc+O7n5yvq0Q0ra3DXS0149M39+MbZNbjjxY/j6msgOPmLYfD6k34MQqiOKhJC/z2swRBUy54OOfhmOvbYTZHSbJt6j9aIpx/JGkNvMAisqi3Bgg3LcbTfE735if2snsEE+dsGlQHUaEHponDuVuztHIDdYgwviuILwGExwWAIv15fnhN9X7fbr9pYnBwzv60o4dN3ZS+bEMBta2oVE91vW1OrGAPe1uuF1WLApstPQrfbj1y7GT2DPrT3ejE7N/yUc8eR3miahkiZbnl+V9yKnokmExdm8gkWERDurbOZjbh7625cf85cdLl8+N99Hbj/0sXY3daHYCi88NK3VtTgJy/vwUUnlY869A0YThj94o7DuOeiBtz47I64ub6R9m7+t5aj5ZgLdosprleepr/IcMWQlKpt9d6jA5AyFLcKciR3nd1iwuNvHcQ1Z1ajMs+GQz2D0dU0AeCjQz2q9XV3ez+qCzPHvFYfz/WdvdLpp0NjTl49Otqvft/XMeBh3sZJdrxxAAO7MSQr0Mqxm3Dr+bW4bctwEHLr+bXIcQx3ySd7DL3BIFBdmJlwmIbTqp5TLsuqHCagJSj1BIJo7nLHDTkoHBGMaem5zHWYVAO23ExlufIcFmRFVtiMCSZzY/LYWYwGfO+PO+PKHjvZvS3Bk9C2PuWTUE4mJhpdyzEXBocSRHsCITz73iFcfmol7tnahNUNZTAagB+eXwuH1Rhd0GKshNHf/tw8/P5vzbhx1UJ8fmEx6suyEz6s+qS9n3OQZrDIcMX/87mahMHbRSeVoyw7IzpXWkrgyXea0e32od8TQGuvB396/xBu+sJChCRw0UnlePa9Q2jt9UR7jUfW1z3t/Tix1DnqkMjjvb5HeqVHW8SM9CVvqHd4ZH3JtVtGeZc+8EFCaiQjDmBgN4pkBlpHejz4w7vN0YmoNosJj77xKeYU2FGZH74YHOxSX8xkQUnWpIyhL8rKUA1URvaMaQm0Bn1B1Z64TZefpPgsLXPeWnu8eGZ7i+Jv9cRb+1GV70BF3vDfocflx8anP4oP2v75VFTmh3/XkoslP1O98Y1NdA4gOlcjNu3DE283czIx0RBHhgk2iwmV+TbML85Ct9uHJ99pxldPqcCsHBssRgM+ae+HAeHe9n9/fV9cjsx7L2pAWY4Nn5mdg3yHBb5gCOeeWBwN4hI9rOIcJIoMVxzwBrGo3KoI3rbuasUlS8tRlm1D96APDotJMb9847nzEAxJbDx3HmqKMvGjrU1o7hpUBIVbPjocl5cx8m+nn5A/aj073vqpZRQO6Utmhgm3r6nFLTH3TrevqUVWhv5vvfkgITWScR3Tf+1KoWTeKBQ7rdhzdAAb/vBB9LWRTz+6XF7VXHATzZEzVm+jEIDDYlSs8uiwGDFy+Hdrjxf//vo+RUDz76/vQ1X+omiglSiAGhwxT62uxInb19Thlhd2xTR0dagrcUa3cfkC2N7ci+3NHyje6x6RCLx1lJ62yHKoiZ46Fcf0NuYm6E3NtStPj2KnFd1uHx55bZ/is/gEiyjc3gRDEiEZxLVnzcV9L++OBm1Gg0B7n0fxEOmm8xbg+6sW4uAxF+67eBH8wRBOKHSgrixnQjerXBmTIrkOraZwr29ptg23bWlErt0St4jFTectwE8vXYymtj6ccUI+2vu8+F7MMN9IwNba68GD2/Zi/VnVKHZa4fL6VXv7xroOJKN+jjUKh/QlEAwBkIpRRW6fH4GQ/ieq8UFCaiSjnWBgNwqtf2AtwzW1PP2wGA2qq0FOJEeOlt7G1l4Pfv6X/dEVKoMh4Od/2Y95JVmKHii3L4DmrkFFQBN5PaIwU70nLj9zxFDMAS8eeX2vIkh85PW9WFqVi2pbuAewUmMi8IRDRGNWKNPyd6/Oy0Jz16Ci8TWbBKrzsxT7q8p34JeXL0H/YDCaXD3LauQTLJrxfL4g3vtHN1qOuVGZb8dtL4Yfkjz5TriHe35xFr75+/cVbdtdf96Nf//KEgx4g9hztB8nVebAkWHGuwe6JjTsnUOHqCLXjpu+sBD+gERbrxd5DjPuu3gRHBlGfOM/4utfZJW/nYd7cd/Le+KuvZH8dR5/COU5NnS5fHji7WZcfmql4gGslp4MJnyeeQa8Qdzywsdxx/zRK5amsFTa8UHC1EvGdYyB3Si0/IG1DtfU8vRDy7BBrbT0NmrtgcpzqAdteTHjxAuyLKpDDgqzlMMZ2/s8qkFibLA8p8CBh7/yGew41IuQBIwCqC/PjksEXlvqxI8vbsDeowPR7eYWZaK2dDjZucEg8PmFxdi8/tShxPBW1JYqc/Ad6h3EXS+F5wBFgs0XdxxGzdey4gJ4teMTCkk+xaIZKxAI4X8PdMBsMCIrw6RY1ba114NHXtuHey6sVz13vIEQTqrIwZwCBz5u7ccXH3pzwsPeOXSIDve6EQyFV6j0+EPItpvhtBnR2a+eGqcq34EfbW3C+YvKRl3Ex2o2IC8zAz/9n71o7fVg8/YWbF5/Kgb9QU09GaGQxIGugbipD6yf01u/Vz2P54A3kOAd+pKsNSZIu2RcxxjYjULLH3g8wzXHevqhZdigVlp6G7VWIF8wGDcPZsOKGvhjhhPMznXgcJ47vPqky49chxkmY/j1WFpXljQbBeYVZUV7xszG+MbEYBCwmpWpGaxmo6LhCYUk3j/UhWBQwBcIB8nvH+rC0oqC6HbtfR6sbSjBGTUF0ZxYRgTjemb3dvTB7QviQKcrGkhWFTiwt6MPC0tzQDQTHejqQ67NgqP9XmRaTbCa43OBdbt9CRZqMuH0EwqSMuydQ4eox+VH72AAR3sGcNrcYrT3e2G3mFDiVJ9HnWU14d/W1UMI4NE34/9dDi2WctcF9Xh512G09npgNRtw46qFqB/HkOFIDtRcuyU6WsUggBNLs1g/p7GiBHk8CzL1v2JvshfzI22ScR1jYDcKLX/gZM7rqMi14851dXGJwCty7eMuu5bexvjeLBtqS51xFSjfkYHN21sUwyc3b2/BqroSxXY97mBcIzCS0QBsPHce7n9FOWk9dmXJf3S7cMzljwug/tHtii40A4RX4Nt3dACb3tivWPylpigzOpT0cK8LBzsG43oSS7Nd0XQHFflW/ONYJq78zd8U28zOUwabA94gjEJiaWUujrn8yHOY0eP2YsA7/h5VoukgFJJo7/MCMMAbkMi2GeHIMMYtMFGWa8e/rlqAH23drTjvy3JsMAzNv0tGO8qhQzOb2x+EDPpwak0RBjwBCAB9g34YBOJWybxhZQ2OdLsx4Atidp7atbcexwY8uObMajzw6h5sPHc+Lj25EnmOjHHfaEXqd6QHO+L0E/K58NY0VpZtwJ1r63BzTL27c20dynP0v5Q2F6NKneO9jjGwG8NEe9kmMm6+pduNh7Yp5589tG0vllTkjvsAa+mNC4UkXm5qH/OJTFW+AzeuWpiUnsuOAS9yRqQocHv96BzwRi9wXf0++ANBRY+d2+tHV78vutolALT3efFBSxd+OdRLmOcw43fvHMCSitzoZ7V2e7GnrQe/vWoZOoaC81caD6Mq3xEN7A4n2qbAgbKc4bKbDEDmiFQQmVYzTPpvo4kmxaEeFyryM9DWG364IQRgNwNVeRn47VUno9vlx64jfWjtcaO+PEexUFN1oQOVQ20I58dRMkgZwuyCbPS6A+EeOZsRdrMJ3W4fTih04LErl6JvMID8TAtCMgi3L4RHtnyM5q5BVObb8NNLF8MXCCLPkYGm1l70eYP40/vhVAc3PrsDL03wppb1e2bq9wKnz83CE1ctQ/vQaKDyXCP6vUDJ2G9PKS5Glb4Y2I1hrDHGyZzXoWX+mVZaehu1BmMGg8CKmkI8efUytPV5UeLMQP2IeWrtfR58obYYF59cgc5+LwqzMvDM31viym4zG5FtN8FpsyDUL5GfaYHZKBVDKoMIIS8zAxajAYGQhNNqgtVsQBDKRsZokLj6zDkQMMJo8CPDZMTVZ86BySCHtzFKnL2gGFJKSAmEZPh3o1EqPuf8RbMQGfYuBHD+olkISKnYn9lghN0SVOzPMvQ6Abt27cQXv3xN3OuzCnLwq4d+koIS0WSzGAJo7wvB4w/CbBQwCgGbGTAZDTAaBHzBEB77635cc2Y1AGDd4jLV9ojz4+h4hUISPe4AQjKEYmd4oZOsDBOMBgGDQSAYknB7g8gdyh17zBVAQWYG7lhbC7PRAECg2+1DfmYGPj4SDuqMArj2rGo8+/4hLJ9XhD3t/QCguccucv/Q5fLinosacGPMqpvJqt+cB6VfWSojLg0JXtcbPoxIX9M6sBNCrALwAAAjgEellD8az/u1jDEez3jYsRrgyFLNkUU8AGDLR4dVT6RAIITG1l7FEErTiK4jrzeAzgEvOvp9MAiB0swM2GzDPU5an8j4fEH85dMO+AMSbm8A7f3AMXcHzqkpgsUSDmrK86w4e0ER3tnfhZAEPu0YwNkLilCWqyy7UQDegMTbMdudUOhA7BS6DJMRGaYgTIbwktVmowFCSGSYlAGU02rCh//oixtmuXj2cOoEu8WEw90e7OvoUeyvJmaMe47NhPea4z/npEqnYn9mE9DS6onbbtFs/ScbnQo+aUDWOV+Pe/3lh25gwDcNDQ760dztgzPDggyTEb2DAQSlhCcAFGcZsb8rgFy7GRtW1GDz9hZctKQs4egHzo+j49VyzAVvIACjwYA3F5pYTwAAIABJREFU93VGh/GfUGhHls2CAV8AWTYzuga8qMizId+RgbY+D4qzrPCHAnB7ww8Y+z0BLK7IQVufF0e6B2E0Clx5RhX+338OD6d7+CufwZz8zOicbLW6OvL+oTLfhk2XL4XZKJIWgHEeVHJMZnA86I//3WZW31ZPqvId+P0/nwx/QETbZLNJ8mFbGpi2gZ0QwgjgEQDnAjgE4O9CiBeklB9r/Yzx9GiNNR5WSwNckWvHt1bUjDnHLhAI4bmPDsdtt25RWTS4Gxz0Y8uutrh8cefXlUSDO61PZD452o9jLn9cnrdPjvajvjy8aEhnnw+Hezxx891m5/owO3f4swZ9IdXtynOGv6PZINDa440LoAocysdcPe5gdJvI8bnlhUY8cdWy6DZur/r+ymL21zWQ+HNOKBreX6+G/VG8RAHfkdceTUFpKFkO9rjg9oVwsLMPtyrO1TqcUZOFbpcPLq8fm7e34MZVC8e8IeD8ODoe7X1eODIs+LRjeN51Zb4N159Tgye27cVFSypw24vv47Q5eVhVX6qos/deVI9jLr9iDuh3Pz8ff/h7C756SiXcMasb5tot2Ns+gOt//8GowdTI+4fmrkGsf3L7hIdzquE8qOM3mcFxAMDfD8Y/ND69xjnme1PN6w1gX/tg3D3kwsKAooOA9Gc6zw5aBmCflHK/lNIH4CkAa8fzAaP1aI1Xogb4YJcruk1LtzsarEW2ufm5XWjpdis+q7G1V3W7xtbe6DY72/qiJ2Rkm1te2IWdbX3RbSLDn6zmcDVINDxkwBuIBnWRz7ptS6Niyd4+byC6jHNkmwde3Yv+Ecv69vsSbBeTE6/Pox5A9XuUi5S093tVj097v3e47An2NxCzPy2fM57tiGaCvsEgjMIQvUEGhtuZw8eCyM+0INtmwW++tow9CDTpXL4ATAahaO9XN5ThB8/vwhWnV0dzK37tzDlxdXZfhysa1EVeu+/lT7C6oQz3v7IHpTEPAi9cUh53TRl5LQeSe/+QyFTsY7rTcm82UUeOqd/LHDmm/wXXtNxDkj5N58CuDMA/Yn4/NPSaZpEerVgTHWOspQHW2ki39qpv19Yb+1kJgpC+4SAkMvzppQ3L8dT6U/DShuWqN2DHXOo5gI65fNHfvX71HHwjX0u0nTfmtaMaA6hiZ4bq8Sl2Dvfsadmfls8Zz3ZEM8HRfi+OufwJz9X7/ns3aorDuSAZ1NFkq8xzwDUiF6wQ4fo4GNPj1q1SZ0MSqvU4+v6YB4GR10ZuO/I6ncz7h0SmYh/T3WQGx+n8MFjLPSTp03QO7DQRQqwXQmwXQmzv6OhQ/JvWHi0ttDTAWhvp0myb6nYl2bGfpS0IiQx/OrW6IOENWFmuXfWzynJs0d/n5GeqbjNnxN9Ky3YlCcpeMqLs9SVO3L6mTnF8bl9Th/qS4WEOWvZXW5Kp+jm1JcqhLFr2d7xGq49EU220+ljizECew5ywnbn6zBM4H4OSarT6OKfAgYJM9fpozzBFX1ers0YB1fdF8tjNyh2+5ibaduR1Opn3D4lMxT6mu+MNjkerk+n8MDidyz7TTds5dgAOA5gd83v50GsKUspNADYBwNKlSxXLICZzQr+WVd+0rgxXW+pUzXdXW5od3SYShIwcHz2RIKSu1BmXA+iOtXWomzW8vxOKMvGTSxbhO898FN3mJ5cswglFyuBIy3b1pdnqZY/5fgBgs5lxfl0JqgrsaO/zotiZgfoSp2L8t5b9OWwZ+GJdEaoKlkU/p7YkEw5bxrj3d7xGq48zxT9/6zs40tmj+m+pWnAlUZmm+wIwo9XH+tJsvN3cidvW1MbNsavIzcDSyjz21FFSjXW9PrkyH/de1IDvDa0+ueWjw7hzbR0ef2s/bl1di9tebMRv/nogrs6eUJgZl2fxu5+fjyffOYifXLIYp1bl46Wh+4ASpxXzS5xjXqenYkEgLjp0/I53Rd7R6uS8Eofqvcy8Ev0H3sm8h6SpNZ0Du78DqBFCzEE4oPsSgK+M90OSNaFfSwOstZE2mQxYt6gMNUWZaOv1oCTbitrSbMWqmMkMQkwmAy5YXIZ5xYn3ZzAInFdXioWlzlHLrmU7q9WENfWlmBNb9tJsWK3x1dVmM2PZnPy418dbLoctA8vmjP0kaqz90fE70tmjutgKkLoFVxKVaSYvAGO1mnBaZQEOdA/g8auW4Wh/+FydV+JAjo1DwWjqmUwGrG6Yhbqy7Gh7X5Frx+LZOTjaP4jHr1qGzgEvKvPteOKqZTg64EWJ04ra4ix8esyFX12xFIO+IPIcFnj8Afz6ymWYU+CIuw+oyHNoCqamYkEgLjp0fCYzOM6xWfH5ukLFQ+N0aR+n4kE2TY5pG9hJKQNCiOsB/DfC6Q5+LaVsTGWZtDTAWhtpk8mARbNzsWh24m2SGYRo2Z/WsmvZzmo14eQklZ0XPqLJYbWasLA0J9XFIIpSa+/nFmdhbnHWqO+rK9Nej3lNmV4m83jm2KxYNkf/gZwaPshOT9M2sAMAKeVLAF5KdTmIKF6ihOYf796DU84Z32clGip5YN8nmDN3vubXp/vQSiIiIpq+pnVgR0T6lSi/nWfntxO+Z9Rg8Bv3xr3evfPbaFDZR6LX02lo5XiDWYCBKxER0XTGwI6I0sZEgsHpKtG8v0RBK5BegSsRERGNj5ByRi68p0oI0QGgeQp2VQCgcwr2MxlY9uTolFKuGm2DMeqjnr6LGr2XD9B/Gae6fKPWyTStjyzX+OmlbNOxPiYTv+PUmu7X7NGw7KkxWtkT1kcGdikghNgupVya6nJMBMuuD3r/LnovH6D/Muq9fLH0WlaWa/z0XDatpsN3GAu/Y3pJ5+/CsqfGRMs+4xOUExERERERpTsGdkRERERERGmOgV1qbEp1AY4Dy64Pev8uei8foP8y6r18sfRaVpZr/PRcNq2mw3cYC79jeknn78Kyp8aEys45dkRERERERGmOPXZERERERERpjoEdERERERFRmmNgF2PVqlUSAH/4MxU/Y2J95M8U/4yK9ZE/U/wzKtZH/kzxz5hYJ/kzhT8JMbCL0dmZrjkMaTpifSQ9YX0kPWF9JL1hnSQ9YGBHRERERESU5hjYERERERERpTkGdkRERERERGnOlOoCUHoIhSQOdrnQ3udBsdOKqnwHDAaR6mIRkQY8f4kmjucPTRTrDk01BnY0plBIYmtjGzY+/SE8/hCsZgPuv3QxVtWWsIEi0jmev0QTx/OHJop1h1KBQzFpTAe7XNGGCQA8/hA2Pv0hDna5UlwyIhrLZJ+/fr8fO3bsUPz4/f6kfDZRqvH6RxPFukOpwB47GlN7nyfaMEV4/CEc7fegujAzRaUiIi0m+/xtamrCNx7ZgqziCgBAf3sLfn4d0NDQcNyfTZRqvP7RRLHuUCowsKMxFTutsJoNigbKajagKMuawlIRkRZTcf5mFVcgp3xu0j6PSC94/aOJYt2hVOBQTBpTVb4D91+6GFZzuLpExolX5TtSXDIiGgvPX6KJ4/lDE8W6Q6nAHjsak8EgsKq2BAs2LMfRfg+KsriyE1G64PlLNHE8f2iiWHcoFRjYkSYGg0B1YSbHhROlIZ6/RBPH84cminWHphqHYhIREREREaU5BnZERERERERpjoEdERERERFRmmNgR0RERERElOYY2BEREREREaU5BnZERERERERpjoEdERERERFRmmNgR0RERERElOYY2BEREREREaU5BnZERERERERpjoEdERERERFRmpsWgZ0Q4ttCiEYhxC4hxB+EEFYhxBwhxLtCiH1CiM1CCEuqy0lERERERDQZ0j6wE0KUAdgAYKmUsg6AEcCXANwD4KdSyrkAugFck7pSEhERERERTZ60D+yGmADYhBAmAHYArQBWAPjj0L8/DmBdispGREREREQ0qdI+sJNSHgZwH4AWhAO6XgDvAeiRUgaGNjsEoCw1JSQiIiIiIppcaR/YCSFyAawFMAfALAAOAKvG8f71QojtQojtHR0dk1RKIm1YH0lPWB9JT1gfSW9YJ0lv0j6wA/A5AAeklB1SSj+APwE4A0DO0NBMACgHcFjtzVLKTVLKpVLKpYWFhVNTYqIEWB9JT1gfSU9YH0lvWCdJb6ZDYNcC4FQhhF0IIQCsBPAxgNcAXDy0zZUAnk9R+YiIiIiIiCZV2gd2Usp3EV4k5X0AOxH+TpsA3AhgoxBiH4B8AI+lrJBERERERESTyDT2JvonpbwVwK0jXt4PYFkKikNERERERDSl0r7HjoiIiIiIaKZjYEdERERERJTmGNgRERERERGlOQZ2REREREREaY6BHRERERERUZpjYEdERERERJTmGNgRERERERGlOQZ2REREREREaY6BHRERERERUZpjYEdERERERJTmGNgRERERERGlOQZ2REREREREaY6BHRERERERUZpjYEdERERERJTmGNgRERERERGlOQZ2REREREREaY6BHRERERERUZrTXWAnhDhTCHHV0P8XCiHmpLpMREREREREeqarwE4IcSuAGwF8f+glM4Dfpa5ERERERERE+qerwA7ABQDWAHABgJTyCICslJaIiIiIiIhI5/QW2PmklBKABAAhhCPF5SEiIiIiItI9vQV2TwshfgkgRwjxzwD+B8CvUlwmIiIiIiIiXTOlugCxpJT3CSHOBdAHYD6AW6SUr6S4WERERERERLqmq8BuaAXMNyPBnBDCJoSoklIeHON9OQAeBVCH8DDOqwF8AmAzgCoABwFcKqXsnrTCExERERERpYjehmI+AyAU83tw6LWxPABgq5RyAYBFAJoA/CuAV6WUNQBeHfqdiIiIiIho2tFbYGeSUvoivwz9v2W0NwghsgGcBeCxyHuklD0A1gJ4fGizxwGsm5QSExERERERpZjeArsOIcSayC9CiLUAOsd4zxwAHQB+I4T4QAjx6NBqmsVSytahbdoAFKu9WQixXgixXQixvaOjIwlfgWjiWB9JT1gfSU9YH0lvWCdJb/QW2F0L4CYhRIsQ4h8IJyv/lzHeYwKwBMDPpZSfQTgHnmLYZWwKhZGklJuklEullEsLCwuP+wsQHQ/WR9IT1kfSE9ZH0hvWSdIbXS2eIqX8FMCpQojMod8HNLztEIBDUsp3h37/I8KBXbsQolRK2SqEKAVwdFIKTURERERElGK6COyEEP8kpfydEGLjiNcBAFLK+xO9V0rZJoT4hxBivpTyEwArAXw89HMlgB8N/ff5ySo/ERERERFRKukisAPgGPpv1gTf/y0A/yGEsADYD+AqhIeZPi2EuAZAM4BLj7uUREQ0Ln6/H01NTYrXFi5cCLPZnKISERERTU+6COyklL8UQhgB9EkpfzqB938IYKnKP6087sIREdGENTU14RuPbEFWcQUAoL+9BT+/DmhoaEhxyYiIiKYXXQR2ACClDAohvgxg3IEdERHpV1ZxBXLK56a6GERERNOabgK7If8rhHgYwGaEV7cEAEgp309dkYiIiIiIiPRNb4Hd4qH/3h7zmgSwIgVlISIiIiIiSgt6C+wukVKOlZCciIiIiIiIYugiQbkQ4nwhRAeAHUKIQ0KI01NdJiIiIiIionShi8AOwL8BWC6lnAXgIgB3p7g8REREREREaUMvgV1ASrkbAKSU72Li+eyIiIiIiIhmHL3MsSsSQmxM9LuU8v4UlImIiIiIiCgt6CWw+xWUvXQjfyciIiIiIqIEdBHYSSlv07KdEOL7UkrOvyMiIiIiIoqhlzl2Wl2S6gIQERERERHpTboFdiLVBSAiIiIiItKbdAvsZKoLQEREREREpDfpFtixx46IiIiIiGgEXQV2QogzxnjtmSksDhERERERUVrQVWAH4KHRXpNS3jWFZSEiIiIiIkoLukh3IIQ4DcDpAApHJCp3AjCmplRERERERETpQReBHQALgEyEyxObmLwPwMUpKREREREREVGa0EVgJ6X8C4C/CCF+K6VsTnV5iIiIiIiI0okuArsYGUKITQCqEFM2KeWKlJWIiIiIiIhI5/QW2D0D4BcAHgUQTHFZiIiIiIiI0oLeAruAlPLnqS4EERERERFROtFbuoMtQohvCiFKhRB5kR8tbxRCGIUQHwghXhz6fY4Q4l0hxD4hxGYhhGVyi05ERERERJQaegvsrgTwfwG8BeC9oZ/tGt97A4CmmN/vAfBTKeVcAN0ArkliOYmIiIiIiHRDV4GdlHKOyk/1WO8TQpQD+CLCc/MghBAAVgD449AmjwNYN1nlJiIiIiIiSiVdBXZCCLsQ4uahlTEhhKgRQqzW8NafAfgegNDQ7/kAeqSUgaHfDwEoS3qBiYiIiIiIdEBXgR2A3wDwATh96PfDAO4c7Q1Dgd9RKeV7E9mhEGK9EGK7EGJ7R0fHRD6CKGlYH0lPWB9JT1gfSW9YJ0lv9BbYnSClvBeAHwCklG4AYoz3nAFgjRDiIICnEB6C+QCAHCFEZNXPcoSDxDhSyk1SyqVSyqWFhYVJ+ApEE8f6SHrC+kh6wvpIesM6SXqjt3QHPiGEDYAEACHECQC8o71BSvl9AN8f2v5sAN+VUn5VCPEMgIsRDvauBPD8JJabiIg0CAUD2LNnj+K1hQsXwmw2p6hERERE04PeArtbAWwFMFsI8R8I98Z9bYKfdSOAp4QQdwL4AMBjSSkhzWihkMTBLhfa+zwodlpRle+AwTBWpzLR5Em3OunqPIK7t3hRWDkIAOhvb8HPrwMaGhpSXDKaLtLtnKDpi3WRppquAjsp5StCiPcBnIrwEMwbpJSd43j/6wBeH/r//QCWTUIxSUcCgRAaW3vR2utBabYNtaVOmEzKEcbJalhDIYmtjW3Y+PSH8PhDsJoNuP/SxVhVW8KGWqfUjj2ACdUHPV6g07VOZhaWI6d8bqqLQdPQeM6JyDnd5fLCYjTA7Quqntt6PPdJ/0IhiT/vasN3nhmuiz+5ZDHOq9N3+0zpTVeBnRDiDAAfSin/SwjxTwBuEkI8IKVsTnXZSH8CgRCe++gwbn5uV7TRvHNdHdYtKosGd8m88T3Y5Yp+DgB4/CFsfPpDLNiwHNWFmUn/fnR8Eh17i0ng+t9/MK76oNcAinWSSEnrORE5p+/Z2oTLllbgwW17Vc9tvZ77pH+fdgxEgzogXBe/88yHmFd8JmqKs1JcOpqu9LZ4ys8BuIUQiwBsBPApgCdSWyTSq8bW3mhQB4QbzZuf24XG1t7oNoku8ge7XOPeX3ufJ/o5ER5/CEf7PcfxLWiyJDr2Ow71jrs+JLMeJRPrJJGS1nMick6vbiiLBnWRbWPPbb2e+6R/BzpdqnXxQCfrDk0evQV2ASmlBLAWwCNSykcA8LEGqWrtVb+At/UOX8CTeeNb7LTCalaeMlazAUVZ1nF/Fk2+RMc+JBH32lj1Qa8BFOskkZLWcyJyTguBUc9tvZ77pH9Ws0G1Lo58jSiZ9Fa7+oUQ3wdwOYD/EkIYAHCpNFJVmm1TbTRLsocv4Mm88a3Kd+D+SxdHPy8yJCcyb4v0JdGxHzl6Skt90GsAxTpJpKT1nIg9p0c7t/V67pP+ZVvNuGFljaKe3bCyBk4rb2tp8uhqjh2AywB8BcDVUso2IUQFgB+nuEykU7WlTty5ri5ujl1taXZ0m8hFfuT8iInc+BoMAqtqS7Bgw3Ic7fegKIuT6PUs0bG3mASsZsO46kMy61EysU4SKWk9JyLn9D1bm7BhRU3cHLvIua3Xc5/0b2GJE83dbqw/qxohCRgEUJZrw4klzlQXjaYxXQV2Q8HcswBqhl7qBPCfKSwS6ZjJZMC6RWWoKcpEW68HJdlW1JZmK1bFTPaNr8EgUF2YyYUp0kCiYw8AL42zPug5gGKdJFLSck5Ez+mSLBxzebF5/amqq2Lq+dwnfbNYjFi1sAQ7snujK6o2zMqGxWJMddFoGtNVYCeE+GcA6wHkATgBQBmAXwBYmcpykX6ZTAYsmp2LRbMTb8Mb35kr0bGfSH1gPSKaXrSe0zz3aaIsFiOWVuWluhg0g+gqsANwHcK5594FACnlXiFEUWqLRMk2lbnntO6P9CX2+NstJviCQeQ7MlCRa0dLt3tK80mx/hBNTxM5tyeaG5O58GamwUE/drb1ob3Pi2JnBupLnLDZOMeOJo/eAjuvlNInRLixE0KYAMjR30LpZKpzz2nZH+mL2vHfsKIG23a34UvLKhXHcrLzSbH+EE1PEzm3J5obk7nwZqbBQT+27GrDLS8M17Hb19Th/LoSBnc0afR2Z/IXIcRNAGxCiHMBPANgS4rLREk01bnntOyP9EXt+D+4bS+uOL067lhOdj4p1h+i6Wki5/ZEc2MyF97MtLOtLxrUAeHjfssLu7CzrS/FJaPpTG+B3Y0AOgDsBPAvAF4CcHNKS0RJNdW557Tsj/Ql0fEf9AWmPJ8U6w/R9DSRc3uiuTGZC29mau/zqh739j5vikpEM4FuhmIKIYwAGqWUCwD8KtXlockRyT0X29glyj03cpuJ5A3Ssj/Sl0TH324xJa1eaMX6QzQ9TeTcTtQ2jZUbM5nXNEofxc4M1eNe7MxIYaloutNNj52UMgjgk6HcdTRNRXLPxSbsTJR7LhlJl7Xsj/RF7fhvWFGDx9/aH3csJzufFOsP0fQ0kXM70bWpoTx71HYpmdc0Sh/1JU7cvkZZx25fU4d65rGjSaSbHrshuQAahRB/AxAdfC6lXJO6IlEyTXXuOS37I32JPf7hVTGN8AdDWFVXgopcO5ZU5E5ZPinWH6LpaSLn9kRzYzIX3sxks5lxfl0JqgrsXBWTpozeArsfpLoANPmmOveclv2Rvox2/Kc6nxTrD9H0NJFze6K5MZkLb2ay2cxYNic/1cWgGUQXgZ0QwgrgWgBzEV445TEpZSC1paKZRGuOIeYiml60HE8ec6L0NPLcTUUeTJrZmAeVppouAjsAjwPwA3gTwHkATgRwQ0pLRDOG1hxDzEU0vWg5njzmROlp5LlbmW/Dt1bUTGkeTJrZmAeVUkEvNetEKeU/SSl/CeBiAMtTXSCaObTmGGIuoulFy/HkMSdKTyPP3dUNZVOeB5NmNuZBpVTQS2Dnj/zPTB+CGQpJ7O8YwNufdmJ/xwBCIxPkUNJpzTGUTrmIWI/GpuV4ptMxJ6JhI89dIZDyc5nt8szCPKg0EcfbTuhlKOYiIUTf0P8LALah3wUAKaWcEWvDJnvYVzLnBk3neUZacwylSy6i0eoRgLjjqPbadDm2I8XWYy158dLlmBORUlGW+rk70XM50nZ0ubywGA1w+4Ljai85rHvmYR5UGq9ktBO66LGTUhqllM6hnywppSnm/2dEUAckd9hXpHJ84cE38eVfvYsvPPgmtja2TegJYTI/S4+05hiqyLWr5j2qyLVPeZlHk6getRxzqR7HbZ+0T9tjG2tkPd7w1Ptj5sVj/imi9BMKSRzoGsANK2ui5+6Wjw5POA9mpO246rd/w98PdOOyTe+Mu73ksO6ZZ16BQzWP3bwCroxK6pLRTuilx44w+rCv8S6RnKhyLNiwPKWfpUdacwy1dLvx0La9uObMaggBSAk8tG0vllTk6urvkKgetfd5VY/j+rOqp+2xjTWyHjd3DeKhbXuxef2pGPQHmX+KaJo42OXC9b//ALl2S7S9Ngjg5KrcUfPNjfZ5G5/+ENecWY0Ht+2dUHuZzOs7pYfG9n488rrynuGR1/eiutCBpVV5qS4e6VAy2gkGdjqSzGFfybyIzKQLkhzlwWt7nwfNXYN45LV9itf19ndIVI9cvoDqcRz5sDnVx3ayhv2q1ePmrkEM+oM4tbog4fuYf4oovaid6yEJdPR7sbQqf8LXwNHm6Y31mRzWPfO0JbhnaO/jHDtSl4x2Iu0DOyHEbABPACgGIAFsklI+IITIA7AZQBWAgwAulVJ2p6qcWkSGfY0cWzuRYV/JvIgUO62ozLdhdUMZxND99ZaPDk+bC5LWMc3pcmFOVI8q8xyq5R8ZM433Ox3v3JORnzVZ81DS5fgR0fGJXLMuW1oR7WGzmg2oKcrEkpAcNVdlUZYVRkN44YtIOxZpO4CJz9NL5vWd0kNptvq9U4mT1xxSl4x2Iu0DOwABAN+RUr4vhMgC8J4Q4hUAXwPwqpTyR0KIfwXwrwBuTGE5x5TMYV/JvIiUZ9tw3Tk1uOX54Vwst6+tQ3m2bdyfpUdah5qmy4U5UT0CoFp+i0lEb1bG+50igdg9W5vibqImEpAlY9hvoh6/dDl+RHR8qvIduGNtPdY/uV3Rltz47A7Ul2Ur2hK1h0k3rKzBE283o9vtw/2XLsbnFxbj/ksX456tTdiwoiaundPShnBY98xT4DDjm2fPxa0vNEbry21ralGQaU510UinktFOpH1gJ6VsBdA69P/9QogmAGUA1gI4e2izxwG8Dp0HdkDyhn0l8yLS1N4XDeqA8AXylud3YX5xJhbNzj2ucurBeIaaWkwC68+qRkiG52xYTPq8KCeqR4kCvonMOwGSM/ck1vEO+x2rx483VpMvFAxgz5490d/37NkDOdoYZ6IkMxgETAaRYK6xsi1Re5j0wKvheVGPvLYPG5/+EC9tWB5uO0qycMzlxeb1p05oZAKHdc8sh3o80aAOCNetW19oxGNXLkVlQVaKS0d6dbztRNoHdrGEEFUAPgPgXQDFQ0EfALQhPFRzRtFSObTMZxotF8ui2eMvl5Z9+nxB7DjSi7Y+D0qdVtTPyobFYhz/zjTQOkTvYJcLd7z4cXRYRUgCd7z4MaoL0uNCPfLvXpFrV/y+tCIPLd1uvHugK3pcQiGJxtZetPd5kO/IQAgS+Y6M6DFLxtyTWBMZLjkyhcE9W5sSBpi8sZp8rs4juHuLF4WVgwCAto//huyq+hSXimYap82EDSvnRucQP/veIXS7fbDHXEdCIYnmLpdq27WgJAsbz50Hi0mga8AbbSeXVOTxYRBp0u9Rn9fe75nR6Zppkk2bwE4IkQngWQC22WFhAAAgAElEQVT/R0rZJ8RwwyullEII1UfGQoj1ANYDQEVFxVQUVTe0zmdKmItlAuPEtezT5wviuR1H4oZ+rmuYNSnBndYhel0ub9xwww0ranDM5U1aoDBZ9VHt737nujo8tG0vmrsGUZlvw7dW1ODm54b/5g9/5TPocfsVr21YUYPN21tw46qFWFVbkpS5J7HGO1xS7XttWFGDJ99pRutQEthULwaTziZaHzMLy5FTPhcA0N/eMillo5lHa30MhST2tA9g0xv7FcMr7WYj/MFQdJttn7TDH5Sqbdfeo/14ZvshXHFaJS7/9d+Ye45UjVYnS7MzVOtWqTNjSstIM4su8tgdLyGEGeGg7j+klH8aerldCFE69O+lAI6qvVdKuUlKuVRKubSwsHBqCqwTWvNl5NhNuPX8WsUN/K3n1yLHMf5x4lr2ueNIr+rQzx1Heif0PccSGaL30obleGr9KdFhNyMv3BajIW644YPb9sJsTN5pNFn1Ue3vfvNzu7C6oQwAsLqhLBrARf59x6HeuNce3LYXqxvKoscsEoht+egwNqyoUdSRicxf03osRvteD27biwuXlEe34QIpE5eK9tHv92PHjh2KH7/fPyX7Jn3TWh8Pdrlw03/ujBte6fYHkefIiG6z41AvfvTnpri26werT8Qz2w/hwiXleODV+CHmzD1HEaPVyWAIilyKkQcMQY5Mp0mU9j12Itw19xiAJinl/TH/9AKAKwH8aOi/z6egeLqmdT7TkR4P/vBuM+69eBEGfQHYLCY8+sanmFNgR2V+8lMntCXMwzZ5SwRrGaLn9gVVy+X2BSetXMmS6O8e6dhWG0oZkurDKyPbRo5ZMuaexBrPcMlE3ysSa3OBlPTT1NSEbzyyBVnF4aff/e0t+Pl1QENDQ4pLRukiUbswO88ebQva+zwIyXDKkyffaVbkGuv3+NHa60naEHOamQ73ePDE28q69cTbzSjPtWNJZapLR9NV2gd2AM4AcDmAnUKID4deuwnhgO5pIcQ1AJoBXJqi8umW1vlMxU4r9hwdwIY/fDDqdsnaZ2mCbYpVhn5OVs6z8ZRdrVx6k6jssWtajPx3o1AfXiml8piNFYhN5jFK9L1WLijC6Sfkc4GUNJVVXBEdykk0XonahYUlzmhbUOy0Rtu41l5PNNeY1WzAfRcvwvUr5mJ+cRZTpNCEFTsz0O32KfLYhe8ZOBSTJk/aD8WUUv5VSimklA1SysVDPy9JKbuklCullDVSys9JKY+luqx6ExlGN9bwOa3bJWuf9bOycfvaOsU2t6+tQ8OsbMVnReZXfeHBN/HlX72LLzz4JrY2tiE0MuN2kiTz7zDV1Mp+57o6vLjjMIBwbp071yn/5vXl2XGvbVhRgxd3HNb8vSf7GCU6JvVlOTi1uiC6YAoRzRyJ2oU5BQ7FNvXl2XFD5W49vxY/fnk3Ht62D/e9vDtuGkK6tPmUeieWOHD7mhH3MmvqcGIJ6w9NnunQY0cTZDAIfH5hMTavPxWtvR6UZltRW5oddyOczGXitXyWxWLEmrpSzMm3o63PixJnBupL41fF1JrzLBAIobG1d+g72lBb6oTJpHymoaVXKd2Xyz+xNAuPX7UMbl8AFXkOVObZsaQiF0f7PShxWiElFP8+pyC8KmZNUSba+7zIc1ggIbGqrkTz944co1y7BV89pQKFmRkISYmPW3txokpdizUTjgkRJV+kXTjxhuVo7/PC5QugMteOlmMuReLxFfOLUVOUiZMqctEx4EVhVgbu3dqE5q7wiq7NXYP4xV/24Ymrl6FzwIuybBtqZ43ebhFFHB0IYE9bN3571TJ0DF2fXmk8jKVVucicHmmASYcY2M1goZDEy03tY66KCSQ3/46WoXvb9naMWa72Pg9y7RZcuKQ8Olfs2fcOKeY/BAIhPPfRYcXKjneuq8O6RWXR4E7r6qDJ/jtMlUTfb06BA9WFmajKdyT8d5PJEJercDxDKyPH6GunV+Gn/7NHsUJdW58HK+YXq753uh8TIpp8H7f2j5l4vPFIf9yqur6AxPJ5RdHryoct3bjrz59wVUwalx6XD7VleXjr006EJLDv6ABqy/LQ4/alumg0jTGwm0Jaeo6mktYeL6203vCPtZ3WcpVmW3HVGVW4/5XhgGHjufMUaRgaW3txuKsfj1+1DEf7w/t7e287Glt7owHLwS4X9rZ2x22zoCRrWgQLB7tc+PVfPw0vfuMNwGkzoWvAg9f3HEVVvgNSIvr3Ls224orTKhEKSby6ux3VBZmYUzB8fCIB1z1bm7C6oQxGA/CZ2Tkoyc6Axx+CQQh4A+H/HnN5UZCZgXsvqseRXg9+fPEiHO5x4/G3mvHAq3ux/qxq5NosMBlF3IIrya6bRDSztBxzYXdbH76+vBo2swG5djOyrBbce1E9nFYz+rwBbPukHXkOM358cQOybWYc7HLB4wviy6dU4rYtjYp0O7+8/CQc6BzAJ219qMizod8TmPR53SNN5ZxySgIB2DMMmFeUBZc3AIfVBPPkpOMlimJgN0W09BxNNa2rYmqhtYdFy3ZayxUIymhQF9nm/lf2YMX8oug2BhHCnCIn/jfmidkJRU4YxPDnB0IBlOZm4srfDOcqun1NLYKh6ZFEtHfQh4uWVOB7f/wo+v1uPb8WP3tlD/YcHcB9Fy+KBnXXnlUNtz+I78ZsG3t8Dna5cM/WJlx9+hx0uX0ISeDDf/SgatABSOBnr+6Jy/UX+5T8hpU1uPasavzijf2wW4zY3d6PO178OG5fyaybRDSzhEIS77f0xOWxe2jbp7CYBK797FxF4LZhRQ3u2/4Jrj1rLixWgSO9g/j68moA4VEgtzy/C+vPqobVZMRzHx5GkdOKh7fti/b8TUUP3nhGMZA+WM0GuH0hHOh0ISTDC5JVFTiic+6IJgMDuyTQ8hStsTU+J9jNz+1CTVFm3FC3qaJ1VUwtIjf8kWV9AeCerU1xvV5aemK0lqul26168/+PbjfmFmcBAPxBgcM9nrgLfHmuPfqeHncQt7zQqMyb90Ijnrh6Wdz31OsT09HKFQwBt72o/H63bWnELy8/CX8/2A0MrQx34ZJydLl90b9VZNuNT3+I+d9aDiGAPe39+NLJFXD7g3F/U6MI58MbmevvgVf34oaVNRjwBjHoD2J2nh1XnV6JWTn2aAAZu68FG5YntW4S0cxysMuFB17do7gePfX3lmh+y0hQBwznvrzmzGr84o19uP6cGkXbtmFFDZ58pxkhieh2d7z4Ma45sxqPvLZP80iC4712cBRD+nH5gugfVObg7B/0w5UGKZIofTGwO05an6K19qr3QLT1erBo9lSXOiyyctjIsk9kxa8ulzeup2bDihocc3kVFx0tPTFay+WwmFRv/u2W4Wrd7w3EJZh94NW9+OXlJ8WUyas6V6+9z6vYn16fmI5Vrs4Br3pewF4PHt62D5X5Nty+tg6Hut0Jc9c1tfXhu898hK8vr8aC4qy4gOypv7fgrgvqcbjHg68vr8az7x1Ca28472Cu3QKnzRw9DlazAXesrRs1R9Syqvyk1U0imlkSXY8MBmDAG1Rt78XQg6kfPL8rLuhbf1Y1giFlHs/Ie7WMJEjGtYOjGNJPIBiCJxBSPCjYeO48BIKhsd9MNEHsDz5OiZ6iHexyKbYrzbbFdb9bzQaUZKeuByJ2Vcxf/NMSbF5/Gj6/UH0xi7FYjIa4npoHt+2F2aj8zpGemFgje2IiK5q9tGE5nlp/Cl7asFz1AujIMMYtVX3Dyho4MoYHsXv86knFvTGvzc4Nzyt77K/78fC2fXj0zf244rRKlOcoj43WYz3VxipXea4NG1bOxfUrwj+l2eFjYB0KgJu7BvHIa3vx2ZrCaF6nWFazAXva++Hxh/Dse4dgMCgDstJsKy5bWoFrHt+O7/1xBx59cz8uP7USpUN1+5Kl5dHhlpHy/eD5XRAJ9lWUZdVcB4iIRrIY1K9HFbl2ZGYYVdt7h8UIo0H9YVNFrh1/ev8QrGYDHBYjNqyci9k5Nly/Yi4q821jjiRIxrVDy7WT9EVKqE4XkZOTkYkIAAO74zbaU7RYtaXOuJxgd66rQ22pMjfbVIqsinnZpndw7e/ex2Wb3sbLTe0TyjHm9qkHUO4RQw605oKLrHQ4Wi6yQX8QdrMR68+qxvUr5mL9WdWwm43w+If3WZVvV70YVuYr1xpW69UTI3ap9VhPtUTlau/z4GDnAPa2D2DTG8qbmLsvqMejb3wa3b65axASUjWv053r6vDM9kMAwj3PA56A4m964ZJy1ZuoC5eUw2o2oCLPrlq+/R0ubFhRk7AuaKkDREQjHXP71K9H/iCkVG/vAWBReY7q9aKtz4Nutw/fX7UARoPApjf248Y/7cSjb+7H9efUoDx79LXrk3HtSOc8qjOVy6t+X+TycigmTR4OxTxOWucCGQwChVkW3HfxIrh8ATgsJmTZjCm9WR3PmP2x5gcUO62ozLdhdUNZNCDa8tFhFDvj/w5a8o75fEHsONKLtj4PSp1W1M+Kz2OX78jAlh2HccXp1Rj0BWC3mPD4W/uxfF5hdJsMkxF3X1CP7//nzuhQiLsvqEeGafizDveoX3QP93iwuGL4Na3Heqrn4SUqlz8o8eddbao3MT+7dDHOGlpk5rz6UszOtaPb7UdVvgNlOVbUlWVj0BeE3WJE76Aft62tRTAo0Tfoh9sXwE3nLcBdf94Njz+U8Cn3iaVZeHr9qZAQquVbXlMAfzCEzetPjVsVk4hoIkIhiQyTIUFbnQGHxaTaXp1QmAmTAbjrgnrcFHO9uOuCemRaTXjy6mUwCIGvPvZu3OiDYqcVZ84tSNh2JWPOMHN2pp9Mq/p0kUwrb71p8rB2HSet88EOdrnwL0++H3eCv5TCic+j9fTElknL/ICKXDu+taImbtXPiphFSiK83gA6B7zo6PfBIARKMzNgs5mj/+7zBfHcjiO45fnhz7p9bR3WNcxSBHfl2TZ8aVmlYrXHO9fVKZ6edrm8EAJYf1Y1QhIwCECI8OtVBeHvmJdpUW188xwWRbkrcu2qF/3Y75iKeXhqdfCeixrw4Kuf4CunVKke4z1HB/Dom/txx9o6DPoCilUw/+2CeuTaTRjwBrDhqQ+Qa7fgitMqFXPkfnLJIvzmayfj7f1dqCnKUv37LSx1orowE6GQVD1H6styeFNCREkTaX8PHXPhhpU1ijbrhpU18AVD2N3Wp9pehaTEwc5B/PqtA7jmzGpk20yYW5iJ215sRHPXYLRdVWtPtzcfQ3muLeG1PFnz2ZmzM714A0FsWFETN9fTF2CPHU0eBnbHSetTND1OfLYnXHxE2TOmpWevpdutuurnkopcxfcbHPRjy6423PJCTNC2pg7n15VEg7udrb3RoC7yWbc8vwsnFDpwUmVe9LN2H+1T3ee84kw0lIdXGg0EJf71TzvjvuPjVw2veJlnN+HOdXVxQWmeQ3l6HOlzw24xKHpdTcbw6xV5mZr/VsmmVge7XF6cUl0IS4In11UFjujT5vVnVUf/PdduQXOXC1kZ2bAYDfjm2XMxp8CBx9/ar1hh7t7/3o0vnVwBm9mI+17eHXfxuveiBsWQSj5pJqLJFlmd+UsnV6As14YbVtagIDMD+Q4LMswGfHp0AK/tPoofrD5RkWZlw4oa3P3nJqxdXDY053gfHvzyZ/DN37+vaMv3dwyotqfBEEa9lrMNnJkyM0zYvL0leu2UEti8vQU/uXhRqotG0xgDuzFoGVan5SmaHpdv9wXVnyb5R6zYpCUo1Rq47mzriwZ1kW1ueWEXqgrsWDYnH8DoK4jGak0whLKtx4uG8KrW6BhQn2vROTC84qUQgMVkUPTqWUyGuDl2nX0+HOh0xz0FLsq0oiJP+99qMsTWwVBIot8TgNEAHOp2qx7jIz3uaNkiUyobypy49uy5OHTMhY4Br+LG59bVtfjFG/uiT643rKiBEMDjbzXjkqXlKMux4t6LF+FgpwuBUAjlOTbFecInzUQ02bpcXnxlWSV++j97MK8oE18+pTK6ymVkRcINn6uB2xdU3Gw/+U4zWns9iJ1ePugNxLXlT28/hDvW1ik+8werT8SmNz7FRUvKRi0b28CZp9fjj9bHSH359ufmoc/rH/vNRBPEwG4UyRxWp3UoRjLnZ431WfmODNWnSavqShSfU5SlHpQWZg4HpUWZGarbFDgyFJ/V3pdg6f2Y1AL5DvWhkbkjhkbaLUYsrcwOz7HzBmDPCM+xs1oMis9Sm/sXO8yyxx3E9/64I25/I/PYDfjUUydsikmdkOoA3ucL4oNDPejo92JpZS7cviDu/nNT3DFe3VAWLZtBhFe2vPKMKuxu60NNURb+74h0Bre92BjN2xRZHOXeixehtdeDB1/dh+tXzMXD2/ZFy7GgJGtKvi8RUUSG0RC9iV4+ryguX939r+yJPsB77K/749rpU+bkIfO8+QiGJGbnh1cTDklE07d0u32oyLfh+nPmwhMIQUrA5fHjxlULuYgJxcnKMOP3f2tWXH9//7dm/DhNeuz0mreXRsfAbhTJHFanZShGMgPJZM6LMxmBW8+vjV4krWYDbj2/FjHrj2AwEMTGc+dFl/aNPB31jBhLXuzMSLDIynAA6LAYVfeXOWKIaLHTguvOmQujwYBQSCLbZsJ158xFiXM4aMtzmHDd2XOjCcjDQz9rkWsfrvpdCXr1ugZ8I15TX+Eq9rVk5gYcr0AghBd2HlEcz5vOW4Bvnj0Xt77QiFy7BZcsLce3VtSg1+3HTefNR3G2Dbl2M5ZU5KCptR+b3tiPry+vVv2esT2YHn8IBzvDS3VbzQbF8s1WswFlY6wSR0SUbJ0xbXmiPJllOTZs3dmK+y9djN1tfQjJ8DXosqUVuPm5nbhsaQWe3v4PGITAM9sPodvtw4YVNdi8vQXXfnYu9rb1wyAEnn0v/G+b15/K+cKkasDrx9Wnz0GX24eQBEwG4OrT58CVBj12oVB48bXvPDN8L/OTSxbjvDqmHdI7BnajSPawurGGYiQzkNQ6L+6hbXsVT5Me2rY3bl5ce58Xv/jLPsV2v/jLPswtXITK/PB2R/s9yDAqhzNmGA3oGJn2oSQT151dEzfHrrZkeH81BZnocnmx6fKTcMzlR57DjGAohJoC5d/AFwSO9vtwa0zQdtuaWhTG9I71Dgbx9PYW3HvxIkWvXlXBwug2mVaTarA5cuWqbJtZdbts2/B2qZxL0djaGzfn8K4/78bGz9Xg8atORsuxQTz82l6sbiiD0QAsLHXi56/tg8Uk8O1zF6AgMwM/vngRugY8qr2OI4M3b2A4UH7k9X3R1+9cV4faWalL40FEM5MjIzxvPNduwYmlWXE9blazAV5/EF9cNEvx8O0Hq0/E5r+1oLlrEJu3t+A7n1+AfUf7cdMXFuKul5rw4LbwyIy2nkE8+Pp+dLt9WH9WNRaUOBnUUULZNguOWX3Iz8yCyxuAw2qCxx9AttUy9ptTbH/HQDSoA8L3E9955kPMLz4Tc4s5IkfPGNiNYqqH1SUzkNQ6Ly4yUTzWyP31DvpVt+sZHH7q5LRacPfWD+L+Vr+75hTFe/Z1uPHI63sVC3E88vpezC/OxKKKcK9da78Hh3s8uPO/mqIX3pu/uBCteR5Ux6yeOeANRoO6yPe79YVG/DZmYZRBfwAXLalQrJx56+paDPoD0W2ybSZc+9m5cT2E2SMCO7NJ4BufnYsfxmz3w/NrYTKOf87lZEg0NzEEoN8bxMOv7cVlSysU8+3uuqAOvoDENY//XfGd/t8XFuLfXmpSBMz/HhO83bG2DhaTwH0XL8IJhXb85JLF6BzwojTbitrSbJhMTJFJRFOr2JmBuy+sR0e/FxufHm7zIz1uX1lWidJsG7711AeK68YdL36Ma86sRseAD5ctVV4vNqyoCc/B6/HgZ6/uQ+vQXO/a0myce2IxgzpKyGwQ8AeBm5/7SHFvMfKeQY8OdLlU7ycOdLkY2OkcA7tRTPWwumQGklo+S+v+nFaz6nZOa0yKAn8IuXYLLlxSHg3Ynn3vEHwBZcPQ4fLGBRcbVtSgwzU8x66tbzioA8KNyZ3/1YRfX7kU1UXDDUpHv/p8vc7+4c+ymU247UXlyma3vdiIJ2KCP39Axs3FuG1LI/5jRFAaColoUBfZ7odbGuOC11QpzbapHqfyHDt2HOrB6oayuETiB7vc2PTG/rjv9NNLF2P9WdWYX5wFKQGrxYC7L6zHMZcfe9r7cf8re9Dt9uH+Sxdjfkk2b25owkLBAPbs2RP9fc+ePZCx3cNEGs1y2nA0x4vvx6yEHDsn+HC3GzuP9CYcan7hkvK4NvLBbXux/qxqtHQPRoM6q9mAkuwMtns0KpcvqHpv8ZuvnZziko3NkWHUtGo66Q8Du1FM9bC6ZAaSWj5L6/6KnRmqOYFi58U57SZ847PV6HSFx5IbBfCNz1bDOaLXy2k1q144YxcqGVBZjczjD2HAq5yvV+JUX7ClKKZciRZrORqzKmZ7f4J8fiOGkR5zqc/FO+ZSzsWbSrGTm0uc1rg8ezesDK9yGpJQTSQekurzUPo9flTk2f8/e3ceHkd15ov/e3rftO+WLNvCMrYlLxhjnBlwJnZiDGOwWYfkhi1O/LsJxJ4QJgQuSzBLICEwIRAyJBCWSdgnbNdsARLCDRBsdmO8W7KNLcmSrKVbvZ/fH13d6pKqW9Vyt7pb+n6eR4+l6uqq01XHp+utes85ONDtwYzqQkwrd2BySaRezK4pxD8dU8Yhuykt3Ie/wE+f96FiygAA4NBn/0DR1DkJ1w8EAti6datq2axZs2A2mxO8gyaKrW29OJQgc2F7W1/s70Sp5lptpDcQmbz8jle3xda9aXUzmmqYbk7JJey/n8VrBr0cZpPmdZ/TwrAh1/EMjWCs0+osJjFk2P3RXTTrCUr1Bq51xQ7UldhV5aorsaOueHCQFX8gDLc/FHv6E5sQdsgTuz5vIGEgEVVo135CWDAkSKwoMGLDGc3D+utVFgzeUapKFPy5rHHb0V6nwqUe0dNl076D5bJm5w5WdICc217aipVza1FkM2JSsQPrlzWi1GFBRYEV29r68EXPAJ7/6AB+vGLWsPIbhfZFzuRSB8JSorrIhsVTy1SplRyym9LNVVGH4rrpAIC+ttak627duhXfved5FFTVx9a/91Jg7ty5GS8n5baDPV4UO7S/PwwCsYFShk4Bc/OZc2AxChTbtUdk7vUEsGp+LaZXuDC1zIHZTDcnHRJdf1QVWJO8Kzd4/CE8/LZ6RM+H327B/MnF2S4ajYCBXQ7Z2+nGZX8c3k9t4ygnt9YTlOpZp7Xbg9tf2RYbNCQUBm5/ZRuaJhXF3qdnKgAAKEiQ1llgHbzb7vEHNedei+8XBwBuH1BkN+C+C45HtyeAEocZA/4A4rI6UWAzYsMZTcNGxSywDQZjFqNBcxROy5Av7kR3sBxZuoMVnYw3mtq65qQG3BE3TcFlS6fjd3/bjRKHBRcsnoL739o1bGLeaeXOYfMy3XBGEzr6BtBQUYDZNUyzpNxTUFUfCwSJomqK7PhkfzduOKNJNajW5V+bgWMqnAiGJOqK7bhXGQzMaABmVRfi3r/sxPb2fly1YiZ+8NUZqnnHblrdHJv3tLbYjrmTS7L8KSlflDq1bz6XOnM/nbGq0IZuj181tkK2514mfRjYjWAs5/HI1uTWI9EzyIqeqQAAwBfQnhTdHxpMsyywmjXn17t9yNwvRwYCuOXFbcNGqbzlzME0Ln8ghEnFVvz+4hNwuN+HcpcVoXAI/rhpGFq7BvDouy2RkTP9QdgtJvzuzV1Yu+QYzIv7Eu/2+DXvYM0a4znbonVye1sfzj+hHgOBEL59cgMmF9tVx/vpzftjx/qRdyITiZfYzbjvguPR3ueD02JCR58X06uceOiSRWjv86LMZcUX3W7YLRYGdUSUV4odJjhtZgSCIfzuwoU4MhBAgc0Ek0Gg3xeCNxhC74Af5x4/Gd5gGBajAfu63Lj4n6eh2G7GDS9sgT8osXZJAxrKXTjc78Xhfh9ufTGShnlcik8rOA/YxNbeF8AxFVY8fMkitPV5UVVgg9EQiiyvzHbpkjMaoHkj28gH1TmPgV0S6ZxXTo9sTG6t54tHT7lqirUH7qgpVqccFDksmkHbSdOPi61TWWjVnF8vvu8cAPiCIc2BWOLTP11WC/7wbgtWL4ikbUkAz314AGuXNKr2t729H+se/UBV9ooh6RIOi0nzDpZ9DJ/YxdfJDatmY+GUErT1+VDmtMBiNKjOwcEeLx5XpnrY3tYHi9GAw/0+XP7k4AhdPz1zDowGAZMQKLKb0e8NYt7kUhxT4eIFCBHllS+OeOEe8KKxuhhCCFyhtHVDsxfufmOn6jvj5y9vw6X/0oD/WD4T7ribe9vb+7HmpAYAkba+vlR/f/exvn6g3GM1GfHpgZ7Y2AM72vtR7rSguTb3+2ce7PFq3sg+rr4YU8vZFSOXMbBLIp3zyukxtcyJu79xHD7e3xMbgGROXZHm4Cl6AjK/P4SPv+jBoV4vagptmDOpCJa4EY30fvFMLXPigYuPRzCE2LxyJiNU5Tq2ogA/PXMOroobuOOnZ87BsRWFqjKFwmHNoC0cNwpepcOKySXW2FOkSAAZQqVjSJBosyAQ8MfWqyq04e0dbarROq1WgS8dU4lvPfieqlw26+Dnm1xi1UyXmFyq3l9dqVXzM9aVjl2+/IEeN8qdFvz8nHmoKrTCZTOiutgBswAOe8K4aXWz6tj+7yXT8YtXPkdL5wBsZgOuWjET65c1YnKpA7OqCzGtnHeQiWh8qC+zwWIqRTAchNNiibXrT2/eH3v68Mg7LVi7pAHHVLjwxZEBPPx2C7o9fpQ6rfAGw6oUzhtXNWPjx19gSpkdN66ag/Y+L4QA6kscaO32JP3+HavrBz4VzF2TigzY323B4bjBUoodFkwqyv3HXlWFNoeQo+gAACAASURBVNQWW3FsdUFsDuDaYitTMfPAuA7shBArAPwSgBHA76SUt6by/nSnRuoKxoJSNQDJHefN19zO69vahgWAS48dnFPH7w/hpa2HsLO9P3KnqK0PB3oGsGJWdSy40/vFEwyG0ecNIRCU8AVC6PUKmI0CwWA4tq029wAcFgNuP2ce3P4gnBYTzMbI8smWwW25rAaUOSPpgN3uAEqUyced1sGGruWIW/P4tRxxY6Z9MBXGZTNg5qRidPT54PGF0N7nw8xJxSiwD27L5w/CYTWqy2US8PkH++v5AkHMqXUOpksU2lBgFfAN6dPn8QVRXzo0rSIMj0+9XqZ4vUG8t+eIatTLW86cgxlVDjgtBpQ5DJhTW4BHvrUIXW4/ihyRY3vb2fPQ5fahwGaGNxhCQ5mLAR0RjSvhsMTH+/pQYBcwChN2tLsxoyrarvtQW2TDH799Itp6fSiwmVBgNaHUacFPzmiCy2rEna9uw7JZ1bj9nHkwGIDOPj/ufmMHvr+0EQVWE9Y+sgneQBhTyuzDbk5q3RAdi64VfCqYHpkMjqeU2VBdaItdM1jzZPDeuiI7vrPkGASCkZvuQgDfWXIM6orsWS4ZjWTcBnZCCCOAewB8DcB+AO8JIZ6TUn6mdxvpTI3U0wDrDbRau9zY0dY/bATK6RWu2CPyrYd6caB7YNg6Ww/1Yl59pN+Y3i+ebe196HIHhg0usq29D3PqIoFWR48fuw97huVjV7hsiO9r7vFJHOr1D9tWadzTOCGAlk7vsAFP5k22DCmrRJcnOGxb1f7Bp399XondHe7hUzXEncNgGPjkQP+w/R1Xr37aGJbAro4BjXKNTUv9eXtvLKiLfP4wrv7TJ3jsOyciEAZ6eiX8wchQyhISLZ0eFNnNKHUYYTcbManIzoCOiMal1i43pAzhiyMS1z37QSwIu+wrjXj8vRacvaAeN7ywBV+aVooVc2pUT+ZuO3sO/nVuLW576XPV0zp/UOKaZz7F+mWNsXZ35dzaWFAHJP6eHouuFWOdVTQeZTI4Ptgbxtd/+49hdeDR75yI6hwfXHJ3p/Z13+7OPsysyfHCT3C5/zx49BYB2Cml3C2l9AN4DMCqVDYQnefNZo4cpqOZVy5RA7y3c/DpVLJAK15br09zBMq23sHhIHu8Ac11euKmFYh+8cTT+uLp9wU1J9nsj3tS1ZdgVMw+/5CRLAPaE3a6A4ODmfR4QrHgKbrOdc9tQY9HPY9dosk/3f7B9fp8CcoVV/Zut/b+ut3q/ektV6YknJOv148j7hD6fEG09fngsplQaDPDaTFhRlUBFjWU4Sszq3BMJfvN0cQWCATw8ccfq34CgcDIb6Sc19brg8tmwXXPDgZdK+fW4tpnP8WF/9SAG16ItN0XnzQtFtQBkTZ0V4c7FtRFl1377Kc4a0EdvIEwSh2DNxWF0J7rbuj3dDqvHxJ/Zn3XDJSYnmuz0WpP8J3d0Zv789h1e7Sv+7o9Y5OhRKM3bp/YAagFsC/u7/0ATkxlA+mcoFzP0zG9d/jcfu0JvD1xQZTHrz1K5UBc0KN3gnI9E3P7EoyK6RuyrDvBtrrjttXWp90YtvX5VMsSlSt+W/3eBJOdewePld796V0vU+wW7Xn0rBYD2vp8MBkEqgqssJoM6HD7UGA3pvUigigXhUNBbN++XbUs0YTlnANv/HL7g+gdULf30SBswDe4vNs9fC7VsNQO1oQyz6fTqr5U0vM9nc7rh0SyMeDaeJPJlNlk39m5rjPB9VU+TK4+0eV+7cowIcRaIcQmIcSmjo6OYa9H53lb3FCOhqMYKVDP0zG9d/imlDo1txU/YtckZZTKoevUFA3uL/rFs3HdyXhs7YnYuO5kzfSD2hKH5rZqiwdzraeVuTTXmTak7LXFI28rOqnn0HWqhoyKWZfgM9YWD37GRNuqjBvxUu/+9K53NJLVR5c1Mo9efP1YvyzS/6Oq0IqKAgsK7AI2i0BjpQsnT6/kEzo6KiO1j7nAffgL/PT5j/Gjpz7Cj576CN+953ls3bo14frROfCK66bHAjzKD8nq45RSJ0qdZs022mE1xZZrrWNUArih7zMI4NqVs1HmssRef/6jA7hpdbOuJ3Hpun5IZCyeCo53ejOXEhntd3auq0lwXKoLedMg143nwO4AgMlxf9cpy1SklPdJKRdKKRdWVFRkrDB6GmC9gda0cu1tTSsf3Nbs6kLcfOYc1To3nzkHs2vUw+zq+eJprinEjavUX2Q3rmpG86TBbR1T6cIvzp2nWucX587DMZXqO17Nk0be1pzqQmw4Q73OhjOaMae6cMi2ihJsazD/u7lGe505cftrri7Q3F/zkPnp9JbraCSrjzMqHagttmPtkgZctnQ61i5pQG2xHRajxKQSI2qKjWisKMax1cWYUsa0Szp6Y9U+Hi1XRV0sWHOWT8L27dtHlW7JVM3clqw+Tit3wmQANpzRpArCblzVjIf+vhvXr4ws//1be3BD3Do2swHHVLhwy5Dvy2tXzsa8uiLUldhwwpTS2Pfy7y9ehNXzakf8nh4Leq8ZKLGjDY6T1cmGBN/ZDZWOtH+OdJszqQgbhlw7bVjVjLmTcn+qhoku928bjN57ABqFENMQCejOB/CNbBVGb1pGNNBKlgKgZ1smkwGr5k7C9AonDvV4UV1kQ1NNEUym1GN5k8mAM+fXYkaVK+G2DAaBU5trMKumMOnn07Mtu92M05urMbXcgbZeH6oKrZhTXQi73ZzytiwWI1bNnYRp5c7YiFdzh0z74LBbsLK5SrW/5uoCOOzqwVr0litTXHYblh5bhuoiW2T/BVZUFBhhNwPFNhdstvH835lIn8gTPB8qpgwASC3dkqma+ctgEFjcUIF9Xf14+JJFaO/3odxlgctqwhWnzEQoHMZDlyzC4X4fppQ58NAli9DR50N1kQ3N1YU41O/FI99ahCOeABxWI2wmI8pcFtSXOjW/l0f6nh4req4ZKLFMpswW221YEv+dXWjFjGoniu25/9TLYjFi9dxJaEhy7US5adxeCUopg0KIywC8jMh0Bw9IKbdks0zpbID1bMtkMmDe5BLMm5xwFd30bEvv59OzLbvdjEXTytJSLovFiIVTS5Nux2G36Nqf3nJlistuw6Jpuf+lQJRN0Sd4oxFN1aT8YzAITCkvwJTy1N871ebixMsTVCaD4+I8/s7Wc+1EuWfcBnYAIKXcCGBjtstBRESjM3RwlO3bt0NKmeQdmds3kHhgFkouEAgM6/vIY0lElF7jOrAjIqLM62trjf3u7jwEo9eHIw57Wv5u3/Y+rvnAjeLqTwAAnXs+Q2H9LAghdL2/r60V27dHft++fbuqrPGvab0+dN+erjZc982vYsaMGWk5bhPJ9u3bseG//wxHaRWAyLH87w3fY5orEVEaibG685kPhBAdAFrGYFflAA6PwX4ygWVPj8NSyhXJVhihPubSZ9GS6+UDcr+MY12+pHUyT+sjy5W6XCnbeKyP6cTPOLbG+3d2Mix7diQre8L6yMAuC4QQm6SUC7NdjtFg2XNDrn+WXC8fkPtlzPXyxcvVsrJcqcvlsuk1Hj7DSPgZ80s+fxaWPTtGW/bxPN0BERERERHRhMDAjoiIiIiIKM8xsMuO+7JdgKPAsueGXP8suV4+IPfLmOvli5erZWW5UpfLZdNrPHyGkfAz5pd8/iwse3aMquzsY0dERERERJTn+MSOiIiIiIgozzGwIyIiIiIiynMM7IiIiIiIiPIcA7s4K1askAD4w5+x+BkR6yN/xvgnKdZH/ozxT1Ksj/wZ458RsU7yZwx/EmJgF+fw4XydnJ7GI9ZHyiWsj5RLWB8p17BOUi5gYEdERERERJTnGNgRERERERHluYwHdkKIvUKIT4QQHwohNinLSoUQrwohdij/lijLhRDiLiHETiHEx0KIBXHbuUhZf4cQ4qK45ccr29+pvFck28dEEg5L7O7ox9u7DmN3Rz/C4aRpuZQHeE4p17BOEhFpY/tIY800Rvv5ipQyPvn4xwBek1LeKoT4sfL3lQBOBdCo/JwI4F4AJwohSgFcD2AhIp0GNwshnpNSdivrfAfAuwA2AlgB4MUk+5gQwmGJl7YcwuVPfAhvIAyb2YA7zpuPFU3VMBhEtotHo8BzSrmGdZKISBvbR8qGbKVirgLwkPL7QwBWxy1/WEa8A6BYCFED4BQAr0opu5Rg7lUAK5TXCqWU70gpJYCHh2xLax8Twt5Od6wxAQBvIIzLn/gQezvdWS4ZjRbPKeUa1kkiIm1sHykbxiKwkwBeEUJsFkKsVZZVSSkPKr8fAlCl/F4LYF/ce/cry5It36+xPNk+VIQQa4UQm4QQmzo6OlL+cLmqrdcba0yivIEw2vu8WSoR6ZGsPvKc0lgbqX1knaSxlOr3dSgUwp49e2I/oVBoDEpJEwm/synXjEVgd5KUcgEiaZaXCiGWxL+oPGnLaNJxsn1IKe+TUi6UUi6sqKjIZDHGVFWhDTaz+vTazAZUFtiyVCLSI1l95DmlsTZS+8g6SWMp1e/r1tZWrLnnRXz/0fex5p4X0draOgalpImE39mUazIe2EkpDyj/tgP4E4BFANqUNEoo/7Yrqx8AMDnu7XXKsmTL6zSWI8k+JoSpZU7ccd78WKMSze2eWubMcslotHhOKdewTlKuc5RWwVU+CY5SzaQdooxh+0jZkNHBU4QQTgAGKWWf8vtyABsAPAfgIgC3Kv8+q7zlOQCXCSEeQ2TwlB4p5UEhxMsAbokb2XI5gKuklF1CiF4hxGJEBk+5EMCv4raltY8JwWAQWNFUjZnrTkZ7nxeVBTZMLXOyw24e4zmlXMM6SUSkje0jZUOmR8WsAvAnZQYCE4A/SilfEkK8B+AJIcQaAC0AzlPW3wjgNAA7AXgAXAIASgB3I4D3lPU2SCm7lN+/B+BBAHZERsN8UVl+a4J9TBgGg0BDhQsNFa5sF4XShOeUcg3rJBGRNraPNNYyGthJKXcDmKexvBPAMo3lEsClCbb1AIAHNJZvAtCsdx9ERERERETjTbamOyAiIiIiIqI0YWBHRERERESU5xjYERERERER5TkGdkRERERERHmOgR0REREREVGeY2BHRERERESU5xjYERERERER5TkGdkRERERERHmOgR0REREREVGeY2BHRERERESU50zZLgARERHRWAmFQmhtbY39XV9fD6PRmMUSERGlBwM7IiIimjBaW1ux5p4X4SitgqerDfdfeiqmTZuW7WIRER01BnZEREQ0oThKq+Aqn5TtYhARpRUDOyIiIhoX4tMs9+3bBymzXCAiojHEwI6IiIjGhfg0y849W+CaND3bRSIiGjMcFZOIiIjGjWiapb2oPNtFISIaUwzsiIiIiIiI8hwDOyIiIiIiojzHwI6IiIiIiCjPMbAjIiIiIiLKc2MS2AkhjEKID4QQLyh/TxNCvCuE2CmEeFwIYVGWW5W/dyqvT43bxlXK8m1CiFPilq9Qlu0UQvw4brnmPoiIiIiIiMabsXpitx7A1ri/bwNwp5RyOoBuAGuU5WsAdCvL71TWgxBiNoDzATQBWAHg10qwaARwD4BTAcwG8HVl3WT7ICIiIiIiGlcyHtgJIeoA/CuA3yl/CwBLATylrPIQgNXK76uUv6G8vkxZfxWAx6SUPinlHgA7ASxSfnZKKXdLKf0AHgOwaoR9EBERERERjStj8cTuPwH8CEBY+bsMwBEpZVD5ez+AWuX3WgD7AEB5vUdZP7Z8yHsSLU+2DyIiIiIionElo4GdEGIlgHYp5eZM7udoCCHWCiE2CSE2dXR0ZLs4NMGxPlIuYX2kXML6SLmGdZJyTaaf2P0zgDOEEHsRSZNcCuCXAIqFECZlnToAB5TfDwCYDADK60UAOuOXD3lPouWdSfahIqW8T0q5UEq5sKKiYvSflCgNWB8pl7A+Ui5hfaRcwzpJuSajgZ2U8iopZZ2Ucioig5+8LqX8XwDeAHCOstpFAJ5Vfn9O+RvK669LKaWy/Hxl1MxpABoB/APAewAalREwLco+nlPek2gfRERERERE44pp5FUy4koAjwkhbgLwAYD7leX3A3hECLETQBcigRqklFuEEE8A+AxAEMClUsoQAAghLgPwMgAjgAeklFtG2AcRERERZDiEffsGu+rX19fDaDRmsURERKM3ZoGdlPIvAP6i/L4bkREth67jBXBugvffDOBmjeUbAWzUWK65DyIiIiIAGDhyGFc/9QWKqzvg6WrD/ZeeimnTpmW7WEREo5KtJ3ZEREREWWcvqYSrfFK2i0FEdNTGaoJyIiIiIiIiyhAGdkRERERERHmOgR0REREREVGeYx87IiIiGtfiR7/ct28fpMxygYiIMoCBHREREY1r8aNfdu7ZAtek6dkuEhFR2jEVk4iIiMa96OiX9qLybBeFiCgjGNgRERERERHlOQZ2REREREREeY6BHRERERERUZ5jYEdERERERJTnGNgRERERERHlOd3THQghKgB8B8DU+PdJKb+V/mIRERERERGRXqnMY/csgL8B+DOAUGaKQ0RERERERKlKJbBzSCmvzFhJiIiIiIiIaFRS6WP3ghDitIyVhIiIiIiIiEYllcBuPSLBnVcI0SuE6BNC9GaqYERERERERKSP7lRMKWVBJgtCREREREREo6P7iZ2I+KYQ4lrl78lCiEWZKxoRERERERHpkUoq5q8BfAnAN5S/+wHck/YSERERERERUUpSCexOlFJeCsALAFLKbgCWZG8QQtiEEP8QQnwkhNgihLhBWT5NCPGuEGKnEOJxIYRFWW5V/t6pvD41bltXKcu3CSFOiVu+Qlm2Uwjx47jlmvsgIiIiIiIab1IJ7AJCCCMACcQmLA+P8B4fgKVSynkA5gNYIYRYDOA2AHdKKacD6AawRll/DYBuZfmdynoQQswGcD6AJgArAPxaCGFUynMPgFMBzAbwdWVdJNkHERERERHRuJJKYHcXgD8BqBRC3AzgLQC3JHuDjOhX/jQrPxLAUgBPKcsfArBa+X2V8jeU15cJIYSy/DEppU9KuQfATgCLlJ+dUsrdUko/gMcArFLek2gfRERERERE40oqo2L+QQixGcAyAALAainl1pHepzxV2wxgOiJP13YBOCKlDCqr7AdQq/xeC2Cfsr+gEKIHQJmy/J24zca/Z9+Q5Scq70m0DyIiIiIionEllVEx7wJQKqW8R0p5t56gDgCklCEp5XwAdYg8YZs5uqJmhhBirRBikxBiU0dHR7aLQxMc6yPlEtZHyiWsj5RrWCcp16SSirkZwDVCiF1CiNuFEAtT2ZGU8giANxAZWbNYCBF9WlgH4IDy+wEAkwFAeb0IQGf88iHvSbS8M8k+hpbrPinlQinlwoqKilQ+ElHasT5SLmF9pFzC+ki5hnWSco3uwE5K+ZCU8jQAJwDYBuA2IcSOZO8RQlQIIYqV3+0AvgZgKyIB3jnKahcBeFb5/Tnlbyivvy6llMry85VRM6cBaATwDwDvAWhURsC0IDLAynPKexLtg4iIiIiIaFzR3ccuznRE0imnIBKkJVMD4CGln50BwBNSyheEEJ8BeEwIcROADwDcr6x/P4BHhBA7AXQhEqhBSrlFCPEEgM8ABAFcKqUMAYAQ4jIALwMwAnhASrlF2daVCfZBREREREQ0rugO7IQQPwNwJiKDnzwO4EYlvTIhKeXHAI7TWL4bkf52Q5d7AZybYFs3A7hZY/lGABv17oOIiIiIiGi8SeWJ3S4AX5JSHs5UYYiIiIiIiCh1IwZ2QoiZUsrPEenPVi+EqI9/XUr5fqYKR0RERERERCPT88TucgBrAfxC47XoZONERERERESUJSMGdlLKtUIIA4BrpJT/bwzKRERERERERCnQNd2BlDIM4O4Ml4WIiIiIiIhGIZUJyl8TQpwthBAZKw0RERERERGlLJXA7v8D8CQAvxCiVwjRJ4TozVC5iIiIiIiISCfd0x1IKQsyWRAiIiIiIiIaHV2BnRDCBOBUADOVRZ8BeFlKGcxUwYiIiIiIiEifEVMxhRC1ALYA+CGASQBqAfwIwBYhxKTMFo+IiIiIiIhGoueJ3c0A7pVS/mf8QiHEOgA/BXBRJgpGRERERERE+ugJ7BZLKS8eulBKeZcQYlv6i0REREQ0tmQ4hH379gEA6uvrYTQas1wiIqLU6BkVcyDJa550FYSIiIgoWwaOHMbVT72PNfe8iNbW1mwXh4goZXqe2BUJIc7SWC4AFKa5PERERERZYS+phNVqzXYxiIhGRU9g91cApyd47c00loWIiIiIiIhGYcTATkp5iZ4NCSEuklI+dPRFIiIiIiIiolTo6WOn1/o0bouIiIiIiIh0SmdgJ9K4LSIiIiIiItJJTx87vWQat0U5JhyW2NvpRluvF1WFNkwtc8JgYCyfDTwXlEtYH4mItLF9pLGWzsCONXWcCoclXtpyCJc/8SG8gTBsZgPuOG8+VjRVs4EaYzwXlEtYH4mItLF9pGxIZyrm/0vjtiiH7O10xxomAPAGwrj8iQ+xt9Od5ZJNPDwXlEtYH4mItLF9pGzQHdgJIdYLIQpFxP1CiPeFEMujr0spL9N4z2QhxBtCiM+EEFuEEOuV5aVCiFeFEDuUf0uU5UIIcZcQYqcQ4mMhxIK4bV2krL9DCHFR3PLjhRCfKO+5Swghku2DUtfW6401TFHeQBjtfd4slWji4rmgXML6SESkje0jZUMqT+y+JaXsBbAcQAmACwDcOsJ7ggB+KKWcDWAxgEuFELMB/BjAa1LKRgCvKX8DwKkAGpWftQDuBSJBGoDrAZwIYBGA6+MCtXsBfCfufSuU5Yn2kTXhsMTujn68veswdnf0IxzOj26JVYU22MzqqmIzG1BZYMtSiSau0Z6LfK17lNvS3TawnhLReMFrJ8qGVAK7aELwaQAekVJuwQj96qSUB6WU7yu/9wHYCqAWwCoA0TnvHgKwWvl9FYCHZcQ7AIqFEDUATgHwqpSyS0rZDeBVACuU1wqllO9IKSWAh4dsS2sfWRHNtT7trr/h6799F6fd9Te8tOVQXly4TC1z4o7z5scaqGie+NQyZ5ZLNvGM5lzkc92j3JbOtoH1lIjGk/oSB25a3axqH29a3Yz6EkeWS0bjWSqDp2wWQrwCYBqAq4QQBQDCI7wnRggxFcBxAN4FUCWlPKi8dAhAlfJ7LYB9cW/bryxLtny/xnIk2cfQcq1F5Okg6uvr9X6clCXKtZ657mQ0VLgytt90MBgEVjRVY+a6k9He50VlAUd2ypSR6uNozkU+1z3KrkzUx0RYT2kkY/V9TaRXsjrZ2u3Br17fgTUnNUAIQErgV6/vwIL6ErZplDGpBHZrAMwHsFtK6RFClAG4RM8bhRAuAE8D+HcpZa/SDQ4AIKWUQoiM3pJNtg8p5X0A7gOAhQsXZqwcyXKt8+E/uMEg0FDhyouy5jM99THVc5HvdY+yJxP1MRHWUxrJWH1fE+mVrE629XrR0jmAe97YqXoP2zTKJN2pmFLKMCJ95pYIIc4C8GUA00d6nxDCjEhQ9wcp5f8oi9uUNEoo/7Yryw8AmBz39jplWbLldRrLk+0jK5hrTdnCukf5gPWUiMYTtmmUDamMivkAgAcAnA3gdOVn5QjvEQDuB7BVSnlH3EvPAYiObHkRgGfjll+ojI65GECPkk75MoDlQogSZdCU5QBeVl7rFUIsVvZ14ZBtae0jK9hPjbKFdY/yAespEY0nbNMoG1JJxVysjG6Zin9GZPTMT4QQHyrLrkZkNM0nhBBrALQAOE95bSMig7PsBOCBkuoppewSQtwI4D1lvQ1Syi7l9+8BeBCAHcCLyg+S7CMr2E+NsoV1j/IB6ykRjSds0ygbUgns3hZCzJZSfqb3DVLKt5B45MxlGutLAJcm2Fb0ieHQ5ZsANGss79TaRzaxnxplC+se5QPWUyIaT9im0VhLJbB7GJHg7hAAHyIBm5RSzs1IyYiIiIiIiEiXVAK7+6GkVSKFaQ6IiIiIiIgos1IJ7DqklM9lrCREREREREQ0KqkEdh8IIf4I4HlEUjEBAHFTGBAREREREVEWpBLY2REJ6JbHLZMAGNgRERERERFl0YiBnRBispRyn5TyEo3Xks5jR0RERERERJmnZ4LyV4UQU4cuFEJcAuCX6S4QERERERERpUZPYHc5gFeEEI3RBUKIq5TlX85UwYiIiIiIiEifEVMxpZQbhRA+AC8KIVYD+DaARQCWSCm7M13AiSYcltjb6UZbrxdVhTZMLXPCYEg0xzuRNtYjmohY74mIKJ8d7feYrsFTpJSvKamXfwHwdwBLpZTeUZWYEgqHJV7acgiXP/EhvIEwbGYD7jhvPlY0VfPihHRjPaKJiPWeiIjyWTq+x0ZMxRRC9AkhegFsBFAIYBmA9rjlE144LLG7ox9v7zqM3R39CIflqLazt9MdO5kA4A2EcfkTH2Jvpzudxc0L6TqmE0X88frkwJFxW49YL8avoz23qbafrEtElGnBYBgf7evGS58exEf7jiAYDGe7SJTD0hEH6EnFLBh9Ece/dN4lbuv1xk5mlDcQRnufFw0VrnQWO6fxzntqhh6vdcumj8t6xHoxfqXj3KbSfrIuEVGmBYNhPPPRAVzzzKexduam1c1YPa8WJpOeIS5ooklHHMCadZTS+ZStqtAGm1l9SmxmAyoLbGkpa77gk8vUDD1eYYlxWY9YL8avdJzbVNpP1iUiyrQtB3tiQR0QaWeueeZTbDnYk+WSUa5KRxzAwG4EI6XrJIuuUzW1zIk7zpsfO6nRu8hTy5yj/wB5KJ3HdDwaWieHHq+nN+/HuqWN464esV7ktqNJbUzHuU2l/WRdIqJMO9ij3c4c6mE7Q9rSEQfoGjxlotKTrhONruP/84726YjBILCiqRoz152M9j4vKgsm5qhu6Tym441WnfztBQtVx+tgjxePb2rF42sXYyAQGjf1HftHcwAAIABJREFUiPUidx1tamM6zm0q7SfrEhFlWk2RXbOdqS5kO0Pa0hEH8IldEnrSddL9lM1gEGiocGFxQzkaKlx5fzE+GnxymZhWnbzm2U9w29lzVcfryhWzMKe2eFzVI9aL3HW0qY3pOrd620/WJSLKtGKHCdef3qRqZ64/vQnFTnOWS0a57GjjAD6xS0JPJ0Y+ZUs/HtPEtOpkS+cAaott2DjOjxfrRe462g7fY31uWZeIKNO+OOLFo++24GfnzMOAPwi7xYTfvbkL08odmFKWvwOZUW5jYJeE3nSdaHSdzyMO5hoeU22J6mSp0zohjhfrRW5KVyrlWJ5b1iUiyqSqQhu2t/dj3aMfxJYx5ZsyjamYSTBdh3IN6yTlItZLIiI1touUDXxilwTTdTIjHJbY2+lGW68XVYU8pqmYaHWSdSU/5GO9ZN0iokwyGASWz6rC42sX42CPFzVFdjTVFLKdoYzKaGAnhHgAwEoA7VLKZmVZKYDHAUwFsBfAeVLKbiGEAPBLAKcB8AC4WEr5vvKeiwBco2z2JinlQ8ry4wE8CMAOYCOA9VJKmWgfo/kMTNdJL04MfPQmSp1kXckv+VQvWbeIKNPCYYlXtraxnaExlelUzAcBrBiy7McAXpNSNgJ4TfkbAE4F0Kj8rAVwLxALBK8HcCKARQCuF0KUKO+5F8B34t63YoR9UJZxYmDSi3WFMoV1i4gyje0MZUNGAzsp5ZsAuoYsXgXgIeX3hwCsjlv+sIx4B0CxEKIGwCkAXpVSdilP3V4FsEJ5rVBK+Y6UUgJ4eMi2tPZBWcaJgUkv1hXKFNYtIso0tjOUDdnoY1clpTyo/H4IQJXyey2AfXHr7VeWJVu+X2N5sn3kvPHe74MTAx+d8V4/4rGu5Id8rJOsW0SUaVWFNkwps2Pl3FoIpUl8/qMDbGcoo7I6eIrSH05mcx9CiLWIpH6ivr4+k0UZ8QIo3f0+9FxwpfOiTM+2ppY5cfc3jsPH+3sQloBRAHPqijhKlCJZfUy1fkTPR6fbB4vRAI8/hMoCGwwC2NPphtNiQlWhFfWluXchHg5LSAncfs487GjvwxOb9qPb4+eIYmNspPbxaNosrfpZVWhDfYkDrd2elNukVNoytkP5aSy/r4n0SFYn60sc+NEpM7GtrS/WzvzolJmoL3Fko6g0QWQjsGsTQtRIKQ8q6ZTtyvIDACbHrVenLDsA4F+GLP+LsrxOY/1k+xhGSnkfgPsAYOHChRkLMvVcACXKx5657uSUByTQs790BpKpbMsflLjvzd2q9SgiWX1MpX5Ez8dtL23Fvy2sx12v74gd7/XLGvHw2y3o9vixflkjGqtcWHpsVc4Ed1p16ZYz52BBfXFOBqHj2Ujt42jbrGT186bVzfjV6zvQ0jmgu00aTVvGdij/jNX3NZFeyepkS6cbrV0eVTuzflkjWjrdOKaqICvlpfEvG/PYPQfgIuX3iwA8G7f8QhGxGECPkk75MoDlQogSZdCU5QBeVl7rFUIsVkbUvHDItrT2kTV6OtGmMx9bz/7S2bFX77bS3Zk4HJbY3dGPt3cdxu6OfoTD4/e7PpX6ET3OK+fWxi6ao+v/8rUdOGtBXex3rz+MTw705Myx06ojV//pE4QlGNTlmNG0WeGwxCcHjuDzQ724YvnMYfXzmmc+xcq5tbG/9bQPqbYrHNSAiDLtYK838h075Pv3YC/72FHmZHq6g0cRedpWLoTYj8jolrcCeEIIsQZAC4DzlNU3IjLVwU5Epju4BACklF1CiBsBvKest0FKGR2Q5XsYnO7gReUHSfaRNckugKJ3ttPZ70PP/vSsExUMhrHlYI9qLhaTafC+gN5tpbLPkYTDEq9vaxuWTpVLT5/SSW/9iKakeQNhCAHN4x3N9/cGwth92I3t7X3Y1+VB06RChCXQ3pe9/lLpriP51v8rEzJ1HFJps4LBMD472IO9nR7s6ujHk5v249yFdUnrZ/Tvkc59qnVGz/qsO5nDY0sTQZ83qNnO9HmDWSoRTQQZDeyklF9P8NIyjXUlgEsTbOcBAA9oLN8EoFljeafWPrJJTyfaqWVO3HHe/GHpRKPp96HngquyQHudCpf6oiwYDOOZjw7gmmc+VaVLrZ5XGwvu9HYSTmfw2tLpxo62/mFpDseUuzAtD+bSSpVW/bjt7LmqfP1oStq2Q72wmSPnRut4Szn4+/RKFy5/4kOUOCy48EtTYncYszXnTrrqCOcqi8jkcdDbZmm1IeuWNkJCap7rGVUFuGzpdAD6BhvQ25ZFjVTHWHcyJx+OrQyHsG9fZMy2UCgEADAajQAi/aiivxMlU1VoTdDOWLNYKhrvsjp4ykRSX+LA95c2DguO4i/KDQaBFU3VmLnuZLT3eVFZMPo7mXouuIwG4PKvzcAdr26PrXP512bAOCRBd8vBnli5gcF0qcZKF+ZNLtH9+aLl+q8LFqBvIAS3LwinzYQCm3HYhaCeO7pf9AxopjnMrSsal4GdwSCwfFYV7rtgITa1dCEUBu54dRvMRkPsoiiaYlbisGDd0kY8vqkVP/jqDNz55+2q4Pfht1tgMxtw7crZeOCtXVhzUgOmljmwv9uDEocFB3u8R9XH82ik6wZHOvus5rNMHweLSWDtkoZIqqyI/D3UZ0PakBKHBd5gCMeUO3Htytm48YXPYud6w6pm/OKVz2N97G5ePQcmI2KpwlrtgtEArF/WqLopsX5Z47C2LGqkOsa6kzn5cGwHjhzG1U99geLqDnTu2QKjvRDF1ZPh6WrD/ZeeimnTpmW7iJQHjEJoXmOZcuQGBo1PDOzGSGu3RzM4WlBfovoyMxgEGipcR/0FpydI7Oj3odhmwu3nzIPbH4TTYoLHF8Dhfh+mlg/uP3qRH88bCONQjxfzJg9+vl+9vgNrTmqIPbH71es7hn2+cFiio88/LAAMh2XKg7r0+7TTHNy+kGrZeEr7ae32YO0jm1SfO/6iKJpidrDHi0feacGFX5qCeXVFsXPsD4RRWWTDv3+1ETVFNhzqHcDSmdWqwSvWLW3EI++0xM77aFIg46V6/NN1gyOdKZ35LJPHYW+nG5f98YNhd6Q3xl2kR86/J7ZOTZENFyyegtc/P4TplQVwWYEHL1mEcCgMp82EG1/Yonryf9fr2/HjFbOw57Abbl9Ys1042OPFw2+3xNofKYGH327BcfXFqrYsaqQ6xrqTOflybO0llXCVT4Knqw1GZzFc5ZOyXSTKM50eH6qLrLjvguPR7Q6gxGnGkQE/ujy+bBeNxjEGdmNE75dZOoOQkYJEu9mITk8A1z3/meou92yzOs2kpsiumU5QXTSY5tTp9g0b3W7d0kZ0uX2q/et5+qf3jm6Fy6qZ/lnmssTWyYe0n1Qkqkfb2/oARC6abWZDLK2y0G7GxQ++F/vs169swi0bP4s9DblxVTMe39SqOtZ3KQH6PW/sTNqHT08dHe3xT8cNDs5VFpHJ45CoPrb1eiEl0NLlhstiwq6O/lgZzlpQh9c/P4SzF9TjR099FKsXN65qxrF2F06fW4tOjz/Wb/Zb/zQN+7rcmFTswBXK+tH9RNuFqkIbuj1+3PPGTt2fMVkdY93JHB5bmijKHFb0DASxqaU70p4dBqaWO1HqYComZQ4DuxGkK9DS82U21vPYuX0hzVTGBfUlqu001RTi5+fMxY72/tjF1vRKF5pqimLrWIyGYaPb3fX6Djy+drFqW3qe/rX1elHisOCsBXWxgO3pzfvR1qsOgoUA/veXp+OG57cMBi6nNyH+UO3tdOOBt3bhZ+fMw4AvCIfVhAfe2oWZ1QU5dXdYr5oiG65ccSxKHRY4rCYcOOLBo/9oxScHevHvj3+IO86bj4fXLETvQBid/T58cWRAlVr5mzd34ofLZ8YCwbvf2IGVc2tVF8TRwSui0wzEp0DGD1O/cm4tjAbghCml+FJDmWownahspl2ls89qPsvkcagpsuHqU49FTbEDHl8Qh90+PLFpHwIhiX/91d/gDYRx9anHwmyM1CWLyYBQKIxF00pw3bPqGzzXPvspnli7GJWFNnR6/ACAZz48gPNPqEdDhQtH3P6EN8cWTS1L62dk3ckcHluaKPzhMELhMGZUFsS6nngDQfjD4ZHfTDRKDOySSGegpefLbKznsfP4tVMZPX71iE0Gg4BBCNUgJb84d57qGHj8oQTbUqdF1hZrP/2rKR4McAtsJnz3yw047B68a//dLzegwKaurgYhYkFddH83PL9FFUz2DPiHPRm4fmUTegb8uo9lrgiHJbZ80TesH9HlX5uBZ94/AG8gjNte2or1y2bg6j99Miy1EgD+baH6WKxb2oi6EltsoIqnN0cmAj+2qgBrlzRgQX2x6jzvOezWnHvsp2fOQV2pHWVOa86ktKWzz2o+y9RxCIcldnb0IySB/4irUzetbsZ/v7Mba05qQIHNiBKnFdc+qx405Y4/b8e/LayPpfwCkX53Ozrc+D9D6u5j77XiJ6c3YVKJdtthEJG+pctnVWFjmj4j607m8NjSRGEAEAgB1zwTd/1xelNW5hkbjfHUjWUiYWCXRDoDrejAF4+vXayaMiD+P0kqF8Ej/Yfbc1i77Md+/2QcUxnZVqlTe8SmUodFta/dHf344ZPqFKgfPvkRjq0qwHRlks1Eo9INTa8RAK4+daYqaCtzWmDAYNn9Qe27WUOXu30jB5PBkMQNLwwJ/l7Ygke+tUhzH7lsb6cbP3t5q6of42PvtWLV/Fqce0I9Pm/rx8q5tbGgDlCnVgLQfKp653nzIWVkMJ3rTp+NUDiMn7/8OS7/2rGoK44MfhOtbzva+zTnxrvqT59gzUkNuP+t3aobCKmOVphu6eqzmu8ycRz2drrx8f4ePPvhAXWd/EcLzlowGRte+AxXLJ8RC+oAdX2MT/kFgHMX1sWCuqHrtvf68Ou/7sT1pzepntCvX9aIzS3d+OVrO2L1Ll2fkXUnc3hsaSLwh6TmzeffX3xClks2svHWjWUiYWCXRLrn03pla1vS/yR6pwzQ8x+upcutmc7Y2uWOBXb+UAjrljYO6xcXGJImsEeZE23ocdjT6Y4FdgJSc1Q6AfWk1+39XvhDUvX074rlx6K9f3DCzkA4DLc/NGwag6HlclpNmkGDwzLYR7Ct16fdB6hP3Xl5pHn6ckGn24dvLJqiGuHyB1+dAbvFiM8P9eKsBXUoshlVF9lPb96Pgz1eCBEZsVDrWAwEQrj/rcFjffPqOTj/hHrc8eo2AEB9qR2tXQO48umP8fNz5sFo0N6O1WTAmpMasL/LjfdbuxEIheG0mnD1qTNxy4ufq85lotEKKX+09XpR7rQOq5N3njcfP3jiQ8yodKHcZdOsK/WldpQ4LLF6YDMb0FDu0lzXaIgM9NTSOYDf/HUnfnbOPGxv64PTYkQoLFFZaMO3T27AbS9tzdsUayIafzr7tdPHO925nzGUD6PXkjYGdkmks5O3nv8keqcM0LOtIpt52Jxk65c1otBmjm2nzGnF45taVSPJPb6pFSuaq1X7s5uNmsfBHjfIyq7Dbs1R6aZXutBQWRBbr9Bmwe2vfKAq++2vbMN/rzkxto4/KDX7/v3uwoWqcvlDoWFD+f/gqzMQCA2WszLRPDKuwc7LeubpywUGIWKfFYgclzv/vB0PXbIIbl8QtcU2+EMSd/xZnfb2+KZWHKsE4FrHYm9c4O4NhPF/nvkEPz9nHk6fV4s7Xt2Gq0+bjV0d/fj2yQ2wmAyYXVOouZ3pla5YmuY3739XdU7WL2uE2x8acbTCXMa0FLWaIhsMQuCi3/9DVX+2HuqFNxDGt5ccg13tfZp15cCRgciIrZOLUFs8B3aLCZ39Xs1159UV45d/3g4AaOkcwPa2Pjy9eT8uWDwFd7+xU1XXhw7WdDR4vonoaCSax64qD+axy5fRa2m43LlqzUHRfnE28+BEz6Pt5J3sP0lUoikRWrs9urbV1ju4LbNRaAZHZuPghcnUMieuXDEL97+1G3e/vhP3v7UbV66YNezzOS1GrF/WqDoO65c1whn3ZMxmNsZGpbv79Z24542d6Pb4YRsywubhfu0naJ39g0/Q3AmmMej3qfv+lTutcFqNWLukAZctnY61SxrgtBpR5hxsNEscRmw4o0lV9g1nNKHYOViuRCN1bjnYg1zSmeDYHe73odBmgkEYhn2Ou17fgStXzMItG7filo1bsW6p+jxeu3I2nty0H0DkQv3Sr0zHt0+OpG0+/9EB/HD5sfD4grjvzUgdWf/YBwiHJa4/XX1Mrz+9KTagytA0zTv/vB39vhCe3hzZz7kL62A3m2LzkuWD6FPy0+76G77+23dx2l1/w0tbDuXVZ0i3UFj7/3NYRurEgC+IJzbt16xzDosRj73XivdbjuDKpz/Bukc/wH1v7sHNZ84Ztu69f9mB5U2Rm01TyuyYWVWAq0+bpZlWbE7To2CebyI6WhajwE+GfFf+5PQmWIy5f4Mo2o0i3lh2o6DR4xO7JNLZyVvP0z+9d0gclpFTEN0JBjNxx/U/09PvDwA8gQCqC22qSYirC23wBAYDLZtZaKZiDm0YnInKbh2siuUui+Y65U51378+XwC//dvuWOpqWAK//dtuzK0bHK3TFwSKnRbVXH0WswHx48MkGqnzYNxInbkg0bFz2UwIhiW63NqB3872/tgAFR/u68QDF5+Ajl4fylwW2C0GdHv8sbnF4tNyr105GzazETe/8pn66fCTH+E331yA3198Aro9fpQ6LdjZ1oeWzgGIBOmedrNBtf373tydtXz9VJ/EBINhfLj/CD4/1Itvn9wQS2+d6Gkp7X1eFNiG18nnPzoQGwGz2+PHS58exH9dcDw6en1o7fbg7tcjN33WLW2EIa556Pb4UWw34YrlM1BVaIfTYkQgLOEPSlQX2rBwShHOPr4eVzz1Eb59csOI/WtHkiz9mmlIRHS02vr9MEAOmyu4vT/3UzGNBmhe07EbRe5jYDeCdHXy1jMqpt7Uz4R94+JSEBNtq6pQPb3CSP3+AMBiNOEP7+7Ahf/UEJsy4KG/R57uRZmEAU6LURX8OS1GmIT6grnQbtJsLArjRrzs9wU0Uyz7h4zW2eX2a/Y564rLX/cFQlj36PAJlP+wZnDwlDKndiBZNiSQBLLbF89mNmoeu0AwjC+ODGD+5GLNz2EzCVz6lekod5oxpdyJq/7n49g8dhtWNeOurx+HTw/0DHsCcuMLn2HtkgbN0Qs7+vyqkQ5/euZcTCmzx/Y5tAyLG8pi6ZnR7V/+xIeoWnMiTEYBjz80JuluqXYI10rTjZ/AfSKnpVQV2rD7cP+wOnn+CfWYUemEPyzxs7Pn4ojHD4MQuCZuEBUgkvZ946pmXLZ0OowCKHVYYDcb4LSaVaNsXn96E7yBIK5cMQsXPDCY9jlS+5bMSOnXybIiJur5JqLUlDjMuPn/fjZs3ITbz5mX3YLpcLDHixc/OaiaKuq3b+7C/Mn5141iomFgN0b0PP3TO7+Pnr5x6Zxeoc8XwNdm16iGyf/BV2eg3xeIrdPW58NTm/fj20uOwYA/CIcl0ghUDrnQaix3osvtw30XHI9udwAlTjNC4TAaywfLZRAG/PEf6v56f/xHC25c1azalt1sUq0HRNb72dmDjebhfr/mIDKd/YNlD8kQbjt7DnZ1uGMjdTZUOBGW6rv/2e6LV+ayoKHcjt9ffAI6+n2ocFnRO+CDxRQJqjv6vLh25Wzc+MLghPM/O2cu3L4Q7vjzFpQ4LDh3YR3+/asz0NXvx+/e2oPrnv0Uv/y3+agttmteyIYlNEcvvPuNHarj/od39+DWs+Zic0s37jxvPm59aWsseLzjvPmQkMO2X+Kw4PO2PlV5M/0UL9UnMVpputHjcf9buyf0pMpTy5zo9vhwwtQS3H/RQnS5A7CaDPAGAtjR7sZVf/oEVyyfgV5vEAbhVtWXN7e1Y0VzDdY+sjlWL502E8xGI57e3Kpa9zd/3YmrT52Fjri0z6c37x92cyuVNPlE6deNlS7Mm1yiKyuCiCgZty+Iy74yHXazKTaP3ZTS6cOmlMpFBTYTVs6bpLruu/xrM4ZNO0W5h2doDI309E9v6me0b1yyoE3PtvSmfhZYzZqDdsRPGVBXYsd5J0zGzva+WHB03gmTUVdiV21/V6cHh3r9wyYV39XpwZy6yBMyl9WENf88LTYlgskArPnnaXBZ1dV1IBAcNp/auqWN8MaliBbaE8yJZx+8QLObzeh2u1WjcP54xcxhF4kjXQxmWrXLhn8MdGP94+/FyrnhjGZ09vWi1OlAGECZ04j7Ljgem1q6Mau6EGajwI+e+hglDsuwVMsffHUGHvz7XgCR/nXrlk1HWA6OpGkzGyDl4MiEQOTidnZNIWwmY2xbU8rs+N6/TMclDw6WKzqvXYXLilA4Mkrr+mXT8cSm/bEnf+curIsFddHjmel0t6F1vqbIhrMW1MUmbB/6fyRRmq7RgAk/qXI4LHHgiBcHugdUT+zu/V8L8N0/vI8ShwUFVjMK7SYYDUbc/9ZWVZrvfW/u0qyX169swm/e3Bm7MbBuaSMkIn1qo8HWwR4vHnmnBWuXNKCppgiNVa5h5y5Zym2i83pISb/WkxVBRJRMid2Mgz0+XDEkA6HYbh75zVnmDYRwx6vq6747Xt2OBfX5N1XURMPALsfoSf1Mte+fTNDfX2/q55GBgOZFUM/A4FMvu9mIPm9w2BQF9iGDp/T7giPO6+IPBmExGVXb+snpTQiE1He57GaT5gAKD10y2PDYTEbNqRNspsFyeQMh3PrS56rt3PrS53j4EvVcMyNdDGbap4d6cd1z6sDyuuc+xYOXnICwDOP1zw5hUokLNUWRgKrb48chJZA5a0Gd5qAm65c1ot8fwvrHP1RdwD6+qRXfWDQFD/59bySVclopZn7jOIQlUGw3q7a1cm4trn9OfU6v+tMn+L/fPxmfHexT3YBYv6wRD7/dgm6PH5NLHLpuLKRTfJ3X6lc49IlhTZH2pNhLGiuwoL5kQo+SuOWLHuxs74/93wIi5++DfUdiAVvPgB/TqwrwvT+8PyzNN9Hcije8sCX2hDj6f/rhby1CVaFVlfbZ7fHDbjbi2GrXsNSgkVJuE53X6qJI26d3xGCaWGQ4hH379sX+rq+vh9HIp7ikzaNc3yS73slV3R7t675uTyDBOyhXsBtknooGgIsbytFQ4Rp2galnVDe9o34W2MyaoyMVxE2dcKjXi8fei1wIXbY0MrLiY++14lDcSJ1ApF+cVmMR3y/OYDDiJ0Maw588vwUC6i/QzgSDhXS6B0fY9PhDmqODegKDaZY9A9qjcPYMqAPJ6MXg0OMQvRjMtEMJ+/34YDIYsWz2JLgsRhTYTagosKJ3IIBZytQEiQY1mVLm0JwU+j+Wz4TNZIDFJHDLmXPQ1ufD5U9+hMse/QB/392p2laibbd2DU97jEwkPQ9rlzSgvc+reTyjNxbCYYndHf14e9dh7O7oT8uIhPF1XivYvfyJD7G30x1bv6mmEDetblb9H7lpdTPm1xVP6KAOAA70DCAsh597q8mAC78UCZj9IYkP9x1J+NQzUd2J75rrDYTR4wmgtsiBxiqXahTcxioX6kuHPzVNlHIbPbeJzmtTTWTgJb0jBtPEMnDkMK5+6n18/9H3seaeF9Ha2prtIlEO60xwvZMP89gluu6LnzKLchOf2I1TevoS6X3y57JqD9rhtA4GWr5gSDMt0h9UN2q1xdp3ymuLB1M2O/q0A7aOfvWk4pUF2nPEVMTNUdfv1w7a3L7BwE5rZL/oaJPxmmoK8cvz5yMQlLF8ebNRxC4GM60mwRPWUqcFnf1+OKwGdHoCuO75TbFzcPWpM3H9ytk41Ks9R5jLatI8Pp+39eF3f9uNBy9ZBEDi4t+/F1svOpz90G0N/duZYNudbj/m1hXhxhc+S9hPKtVBTvSKr/Pb2/pGfGJoMhmwel4tGitdONTjRXWRDU01RTk1v2G2lLusMIrh89QV2Uxw2SzwBsLwBhMPdDKzuhDbDvVqvhafZWAzG2A1G/DK1jYsn1WFhnLXiJkKI6WZj3Re0zkiMo0v9pJKuMonZbsYlAcSjvDtGj4wW64ptGlf97lsfEKd63h1Mk7pmTcPGPnJHwAEQuHYdAfRO+XVhTYE4/qbFFjNmmmRQ/vFWU1Ccw40q2lwvxVKwBbPZjagYsikngU2Izacob7rvuGMZhTG9Z8rdVg0t1XiGLzr5AsGh821tW5pI3xB9eAp4bBEz0AQVzz1Ea78n09wxZMfoWcgOGZzW7lsBtwwZE6+G85ogskgUWQ3wW42DXs6ecuLnyMQCmNuXZEyfYF6LsLPDvZqHp9o37q/7zqMt3aqn9A9vXm/alvPf3Rg2Dldv6wRXW5/bKTM+G1vb+vDtDIXfn/xIiyaVoLH1y7Go985ERvXnRwL3EZ64nI0onV+RlVB0ieGUSaTAfMml+CU5hrMm1zCoE5RWWDFMRWuYXNc1pY6sedwP2xmA+xmA57/6MCw/183rW7Gk++1wmYePs/kDWc04YWPD8T+vv70JhTazLj8iQ/R2u0Zsb0CBlNu4w09tyOdVz1tIxFRIrYE1zu2PPgO8frDsZHOY/MEW4zw+dnPONfxid04pbf/nB4GIfCfr22PDdkbCgP/+dp23HX+cbF1EqUcdA1JOdjT6cGj77ZEhtD1B2G3mPC7N3ehyH4MZk0qBgBIGcb1K5twwwtxA6ysbIKU6u0f8QRxz192qPrB3POXHfjZ2XNj64SkxH+ccix+/vK22Lb+45RjEYp7JOCwmPH654diZXJYItM5nDBVPSDKx1/04Lpnh/Rxe/ZTNJQ7sXBqacrHNVV7Dg/gpU8ic4Id8QRQ7DDjwbf24Mzj6zClxIF93QOa58BqMmJHez9mVRfgv755PHq9Qexo78PDb7cAgOYgES99ehDrlk1HbZEdDqsJC6cU4cSGiliKnAyHVccP/UYpAAAgAElEQVQ9EAzFprqQErF+dPd+83h89783x7Z97crZ6PMG0O3xYeHUMs2+dNFBL0bb/07vPHV6R6FNdd67iaTAbkSD2YVff2MB3P4Q2noHcLjPF5uY3GQAvvvl6bj3rzux5qQGGA3AnElFONgzgBXNNfiiZyBWl4wG4EsNZfjFK5/H2hopI6Ni3riqWXX+o+ek0+2DxWgYNl2G3nNLRJQpobCA0yIio4B7AihxmHHE40M+jMF0qNeLe/+6OzaieCgM3PvX3bh25ayR30xZxcBunErnhY3HH0JL50BsuPv45VGlCeaCK3GoUw4qXFZsb+/Hukc/UK0Xnz4pIfDGtkgAE50S4Q/v7MG0imNU2+pyBzTLFd+51+MPwiSEan49kxCq4YYlQvi3E6aohvW9cVUzwlC3vod6vZpTJ7QN6UeYKTVFdry9pwtvbD8cW2YzG3D28ZPR4w2gslA7NfVAzwCe3LQf5y6sQ2OlC2ajAXe9NnjMHnknMmXEjCoXdnX046VPD+LUOTWqFIwbzmjCr///9u48PpK6zv/465NO55hccydhhrlguDLD5YCAqyIIDi6XiojrLrq64s+fCsrqzwu5Ft11VVZUVkVFhXVXUBQHRIHlWHARZYBhDkBmuOZgjsyV+87n90dX93Qn3Uknk6SrMu/n49GPdKq/XfWpqk9X16er6lsP7+up8Lrzl3D36pfp7nXeefxcSotjqR41gdQyKi02vnDWEUyrKMEwXtrZRl9/Pztbu+nv95zXhuY6RS/bDxPphdfsqjJe3tXKx//z6ayncA4s0s48spZ7hjjdbrxOCZ0MtjZ1Uh4v5qP/8UTGevr6BUvZ097NrY+/yueWH05pPJYq1MrjMV7a2ZZx78mrz2lg/fa9XHjCfHr6+rnoxPlMKYmxcXc7rV19dPc67d19zJ9RTl11GS/uaOW5rc1s3tNOeTzGPwcdH5XFi/jqu47moKllzKospaQ483NfUnxgry8RmVhNnd00dfSxoXFPqlfumZWlVJWF/xq72poy9rR3Z+xflcWLqD2Ab/ETFSrsJqmxvEYkn5ud11aXcvU5DalOT5I7bLU1madPlsVjXHVOw6DbHZSl9Z7Z09fHWw6v5yO3PplxxK63L/PUyJzXxqWd/llVGueffz/4BuXpt2ooIjbovmzfeWj9oJuIHlRTxsUnzx90znn9BHWe0lBfzbXnLUkdNUwul5seeZGPn7aY6RUlXHteA1f+Zl1GfL9bszWj98fLTj900HKLFUF3bz+LZ1ex5KAaLv35vmXW2dPPVSsyeyq84s61fPui49jQ2JqxPD6//Ag6e/tTO+4/fLSIr7/7GF7b25HqOjkZ18bdbYN6M0yegjltSkle9ynLVnhddvpipk0pSfVimry2dMGMipxFWq6jgCO9710hFOqIYm11Geu3Nw86ur5gZkXq2oyykmKKY4n86neYO20KX78vswfaq+9ax08/eAJPvbo3I5cuPW0xdz2zhYtPns/eti6+/I6lWXtZTV/Xn71jderoX3pvnZD43N8zgvWmI7Uisj+mlZeweXdHxjB3Z2p5+K+xKzKy7qvFwn8W6QFvUhd2ZrYcuAGIAT90938pcEgFket2B/nK5+hfbUUZ06bE+foFx9DW3UtFSaJjkdqKzKJne0snMTyjXXtXD9tbOlkatInHYqkdRdjXBfpPBtx+IF5sWS/ujaf9Mr+zNXtHLDvTes5s7uzJ2vFLS1dmt74dPdl72Lz5/csy2o3XDmFxcRGHza5ILbvkaawv7GiluryY3W3dHDKzkls/eCLbm7uoKI3xhV+vHdT74+0rN6eW27QpJYOK1S+dfVRqZzl9mQ3sqXBvR8+g5bGrvXtQ9/cvbG8ZNOyGB9Zz/Lxpgwq75LWhyfuUJYvtNx46kxMWTB+0HLMVXjc8kHlD9fRrS0dapOV7r8dCKeQRxQUzKti8p507Hn0p49TqLXs7ueWPiXVXURrj2deaM243culpi7n18VdT+dXZ009b5+DPVvJG8Dc8sJ6Pv+VQamv681rXZmTtrXMk601HakVkf3X19We93VJXBM7FrCkv4aHnB5859fqFDYUOTYYxaQs7M4sBNwJnAJuBJ8xshbs/W9jIJsZY7pjkc/Rv7bbm1L3QksriRfzHh16fcf1ZdVk8dZpcertb0u4919KZvSfLls7M2w/Ei4qoKivOON2qqqyYeNG+n5Rm5eo5s2LfkcQpJdnvhzfwXjOtXdnjak3rYXO8dwgtOI00ed+4RIcxDZQXx2ju6MXpoTxezPTKOLvbetjT3j2oS/mtTYkd7x/83evAjA/fsjJj3v/p7me55E2LMk7XzNZT4YyKkkHLI9sOda6d7PTTYZPSjw5vberkxoc2UBYv4p3HzRlR74fpRWjyFM7RFGljea3qeCjkEcX+fmdWVcmg05h/8vcnpE7hOXHhCYNucpss2JLFWFm8iLbuvpzrsbOnn+6+fqaUZO9ldeC6dk/cRmF/1lsUjtSKSLj19HrWH4N/8HfLhnln4c2bNoUzGw7KOHPquvOXMG/alEKHJsOYzAdVTwQ2uPtL7t4N/Bw4r8AxTZix7lVwuB7ict9jLfP6s7YcxVH6Tn5Nefb7p9SUZ94/pbmzm76+zMORfX1Oc+e+I20Og3rtu+z0xXha+LkKydYBheSsyuy9dc6o2HdaxXj25gjQ1t3LjQ9vSN0v8EN/tYgbH95AW3cvNeXFVJbE2d7cxebd7alTKWLBTm66Pe3dPPHqnpzFzrzpUzJ7Gz1vSUZPhZeetpgXdrQMGm+2aWUbVhYvynr/sXzvrZiUq/fDZHqmvz+fnhL3N56Jlm/vt+Ph+e3NbGvqSp3GnMzHXz25MdVbba7PVvJ0nuT1mztbs9/T0INbayybP53a6uyfv/R1felpi/nVU5u548nNgz73I1lvhVyuIjI5dPRk3/519PTleEd4bNzTzhV3ZnYWd8Wda9m4p73AkclwJu0RO2AOsCnt/83A6wsUy4Sb6FPIct1jLf06PIB50yuytjs4bSd/dlUpl59xWMY1WZefcRizB9zuYNqUMq567NmM3jpvfuzlzN46W7tTp4Ule9m75Y+vsijtFMCq8lj2a/UG3K9lT3s3nz7zcL5+374eNj995uHs7dh3IfR4L/eWzt6sHca0dvYRi0E8VkRtVSkv7+ylp7efRTPLWTCjnLnTpvCltGvzvvKOpXzt3r/whbcfmXXetzV1ppbZEbVVdPf1cu15S3jilT24JzpcgcE9ah4yu5LPLj+Cr6Z1aLF0bg3fePex/OMvMo9iLpw5eCd7pNeG5jpN+Kj6Kk45ZEbG+0fToVDY72dWyCOKe9p7eHrT3qz5+K5lB/ODi5dhZD9ytmz+NL76rqWUlxTz6yc38Y7XzR10WvWlpy3mtpUb+eq7juaURTMoKrJB6+8r71jKYbWVnLxoBj19zpd+s4atTYkicXFtJb/9xBtpbB35egv7kVoRCb9pU+I5OpUL/6532C9DkNzCn13jzMwuAS4BmDdvXoGjGTsTvWOy9KCaQR17XHveEo4+KPPm3QtnZt+5Tt/Jnze9gkWzKjJOsVw0q2LQEZ6G+mo+cdri1K9KyVMF0m8YftDU8qw9O9WndepSGotlvSVCaXFmYVdaHOPWx1/JKBJvffwVvnz+0lSb/V3uw+XjlHj2IrSspIiunn46+vp5+pWdHFKbuHXEE6/spaOnn6qyGF+/4BgqSmPMn1FBkSUK1S172wddIJ1+DVRZvIjvvu94vnbX85xzzBx++GhmhxS3rdzI1y84hue3t+AO/3zP85QUG7ddchIdPX2pHWqAI+vzK46SR4fz+fIYqvAaeP3eaIu0kcQz0ca7W/+h8rGzpy/nzer7+iAeM75+7/ODOlW6/IzD+Owda1LX2JXFizhx0QxiBj96/zK6e/uZPqWEnv5+li+py1hHQ62//n7nxx84cdBrh8we+XrT7RLCabJ+X0t0DZWTJbEYXzjrCL7yu30/dH7hrCOIx8J/k2/9uBVdk7mw2wIcnPb/3GBYBne/CbgJYNmyZRNzp+kJMNE7JiUlMc4/+iAWzaxIdRpy9EE1lJRkbsDy2bkuKjJOO7yWRTMrh9wBLy4u4vxj5rB4diXbmjqpqymjob4m40bDR9VVc935SwYVf0elFX8lsSJmVZZkFJKzKkuID+j+qWZKMRedMG9QZy015fs+Rvu73IfLx+qyeNYOY6aUFPOtB57l0287nJKSEspLiigtLuIX921K3aLg+guP5c2HzU51+3/9hcfy1d8/x8fefAjfvug4mjt7qJlSwj/dvS5V1H31XUfzhkUz+fEHTmR3WxeLZ1fy2TtWp6b92eVHUlJsqYIvOZ2lc6YOWl/jVRyNtBAMa5E2GuN9RHGofFw0o4LrfvvsoKO2V5/TQHk8RllJERcum8ftKzfyrxccQ1dPH/NnTOG1vR3saU8c5S6LF/Gptx7Gf/75VT67/EhOXjRzyNiHWn9juW7DfqT2QBXV7+u+vj42btyY+n/evHnEIrBzL8MbKidL48a0isx9i2kVJZTGw78d0Y9b0TWZC7sngMVmtpBEQXcR8DeFDWniFGLHpKQklteNuvPZAct3J624uIhjDp7GMQfnfn244u+w2VVsaergsNlV+3r0LDYOn12VMa7FM6rYuKsjYyM9b/oUFs/c1268l/uRddW8uqc9I4Y5U8v5/ZrNXHTifDZsb2V2dSlmcOL8GVmPYGTEWVfF7rYuyuIxqrvi9PT18a33HEd7T+YNn5Pr4vh+Z+mcmoxxAkPeC07GV6GK1YWzKvl/bzuCf733+dQtBo6sq6bIoLu/l+K+OCcdMp1FsyrZ3tzJwpkVLKmrZnZVKT/9+xNp7+5lRkUJ3X39nHFUbejyZrL9CCCFs3HjRj504++YMr2W9t3b+dHHzmLhwoWFDkvG2cFTy9nZ2sXJi2aws7WLmZWl9PX3MW9qeaFDG5Z+3IquSVvYuXuvmX0cuJfE7Q5udvd1BQ5rQmnHJGG44q+kJMZbFs9m9WtNQx5tLCsr5vTDZjO7qpTtzV3UVpeytL6GsrLMj9F4LveSkhjLj6xjdU0T25o7mV1VSr/3sXzpXPr6+4AiaqtLmTe9Ytg4RhNnrvcozw48RUXGWUvqOWx2FS/vaqM0XkRVSTFV5UUsnFGd+vFk3oDfehaUVQ46TVYkary/j02bEpfx9wX3WE0ehUuekpc8Srdp0ybKp9VSOfOgjPcl2+Zz9C7XUT8dDRy98V52FeWlHDd3Kuu2tQJGrMg4+qCpVJSXDvveMNA+ZDRN2sIOwN3vAe4pdBwSfvkebSwrK+aEhTMmIKLc8o1VZLwVFRmL66pYXFc1fGORSaRj706+8MvXmFrXyK6X1xErr2Zq3cGpI3JA6ijdrpfXUXnQoYPeN5Kjd7mO+ulo4OhNxLKrKC/lxIXRKORkcpjUhZ2IiIjIeCifNpvKmQfRvns7sYqpVM48KOP1KdNrU69ne99IJceX73AZnpadTDYq7ERERGTSSBZSHU07iXV301paOqLnfR3No3pfa2kp7bu3p061HC6O9LbD2bRpU2p86e/LNVyGN3DZiUwG5h6ZjqXGnZk1Aq9OwKRmAjsnYDrjQbGPjZ3uvnyoBsPkY5jmJZuwxwfhj3Gi4xsyJyOaj4pr5MIS22TMx7GkeZxYk/07eyiKvTCGij1nPqqwKwAzW+nuywodx2go9nAI+7yEPT4If4xhjy9dWGNVXCMX5tjyNRnmYTiax2iJ8rwo9sIYbexFwzcRERERERGRMFNhJyIiIiIiEnEq7ArjpkIHsB8UeziEfV7CHh+EP8awx5curLEqrpELc2z5mgzzMBzNY7REeV4Ue2GMKnZdYyciIiIiIhJxOmInIiIiIiIScSrsREREREREIk6FnYiIiIiISMSpsEuzfPlyB/TQYyIew1I+6jHBjyEpH/WY4MeQlI96TPBjWMpJPSbwkZMKuzQ7d0b15vQyGSkfJUyUjxImykcJG+WkhIEKOxERERERkYhTYSciIiIiIhJxxYUOYDTMrAx4BCglMQ+/dPerBrQpBW4BXgfsAt7j7q9McKgj1t/vvLKrje3NndRWl7FgRgVFRVbosCRilEdyIFLeS5gpP0VkvEWysAO6gNPcvdXM4sAfzOx37v54WpsPAXvc/VAzuwj4KvCeQgSbr/5+5/frtnH57avo7OmnLF7E9Rcey/KGOm38JW/KIzkQKe8lzJSfIjIRInkqpie0Bv/Gg8fAXmLOA34aPP8lcLqZhXrr+cquttRGH6Czp5/Lb1/FK7vaChyZRInySA5EynsJM+WniEyESBZ2AGYWM7NVwA7gfnf/04Amc4BNAO7eCzQBM7KM5xIzW2lmKxsbG8c77CFtb+5MbfSTOnv62dHSWaCIZKKNRT4qj2SshGn7OBzl/eQXpXwcSPk5OeWbk3MOnoeZjfgx5+B5Ezg3MhlE9VRM3L0PONbMpgK/NrMl7r52FOO5CbgJYNmyZUPeG2K81VaXURYvytj4l8WLmF1VVsCoZCKNRT4qj2SshGn7OBzl/eQXpXwcSPk5OeWbk69t3sR7vv/YiMd/20dOGX1wckCK7BG7JHffCzwELB/w0hbgYAAzKwZqSHSiEloLZlRw/YXHUhZPrJbkOfgLZlQUODKJEuWRHIiU9xJmyk8RmQiRPGJnZrOAHnffa2blwBkkOkdJtwJ4P/BH4ALgQXcP9S98RUXG8oY6jrj0jexo6WR2lXrNkpFTHsmBSHkvYab8FJGJEMnCDqgHfmpmMRJHHW9397vN7FpgpbuvAH4E3GpmG4DdwEWFCzd/RUXGolmVLJpVWehQJMKUR3IgUt5LmCk/RWS8RbKwc/fVwHFZhl+Z9rwTePdExiUiIiIiIlIIkb/GTkRERERE5ECnwk5ERERERCTiVNiJiIiIiIhEnAo7ERERERGRiFNhJyIiIiIiEnEq7ERERERERCJOhZ2IiIiIiEjEqbATERERERGJOBV2IiIiIiIiEafCTkREREREJOJU2ImIiIiIiEScCjsREREREZGIU2EnIiIiIiIScSrsREREREREIk6FnYiIiIiISMRFrrAzs4PN7CEze9bM1pnZZVnanGpmTWa2KnhcWYhYRUREREREJkJxoQMYhV7gH939KTOrAp40s/vd/dkB7R5197MLEJ+IiIiIiMiEitwRO3ff6u5PBc9bgOeAOYWNSkREREREpHAiV9ilM7MFwHHAn7K8fLKZPWNmvzOzhgkNTEREREREZAJFtrAzs0rgDuCT7t484OWngPnufgzwbeDOIcZziZmtNLOVjY2N4xewSB6UjxImykcJE+WjhI1yUsImkoWdmcVJFHU/c/dfDXzd3ZvdvTV4fg8QN7OZ2cbl7je5+zJ3XzZr1qxxjVtkOMpHCRPlo4SJ8lHCRjkpYRO5ws7MDPgR8Jy7X5+jTV3QDjM7kcR87pq4KEVERERERCZOFHvFfAPwd8AaM1sVDPsCMA/A3b8HXAB81Mx6gQ7gInf3QgQrIiIiIiIy3iJX2Ln7HwAbps13gO9MTEQiIiIiIiKFFblTMUVERERERCSTCjsREREREZGIU2EnIiIiIiIScSrsREREREREIk6FnYiIiIiISMSpsBMREREREYk4FXYiIiIiIiIRp8JOREREREQk4lTYiYiIiIiIRJwKOxERERERkYhTYSciIiIiIhJxKuxEREREREQiToWdiIiIiIhIxKmwExERERERiTgVdiIiIiIiIhEXycLOzA42s4fM7FkzW2dml2VpY2b2LTPbYGarzez4QsQqIiIiIiIy3ooLHcAo9QL/6O5PmVkV8KSZ3e/uz6a1OQtYHDxeD3w3+CsiIiIiIjKpRPKInbtvdfenguctwHPAnAHNzgNu8YTHgalmVj/BoYqIiIiIiIy7SBZ26cxsAXAc8KcBL80BNqX9v5nBxZ+IiIiIiEjkRbqwM7NK4A7gk+7ePMpxXGJmK81sZWNj49gGKDJCykcJE+WjhInyUcJGOSlhE9nCzsziJIq6n7n7r7I02QIcnPb/3GBYBne/yd2XufuyWbNmjU+wInlSPkqYKB8lTJSPEjbKSQmbSBZ2ZmbAj4Dn3P36HM1WABcHvWOeBDS5+9YJC1JERERERGSCRLVXzDcAfwesMbNVwbAvAPMA3P17wD3A24ENQDvw9wWIU0REREREZNxFsrBz9z8ANkwbBz42MRGJiIiIiIgUTiRPxRQREREREZF9VNiJiIiIiIhEnAo7ERERERGRiFNhJyIiIiIiEnEq7ERERERERCKuYIWdmcXM7FOFmr6IiIiIiMhkUbDCzt37gPcWavoiIiIiIiKTRaHvY/e/ZvYd4DagLTnQ3Z8qXEgiIiIiIiLRUujC7tjg77Vpwxw4rQCxiIiIiIiIRFJBCzt3f0shpy8iIiIiIjIZFLRXTDOrNbMfmdnvgv+PMrMPFTImERERERGRqCn07Q5+AtwLHBT8/wLwyYJFIyIiIiIiEkGFLuxmuvvtQD+Au/cCfYUNSUREREREJFoKXdi1mdkMEh2mYGYnAU2FDUlERERERCRaCt0r5uXACuAQM/tfYBZwQWFDEhERERERiZZC94r5lJm9GTgcMOAv7t5TyJhERERERESiptCnYgKcCBwDHA+818wuHu4NZnazme0ws7U5Xj/VzJrMbFXwuHKMYxYREREREQmNgh6xM7NbgUOAVezrNMWBW4Z560+A7wzT7lF3P3t/YxQREREREQm7Ql9jtww4yt19JG9y90fMbMG4RCQiIiIiIhIxhT4Vcy1QN07jPtnMnjGz35lZQ65GZnaJma00s5WNjY3jFIpIfpSPEibKRwkT5aOEjXJSwqbQhd1M4Fkzu9fMViQfYzDep4D57n4M8G3gzlwN3f0md1/m7stmzZo1BpMWGT3lo4SJ8lHCRPkoYaOclLAp9KmYV4/HSN29Oe35PWb272Y20913jsf0RERERERECqnQhd2hwCPuvn4sR2pmdcB2d3czO5HEkcldYzkNERERERGRsCh0YTcP+H7QEcqTwCMkerNcNdSbzOy/gFOBmWa2GbgKiAO4+/dI3OT8o2bWC3QAF420gxYREREREZGoKPQNyq8CMLNy4MPAZ4BvArFh3vfeYV7/DonbIYiIiIiIiEx6hb6P3RXAG4BK4Gng08CjhYxJREREREQkagp9KuY7gV7gt8D/AH90967ChiQiIiIiIhItBb3dgbsfD7wV+DNwBrDGzP5QyJhERERERESiptCnYi4B3gi8GVgGbEKnYoqIiIiIiIxIoU/FvAF4CLgReNrdWwscj4iIiIiISOQU5FRMMys2s38FjiFxnd0NwMtm9q9mFi9ETCIiIiIiIlFVqGvsvgZMBxa6+/HBtXaHAFOBrxcoJhERERERkUgqVGF3NvBhd29JDnD3ZuCjwNsLFJOIiIiIiEgkFaqwc3f3LAP7gEHDRUREREREJLdCFXbPmtnFAwea2d8CzxcgHhERERERkcgqVK+YHwN+ZWYfBJ4Mhi0DyoF3FCgmERERERGRSCpIYefuW4DXm9lpQEMw+B53f6AQ8YiIiIiIiERZQe9j5+4PAg8WMgYREREREZGoK9Q1diIiIiIiIjJGVNiJiIiIiIhEXCQLOzO72cx2mNnaHK+bmX3LzDaY2WozO36iYxQREREREZkoBb3Gbj/8BPgOcEuO188CFgeP1wPfDf5OGv39ziu72tje3EltdRkLZlRQVGQjblOI6Y3luHp7+1m3tYmtTZ3U15TTUF9NcXHRiMc1lstqPHV29rJmaxPbmruorS6lfmop9VVT2LinPWfs6fM2u6qMWBHB8iqjrx92tHQypaSY7r4+ZlSUhnbeJZw6OnpYs62Z7UFOLqmrYltrN9ub9+XY9uZOKkpitHb30t7dx6IZFSycVak8kzE3cFs+b1pi+9jU0U1/kItzppXT2+dsb+lkZmUpdTWl9PQmtoXp28UwfxeIiGQTycLO3R8xswVDNDkPuCW4CfrjZjbVzOrdfeuEBDjO+vud36/bxuW3r6Kzp5+yeBHXX3gsyxvqUl9A+bQpxPTGcly9vf3c+cwWrrhzbarddecv4fxj5qSKu4leVuOps7OXFWu2cuWKffN77bkNHDStjS/+eh2v7uoYFHu2ebvs9MX8bs1Wzlpazw0PrE8Nv/S0xdy2ciOfXX5k6OZdwqmjo4e71m4bkJNLeGHbHn67tpGLT57PDQ+sZ9qUktTzZLtvvPtYzlqiPJOxk217d935S7hv3Wu85Yh6rrlrHYfNruS9r5/PNXetS7W55twGfrFyI1v2dg3K0zB+F4iI5BLJUzHzMAfYlPb/5mDYpPDKrrbUFxdAZ08/l9++ild2tY2oTSGmN5bjWre1KVXUJdtdceda1m1tKtiyGk9rtjaldqAhEeeVK9YRsxhnHz0nNSw99mzzdsMD6/mHNx2S2nlJDv/Wg+s5++g5oZx3Cac125qz5ORa3towh3cePzeVY+nPk+3+8RfKMxlb2bZ3V9y5lvedtDBVyP3Dmw5JPU+2uWrFOi4+ZVHWPNX2UESiZLIWdnkzs0vMbKWZrWxsbCx0OHnZ3tyZ+uJJ6uzpZ0dL54jaFGJ6YzmurU3Z221rGtm4xnJZ7a+h8nFbc1fWOHe2dmGWOSwZe6556+jqzTrcrHDzLuEz3PZxe46cbGzpTOVSYjyE5jMm0TV8Pmbf3u1t60kNz7Xt6+juVZ7KiEVxH1Imt8la2G0BDk77f24wbBB3v8ndl7n7slmzZk1IcPurtrqMsnjmqiuLFzG7qmxEbQoxvbEcV31NedZ2dTUjG9dYLqv9NVQ+1lWXZo1zZmUp7pnDkrHnmrcppcVZh7sXbt4lfIbbPtbmyMlZQf6kvxaWz5hE1/D5mH17N7Uinhqea9tXXlKcej7wNeWp5BLFfUiZ3CZrYbcCuDjoHfMkoGmyXF8HsGBGBTqabwQAACAASURBVNdfeGzqCyh5HcCCGRUjalOI6Y3luBrqq7nu/CUZ7a47fwkN9TUFW1bjaWl9Ddeemzm/157bQJ/3cffqLalh6bFnm7fLTl/MDx55kctOX5wx/NLTFnP36i2hnHcJp6V11Vlycgn/vW4Ldzy5OZVj6c+T7b7xbuWZjK1s27vrzl/Czx5/mavOaaAsXsQPHnkx9TzZ5ppzG7jlsZey5qm2hyISJebpP/VHhJn9F3AqMBPYDlwFxAHc/XtmZiR6zVwOtAN/7+4rhxvvsmXLfOXKYZuFQrLnrx0tiZ4Oh+rpcag2hZjeWI4r2SvmtqZO6mrKaKivydkr5kQtqzwNO/Js+ZjRK2ZVKfXT9vWKmSv29HmbVZnoFXNbcyd11em9Ysbo6etnunrFPJANudJzbR9z9Yq5oyUzx8rjMdqCXjEXzqhgkXrFlKGNKh8HbsuTvWI2d3Qnemht6WROTTm9/UGvmBWl1E1N9IrZ2LovZxtbJ+y7QKJhVN/ZqTeb8Z7vPzbiid72kVOI4n66jLuc+RjJwm68RKmwk8jbry8JkXEwqh1pkXGifJQwUWEnYZIzHyfrqZgiIiIiIiIHDBV2IiIiIiIiEafCTkREREREJOJU2ImIiIiIiEScCjsREREREZGIU2EnIiIiIiIScSrsREREREREIk6FnYiIiIiISMSpsBMREREREYk4FXYiIiIiIiIRp8JOREREREQk4lTYiYiIiIiIRJwKOxERERERkYhTYSciIiIiIhJxKuxEREREREQiToWdiIiIiIhIxEWysDOz5Wb2FzPbYGafy/L6B8ys0cxWBY9/KEScIiIiIiIiE6G40AGMlJnFgBuBM4DNwBNmtsLdnx3Q9DZ3//iEBygiIiIiIjLBonjE7kRgg7u/5O7dwM+B8wock4iIiIiISMFEsbCbA2xK+39zMGygd5nZajP7pZkdnGtkZnaJma00s5WNjY1jHavIiCgfJUyUjxImykcJG+WkhE0UC7t83AUscPejgfuBn+Zq6O43ufsyd182a9asCQtQJBvlo4SJ8lHCRPkoYaOclLCJYmG3BUg/Ajc3GJbi7rvcvSv494fA6yYoNhERERERkQkXxcLuCWCxmS00sxLgImBFegMzq0/791zguQmMT0REREREZEJFrldMd+81s48D9wIx4GZ3X2dm1wIr3X0FcKmZnQv0AruBDxQsYBERERERkXEWucIOwN3vAe4ZMOzKtOefBz4/0XGJiIiIiIgUQhRPxRQREREREZE0KuxEREREREQiToWdiIiIiIhIxKmwExERERERiTgVdiIiIiIiIhGnwk5ERERERCTiVNiJiIiIiIhEnAo7ERERERGRiFNhJyIiIiIiEnEq7ERERERERCJOhZ2IiIiIiEjEqbATERERERGJOBV2IiIiIiIiEafCTkREREREJOJU2ImIiIiIiERcZAs7M1tuZn8xsw1m9rksr5ea2W3B638yswUTH6WIiIiIiMj4Ky50AKNhZjHgRuAMYDPwhJmtcPdn05p9CNjj7oea2UXAV4H3jHRavb39rNvaxNamTuprymmor6a4OLMe7u93XtnVxvbmTmqry1gwo4KiIhs0rs7OXtZsbWJbcxd11aUsra+hrCxzFXR397H6tSa2NXdSX13G0oNqKCmJDRpXPtNs7+hm7bYWtjd3UVtdypK6KqaUl4y4DcDejk5e2NaWandYXQVTy8tG3Cas4xrL6eWbD6PR2tHJs2nTn1UZozyeeK2rD0pjsHlvP40tXVSVFVNVGiNWVERLVy972ruZWVmKu7OnvYeZlSW0dfVSUx6nu8/Z0dzF7OpSOrp7qSyL09PXx972XqrLi6kpj9PS2cvO1m7qq8sojxfR1NnL7rZuZlaVAn24F9Hc0UtVWYyy4hiNrV3U1ZRTEY+xaW87pcUxmjoS051ZWQpAY2sXJbEi2rv7UssKYOPuNna1dtPZ20d7dx815XFmV5Uyd+oUNu5pz1i2QMbynjdtX5v6mjL6+mFHy77XXt3dzqu726goKaauppTevn2vZ1tXyc/k9uZOZleVUhwzaspLxnS97o988m28crKlo5NXd3XS0tXLrtZuaqtLOXhajH5gW1M/Xb19zK6Ks7utj+3NXcyoLKGiJEZvn7O3s5uq0jgdvb2UFRfT1NFDTVmc5s4eyuIxYkVQWhyjtqaIV3d2U1tVTm11PJX/c6aWETN4rbmLmrJi5kwtY2drD1uDbedRtVW8sLM157a7o6OHNduaU5+lpXXVlCc/TOS3vZbRGa987O3t59XdzbR2Od29fexp76E8XkxJsRErMlq6eqgpK6HInM5eZ2drNzMrSygvLmJPRw9TSorp6++juCjGjpYupleUMKUkxu72bipKinH6KSmK4cD25sQ2dnpFnENnVrG5qWPQ/KTP50FTy9jbFuRnjn2J0Sw3YNy+bw4U4/mdLTLRovotdSKwwd1fAjCznwPnAemF3XnA1cHzXwLfMTNzd893Ir29/dz5zBauuHMtnT39lMWLuO78JZx/zJzUBrm/3/n9um1cfvuqVJvrLzyW5Q11GRuGzs5eVqzZypUr9o3r2nOXcO7S+tTOQnd3H3eufo0rf5PW5rwlnH/0QRnFXT7TbO/o5u612wdN7+wltanCLZ82kChm7lvbOKjdmUtmpYqafNqEdVxjOb1882E0Wjs6uWfQ9Bs4qr6CmZUxSmPwhxdbMvL12nOPAowrV6xLDbvs9MXc8sdX2dPezZVnH8m25i6uSnv96nMa6N3TwXW/fS5jOjc+vIFXd3WwbH4N7142L/We+TPK+b+nHpoxjvRpfOGsI+jo6eff/vuF1OvXnNtAedzY2tSdMfz6C4+lorSIV3e209bdxw0PrE+99pm3Hc7sqlI+88vVqWHf+Zvj6O71jOV93flL+PaD6+nudS4+eX5qHPNnlPOJ0xanls/8GeX8nzcfyjV3rcu5rrJ9Jq86u4E7ntrIB//qkDFZr/sjn3wbr5xs6ejksZf2sLutJ2MZ3vbhE3h1dxd727tpOKiCJ19tycjZq85pIB6Dts4+7lq9gXcdP4/vPbKO9yybx7ceXJ+RQxUlMTbvibN0TiU/e/xlDqubxpUr1jJtSknGuk3m1L8HOTp/RjkfO3VxxnTTt90dHT3ctXbboM/yOUvqKC+P57W9ltEZr3zs7e3nyU272NHczZ62br7yu+cH5ZKZ0VLeQ1u3Z+TsVec0UFwE9z/7CqcfWZ+xLbvqnAZqyoq58cH1XHzyAva29wwa94uNbfz0sZdZ+WpTan7OPLKW+57bzuW3r+Kw2ZW89/XzM6Y5cF9iNMst2/ZvrL5vDhTj+Z0tUghRPRVzDrAp7f/NwbCsbdy9F2gCZoxkIuu2NqV2AgE6e/q54s61rNvalGrzyq621AYh2eby21fxyq62jHGt2dqU2klItrtyxVrWpI1r9WtNqR3IVJvfrGX1a00Z48pnmmu3tWSd3tptLSNqA/DCtras7V7Y1jaiNmEd11hOL998GI1ns05/He3dsHF3Hxt39w3K1ykl8VRRlxx2wwPreefxc+ns6c8o6pKvX33XOna0dA2aztlHJz5iF5+yKOM9Zx89Z9A40qexs21f8ZZ8/aoV65g6pXTQ8MtvX0VLRx8727pTO+3J1752719Yv6M1Y9jqzU2DlvcVd67l7KPn8M7j52aM4+yj52Qsn7OPnpPa0cq1rrJ9Jq+5ex0Xn7JozNbr/sgn38YrJ5/b1kZPrw9ahl29xobGVna2ddPXHxuUs9fctY6y4mJ2tXdz8SmLuObuRG4li7pkuxseWM/Otm42NLbR2NLHWxvmpMY1cN0mcyqZo2cfPWfQdNO33Wu2NWffHm9rTryex/ZaRme88nHd1ibwItbvaE0VXsnxJ3OpsbWLqVNKB+XsNXetozxezPtOWjhoW3bNXevo98R2b0NjW9Zxr9/RysWnLMqYn3Vb922b/uFNhwya5sB9idEst2zbvzBsl6JkPL+zRQohqoXdmDGzS8xspZmtbGxszHhta1Nn6sOe1NnTz7amztT/25uzt9nR0pkxbFtzV9Z225u70tpkH9f25sxx5TPN7XlML582B8K4xnZ6+eVDLkPlY87pt3SmHgNfb+vqzfoeC36I7Heyvt4/4Lh2+ns6BozTLPs4hpvGnraerMPbunvzjitXO7PBcQ33f/K96esq12eyo7t3ROt1vOS3LRh9Tg6Xj9nya3tLJ/2eWDfZcjJ9HSdzKde6SB9PY9q4hsu5XK8nt93DfZbz2V7L6IxXPm5t6mRHkHtD5dLuIbY7Q22TOobZLnV092YMS99/GLjNTLZJ35cYTrblliueQm+XomQ8v7NFCiGqhd0W4OC0/+cGw7K2MbNioAbYNXBE7n6Tuy9z92WzZs3KeK2+ppyyeOYiKosXUVez7/S82uqyrG1mV2Ved1VXXZq1XW116b7p5RhXbXXmuPKZZm0e08unzYEwrrGdXn75kMtQ+Zhz+lVliUeWaVeUFWd9T/KE5JiR9fWBZ6Ckv2dKafZxjnQa0yriWYdXlBTnHVeudslp5xPnwP/T11Wuz2R5SfGI1ut4yW9bMPqcHC4fs+VXbXXi2reY5Z52RUkxRZaZS7nWd5FBbVUZs6syxzXUes/1enLbPdxnOZ/ttYzOeOVjfU05s6vKhtx2FBlMH2K7M9Rr5cNsl8pLijOG1dfsm89c28z0fYnhZFtuueIp9HYpSsbzO1ukEKJa2D0BLDazhWZWAlwErBjQZgXw/uD5BcCDI7m+DqChvprrzl+SseNx3flLaKivSbVZMKOC6y88NqPN9Rcem7qoOWlpfQ3Xnps5rmvPXcLStHEtPaiGa88b0Oa8JRx9UE3GuPKZ5pK6qqzTW1JXNaI2AIfVVWRtd1hdxYjahHVcYzm9fPNhNI7KOv0GppTAvOkx5k2LDcrX9q4erj23IWPYZacv5ldPbU7tqF4z4PWrz2lgdlXpoOncvTrx28lPH3sp4z13PbNl0DjSpzGjooRPvfWwjNevObeBve1dg4Zff+GxVJXHmFFRwmWnL8547TNvO5zFsyszhi2dWzNoeV93/hLuXr2FO57cnDGOu57ZkrF87npmC1ed0zDkusr2mbzq7AZueeylMVuv+yOffBuvnDyyroJ4zAYtw9KYc8isSmZUlBCzvkE5e9U5DXT29jJjSgk/fewlrjq7gbue2cKlpy0elEMzK0o4dFYFs6pi3L9uS2pcA9dtMqeSOXrXM1sGTTd92720rjr79riuOvF6HttrGZ3xyseG+mqwfg6dXckXzjoiay7Nqixlb3vXoJy96pwGOnp6+Y/HXx60LbvqnAaKDG557CUOmVWRddyLZ1dyy2MvZcxPQ/2+bdMPHnlx0DQH7kuMZrll2/6FYbsUJeP5nR1Fcw6eh5mN+DHn4HmFDl0CNsJaJzTM7O3AN4EYcLO7f9nMrgVWuvsKMysDbgWOA3YDFyU7W8ll2bJlvnLlyoxhyV4xtzV1UldTRkN9Tc5eMXe0dDK7avheMVO9sA3RK2ayd6ajh+kVc6hpqlfMwkwvz3wY9qrsbPmY0StmVSmzqrL3irmztYvK0mIqS2MU5+gVc0ZFCe3dab1itnQxu7KUjp5eKkvj9PT309TeQ1VZ0CtmV6JXzLrqMqbE03rFrCwF68fdaOnopaIsRnlxjMbWbuqqS6koKU71itnc0cP0ihJmVSWOfOxs7SI+TK+YHUGvmLPSesVMX7ZAxvJO9oq5o6WTuupEr5iNrftee3V3Oxt3tzElrVfM5OtR7hVzqHwbi5zMlo8ZvWK2dVNbNUSvmC1dzKjY1ytmU2c3FaVxunp7KS0upqmzh+rSOC1dPZQVxygqgrJYjNqpiV4xZ1eVU5elV8ytQe+Ec4NeMbcF286GoFfMXNvufHvFHGp7LaMzXvmY2Stmf9ArZiyzV8zSEoqKoLO3n52t3cyoKGFKvIi9HT2UlxTT733ELEuvmPFi3Ab3ijmtIs7ioFfMgfOTPp/1NYleMbc1596XGM1yA/La/5DcxvM7O/VmM97z/cdGHNttHzmFidxPj0qckjsfI1vYjYehPpQiY2y/viRExsGId6RFxpHyUcJEhd0QVNhNuJz5GNVTMUVERERERCSgwk5ERERERGSMTfR1izoVM42ZNQKvTsCkZgI7J2A640Gxj42d7r58qAbD5GOY5iWbsMcH4Y9xouMbMicjmo+Ka+TCEttkzMexpHmcWJP9O3soir0whoo9Zz6qsCsAM1vp7ssKHcdoKPZwCPu8hD0+CH+MYY8vXVhjVVwjF+bY8jUZ5mE4msdoifK8KPbCGG3sOhVTREREREQk4lTYiYiIiIiIRJwKu8K4qdAB7AfFHg5hn5ewxwfhjzHs8aULa6yKa+TCHFu+JsM8DEfzGC1RnhfFXhijil3X2ImIiIiIiEScjtiJiIiIiIhEnAq7MWBmB5vZQ2b2rJmtM7PLguHTzex+M1sf/J0WDDcz+5aZbTCz1WZ2fNq4+sxsVfBYEcLYjzCzP5pZl5l9esC4lpvZX4L5+lzEYn/FzNYEy33leMc+WhO9jPNhZjeb2Q4zW5s2LOs6KFB8I8qTAsVYZmZ/NrNnghivCYYvNLM/Bev7NjMrKVSM2YQlH4dYx1eb2Za0berbCxTfoO1LofPPzA5PWy6rzKzZzD4ZlmU2WmHJyVxG8b1llnt/4f1B+/Vm9v604a8L8m1D8F4bahrjNJ8xM3vazO4O/s+6LTOz0uD/DcHrC9LG8flg+F/M7G1pw7Ou4zBuL8Oej0OxLN/tUZHrcxYFufYH8ubueuznA6gHjg+eVwEvAEcB/wp8Lhj+OeCrwfO3A78DDDgJ+FPauFpDHvts4ATgy8Cn08YTA14EFgElwDPAUVGIPXjtFWBmoXNpmPmd8GWcZ1xvAo4H1qYNy7oOChTfiPKkQDEaUBk8jwN/CrYNtwMXBcO/B3y00Os7LebQ5OMQ6/jqgZ/1AsU3aPsSsvyLAduA+WFZZvsxH6HIySFiHJP9BWA68FLwd1rwfFrw2p+Dtha896yJzjngcuA/gbuD/7Nuy4D/C3wveH4RcFvw/Khg/ZUCC4P1GhtqHYdtexmFfBwm/kHf7VF55PqcFTquPGPPuj+Q7/t1xG4MuPtWd38qeN4CPAfMAc4Dfho0+ylwfvD8POAWT3gcmGpm9RMcNjDy2N19h7s/AfQMGNWJwAZ3f8ndu4GfB+OIQuxRMeHLOB/u/giwe8DgXLk/4Ubx+ZxwwbagNfg3HjwcOA34ZTC8oDFmEZp8HGIdh1lo8g84HXjR3XPdXDkqQpOTuYzh/sLbgPvdfbe77wHuB5YHr1W7++Oe2DO8ZcC4xj3nzGwu8NfAD4P/jdzbsvSYfgmcHrQ/D/i5u3e5+8vABhLrN+s6HmYahRL6fBxKju/2SIjodwIw5P5AXlTYjbHgNILjSFTYte6+NXhpG1AbPJ8DbEp722b2JVyZma00s8fNbEI3SnnGnstQ8zTu9jN2SHxo7jOzJ83sknEJcv8VdBmP0GjWwbgbgzwZN8GpS6uAHSR20l4E9rp7b9AkbOs7lPk4YB0DfDw4he3miT7dMU227UuY8u8i4L/S/g/DMhuNUOZkLvu5vzDU8M1ZhjPENMbaN4H/B/QH/88g97YsNR/B601B+5HO91DTKJRI5eNkleU7IfQG7g+4e96xq7AbQ2ZWCdwBfNLdm9NfC345y6finu+JO83/DfBNMztk7CMdbIxiL4gxiv2v3P144CzgY2b2prGP9MAUlvwJe467e5+7HwvMJfFL7xGFjCeKsqzj7wKHAMcCW4FvFCi0Ibcvhcy/4Dqkc4FfBIPCsswmtUJvj8ZrGmZ2NrDD3Z8c63GLjNRQn7MwG7g/YGZL8n2vCrsxYmZxEsnzM3f/VTB4e/IUy+DvjmD4FuDgtLfPDYbh7sm/LwEPk/iVIUyx55JznsbTGMWevtx3AL8msWMdNgVZxqM04nUwnsYqTyaCu+8FHgJOJnHaVXHwUtjWd6jyMds6dvftwRdkP/ADCvS5zrF9CUv+nQU85e7bgxhDscxGKVQ5mcsY7S8MNXxuluFDTWMsvQE418xeIXHq4WnADeTelqXmI3i9BtjFyOd71xDTKJRI5ONkleNzFilp+wPL832PCrsxEJzb/SPgOXe/Pu2lFUCyp6r3A79JG36xJZwENLn7VjObZmalwThnkthAPhuy2HN5Algc9EpVQuLUnnHt1XOsYjezCjOrSj4HzgTC2AvUhC/j/TDS/Bk3Y5jj48bMZpnZ1OB5OXAGiWsCHgIuCJoVNMYsQpOPudbxgGuX30EBPtdDbF/Ckn/vJe00zDAss/0QmpzMZaz2F4B7gTOD/YZpJPLq3uC1ZjM7KZjWxQPGNa455+6fd/e57r6AxPJ/0N3fR+5tWXpMFwTtPRh+kSV6zVwILCbRKUzWdRy8J2zby9Dn42Q1xOcs9HLsDzyf9wg8BD3ARP0B/BWJUxpWA6uCx9tJnPP9ALAe+G9guu/r8eZGEtfQrAGWBcNPCf5/Jvj7oRDGXkfiPPFmYG/wvDp47e0keh56EfhiVGIn0WPVM8Fj3UTEvh/zPKHLOM+Y/ovEKVs9wTL9UK51UKD4RpQnBYrxaODpIMa1wJXB8EUkdmY2kDhVrrTQ63tA3KHIxyHW8a3BtnQ1iR2q+gLElnX7Eob8AypIHOmoSRtW8GW2n/MUipwcIr4x2V8IXvtgsG3YAPx92vBlwXbkReA7gBUi54BT2dcrZtZtGVAW/L8heH1R2vu/GMzDXwh69hxqHYdxexn2fBwm9kHf7YWOaQSxZ/2cFTquPGPPuj+Q7yP5YRcREREREZGI0qmYIiIiIiIiEafCTkREREREJOJU2ImIiIiIiEScCjsREREREZGIU2EnIiIiIiIScSrsJKvgnjl/MLOz0oa928x+X8i4RERERCQ8zGyumf3GzNab2YtmdkNw7z6ZYCrsJCtP3Afj/wDXm1mZmVUCXwE+NprxmVnxWMYn4WdmXzSzdWa22sxWmdnrh2j7EzO7IHj+sJktC57fk7xR5xjFdKqZNQXxPGdmV+Vot8zMvjVW05XCMLO+YF2vNbNfmNmUAsRwqpmdMkybq81sS1qs5+Zo93/M7OLxiVQmmpm1pj1/u5m9YGbzx2E6PzGzl83smWAat5jZ3CHa/9DMjhrrOGRyCm4G/ivgTndfDBwGVAJfnoBpa99yABV2kpO7rwXuAj4LXAn8B/BFM/uzmT1tZucBmNkCM3vUzJ4KHqcEw08Nhq8Ani3UfMjEM7OTgbOB4939aOCtwKaRjsfd3+7ue8c4vEfd/VgSN/H9WzM7Pv1FMyt295XufukYT1cmXoe7H+vuS4BuEj9WpUzQTsGpwJCFXeDfgrx8N3CzmWV8Pwd5+T13v2UcYpQCMrPTgW+RuAn3q+M0mc+4+zHA4SRufvxgtiMqZhZz939wd31nS75OAzrd/ccA7t4HfAr4oJk9ZGZHAwT7jVcGz681sw8H+4kPm9kvzex5M/tZUChiZq8zs/8xsyfN7F4zqw+GP2xm3zSzlcBlhZjhMFNhJ8O5Bvgb4CygDHjQ3U8E3gJ8zcwqgB3AGe5+PPAeEl9QSccDl7n7YRMbthRYPbDT3bsA3H2nu7+Wa0Odi5m9YmYzgx8PnjOzHwRHAe8zs/KgzQlpRwW/ZmZr8wnQ3duAJ4FDgyMmt5rZ/wK3Bl82dwfjrzSzH5vZmmA67wqGn2lmfwx+zPhFcFRbwutREus64wcnM4sFefNEsH4/AmBm9Wb2SNpRtDcGw7Ou9yBXrwmGrzGzI8xsAYli8lPBeN44XJDu/hzQC8wcuAMT5Omng+kdamb/HRyFecrMDgmGfyZtXq4Z86UoY8rM3gT8ADjb3V8Mhv3EzL5rZo+b2UtBzt4cbAN/ErSJBe3WBvn2qXym5wn/Bmwj8b2OmbWa2TfM7Bng5CDvllniCPHX0mL9gJl9J3j+t5b4kXeVmX3fzGJp4/pykJePm1nt2C0tCakGEt+lKe7eDGwEHgLeaGY1JLZrbwiavBF4JHh+HPBJ4ChgEfAGM4sD3wYucPfXATeTeQSwxN2Xufs3xmeWokuFnQwp2Pm9DbgVOAP4nJmtAh4mUejNA+LAD8xsDfALEh/OpD+7+8sTGrSEwX3AwZY47effzezNeWyoh7MYuNHdG4C9wLuC4T8GPhIc7ejLd2RmNgM4CVgXDDoKeKu7v3dA0y8BTe6+NDj6+KCZzQSuCNofD6wELh/BvMgEssSRubOANcGg9B+cPkRi/Z4AnAB82MwWkvhB694gr44BVuWx3ncGw78LfNrdXwG+R3A0zt0fzSPW1wP9QGMwKNcOzM9IfB6OIXFEcKuZnUnic3IicCzwuqBwkHAqBe4Eznf35we8Ng04mcSRjxXAv5HYgV5qZseSWL9z3H2Juy8lsR0ciaeAI4LnFcCf3P0Yd/9DWps7gHek/f8e4OdmdmTw/A1p2933pY3r8SAvHwE+PMK4ZHL5H+BNJAq63wKVljglfqG7/yVo82d33+zu/cAqYAGJI8tLgPuDfc4rgPTTh2+boPgjR+emSj76g4cB70r7MAKJ60OA7SR2foqAzrSX2yYoRgkRd281s9eR+FXuLSQ2wtexb0MNEAO2jmC0L7v7quD5k8ACS1x/V+XufwyG/yeJU0CH8kYze5pETv+Lu68zs3cDK9y9I0v7twIXpc3bHjM7m0Qh+L/BvJQAf8zyXims8mCnABJH7H5EoghK/8HpTOBoC67xBGpIFEdPkDglMk7i2pFVZvZmhl7vvwr+Pgm8c4SxfsrM/hZoAd7j7h5MY9AOjJlVkdip/zWAu3cGw88M5ufpoGllMC+PDByHhEIP8BiJHxcGnlJ2V5ADa4Dt7r4GwMzWkdjx/R9gkZl9m8QO830jnLalPe8jUcRlcPfG4IjhScB6EoXg/5K41v51ZUry3QAABAhJREFUwBNBjpaTOHMHEqc83x08f5LED8IyuT0LXJA+wMyqSfzw/zSJyx5eAu4HZpIo9tOP8HWlPe8jUZsYsM7dT84xTe1b5qDCTkbiXuATZvaJ4AvnOHd/msSO0GZ37zez95PYYZcDXHCe/cPAw8HOyccYekM9nIEb//JRjudRd89W/I3ki8KA+7Mc3ZNw6QiOKKQEO6Lp69qAT7j7vQPfHBzt+mvgJ2Z2PbCHodd7MkeTOycj8W/u/vUsw0eal//s7t8f4bSlMPqBC4EHzOwL7v6VtNe60tp0DXhPcfAD0zHA20ic7nsh8MERTPs44IHgeWewvc7m58G4nwd+HXz3G/BTd/98lvY9QedrMLrPgUTPA8C/mNnF7n5LcFruN4CfuHuzmW0ice3wtcAs4OvBYyh/AWaZ2cnu/sfgB7bD3H3dMO874OlUTBmJfyJx2uXq4FfDfwqG/zvw/uD8/CPQLykHPDM73MwWpw06FniOYEMdtImbWcP+TCfoWKXF9vW4edFQ7UfpftJ6gzWzacDjJK4DODQYVmFmuo40mu4FPhrsOGBmhwXrcz6JIyU/AH5I4vTN0az3FqBqLAN29xZgs5mdH8RRGpzedC+JDguS1/3NMbPZYzltGVvu3k7ix4P3mdmH8n1fcFpwkbvfQeI0teOHeUvyfWZml5K4Djqf2xf9GjgPeC+JIg8SO/IXJHPLzKbbOPTmKdEQFPLvAN5tZuuBF0icufWFoMmjwI7gjJhHSZxSOeRp6e7eTeIo4FeDfctV5NcJ1QFPv6TIsNz96rR/P5Ll9fXA0WmDPhsMf5jEERs58FQC3w5OlewFNgCXADcB3woupC4Gvsm+a9xG60MkrvHsJ3F6UtN+jm+g64AbLdEpSx9wjbv/ysw+APyXmZUG7a4g8YUm0fJDEqe2PRUciWgEzifRm+VnzKwHaAUuDk5N+wAjW+93Ab+0RC/Cn8jnOrs8/R3wfTO7lsQpfe929/uC65/+GByZbAX+ln2nyUkIuftuM1sOPGJmjcO+IWEO8GPb13tqtqNn6b5mZl8CppD4geItwc7zcLHtMbPngKPc/c/BsGfN7ArgvmD6PSR+/BqvHj0l5Nx9E3BOjte+ROJaddz9NdJOAx64n+juH097vorE9XkDx3fq2EQ9Odm+I+YiItFjZpXu3ho8/xxQ7+7qAllEREQOKDpiJyJR99dm9nkS27NXgQ8UNhwRERGRiacjdiIy6ZjZ24CvDhj8sru/I1t7kYlgZl8k0YlAul+4+0hu+yGSlZndyL77hCXd4MGNo0Vk8lNhJyIiIiIiEnHqFVNERERERCTiVNiJiIiIiIhEnAo7ERERERGRiFNhJyIiIiIiEnEq7ERERERERCLu/wOV+TSz9wbF1gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhuZrtFp4wPp"
      },
      "source": [
        "i) We know that name can have no relation with the selling price so we exculde that.\n",
        "\n",
        "ii) As we can see as year increases the selling prise is also increasing so we can troansform year column into age(2020-year)  we taking year-2020 for conerting negative correlation into possitive correlation.\n",
        "\n",
        "iii)And by the above pair plot we can say that not much of multicollinearity is observed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-gjw3n8AsHJ",
        "outputId": "860c580d-567a-4479-ea66-8edede1c9e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "car_data['Year']=car_data['Year']-2020\n",
        "print(car_data[[\"Year\",\"Selling_Price\"]].corr())\n",
        "sns.regplot(x=\"Year\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   Year  Selling_Price\n",
            "Year           1.000000       0.236141\n",
            "Selling_Price  0.236141       1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5a01a2940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rlnTWO7Q5IxYRRlP+HVk2AQPxyNyuNTptchCRhao6CFR8D6Wq1xY5/OVKz2OMKW7zZeu44e5drFgUzauBZOsRaufM5QvZ1zfM0NhEAl7QGmPt0rmdgMu59/z34PNO/FpKO3M+rBKeMXW0af0ybrz6bJYtaOXkaIplC1qtOF6Nbb5sHfFohBWLWnnN8gWsWNRKPBqZ8wm47Kqs9WZVWY0x9ZJZ59CMCwJnWpW1nG6lC6d6XlWfrPRNjTGmmczHBYHlDEh/ZornFLi8RrEYY0xDmo8rpKdNDqr6+tkIxBhjGlGYJUoaWTndSv9lqudV9T9qF44xxsxcGFf4uRsfAbTHoySSaVshDbx5iucUsORgjKm7sK7w5+vGR+V0K/332QjEGGOqEdYV/uqudo4OjWXPC/Nj46Oy19iLyHIR+bKI3Bc8PktE3h1eaMYYU779AwnaYpG8Y7W4wp+vGx9VUoDlq8D3gVcFj/cAf1rrgIwxZiZWd7XTNzxO77Fhdh8epPfYMH3D41Vf4c/XhYaV1FZaqqq3i8hHAVQ1LSLudC8yxpjZcMm6bh7r7Qf8wdC065JIuvz+xlOrPndY6xwaeYpsJclhRESW4P/cEZGLgZNTv8QYY/KF1SDe+5xf7DlT80HxS0ff+9whPnDlmVWdO4yYG32KbCXdSn8O3A28WkR+jL8v9PtDicoYMyeFuavaS8eGmVwMSIPj1Qgr5twBdBH/cywiDbOL37TJQUQuEpEVQZmM3wT+Gn8/hx8AB0KOzxgzh4TZIGb2NxKZ+Mg9PlNhbcUa1gB6rZRz57AFSAZfvw74G+ALwADBFp7GGFOOMBvEaCYb5PYr5R6foT1HBoOtVzVvK9a9R6rbinV1Vzujqfxh20aaIltOcoio6vHg698Dtqrqd1T174DTwwvNGDPXhNkgnr6sk8z+O5li0xHxj1cjdytWEcluyVrtVqyNPkW2rOQgIpmB6yuAh3Oeq2RA2xgzz4XZIH74qvV0d8RpiTpEHWiJOnR3xPnwVeurOm886oCCp4qieKo12Yq10afIltO4fwv4kYj0AaPA/w8gIqdjs5WMMRXYtH4ZN0IoeyNsWr+Mf3r7eTU/9xnLFvCzw4OcGE3hKTgCi9tinLFsQU1ibpRkMFk55TP+p4g8BKwEfqATuwM5TDNbSUS+AvwOcFRVXxsc6wa+DazB30P6Hao6MNN/gDGmuYTZIIZx7tz1E+B3WQ0kUlyyrrum79NoyrovUtXHVPVOVR3JObanjI1+vgpcNenYR4CHVPUM4KHgsTHGNKR7nzuEM2lM25GJdRVzVahjBqq6XUTWTDr8FmBT8PXXgG3Ah8OMw5hyNfKKVTO9MP7/9vUniDhC3Jm4lnY9j339jTHlNCzVjajMzHJVzaTcw8DyUt8oIteJyA4R2XHs2LHZic7MW2Eu0DLhC/P/z/WU8bTLWMplPO3ielUunmgC9UgOWcH4RcmfsqpuVdUNqrqhp6dnFiMz89GW7b2k3PzFTim3+sVOZnaEtcCupzOOq+D5k5Tw1F9Y19MZrzrmbbuPcu3Wx7j0poe5dutjNb0QyZw71rPmnJm8vh7J4YiIrAQIPttlmWkIe48O0TeUJO0pEUdIe0rfUJK9R4fqHZopQ1gL7Ba0xgoaSic4Xo1tu4/yoTue4an9AxwZHOOp/QN86I5napIgcu+iUC89k3PUIzncDbwr+PpdwF11iMGYAsm0BwKOCILgiIAEx03DC2uB3bHh8aI1m44Nj1d13k/d9yInEinUg4gI6sGJRIpP3fdiVeeFwo2PZiLU5CAi3wIeBV4jIgeCzYE+BbxBRPYCVwaPjam7WLC81vMUVcUL+pXjkerKL5jZsfmydQyOpth7ZIgXD51k75EhBkdTVS+wS4y7RZNDYry6HQv29SdwJH/ltSPUZKC72F1UpcKerXRtiaeuCPN9jZmJM5cvZF/fMENjaZKuRzzisKA1xtql1ZVfMLNneCzFWNpvytOeh4ylqj7nWLp4Eih1vBEU29q0UnUdkDamkWy+bB3xaIQVi1p5zfIFrFjUSjwaaZhaN2Zqf/vd5xhN51/jj6aVv/3uc1WdV0tMmSl1vFzrlnbgTSrL4al/vFq5ZUpmypKDMYFGr3UzV4Q1Q+fAibGKjpdr8gK46Y6X68NXraerPYYAaddDgK72WNW1oCD/dxlxZnT7YIXzjMnRyLVu5oJG3/2smHjUYTRVOCmhFoX33nnxaXzpkX2kkko86vDOi0+r2c8h87ssm1+e0a2T3TkYY2ZNo+9+VkxHS/Fr6FLHy7Vt91G+8dgrJNMegpJMe3zjsVcaZtGlJQdjzKxp9N3Piik166fa2UA33b+bgUQKBaIRB8Uv6HfT/burOm+tWLeSMaaoMOoUre5q5+X+YQZHJ2aELWyLsmZJ9TPCVi1q4cDJwrUHqxa1VHXevhLrGUodL1dv34g/lTXYqU4EVJTevpFpXjk77M7BGFMgrDpFl6zr5sjgOCNJl5SrjCRdjgyO16T8dWdr8WvdUsfLNVZkvGGq45XwNL9mk1ftFKgcqlpVDShLDsaYAlu295JM59eZSqarrzN173OHQCEz0UcAtDblr3cfKX7FXep4uTLbgopMfOQen6mezjhpL79mU9qrvGZTyvVIJNOcHE3RNzzO4ZNj7D+eYF/fCP1V3N1Yt5IxpsCeI8HOZ57fcKVdl7G0S9qt7mp5X3+CaESINFH56454hMGxdEGJ0I54dWMOnS1RHPzTKn6ilOD4ZK6npFyPpOuRdoOv0x7pYDV/GCw5GGMKjKY8XC9osARQcD1I1KArpdmc/apFPHfwBMM55TI6WyKc/apFVZ13OOmyuruNvuEkSdcj5ghLO+MMjacZGEmS8jxSrpJ2vbqUCLduJWNMgVRusUEtcXwG1i3twPWUsaCffSzYG6EWq4LDcsm6bkZTHrGI0BIVYhFhNOXNaJwk7XqMJl0Gx1KsWNhKIumPM6iqX68p6dLT2cpAIsnwWJrxVP32jrA7B2NMAccRovjlHFT9u4eIVN/P/qbXruDFwzkl0BXc4HijerT3OK1RYSQ5kRg74g6P9h7nA0W+P5Xp+vE8UkHXT7EuoHNetZAnfzGAE4xjpFyP4wmPN59b3R1JrVhyMMYUWLuknT1HhrP94QSb3ZzeU13563ufO0REJgZhhYn9mD9w5ZnVBx6C5w+eyEsMACNJj+cODHAikfS7fjw/IVQyBvDU/pMs6YgxPO6Scj1iEYfOlghP7T/JO8t4/WjSpW94nOMjSfqGk/SPjNM/nKR/JEn/8Dj9I0mOjyRn8C/2WXIwpsmFsR7ht89Zye4je7OPM0nit89ZWdV5MwPPmeZTJx1vFKpK0vWy022LSaS8qhrfQ4OjxCIO/r2TLxZx+OXJBAcHRunLNPZBQ98fJIC+Yb/RT5SIq1YsORjTxDK7iQ2Pp3E9pW94nA/d8Qyffvt5VSWIsK7w066HO+nC2lX80e4qRYSCc2eOl5JyveBDJ75O+3cCGaW6/CsdChhNuRwfTmYb/ZSrHMlJLmnPYzRYxPfOrzxe0bkXtcVY2hlnSWcLSzriLOmMs6SjhVMWt/F7N1UWZ4YlB2OaWGY3sYhIwW5i1SSHff0Jgo3w/HUJQb94tVf4pdrTWgy5lmqsXYXh8TTpIAlU2gUkJeLL5JyxlJvtysl28WSu9jNX/MPjJe9AprKoLRY09HG6O+IsDRr/7s44PcHXXR3x4A6kULFpseWy5GBME9vXn0BVSalmB46F6htxz1OCXVNB/EHptIJU2YzX6io8+zrPH/hNuzplZEcHKy/bPR40+lMltDd//hFGZrAj3MLWqL92YhIBHAe+8e5fo7s9XnXl12pYcjAmRxj992FyvfxumsyFsHjVddPEIg4pz80ORucen02qSsr1y0BkkkDa9Uh5M5//n0x72cHbyQO5x4fH6QsGcoeKNN6TTU4MC1ujLOlsCa7y40EXT05XT2dLttH/rc9txw2SWu4iOEeEFQtbK/531VrdkoOIvAwM4Y/GpFV1Q71iMQaac68BR4p3emSKuc34vE7xRjdS4ngtnEgkSXsadPn4DX+t5vj/5f99JtvNU06jX66PvfkslgTdPd0dlV3pd7fHODI0MeaQSRI97bGaxVeNet85vF5V++ocgzGAX08o5br0D+dXDN2yvbdhk0OxAdipjpcrlS5+gmSJ46WkXX9+f9pTXFen7L8vNfMnmfY4npg0aydvBk9y2hpCO39xoujxzpZotk8/fzA36N/vjPMHXyo9OPybZ/ZM+b5T6WiJwVDhv7mjpXhykGBcyXEg4giOZD78x4Igjn9hkPm+qDPzO716JwdjGsbeo0OcTKRwHCHiCGlP6RtKknKHpn9xnXglrqxLHS/XeInskjmeqfiZ2/BnrvbT3sRzkwd8p+q/v/Opg8GArj+wm2n4T46mqvq3AFxz0ersgO7STn9wd0lHnJYq92QoV7Yxd4KGW+DI4GjR7z06OMqrFrdlG35HJJgQUOW+pBWqZ3JQ4AciosAWVd06+RtE5DrgOoBTTz11lsMz800yGIHNra/vib+6tVq3PriHLz2yj5GkS0c8wnsuXVuTRV+ZJDZZdIYrmVW16PlyvdI/MmV3T9r15//3T565Mzz1moD//fBL08bXEY8U9Ol3d7bQ0xnnxu+9WPJ11122btpzl+JI8QFzR/zd4BzxLyaKXdVHgguNYsaCu7DcNl8VRtNK6ywlranUMzlcqqoHRWQZ8ICI7FbV7bnfECSMrQAbNmyoT4ERM2/4NXP8q26RicHd+FQT5ctw64N7+NxDe7MNzOBYms895C8wqzZBLG6L0jdSeGW9qM3/03Y9xQuu8lXBzX7tf3ZV8bzguJs/v7+U5w+enOjOmbwqdzjJiRle6a/uasvOz8/r6sn5eqrd16ZKDpNlu2SCxjySe1UfNOiZq/Y3n7uCu545XHCOt5y3kuVVDBxn76q0xPE6q1tyUNWDweejInInsBHYPvWrjAnPmcsXsq9vmKGxiTGHBa0x1i6tbpeyf/1Rb8GVp6f+8UqTQ6ZR99TfKGZ4vPjg6tB4mn19I2U1NGnXYyCRyivFMJX3/ftTZcfbHo9ku3CWdrbw0BSbBX3tjzeWfd6MTD/8dD0uKxe1+YkguJqvpIvmlmt/FUee5O5nD+N6SsQRrj53BTdfc2HF8ebqbIkyMp72B6KDacgO1e9NXSt1iUJEOgBHVYeCr98I3FiPWEx4mm1a6ObL1nHD3btYsShKWyzCaMrfrWxzFV0SAIlUqfILfk0dL1ijkLnKz17Je7lX95p9PlfS1aIrmTOlno8H0zL7MrV2clboZsoxnAj2Ma5Ea8zJLsjKDOQW9Ol3xmmP5zcxUyUHINsV4zhC1JGg4F/+sfxunPIa+bYq9164+ZoLufmaqk5R4D2XruWWh1/yCxoG/4ee+scbQb1S1HLgziB7R4F/V9X76xSLCUHY00LDSDyb1i/jRvxZSwcGEqwq87zFumg8VTT4eir7j1e+WM31lIGE3+jHIg7jOWMiij9TSYA33rx9Ro3+ko4WDp4oPlgaAe56/68XNPrFiASNedCol+p7z1i7tGPWB13rKXPXGMZYVC3UJTmoai9wXj3eO2xhDTw2my3be4lFJNuItMejJJLpmkwL3bb7KH95xzMMjaVJex59Q+P85R3P8E9V1BPygqvyS05fwsWvXpJ3xX58JJntp8905+T24U/XdeMAxXryJ08ydD3lRCJZtMhaX9Cff3wkyUAiOe2K4slPt0QdlnTGc67243R3tEws1OpoYemCiSv9yz/zo6LndYHF7XGijpPXTZPpr88MwFZyVZ8xnxJDxrmrFnP2qxZlL3LOXbW43iFlNUbn1hxx64N7uOXhl3AEoo5faOuWYAbGfEsQ+wcSLG7Ln6/dFotwYKD66ps33b+b/uEkGgwau+qRHE5y0/272bR+md8Fo5o3GJvbRZPpwsl242i4g4CnLWkvWs6iozXK39z5vD+oO5JkYGT6Rn+yTLmM3Ne1Rh2WLWjlA1eenu326YhHija+uTNqIjldNlNZ1VVd2W7ja/RFl5YcauhLj+wLEoN/TeiIX2nxS4/sm3fJYXVXO0eHxvK6H0ZTblkNS+Yq3p3cFx804u9aLh4AABWSSURBVHuODPlX4pnJHkF/+54jQ2UPwpbyjZ+8zO07DzCacmmLRXjHr67ina9bUzxOVU4kUnlTNY+P5Pfp942MM1BicdfQWJpHe/uLPhePOsEV/USf/tJOf9rm0qDw2tKOFt7z9SdIjLsM5xR1izpC0nX5tbVLiOR06UQdh0gk93FlA7PzWRg9AmHeXdeCJYcaGkm6TF497wgzqsbYTDwvqA+TacBR/vvr1nDjPS/gaYrW6MTg7u9vXE3f8Hh24NXL9M/ndNdMZ6pVwdUmhq899kp2UHcs5fLVR19hz5EhzlyxcKLRz6nAOdO1Zi1Rh4vXLclbkZs7f7+zJVqy4Y7kNPZpV/MSA/h7E7fGI6zunvkV/nSVSBvtvADrl3ew+8hI0ePVuPXBPdz84N5s3INjaW5+sPqpyGHeXdeCJYca6ohHGEmmUXUnKmQKdJQxeDfbJs9/zzTSuQ125krd0/xGPPP9mYRQzOnLO7l+0+nc9sR+Dg+OsmJhG9dctJqzT1nEYJUrXsvtw5/MU2VwNJW9ovdn7kz07z/68/6Jxj7nn/Xj3uP8uPf4lOeORSSYstlSUFf/jh37+Xlf/h/8eNrjZGKcj735rIJzRR2HaET8j8zXQTKIRfKv9o8niv8sp5uOOp22eKToZjLVzvrpaHEYHi/83+tsqb6gX1dHKxFGyI06Ehyvxhe2/bwgoWlwvJrksLqrnZf7hxkczS/XsmZJdVOna6XxWq0mdsX6Hu58+lD2sQaVtK5YP/P6K7lyG2qd9DnbWOfMlilo7HO+ng0b13WzcQabsE8nHnMYSxU2MNGo8MTLxwtW5WYWah0fSU67+reUs1YuzE7RzNTc6c4puLawtfSV/k33/6zo8acPDNLdESfiCLGI01BdPd1t0aLJobutuibjnFO6eGb/AImc/7/2mMNrT+mq6rwAe44M4kSEKJJdxOih7D0yWNV5x0uskC91vFyXrOvm8ZePB4vtIOl6HB1Kcu1Ftf+bmQlLDjV0eDDJ4ja/TrsXTCdc2BrhlyfHGUu5+VfoQeIodpWuwcbuXs5VvQI//Xk/tz2xn0ODo6wMrsTDaHwbhaoyOJrODtj6M3fGS9YNSqaVD3/nubLOHXVkogRD0MDf8+yhosmjNebw+d+/oKzzRhwhGnGCK32ZtvDZ4vZ4WeedbaMlGr6xGjSIP92XP84ylva4pAa/x6mgvzEzS0rE/xtKVluFMCSP9h6npzM+adFllEd7j/OBegdHEyWH3YeHuHbrY6EvpMr0n0/uC892rXglvlZlX98wPQtaWbaQicFSlFf6R/hliXnj5Xq89zi3PLyXqCMsbI3SPzLOLQ/v5YOc0XQJQlUZGksHDf5EkbW+SVf7x0eS2T/4ckUcyZmqWbybZ2lHCwvaogVlrR976RhHhouUomiZ6EpxxO/uiWUSQMTv6ok4QsxxKp6+Wa2lnfGiXUhLO6tLOilXiUXyk1va86puaO97/nB234LcPQzue/5w1QO88ajDaNLF05zyJ0pdN8yZyv6BBEs7W+hZMNHtpao25lCpqCMlp3plpi5OHtjMvRqfPE89t/88c6Ve7XTGFQvb6B8Zz6v/MpbyWLGwrarzAtz2xH5SaZcTSX9VbSzi0BGPcNsT+xsmOWhQziHTrZPdMjG3Bk/Q8M+00e/uiJMYT/OLgVEcmWhkVOGaDat492XrZryXQalKpClPedXitmzXTyP59NvPY/O/7czr4miJOnz67dUtIwqroe3tGyHiCPGcpON6Hr19hQPJlTpj2YLCPvyOWNV9+KsWtXDgZGFJ8FWLWqo6bzUz+mZD0yQH1WB3KjfNLQ/tZV1P56z2n5fjmotWc8vDexlNubQG/eJpT7nmotVVn/vl/mGGx9MI/lz0tKucGE3hesM1iHxquY3+8ZH8/vy+nP78vuHKG31HCEot+FM0l+Qt0pro31/UFss2+n/+7WdIJNOMBIkyHiTKFw8Pl5UYMit3Y8FVfyzqEHMcv26OTCSbzBqCtEdDVMksZUFrFBlPZ+v+VLNvcEZYDW2Ywip/8sm3ncv7v/UkI0kXT4NqrPEIn3zbuTWJN5FM1zTeWmme5IDfjdMSdTh0crSs6pGzbeO6bj7IGQUzdGpxZV+sP9Wtsj9VVRkZdye2TMzZJjFvQ5WRZMVlqyWIMeII8WBrSRHhjb+ynA1ru7JTNxe1xSq+Gj80OEpXR5zujonXKcrhnPr4mQQQj/rdP5kEkOkSKhqzSLb0hP/zKT4rqlJL2qP0JwoL5C1pr/7Pb8v2Xha1xVi5aOLutBZz5cNqaNcuaeelYyNITuVbT+H0pdVfLc+0/Ek5533vb6wrWOdQi/P+6tMHCgr6NcIaB2ii5JBRq26ax3uPhzK4G9oMnajDeKq823xVZSTpZksoZxr4/Mf+15XOuHAEujr8fvvuSXP0s336nS184j9f4HgimdfFNppy6e0b4forTq/qZ7Ey6L7L3I6L+L8Xq7vbedXituxYwExEHLLdjRLMIqnWZ95xAX/ybzuy9fsBWqPCZ95R3iD3VMKaKx9WQ/uRN/0KH7rjGYZz7nQWt8T4yJt+parz5sZd68Z12+6j3PHkQXoWtHBqkCjvePIg565aXNV73frgHr6bM7vR85TvPn2ItUv3NMSi2aZKDqMptybdNM04uHtadwcHBoYZHvev4CKO0BqP0BaL8IUfvpTTv19Fo98ez2nwJ+roL82pqb+4PV7Wlf7hoTEWtub/erXGnLyr++nkdv9EI/6AbywqXP/6V/P333uBtOdlr2o9hfdtOr2q7p9YZGKTltypkNXu57Bp/TL+9b9tqHlDC7MzV76WHbeb1i/jDy8+zb8Kd13aIg5/ePFpNWvQwyjIGNZK5n/5Uf76Cc05bsmhAp6nLOloqckV/m1P7CfqSPaqNtPA1HNwN5FM562+zSu4NpLklwOj9I1MdE24rpJ0XYbGXb7z5MGS5xVgcXsspwRDUF6505/GmZmn31Vmo1+ulWUOzmdKOsQyU0AzSSBS+ur/yrNXEI04NW9sw9rPIVetR8jCmiu/bffRvH72X54Y5fmDJ/jf115Y1c952+6jfP2xV0i6Xjberz/2StVX4Zlzv/frT5BZQnFgYJQdL/fzxT+8qKpz7x9I8MuB0bwuRgeq3iFwtMhanamOz7amSQ5rezr57O/VppDrocHRqq9qyzWadPM2UTkeVNrsnzSwO1qi5v9UBFixqJXTlrT7g7cdLXRnu3n8q//MIqvZlhmcH0u7tEYjjKf9Rua9v7GWFYtai672nYlaNrZh9bOHWWDt0d7jLGiJMDiWJhUMli6swVz5v73zWYbGJ34nPYWhcZe/vfNZHvnolTM+76fue5ETiZRfwVUE9eBEIsWn7nux6p/F9f++k8ntasrzjz9/45tmfN7DJ0YLxp484FCV09MbXdMkh1oq96p2KqPJiYHcyQ197vFiq0yn09UemzSDJ84Pdx9jLOXSHo9kV9KOpT2WdLTwD287p+L3qCVHMgO+E/P+33rhKSxb0MIXH9lX866UsBrbsPrZwyywtufIICNJl5jjZLvCRpJu1auCi03dnOp4ufb1J/y7nJyJFepp0aq1lRpOFr/iLnW8XOkSVyCljs8V8zI5TDXldDTl5u+WNXkgN0gCM2n0F7fFgmqaE6tyJ/fpd3fEi3an/OCFI3R1xJCcEmVh3e0UUywB+FNBS8/9v/ys5Vx+1vKaxzIb1Sxr+Xe/fyBBRKD32HC2u2ppZ7wmi51SrgYz2fIjbtRVwRDE5ub//bREG2v9iGmi5NB7bJg///YzMx5zGEu52cY9kUrzq6d28fi+4xwbHifqOLTFInzinhdmVEF1UVssrysndwZPph5Pd0e85BTKctTibmc6mdW/8YhTdgKoh7Bm6ITVz94Zj7D7yMR6lJTrMnJ8lPXLqx/LSKaL/76WOl53WuIqvtRxUzdNkxwijhSdVTSebfST2Ro8uaUYMncBI+NT/bF4RTdqX9gazZuimZ2y2Znfv5/b6BebIvsrKxdW/e+v1QK7Yglg649+zld/8jKJlFfT3euuunlbXgnl9cs7uP/PNlV93rBWlobVz14qadXizmGsRN9GqeP1VurPcMo/T1MXdUsOInIVcAt+Vd0vqeqnpvp+1/NX6Y6nPT5574ss6YzTP5ws2qhPp2SjP2kgt9JSAWFOkd24rpurDi8v2Iim2HlL3QEUm/9/64N7+Ocf9dZ897rJiQFg95ERrrp5W9UJIqyVpWH1s4fVF25MmOqSHEQkAnwBeANwAHhCRO5W1RdKvSbtKQM5tetLXel3T+rOmTxffyaNfrnCnCL7eO9x7n/hCN0dcVpj/qby33/hCOetXsxlr1k2kQwqXAAW1u51xTZdmep4JcIaODbGTKjXncNG4CVV7QUQkduAtwAlk4MA7bEICHTGo7zjolUFV/v1rr5Y6ymyuXcAdzx5gJaoQ0dLFAFaY/4g7Ld3HOCtF66acczNuntdGCthjTET6pUcTgH25zw+APza5G8SkeuA6wBau5azpDNO2lM+eHljrmSeyaDx5C6gUncAhwfHWNwWy1sXUItB2I64f3eTO97sqX98vmmJOkVXlrdUedERVlntsM8dhkvWdvHovoGix6u1sNXfS6XY8WqEtf1oWOetlcYsdB5Q1a2qukFVN0TaFrKko6VhEwP4g8ZpTxlNuSg6Ue5j42riwVX/4vY4Sxe08KrFbZza3c6apR2s6mpn2cLWYEexGK2xSEHX0Oqu9oKFcrUYhH3PpWvx1O9K8tQLPvvHq1HqF7xRfvGLed+mVxfsZSzB8Wp8+u3n0T4p2bbHI1WX1Q7z3KUa62ob8W9tfl3BOS5Z28W3Nr+uqvMC3HrNBQWJvCXqcOs11dWwuv/PNhX83tZickVY560VqXYPgxm9qcglwMdV9beCxx8FUNX/Veo155x/od71wPZZirBymTpAO14+zjd/+gsOnRzllMV+X/gVNZjrn7vwK3cQ9sarz666e+XWB/cUVJxs5NlKYQrrZ5Gp+RPGGElY5752y0/yrvJr1YiHKcyfc7MSkZ2quqHi19UpOUSBPcAVwEHgCeD3VXVXqdc0SnKIBsXfoo5DPOJ/nVkLEDb7xTfGVGqmyaEuYw6qmhaR64Hv409l/cpUiWG2RXJXATsOsWBPgHhk9reBzGWDsMaY2VK3dQ6qei9wb73eP7ccRLYkdAOuBjbGmHpomhXSMzH5DsASgDHGlKfpk0OjdgEZY0wza5rkIAKdLdGGLghnjDFzRdMkh3jEYdnC1nqHYYwx80JDL4IzxhhTH5YcjDHGFLDkYIwxpoAlB2OMMQUsORhjjClgycEYY0wBSw7GGGMKWHIwxhhTwJKDMcaYAnXZz2EmROQY8EoIp14K9IVw3jA1W8zNFi9YzLOh2eKF5oz5Naq6oNIXNU35DFXtCeO8IrJjJhth1FOzxdxs8YLFPBuaLV5o3phn8jrrVjLGGFPAkoMxxpgClhxga70DmIFmi7nZ4gWLeTY0W7wwj2JumgFpY4wxs8fuHIwxxhSw5GCMMabAvE0OIvJfRWSXiHgismHSc+eKyKPB88+JSN23oJsq3uD5U0VkWEQ+VI/4iikVs4i8QUR2Bj/bnSJyeT3jzDXN78VHReQlEfmZiPxWvWKcioicLyKPicjTIrJDRDbWO6bpiMj7RWR38HP/x3rHUy4R+QsRURFZWu9YpiIi/xT8fJ8VkTtFZHE5r5u3yQF4HvgvwPbcgyISBf4N+BNVPRvYBKRmPbpCRePN8VngvtkLpyylYu4D3qyq5wDvAr4x24FNodTvxVnANcDZwFXAP4tIZPbDm9Y/An+vqucDNwSPG5aIvB54C3Be8Pf26TqHVBYRWQ28EfhFvWMpwwPAa1X1XGAP8NFyXtQ0i+BqTVVfBBCRyU+9EXhWVZ8Jvq9/lkMraop4EZG3AvuAkVkOa0qlYlbVp3Ie7gLaRKRFVcdnMbyipvg5vwW4LYhxn4i8BGwEHp3dCKelwMLg60XAL+sYSzn+B/CpzP+9qh6tczzluhn4K+CuegcyHVX9Qc7Dx4C3l/O6+XznUMqZgIrI90XkSRH5q3oHNBUR6QQ+DPx9vWOZod8FnmyExDCNU4D9OY8PBMcazZ8C/yQi+/Gvwsu6SqyjM4HfEJGfisiPROSiegc0HRF5C3AwcwHZZP6YMnsY5vSdg4g8CKwo8tTfqGqpjB8FLgUuAhLAQyKyU1UfCinMrBnG+3HgZlUdLnZXEbYZxpx57dnATfh3a7OmmpgbwVTxA1cAf6aq3xGRdwBfBq6czfgmmybeKNANXIz/N3e7iKzTOs+xnybmv2aWf2enU87vtIj8DZAGvlnOOed0clDVmfxRHAC2q2ofgIjcC1wIhJ4cZhjvrwFvDwbyFgOeiIyp6udrG11xM4wZEVkF3An8oar+vLZRTW2GMR8EVuc8XhUcm3VTxS8iXwc+GDz8v8CXZiWoKUwT7/8A/iNIBo+LiIdf3O7YbMVXTKmYReQcYC3wTHAxtgp4UkQ2qurhWQwxz3S/0yLyR8DvAFeUm3itW6nQ94FzRKQ9GJz+TeCFOsdUkqr+hqquUdU1wOeAf5itxDBTwWyJe4CPqOqP6x1Pme4GrhGRFhFZC5wBPF7nmIr5Jf7vLMDlwN46xlKO7wKvBxCRM4E4DVz1VFWfU9VlOX9zB4AL65kYpiMiV+GPj1ytqolyXzdvk4OIvE1EDgCXAPeIyPcBVHUAf+bPE8DT+P3h99QvUl+peBvZFDFfD5wO3BBMuXxaRJbVLdAcU/xe7AJux79QuB94n6q69Yu0pPcCnxGRZ4B/AK6rczzT+QqwTkSeB24D3lXvLqU56PPAAuCB4G/tX8t5kZXPMMYYU2De3jkYY4wpzZKDMcaYApYcjDHGFLDkYIwxpoAlB2OMMQUsORhTgvgeEZE35Rz7ryJyfz3jMmY22FRWY6YgIq/FX2l8AX5FgaeAq2ayqltEoqqarnGIxoTCkoMx0whKk4wAHcHn04DXAjHg46p6l4iswS893hG87HpV/YmIbAI+AQwA61X1zNmN3piZseRgzDREpAN4EkgC3wN2qeq/BWVAHse/q1DAU9UxETkD+JaqbgiSwz349fT31edfYEzl5nThPWNqQVVHROTbwDDwDuDNOTvutQKn4tc0+ryInA+4+KWoMx63xGCajSUHY8rjBR8C/K6q/iz3SRH5OHAEOA9/osdYztMNtQmTMeWw2UrGVOb7wPslqNcsIhcExxcBh1TVA94JNOIWosaUzZKDMZX5BP5A9LMisit4DPDPwLuCaqjrsbsF0+RsQNoYY0wBu3MwxhhTwJKDMcaYApYcjDHGFLDkYIwxpoAlB2OMMQUsORhjjClgycEYY0yB/wd0yNCsNauR1QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bC4qXfbXUd8V"
      },
      "source": [
        "i) We can see the positive corelation which means newer the car is higher the price will be. Though the correlation is not so high it does have some effect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UI7yowPiUUOf",
        "outputId": "fd9d1756-c1f6-47bc-a406-32f0c8c4d8a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "print((car_data[['Present_Price','Selling_Price']]).corr())\n",
        "sns.regplot(x=\"Present_Price\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Present_Price  Selling_Price\n",
            "Present_Price       1.000000       0.878983\n",
            "Selling_Price       0.878983       1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5a06283c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o5aSGhdYwqm"
      },
      "source": [
        "i)  Good amount of correlation can be seen inbetween attributed Present price and selling price so higher the present price higher the selling price will be."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTceEzm75wK0",
        "outputId": "ff1a3e1e-6dce-401f-b4b0-ef5412efe609",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "print((car_data[['Kms_Driven','Selling_Price']]).corr())\n",
        "sns.regplot(x=\"Kms_Driven\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Kms_Driven  Selling_Price\n",
            "Kms_Driven       1.000000       0.029187\n",
            "Selling_Price    0.029187       1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5a0675f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5UYjXkPfzzO"
      },
      "source": [
        "i) The value of corelation between Kms driven and selling price is very small and it is positive so according to correlation value we can coclude that as kilometers driven increases the cost increases by a small value. But we know that it should be in reverse the way it is now this might be beacause of the limited dataset in which data doesnot reflect the quality described."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7ZBVt9U5x6b",
        "outputId": "50f6a351-cb73-4e9a-86db-5b135be4d15a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "sns.barplot(x=\"Fuel_Type\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa59e91be48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBwO4YrrsnWL"
      },
      "source": [
        "As attribute fuel type has categorical data corelation cant be caluculated. By barplot we can say that cars of fuel type - diesel cost is more compared to petrol and cng types."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4OioprNtaPO",
        "outputId": "3876de7b-5b8f-4da8-fee4-ab1eb976f13b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "sns.barplot(x=\"Seller_Type\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa59e806828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iu1QrI2Otf1P"
      },
      "source": [
        "This barplot tells us that cars sold by dealars are way costly than cars sold by individuals."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEfimv6stt2h",
        "outputId": "bba5fa2a-ff10-4533-cdec-c6851e85e9e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.barplot(x=\"Transmission\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa59e7d01d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9_Aub_ZAiec"
      },
      "source": [
        "So by this we can say that automatic trasmission gear cars are more costly manual gear trabsmission."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEC0D6iCBQeu",
        "outputId": "6e5597f2-9168-46b9-e447-88000db42b79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.barplot(x=\"Owner\",y='Selling_Price',data=car_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa59e737940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1z5H2he5BwEn"
      },
      "source": [
        "Newer the car is higer the cost will be"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYTDRrHuH3KO"
      },
      "source": [
        "...All above mentioned bargraphs represent the range of selling prices of each value in x-axis which are catogorical. The line in the center is is the range of selling prices from its center(top of the rectangle)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5-jOVGjFbRF"
      },
      "source": [
        "-->>As the output expected is continous and we found not much of colinearity exists between independent variables so i am going to apply linear regression."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlGP8d8PMJ-f"
      },
      "source": [
        "# conclusions\n",
        "from above plots we can say that every variable is related to the dependent variable(selling price) among all kilometers travelled showed least correlation so we can try building a model and check which one gives us better estimates.\n",
        "\n",
        "1) with 'kms_travelled'\n",
        "\n",
        "2) without 'kms_travelled'\n",
        "\n",
        "and we can see that, though values of attribute owner are numerical they are categorical so 2 models can be made :\n",
        "\n",
        "1) taking owner attribute as numerical  \n",
        "\n",
        "2) taking owner attribute as catagorical and use dummy variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWav4FAUQgQ9",
        "outputId": "17f1edaa-d2ac-420b-e129-cd59876a152b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "car_data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
              "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkZc2I5uNT7H",
        "outputId": "e7c200f4-1417-4202-91fd-f7d7a7a1b2a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "car_data=car_data[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven','Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]\n",
        "cd_with_dummies=pd.get_dummies(car_data,drop_first=True)\n",
        "cd_with_dummies.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven', 'Owner',\n",
              "       'Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual',\n",
              "       'Transmission_Manual'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itvobQxCLDv0"
      },
      "source": [
        "1)  taking owner attribute as numerical  and ignoring kms_travelled"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1en_I4C_7pfu",
        "outputId": "a2bfdccc-6a93-4561-b283-38bbd64d5179",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "y1=cd_with_dummies['Selling_Price']\n",
        "x1=cd_with_dummies[['Year', 'Present_Price', 'Owner','Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual','Transmission_Manual']]\n",
        "x1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6</td>\n",
              "      <td>5.59</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-7</td>\n",
              "      <td>9.54</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-3</td>\n",
              "      <td>9.85</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-9</td>\n",
              "      <td>4.15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-6</td>\n",
              "      <td>6.87</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>-4</td>\n",
              "      <td>11.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>-5</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>-11</td>\n",
              "      <td>11.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>-3</td>\n",
              "      <td>12.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>-4</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  Present_Price  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0      -6           5.59  ...                       0                    1\n",
              "1      -7           9.54  ...                       0                    1\n",
              "2      -3           9.85  ...                       0                    1\n",
              "3      -9           4.15  ...                       0                    1\n",
              "4      -6           6.87  ...                       0                    1\n",
              "..    ...            ...  ...                     ...                  ...\n",
              "296    -4          11.60  ...                       0                    1\n",
              "297    -5           5.90  ...                       0                    1\n",
              "298   -11          11.00  ...                       0                    1\n",
              "299    -3          12.50  ...                       0                    1\n",
              "300    -4           5.90  ...                       0                    1\n",
              "\n",
              "[301 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5C4CONILn3n"
      },
      "source": [
        "from sklearn.linear_model  import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osMsQ92pOJKw"
      },
      "source": [
        "x1_train, x1_test, y1_train, y1_test = train_test_split(x1, y1, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yG2RCNLOSl9",
        "outputId": "e354d713-ae28-4a44-c9ea-19df1014e5a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "reg=LinearRegression()\n",
        "reg.fit(x1_train,y1_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIz_uadpOZbt",
        "outputId": "163b4fab-99a8-4c13-cd70-40913b01bcb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "print(reg.intercept_)\n",
        "print(reg.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.668378690093171\n",
            "[ 0.4284824   0.42582322 -0.9586005   2.42568204  0.61156513 -1.11948485\n",
            " -1.30065782]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IiC3DfIwOica",
        "outputId": "63e230f2-809d-4b4c-b9ee-f4aa0f301899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y1_train, y_pred=reg.predict(x1_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.306272667304246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTMIt77eOq22",
        "outputId": "453d9ca1-8601-4e6f-e089-a007a27ffed6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y1_train, y_pred=reg.predict(x1_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8717051916959566"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moFEC_Ev48Px",
        "outputId": "581fbf88-4475-452f-af94-ce83a2645001",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y1_test, y_pred=reg.predict(x1_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2379931905684907"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rq2sKgQ5aWVp",
        "outputId": "0eb05b11-748c-445f-9066-3cec730a1066",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y1_test, y_pred=reg.predict(x1_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9114623972338541"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-118O3t44Mt"
      },
      "source": [
        "by R squared value we can say that our regression line or model is able to predict 91 ppercent correctly on unseen data(test data) which is a good sign."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nFxDR1XaTJ3"
      },
      "source": [
        "2) taking dummy variable of owner attribute as  and ignoring kms_travelled"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnoHVTDE43Ov",
        "outputId": "ea0f29c8-56f6-45ca-dd1d-9f0ed4a3b714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        }
      },
      "source": [
        "y2=y1\n",
        "y2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3.35\n",
              "1       4.75\n",
              "2       7.25\n",
              "3       2.85\n",
              "4       4.60\n",
              "       ...  \n",
              "296     9.50\n",
              "297     4.00\n",
              "298     3.35\n",
              "299    11.50\n",
              "300     5.30\n",
              "Name: Selling_Price, Length: 301, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcrXWPGHO34t",
        "outputId": "785bacf8-8668-4d98-880d-780fea2de379",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "source": [
        "x2=x1\n",
        "x2[\"Owner\"].replace({1:'one',0:'zero',3:'three'},inplace=True)\n",
        "x2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:4569: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  method=method,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6</td>\n",
              "      <td>5.59</td>\n",
              "      <td>zero</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-7</td>\n",
              "      <td>9.54</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-3</td>\n",
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              "      <td>zero</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-9</td>\n",
              "      <td>4.15</td>\n",
              "      <td>zero</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-6</td>\n",
              "      <td>6.87</td>\n",
              "      <td>zero</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>-4</td>\n",
              "      <td>11.60</td>\n",
              "      <td>zero</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <th>297</th>\n",
              "      <td>-5</td>\n",
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              "      <td>zero</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>-11</td>\n",
              "      <td>11.00</td>\n",
              "      <td>zero</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>-3</td>\n",
              "      <td>12.50</td>\n",
              "      <td>zero</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>-4</td>\n",
              "      <td>5.90</td>\n",
              "      <td>zero</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  Present_Price  ... Seller_Type_Individual  Transmission_Manual\n",
              "0      -6           5.59  ...                      0                    1\n",
              "1      -7           9.54  ...                      0                    1\n",
              "2      -3           9.85  ...                      0                    1\n",
              "3      -9           4.15  ...                      0                    1\n",
              "4      -6           6.87  ...                      0                    1\n",
              "..    ...            ...  ...                    ...                  ...\n",
              "296    -4          11.60  ...                      0                    1\n",
              "297    -5           5.90  ...                      0                    1\n",
              "298   -11          11.00  ...                      0                    1\n",
              "299    -3          12.50  ...                      0                    1\n",
              "300    -4           5.90  ...                      0                    1\n",
              "\n",
              "[301 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z83HfVx7f_-x",
        "outputId": "356ebc13-4c00-4443-b8c8-5afb056a0af4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "x2=pd.get_dummies(x2,drop_first=True)\n",
        "x2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "      <th>Owner_three</th>\n",
              "      <th>Owner_zero</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>4</th>\n",
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              "      <th>...</th>\n",
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              "      <td>-4</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>-5</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>-11</td>\n",
              "      <td>11.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>-3</td>\n",
              "      <td>12.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>-4</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  Present_Price  ...  Owner_three  Owner_zero\n",
              "0      -6           5.59  ...            0           1\n",
              "1      -7           9.54  ...            0           1\n",
              "2      -3           9.85  ...            0           1\n",
              "3      -9           4.15  ...            0           1\n",
              "4      -6           6.87  ...            0           1\n",
              "..    ...            ...  ...          ...         ...\n",
              "296    -4          11.60  ...            0           1\n",
              "297    -5           5.90  ...            0           1\n",
              "298   -11          11.00  ...            0           1\n",
              "299    -3          12.50  ...            0           1\n",
              "300    -4           5.90  ...            0           1\n",
              "\n",
              "[301 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrU5Zv2xciv4",
        "outputId": "619a98ee-ff98-4a5a-f27b-03f5ecabc7b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "x2_train, x2_test, y2_train, y2_test = train_test_split(x2, y2, test_size=0.2, random_state=0)\n",
        "reg2=LinearRegression()\n",
        "reg2.fit(x2_train,y2_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAVtTjkXh2c8",
        "outputId": "5cff79fe-21fa-4e4b-f6a5-09a49d058581",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "reg2.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.87817403e-01,  4.30712413e-01,  2.42039017e+00,  5.51288629e-01,\n",
              "       -1.14147250e+00, -1.53044037e+00, -6.50892284e+00, -7.65922863e-01,\n",
              "       -5.44397659e-06])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYnfm_5Qh7dJ",
        "outputId": "d831e178-9661-4144-eeb7-d84f20882b5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "reg2.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.58078948173715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP17BoPch-YE",
        "outputId": "6afc24a9-f1f6-4238-de61-2e4a896c3db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y2_train, y_pred=reg2.predict(x2_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.1702682875264987"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9L-1QGKwiUaQ",
        "outputId": "fe3b1622-30f6-4806-ddd1-791aa2b71b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y2_train,y_pred=reg2.predict(x2_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8769826317584918"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LufRWz5ikhi",
        "outputId": "a6d06614-cd63-451d-c820-9dfd3e7745ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y2_test, y_pred=reg2.predict(x2_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1170164430622362"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVelgzovizng",
        "outputId": "e7c63447-90c6-4b5b-b5df-68b1003a597c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y2_test, y_pred=reg2.predict(x2_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9179827767356279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1d3Fxq_i-as"
      },
      "source": [
        "upon taking dummy variables for owner attribute we can see a slight elvation in Rsquared values of both test and train datasets and mean square error has also decreased a bit. So this a bit better model as data is well prossed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkPey5gbj9QT"
      },
      "source": [
        "3) now lets include kms driven and see its effect"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RV1AzgMPi6ym",
        "outputId": "7605bb4f-a245-4993-dd8d-57b9f03584c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "y3=y2\n",
        "x3=x2\n",
        "x3[\"Kms_Driven\"]=car_data['Kms_Driven']\n",
        "x3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "      <th>Owner_three</th>\n",
              "      <th>Owner_zero</th>\n",
              "      <th>Kms_Driven</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6</td>\n",
              "      <td>5.59</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>27000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-7</td>\n",
              "      <td>9.54</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>43000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-3</td>\n",
              "      <td>9.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-9</td>\n",
              "      <td>4.15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-6</td>\n",
              "      <td>6.87</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42450</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>-4</td>\n",
              "      <td>11.60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>33988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>-5</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>-11</td>\n",
              "      <td>11.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>87934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>-3</td>\n",
              "      <td>12.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>-4</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5464</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  Present_Price  Fuel_Type_Diesel  ...  Owner_three  Owner_zero  Kms_Driven\n",
              "0      -6           5.59                 0  ...            0           1       27000\n",
              "1      -7           9.54                 1  ...            0           1       43000\n",
              "2      -3           9.85                 0  ...            0           1        6900\n",
              "3      -9           4.15                 0  ...            0           1        5200\n",
              "4      -6           6.87                 1  ...            0           1       42450\n",
              "..    ...            ...               ...  ...          ...         ...         ...\n",
              "296    -4          11.60                 1  ...            0           1       33988\n",
              "297    -5           5.90                 0  ...            0           1       60000\n",
              "298   -11          11.00                 0  ...            0           1       87934\n",
              "299    -3          12.50                 1  ...            0           1        9000\n",
              "300    -4           5.90                 0  ...            0           1        5464\n",
              "\n",
              "[301 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGCawHc8lGeT",
        "outputId": "10a8f29b-82c5-4c01-cb49-0b64ce8aac12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "x3_train, x3_test, y3_train, y3_test = train_test_split(x3, y3, test_size=0.2, random_state=0)\n",
        "reg3=LinearRegression()\n",
        "reg3.fit(x3_train,y3_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LymWIWnrYLb",
        "outputId": "074eca5d-a4da-4a98-a4fc-fe3e261edf3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "reg3.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.87817403e-01,  4.30712413e-01,  2.42039017e+00,  5.51288629e-01,\n",
              "       -1.14147250e+00, -1.53044037e+00, -6.50892284e+00, -7.65922863e-01,\n",
              "       -5.44397659e-06])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdFnZusilUC-",
        "outputId": "eb9a0975-11c0-49f1-b426-c828b4d8a5a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y3_train, y_pred=reg3.predict(x3_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.1702682875264987"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgXtWN9NoHRa",
        "outputId": "cbb44146-a1b0-4a84-9111-10e0c86a4ae2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y3_train, y_pred=reg3.predict(x3_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8769826317584918"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnrL_0uNq5lx",
        "outputId": "b254b86b-9051-41f7-d643-3d1d14c9d40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "mean_squared_error(y_true=y3_test, y_pred=reg3.predict(x3_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.073175480703054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmdRY9D0rDJj",
        "outputId": "521fcbca-8d9a-4edc-acd5-538fcfe53802",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "r2_score(y_true=y3_test, y_pred=reg3.predict(x3_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9179827767356279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMGcRGNMrdZb"
      },
      "source": [
        "Adding kms driven attribute made no difference so it can be excluded as we can the coefficent of kms driven is very small so that is the reason why it had no effect on the results of model. So for this dataset kms driven is of no use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLP2QiyGF7-r"
      },
      "source": [
        "work from previous notebook:\n",
        "i followed this notebook as a refernce https://www.kaggle.com/anzeee/linear-regression-for-car-price where relation between every variable to selling price is stated and a linear regression model is built."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTkwZkfhGdud"
      },
      "source": [
        "My findings were elimination of Kms_travelled attribute as it has nothing to do in the model because of its inverse behaviour.\n",
        "\n",
        "And i have tried using owner atrribute as categorical data and it was observed that a bit of improvement is there."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMfUEYIprGxS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}